From fce26add53261187695c56f1253a2d7186db1ade Mon Sep 17 00:00:00 2001
From: cheminfo bot <admin@cheminfo.org>
Date: Wed, 7 Oct 2020 16:39:28 +0000
Subject: [PATCH] Release v5.2.0

---
 bower.json         |     2 +-
 dist/ml.js         | 26542 ++++++++++++++++++++++++-------------------
 dist/ml.js.map     |     2 +-
 dist/ml.min.js     |     4 +-
 dist/ml.min.js.map |     2 +-
 package.json       |    10 +-
 6 files changed, 14665 insertions(+), 11897 deletions(-)

diff --git a/bower.json b/bower.json
index b604599..f281f62 100644
--- a/bower.json
+++ b/bower.json
@@ -1,6 +1,6 @@
 {
   "name": "ml",
-  "version": "5.1.1",
+  "version": "5.2.0",
   "main": [
     "dist/ml.js",
     "dist/ml.min.js"
diff --git a/dist/ml.js b/dist/ml.js
index ccd022b..d90a48b 100644
--- a/dist/ml.js
+++ b/dist/ml.js
@@ -1,31 +1,24 @@
 /**
  * ml - Machine learning tools
- * @version v5.1.1
+ * @version v5.2.0
  * @link https://github.com/mljs/ml
  * @license MIT
  */
 (function (global, factory) {
   typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
   typeof define === 'function' && define.amd ? define(['exports'], factory) :
-  (global = global || self, factory(global.ML = {}));
-}(this, function (exports) { 'use strict';
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.ML = {}));
+}(this, (function (exports) { 'use strict';
 
   const toString = Object.prototype.toString;
-
   function isAnyArray(object) {
     return toString.call(object).endsWith('Array]');
   }
 
-  var src = isAnyArray;
-
-  /**
-   * Computes the maximum of the given values
-   * @param {Array<number>} input
-   * @return {number}
-   */
-
   function max(input) {
-    if (!src(input)) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+    if (!isAnyArray(input)) {
       throw new TypeError('input must be an array');
     }
 
@@ -33,23 +26,32 @@
       throw new TypeError('input must not be empty');
     }
 
-    var maxValue = input[0];
+    var _options$fromIndex = options.fromIndex,
+        fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,
+        _options$toIndex = options.toIndex,
+        toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;
 
-    for (var i = 1; i < input.length; i++) {
+    if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {
+      throw new Error('fromIndex must be a positive integer smaller than length');
+    }
+
+    if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {
+      throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');
+    }
+
+    var maxValue = input[fromIndex];
+
+    for (var i = fromIndex + 1; i < toIndex; i++) {
       if (input[i] > maxValue) maxValue = input[i];
     }
 
     return maxValue;
   }
 
-  /**
-   * Computes the minimum of the given values
-   * @param {Array<number>} input
-   * @return {number}
-   */
-
   function min(input) {
-    if (!src(input)) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+    if (!isAnyArray(input)) {
       throw new TypeError('input must be an array');
     }
 
@@ -57,9 +59,22 @@
       throw new TypeError('input must not be empty');
     }
 
-    var minValue = input[0];
+    var _options$fromIndex = options.fromIndex,
+        fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,
+        _options$toIndex = options.toIndex,
+        toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;
+
+    if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {
+      throw new Error('fromIndex must be a positive integer smaller than length');
+    }
+
+    if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {
+      throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');
+    }
+
+    var minValue = input[fromIndex];
 
-    for (var i = 1; i < input.length; i++) {
+    for (var i = fromIndex + 1; i < toIndex; i++) {
       if (input[i] < minValue) minValue = input[i];
     }
 
@@ -69,7 +84,7 @@
   function rescale(input) {
     var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
 
-    if (!src(input)) {
+    if (!isAnyArray(input)) {
       throw new TypeError('input must be an array');
     } else if (input.length === 0) {
       throw new TypeError('input must not be empty');
@@ -78,7 +93,7 @@
     var output;
 
     if (options.output !== undefined) {
-      if (!src(options.output)) {
+      if (!isAnyArray(options.output)) {
         throw new TypeError('output option must be an array if specified');
       }
 
@@ -112,442 +127,169 @@
     return output;
   }
 
-  /**
-   * @private
-   * Check that a row index is not out of bounds
-   * @param {Matrix} matrix
-   * @param {number} index
-   * @param {boolean} [outer]
-   */
-  function checkRowIndex(matrix, index, outer) {
-    let max = outer ? matrix.rows : matrix.rows - 1;
-
-    if (index < 0 || index > max) {
-      throw new RangeError('Row index out of range');
-    }
+  const indent = ' '.repeat(2);
+  const indentData = ' '.repeat(4);
+  function inspectMatrix() {
+    return inspectMatrixWithOptions(this);
   }
-  /**
-   * @private
-   * Check that a column index is not out of bounds
-   * @param {Matrix} matrix
-   * @param {number} index
-   * @param {boolean} [outer]
-   */
-
-  function checkColumnIndex(matrix, index, outer) {
-    let max = outer ? matrix.columns : matrix.columns - 1;
-
-    if (index < 0 || index > max) {
-      throw new RangeError('Column index out of range');
-    }
+  function inspectMatrixWithOptions(matrix, options = {}) {
+    const {
+      maxRows = 15,
+      maxColumns = 10,
+      maxNumSize = 8
+    } = options;
+    return `${matrix.constructor.name} {
+${indent}[
+${indentData}${inspectData(matrix, maxRows, maxColumns, maxNumSize)}
+${indent}]
+${indent}rows: ${matrix.rows}
+${indent}columns: ${matrix.columns}
+}`;
   }
-  /**
-   * @private
-   * Check that the provided vector is an array with the right length
-   * @param {Matrix} matrix
-   * @param {Array|Matrix} vector
-   * @return {Array}
-   * @throws {RangeError}
-   */
-
-  function checkRowVector(matrix, vector) {
-    if (vector.to1DArray) {
-      vector = vector.to1DArray();
-    }
 
-    if (vector.length !== matrix.columns) {
-      throw new RangeError('vector size must be the same as the number of columns');
-    }
+  function inspectData(matrix, maxRows, maxColumns, maxNumSize) {
+    const {
+      rows,
+      columns
+    } = matrix;
+    const maxI = Math.min(rows, maxRows);
+    const maxJ = Math.min(columns, maxColumns);
+    const result = [];
 
-    return vector;
-  }
-  /**
-   * @private
-   * Check that the provided vector is an array with the right length
-   * @param {Matrix} matrix
-   * @param {Array|Matrix} vector
-   * @return {Array}
-   * @throws {RangeError}
-   */
+    for (let i = 0; i < maxI; i++) {
+      let line = [];
 
-  function checkColumnVector(matrix, vector) {
-    if (vector.to1DArray) {
-      vector = vector.to1DArray();
-    }
+      for (let j = 0; j < maxJ; j++) {
+        line.push(formatNumber(matrix.get(i, j), maxNumSize));
+      }
 
-    if (vector.length !== matrix.rows) {
-      throw new RangeError('vector size must be the same as the number of rows');
+      result.push(`${line.join(' ')}`);
     }
 
-    return vector;
-  }
-  function checkIndices(matrix, rowIndices, columnIndices) {
-    return {
-      row: checkRowIndices(matrix, rowIndices),
-      column: checkColumnIndices(matrix, columnIndices)
-    };
-  }
-  function checkRowIndices(matrix, rowIndices) {
-    if (typeof rowIndices !== 'object') {
-      throw new TypeError('unexpected type for row indices');
+    if (maxJ !== columns) {
+      result[result.length - 1] += ` ... ${columns - maxColumns} more columns`;
     }
 
-    let rowOut = rowIndices.some(r => {
-      return r < 0 || r >= matrix.rows;
-    });
-
-    if (rowOut) {
-      throw new RangeError('row indices are out of range');
+    if (maxI !== rows) {
+      result.push(`... ${rows - maxRows} more rows`);
     }
 
-    if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);
-    return rowIndices;
+    return result.join(`\n${indentData}`);
   }
-  function checkColumnIndices(matrix, columnIndices) {
-    if (typeof columnIndices !== 'object') {
-      throw new TypeError('unexpected type for column indices');
-    }
-
-    let columnOut = columnIndices.some(c => {
-      return c < 0 || c >= matrix.columns;
-    });
 
-    if (columnOut) {
-      throw new RangeError('column indices are out of range');
-    }
+  function formatNumber(num, maxNumSize) {
+    const numStr = String(num);
 
-    if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);
-    return columnIndices;
-  }
-  function checkRange(matrix, startRow, endRow, startColumn, endColumn) {
-    if (arguments.length !== 5) {
-      throw new RangeError('expected 4 arguments');
+    if (numStr.length <= maxNumSize) {
+      return numStr.padEnd(maxNumSize, ' ');
     }
 
-    checkNumber('startRow', startRow);
-    checkNumber('endRow', endRow);
-    checkNumber('startColumn', startColumn);
-    checkNumber('endColumn', endColumn);
+    const precise = num.toPrecision(maxNumSize - 2);
 
-    if (startRow > endRow || startColumn > endColumn || startRow < 0 || startRow >= matrix.rows || endRow < 0 || endRow >= matrix.rows || startColumn < 0 || startColumn >= matrix.columns || endColumn < 0 || endColumn >= matrix.columns) {
-      throw new RangeError('Submatrix indices are out of range');
+    if (precise.length <= maxNumSize) {
+      return precise;
     }
+
+    const exponential = num.toExponential(maxNumSize - 2);
+    const eIndex = exponential.indexOf('e');
+    const e = exponential.slice(eIndex);
+    return exponential.slice(0, maxNumSize - e.length) + e;
   }
-  function newArray(length) {
-    let value = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0;
-    let array = [];
 
-    for (let i = 0; i < length; i++) {
-      array.push(value);
-    }
+  function installMathOperations(AbstractMatrix, Matrix) {
+    AbstractMatrix.prototype.add = function add(value) {
+      if (typeof value === 'number') return this.addS(value);
+      return this.addM(value);
+    };
 
-    return array;
-  }
+    AbstractMatrix.prototype.addS = function addS(value) {
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) + value);
+        }
+      }
 
-  function checkNumber(name, value) {
-    if (typeof value !== 'number') {
-      throw new TypeError("".concat(name, " must be a number"));
-    }
-  }
+      return this;
+    };
 
-  function sumByRow(matrix) {
-    let sum = newArray(matrix.rows);
+    AbstractMatrix.prototype.addM = function addM(matrix) {
+      matrix = Matrix.checkMatrix(matrix);
 
-    for (let i = 0; i < matrix.rows; ++i) {
-      for (let j = 0; j < matrix.columns; ++j) {
-        sum[i] += matrix.get(i, j);
+      if (this.rows !== matrix.rows || this.columns !== matrix.columns) {
+        throw new RangeError('Matrices dimensions must be equal');
       }
-    }
-
-    return sum;
-  }
-  function sumByColumn(matrix) {
-    let sum = newArray(matrix.columns);
 
-    for (let i = 0; i < matrix.rows; ++i) {
-      for (let j = 0; j < matrix.columns; ++j) {
-        sum[j] += matrix.get(i, j);
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) + matrix.get(i, j));
+        }
       }
-    }
 
-    return sum;
-  }
-  function sumAll(matrix) {
-    let v = 0;
+      return this;
+    };
 
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        v += matrix.get(i, j);
-      }
-    }
+    AbstractMatrix.add = function add(matrix, value) {
+      const newMatrix = new Matrix(matrix);
+      return newMatrix.add(value);
+    };
 
-    return v;
-  }
-  function productByRow(matrix) {
-    let sum = newArray(matrix.rows, 1);
+    AbstractMatrix.prototype.sub = function sub(value) {
+      if (typeof value === 'number') return this.subS(value);
+      return this.subM(value);
+    };
 
-    for (let i = 0; i < matrix.rows; ++i) {
-      for (let j = 0; j < matrix.columns; ++j) {
-        sum[i] *= matrix.get(i, j);
+    AbstractMatrix.prototype.subS = function subS(value) {
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) - value);
+        }
       }
-    }
 
-    return sum;
-  }
-  function productByColumn(matrix) {
-    let sum = newArray(matrix.columns, 1);
+      return this;
+    };
 
-    for (let i = 0; i < matrix.rows; ++i) {
-      for (let j = 0; j < matrix.columns; ++j) {
-        sum[j] *= matrix.get(i, j);
-      }
-    }
+    AbstractMatrix.prototype.subM = function subM(matrix) {
+      matrix = Matrix.checkMatrix(matrix);
 
-    return sum;
-  }
-  function productAll(matrix) {
-    let v = 1;
+      if (this.rows !== matrix.rows || this.columns !== matrix.columns) {
+        throw new RangeError('Matrices dimensions must be equal');
+      }
 
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        v *= matrix.get(i, j);
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) - matrix.get(i, j));
+        }
       }
-    }
 
-    return v;
-  }
-  function varianceByRow(matrix, unbiased, mean) {
-    const rows = matrix.rows;
-    const cols = matrix.columns;
-    const variance = [];
-
-    for (let i = 0; i < rows; i++) {
-      let sum1 = 0;
-      let sum2 = 0;
-      let x = 0;
-
-      for (let j = 0; j < cols; j++) {
-        x = matrix.get(i, j) - mean[i];
-        sum1 += x;
-        sum2 += x * x;
-      }
-
-      if (unbiased) {
-        variance.push((sum2 - sum1 * sum1 / cols) / (cols - 1));
-      } else {
-        variance.push((sum2 - sum1 * sum1 / cols) / cols);
-      }
-    }
-
-    return variance;
-  }
-  function varianceByColumn(matrix, unbiased, mean) {
-    const rows = matrix.rows;
-    const cols = matrix.columns;
-    const variance = [];
-
-    for (let j = 0; j < cols; j++) {
-      let sum1 = 0;
-      let sum2 = 0;
-      let x = 0;
-
-      for (let i = 0; i < rows; i++) {
-        x = matrix.get(i, j) - mean[j];
-        sum1 += x;
-        sum2 += x * x;
-      }
-
-      if (unbiased) {
-        variance.push((sum2 - sum1 * sum1 / rows) / (rows - 1));
-      } else {
-        variance.push((sum2 - sum1 * sum1 / rows) / rows);
-      }
-    }
-
-    return variance;
-  }
-  function varianceAll(matrix, unbiased, mean) {
-    const rows = matrix.rows;
-    const cols = matrix.columns;
-    const size = rows * cols;
-    let sum1 = 0;
-    let sum2 = 0;
-    let x = 0;
-
-    for (let i = 0; i < rows; i++) {
-      for (let j = 0; j < cols; j++) {
-        x = matrix.get(i, j) - mean;
-        sum1 += x;
-        sum2 += x * x;
-      }
-    }
-
-    if (unbiased) {
-      return (sum2 - sum1 * sum1 / size) / (size - 1);
-    } else {
-      return (sum2 - sum1 * sum1 / size) / size;
-    }
-  }
-  function centerByRow(matrix, mean) {
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        matrix.set(i, j, matrix.get(i, j) - mean[i]);
-      }
-    }
-  }
-  function centerByColumn(matrix, mean) {
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        matrix.set(i, j, matrix.get(i, j) - mean[j]);
-      }
-    }
-  }
-  function centerAll(matrix, mean) {
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        matrix.set(i, j, matrix.get(i, j) - mean);
-      }
-    }
-  }
-  function getScaleByRow(matrix) {
-    const scale = [];
-
-    for (let i = 0; i < matrix.rows; i++) {
-      let sum = 0;
-
-      for (let j = 0; j < matrix.columns; j++) {
-        sum += Math.pow(matrix.get(i, j), 2) / (matrix.columns - 1);
-      }
-
-      scale.push(Math.sqrt(sum));
-    }
-
-    return scale;
-  }
-  function scaleByRow(matrix, scale) {
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        matrix.set(i, j, matrix.get(i, j) / scale[i]);
-      }
-    }
-  }
-  function getScaleByColumn(matrix) {
-    const scale = [];
-
-    for (let j = 0; j < matrix.columns; j++) {
-      let sum = 0;
-
-      for (let i = 0; i < matrix.rows; i++) {
-        sum += Math.pow(matrix.get(i, j), 2) / (matrix.rows - 1);
-      }
-
-      scale.push(Math.sqrt(sum));
-    }
-
-    return scale;
-  }
-  function scaleByColumn(matrix, scale) {
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        matrix.set(i, j, matrix.get(i, j) / scale[j]);
-      }
-    }
-  }
-  function getScaleAll(matrix) {
-    const divider = matrix.size - 1;
-    let sum = 0;
-
-    for (let j = 0; j < matrix.columns; j++) {
-      for (let i = 0; i < matrix.rows; i++) {
-        sum += Math.pow(matrix.get(i, j), 2) / divider;
-      }
-    }
-
-    return Math.sqrt(sum);
-  }
-  function scaleAll(matrix, scale) {
-    for (let i = 0; i < matrix.rows; i++) {
-      for (let j = 0; j < matrix.columns; j++) {
-        matrix.set(i, j, matrix.get(i, j) / scale);
-      }
-    }
-  }
-
-  function inspectMatrix() {
-    const indent = ' '.repeat(2);
-    const indentData = ' '.repeat(4);
-    return "".concat(this.constructor.name, " {\n").concat(indent, "[\n").concat(indentData).concat(inspectData(this, indentData), "\n").concat(indent, "]\n").concat(indent, "rows: ").concat(this.rows, "\n").concat(indent, "columns: ").concat(this.columns, "\n}");
-  }
-  const maxRows = 15;
-  const maxColumns = 10;
-  const maxNumSize = 8;
-
-  function inspectData(matrix, indent) {
-    const {
-      rows,
-      columns
-    } = matrix;
-    const maxI = Math.min(rows, maxRows);
-    const maxJ = Math.min(columns, maxColumns);
-    const result = [];
-
-    for (let i = 0; i < maxI; i++) {
-      let line = [];
-
-      for (let j = 0; j < maxJ; j++) {
-        line.push(formatNumber(matrix.get(i, j)));
-      }
-
-      result.push("".concat(line.join(' ')));
-    }
-
-    if (maxJ !== columns) {
-      result[result.length - 1] += " ... ".concat(columns - maxColumns, " more columns");
-    }
-
-    if (maxI !== rows) {
-      result.push("... ".concat(rows - maxRows, " more rows"));
-    }
-
-    return result.join("\n".concat(indent));
-  }
-
-  function formatNumber(num) {
-    const numStr = String(num);
-
-    if (numStr.length <= maxNumSize) {
-      return numStr.padEnd(maxNumSize, ' ');
-    }
-
-    const precise = num.toPrecision(maxNumSize - 2);
+      return this;
+    };
 
-    if (precise.length <= maxNumSize) {
-      return precise;
-    }
+    AbstractMatrix.sub = function sub(matrix, value) {
+      const newMatrix = new Matrix(matrix);
+      return newMatrix.sub(value);
+    };
 
-    const exponential = num.toExponential(maxNumSize - 2);
-    const eIndex = exponential.indexOf('e');
-    const e = exponential.substring(eIndex);
-    return exponential.substring(0, maxNumSize - e.length) + e;
-  }
+    AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;
+    AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;
+    AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;
+    AbstractMatrix.subtract = AbstractMatrix.sub;
 
-  function installMathOperations(AbstractMatrix, Matrix) {
-    AbstractMatrix.prototype.add = function add(value) {
-      if (typeof value === 'number') return this.addS(value);
-      return this.addM(value);
+    AbstractMatrix.prototype.mul = function mul(value) {
+      if (typeof value === 'number') return this.mulS(value);
+      return this.mulM(value);
     };
 
-    AbstractMatrix.prototype.addS = function addS(value) {
+    AbstractMatrix.prototype.mulS = function mulS(value) {
       for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + value);
+          this.set(i, j, this.get(i, j) * value);
         }
       }
 
       return this;
     };
 
-    AbstractMatrix.prototype.addM = function addM(matrix) {
+    AbstractMatrix.prototype.mulM = function mulM(matrix) {
       matrix = Matrix.checkMatrix(matrix);
 
       if (this.rows !== matrix.rows || this.columns !== matrix.columns) {
@@ -556,34 +298,39 @@
 
       for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + matrix.get(i, j));
+          this.set(i, j, this.get(i, j) * matrix.get(i, j));
         }
       }
 
       return this;
     };
 
-    AbstractMatrix.add = function add(matrix, value) {
+    AbstractMatrix.mul = function mul(matrix, value) {
       const newMatrix = new Matrix(matrix);
-      return newMatrix.add(value);
+      return newMatrix.mul(value);
     };
 
-    AbstractMatrix.prototype.sub = function sub(value) {
-      if (typeof value === 'number') return this.subS(value);
-      return this.subM(value);
+    AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;
+    AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;
+    AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;
+    AbstractMatrix.multiply = AbstractMatrix.mul;
+
+    AbstractMatrix.prototype.div = function div(value) {
+      if (typeof value === 'number') return this.divS(value);
+      return this.divM(value);
     };
 
-    AbstractMatrix.prototype.subS = function subS(value) {
+    AbstractMatrix.prototype.divS = function divS(value) {
       for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - value);
+          this.set(i, j, this.get(i, j) / value);
         }
       }
 
       return this;
     };
 
-    AbstractMatrix.prototype.subM = function subM(matrix) {
+    AbstractMatrix.prototype.divM = function divM(matrix) {
       matrix = Matrix.checkMatrix(matrix);
 
       if (this.rows !== matrix.rows || this.columns !== matrix.columns) {
@@ -592,98 +339,16 @@
 
       for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - matrix.get(i, j));
+          this.set(i, j, this.get(i, j) / matrix.get(i, j));
         }
       }
 
       return this;
     };
 
-    AbstractMatrix.sub = function sub(matrix, value) {
+    AbstractMatrix.div = function div(matrix, value) {
       const newMatrix = new Matrix(matrix);
-      return newMatrix.sub(value);
-    };
-
-    AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;
-    AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;
-    AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;
-    AbstractMatrix.subtract = AbstractMatrix.sub;
-
-    AbstractMatrix.prototype.mul = function mul(value) {
-      if (typeof value === 'number') return this.mulS(value);
-      return this.mulM(value);
-    };
-
-    AbstractMatrix.prototype.mulS = function mulS(value) {
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * value);
-        }
-      }
-
-      return this;
-    };
-
-    AbstractMatrix.prototype.mulM = function mulM(matrix) {
-      matrix = Matrix.checkMatrix(matrix);
-
-      if (this.rows !== matrix.rows || this.columns !== matrix.columns) {
-        throw new RangeError('Matrices dimensions must be equal');
-      }
-
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * matrix.get(i, j));
-        }
-      }
-
-      return this;
-    };
-
-    AbstractMatrix.mul = function mul(matrix, value) {
-      const newMatrix = new Matrix(matrix);
-      return newMatrix.mul(value);
-    };
-
-    AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;
-    AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;
-    AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;
-    AbstractMatrix.multiply = AbstractMatrix.mul;
-
-    AbstractMatrix.prototype.div = function div(value) {
-      if (typeof value === 'number') return this.divS(value);
-      return this.divM(value);
-    };
-
-    AbstractMatrix.prototype.divS = function divS(value) {
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / value);
-        }
-      }
-
-      return this;
-    };
-
-    AbstractMatrix.prototype.divM = function divM(matrix) {
-      matrix = Matrix.checkMatrix(matrix);
-
-      if (this.rows !== matrix.rows || this.columns !== matrix.columns) {
-        throw new RangeError('Matrices dimensions must be equal');
-      }
-
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / matrix.get(i, j));
-        }
-      }
-
-      return this;
-    };
-
-    AbstractMatrix.div = function div(matrix, value) {
-      const newMatrix = new Matrix(matrix);
-      return newMatrix.div(value);
+      return newMatrix.div(value);
     };
 
     AbstractMatrix.prototype.divide = AbstractMatrix.prototype.div;
@@ -1425,14869 +1090,17859 @@
     };
   }
 
-  class AbstractMatrix {
-    static from1DArray(newRows, newColumns, newData) {
-      let length = newRows * newColumns;
+  /**
+   * @private
+   * Check that a row index is not out of bounds
+   * @param {Matrix} matrix
+   * @param {number} index
+   * @param {boolean} [outer]
+   */
+  function checkRowIndex(matrix, index, outer) {
+    let max = outer ? matrix.rows : matrix.rows - 1;
 
-      if (length !== newData.length) {
-        throw new RangeError('data length does not match given dimensions');
-      }
+    if (index < 0 || index > max) {
+      throw new RangeError('Row index out of range');
+    }
+  }
+  /**
+   * @private
+   * Check that a column index is not out of bounds
+   * @param {Matrix} matrix
+   * @param {number} index
+   * @param {boolean} [outer]
+   */
 
-      let newMatrix = new Matrix(newRows, newColumns);
+  function checkColumnIndex(matrix, index, outer) {
+    let max = outer ? matrix.columns : matrix.columns - 1;
 
-      for (let row = 0; row < newRows; row++) {
-        for (let column = 0; column < newColumns; column++) {
-          newMatrix.set(row, column, newData[row * newColumns + column]);
-        }
-      }
+    if (index < 0 || index > max) {
+      throw new RangeError('Column index out of range');
+    }
+  }
+  /**
+   * @private
+   * Check that the provided vector is an array with the right length
+   * @param {Matrix} matrix
+   * @param {Array|Matrix} vector
+   * @return {Array}
+   * @throws {RangeError}
+   */
 
-      return newMatrix;
+  function checkRowVector(matrix, vector) {
+    if (vector.to1DArray) {
+      vector = vector.to1DArray();
     }
 
-    static rowVector(newData) {
-      let vector = new Matrix(1, newData.length);
+    if (vector.length !== matrix.columns) {
+      throw new RangeError('vector size must be the same as the number of columns');
+    }
 
-      for (let i = 0; i < newData.length; i++) {
-        vector.set(0, i, newData[i]);
-      }
+    return vector;
+  }
+  /**
+   * @private
+   * Check that the provided vector is an array with the right length
+   * @param {Matrix} matrix
+   * @param {Array|Matrix} vector
+   * @return {Array}
+   * @throws {RangeError}
+   */
 
-      return vector;
+  function checkColumnVector(matrix, vector) {
+    if (vector.to1DArray) {
+      vector = vector.to1DArray();
     }
 
-    static columnVector(newData) {
-      let vector = new Matrix(newData.length, 1);
-
-      for (let i = 0; i < newData.length; i++) {
-        vector.set(i, 0, newData[i]);
-      }
+    if (vector.length !== matrix.rows) {
+      throw new RangeError('vector size must be the same as the number of rows');
+    }
 
-      return vector;
+    return vector;
+  }
+  function checkIndices(matrix, rowIndices, columnIndices) {
+    return {
+      row: checkRowIndices(matrix, rowIndices),
+      column: checkColumnIndices(matrix, columnIndices)
+    };
+  }
+  function checkRowIndices(matrix, rowIndices) {
+    if (typeof rowIndices !== 'object') {
+      throw new TypeError('unexpected type for row indices');
     }
 
-    static zeros(rows, columns) {
-      return new Matrix(rows, columns);
+    let rowOut = rowIndices.some(r => {
+      return r < 0 || r >= matrix.rows;
+    });
+
+    if (rowOut) {
+      throw new RangeError('row indices are out of range');
     }
 
-    static ones(rows, columns) {
-      return new Matrix(rows, columns).fill(1);
+    if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);
+    return rowIndices;
+  }
+  function checkColumnIndices(matrix, columnIndices) {
+    if (typeof columnIndices !== 'object') {
+      throw new TypeError('unexpected type for column indices');
     }
 
-    static rand(rows, columns) {
-      let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+    let columnOut = columnIndices.some(c => {
+      return c < 0 || c >= matrix.columns;
+    });
 
-      if (typeof options !== 'object') {
-        throw new TypeError('options must be an object');
-      }
+    if (columnOut) {
+      throw new RangeError('column indices are out of range');
+    }
 
-      const {
-        random = Math.random
-      } = options;
-      let matrix = new Matrix(rows, columns);
+    if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);
+    return columnIndices;
+  }
+  function checkRange(matrix, startRow, endRow, startColumn, endColumn) {
+    if (arguments.length !== 5) {
+      throw new RangeError('expected 4 arguments');
+    }
 
-      for (let i = 0; i < rows; i++) {
-        for (let j = 0; j < columns; j++) {
-          matrix.set(i, j, random());
-        }
-      }
+    checkNumber('startRow', startRow);
+    checkNumber('endRow', endRow);
+    checkNumber('startColumn', startColumn);
+    checkNumber('endColumn', endColumn);
 
-      return matrix;
+    if (startRow > endRow || startColumn > endColumn || startRow < 0 || startRow >= matrix.rows || endRow < 0 || endRow >= matrix.rows || startColumn < 0 || startColumn >= matrix.columns || endColumn < 0 || endColumn >= matrix.columns) {
+      throw new RangeError('Submatrix indices are out of range');
     }
+  }
+  function newArray(length, value = 0) {
+    let array = [];
 
-    static randInt(rows, columns) {
-      let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+    for (let i = 0; i < length; i++) {
+      array.push(value);
+    }
 
-      if (typeof options !== 'object') {
-        throw new TypeError('options must be an object');
-      }
+    return array;
+  }
 
-      const {
-        min = 0,
-        max = 1000,
-        random = Math.random
-      } = options;
-      if (!Number.isInteger(min)) throw new TypeError('min must be an integer');
-      if (!Number.isInteger(max)) throw new TypeError('max must be an integer');
-      if (min >= max) throw new RangeError('min must be smaller than max');
-      let interval = max - min;
-      let matrix = new Matrix(rows, columns);
+  function checkNumber(name, value) {
+    if (typeof value !== 'number') {
+      throw new TypeError(`${name} must be a number`);
+    }
+  }
 
-      for (let i = 0; i < rows; i++) {
-        for (let j = 0; j < columns; j++) {
-          let value = min + Math.round(random() * interval);
-          matrix.set(i, j, value);
-        }
-      }
+  function sumByRow(matrix) {
+    let sum = newArray(matrix.rows);
 
-      return matrix;
+    for (let i = 0; i < matrix.rows; ++i) {
+      for (let j = 0; j < matrix.columns; ++j) {
+        sum[i] += matrix.get(i, j);
+      }
     }
 
-    static eye(rows, columns, value) {
-      if (columns === undefined) columns = rows;
-      if (value === undefined) value = 1;
-      let min = Math.min(rows, columns);
-      let matrix = this.zeros(rows, columns);
+    return sum;
+  }
+  function sumByColumn(matrix) {
+    let sum = newArray(matrix.columns);
 
-      for (let i = 0; i < min; i++) {
-        matrix.set(i, i, value);
+    for (let i = 0; i < matrix.rows; ++i) {
+      for (let j = 0; j < matrix.columns; ++j) {
+        sum[j] += matrix.get(i, j);
       }
-
-      return matrix;
     }
 
-    static diag(data, rows, columns) {
-      let l = data.length;
-      if (rows === undefined) rows = l;
-      if (columns === undefined) columns = rows;
-      let min = Math.min(l, rows, columns);
-      let matrix = this.zeros(rows, columns);
+    return sum;
+  }
+  function sumAll(matrix) {
+    let v = 0;
 
-      for (let i = 0; i < min; i++) {
-        matrix.set(i, i, data[i]);
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        v += matrix.get(i, j);
       }
-
-      return matrix;
     }
 
-    static min(matrix1, matrix2) {
-      matrix1 = this.checkMatrix(matrix1);
-      matrix2 = this.checkMatrix(matrix2);
-      let rows = matrix1.rows;
-      let columns = matrix1.columns;
-      let result = new Matrix(rows, columns);
+    return v;
+  }
+  function productByRow(matrix) {
+    let sum = newArray(matrix.rows, 1);
 
-      for (let i = 0; i < rows; i++) {
-        for (let j = 0; j < columns; j++) {
-          result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));
-        }
+    for (let i = 0; i < matrix.rows; ++i) {
+      for (let j = 0; j < matrix.columns; ++j) {
+        sum[i] *= matrix.get(i, j);
       }
-
-      return result;
     }
 
-    static max(matrix1, matrix2) {
-      matrix1 = this.checkMatrix(matrix1);
-      matrix2 = this.checkMatrix(matrix2);
-      let rows = matrix1.rows;
-      let columns = matrix1.columns;
-      let result = new this(rows, columns);
+    return sum;
+  }
+  function productByColumn(matrix) {
+    let sum = newArray(matrix.columns, 1);
 
-      for (let i = 0; i < rows; i++) {
-        for (let j = 0; j < columns; j++) {
-          result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));
-        }
+    for (let i = 0; i < matrix.rows; ++i) {
+      for (let j = 0; j < matrix.columns; ++j) {
+        sum[j] *= matrix.get(i, j);
       }
-
-      return result;
     }
 
-    static checkMatrix(value) {
-      return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);
-    }
+    return sum;
+  }
+  function productAll(matrix) {
+    let v = 1;
 
-    static isMatrix(value) {
-      return value != null && value.klass === 'Matrix';
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        v *= matrix.get(i, j);
+      }
     }
 
-    get size() {
-      return this.rows * this.columns;
-    }
+    return v;
+  }
+  function varianceByRow(matrix, unbiased, mean) {
+    const rows = matrix.rows;
+    const cols = matrix.columns;
+    const variance = [];
 
-    apply(callback) {
-      if (typeof callback !== 'function') {
-        throw new TypeError('callback must be a function');
-      }
+    for (let i = 0; i < rows; i++) {
+      let sum1 = 0;
+      let sum2 = 0;
+      let x = 0;
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          callback.call(this, i, j);
-        }
+      for (let j = 0; j < cols; j++) {
+        x = matrix.get(i, j) - mean[i];
+        sum1 += x;
+        sum2 += x * x;
       }
 
-      return this;
+      if (unbiased) {
+        variance.push((sum2 - sum1 * sum1 / cols) / (cols - 1));
+      } else {
+        variance.push((sum2 - sum1 * sum1 / cols) / cols);
+      }
     }
 
-    to1DArray() {
-      let array = [];
+    return variance;
+  }
+  function varianceByColumn(matrix, unbiased, mean) {
+    const rows = matrix.rows;
+    const cols = matrix.columns;
+    const variance = [];
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          array.push(this.get(i, j));
-        }
+    for (let j = 0; j < cols; j++) {
+      let sum1 = 0;
+      let sum2 = 0;
+      let x = 0;
+
+      for (let i = 0; i < rows; i++) {
+        x = matrix.get(i, j) - mean[j];
+        sum1 += x;
+        sum2 += x * x;
       }
 
-      return array;
+      if (unbiased) {
+        variance.push((sum2 - sum1 * sum1 / rows) / (rows - 1));
+      } else {
+        variance.push((sum2 - sum1 * sum1 / rows) / rows);
+      }
     }
 
-    to2DArray() {
-      let copy = [];
-
-      for (let i = 0; i < this.rows; i++) {
-        copy.push([]);
+    return variance;
+  }
+  function varianceAll(matrix, unbiased, mean) {
+    const rows = matrix.rows;
+    const cols = matrix.columns;
+    const size = rows * cols;
+    let sum1 = 0;
+    let sum2 = 0;
+    let x = 0;
 
-        for (let j = 0; j < this.columns; j++) {
-          copy[i].push(this.get(i, j));
-        }
+    for (let i = 0; i < rows; i++) {
+      for (let j = 0; j < cols; j++) {
+        x = matrix.get(i, j) - mean;
+        sum1 += x;
+        sum2 += x * x;
       }
-
-      return copy;
     }
 
-    toJSON() {
-      return this.to2DArray();
+    if (unbiased) {
+      return (sum2 - sum1 * sum1 / size) / (size - 1);
+    } else {
+      return (sum2 - sum1 * sum1 / size) / size;
     }
-
-    isRowVector() {
-      return this.rows === 1;
+  }
+  function centerByRow(matrix, mean) {
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        matrix.set(i, j, matrix.get(i, j) - mean[i]);
+      }
     }
-
-    isColumnVector() {
-      return this.columns === 1;
+  }
+  function centerByColumn(matrix, mean) {
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        matrix.set(i, j, matrix.get(i, j) - mean[j]);
+      }
+    }
+  }
+  function centerAll(matrix, mean) {
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        matrix.set(i, j, matrix.get(i, j) - mean);
+      }
     }
+  }
+  function getScaleByRow(matrix) {
+    const scale = [];
 
-    isVector() {
-      return this.rows === 1 || this.columns === 1;
+    for (let i = 0; i < matrix.rows; i++) {
+      let sum = 0;
+
+      for (let j = 0; j < matrix.columns; j++) {
+        sum += Math.pow(matrix.get(i, j), 2) / (matrix.columns - 1);
+      }
+
+      scale.push(Math.sqrt(sum));
     }
 
-    isSquare() {
-      return this.rows === this.columns;
+    return scale;
+  }
+  function scaleByRow(matrix, scale) {
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        matrix.set(i, j, matrix.get(i, j) / scale[i]);
+      }
     }
+  }
+  function getScaleByColumn(matrix) {
+    const scale = [];
 
-    isSymmetric() {
-      if (this.isSquare()) {
-        for (let i = 0; i < this.rows; i++) {
-          for (let j = 0; j <= i; j++) {
-            if (this.get(i, j) !== this.get(j, i)) {
-              return false;
-            }
-          }
-        }
+    for (let j = 0; j < matrix.columns; j++) {
+      let sum = 0;
 
-        return true;
+      for (let i = 0; i < matrix.rows; i++) {
+        sum += Math.pow(matrix.get(i, j), 2) / (matrix.rows - 1);
       }
 
-      return false;
+      scale.push(Math.sqrt(sum));
     }
 
-    isEchelonForm() {
-      let i = 0;
-      let j = 0;
-      let previousColumn = -1;
-      let isEchelonForm = true;
-      let checked = false;
+    return scale;
+  }
+  function scaleByColumn(matrix, scale) {
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        matrix.set(i, j, matrix.get(i, j) / scale[j]);
+      }
+    }
+  }
+  function getScaleAll(matrix) {
+    const divider = matrix.size - 1;
+    let sum = 0;
 
-      while (i < this.rows && isEchelonForm) {
-        j = 0;
-        checked = false;
-
-        while (j < this.columns && checked === false) {
-          if (this.get(i, j) === 0) {
-            j++;
-          } else if (this.get(i, j) === 1 && j > previousColumn) {
-            checked = true;
-            previousColumn = j;
-          } else {
-            isEchelonForm = false;
-            checked = true;
-          }
-        }
-
-        i++;
+    for (let j = 0; j < matrix.columns; j++) {
+      for (let i = 0; i < matrix.rows; i++) {
+        sum += Math.pow(matrix.get(i, j), 2) / divider;
       }
-
-      return isEchelonForm;
     }
 
-    isReducedEchelonForm() {
-      let i = 0;
-      let j = 0;
-      let previousColumn = -1;
-      let isReducedEchelonForm = true;
-      let checked = false;
-
-      while (i < this.rows && isReducedEchelonForm) {
-        j = 0;
-        checked = false;
-
-        while (j < this.columns && checked === false) {
-          if (this.get(i, j) === 0) {
-            j++;
-          } else if (this.get(i, j) === 1 && j > previousColumn) {
-            checked = true;
-            previousColumn = j;
-          } else {
-            isReducedEchelonForm = false;
-            checked = true;
-          }
-        }
-
-        for (let k = j + 1; k < this.rows; k++) {
-          if (this.get(i, k) !== 0) {
-            isReducedEchelonForm = false;
-          }
-        }
-
-        i++;
+    return Math.sqrt(sum);
+  }
+  function scaleAll(matrix, scale) {
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.columns; j++) {
+        matrix.set(i, j, matrix.get(i, j) / scale);
       }
-
-      return isReducedEchelonForm;
     }
+  }
 
-    echelonForm() {
-      let result = this.clone();
-      let h = 0;
-      let k = 0;
-
-      while (h < result.rows && k < result.columns) {
-        let iMax = h;
-
-        for (let i = h; i < result.rows; i++) {
-          if (result.get(i, k) > result.get(iMax, k)) {
-            iMax = i;
-          }
-        }
-
-        if (result.get(iMax, k) === 0) {
-          k++;
-        } else {
-          result.swapRows(h, iMax);
-          let tmp = result.get(h, k);
-
-          for (let j = k; j < result.columns; j++) {
-            result.set(h, j, result.get(h, j) / tmp);
-          }
+  class AbstractMatrix {
+    static from1DArray(newRows, newColumns, newData) {
+      let length = newRows * newColumns;
 
-          for (let i = h + 1; i < result.rows; i++) {
-            let factor = result.get(i, k) / result.get(h, k);
-            result.set(i, k, 0);
+      if (length !== newData.length) {
+        throw new RangeError('data length does not match given dimensions');
+      }
 
-            for (let j = k + 1; j < result.columns; j++) {
-              result.set(i, j, result.get(i, j) - result.get(h, j) * factor);
-            }
-          }
+      let newMatrix = new Matrix(newRows, newColumns);
 
-          h++;
-          k++;
+      for (let row = 0; row < newRows; row++) {
+        for (let column = 0; column < newColumns; column++) {
+          newMatrix.set(row, column, newData[row * newColumns + column]);
         }
       }
 
-      return result;
+      return newMatrix;
     }
 
-    reducedEchelonForm() {
-      let result = this.echelonForm();
-      let m = result.columns;
-      let n = result.rows;
-      let h = n - 1;
-
-      while (h >= 0) {
-        if (result.maxRow(h) === 0) {
-          h--;
-        } else {
-          let p = 0;
-          let pivot = false;
+    static rowVector(newData) {
+      let vector = new Matrix(1, newData.length);
 
-          while (p < n && pivot === false) {
-            if (result.get(h, p) === 1) {
-              pivot = true;
-            } else {
-              p++;
-            }
-          }
+      for (let i = 0; i < newData.length; i++) {
+        vector.set(0, i, newData[i]);
+      }
 
-          for (let i = 0; i < h; i++) {
-            let factor = result.get(i, p);
+      return vector;
+    }
 
-            for (let j = p; j < m; j++) {
-              let tmp = result.get(i, j) - factor * result.get(h, j);
-              result.set(i, j, tmp);
-            }
-          }
+    static columnVector(newData) {
+      let vector = new Matrix(newData.length, 1);
 
-          h--;
-        }
+      for (let i = 0; i < newData.length; i++) {
+        vector.set(i, 0, newData[i]);
       }
 
-      return result;
+      return vector;
     }
 
-    set() {
-      throw new Error('set method is unimplemented');
+    static zeros(rows, columns) {
+      return new Matrix(rows, columns);
     }
 
-    get() {
-      throw new Error('get method is unimplemented');
+    static ones(rows, columns) {
+      return new Matrix(rows, columns).fill(1);
     }
 
-    repeat() {
-      let options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
-
+    static rand(rows, columns, options = {}) {
       if (typeof options !== 'object') {
         throw new TypeError('options must be an object');
       }
 
       const {
-        rows = 1,
-        columns = 1
+        random = Math.random
       } = options;
+      let matrix = new Matrix(rows, columns);
 
-      if (!Number.isInteger(rows) || rows <= 0) {
-        throw new TypeError('rows must be a positive integer');
+      for (let i = 0; i < rows; i++) {
+        for (let j = 0; j < columns; j++) {
+          matrix.set(i, j, random());
+        }
       }
 
-      if (!Number.isInteger(columns) || columns <= 0) {
-        throw new TypeError('columns must be a positive integer');
+      return matrix;
+    }
+
+    static randInt(rows, columns, options = {}) {
+      if (typeof options !== 'object') {
+        throw new TypeError('options must be an object');
       }
 
-      let matrix = new Matrix(this.rows * rows, this.columns * columns);
+      const {
+        min = 0,
+        max = 1000,
+        random = Math.random
+      } = options;
+      if (!Number.isInteger(min)) throw new TypeError('min must be an integer');
+      if (!Number.isInteger(max)) throw new TypeError('max must be an integer');
+      if (min >= max) throw new RangeError('min must be smaller than max');
+      let interval = max - min;
+      let matrix = new Matrix(rows, columns);
 
       for (let i = 0; i < rows; i++) {
         for (let j = 0; j < columns; j++) {
-          matrix.setSubMatrix(this, this.rows * i, this.columns * j);
+          let value = min + Math.round(random() * interval);
+          matrix.set(i, j, value);
         }
       }
 
       return matrix;
     }
 
-    fill(value) {
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, value);
-        }
-      }
+    static eye(rows, columns, value) {
+      if (columns === undefined) columns = rows;
+      if (value === undefined) value = 1;
+      let min = Math.min(rows, columns);
+      let matrix = this.zeros(rows, columns);
 
-      return this;
-    }
+      for (let i = 0; i < min; i++) {
+        matrix.set(i, i, value);
+      }
 
-    neg() {
-      return this.mulS(-1);
+      return matrix;
     }
 
-    getRow(index) {
-      checkRowIndex(this, index);
-      let row = [];
+    static diag(data, rows, columns) {
+      let l = data.length;
+      if (rows === undefined) rows = l;
+      if (columns === undefined) columns = rows;
+      let min = Math.min(l, rows, columns);
+      let matrix = this.zeros(rows, columns);
 
-      for (let i = 0; i < this.columns; i++) {
-        row.push(this.get(index, i));
+      for (let i = 0; i < min; i++) {
+        matrix.set(i, i, data[i]);
       }
 
-      return row;
+      return matrix;
     }
 
-    getRowVector(index) {
-      return Matrix.rowVector(this.getRow(index));
+    static min(matrix1, matrix2) {
+      matrix1 = this.checkMatrix(matrix1);
+      matrix2 = this.checkMatrix(matrix2);
+      let rows = matrix1.rows;
+      let columns = matrix1.columns;
+      let result = new Matrix(rows, columns);
+
+      for (let i = 0; i < rows; i++) {
+        for (let j = 0; j < columns; j++) {
+          result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));
+        }
+      }
+
+      return result;
     }
 
-    setRow(index, array) {
-      checkRowIndex(this, index);
-      array = checkRowVector(this, array);
+    static max(matrix1, matrix2) {
+      matrix1 = this.checkMatrix(matrix1);
+      matrix2 = this.checkMatrix(matrix2);
+      let rows = matrix1.rows;
+      let columns = matrix1.columns;
+      let result = new this(rows, columns);
 
-      for (let i = 0; i < this.columns; i++) {
-        this.set(index, i, array[i]);
+      for (let i = 0; i < rows; i++) {
+        for (let j = 0; j < columns; j++) {
+          result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));
+        }
       }
 
-      return this;
+      return result;
     }
 
-    swapRows(row1, row2) {
-      checkRowIndex(this, row1);
-      checkRowIndex(this, row2);
-
-      for (let i = 0; i < this.columns; i++) {
-        let temp = this.get(row1, i);
-        this.set(row1, i, this.get(row2, i));
-        this.set(row2, i, temp);
-      }
-
-      return this;
-    }
-
-    getColumn(index) {
-      checkColumnIndex(this, index);
-      let column = [];
-
-      for (let i = 0; i < this.rows; i++) {
-        column.push(this.get(i, index));
-      }
-
-      return column;
+    static checkMatrix(value) {
+      return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);
     }
 
-    getColumnVector(index) {
-      return Matrix.columnVector(this.getColumn(index));
+    static isMatrix(value) {
+      return value != null && value.klass === 'Matrix';
     }
 
-    setColumn(index, array) {
-      checkColumnIndex(this, index);
-      array = checkColumnVector(this, array);
-
-      for (let i = 0; i < this.rows; i++) {
-        this.set(i, index, array[i]);
-      }
-
-      return this;
+    get size() {
+      return this.rows * this.columns;
     }
 
-    swapColumns(column1, column2) {
-      checkColumnIndex(this, column1);
-      checkColumnIndex(this, column2);
-
-      for (let i = 0; i < this.rows; i++) {
-        let temp = this.get(i, column1);
-        this.set(i, column1, this.get(i, column2));
-        this.set(i, column2, temp);
+    apply(callback) {
+      if (typeof callback !== 'function') {
+        throw new TypeError('callback must be a function');
       }
 
-      return this;
-    }
-
-    addRowVector(vector) {
-      vector = checkRowVector(this, vector);
-
       for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + vector[j]);
+          callback.call(this, i, j);
         }
       }
 
       return this;
     }
 
-    subRowVector(vector) {
-      vector = checkRowVector(this, vector);
+    to1DArray() {
+      let array = [];
 
       for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - vector[j]);
+          array.push(this.get(i, j));
         }
       }
 
-      return this;
+      return array;
     }
 
-    mulRowVector(vector) {
-      vector = checkRowVector(this, vector);
+    to2DArray() {
+      let copy = [];
 
       for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * vector[j]);
-        }
-      }
-
-      return this;
-    }
-
-    divRowVector(vector) {
-      vector = checkRowVector(this, vector);
+        copy.push([]);
 
-      for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / vector[j]);
+          copy[i].push(this.get(i, j));
         }
       }
 
-      return this;
+      return copy;
     }
 
-    addColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + vector[i]);
-        }
-      }
-
-      return this;
+    toJSON() {
+      return this.to2DArray();
     }
 
-    subColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
+    isRowVector() {
+      return this.rows === 1;
+    }
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - vector[i]);
-        }
-      }
+    isColumnVector() {
+      return this.columns === 1;
+    }
 
-      return this;
+    isVector() {
+      return this.rows === 1 || this.columns === 1;
     }
 
-    mulColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
+    isSquare() {
+      return this.rows === this.columns;
+    }
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * vector[i]);
+    isSymmetric() {
+      if (this.isSquare()) {
+        for (let i = 0; i < this.rows; i++) {
+          for (let j = 0; j <= i; j++) {
+            if (this.get(i, j) !== this.get(j, i)) {
+              return false;
+            }
+          }
         }
+
+        return true;
       }
 
-      return this;
+      return false;
     }
 
-    divColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / vector[i]);
-        }
-      }
+    isEchelonForm() {
+      let i = 0;
+      let j = 0;
+      let previousColumn = -1;
+      let isEchelonForm = true;
+      let checked = false;
 
-      return this;
-    }
+      while (i < this.rows && isEchelonForm) {
+        j = 0;
+        checked = false;
 
-    mulRow(index, value) {
-      checkRowIndex(this, index);
+        while (j < this.columns && checked === false) {
+          if (this.get(i, j) === 0) {
+            j++;
+          } else if (this.get(i, j) === 1 && j > previousColumn) {
+            checked = true;
+            previousColumn = j;
+          } else {
+            isEchelonForm = false;
+            checked = true;
+          }
+        }
 
-      for (let i = 0; i < this.columns; i++) {
-        this.set(index, i, this.get(index, i) * value);
+        i++;
       }
 
-      return this;
+      return isEchelonForm;
     }
 
-    mulColumn(index, value) {
-      checkColumnIndex(this, index);
-
-      for (let i = 0; i < this.rows; i++) {
-        this.set(i, index, this.get(i, index) * value);
-      }
+    isReducedEchelonForm() {
+      let i = 0;
+      let j = 0;
+      let previousColumn = -1;
+      let isReducedEchelonForm = true;
+      let checked = false;
 
-      return this;
-    }
+      while (i < this.rows && isReducedEchelonForm) {
+        j = 0;
+        checked = false;
 
-    max() {
-      let v = this.get(0, 0);
+        while (j < this.columns && checked === false) {
+          if (this.get(i, j) === 0) {
+            j++;
+          } else if (this.get(i, j) === 1 && j > previousColumn) {
+            checked = true;
+            previousColumn = j;
+          } else {
+            isReducedEchelonForm = false;
+            checked = true;
+          }
+        }
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          if (this.get(i, j) > v) {
-            v = this.get(i, j);
+        for (let k = j + 1; k < this.rows; k++) {
+          if (this.get(i, k) !== 0) {
+            isReducedEchelonForm = false;
           }
         }
+
+        i++;
       }
 
-      return v;
+      return isReducedEchelonForm;
     }
 
-    maxIndex() {
-      let v = this.get(0, 0);
-      let idx = [0, 0];
+    echelonForm() {
+      let result = this.clone();
+      let h = 0;
+      let k = 0;
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          if (this.get(i, j) > v) {
-            v = this.get(i, j);
-            idx[0] = i;
-            idx[1] = j;
+      while (h < result.rows && k < result.columns) {
+        let iMax = h;
+
+        for (let i = h; i < result.rows; i++) {
+          if (result.get(i, k) > result.get(iMax, k)) {
+            iMax = i;
           }
         }
-      }
 
-      return idx;
-    }
+        if (result.get(iMax, k) === 0) {
+          k++;
+        } else {
+          result.swapRows(h, iMax);
+          let tmp = result.get(h, k);
 
-    min() {
-      let v = this.get(0, 0);
+          for (let j = k; j < result.columns; j++) {
+            result.set(h, j, result.get(h, j) / tmp);
+          }
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          if (this.get(i, j) < v) {
-            v = this.get(i, j);
+          for (let i = h + 1; i < result.rows; i++) {
+            let factor = result.get(i, k) / result.get(h, k);
+            result.set(i, k, 0);
+
+            for (let j = k + 1; j < result.columns; j++) {
+              result.set(i, j, result.get(i, j) - result.get(h, j) * factor);
+            }
           }
+
+          h++;
+          k++;
         }
       }
 
-      return v;
+      return result;
     }
 
-    minIndex() {
-      let v = this.get(0, 0);
-      let idx = [0, 0];
+    reducedEchelonForm() {
+      let result = this.echelonForm();
+      let m = result.columns;
+      let n = result.rows;
+      let h = n - 1;
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          if (this.get(i, j) < v) {
-            v = this.get(i, j);
-            idx[0] = i;
-            idx[1] = j;
+      while (h >= 0) {
+        if (result.maxRow(h) === 0) {
+          h--;
+        } else {
+          let p = 0;
+          let pivot = false;
+
+          while (p < n && pivot === false) {
+            if (result.get(h, p) === 1) {
+              pivot = true;
+            } else {
+              p++;
+            }
+          }
+
+          for (let i = 0; i < h; i++) {
+            let factor = result.get(i, p);
+
+            for (let j = p; j < m; j++) {
+              let tmp = result.get(i, j) - factor * result.get(h, j);
+              result.set(i, j, tmp);
+            }
           }
+
+          h--;
         }
       }
 
-      return idx;
+      return result;
     }
 
-    maxRow(row) {
-      checkRowIndex(this, row);
-      let v = this.get(row, 0);
+    set() {
+      throw new Error('set method is unimplemented');
+    }
 
-      for (let i = 1; i < this.columns; i++) {
-        if (this.get(row, i) > v) {
-          v = this.get(row, i);
-        }
+    get() {
+      throw new Error('get method is unimplemented');
+    }
+
+    repeat(options = {}) {
+      if (typeof options !== 'object') {
+        throw new TypeError('options must be an object');
       }
 
-      return v;
-    }
+      const {
+        rows = 1,
+        columns = 1
+      } = options;
 
-    maxRowIndex(row) {
-      checkRowIndex(this, row);
-      let v = this.get(row, 0);
-      let idx = [row, 0];
+      if (!Number.isInteger(rows) || rows <= 0) {
+        throw new TypeError('rows must be a positive integer');
+      }
 
-      for (let i = 1; i < this.columns; i++) {
-        if (this.get(row, i) > v) {
-          v = this.get(row, i);
-          idx[1] = i;
+      if (!Number.isInteger(columns) || columns <= 0) {
+        throw new TypeError('columns must be a positive integer');
+      }
+
+      let matrix = new Matrix(this.rows * rows, this.columns * columns);
+
+      for (let i = 0; i < rows; i++) {
+        for (let j = 0; j < columns; j++) {
+          matrix.setSubMatrix(this, this.rows * i, this.columns * j);
         }
       }
 
-      return idx;
+      return matrix;
     }
 
-    minRow(row) {
-      checkRowIndex(this, row);
-      let v = this.get(row, 0);
-
-      for (let i = 1; i < this.columns; i++) {
-        if (this.get(row, i) < v) {
-          v = this.get(row, i);
+    fill(value) {
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, value);
         }
       }
 
-      return v;
+      return this;
     }
 
-    minRowIndex(row) {
-      checkRowIndex(this, row);
-      let v = this.get(row, 0);
-      let idx = [row, 0];
+    neg() {
+      return this.mulS(-1);
+    }
 
-      for (let i = 1; i < this.columns; i++) {
-        if (this.get(row, i) < v) {
-          v = this.get(row, i);
-          idx[1] = i;
-        }
+    getRow(index) {
+      checkRowIndex(this, index);
+      let row = [];
+
+      for (let i = 0; i < this.columns; i++) {
+        row.push(this.get(index, i));
       }
 
-      return idx;
+      return row;
     }
 
-    maxColumn(column) {
-      checkColumnIndex(this, column);
-      let v = this.get(0, column);
+    getRowVector(index) {
+      return Matrix.rowVector(this.getRow(index));
+    }
 
-      for (let i = 1; i < this.rows; i++) {
-        if (this.get(i, column) > v) {
-          v = this.get(i, column);
-        }
+    setRow(index, array) {
+      checkRowIndex(this, index);
+      array = checkRowVector(this, array);
+
+      for (let i = 0; i < this.columns; i++) {
+        this.set(index, i, array[i]);
       }
 
-      return v;
+      return this;
     }
 
-    maxColumnIndex(column) {
-      checkColumnIndex(this, column);
-      let v = this.get(0, column);
-      let idx = [0, column];
+    swapRows(row1, row2) {
+      checkRowIndex(this, row1);
+      checkRowIndex(this, row2);
 
-      for (let i = 1; i < this.rows; i++) {
-        if (this.get(i, column) > v) {
-          v = this.get(i, column);
-          idx[0] = i;
-        }
+      for (let i = 0; i < this.columns; i++) {
+        let temp = this.get(row1, i);
+        this.set(row1, i, this.get(row2, i));
+        this.set(row2, i, temp);
       }
 
-      return idx;
+      return this;
     }
 
-    minColumn(column) {
-      checkColumnIndex(this, column);
-      let v = this.get(0, column);
+    getColumn(index) {
+      checkColumnIndex(this, index);
+      let column = [];
 
-      for (let i = 1; i < this.rows; i++) {
-        if (this.get(i, column) < v) {
-          v = this.get(i, column);
-        }
+      for (let i = 0; i < this.rows; i++) {
+        column.push(this.get(i, index));
       }
 
-      return v;
+      return column;
     }
 
-    minColumnIndex(column) {
-      checkColumnIndex(this, column);
-      let v = this.get(0, column);
-      let idx = [0, column];
+    getColumnVector(index) {
+      return Matrix.columnVector(this.getColumn(index));
+    }
 
-      for (let i = 1; i < this.rows; i++) {
-        if (this.get(i, column) < v) {
-          v = this.get(i, column);
-          idx[0] = i;
-        }
+    setColumn(index, array) {
+      checkColumnIndex(this, index);
+      array = checkColumnVector(this, array);
+
+      for (let i = 0; i < this.rows; i++) {
+        this.set(i, index, array[i]);
       }
 
-      return idx;
+      return this;
     }
 
-    diag() {
-      let min = Math.min(this.rows, this.columns);
-      let diag = [];
+    swapColumns(column1, column2) {
+      checkColumnIndex(this, column1);
+      checkColumnIndex(this, column2);
 
-      for (let i = 0; i < min; i++) {
-        diag.push(this.get(i, i));
+      for (let i = 0; i < this.rows; i++) {
+        let temp = this.get(i, column1);
+        this.set(i, column1, this.get(i, column2));
+        this.set(i, column2, temp);
       }
 
-      return diag;
+      return this;
     }
 
-    norm() {
-      let type = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'frobenius';
-      let result = 0;
+    addRowVector(vector) {
+      vector = checkRowVector(this, vector);
 
-      if (type === 'max') {
-        return this.max();
-      } else if (type === 'frobenius') {
-        for (let i = 0; i < this.rows; i++) {
-          for (let j = 0; j < this.columns; j++) {
-            result = result + this.get(i, j) * this.get(i, j);
-          }
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) + vector[j]);
         }
-
-        return Math.sqrt(result);
-      } else {
-        throw new RangeError("unknown norm type: ".concat(type));
       }
+
+      return this;
     }
 
-    cumulativeSum() {
-      let sum = 0;
+    subRowVector(vector) {
+      vector = checkRowVector(this, vector);
 
       for (let i = 0; i < this.rows; i++) {
         for (let j = 0; j < this.columns; j++) {
-          sum += this.get(i, j);
-          this.set(i, j, sum);
+          this.set(i, j, this.get(i, j) - vector[j]);
         }
       }
 
       return this;
     }
 
-    dot(vector2) {
-      if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();
-      let vector1 = this.to1DArray();
+    mulRowVector(vector) {
+      vector = checkRowVector(this, vector);
 
-      if (vector1.length !== vector2.length) {
-        throw new RangeError('vectors do not have the same size');
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) * vector[j]);
+        }
       }
 
-      let dot = 0;
+      return this;
+    }
 
-      for (let i = 0; i < vector1.length; i++) {
-        dot += vector1[i] * vector2[i];
+    divRowVector(vector) {
+      vector = checkRowVector(this, vector);
+
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) / vector[j]);
+        }
       }
 
-      return dot;
+      return this;
     }
 
-    mmul(other) {
-      other = Matrix.checkMatrix(other);
-      let m = this.rows;
-      let n = this.columns;
-      let p = other.columns;
-      let result = new Matrix(m, p);
-      let Bcolj = new Float64Array(n);
+    addColumnVector(vector) {
+      vector = checkColumnVector(this, vector);
 
-      for (let j = 0; j < p; j++) {
-        for (let k = 0; k < n; k++) {
-          Bcolj[k] = other.get(k, j);
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) + vector[i]);
         }
+      }
 
-        for (let i = 0; i < m; i++) {
-          let s = 0;
+      return this;
+    }
 
-          for (let k = 0; k < n; k++) {
-            s += this.get(i, k) * Bcolj[k];
-          }
+    subColumnVector(vector) {
+      vector = checkColumnVector(this, vector);
 
-          result.set(i, j, s);
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) - vector[i]);
         }
       }
 
-      return result;
+      return this;
     }
 
-    strassen2x2(other) {
-      other = Matrix.checkMatrix(other);
-      let result = new Matrix(2, 2);
-      const a11 = this.get(0, 0);
-      const b11 = other.get(0, 0);
-      const a12 = this.get(0, 1);
-      const b12 = other.get(0, 1);
-      const a21 = this.get(1, 0);
-      const b21 = other.get(1, 0);
-      const a22 = this.get(1, 1);
-      const b22 = other.get(1, 1); // Compute intermediate values.
+    mulColumnVector(vector) {
+      vector = checkColumnVector(this, vector);
 
-      const m1 = (a11 + a22) * (b11 + b22);
-      const m2 = (a21 + a22) * b11;
-      const m3 = a11 * (b12 - b22);
-      const m4 = a22 * (b21 - b11);
-      const m5 = (a11 + a12) * b22;
-      const m6 = (a21 - a11) * (b11 + b12);
-      const m7 = (a12 - a22) * (b21 + b22); // Combine intermediate values into the output.
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) * vector[i]);
+        }
+      }
 
-      const c00 = m1 + m4 - m5 + m7;
-      const c01 = m3 + m5;
-      const c10 = m2 + m4;
-      const c11 = m1 - m2 + m3 + m6;
-      result.set(0, 0, c00);
-      result.set(0, 1, c01);
-      result.set(1, 0, c10);
-      result.set(1, 1, c11);
-      return result;
+      return this;
     }
 
-    strassen3x3(other) {
-      other = Matrix.checkMatrix(other);
-      let result = new Matrix(3, 3);
-      const a00 = this.get(0, 0);
-      const a01 = this.get(0, 1);
-      const a02 = this.get(0, 2);
-      const a10 = this.get(1, 0);
-      const a11 = this.get(1, 1);
-      const a12 = this.get(1, 2);
-      const a20 = this.get(2, 0);
-      const a21 = this.get(2, 1);
-      const a22 = this.get(2, 2);
-      const b00 = other.get(0, 0);
-      const b01 = other.get(0, 1);
-      const b02 = other.get(0, 2);
-      const b10 = other.get(1, 0);
-      const b11 = other.get(1, 1);
-      const b12 = other.get(1, 2);
-      const b20 = other.get(2, 0);
-      const b21 = other.get(2, 1);
-      const b22 = other.get(2, 2);
-      const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;
-      const m2 = (a00 - a10) * (-b01 + b11);
-      const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);
-      const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);
-      const m5 = (a10 + a11) * (-b00 + b01);
-      const m6 = a00 * b00;
-      const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);
-      const m8 = (-a00 + a20) * (b02 - b12);
-      const m9 = (a20 + a21) * (-b00 + b02);
-      const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;
-      const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);
-      const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);
-      const m13 = (a02 - a22) * (b11 - b21);
-      const m14 = a02 * b20;
-      const m15 = (a21 + a22) * (-b20 + b21);
-      const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);
-      const m17 = (a02 - a12) * (b12 - b22);
-      const m18 = (a11 + a12) * (-b20 + b22);
-      const m19 = a01 * b10;
-      const m20 = a12 * b21;
-      const m21 = a10 * b02;
-      const m22 = a20 * b01;
-      const m23 = a22 * b22;
-      const c00 = m6 + m14 + m19;
-      const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;
-      const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;
-      const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;
-      const c11 = m2 + m4 + m5 + m6 + m20;
-      const c12 = m14 + m16 + m17 + m18 + m21;
-      const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;
-      const c21 = m12 + m13 + m14 + m15 + m22;
-      const c22 = m6 + m7 + m8 + m9 + m23;
-      result.set(0, 0, c00);
-      result.set(0, 1, c01);
-      result.set(0, 2, c02);
-      result.set(1, 0, c10);
-      result.set(1, 1, c11);
-      result.set(1, 2, c12);
-      result.set(2, 0, c20);
-      result.set(2, 1, c21);
-      result.set(2, 2, c22);
-      return result;
-    }
+    divColumnVector(vector) {
+      vector = checkColumnVector(this, vector);
 
-    mmulStrassen(y) {
-      y = Matrix.checkMatrix(y);
-      let x = this.clone();
-      let r1 = x.rows;
-      let c1 = x.columns;
-      let r2 = y.rows;
-      let c2 = y.columns;
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          this.set(i, j, this.get(i, j) / vector[i]);
+        }
+      }
 
-      if (c1 !== r2) {
-        // eslint-disable-next-line no-console
-        console.warn("Multiplying ".concat(r1, " x ").concat(c1, " and ").concat(r2, " x ").concat(c2, " matrix: dimensions do not match."));
-      } // Put a matrix into the top left of a matrix of zeros.
-      // `rows` and `cols` are the dimensions of the output matrix.
+      return this;
+    }
 
+    mulRow(index, value) {
+      checkRowIndex(this, index);
 
-      function embed(mat, rows, cols) {
-        let r = mat.rows;
-        let c = mat.columns;
+      for (let i = 0; i < this.columns; i++) {
+        this.set(index, i, this.get(index, i) * value);
+      }
 
-        if (r === rows && c === cols) {
-          return mat;
-        } else {
-          let resultat = AbstractMatrix.zeros(rows, cols);
-          resultat = resultat.setSubMatrix(mat, 0, 0);
-          return resultat;
-        }
-      } // Make sure both matrices are the same size.
-      // This is exclusively for simplicity:
-      // this algorithm can be implemented with matrices of different sizes.
+      return this;
+    }
 
+    mulColumn(index, value) {
+      checkColumnIndex(this, index);
 
-      let r = Math.max(r1, r2);
-      let c = Math.max(c1, c2);
-      x = embed(x, r, c);
-      y = embed(y, r, c); // Our recursive multiplication function.
+      for (let i = 0; i < this.rows; i++) {
+        this.set(i, index, this.get(i, index) * value);
+      }
 
-      function blockMult(a, b, rows, cols) {
-        // For small matrices, resort to naive multiplication.
-        if (rows <= 512 || cols <= 512) {
-          return a.mmul(b); // a is equivalent to this
-        } // Apply dynamic padding.
+      return this;
+    }
 
+    max() {
+      let v = this.get(0, 0);
 
-        if (rows % 2 === 1 && cols % 2 === 1) {
-          a = embed(a, rows + 1, cols + 1);
-          b = embed(b, rows + 1, cols + 1);
-        } else if (rows % 2 === 1) {
-          a = embed(a, rows + 1, cols);
-          b = embed(b, rows + 1, cols);
-        } else if (cols % 2 === 1) {
-          a = embed(a, rows, cols + 1);
-          b = embed(b, rows, cols + 1);
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          if (this.get(i, j) > v) {
+            v = this.get(i, j);
+          }
         }
+      }
 
-        let halfRows = parseInt(a.rows / 2, 10);
-        let halfCols = parseInt(a.columns / 2, 10); // Subdivide input matrices.
-
-        let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);
-        let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);
-        let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);
-        let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);
-        let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);
-        let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);
-        let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);
-        let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1); // Compute intermediate values.
-
-        let m1 = blockMult(AbstractMatrix.add(a11, a22), AbstractMatrix.add(b11, b22), halfRows, halfCols);
-        let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);
-        let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);
-        let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);
-        let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);
-        let m6 = blockMult(AbstractMatrix.sub(a21, a11), AbstractMatrix.add(b11, b12), halfRows, halfCols);
-        let m7 = blockMult(AbstractMatrix.sub(a12, a22), AbstractMatrix.add(b21, b22), halfRows, halfCols); // Combine intermediate values into the output.
+      return v;
+    }
 
-        let c11 = AbstractMatrix.add(m1, m4);
-        c11.sub(m5);
-        c11.add(m7);
-        let c12 = AbstractMatrix.add(m3, m5);
-        let c21 = AbstractMatrix.add(m2, m4);
-        let c22 = AbstractMatrix.sub(m1, m2);
-        c22.add(m3);
-        c22.add(m6); // Crop output to the desired size (undo dynamic padding).
+    maxIndex() {
+      let v = this.get(0, 0);
+      let idx = [0, 0];
 
-        let resultat = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);
-        resultat = resultat.setSubMatrix(c11, 0, 0);
-        resultat = resultat.setSubMatrix(c12, c11.rows, 0);
-        resultat = resultat.setSubMatrix(c21, 0, c11.columns);
-        resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);
-        return resultat.subMatrix(0, rows - 1, 0, cols - 1);
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          if (this.get(i, j) > v) {
+            v = this.get(i, j);
+            idx[0] = i;
+            idx[1] = j;
+          }
+        }
       }
 
-      return blockMult(x, y, r, c);
+      return idx;
     }
 
-    scaleRows() {
-      let options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
+    min() {
+      let v = this.get(0, 0);
 
-      if (typeof options !== 'object') {
-        throw new TypeError('options must be an object');
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          if (this.get(i, j) < v) {
+            v = this.get(i, j);
+          }
+        }
       }
 
-      const {
-        min = 0,
-        max = 1
-      } = options;
-      if (!Number.isFinite(min)) throw new TypeError('min must be a number');
-      if (!Number.isFinite(max)) throw new TypeError('max must be a number');
-      if (min >= max) throw new RangeError('min must be smaller than max');
-      let newMatrix = new Matrix(this.rows, this.columns);
-
-      for (let i = 0; i < this.rows; i++) {
-        const row = this.getRow(i);
-        rescale(row, {
-          min,
-          max,
-          output: row
-        });
-        newMatrix.setRow(i, row);
-      }
-
-      return newMatrix;
-    }
-
-    scaleColumns() {
-      let options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
-
-      if (typeof options !== 'object') {
-        throw new TypeError('options must be an object');
-      }
-
-      const {
-        min = 0,
-        max = 1
-      } = options;
-      if (!Number.isFinite(min)) throw new TypeError('min must be a number');
-      if (!Number.isFinite(max)) throw new TypeError('max must be a number');
-      if (min >= max) throw new RangeError('min must be smaller than max');
-      let newMatrix = new Matrix(this.rows, this.columns);
-
-      for (let i = 0; i < this.columns; i++) {
-        const column = this.getColumn(i);
-        rescale(column, {
-          min: min,
-          max: max,
-          output: column
-        });
-        newMatrix.setColumn(i, column);
-      }
-
-      return newMatrix;
+      return v;
     }
 
-    flipRows() {
-      const middle = Math.ceil(this.columns / 2);
+    minIndex() {
+      let v = this.get(0, 0);
+      let idx = [0, 0];
 
       for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < middle; j++) {
-          let first = this.get(i, j);
-          let last = this.get(i, this.columns - 1 - j);
-          this.set(i, j, last);
-          this.set(i, this.columns - 1 - j, first);
+        for (let j = 0; j < this.columns; j++) {
+          if (this.get(i, j) < v) {
+            v = this.get(i, j);
+            idx[0] = i;
+            idx[1] = j;
+          }
         }
       }
 
-      return this;
+      return idx;
     }
 
-    flipColumns() {
-      const middle = Math.ceil(this.rows / 2);
+    maxRow(row) {
+      checkRowIndex(this, row);
+      let v = this.get(row, 0);
 
-      for (let j = 0; j < this.columns; j++) {
-        for (let i = 0; i < middle; i++) {
-          let first = this.get(i, j);
-          let last = this.get(this.rows - 1 - i, j);
-          this.set(i, j, last);
-          this.set(this.rows - 1 - i, j, first);
+      for (let i = 1; i < this.columns; i++) {
+        if (this.get(row, i) > v) {
+          v = this.get(row, i);
         }
       }
 
-      return this;
+      return v;
     }
 
-    kroneckerProduct(other) {
-      other = Matrix.checkMatrix(other);
-      let m = this.rows;
-      let n = this.columns;
-      let p = other.rows;
-      let q = other.columns;
-      let result = new Matrix(m * p, n * q);
+    maxRowIndex(row) {
+      checkRowIndex(this, row);
+      let v = this.get(row, 0);
+      let idx = [row, 0];
 
-      for (let i = 0; i < m; i++) {
-        for (let j = 0; j < n; j++) {
-          for (let k = 0; k < p; k++) {
-            for (let l = 0; l < q; l++) {
-              result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));
-            }
-          }
+      for (let i = 1; i < this.columns; i++) {
+        if (this.get(row, i) > v) {
+          v = this.get(row, i);
+          idx[1] = i;
         }
       }
 
-      return result;
+      return idx;
     }
 
-    transpose() {
-      let result = new Matrix(this.columns, this.rows);
+    minRow(row) {
+      checkRowIndex(this, row);
+      let v = this.get(row, 0);
 
-      for (let i = 0; i < this.rows; i++) {
-        for (let j = 0; j < this.columns; j++) {
-          result.set(j, i, this.get(i, j));
+      for (let i = 1; i < this.columns; i++) {
+        if (this.get(row, i) < v) {
+          v = this.get(row, i);
         }
       }
 
-      return result;
+      return v;
     }
 
-    sortRows() {
-      let compareFunction = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : compareNumbers;
+    minRowIndex(row) {
+      checkRowIndex(this, row);
+      let v = this.get(row, 0);
+      let idx = [row, 0];
 
-      for (let i = 0; i < this.rows; i++) {
-        this.setRow(i, this.getRow(i).sort(compareFunction));
+      for (let i = 1; i < this.columns; i++) {
+        if (this.get(row, i) < v) {
+          v = this.get(row, i);
+          idx[1] = i;
+        }
       }
 
-      return this;
+      return idx;
     }
 
-    sortColumns() {
-      let compareFunction = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : compareNumbers;
+    maxColumn(column) {
+      checkColumnIndex(this, column);
+      let v = this.get(0, column);
 
-      for (let i = 0; i < this.columns; i++) {
-        this.setColumn(i, this.getColumn(i).sort(compareFunction));
+      for (let i = 1; i < this.rows; i++) {
+        if (this.get(i, column) > v) {
+          v = this.get(i, column);
+        }
       }
 
-      return this;
+      return v;
     }
 
-    subMatrix(startRow, endRow, startColumn, endColumn) {
-      checkRange(this, startRow, endRow, startColumn, endColumn);
-      let newMatrix = new Matrix(endRow - startRow + 1, endColumn - startColumn + 1);
+    maxColumnIndex(column) {
+      checkColumnIndex(this, column);
+      let v = this.get(0, column);
+      let idx = [0, column];
 
-      for (let i = startRow; i <= endRow; i++) {
-        for (let j = startColumn; j <= endColumn; j++) {
-          newMatrix.set(i - startRow, j - startColumn, this.get(i, j));
+      for (let i = 1; i < this.rows; i++) {
+        if (this.get(i, column) > v) {
+          v = this.get(i, column);
+          idx[0] = i;
         }
       }
 
-      return newMatrix;
+      return idx;
     }
 
-    subMatrixRow(indices, startColumn, endColumn) {
-      if (startColumn === undefined) startColumn = 0;
-      if (endColumn === undefined) endColumn = this.columns - 1;
+    minColumn(column) {
+      checkColumnIndex(this, column);
+      let v = this.get(0, column);
 
-      if (startColumn > endColumn || startColumn < 0 || startColumn >= this.columns || endColumn < 0 || endColumn >= this.columns) {
-        throw new RangeError('Argument out of range');
+      for (let i = 1; i < this.rows; i++) {
+        if (this.get(i, column) < v) {
+          v = this.get(i, column);
+        }
       }
 
-      let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);
+      return v;
+    }
 
-      for (let i = 0; i < indices.length; i++) {
-        for (let j = startColumn; j <= endColumn; j++) {
-          if (indices[i] < 0 || indices[i] >= this.rows) {
-            throw new RangeError("Row index out of range: ".concat(indices[i]));
-          }
+    minColumnIndex(column) {
+      checkColumnIndex(this, column);
+      let v = this.get(0, column);
+      let idx = [0, column];
 
-          newMatrix.set(i, j - startColumn, this.get(indices[i], j));
+      for (let i = 1; i < this.rows; i++) {
+        if (this.get(i, column) < v) {
+          v = this.get(i, column);
+          idx[0] = i;
         }
       }
 
-      return newMatrix;
+      return idx;
     }
 
-    subMatrixColumn(indices, startRow, endRow) {
-      if (startRow === undefined) startRow = 0;
-      if (endRow === undefined) endRow = this.rows - 1;
+    diag() {
+      let min = Math.min(this.rows, this.columns);
+      let diag = [];
 
-      if (startRow > endRow || startRow < 0 || startRow >= this.rows || endRow < 0 || endRow >= this.rows) {
-        throw new RangeError('Argument out of range');
+      for (let i = 0; i < min; i++) {
+        diag.push(this.get(i, i));
       }
 
-      let newMatrix = new Matrix(endRow - startRow + 1, indices.length);
+      return diag;
+    }
 
-      for (let i = 0; i < indices.length; i++) {
-        for (let j = startRow; j <= endRow; j++) {
-          if (indices[i] < 0 || indices[i] >= this.columns) {
-            throw new RangeError("Column index out of range: ".concat(indices[i]));
-          }
+    norm(type = 'frobenius') {
+      let result = 0;
 
-          newMatrix.set(j - startRow, i, this.get(j, indices[i]));
+      if (type === 'max') {
+        return this.max();
+      } else if (type === 'frobenius') {
+        for (let i = 0; i < this.rows; i++) {
+          for (let j = 0; j < this.columns; j++) {
+            result = result + this.get(i, j) * this.get(i, j);
+          }
         }
-      }
 
-      return newMatrix;
+        return Math.sqrt(result);
+      } else {
+        throw new RangeError(`unknown norm type: ${type}`);
+      }
     }
 
-    setSubMatrix(matrix, startRow, startColumn) {
-      matrix = Matrix.checkMatrix(matrix);
-      let endRow = startRow + matrix.rows - 1;
-      let endColumn = startColumn + matrix.columns - 1;
-      checkRange(this, startRow, endRow, startColumn, endColumn);
+    cumulativeSum() {
+      let sum = 0;
 
-      for (let i = 0; i < matrix.rows; i++) {
-        for (let j = 0; j < matrix.columns; j++) {
-          this.set(startRow + i, startColumn + j, matrix.get(i, j));
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          sum += this.get(i, j);
+          this.set(i, j, sum);
         }
       }
 
       return this;
     }
 
-    selection(rowIndices, columnIndices) {
-      let indices = checkIndices(this, rowIndices, columnIndices);
-      let newMatrix = new Matrix(rowIndices.length, columnIndices.length);
+    dot(vector2) {
+      if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();
+      let vector1 = this.to1DArray();
 
-      for (let i = 0; i < indices.row.length; i++) {
-        let rowIndex = indices.row[i];
+      if (vector1.length !== vector2.length) {
+        throw new RangeError('vectors do not have the same size');
+      }
 
-        for (let j = 0; j < indices.column.length; j++) {
-          let columnIndex = indices.column[j];
-          newMatrix.set(i, j, this.get(rowIndex, columnIndex));
-        }
+      let dot = 0;
+
+      for (let i = 0; i < vector1.length; i++) {
+        dot += vector1[i] * vector2[i];
       }
 
-      return newMatrix;
+      return dot;
     }
 
-    trace() {
-      let min = Math.min(this.rows, this.columns);
-      let trace = 0;
+    mmul(other) {
+      other = Matrix.checkMatrix(other);
+      let m = this.rows;
+      let n = this.columns;
+      let p = other.columns;
+      let result = new Matrix(m, p);
+      let Bcolj = new Float64Array(n);
 
-      for (let i = 0; i < min; i++) {
-        trace += this.get(i, i);
-      }
+      for (let j = 0; j < p; j++) {
+        for (let k = 0; k < n; k++) {
+          Bcolj[k] = other.get(k, j);
+        }
 
-      return trace;
-    }
+        for (let i = 0; i < m; i++) {
+          let s = 0;
 
-    clone() {
-      let newMatrix = new Matrix(this.rows, this.columns);
+          for (let k = 0; k < n; k++) {
+            s += this.get(i, k) * Bcolj[k];
+          }
 
-      for (let row = 0; row < this.rows; row++) {
-        for (let column = 0; column < this.columns; column++) {
-          newMatrix.set(row, column, this.get(row, column));
+          result.set(i, j, s);
         }
       }
 
-      return newMatrix;
+      return result;
     }
 
-    sum(by) {
-      switch (by) {
-        case 'row':
-          return sumByRow(this);
+    strassen2x2(other) {
+      other = Matrix.checkMatrix(other);
+      let result = new Matrix(2, 2);
+      const a11 = this.get(0, 0);
+      const b11 = other.get(0, 0);
+      const a12 = this.get(0, 1);
+      const b12 = other.get(0, 1);
+      const a21 = this.get(1, 0);
+      const b21 = other.get(1, 0);
+      const a22 = this.get(1, 1);
+      const b22 = other.get(1, 1); // Compute intermediate values.
 
-        case 'column':
-          return sumByColumn(this);
+      const m1 = (a11 + a22) * (b11 + b22);
+      const m2 = (a21 + a22) * b11;
+      const m3 = a11 * (b12 - b22);
+      const m4 = a22 * (b21 - b11);
+      const m5 = (a11 + a12) * b22;
+      const m6 = (a21 - a11) * (b11 + b12);
+      const m7 = (a12 - a22) * (b21 + b22); // Combine intermediate values into the output.
 
-        case undefined:
-          return sumAll(this);
+      const c00 = m1 + m4 - m5 + m7;
+      const c01 = m3 + m5;
+      const c10 = m2 + m4;
+      const c11 = m1 - m2 + m3 + m6;
+      result.set(0, 0, c00);
+      result.set(0, 1, c01);
+      result.set(1, 0, c10);
+      result.set(1, 1, c11);
+      return result;
+    }
 
-        default:
-          throw new Error("invalid option: ".concat(by));
-      }
+    strassen3x3(other) {
+      other = Matrix.checkMatrix(other);
+      let result = new Matrix(3, 3);
+      const a00 = this.get(0, 0);
+      const a01 = this.get(0, 1);
+      const a02 = this.get(0, 2);
+      const a10 = this.get(1, 0);
+      const a11 = this.get(1, 1);
+      const a12 = this.get(1, 2);
+      const a20 = this.get(2, 0);
+      const a21 = this.get(2, 1);
+      const a22 = this.get(2, 2);
+      const b00 = other.get(0, 0);
+      const b01 = other.get(0, 1);
+      const b02 = other.get(0, 2);
+      const b10 = other.get(1, 0);
+      const b11 = other.get(1, 1);
+      const b12 = other.get(1, 2);
+      const b20 = other.get(2, 0);
+      const b21 = other.get(2, 1);
+      const b22 = other.get(2, 2);
+      const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;
+      const m2 = (a00 - a10) * (-b01 + b11);
+      const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);
+      const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);
+      const m5 = (a10 + a11) * (-b00 + b01);
+      const m6 = a00 * b00;
+      const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);
+      const m8 = (-a00 + a20) * (b02 - b12);
+      const m9 = (a20 + a21) * (-b00 + b02);
+      const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;
+      const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);
+      const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);
+      const m13 = (a02 - a22) * (b11 - b21);
+      const m14 = a02 * b20;
+      const m15 = (a21 + a22) * (-b20 + b21);
+      const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);
+      const m17 = (a02 - a12) * (b12 - b22);
+      const m18 = (a11 + a12) * (-b20 + b22);
+      const m19 = a01 * b10;
+      const m20 = a12 * b21;
+      const m21 = a10 * b02;
+      const m22 = a20 * b01;
+      const m23 = a22 * b22;
+      const c00 = m6 + m14 + m19;
+      const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;
+      const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;
+      const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;
+      const c11 = m2 + m4 + m5 + m6 + m20;
+      const c12 = m14 + m16 + m17 + m18 + m21;
+      const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;
+      const c21 = m12 + m13 + m14 + m15 + m22;
+      const c22 = m6 + m7 + m8 + m9 + m23;
+      result.set(0, 0, c00);
+      result.set(0, 1, c01);
+      result.set(0, 2, c02);
+      result.set(1, 0, c10);
+      result.set(1, 1, c11);
+      result.set(1, 2, c12);
+      result.set(2, 0, c20);
+      result.set(2, 1, c21);
+      result.set(2, 2, c22);
+      return result;
     }
 
-    product(by) {
-      switch (by) {
-        case 'row':
-          return productByRow(this);
+    mmulStrassen(y) {
+      y = Matrix.checkMatrix(y);
+      let x = this.clone();
+      let r1 = x.rows;
+      let c1 = x.columns;
+      let r2 = y.rows;
+      let c2 = y.columns;
 
-        case 'column':
-          return productByColumn(this);
+      if (c1 !== r2) {
+        // eslint-disable-next-line no-console
+        console.warn(`Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`);
+      } // Put a matrix into the top left of a matrix of zeros.
+      // `rows` and `cols` are the dimensions of the output matrix.
 
-        case undefined:
-          return productAll(this);
 
-        default:
-          throw new Error("invalid option: ".concat(by));
-      }
-    }
+      function embed(mat, rows, cols) {
+        let r = mat.rows;
+        let c = mat.columns;
 
-    mean(by) {
-      const sum = this.sum(by);
+        if (r === rows && c === cols) {
+          return mat;
+        } else {
+          let resultat = AbstractMatrix.zeros(rows, cols);
+          resultat = resultat.setSubMatrix(mat, 0, 0);
+          return resultat;
+        }
+      } // Make sure both matrices are the same size.
+      // This is exclusively for simplicity:
+      // this algorithm can be implemented with matrices of different sizes.
 
-      switch (by) {
-        case 'row':
-          {
-            for (let i = 0; i < this.rows; i++) {
-              sum[i] /= this.columns;
-            }
 
-            return sum;
-          }
+      let r = Math.max(r1, r2);
+      let c = Math.max(c1, c2);
+      x = embed(x, r, c);
+      y = embed(y, r, c); // Our recursive multiplication function.
 
-        case 'column':
-          {
-            for (let i = 0; i < this.columns; i++) {
-              sum[i] /= this.rows;
-            }
+      function blockMult(a, b, rows, cols) {
+        // For small matrices, resort to naive multiplication.
+        if (rows <= 512 || cols <= 512) {
+          return a.mmul(b); // a is equivalent to this
+        } // Apply dynamic padding.
 
-            return sum;
-          }
 
-        case undefined:
-          return sum / this.size;
+        if (rows % 2 === 1 && cols % 2 === 1) {
+          a = embed(a, rows + 1, cols + 1);
+          b = embed(b, rows + 1, cols + 1);
+        } else if (rows % 2 === 1) {
+          a = embed(a, rows + 1, cols);
+          b = embed(b, rows + 1, cols);
+        } else if (cols % 2 === 1) {
+          a = embed(a, rows, cols + 1);
+          b = embed(b, rows, cols + 1);
+        }
 
-        default:
-          throw new Error("invalid option: ".concat(by));
-      }
-    }
+        let halfRows = parseInt(a.rows / 2, 10);
+        let halfCols = parseInt(a.columns / 2, 10); // Subdivide input matrices.
 
-    variance(by) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+        let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);
+        let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);
+        let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);
+        let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);
+        let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);
+        let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);
+        let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);
+        let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1); // Compute intermediate values.
 
-      if (typeof by === 'object') {
-        options = by;
-        by = undefined;
+        let m1 = blockMult(AbstractMatrix.add(a11, a22), AbstractMatrix.add(b11, b22), halfRows, halfCols);
+        let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);
+        let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);
+        let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);
+        let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);
+        let m6 = blockMult(AbstractMatrix.sub(a21, a11), AbstractMatrix.add(b11, b12), halfRows, halfCols);
+        let m7 = blockMult(AbstractMatrix.sub(a12, a22), AbstractMatrix.add(b21, b22), halfRows, halfCols); // Combine intermediate values into the output.
+
+        let c11 = AbstractMatrix.add(m1, m4);
+        c11.sub(m5);
+        c11.add(m7);
+        let c12 = AbstractMatrix.add(m3, m5);
+        let c21 = AbstractMatrix.add(m2, m4);
+        let c22 = AbstractMatrix.sub(m1, m2);
+        c22.add(m3);
+        c22.add(m6); // Crop output to the desired size (undo dynamic padding).
+
+        let resultat = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);
+        resultat = resultat.setSubMatrix(c11, 0, 0);
+        resultat = resultat.setSubMatrix(c12, c11.rows, 0);
+        resultat = resultat.setSubMatrix(c21, 0, c11.columns);
+        resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);
+        return resultat.subMatrix(0, rows - 1, 0, cols - 1);
       }
 
+      return blockMult(x, y, r, c);
+    }
+
+    scaleRows(options = {}) {
       if (typeof options !== 'object') {
         throw new TypeError('options must be an object');
       }
 
       const {
-        unbiased = true,
-        mean = this.mean(by)
+        min = 0,
+        max = 1
       } = options;
+      if (!Number.isFinite(min)) throw new TypeError('min must be a number');
+      if (!Number.isFinite(max)) throw new TypeError('max must be a number');
+      if (min >= max) throw new RangeError('min must be smaller than max');
+      let newMatrix = new Matrix(this.rows, this.columns);
 
-      if (typeof unbiased !== 'boolean') {
-        throw new TypeError('unbiased must be a boolean');
+      for (let i = 0; i < this.rows; i++) {
+        const row = this.getRow(i);
+        rescale(row, {
+          min,
+          max,
+          output: row
+        });
+        newMatrix.setRow(i, row);
       }
 
-      switch (by) {
-        case 'row':
-          {
-            if (!Array.isArray(mean)) {
-              throw new TypeError('mean must be an array');
-            }
+      return newMatrix;
+    }
 
-            return varianceByRow(this, unbiased, mean);
-          }
+    scaleColumns(options = {}) {
+      if (typeof options !== 'object') {
+        throw new TypeError('options must be an object');
+      }
 
-        case 'column':
-          {
-            if (!Array.isArray(mean)) {
-              throw new TypeError('mean must be an array');
-            }
+      const {
+        min = 0,
+        max = 1
+      } = options;
+      if (!Number.isFinite(min)) throw new TypeError('min must be a number');
+      if (!Number.isFinite(max)) throw new TypeError('max must be a number');
+      if (min >= max) throw new RangeError('min must be smaller than max');
+      let newMatrix = new Matrix(this.rows, this.columns);
 
-            return varianceByColumn(this, unbiased, mean);
-          }
+      for (let i = 0; i < this.columns; i++) {
+        const column = this.getColumn(i);
+        rescale(column, {
+          min: min,
+          max: max,
+          output: column
+        });
+        newMatrix.setColumn(i, column);
+      }
 
-        case undefined:
-          {
-            if (typeof mean !== 'number') {
-              throw new TypeError('mean must be a number');
-            }
+      return newMatrix;
+    }
 
-            return varianceAll(this, unbiased, mean);
-          }
+    flipRows() {
+      const middle = Math.ceil(this.columns / 2);
 
-        default:
-          throw new Error("invalid option: ".concat(by));
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < middle; j++) {
+          let first = this.get(i, j);
+          let last = this.get(i, this.columns - 1 - j);
+          this.set(i, j, last);
+          this.set(i, this.columns - 1 - j, first);
+        }
       }
+
+      return this;
     }
 
-    standardDeviation(by, options) {
-      if (typeof by === 'object') {
-        options = by;
-        by = undefined;
+    flipColumns() {
+      const middle = Math.ceil(this.rows / 2);
+
+      for (let j = 0; j < this.columns; j++) {
+        for (let i = 0; i < middle; i++) {
+          let first = this.get(i, j);
+          let last = this.get(this.rows - 1 - i, j);
+          this.set(i, j, last);
+          this.set(this.rows - 1 - i, j, first);
+        }
       }
 
-      const variance = this.variance(by, options);
+      return this;
+    }
 
-      if (by === undefined) {
-        return Math.sqrt(variance);
-      } else {
-        for (let i = 0; i < variance.length; i++) {
-          variance[i] = Math.sqrt(variance[i]);
-        }
+    kroneckerProduct(other) {
+      other = Matrix.checkMatrix(other);
+      let m = this.rows;
+      let n = this.columns;
+      let p = other.rows;
+      let q = other.columns;
+      let result = new Matrix(m * p, n * q);
 
-        return variance;
+      for (let i = 0; i < m; i++) {
+        for (let j = 0; j < n; j++) {
+          for (let k = 0; k < p; k++) {
+            for (let l = 0; l < q; l++) {
+              result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));
+            }
+          }
+        }
       }
+
+      return result;
     }
 
-    center(by) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+    transpose() {
+      let result = new Matrix(this.columns, this.rows);
 
-      if (typeof by === 'object') {
-        options = by;
-        by = undefined;
+      for (let i = 0; i < this.rows; i++) {
+        for (let j = 0; j < this.columns; j++) {
+          result.set(j, i, this.get(i, j));
+        }
       }
 
-      if (typeof options !== 'object') {
-        throw new TypeError('options must be an object');
+      return result;
+    }
+
+    sortRows(compareFunction = compareNumbers) {
+      for (let i = 0; i < this.rows; i++) {
+        this.setRow(i, this.getRow(i).sort(compareFunction));
       }
 
-      const {
-        center = this.mean(by)
-      } = options;
+      return this;
+    }
 
-      switch (by) {
-        case 'row':
-          {
-            if (!Array.isArray(center)) {
-              throw new TypeError('center must be an array');
-            }
+    sortColumns(compareFunction = compareNumbers) {
+      for (let i = 0; i < this.columns; i++) {
+        this.setColumn(i, this.getColumn(i).sort(compareFunction));
+      }
 
-            centerByRow(this, center);
-            return this;
-          }
+      return this;
+    }
 
-        case 'column':
-          {
-            if (!Array.isArray(center)) {
-              throw new TypeError('center must be an array');
-            }
+    subMatrix(startRow, endRow, startColumn, endColumn) {
+      checkRange(this, startRow, endRow, startColumn, endColumn);
+      let newMatrix = new Matrix(endRow - startRow + 1, endColumn - startColumn + 1);
 
-            centerByColumn(this, center);
-            return this;
-          }
+      for (let i = startRow; i <= endRow; i++) {
+        for (let j = startColumn; j <= endColumn; j++) {
+          newMatrix.set(i - startRow, j - startColumn, this.get(i, j));
+        }
+      }
 
-        case undefined:
-          {
-            if (typeof center !== 'number') {
-              throw new TypeError('center must be a number');
-            }
+      return newMatrix;
+    }
 
-            centerAll(this, center);
-            return this;
-          }
+    subMatrixRow(indices, startColumn, endColumn) {
+      if (startColumn === undefined) startColumn = 0;
+      if (endColumn === undefined) endColumn = this.columns - 1;
 
-        default:
-          throw new Error("invalid option: ".concat(by));
+      if (startColumn > endColumn || startColumn < 0 || startColumn >= this.columns || endColumn < 0 || endColumn >= this.columns) {
+        throw new RangeError('Argument out of range');
       }
-    }
 
-    scale(by) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+      let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);
 
-      if (typeof by === 'object') {
-        options = by;
-        by = undefined;
-      }
+      for (let i = 0; i < indices.length; i++) {
+        for (let j = startColumn; j <= endColumn; j++) {
+          if (indices[i] < 0 || indices[i] >= this.rows) {
+            throw new RangeError(`Row index out of range: ${indices[i]}`);
+          }
 
-      if (typeof options !== 'object') {
-        throw new TypeError('options must be an object');
+          newMatrix.set(i, j - startColumn, this.get(indices[i], j));
+        }
       }
 
-      let scale = options.scale;
+      return newMatrix;
+    }
 
-      switch (by) {
-        case 'row':
-          {
-            if (scale === undefined) {
-              scale = getScaleByRow(this);
-            } else if (!Array.isArray(scale)) {
-              throw new TypeError('scale must be an array');
-            }
+    subMatrixColumn(indices, startRow, endRow) {
+      if (startRow === undefined) startRow = 0;
+      if (endRow === undefined) endRow = this.rows - 1;
 
-            scaleByRow(this, scale);
-            return this;
-          }
+      if (startRow > endRow || startRow < 0 || startRow >= this.rows || endRow < 0 || endRow >= this.rows) {
+        throw new RangeError('Argument out of range');
+      }
 
-        case 'column':
-          {
-            if (scale === undefined) {
-              scale = getScaleByColumn(this);
-            } else if (!Array.isArray(scale)) {
-              throw new TypeError('scale must be an array');
-            }
+      let newMatrix = new Matrix(endRow - startRow + 1, indices.length);
 
-            scaleByColumn(this, scale);
-            return this;
+      for (let i = 0; i < indices.length; i++) {
+        for (let j = startRow; j <= endRow; j++) {
+          if (indices[i] < 0 || indices[i] >= this.columns) {
+            throw new RangeError(`Column index out of range: ${indices[i]}`);
           }
 
-        case undefined:
-          {
-            if (scale === undefined) {
-              scale = getScaleAll(this);
-            } else if (typeof scale !== 'number') {
-              throw new TypeError('scale must be a number');
-            }
+          newMatrix.set(j - startRow, i, this.get(j, indices[i]));
+        }
+      }
 
-            scaleAll(this, scale);
-            return this;
-          }
+      return newMatrix;
+    }
 
-        default:
-          throw new Error("invalid option: ".concat(by));
+    setSubMatrix(matrix, startRow, startColumn) {
+      matrix = Matrix.checkMatrix(matrix);
+      let endRow = startRow + matrix.rows - 1;
+      let endColumn = startColumn + matrix.columns - 1;
+      checkRange(this, startRow, endRow, startColumn, endColumn);
+
+      for (let i = 0; i < matrix.rows; i++) {
+        for (let j = 0; j < matrix.columns; j++) {
+          this.set(startRow + i, startColumn + j, matrix.get(i, j));
+        }
       }
+
+      return this;
     }
 
-  }
-  AbstractMatrix.prototype.klass = 'Matrix';
-
-  if (typeof Symbol !== 'undefined') {
-    AbstractMatrix.prototype[Symbol.for('nodejs.util.inspect.custom')] = inspectMatrix;
-  }
-
-  function compareNumbers(a, b) {
-    return a - b;
-  } // Synonyms
+    selection(rowIndices, columnIndices) {
+      let indices = checkIndices(this, rowIndices, columnIndices);
+      let newMatrix = new Matrix(rowIndices.length, columnIndices.length);
 
+      for (let i = 0; i < indices.row.length; i++) {
+        let rowIndex = indices.row[i];
 
-  AbstractMatrix.random = AbstractMatrix.rand;
-  AbstractMatrix.randomInt = AbstractMatrix.randInt;
-  AbstractMatrix.diagonal = AbstractMatrix.diag;
-  AbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;
-  AbstractMatrix.identity = AbstractMatrix.eye;
-  AbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;
-  AbstractMatrix.prototype.tensorProduct = AbstractMatrix.prototype.kroneckerProduct;
-  class Matrix extends AbstractMatrix {
-    constructor(nRows, nColumns) {
-      super();
+        for (let j = 0; j < indices.column.length; j++) {
+          let columnIndex = indices.column[j];
+          newMatrix.set(i, j, this.get(rowIndex, columnIndex));
+        }
+      }
 
-      if (Matrix.isMatrix(nRows)) {
-        return nRows.clone();
-      } else if (Number.isInteger(nRows) && nRows > 0) {
-        // Create an empty matrix
-        this.data = [];
+      return newMatrix;
+    }
 
-        if (Number.isInteger(nColumns) && nColumns > 0) {
-          for (let i = 0; i < nRows; i++) {
-            this.data.push(new Float64Array(nColumns));
-          }
-        } else {
-          throw new TypeError('nColumns must be a positive integer');
-        }
-      } else if (Array.isArray(nRows)) {
-        // Copy the values from the 2D array
-        const arrayData = nRows;
-        nRows = arrayData.length;
-        nColumns = arrayData[0].length;
+    trace() {
+      let min = Math.min(this.rows, this.columns);
+      let trace = 0;
 
-        if (typeof nColumns !== 'number' || nColumns === 0) {
-          throw new TypeError('Data must be a 2D array with at least one element');
-        }
+      for (let i = 0; i < min; i++) {
+        trace += this.get(i, i);
+      }
 
-        this.data = [];
+      return trace;
+    }
 
-        for (let i = 0; i < nRows; i++) {
-          if (arrayData[i].length !== nColumns) {
-            throw new RangeError('Inconsistent array dimensions');
-          }
+    clone() {
+      let newMatrix = new Matrix(this.rows, this.columns);
 
-          this.data.push(Float64Array.from(arrayData[i]));
+      for (let row = 0; row < this.rows; row++) {
+        for (let column = 0; column < this.columns; column++) {
+          newMatrix.set(row, column, this.get(row, column));
         }
-      } else {
-        throw new TypeError('First argument must be a positive number or an array');
       }
 
-      this.rows = nRows;
-      this.columns = nColumns;
-      return this;
+      return newMatrix;
     }
 
-    set(rowIndex, columnIndex, value) {
-      this.data[rowIndex][columnIndex] = value;
-      return this;
-    }
+    sum(by) {
+      switch (by) {
+        case 'row':
+          return sumByRow(this);
 
-    get(rowIndex, columnIndex) {
-      return this.data[rowIndex][columnIndex];
-    }
+        case 'column':
+          return sumByColumn(this);
 
-    removeRow(index) {
-      checkRowIndex(this, index);
+        case undefined:
+          return sumAll(this);
 
-      if (this.rows === 1) {
-        throw new RangeError('A matrix cannot have less than one row');
+        default:
+          throw new Error(`invalid option: ${by}`);
       }
-
-      this.data.splice(index, 1);
-      this.rows -= 1;
-      return this;
     }
 
-    addRow(index, array) {
-      if (array === undefined) {
-        array = index;
-        index = this.rows;
-      }
+    product(by) {
+      switch (by) {
+        case 'row':
+          return productByRow(this);
 
-      checkRowIndex(this, index, true);
-      array = Float64Array.from(checkRowVector(this, array));
-      this.data.splice(index, 0, array);
-      this.rows += 1;
-      return this;
-    }
+        case 'column':
+          return productByColumn(this);
 
-    removeColumn(index) {
-      checkColumnIndex(this, index);
+        case undefined:
+          return productAll(this);
 
-      if (this.columns === 1) {
-        throw new RangeError('A matrix cannot have less than one column');
+        default:
+          throw new Error(`invalid option: ${by}`);
       }
+    }
 
-      for (let i = 0; i < this.rows; i++) {
-        const newRow = new Float64Array(this.columns - 1);
+    mean(by) {
+      const sum = this.sum(by);
 
-        for (let j = 0; j < index; j++) {
-          newRow[j] = this.data[i][j];
-        }
+      switch (by) {
+        case 'row':
+          {
+            for (let i = 0; i < this.rows; i++) {
+              sum[i] /= this.columns;
+            }
 
-        for (let j = index + 1; j < this.columns; j++) {
-          newRow[j - 1] = this.data[i][j];
-        }
+            return sum;
+          }
 
-        this.data[i] = newRow;
-      }
+        case 'column':
+          {
+            for (let i = 0; i < this.columns; i++) {
+              sum[i] /= this.rows;
+            }
 
-      this.columns -= 1;
-      return this;
-    }
+            return sum;
+          }
 
-    addColumn(index, array) {
-      if (typeof array === 'undefined') {
-        array = index;
-        index = this.columns;
+        case undefined:
+          return sum / this.size;
+
+        default:
+          throw new Error(`invalid option: ${by}`);
       }
+    }
 
-      checkColumnIndex(this, index, true);
-      array = checkColumnVector(this, array);
+    variance(by, options = {}) {
+      if (typeof by === 'object') {
+        options = by;
+        by = undefined;
+      }
 
-      for (let i = 0; i < this.rows; i++) {
-        const newRow = new Float64Array(this.columns + 1);
-        let j = 0;
+      if (typeof options !== 'object') {
+        throw new TypeError('options must be an object');
+      }
 
-        for (; j < index; j++) {
-          newRow[j] = this.data[i][j];
-        }
+      const {
+        unbiased = true,
+        mean = this.mean(by)
+      } = options;
 
-        newRow[j++] = array[i];
+      if (typeof unbiased !== 'boolean') {
+        throw new TypeError('unbiased must be a boolean');
+      }
 
-        for (; j < this.columns + 1; j++) {
-          newRow[j] = this.data[i][j - 1];
-        }
+      switch (by) {
+        case 'row':
+          {
+            if (!Array.isArray(mean)) {
+              throw new TypeError('mean must be an array');
+            }
 
-        this.data[i] = newRow;
-      }
+            return varianceByRow(this, unbiased, mean);
+          }
 
-      this.columns += 1;
-      return this;
-    }
+        case 'column':
+          {
+            if (!Array.isArray(mean)) {
+              throw new TypeError('mean must be an array');
+            }
 
-  }
-  installMathOperations(AbstractMatrix, Matrix);
+            return varianceByColumn(this, unbiased, mean);
+          }
 
-  class BaseView extends AbstractMatrix {
-    constructor(matrix, rows, columns) {
-      super();
-      this.matrix = matrix;
-      this.rows = rows;
-      this.columns = columns;
-    }
+        case undefined:
+          {
+            if (typeof mean !== 'number') {
+              throw new TypeError('mean must be a number');
+            }
 
-  }
+            return varianceAll(this, unbiased, mean);
+          }
 
-  class MatrixColumnView extends BaseView {
-    constructor(matrix, column) {
-      checkColumnIndex(matrix, column);
-      super(matrix, matrix.rows, 1);
-      this.column = column;
+        default:
+          throw new Error(`invalid option: ${by}`);
+      }
     }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(rowIndex, this.column, value);
-      return this;
-    }
+    standardDeviation(by, options) {
+      if (typeof by === 'object') {
+        options = by;
+        by = undefined;
+      }
 
-    get(rowIndex) {
-      return this.matrix.get(rowIndex, this.column);
-    }
+      const variance = this.variance(by, options);
 
-  }
+      if (by === undefined) {
+        return Math.sqrt(variance);
+      } else {
+        for (let i = 0; i < variance.length; i++) {
+          variance[i] = Math.sqrt(variance[i]);
+        }
 
-  class MatrixColumnSelectionView extends BaseView {
-    constructor(matrix, columnIndices) {
-      columnIndices = checkColumnIndices(matrix, columnIndices);
-      super(matrix, matrix.rows, columnIndices.length);
-      this.columnIndices = columnIndices;
+        return variance;
+      }
     }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);
-      return this;
-    }
+    center(by, options = {}) {
+      if (typeof by === 'object') {
+        options = by;
+        by = undefined;
+      }
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);
-    }
+      if (typeof options !== 'object') {
+        throw new TypeError('options must be an object');
+      }
 
-  }
+      const {
+        center = this.mean(by)
+      } = options;
 
-  class MatrixFlipColumnView extends BaseView {
-    constructor(matrix) {
-      super(matrix, matrix.rows, matrix.columns);
-    }
+      switch (by) {
+        case 'row':
+          {
+            if (!Array.isArray(center)) {
+              throw new TypeError('center must be an array');
+            }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);
-      return this;
-    }
+            centerByRow(this, center);
+            return this;
+          }
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(rowIndex, this.columns - columnIndex - 1);
-    }
+        case 'column':
+          {
+            if (!Array.isArray(center)) {
+              throw new TypeError('center must be an array');
+            }
 
-  }
+            centerByColumn(this, center);
+            return this;
+          }
 
-  class MatrixFlipRowView extends BaseView {
-    constructor(matrix) {
-      super(matrix, matrix.rows, matrix.columns);
-    }
+        case undefined:
+          {
+            if (typeof center !== 'number') {
+              throw new TypeError('center must be a number');
+            }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);
-      return this;
-    }
+            centerAll(this, center);
+            return this;
+          }
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(this.rows - rowIndex - 1, columnIndex);
+        default:
+          throw new Error(`invalid option: ${by}`);
+      }
     }
 
-  }
-
-  class MatrixRowView extends BaseView {
-    constructor(matrix, row) {
-      checkRowIndex(matrix, row);
-      super(matrix, 1, matrix.columns);
-      this.row = row;
-    }
+    scale(by, options = {}) {
+      if (typeof by === 'object') {
+        options = by;
+        by = undefined;
+      }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(this.row, columnIndex, value);
-      return this;
-    }
+      if (typeof options !== 'object') {
+        throw new TypeError('options must be an object');
+      }
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(this.row, columnIndex);
-    }
+      let scale = options.scale;
 
-  }
+      switch (by) {
+        case 'row':
+          {
+            if (scale === undefined) {
+              scale = getScaleByRow(this);
+            } else if (!Array.isArray(scale)) {
+              throw new TypeError('scale must be an array');
+            }
 
-  class MatrixRowSelectionView extends BaseView {
-    constructor(matrix, rowIndices) {
-      rowIndices = checkRowIndices(matrix, rowIndices);
-      super(matrix, rowIndices.length, matrix.columns);
-      this.rowIndices = rowIndices;
-    }
+            scaleByRow(this, scale);
+            return this;
+          }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);
-      return this;
-    }
+        case 'column':
+          {
+            if (scale === undefined) {
+              scale = getScaleByColumn(this);
+            } else if (!Array.isArray(scale)) {
+              throw new TypeError('scale must be an array');
+            }
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(this.rowIndices[rowIndex], columnIndex);
-    }
+            scaleByColumn(this, scale);
+            return this;
+          }
 
-  }
+        case undefined:
+          {
+            if (scale === undefined) {
+              scale = getScaleAll(this);
+            } else if (typeof scale !== 'number') {
+              throw new TypeError('scale must be a number');
+            }
 
-  class MatrixSelectionView extends BaseView {
-    constructor(matrix, rowIndices, columnIndices) {
-      let indices = checkIndices(matrix, rowIndices, columnIndices);
-      super(matrix, indices.row.length, indices.column.length);
-      this.rowIndices = indices.row;
-      this.columnIndices = indices.column;
-    }
+            scaleAll(this, scale);
+            return this;
+          }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(this.rowIndices[rowIndex], this.columnIndices[columnIndex], value);
-      return this;
+        default:
+          throw new Error(`invalid option: ${by}`);
+      }
     }
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(this.rowIndices[rowIndex], this.columnIndices[columnIndex]);
+    toString(options) {
+      return inspectMatrixWithOptions(this, options);
     }
 
   }
+  AbstractMatrix.prototype.klass = 'Matrix';
 
-  class MatrixSubView extends BaseView {
-    constructor(matrix, startRow, endRow, startColumn, endColumn) {
-      checkRange(matrix, startRow, endRow, startColumn, endColumn);
-      super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);
-      this.startRow = startRow;
-      this.startColumn = startColumn;
-    }
+  if (typeof Symbol !== 'undefined') {
+    AbstractMatrix.prototype[Symbol.for('nodejs.util.inspect.custom')] = inspectMatrix;
+  }
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(this.startRow + rowIndex, this.startColumn + columnIndex, value);
-      return this;
-    }
+  function compareNumbers(a, b) {
+    return a - b;
+  } // Synonyms
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(this.startRow + rowIndex, this.startColumn + columnIndex);
-    }
 
-  }
+  AbstractMatrix.random = AbstractMatrix.rand;
+  AbstractMatrix.randomInt = AbstractMatrix.randInt;
+  AbstractMatrix.diagonal = AbstractMatrix.diag;
+  AbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;
+  AbstractMatrix.identity = AbstractMatrix.eye;
+  AbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;
+  AbstractMatrix.prototype.tensorProduct = AbstractMatrix.prototype.kroneckerProduct;
+  class Matrix extends AbstractMatrix {
+    constructor(nRows, nColumns) {
+      super();
 
-  class MatrixTransposeView extends BaseView {
-    constructor(matrix) {
-      super(matrix, matrix.columns, matrix.rows);
-    }
+      if (Matrix.isMatrix(nRows)) {
+        return nRows.clone();
+      } else if (Number.isInteger(nRows) && nRows > 0) {
+        // Create an empty matrix
+        this.data = [];
 
-    set(rowIndex, columnIndex, value) {
-      this.matrix.set(columnIndex, rowIndex, value);
-      return this;
-    }
+        if (Number.isInteger(nColumns) && nColumns > 0) {
+          for (let i = 0; i < nRows; i++) {
+            this.data.push(new Float64Array(nColumns));
+          }
+        } else {
+          throw new TypeError('nColumns must be a positive integer');
+        }
+      } else if (Array.isArray(nRows)) {
+        // Copy the values from the 2D array
+        const arrayData = nRows;
+        nRows = arrayData.length;
+        nColumns = arrayData[0].length;
 
-    get(rowIndex, columnIndex) {
-      return this.matrix.get(columnIndex, rowIndex);
-    }
+        if (typeof nColumns !== 'number' || nColumns === 0) {
+          throw new TypeError('Data must be a 2D array with at least one element');
+        }
 
-  }
+        this.data = [];
 
-  class WrapperMatrix1D extends AbstractMatrix {
-    constructor(data) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-      const {
-        rows = 1
-      } = options;
+        for (let i = 0; i < nRows; i++) {
+          if (arrayData[i].length !== nColumns) {
+            throw new RangeError('Inconsistent array dimensions');
+          }
 
-      if (data.length % rows !== 0) {
-        throw new Error('the data length is not divisible by the number of rows');
+          this.data.push(Float64Array.from(arrayData[i]));
+        }
+      } else {
+        throw new TypeError('First argument must be a positive number or an array');
       }
 
-      super();
-      this.rows = rows;
-      this.columns = data.length / rows;
-      this.data = data;
+      this.rows = nRows;
+      this.columns = nColumns;
+      return this;
     }
 
     set(rowIndex, columnIndex, value) {
-      let index = this._calculateIndex(rowIndex, columnIndex);
-
-      this.data[index] = value;
+      this.data[rowIndex][columnIndex] = value;
       return this;
     }
 
     get(rowIndex, columnIndex) {
-      let index = this._calculateIndex(rowIndex, columnIndex);
-
-      return this.data[index];
+      return this.data[rowIndex][columnIndex];
     }
 
-    _calculateIndex(row, column) {
-      return row * this.columns + column;
-    }
+    removeRow(index) {
+      checkRowIndex(this, index);
 
-  }
+      if (this.rows === 1) {
+        throw new RangeError('A matrix cannot have less than one row');
+      }
 
-  class WrapperMatrix2D extends AbstractMatrix {
-    constructor(data) {
-      super();
-      this.data = data;
-      this.rows = data.length;
-      this.columns = data[0].length;
-    }
-
-    set(rowIndex, columnIndex, value) {
-      this.data[rowIndex][columnIndex] = value;
+      this.data.splice(index, 1);
+      this.rows -= 1;
       return this;
     }
 
-    get(rowIndex, columnIndex) {
-      return this.data[rowIndex][columnIndex];
-    }
-
-  }
-
-  function wrap(array, options) {
-    if (Array.isArray(array)) {
-      if (array[0] && Array.isArray(array[0])) {
-        return new WrapperMatrix2D(array);
-      } else {
-        return new WrapperMatrix1D(array, options);
+    addRow(index, array) {
+      if (array === undefined) {
+        array = index;
+        index = this.rows;
       }
-    } else {
-      throw new Error('the argument is not an array');
+
+      checkRowIndex(this, index, true);
+      array = Float64Array.from(checkRowVector(this, array));
+      this.data.splice(index, 0, array);
+      this.rows += 1;
+      return this;
     }
-  }
 
-  class LuDecomposition {
-    constructor(matrix) {
-      matrix = WrapperMatrix2D.checkMatrix(matrix);
-      let lu = matrix.clone();
-      let rows = lu.rows;
-      let columns = lu.columns;
-      let pivotVector = new Float64Array(rows);
-      let pivotSign = 1;
-      let i, j, k, p, s, t, v;
-      let LUcolj, kmax;
+    removeColumn(index) {
+      checkColumnIndex(this, index);
 
-      for (i = 0; i < rows; i++) {
-        pivotVector[i] = i;
+      if (this.columns === 1) {
+        throw new RangeError('A matrix cannot have less than one column');
       }
 
-      LUcolj = new Float64Array(rows);
-
-      for (j = 0; j < columns; j++) {
-        for (i = 0; i < rows; i++) {
-          LUcolj[i] = lu.get(i, j);
-        }
-
-        for (i = 0; i < rows; i++) {
-          kmax = Math.min(i, j);
-          s = 0;
-
-          for (k = 0; k < kmax; k++) {
-            s += lu.get(i, k) * LUcolj[k];
-          }
-
-          LUcolj[i] -= s;
-          lu.set(i, j, LUcolj[i]);
-        }
-
-        p = j;
-
-        for (i = j + 1; i < rows; i++) {
-          if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {
-            p = i;
-          }
-        }
-
-        if (p !== j) {
-          for (k = 0; k < columns; k++) {
-            t = lu.get(p, k);
-            lu.set(p, k, lu.get(j, k));
-            lu.set(j, k, t);
-          }
+      for (let i = 0; i < this.rows; i++) {
+        const newRow = new Float64Array(this.columns - 1);
 
-          v = pivotVector[p];
-          pivotVector[p] = pivotVector[j];
-          pivotVector[j] = v;
-          pivotSign = -pivotSign;
+        for (let j = 0; j < index; j++) {
+          newRow[j] = this.data[i][j];
         }
 
-        if (j < rows && lu.get(j, j) !== 0) {
-          for (i = j + 1; i < rows; i++) {
-            lu.set(i, j, lu.get(i, j) / lu.get(j, j));
-          }
+        for (let j = index + 1; j < this.columns; j++) {
+          newRow[j - 1] = this.data[i][j];
         }
-      }
-
-      this.LU = lu;
-      this.pivotVector = pivotVector;
-      this.pivotSign = pivotSign;
-    }
 
-    isSingular() {
-      let data = this.LU;
-      let col = data.columns;
-
-      for (let j = 0; j < col; j++) {
-        if (data.get(j, j) === 0) {
-          return true;
-        }
+        this.data[i] = newRow;
       }
 
-      return false;
+      this.columns -= 1;
+      return this;
     }
 
-    solve(value) {
-      value = Matrix.checkMatrix(value);
-      let lu = this.LU;
-      let rows = lu.rows;
-
-      if (rows !== value.rows) {
-        throw new Error('Invalid matrix dimensions');
+    addColumn(index, array) {
+      if (typeof array === 'undefined') {
+        array = index;
+        index = this.columns;
       }
 
-      if (this.isSingular()) {
-        throw new Error('LU matrix is singular');
-      }
+      checkColumnIndex(this, index, true);
+      array = checkColumnVector(this, array);
 
-      let count = value.columns;
-      let X = value.subMatrixRow(this.pivotVector, 0, count - 1);
-      let columns = lu.columns;
-      let i, j, k;
+      for (let i = 0; i < this.rows; i++) {
+        const newRow = new Float64Array(this.columns + 1);
+        let j = 0;
 
-      for (k = 0; k < columns; k++) {
-        for (i = k + 1; i < columns; i++) {
-          for (j = 0; j < count; j++) {
-            X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));
-          }
+        for (; j < index; j++) {
+          newRow[j] = this.data[i][j];
         }
-      }
 
-      for (k = columns - 1; k >= 0; k--) {
-        for (j = 0; j < count; j++) {
-          X.set(k, j, X.get(k, j) / lu.get(k, k));
-        }
+        newRow[j++] = array[i];
 
-        for (i = 0; i < k; i++) {
-          for (j = 0; j < count; j++) {
-            X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));
-          }
+        for (; j < this.columns + 1; j++) {
+          newRow[j] = this.data[i][j - 1];
         }
-      }
-
-      return X;
-    }
-
-    get determinant() {
-      let data = this.LU;
-
-      if (!data.isSquare()) {
-        throw new Error('Matrix must be square');
-      }
-
-      let determinant = this.pivotSign;
-      let col = data.columns;
 
-      for (let j = 0; j < col; j++) {
-        determinant *= data.get(j, j);
+        this.data[i] = newRow;
       }
 
-      return determinant;
+      this.columns += 1;
+      return this;
     }
 
-    get lowerTriangularMatrix() {
-      let data = this.LU;
-      let rows = data.rows;
-      let columns = data.columns;
-      let X = new Matrix(rows, columns);
-
-      for (let i = 0; i < rows; i++) {
-        for (let j = 0; j < columns; j++) {
-          if (i > j) {
-            X.set(i, j, data.get(i, j));
-          } else if (i === j) {
-            X.set(i, j, 1);
-          } else {
-            X.set(i, j, 0);
-          }
-        }
-      }
+  }
+  installMathOperations(AbstractMatrix, Matrix);
 
-      return X;
+  class BaseView extends AbstractMatrix {
+    constructor(matrix, rows, columns) {
+      super();
+      this.matrix = matrix;
+      this.rows = rows;
+      this.columns = columns;
     }
 
-    get upperTriangularMatrix() {
-      let data = this.LU;
-      let rows = data.rows;
-      let columns = data.columns;
-      let X = new Matrix(rows, columns);
+  }
 
-      for (let i = 0; i < rows; i++) {
-        for (let j = 0; j < columns; j++) {
-          if (i <= j) {
-            X.set(i, j, data.get(i, j));
-          } else {
-            X.set(i, j, 0);
-          }
-        }
-      }
+  class MatrixColumnView extends BaseView {
+    constructor(matrix, column) {
+      checkColumnIndex(matrix, column);
+      super(matrix, matrix.rows, 1);
+      this.column = column;
+    }
 
-      return X;
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(rowIndex, this.column, value);
+      return this;
     }
 
-    get pivotPermutationVector() {
-      return Array.from(this.pivotVector);
+    get(rowIndex) {
+      return this.matrix.get(rowIndex, this.column);
     }
 
   }
 
-  function hypotenuse(a, b) {
-    let r = 0;
+  class MatrixColumnSelectionView extends BaseView {
+    constructor(matrix, columnIndices) {
+      columnIndices = checkColumnIndices(matrix, columnIndices);
+      super(matrix, matrix.rows, columnIndices.length);
+      this.columnIndices = columnIndices;
+    }
 
-    if (Math.abs(a) > Math.abs(b)) {
-      r = b / a;
-      return Math.abs(a) * Math.sqrt(1 + r * r);
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);
+      return this;
     }
 
-    if (b !== 0) {
-      r = a / b;
-      return Math.abs(b) * Math.sqrt(1 + r * r);
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);
     }
 
-    return 0;
   }
 
-  class QrDecomposition {
-    constructor(value) {
-      value = WrapperMatrix2D.checkMatrix(value);
-      let qr = value.clone();
-      let m = value.rows;
-      let n = value.columns;
-      let rdiag = new Float64Array(n);
-      let i, j, k, s;
-
-      for (k = 0; k < n; k++) {
-        let nrm = 0;
+  class MatrixFlipColumnView extends BaseView {
+    constructor(matrix) {
+      super(matrix, matrix.rows, matrix.columns);
+    }
 
-        for (i = k; i < m; i++) {
-          nrm = hypotenuse(nrm, qr.get(i, k));
-        }
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);
+      return this;
+    }
 
-        if (nrm !== 0) {
-          if (qr.get(k, k) < 0) {
-            nrm = -nrm;
-          }
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(rowIndex, this.columns - columnIndex - 1);
+    }
 
-          for (i = k; i < m; i++) {
-            qr.set(i, k, qr.get(i, k) / nrm);
-          }
+  }
 
-          qr.set(k, k, qr.get(k, k) + 1);
+  class MatrixFlipRowView extends BaseView {
+    constructor(matrix) {
+      super(matrix, matrix.rows, matrix.columns);
+    }
 
-          for (j = k + 1; j < n; j++) {
-            s = 0;
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);
+      return this;
+    }
 
-            for (i = k; i < m; i++) {
-              s += qr.get(i, k) * qr.get(i, j);
-            }
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(this.rows - rowIndex - 1, columnIndex);
+    }
 
-            s = -s / qr.get(k, k);
+  }
 
-            for (i = k; i < m; i++) {
-              qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));
-            }
-          }
-        }
+  class MatrixRowView extends BaseView {
+    constructor(matrix, row) {
+      checkRowIndex(matrix, row);
+      super(matrix, 1, matrix.columns);
+      this.row = row;
+    }
 
-        rdiag[k] = -nrm;
-      }
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(this.row, columnIndex, value);
+      return this;
+    }
 
-      this.QR = qr;
-      this.Rdiag = rdiag;
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(this.row, columnIndex);
     }
 
-    solve(value) {
-      value = Matrix.checkMatrix(value);
-      let qr = this.QR;
-      let m = qr.rows;
+  }
 
-      if (value.rows !== m) {
-        throw new Error('Matrix row dimensions must agree');
-      }
+  class MatrixRowSelectionView extends BaseView {
+    constructor(matrix, rowIndices) {
+      rowIndices = checkRowIndices(matrix, rowIndices);
+      super(matrix, rowIndices.length, matrix.columns);
+      this.rowIndices = rowIndices;
+    }
 
-      if (!this.isFullRank()) {
-        throw new Error('Matrix is rank deficient');
-      }
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);
+      return this;
+    }
 
-      let count = value.columns;
-      let X = value.clone();
-      let n = qr.columns;
-      let i, j, k, s;
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(this.rowIndices[rowIndex], columnIndex);
+    }
 
-      for (k = 0; k < n; k++) {
-        for (j = 0; j < count; j++) {
-          s = 0;
+  }
 
-          for (i = k; i < m; i++) {
-            s += qr.get(i, k) * X.get(i, j);
-          }
+  class MatrixSelectionView extends BaseView {
+    constructor(matrix, rowIndices, columnIndices) {
+      let indices = checkIndices(matrix, rowIndices, columnIndices);
+      super(matrix, indices.row.length, indices.column.length);
+      this.rowIndices = indices.row;
+      this.columnIndices = indices.column;
+    }
 
-          s = -s / qr.get(k, k);
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(this.rowIndices[rowIndex], this.columnIndices[columnIndex], value);
+      return this;
+    }
 
-          for (i = k; i < m; i++) {
-            X.set(i, j, X.get(i, j) + s * qr.get(i, k));
-          }
-        }
-      }
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(this.rowIndices[rowIndex], this.columnIndices[columnIndex]);
+    }
 
-      for (k = n - 1; k >= 0; k--) {
-        for (j = 0; j < count; j++) {
-          X.set(k, j, X.get(k, j) / this.Rdiag[k]);
-        }
+  }
 
-        for (i = 0; i < k; i++) {
-          for (j = 0; j < count; j++) {
-            X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));
-          }
-        }
-      }
+  class MatrixSubView extends BaseView {
+    constructor(matrix, startRow, endRow, startColumn, endColumn) {
+      checkRange(matrix, startRow, endRow, startColumn, endColumn);
+      super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);
+      this.startRow = startRow;
+      this.startColumn = startColumn;
+    }
 
-      return X.subMatrix(0, n - 1, 0, count - 1);
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(this.startRow + rowIndex, this.startColumn + columnIndex, value);
+      return this;
     }
 
-    isFullRank() {
-      let columns = this.QR.columns;
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(this.startRow + rowIndex, this.startColumn + columnIndex);
+    }
 
-      for (let i = 0; i < columns; i++) {
-        if (this.Rdiag[i] === 0) {
-          return false;
-        }
-      }
+  }
 
-      return true;
+  class MatrixTransposeView extends BaseView {
+    constructor(matrix) {
+      super(matrix, matrix.columns, matrix.rows);
     }
 
-    get upperTriangularMatrix() {
-      let qr = this.QR;
-      let n = qr.columns;
-      let X = new Matrix(n, n);
-      let i, j;
+    set(rowIndex, columnIndex, value) {
+      this.matrix.set(columnIndex, rowIndex, value);
+      return this;
+    }
 
-      for (i = 0; i < n; i++) {
-        for (j = 0; j < n; j++) {
-          if (i < j) {
-            X.set(i, j, qr.get(i, j));
-          } else if (i === j) {
-            X.set(i, j, this.Rdiag[i]);
-          } else {
-            X.set(i, j, 0);
-          }
-        }
+    get(rowIndex, columnIndex) {
+      return this.matrix.get(columnIndex, rowIndex);
+    }
+
+  }
+
+  class WrapperMatrix1D extends AbstractMatrix {
+    constructor(data, options = {}) {
+      const {
+        rows = 1
+      } = options;
+
+      if (data.length % rows !== 0) {
+        throw new Error('the data length is not divisible by the number of rows');
       }
 
-      return X;
+      super();
+      this.rows = rows;
+      this.columns = data.length / rows;
+      this.data = data;
     }
 
-    get orthogonalMatrix() {
-      let qr = this.QR;
-      let rows = qr.rows;
-      let columns = qr.columns;
-      let X = new Matrix(rows, columns);
-      let i, j, k, s;
+    set(rowIndex, columnIndex, value) {
+      let index = this._calculateIndex(rowIndex, columnIndex);
 
-      for (k = columns - 1; k >= 0; k--) {
-        for (i = 0; i < rows; i++) {
-          X.set(i, k, 0);
-        }
+      this.data[index] = value;
+      return this;
+    }
 
-        X.set(k, k, 1);
+    get(rowIndex, columnIndex) {
+      let index = this._calculateIndex(rowIndex, columnIndex);
 
-        for (j = k; j < columns; j++) {
-          if (qr.get(k, k) !== 0) {
-            s = 0;
+      return this.data[index];
+    }
 
-            for (i = k; i < rows; i++) {
-              s += qr.get(i, k) * X.get(i, j);
-            }
+    _calculateIndex(row, column) {
+      return row * this.columns + column;
+    }
 
-            s = -s / qr.get(k, k);
+  }
 
-            for (i = k; i < rows; i++) {
-              X.set(i, j, X.get(i, j) + s * qr.get(i, k));
-            }
-          }
-        }
-      }
+  class WrapperMatrix2D extends AbstractMatrix {
+    constructor(data) {
+      super();
+      this.data = data;
+      this.rows = data.length;
+      this.columns = data[0].length;
+    }
 
-      return X;
+    set(rowIndex, columnIndex, value) {
+      this.data[rowIndex][columnIndex] = value;
+      return this;
+    }
+
+    get(rowIndex, columnIndex) {
+      return this.data[rowIndex][columnIndex];
     }
 
   }
 
-  class SingularValueDecomposition {
-    constructor(value) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-      value = WrapperMatrix2D.checkMatrix(value);
-      let m = value.rows;
-      let n = value.columns;
-      const {
-        computeLeftSingularVectors = true,
-        computeRightSingularVectors = true,
-        autoTranspose = false
-      } = options;
-      let wantu = Boolean(computeLeftSingularVectors);
-      let wantv = Boolean(computeRightSingularVectors);
-      let swapped = false;
-      let a;
-
-      if (m < n) {
-        if (!autoTranspose) {
-          a = value.clone(); // eslint-disable-next-line no-console
-
-          console.warn('Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose');
-        } else {
-          a = value.transpose();
-          m = a.rows;
-          n = a.columns;
-          swapped = true;
-          let aux = wantu;
-          wantu = wantv;
-          wantv = aux;
-        }
+  function wrap(array, options) {
+    if (Array.isArray(array)) {
+      if (array[0] && Array.isArray(array[0])) {
+        return new WrapperMatrix2D(array);
       } else {
-        a = value.clone();
+        return new WrapperMatrix1D(array, options);
       }
+    } else {
+      throw new Error('the argument is not an array');
+    }
+  }
 
-      let nu = Math.min(m, n);
-      let ni = Math.min(m + 1, n);
-      let s = new Float64Array(ni);
-      let U = new Matrix(m, nu);
-      let V = new Matrix(n, n);
-      let e = new Float64Array(n);
-      let work = new Float64Array(m);
-      let si = new Float64Array(ni);
-
-      for (let i = 0; i < ni; i++) si[i] = i;
-
-      let nct = Math.min(m - 1, n);
-      let nrt = Math.max(0, Math.min(n - 2, m));
-      let mrc = Math.max(nct, nrt);
+  class LuDecomposition {
+    constructor(matrix) {
+      matrix = WrapperMatrix2D.checkMatrix(matrix);
+      let lu = matrix.clone();
+      let rows = lu.rows;
+      let columns = lu.columns;
+      let pivotVector = new Float64Array(rows);
+      let pivotSign = 1;
+      let i, j, k, p, s, t, v;
+      let LUcolj, kmax;
 
-      for (let k = 0; k < mrc; k++) {
-        if (k < nct) {
-          s[k] = 0;
+      for (i = 0; i < rows; i++) {
+        pivotVector[i] = i;
+      }
 
-          for (let i = k; i < m; i++) {
-            s[k] = hypotenuse(s[k], a.get(i, k));
-          }
+      LUcolj = new Float64Array(rows);
 
-          if (s[k] !== 0) {
-            if (a.get(k, k) < 0) {
-              s[k] = -s[k];
-            }
+      for (j = 0; j < columns; j++) {
+        for (i = 0; i < rows; i++) {
+          LUcolj[i] = lu.get(i, j);
+        }
 
-            for (let i = k; i < m; i++) {
-              a.set(i, k, a.get(i, k) / s[k]);
-            }
+        for (i = 0; i < rows; i++) {
+          kmax = Math.min(i, j);
+          s = 0;
 
-            a.set(k, k, a.get(k, k) + 1);
+          for (k = 0; k < kmax; k++) {
+            s += lu.get(i, k) * LUcolj[k];
           }
 
-          s[k] = -s[k];
+          LUcolj[i] -= s;
+          lu.set(i, j, LUcolj[i]);
         }
 
-        for (let j = k + 1; j < n; j++) {
-          if (k < nct && s[k] !== 0) {
-            let t = 0;
-
-            for (let i = k; i < m; i++) {
-              t += a.get(i, k) * a.get(i, j);
-            }
+        p = j;
 
-            t = -t / a.get(k, k);
+        for (i = j + 1; i < rows; i++) {
+          if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {
+            p = i;
+          }
+        }
 
-            for (let i = k; i < m; i++) {
-              a.set(i, j, a.get(i, j) + t * a.get(i, k));
-            }
+        if (p !== j) {
+          for (k = 0; k < columns; k++) {
+            t = lu.get(p, k);
+            lu.set(p, k, lu.get(j, k));
+            lu.set(j, k, t);
           }
 
-          e[j] = a.get(k, j);
+          v = pivotVector[p];
+          pivotVector[p] = pivotVector[j];
+          pivotVector[j] = v;
+          pivotSign = -pivotSign;
         }
 
-        if (wantu && k < nct) {
-          for (let i = k; i < m; i++) {
-            U.set(i, k, a.get(i, k));
+        if (j < rows && lu.get(j, j) !== 0) {
+          for (i = j + 1; i < rows; i++) {
+            lu.set(i, j, lu.get(i, j) / lu.get(j, j));
           }
         }
+      }
 
-        if (k < nrt) {
-          e[k] = 0;
-
-          for (let i = k + 1; i < n; i++) {
-            e[k] = hypotenuse(e[k], e[i]);
-          }
+      this.LU = lu;
+      this.pivotVector = pivotVector;
+      this.pivotSign = pivotSign;
+    }
 
-          if (e[k] !== 0) {
-            if (e[k + 1] < 0) {
-              e[k] = 0 - e[k];
-            }
+    isSingular() {
+      let data = this.LU;
+      let col = data.columns;
 
-            for (let i = k + 1; i < n; i++) {
-              e[i] /= e[k];
-            }
+      for (let j = 0; j < col; j++) {
+        if (data.get(j, j) === 0) {
+          return true;
+        }
+      }
 
-            e[k + 1] += 1;
-          }
+      return false;
+    }
 
-          e[k] = -e[k];
+    solve(value) {
+      value = Matrix.checkMatrix(value);
+      let lu = this.LU;
+      let rows = lu.rows;
 
-          if (k + 1 < m && e[k] !== 0) {
-            for (let i = k + 1; i < m; i++) {
-              work[i] = 0;
-            }
+      if (rows !== value.rows) {
+        throw new Error('Invalid matrix dimensions');
+      }
 
-            for (let i = k + 1; i < m; i++) {
-              for (let j = k + 1; j < n; j++) {
-                work[i] += e[j] * a.get(i, j);
-              }
-            }
+      if (this.isSingular()) {
+        throw new Error('LU matrix is singular');
+      }
 
-            for (let j = k + 1; j < n; j++) {
-              let t = -e[j] / e[k + 1];
+      let count = value.columns;
+      let X = value.subMatrixRow(this.pivotVector, 0, count - 1);
+      let columns = lu.columns;
+      let i, j, k;
 
-              for (let i = k + 1; i < m; i++) {
-                a.set(i, j, a.get(i, j) + t * work[i]);
-              }
-            }
+      for (k = 0; k < columns; k++) {
+        for (i = k + 1; i < columns; i++) {
+          for (j = 0; j < count; j++) {
+            X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));
           }
+        }
+      }
 
-          if (wantv) {
-            for (let i = k + 1; i < n; i++) {
-              V.set(i, k, e[i]);
-            }
+      for (k = columns - 1; k >= 0; k--) {
+        for (j = 0; j < count; j++) {
+          X.set(k, j, X.get(k, j) / lu.get(k, k));
+        }
+
+        for (i = 0; i < k; i++) {
+          for (j = 0; j < count; j++) {
+            X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));
           }
         }
       }
 
-      let p = Math.min(n, m + 1);
+      return X;
+    }
 
-      if (nct < n) {
-        s[nct] = a.get(nct, nct);
-      }
+    get determinant() {
+      let data = this.LU;
 
-      if (m < p) {
-        s[p - 1] = 0;
+      if (!data.isSquare()) {
+        throw new Error('Matrix must be square');
       }
 
-      if (nrt + 1 < p) {
-        e[nrt] = a.get(nrt, p - 1);
+      let determinant = this.pivotSign;
+      let col = data.columns;
+
+      for (let j = 0; j < col; j++) {
+        determinant *= data.get(j, j);
       }
 
-      e[p - 1] = 0;
+      return determinant;
+    }
 
-      if (wantu) {
-        for (let j = nct; j < nu; j++) {
-          for (let i = 0; i < m; i++) {
-            U.set(i, j, 0);
-          }
+    get lowerTriangularMatrix() {
+      let data = this.LU;
+      let rows = data.rows;
+      let columns = data.columns;
+      let X = new Matrix(rows, columns);
 
-          U.set(j, j, 1);
+      for (let i = 0; i < rows; i++) {
+        for (let j = 0; j < columns; j++) {
+          if (i > j) {
+            X.set(i, j, data.get(i, j));
+          } else if (i === j) {
+            X.set(i, j, 1);
+          } else {
+            X.set(i, j, 0);
+          }
         }
+      }
 
-        for (let k = nct - 1; k >= 0; k--) {
-          if (s[k] !== 0) {
-            for (let j = k + 1; j < nu; j++) {
-              let t = 0;
+      return X;
+    }
 
-              for (let i = k; i < m; i++) {
-                t += U.get(i, k) * U.get(i, j);
-              }
+    get upperTriangularMatrix() {
+      let data = this.LU;
+      let rows = data.rows;
+      let columns = data.columns;
+      let X = new Matrix(rows, columns);
 
-              t = -t / U.get(k, k);
-
-              for (let i = k; i < m; i++) {
-                U.set(i, j, U.get(i, j) + t * U.get(i, k));
-              }
-            }
-
-            for (let i = k; i < m; i++) {
-              U.set(i, k, -U.get(i, k));
-            }
-
-            U.set(k, k, 1 + U.get(k, k));
-
-            for (let i = 0; i < k - 1; i++) {
-              U.set(i, k, 0);
-            }
+      for (let i = 0; i < rows; i++) {
+        for (let j = 0; j < columns; j++) {
+          if (i <= j) {
+            X.set(i, j, data.get(i, j));
           } else {
-            for (let i = 0; i < m; i++) {
-              U.set(i, k, 0);
-            }
-
-            U.set(k, k, 1);
+            X.set(i, j, 0);
           }
         }
       }
 
-      if (wantv) {
-        for (let k = n - 1; k >= 0; k--) {
-          if (k < nrt && e[k] !== 0) {
-            for (let j = k + 1; j < n; j++) {
-              let t = 0;
+      return X;
+    }
 
-              for (let i = k + 1; i < n; i++) {
-                t += V.get(i, k) * V.get(i, j);
-              }
+    get pivotPermutationVector() {
+      return Array.from(this.pivotVector);
+    }
 
-              t = -t / V.get(k + 1, k);
+  }
 
-              for (let i = k + 1; i < n; i++) {
-                V.set(i, j, V.get(i, j) + t * V.get(i, k));
-              }
-            }
-          }
+  function hypotenuse(a, b) {
+    let r = 0;
 
-          for (let i = 0; i < n; i++) {
-            V.set(i, k, 0);
-          }
+    if (Math.abs(a) > Math.abs(b)) {
+      r = b / a;
+      return Math.abs(a) * Math.sqrt(1 + r * r);
+    }
 
-          V.set(k, k, 1);
-        }
-      }
+    if (b !== 0) {
+      r = a / b;
+      return Math.abs(b) * Math.sqrt(1 + r * r);
+    }
 
-      let pp = p - 1;
-      let eps = Number.EPSILON;
+    return 0;
+  }
 
-      while (p > 0) {
-        let k, kase;
+  class QrDecomposition {
+    constructor(value) {
+      value = WrapperMatrix2D.checkMatrix(value);
+      let qr = value.clone();
+      let m = value.rows;
+      let n = value.columns;
+      let rdiag = new Float64Array(n);
+      let i, j, k, s;
 
-        for (k = p - 2; k >= -1; k--) {
-          if (k === -1) {
-            break;
-          }
+      for (k = 0; k < n; k++) {
+        let nrm = 0;
 
-          const alpha = Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));
+        for (i = k; i < m; i++) {
+          nrm = hypotenuse(nrm, qr.get(i, k));
+        }
 
-          if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {
-            e[k] = 0;
-            break;
+        if (nrm !== 0) {
+          if (qr.get(k, k) < 0) {
+            nrm = -nrm;
           }
-        }
 
-        if (k === p - 2) {
-          kase = 4;
-        } else {
-          let ks;
+          for (i = k; i < m; i++) {
+            qr.set(i, k, qr.get(i, k) / nrm);
+          }
 
-          for (ks = p - 1; ks >= k; ks--) {
-            if (ks === k) {
-              break;
-            }
+          qr.set(k, k, qr.get(k, k) + 1);
 
-            let t = (ks !== p ? Math.abs(e[ks]) : 0) + (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);
+          for (j = k + 1; j < n; j++) {
+            s = 0;
 
-            if (Math.abs(s[ks]) <= eps * t) {
-              s[ks] = 0;
-              break;
+            for (i = k; i < m; i++) {
+              s += qr.get(i, k) * qr.get(i, j);
             }
-          }
 
-          if (ks === k) {
-            kase = 3;
-          } else if (ks === p - 1) {
-            kase = 1;
-          } else {
-            kase = 2;
-            k = ks;
+            s = -s / qr.get(k, k);
+
+            for (i = k; i < m; i++) {
+              qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));
+            }
           }
         }
 
-        k++;
+        rdiag[k] = -nrm;
+      }
 
-        switch (kase) {
-          case 1:
-            {
-              let f = e[p - 2];
-              e[p - 2] = 0;
+      this.QR = qr;
+      this.Rdiag = rdiag;
+    }
 
-              for (let j = p - 2; j >= k; j--) {
-                let t = hypotenuse(s[j], f);
-                let cs = s[j] / t;
-                let sn = f / t;
-                s[j] = t;
+    solve(value) {
+      value = Matrix.checkMatrix(value);
+      let qr = this.QR;
+      let m = qr.rows;
 
-                if (j !== k) {
-                  f = -sn * e[j - 1];
-                  e[j - 1] = cs * e[j - 1];
-                }
+      if (value.rows !== m) {
+        throw new Error('Matrix row dimensions must agree');
+      }
 
-                if (wantv) {
-                  for (let i = 0; i < n; i++) {
-                    t = cs * V.get(i, j) + sn * V.get(i, p - 1);
-                    V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));
-                    V.set(i, j, t);
-                  }
-                }
-              }
+      if (!this.isFullRank()) {
+        throw new Error('Matrix is rank deficient');
+      }
 
-              break;
-            }
+      let count = value.columns;
+      let X = value.clone();
+      let n = qr.columns;
+      let i, j, k, s;
 
-          case 2:
-            {
-              let f = e[k - 1];
-              e[k - 1] = 0;
+      for (k = 0; k < n; k++) {
+        for (j = 0; j < count; j++) {
+          s = 0;
 
-              for (let j = k; j < p; j++) {
-                let t = hypotenuse(s[j], f);
-                let cs = s[j] / t;
-                let sn = f / t;
-                s[j] = t;
-                f = -sn * e[j];
-                e[j] = cs * e[j];
+          for (i = k; i < m; i++) {
+            s += qr.get(i, k) * X.get(i, j);
+          }
 
-                if (wantu) {
-                  for (let i = 0; i < m; i++) {
-                    t = cs * U.get(i, j) + sn * U.get(i, k - 1);
-                    U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));
-                    U.set(i, j, t);
-                  }
-                }
-              }
+          s = -s / qr.get(k, k);
 
-              break;
-            }
+          for (i = k; i < m; i++) {
+            X.set(i, j, X.get(i, j) + s * qr.get(i, k));
+          }
+        }
+      }
 
-          case 3:
-            {
-              const scale = Math.max(Math.abs(s[p - 1]), Math.abs(s[p - 2]), Math.abs(e[p - 2]), Math.abs(s[k]), Math.abs(e[k]));
-              const sp = s[p - 1] / scale;
-              const spm1 = s[p - 2] / scale;
-              const epm1 = e[p - 2] / scale;
-              const sk = s[k] / scale;
-              const ek = e[k] / scale;
-              const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;
-              const c = sp * epm1 * (sp * epm1);
-              let shift = 0;
+      for (k = n - 1; k >= 0; k--) {
+        for (j = 0; j < count; j++) {
+          X.set(k, j, X.get(k, j) / this.Rdiag[k]);
+        }
 
-              if (b !== 0 || c !== 0) {
-                if (b < 0) {
-                  shift = 0 - Math.sqrt(b * b + c);
-                } else {
-                  shift = Math.sqrt(b * b + c);
-                }
+        for (i = 0; i < k; i++) {
+          for (j = 0; j < count; j++) {
+            X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));
+          }
+        }
+      }
 
-                shift = c / (b + shift);
-              }
+      return X.subMatrix(0, n - 1, 0, count - 1);
+    }
 
-              let f = (sk + sp) * (sk - sp) + shift;
-              let g = sk * ek;
+    isFullRank() {
+      let columns = this.QR.columns;
 
-              for (let j = k; j < p - 1; j++) {
-                let t = hypotenuse(f, g);
-                if (t === 0) t = Number.MIN_VALUE;
-                let cs = f / t;
-                let sn = g / t;
+      for (let i = 0; i < columns; i++) {
+        if (this.Rdiag[i] === 0) {
+          return false;
+        }
+      }
 
-                if (j !== k) {
-                  e[j - 1] = t;
-                }
+      return true;
+    }
 
-                f = cs * s[j] + sn * e[j];
-                e[j] = cs * e[j] - sn * s[j];
-                g = sn * s[j + 1];
-                s[j + 1] = cs * s[j + 1];
-
-                if (wantv) {
-                  for (let i = 0; i < n; i++) {
-                    t = cs * V.get(i, j) + sn * V.get(i, j + 1);
-                    V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));
-                    V.set(i, j, t);
-                  }
-                }
-
-                t = hypotenuse(f, g);
-                if (t === 0) t = Number.MIN_VALUE;
-                cs = f / t;
-                sn = g / t;
-                s[j] = t;
-                f = cs * e[j] + sn * s[j + 1];
-                s[j + 1] = -sn * e[j] + cs * s[j + 1];
-                g = sn * e[j + 1];
-                e[j + 1] = cs * e[j + 1];
+    get upperTriangularMatrix() {
+      let qr = this.QR;
+      let n = qr.columns;
+      let X = new Matrix(n, n);
+      let i, j;
 
-                if (wantu && j < m - 1) {
-                  for (let i = 0; i < m; i++) {
-                    t = cs * U.get(i, j) + sn * U.get(i, j + 1);
-                    U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));
-                    U.set(i, j, t);
-                  }
-                }
-              }
+      for (i = 0; i < n; i++) {
+        for (j = 0; j < n; j++) {
+          if (i < j) {
+            X.set(i, j, qr.get(i, j));
+          } else if (i === j) {
+            X.set(i, j, this.Rdiag[i]);
+          } else {
+            X.set(i, j, 0);
+          }
+        }
+      }
 
-              e[p - 2] = f;
-              break;
-            }
+      return X;
+    }
 
-          case 4:
-            {
-              if (s[k] <= 0) {
-                s[k] = s[k] < 0 ? -s[k] : 0;
+    get orthogonalMatrix() {
+      let qr = this.QR;
+      let rows = qr.rows;
+      let columns = qr.columns;
+      let X = new Matrix(rows, columns);
+      let i, j, k, s;
 
-                if (wantv) {
-                  for (let i = 0; i <= pp; i++) {
-                    V.set(i, k, -V.get(i, k));
-                  }
-                }
-              }
+      for (k = columns - 1; k >= 0; k--) {
+        for (i = 0; i < rows; i++) {
+          X.set(i, k, 0);
+        }
 
-              while (k < pp) {
-                if (s[k] >= s[k + 1]) {
-                  break;
-                }
+        X.set(k, k, 1);
 
-                let t = s[k];
-                s[k] = s[k + 1];
-                s[k + 1] = t;
+        for (j = k; j < columns; j++) {
+          if (qr.get(k, k) !== 0) {
+            s = 0;
 
-                if (wantv && k < n - 1) {
-                  for (let i = 0; i < n; i++) {
-                    t = V.get(i, k + 1);
-                    V.set(i, k + 1, V.get(i, k));
-                    V.set(i, k, t);
-                  }
-                }
+            for (i = k; i < rows; i++) {
+              s += qr.get(i, k) * X.get(i, j);
+            }
 
-                if (wantu && k < m - 1) {
-                  for (let i = 0; i < m; i++) {
-                    t = U.get(i, k + 1);
-                    U.set(i, k + 1, U.get(i, k));
-                    U.set(i, k, t);
-                  }
-                }
+            s = -s / qr.get(k, k);
 
-                k++;
-              }
-              p--;
-              break;
+            for (i = k; i < rows; i++) {
+              X.set(i, j, X.get(i, j) + s * qr.get(i, k));
             }
-          // no default
+          }
         }
       }
 
-      if (swapped) {
-        let tmp = V;
-        V = U;
-        U = tmp;
-      }
-
-      this.m = m;
-      this.n = n;
-      this.s = s;
-      this.U = U;
-      this.V = V;
+      return X;
     }
 
-    solve(value) {
-      let Y = value;
-      let e = this.threshold;
-      let scols = this.s.length;
-      let Ls = Matrix.zeros(scols, scols);
+  }
 
-      for (let i = 0; i < scols; i++) {
-        if (Math.abs(this.s[i]) <= e) {
-          Ls.set(i, i, 0);
+  class SingularValueDecomposition {
+    constructor(value, options = {}) {
+      value = WrapperMatrix2D.checkMatrix(value);
+      let m = value.rows;
+      let n = value.columns;
+      const {
+        computeLeftSingularVectors = true,
+        computeRightSingularVectors = true,
+        autoTranspose = false
+      } = options;
+      let wantu = Boolean(computeLeftSingularVectors);
+      let wantv = Boolean(computeRightSingularVectors);
+      let swapped = false;
+      let a;
+
+      if (m < n) {
+        if (!autoTranspose) {
+          a = value.clone(); // eslint-disable-next-line no-console
+
+          console.warn('Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose');
         } else {
-          Ls.set(i, i, 1 / this.s[i]);
+          a = value.transpose();
+          m = a.rows;
+          n = a.columns;
+          swapped = true;
+          let aux = wantu;
+          wantu = wantv;
+          wantv = aux;
         }
+      } else {
+        a = value.clone();
       }
 
-      let U = this.U;
-      let V = this.rightSingularVectors;
-      let VL = V.mmul(Ls);
-      let vrows = V.rows;
-      let urows = U.rows;
-      let VLU = Matrix.zeros(vrows, urows);
+      let nu = Math.min(m, n);
+      let ni = Math.min(m + 1, n);
+      let s = new Float64Array(ni);
+      let U = new Matrix(m, nu);
+      let V = new Matrix(n, n);
+      let e = new Float64Array(n);
+      let work = new Float64Array(m);
+      let si = new Float64Array(ni);
 
-      for (let i = 0; i < vrows; i++) {
-        for (let j = 0; j < urows; j++) {
-          let sum = 0;
+      for (let i = 0; i < ni; i++) si[i] = i;
 
-          for (let k = 0; k < scols; k++) {
-            sum += VL.get(i, k) * U.get(j, k);
-          }
+      let nct = Math.min(m - 1, n);
+      let nrt = Math.max(0, Math.min(n - 2, m));
+      let mrc = Math.max(nct, nrt);
 
-          VLU.set(i, j, sum);
-        }
-      }
+      for (let k = 0; k < mrc; k++) {
+        if (k < nct) {
+          s[k] = 0;
 
-      return VLU.mmul(Y);
-    }
+          for (let i = k; i < m; i++) {
+            s[k] = hypotenuse(s[k], a.get(i, k));
+          }
 
-    solveForDiagonal(value) {
-      return this.solve(Matrix.diag(value));
-    }
+          if (s[k] !== 0) {
+            if (a.get(k, k) < 0) {
+              s[k] = -s[k];
+            }
 
-    inverse() {
-      let V = this.V;
-      let e = this.threshold;
-      let vrows = V.rows;
-      let vcols = V.columns;
-      let X = new Matrix(vrows, this.s.length);
+            for (let i = k; i < m; i++) {
+              a.set(i, k, a.get(i, k) / s[k]);
+            }
 
-      for (let i = 0; i < vrows; i++) {
-        for (let j = 0; j < vcols; j++) {
-          if (Math.abs(this.s[j]) > e) {
-            X.set(i, j, V.get(i, j) / this.s[j]);
+            a.set(k, k, a.get(k, k) + 1);
           }
+
+          s[k] = -s[k];
         }
-      }
 
-      let U = this.U;
-      let urows = U.rows;
-      let ucols = U.columns;
-      let Y = new Matrix(vrows, urows);
+        for (let j = k + 1; j < n; j++) {
+          if (k < nct && s[k] !== 0) {
+            let t = 0;
 
-      for (let i = 0; i < vrows; i++) {
-        for (let j = 0; j < urows; j++) {
-          let sum = 0;
+            for (let i = k; i < m; i++) {
+              t += a.get(i, k) * a.get(i, j);
+            }
 
-          for (let k = 0; k < ucols; k++) {
-            sum += X.get(i, k) * U.get(j, k);
+            t = -t / a.get(k, k);
+
+            for (let i = k; i < m; i++) {
+              a.set(i, j, a.get(i, j) + t * a.get(i, k));
+            }
           }
 
-          Y.set(i, j, sum);
+          e[j] = a.get(k, j);
         }
-      }
 
-      return Y;
-    }
+        if (wantu && k < nct) {
+          for (let i = k; i < m; i++) {
+            U.set(i, k, a.get(i, k));
+          }
+        }
 
-    get condition() {
-      return this.s[0] / this.s[Math.min(this.m, this.n) - 1];
-    }
+        if (k < nrt) {
+          e[k] = 0;
 
-    get norm2() {
-      return this.s[0];
-    }
+          for (let i = k + 1; i < n; i++) {
+            e[k] = hypotenuse(e[k], e[i]);
+          }
 
-    get rank() {
-      let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;
-      let r = 0;
-      let s = this.s;
-
-      for (let i = 0, ii = s.length; i < ii; i++) {
-        if (s[i] > tol) {
-          r++;
-        }
-      }
-
-      return r;
-    }
-
-    get diagonal() {
-      return Array.from(this.s);
-    }
-
-    get threshold() {
-      return Number.EPSILON / 2 * Math.max(this.m, this.n) * this.s[0];
-    }
+          if (e[k] !== 0) {
+            if (e[k + 1] < 0) {
+              e[k] = 0 - e[k];
+            }
 
-    get leftSingularVectors() {
-      return this.U;
-    }
+            for (let i = k + 1; i < n; i++) {
+              e[i] /= e[k];
+            }
 
-    get rightSingularVectors() {
-      return this.V;
-    }
+            e[k + 1] += 1;
+          }
 
-    get diagonalMatrix() {
-      return Matrix.diag(this.s);
-    }
+          e[k] = -e[k];
 
-  }
+          if (k + 1 < m && e[k] !== 0) {
+            for (let i = k + 1; i < m; i++) {
+              work[i] = 0;
+            }
 
-  function inverse(matrix) {
-    let useSVD = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;
-    matrix = WrapperMatrix2D.checkMatrix(matrix);
+            for (let i = k + 1; i < m; i++) {
+              for (let j = k + 1; j < n; j++) {
+                work[i] += e[j] * a.get(i, j);
+              }
+            }
 
-    if (useSVD) {
-      return new SingularValueDecomposition(matrix).inverse();
-    } else {
-      return solve(matrix, Matrix.eye(matrix.rows));
-    }
-  }
-  function solve(leftHandSide, rightHandSide) {
-    let useSVD = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
-    leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);
-    rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);
+            for (let j = k + 1; j < n; j++) {
+              let t = -e[j] / e[k + 1];
 
-    if (useSVD) {
-      return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);
-    } else {
-      return leftHandSide.isSquare() ? new LuDecomposition(leftHandSide).solve(rightHandSide) : new QrDecomposition(leftHandSide).solve(rightHandSide);
-    }
-  }
+              for (let i = k + 1; i < m; i++) {
+                a.set(i, j, a.get(i, j) + t * work[i]);
+              }
+            }
+          }
 
-  function determinant(matrix) {
-    matrix = Matrix.checkMatrix(matrix);
+          if (wantv) {
+            for (let i = k + 1; i < n; i++) {
+              V.set(i, k, e[i]);
+            }
+          }
+        }
+      }
 
-    if (matrix.isSquare()) {
-      let a, b, c, d;
+      let p = Math.min(n, m + 1);
 
-      if (matrix.columns === 2) {
-        // 2 x 2 matrix
-        a = matrix.get(0, 0);
-        b = matrix.get(0, 1);
-        c = matrix.get(1, 0);
-        d = matrix.get(1, 1);
-        return a * d - b * c;
-      } else if (matrix.columns === 3) {
-        // 3 x 3 matrix
-        let subMatrix0, subMatrix1, subMatrix2;
-        subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);
-        subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);
-        subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);
-        a = matrix.get(0, 0);
-        b = matrix.get(0, 1);
-        c = matrix.get(0, 2);
-        return a * determinant(subMatrix0) - b * determinant(subMatrix1) + c * determinant(subMatrix2);
-      } else {
-        // general purpose determinant using the LU decomposition
-        return new LuDecomposition(matrix).determinant;
+      if (nct < n) {
+        s[nct] = a.get(nct, nct);
       }
-    } else {
-      throw Error('determinant can only be calculated for a square matrix');
-    }
-  }
-
-  function xrange(n, exception) {
-    let range = [];
 
-    for (let i = 0; i < n; i++) {
-      if (i !== exception) {
-        range.push(i);
+      if (m < p) {
+        s[p - 1] = 0;
       }
-    }
 
-    return range;
-  }
+      if (nrt + 1 < p) {
+        e[nrt] = a.get(nrt, p - 1);
+      }
 
-  function dependenciesOneRow(error, matrix, index) {
-    let thresholdValue = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : 10e-10;
-    let thresholdError = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : 10e-10;
+      e[p - 1] = 0;
 
-    if (error > thresholdError) {
-      return new Array(matrix.rows + 1).fill(0);
-    } else {
-      let returnArray = matrix.addRow(index, [0]);
+      if (wantu) {
+        for (let j = nct; j < nu; j++) {
+          for (let i = 0; i < m; i++) {
+            U.set(i, j, 0);
+          }
 
-      for (let i = 0; i < returnArray.rows; i++) {
-        if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {
-          returnArray.set(i, 0, 0);
+          U.set(j, j, 1);
         }
-      }
-
-      return returnArray.to1DArray();
-    }
-  }
 
-  function linearDependencies(matrix) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-    const {
-      thresholdValue = 10e-10,
-      thresholdError = 10e-10
-    } = options;
-    matrix = Matrix.checkMatrix(matrix);
-    let n = matrix.rows;
-    let results = new Matrix(n, n);
+        for (let k = nct - 1; k >= 0; k--) {
+          if (s[k] !== 0) {
+            for (let j = k + 1; j < nu; j++) {
+              let t = 0;
 
-    for (let i = 0; i < n; i++) {
-      let b = Matrix.columnVector(matrix.getRow(i));
-      let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();
-      let svd = new SingularValueDecomposition(Abis);
-      let x = svd.solve(b);
-      let error = Matrix.sub(b, Abis.mmul(x)).abs().max();
-      results.setRow(i, dependenciesOneRow(error, x, i, thresholdValue, thresholdError));
-    }
+              for (let i = k; i < m; i++) {
+                t += U.get(i, k) * U.get(i, j);
+              }
 
-    return results;
-  }
+              t = -t / U.get(k, k);
 
-  function pseudoInverse(matrix) {
-    let threshold = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : Number.EPSILON;
-    matrix = Matrix.checkMatrix(matrix);
-    let svdSolution = new SingularValueDecomposition(matrix, {
-      autoTranspose: true
-    });
-    let U = svdSolution.leftSingularVectors;
-    let V = svdSolution.rightSingularVectors;
-    let s = svdSolution.diagonal;
+              for (let i = k; i < m; i++) {
+                U.set(i, j, U.get(i, j) + t * U.get(i, k));
+              }
+            }
 
-    for (let i = 0; i < s.length; i++) {
-      if (Math.abs(s[i]) > threshold) {
-        s[i] = 1.0 / s[i];
-      } else {
-        s[i] = 0.0;
-      }
-    }
+            for (let i = k; i < m; i++) {
+              U.set(i, k, -U.get(i, k));
+            }
 
-    return V.mmul(Matrix.diag(s).mmul(U.transpose()));
-  }
+            U.set(k, k, 1 + U.get(k, k));
 
-  function covariance(xMatrix) {
-    let yMatrix = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : xMatrix;
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-    xMatrix = Matrix.checkMatrix(xMatrix);
-    let yIsSame = false;
+            for (let i = 0; i < k - 1; i++) {
+              U.set(i, k, 0);
+            }
+          } else {
+            for (let i = 0; i < m; i++) {
+              U.set(i, k, 0);
+            }
 
-    if (typeof yMatrix === 'object' && !Matrix.isMatrix(yMatrix) && !Array.isArray(yMatrix)) {
-      options = yMatrix;
-      yMatrix = xMatrix;
-      yIsSame = true;
-    } else {
-      yMatrix = Matrix.checkMatrix(yMatrix);
-    }
+            U.set(k, k, 1);
+          }
+        }
+      }
 
-    if (xMatrix.rows !== yMatrix.rows) {
-      throw new TypeError('Both matrices must have the same number of rows');
-    }
+      if (wantv) {
+        for (let k = n - 1; k >= 0; k--) {
+          if (k < nrt && e[k] !== 0) {
+            for (let j = k + 1; j < n; j++) {
+              let t = 0;
 
-    const {
-      center = true
-    } = options;
+              for (let i = k + 1; i < n; i++) {
+                t += V.get(i, k) * V.get(i, j);
+              }
 
-    if (center) {
-      xMatrix = xMatrix.center('column');
+              t = -t / V.get(k + 1, k);
 
-      if (!yIsSame) {
-        yMatrix = yMatrix.center('column');
-      }
-    }
+              for (let i = k + 1; i < n; i++) {
+                V.set(i, j, V.get(i, j) + t * V.get(i, k));
+              }
+            }
+          }
 
-    const cov = xMatrix.transpose().mmul(yMatrix);
+          for (let i = 0; i < n; i++) {
+            V.set(i, k, 0);
+          }
 
-    for (let i = 0; i < cov.rows; i++) {
-      for (let j = 0; j < cov.columns; j++) {
-        cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));
+          V.set(k, k, 1);
+        }
       }
-    }
 
-    return cov;
-  }
+      let pp = p - 1;
+      let eps = Number.EPSILON;
 
-  function correlation(xMatrix) {
-    let yMatrix = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : xMatrix;
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-    xMatrix = Matrix.checkMatrix(xMatrix);
-    let yIsSame = false;
+      while (p > 0) {
+        let k, kase;
 
-    if (typeof yMatrix === 'object' && !Matrix.isMatrix(yMatrix) && !Array.isArray(yMatrix)) {
-      options = yMatrix;
-      yMatrix = xMatrix;
-      yIsSame = true;
-    } else {
-      yMatrix = Matrix.checkMatrix(yMatrix);
-    }
-
-    if (xMatrix.rows !== yMatrix.rows) {
-      throw new TypeError('Both matrices must have the same number of rows');
-    }
-
-    const {
-      center = true,
-      scale = true
-    } = options;
-
-    if (center) {
-      xMatrix.center('column');
-
-      if (!yIsSame) {
-        yMatrix.center('column');
-      }
-    }
-
-    if (scale) {
-      xMatrix.scale('column');
-
-      if (!yIsSame) {
-        yMatrix.scale('column');
-      }
-    }
-
-    const sdx = xMatrix.standardDeviation('column', {
-      unbiased: true
-    });
-    const sdy = yIsSame ? sdx : yMatrix.standardDeviation('column', {
-      unbiased: true
-    });
-    const corr = xMatrix.transpose().mmul(yMatrix);
-
-    for (let i = 0; i < corr.rows; i++) {
-      for (let j = 0; j < corr.columns; j++) {
-        corr.set(i, j, corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)));
-      }
-    }
+        for (k = p - 2; k >= -1; k--) {
+          if (k === -1) {
+            break;
+          }
 
-    return corr;
-  }
+          const alpha = Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));
 
-  class EigenvalueDecomposition {
-    constructor(matrix) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-      const {
-        assumeSymmetric = false
-      } = options;
-      matrix = WrapperMatrix2D.checkMatrix(matrix);
+          if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {
+            e[k] = 0;
+            break;
+          }
+        }
 
-      if (!matrix.isSquare()) {
-        throw new Error('Matrix is not a square matrix');
-      }
+        if (k === p - 2) {
+          kase = 4;
+        } else {
+          let ks;
 
-      let n = matrix.columns;
-      let V = new Matrix(n, n);
-      let d = new Float64Array(n);
-      let e = new Float64Array(n);
-      let value = matrix;
-      let i, j;
-      let isSymmetric = false;
+          for (ks = p - 1; ks >= k; ks--) {
+            if (ks === k) {
+              break;
+            }
 
-      if (assumeSymmetric) {
-        isSymmetric = true;
-      } else {
-        isSymmetric = matrix.isSymmetric();
-      }
+            let t = (ks !== p ? Math.abs(e[ks]) : 0) + (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);
 
-      if (isSymmetric) {
-        for (i = 0; i < n; i++) {
-          for (j = 0; j < n; j++) {
-            V.set(i, j, value.get(i, j));
+            if (Math.abs(s[ks]) <= eps * t) {
+              s[ks] = 0;
+              break;
+            }
           }
-        }
-
-        tred2(n, e, d, V);
-        tql2(n, e, d, V);
-      } else {
-        let H = new Matrix(n, n);
-        let ort = new Float64Array(n);
 
-        for (j = 0; j < n; j++) {
-          for (i = 0; i < n; i++) {
-            H.set(i, j, value.get(i, j));
+          if (ks === k) {
+            kase = 3;
+          } else if (ks === p - 1) {
+            kase = 1;
+          } else {
+            kase = 2;
+            k = ks;
           }
         }
 
-        orthes(n, H, ort, V);
-        hqr2(n, e, d, V, H);
-      }
+        k++;
 
-      this.n = n;
-      this.e = e;
-      this.d = d;
-      this.V = V;
-    }
+        switch (kase) {
+          case 1:
+            {
+              let f = e[p - 2];
+              e[p - 2] = 0;
 
-    get realEigenvalues() {
-      return Array.from(this.d);
-    }
+              for (let j = p - 2; j >= k; j--) {
+                let t = hypotenuse(s[j], f);
+                let cs = s[j] / t;
+                let sn = f / t;
+                s[j] = t;
 
-    get imaginaryEigenvalues() {
-      return Array.from(this.e);
-    }
+                if (j !== k) {
+                  f = -sn * e[j - 1];
+                  e[j - 1] = cs * e[j - 1];
+                }
 
-    get eigenvectorMatrix() {
-      return this.V;
-    }
+                if (wantv) {
+                  for (let i = 0; i < n; i++) {
+                    t = cs * V.get(i, j) + sn * V.get(i, p - 1);
+                    V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));
+                    V.set(i, j, t);
+                  }
+                }
+              }
 
-    get diagonalMatrix() {
-      let n = this.n;
-      let e = this.e;
-      let d = this.d;
-      let X = new Matrix(n, n);
-      let i, j;
+              break;
+            }
 
-      for (i = 0; i < n; i++) {
-        for (j = 0; j < n; j++) {
-          X.set(i, j, 0);
-        }
+          case 2:
+            {
+              let f = e[k - 1];
+              e[k - 1] = 0;
 
-        X.set(i, i, d[i]);
+              for (let j = k; j < p; j++) {
+                let t = hypotenuse(s[j], f);
+                let cs = s[j] / t;
+                let sn = f / t;
+                s[j] = t;
+                f = -sn * e[j];
+                e[j] = cs * e[j];
 
-        if (e[i] > 0) {
-          X.set(i, i + 1, e[i]);
-        } else if (e[i] < 0) {
-          X.set(i, i - 1, e[i]);
-        }
-      }
+                if (wantu) {
+                  for (let i = 0; i < m; i++) {
+                    t = cs * U.get(i, j) + sn * U.get(i, k - 1);
+                    U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));
+                    U.set(i, j, t);
+                  }
+                }
+              }
 
-      return X;
-    }
+              break;
+            }
 
-  }
+          case 3:
+            {
+              const scale = Math.max(Math.abs(s[p - 1]), Math.abs(s[p - 2]), Math.abs(e[p - 2]), Math.abs(s[k]), Math.abs(e[k]));
+              const sp = s[p - 1] / scale;
+              const spm1 = s[p - 2] / scale;
+              const epm1 = e[p - 2] / scale;
+              const sk = s[k] / scale;
+              const ek = e[k] / scale;
+              const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;
+              const c = sp * epm1 * (sp * epm1);
+              let shift = 0;
 
-  function tred2(n, e, d, V) {
-    let f, g, h, i, j, k, hh, scale;
+              if (b !== 0 || c !== 0) {
+                if (b < 0) {
+                  shift = 0 - Math.sqrt(b * b + c);
+                } else {
+                  shift = Math.sqrt(b * b + c);
+                }
 
-    for (j = 0; j < n; j++) {
-      d[j] = V.get(n - 1, j);
-    }
+                shift = c / (b + shift);
+              }
 
-    for (i = n - 1; i > 0; i--) {
-      scale = 0;
-      h = 0;
+              let f = (sk + sp) * (sk - sp) + shift;
+              let g = sk * ek;
 
-      for (k = 0; k < i; k++) {
-        scale = scale + Math.abs(d[k]);
-      }
+              for (let j = k; j < p - 1; j++) {
+                let t = hypotenuse(f, g);
+                if (t === 0) t = Number.MIN_VALUE;
+                let cs = f / t;
+                let sn = g / t;
 
-      if (scale === 0) {
-        e[i] = d[i - 1];
+                if (j !== k) {
+                  e[j - 1] = t;
+                }
 
-        for (j = 0; j < i; j++) {
-          d[j] = V.get(i - 1, j);
-          V.set(i, j, 0);
-          V.set(j, i, 0);
-        }
-      } else {
-        for (k = 0; k < i; k++) {
-          d[k] /= scale;
-          h += d[k] * d[k];
-        }
+                f = cs * s[j] + sn * e[j];
+                e[j] = cs * e[j] - sn * s[j];
+                g = sn * s[j + 1];
+                s[j + 1] = cs * s[j + 1];
 
-        f = d[i - 1];
-        g = Math.sqrt(h);
+                if (wantv) {
+                  for (let i = 0; i < n; i++) {
+                    t = cs * V.get(i, j) + sn * V.get(i, j + 1);
+                    V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));
+                    V.set(i, j, t);
+                  }
+                }
 
-        if (f > 0) {
-          g = -g;
-        }
+                t = hypotenuse(f, g);
+                if (t === 0) t = Number.MIN_VALUE;
+                cs = f / t;
+                sn = g / t;
+                s[j] = t;
+                f = cs * e[j] + sn * s[j + 1];
+                s[j + 1] = -sn * e[j] + cs * s[j + 1];
+                g = sn * e[j + 1];
+                e[j + 1] = cs * e[j + 1];
 
-        e[i] = scale * g;
-        h = h - f * g;
-        d[i - 1] = f - g;
+                if (wantu && j < m - 1) {
+                  for (let i = 0; i < m; i++) {
+                    t = cs * U.get(i, j) + sn * U.get(i, j + 1);
+                    U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));
+                    U.set(i, j, t);
+                  }
+                }
+              }
 
-        for (j = 0; j < i; j++) {
-          e[j] = 0;
-        }
+              e[p - 2] = f;
+              break;
+            }
 
-        for (j = 0; j < i; j++) {
-          f = d[j];
-          V.set(j, i, f);
-          g = e[j] + V.get(j, j) * f;
+          case 4:
+            {
+              if (s[k] <= 0) {
+                s[k] = s[k] < 0 ? -s[k] : 0;
 
-          for (k = j + 1; k <= i - 1; k++) {
-            g += V.get(k, j) * d[k];
-            e[k] += V.get(k, j) * f;
-          }
+                if (wantv) {
+                  for (let i = 0; i <= pp; i++) {
+                    V.set(i, k, -V.get(i, k));
+                  }
+                }
+              }
 
-          e[j] = g;
-        }
+              while (k < pp) {
+                if (s[k] >= s[k + 1]) {
+                  break;
+                }
 
-        f = 0;
+                let t = s[k];
+                s[k] = s[k + 1];
+                s[k + 1] = t;
 
-        for (j = 0; j < i; j++) {
-          e[j] /= h;
-          f += e[j] * d[j];
+                if (wantv && k < n - 1) {
+                  for (let i = 0; i < n; i++) {
+                    t = V.get(i, k + 1);
+                    V.set(i, k + 1, V.get(i, k));
+                    V.set(i, k, t);
+                  }
+                }
+
+                if (wantu && k < m - 1) {
+                  for (let i = 0; i < m; i++) {
+                    t = U.get(i, k + 1);
+                    U.set(i, k + 1, U.get(i, k));
+                    U.set(i, k, t);
+                  }
+                }
+
+                k++;
+              }
+              p--;
+              break;
+            }
+          // no default
         }
+      }
 
-        hh = f / (h + h);
+      if (swapped) {
+        let tmp = V;
+        V = U;
+        U = tmp;
+      }
 
-        for (j = 0; j < i; j++) {
-          e[j] -= hh * d[j];
+      this.m = m;
+      this.n = n;
+      this.s = s;
+      this.U = U;
+      this.V = V;
+    }
+
+    solve(value) {
+      let Y = value;
+      let e = this.threshold;
+      let scols = this.s.length;
+      let Ls = Matrix.zeros(scols, scols);
+
+      for (let i = 0; i < scols; i++) {
+        if (Math.abs(this.s[i]) <= e) {
+          Ls.set(i, i, 0);
+        } else {
+          Ls.set(i, i, 1 / this.s[i]);
         }
+      }
 
-        for (j = 0; j < i; j++) {
-          f = d[j];
-          g = e[j];
+      let U = this.U;
+      let V = this.rightSingularVectors;
+      let VL = V.mmul(Ls);
+      let vrows = V.rows;
+      let urows = U.rows;
+      let VLU = Matrix.zeros(vrows, urows);
 
-          for (k = j; k <= i - 1; k++) {
-            V.set(k, j, V.get(k, j) - (f * e[k] + g * d[k]));
+      for (let i = 0; i < vrows; i++) {
+        for (let j = 0; j < urows; j++) {
+          let sum = 0;
+
+          for (let k = 0; k < scols; k++) {
+            sum += VL.get(i, k) * U.get(j, k);
           }
 
-          d[j] = V.get(i - 1, j);
-          V.set(i, j, 0);
+          VLU.set(i, j, sum);
         }
       }
 
-      d[i] = h;
+      return VLU.mmul(Y);
     }
 
-    for (i = 0; i < n - 1; i++) {
-      V.set(n - 1, i, V.get(i, i));
-      V.set(i, i, 1);
-      h = d[i + 1];
+    solveForDiagonal(value) {
+      return this.solve(Matrix.diag(value));
+    }
 
-      if (h !== 0) {
-        for (k = 0; k <= i; k++) {
-          d[k] = V.get(k, i + 1) / h;
+    inverse() {
+      let V = this.V;
+      let e = this.threshold;
+      let vrows = V.rows;
+      let vcols = V.columns;
+      let X = new Matrix(vrows, this.s.length);
+
+      for (let i = 0; i < vrows; i++) {
+        for (let j = 0; j < vcols; j++) {
+          if (Math.abs(this.s[j]) > e) {
+            X.set(i, j, V.get(i, j) / this.s[j]);
+          }
         }
+      }
 
-        for (j = 0; j <= i; j++) {
-          g = 0;
+      let U = this.U;
+      let urows = U.rows;
+      let ucols = U.columns;
+      let Y = new Matrix(vrows, urows);
 
-          for (k = 0; k <= i; k++) {
-            g += V.get(k, i + 1) * V.get(k, j);
-          }
+      for (let i = 0; i < vrows; i++) {
+        for (let j = 0; j < urows; j++) {
+          let sum = 0;
 
-          for (k = 0; k <= i; k++) {
-            V.set(k, j, V.get(k, j) - g * d[k]);
+          for (let k = 0; k < ucols; k++) {
+            sum += X.get(i, k) * U.get(j, k);
           }
+
+          Y.set(i, j, sum);
         }
       }
 
-      for (k = 0; k <= i; k++) {
-        V.set(k, i + 1, 0);
-      }
+      return Y;
     }
 
-    for (j = 0; j < n; j++) {
-      d[j] = V.get(n - 1, j);
-      V.set(n - 1, j, 0);
+    get condition() {
+      return this.s[0] / this.s[Math.min(this.m, this.n) - 1];
     }
 
-    V.set(n - 1, n - 1, 1);
-    e[0] = 0;
-  }
-
-  function tql2(n, e, d, V) {
-    let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2;
-
-    for (i = 1; i < n; i++) {
-      e[i - 1] = e[i];
+    get norm2() {
+      return this.s[0];
     }
 
-    e[n - 1] = 0;
-    let f = 0;
-    let tst1 = 0;
-    let eps = Number.EPSILON;
-
-    for (l = 0; l < n; l++) {
-      tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));
-      m = l;
+    get rank() {
+      let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;
+      let r = 0;
+      let s = this.s;
 
-      while (m < n) {
-        if (Math.abs(e[m]) <= eps * tst1) {
-          break;
+      for (let i = 0, ii = s.length; i < ii; i++) {
+        if (s[i] > tol) {
+          r++;
         }
-
-        m++;
       }
 
-      if (m > l) {
+      return r;
+    }
 
-        do {
-          g = d[l];
-          p = (d[l + 1] - g) / (2 * e[l]);
-          r = hypotenuse(p, 1);
+    get diagonal() {
+      return Array.from(this.s);
+    }
 
-          if (p < 0) {
-            r = -r;
-          }
+    get threshold() {
+      return Number.EPSILON / 2 * Math.max(this.m, this.n) * this.s[0];
+    }
 
-          d[l] = e[l] / (p + r);
-          d[l + 1] = e[l] * (p + r);
-          dl1 = d[l + 1];
-          h = g - d[l];
+    get leftSingularVectors() {
+      return this.U;
+    }
 
-          for (i = l + 2; i < n; i++) {
-            d[i] -= h;
-          }
+    get rightSingularVectors() {
+      return this.V;
+    }
 
-          f = f + h;
-          p = d[m];
-          c = 1;
-          c2 = c;
-          c3 = c;
-          el1 = e[l + 1];
-          s = 0;
-          s2 = 0;
+    get diagonalMatrix() {
+      return Matrix.diag(this.s);
+    }
 
-          for (i = m - 1; i >= l; i--) {
-            c3 = c2;
-            c2 = c;
-            s2 = s;
-            g = c * e[i];
-            h = c * p;
-            r = hypotenuse(p, e[i]);
-            e[i + 1] = s * r;
-            s = e[i] / r;
-            c = p / r;
-            p = c * d[i] - s * g;
-            d[i + 1] = h + s * (c * g + s * d[i]);
-
-            for (k = 0; k < n; k++) {
-              h = V.get(k, i + 1);
-              V.set(k, i + 1, s * V.get(k, i) + c * h);
-              V.set(k, i, c * V.get(k, i) - s * h);
-            }
-          }
+  }
 
-          p = -s * s2 * c3 * el1 * e[l] / dl1;
-          e[l] = s * p;
-          d[l] = c * p;
-        } while (Math.abs(e[l]) > eps * tst1);
-      }
+  function inverse(matrix, useSVD = false) {
+    matrix = WrapperMatrix2D.checkMatrix(matrix);
 
-      d[l] = d[l] + f;
-      e[l] = 0;
+    if (useSVD) {
+      return new SingularValueDecomposition(matrix).inverse();
+    } else {
+      return solve(matrix, Matrix.eye(matrix.rows));
     }
+  }
+  function solve(leftHandSide, rightHandSide, useSVD = false) {
+    leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);
+    rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);
 
-    for (i = 0; i < n - 1; i++) {
-      k = i;
-      p = d[i];
+    if (useSVD) {
+      return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);
+    } else {
+      return leftHandSide.isSquare() ? new LuDecomposition(leftHandSide).solve(rightHandSide) : new QrDecomposition(leftHandSide).solve(rightHandSide);
+    }
+  }
 
-      for (j = i + 1; j < n; j++) {
-        if (d[j] < p) {
-          k = j;
-          p = d[j];
-        }
-      }
+  function determinant(matrix) {
+    matrix = Matrix.checkMatrix(matrix);
 
-      if (k !== i) {
-        d[k] = d[i];
-        d[i] = p;
+    if (matrix.isSquare()) {
+      let a, b, c, d;
 
-        for (j = 0; j < n; j++) {
-          p = V.get(j, i);
-          V.set(j, i, V.get(j, k));
-          V.set(j, k, p);
-        }
+      if (matrix.columns === 2) {
+        // 2 x 2 matrix
+        a = matrix.get(0, 0);
+        b = matrix.get(0, 1);
+        c = matrix.get(1, 0);
+        d = matrix.get(1, 1);
+        return a * d - b * c;
+      } else if (matrix.columns === 3) {
+        // 3 x 3 matrix
+        let subMatrix0, subMatrix1, subMatrix2;
+        subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);
+        subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);
+        subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);
+        a = matrix.get(0, 0);
+        b = matrix.get(0, 1);
+        c = matrix.get(0, 2);
+        return a * determinant(subMatrix0) - b * determinant(subMatrix1) + c * determinant(subMatrix2);
+      } else {
+        // general purpose determinant using the LU decomposition
+        return new LuDecomposition(matrix).determinant;
       }
+    } else {
+      throw Error('determinant can only be calculated for a square matrix');
     }
   }
 
-  function orthes(n, H, ort, V) {
-    let low = 0;
-    let high = n - 1;
-    let f, g, h, i, j, m;
-    let scale;
-
-    for (m = low + 1; m <= high - 1; m++) {
-      scale = 0;
+  function xrange(n, exception) {
+    let range = [];
 
-      for (i = m; i <= high; i++) {
-        scale = scale + Math.abs(H.get(i, m - 1));
+    for (let i = 0; i < n; i++) {
+      if (i !== exception) {
+        range.push(i);
       }
+    }
 
-      if (scale !== 0) {
-        h = 0;
-
-        for (i = high; i >= m; i--) {
-          ort[i] = H.get(i, m - 1) / scale;
-          h += ort[i] * ort[i];
-        }
+    return range;
+  }
 
-        g = Math.sqrt(h);
+  function dependenciesOneRow(error, matrix, index, thresholdValue = 10e-10, thresholdError = 10e-10) {
+    if (error > thresholdError) {
+      return new Array(matrix.rows + 1).fill(0);
+    } else {
+      let returnArray = matrix.addRow(index, [0]);
 
-        if (ort[m] > 0) {
-          g = -g;
+      for (let i = 0; i < returnArray.rows; i++) {
+        if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {
+          returnArray.set(i, 0, 0);
         }
+      }
 
-        h = h - ort[m] * g;
-        ort[m] = ort[m] - g;
-
-        for (j = m; j < n; j++) {
-          f = 0;
+      return returnArray.to1DArray();
+    }
+  }
 
-          for (i = high; i >= m; i--) {
-            f += ort[i] * H.get(i, j);
-          }
+  function linearDependencies(matrix, options = {}) {
+    const {
+      thresholdValue = 10e-10,
+      thresholdError = 10e-10
+    } = options;
+    matrix = Matrix.checkMatrix(matrix);
+    let n = matrix.rows;
+    let results = new Matrix(n, n);
 
-          f = f / h;
+    for (let i = 0; i < n; i++) {
+      let b = Matrix.columnVector(matrix.getRow(i));
+      let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();
+      let svd = new SingularValueDecomposition(Abis);
+      let x = svd.solve(b);
+      let error = Matrix.sub(b, Abis.mmul(x)).abs().max();
+      results.setRow(i, dependenciesOneRow(error, x, i, thresholdValue, thresholdError));
+    }
 
-          for (i = m; i <= high; i++) {
-            H.set(i, j, H.get(i, j) - f * ort[i]);
-          }
-        }
+    return results;
+  }
 
-        for (i = 0; i <= high; i++) {
-          f = 0;
+  function pseudoInverse(matrix, threshold = Number.EPSILON) {
+    matrix = Matrix.checkMatrix(matrix);
+    let svdSolution = new SingularValueDecomposition(matrix, {
+      autoTranspose: true
+    });
+    let U = svdSolution.leftSingularVectors;
+    let V = svdSolution.rightSingularVectors;
+    let s = svdSolution.diagonal;
 
-          for (j = high; j >= m; j--) {
-            f += ort[j] * H.get(i, j);
-          }
+    for (let i = 0; i < s.length; i++) {
+      if (Math.abs(s[i]) > threshold) {
+        s[i] = 1.0 / s[i];
+      } else {
+        s[i] = 0.0;
+      }
+    }
 
-          f = f / h;
+    return V.mmul(Matrix.diag(s).mmul(U.transpose()));
+  }
 
-          for (j = m; j <= high; j++) {
-            H.set(i, j, H.get(i, j) - f * ort[j]);
-          }
-        }
+  function covariance(xMatrix, yMatrix = xMatrix, options = {}) {
+    xMatrix = new Matrix(xMatrix);
+    let yIsSame = false;
 
-        ort[m] = scale * ort[m];
-        H.set(m, m - 1, scale * g);
-      }
+    if (typeof yMatrix === 'object' && !Matrix.isMatrix(yMatrix) && !Array.isArray(yMatrix)) {
+      options = yMatrix;
+      yMatrix = xMatrix;
+      yIsSame = true;
+    } else {
+      yMatrix = new Matrix(yMatrix);
     }
 
-    for (i = 0; i < n; i++) {
-      for (j = 0; j < n; j++) {
-        V.set(i, j, i === j ? 1 : 0);
-      }
+    if (xMatrix.rows !== yMatrix.rows) {
+      throw new TypeError('Both matrices must have the same number of rows');
     }
 
-    for (m = high - 1; m >= low + 1; m--) {
-      if (H.get(m, m - 1) !== 0) {
-        for (i = m + 1; i <= high; i++) {
-          ort[i] = H.get(i, m - 1);
-        }
+    const {
+      center = true
+    } = options;
 
-        for (j = m; j <= high; j++) {
-          g = 0;
+    if (center) {
+      xMatrix = xMatrix.center('column');
 
-          for (i = m; i <= high; i++) {
-            g += ort[i] * V.get(i, j);
-          }
+      if (!yIsSame) {
+        yMatrix = yMatrix.center('column');
+      }
+    }
 
-          g = g / ort[m] / H.get(m, m - 1);
+    const cov = xMatrix.transpose().mmul(yMatrix);
 
-          for (i = m; i <= high; i++) {
-            V.set(i, j, V.get(i, j) + g * ort[i]);
-          }
-        }
+    for (let i = 0; i < cov.rows; i++) {
+      for (let j = 0; j < cov.columns; j++) {
+        cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));
       }
     }
+
+    return cov;
   }
 
-  function hqr2(nn, e, d, V, H) {
-    let n = nn - 1;
-    let low = 0;
-    let high = nn - 1;
-    let eps = Number.EPSILON;
-    let exshift = 0;
-    let norm = 0;
-    let p = 0;
-    let q = 0;
-    let r = 0;
-    let s = 0;
-    let z = 0;
-    let iter = 0;
-    let i, j, k, l, m, t, w, x, y;
-    let ra, sa, vr, vi;
-    let notlast, cdivres;
+  function correlation(xMatrix, yMatrix = xMatrix, options = {}) {
+    xMatrix = new Matrix(xMatrix);
+    let yIsSame = false;
 
-    for (i = 0; i < nn; i++) {
-      if (i < low || i > high) {
-        d[i] = H.get(i, i);
-        e[i] = 0;
-      }
+    if (typeof yMatrix === 'object' && !Matrix.isMatrix(yMatrix) && !Array.isArray(yMatrix)) {
+      options = yMatrix;
+      yMatrix = xMatrix;
+      yIsSame = true;
+    } else {
+      yMatrix = new Matrix(yMatrix);
+    }
 
-      for (j = Math.max(i - 1, 0); j < nn; j++) {
-        norm = norm + Math.abs(H.get(i, j));
-      }
+    if (xMatrix.rows !== yMatrix.rows) {
+      throw new TypeError('Both matrices must have the same number of rows');
     }
 
-    while (n >= low) {
-      l = n;
+    const {
+      center = true,
+      scale = true
+    } = options;
 
-      while (l > low) {
-        s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));
+    if (center) {
+      xMatrix.center('column');
 
-        if (s === 0) {
-          s = norm;
-        }
+      if (!yIsSame) {
+        yMatrix.center('column');
+      }
+    }
 
-        if (Math.abs(H.get(l, l - 1)) < eps * s) {
-          break;
-        }
+    if (scale) {
+      xMatrix.scale('column');
 
-        l--;
+      if (!yIsSame) {
+        yMatrix.scale('column');
       }
+    }
 
-      if (l === n) {
-        H.set(n, n, H.get(n, n) + exshift);
-        d[n] = H.get(n, n);
-        e[n] = 0;
-        n--;
-        iter = 0;
-      } else if (l === n - 1) {
-        w = H.get(n, n - 1) * H.get(n - 1, n);
-        p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;
-        q = p * p + w;
-        z = Math.sqrt(Math.abs(q));
-        H.set(n, n, H.get(n, n) + exshift);
-        H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);
-        x = H.get(n, n);
+    const sdx = xMatrix.standardDeviation('column', {
+      unbiased: true
+    });
+    const sdy = yIsSame ? sdx : yMatrix.standardDeviation('column', {
+      unbiased: true
+    });
+    const corr = xMatrix.transpose().mmul(yMatrix);
 
-        if (q >= 0) {
-          z = p >= 0 ? p + z : p - z;
-          d[n - 1] = x + z;
-          d[n] = d[n - 1];
+    for (let i = 0; i < corr.rows; i++) {
+      for (let j = 0; j < corr.columns; j++) {
+        corr.set(i, j, corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)));
+      }
+    }
 
-          if (z !== 0) {
-            d[n] = x - w / z;
-          }
+    return corr;
+  }
 
-          e[n - 1] = 0;
-          e[n] = 0;
-          x = H.get(n, n - 1);
-          s = Math.abs(x) + Math.abs(z);
-          p = x / s;
-          q = z / s;
-          r = Math.sqrt(p * p + q * q);
-          p = p / r;
-          q = q / r;
+  class EigenvalueDecomposition {
+    constructor(matrix, options = {}) {
+      const {
+        assumeSymmetric = false
+      } = options;
+      matrix = WrapperMatrix2D.checkMatrix(matrix);
 
-          for (j = n - 1; j < nn; j++) {
-            z = H.get(n - 1, j);
-            H.set(n - 1, j, q * z + p * H.get(n, j));
-            H.set(n, j, q * H.get(n, j) - p * z);
-          }
+      if (!matrix.isSquare()) {
+        throw new Error('Matrix is not a square matrix');
+      }
 
-          for (i = 0; i <= n; i++) {
-            z = H.get(i, n - 1);
-            H.set(i, n - 1, q * z + p * H.get(i, n));
-            H.set(i, n, q * H.get(i, n) - p * z);
-          }
+      let n = matrix.columns;
+      let V = new Matrix(n, n);
+      let d = new Float64Array(n);
+      let e = new Float64Array(n);
+      let value = matrix;
+      let i, j;
+      let isSymmetric = false;
 
-          for (i = low; i <= high; i++) {
-            z = V.get(i, n - 1);
-            V.set(i, n - 1, q * z + p * V.get(i, n));
-            V.set(i, n, q * V.get(i, n) - p * z);
+      if (assumeSymmetric) {
+        isSymmetric = true;
+      } else {
+        isSymmetric = matrix.isSymmetric();
+      }
+
+      if (isSymmetric) {
+        for (i = 0; i < n; i++) {
+          for (j = 0; j < n; j++) {
+            V.set(i, j, value.get(i, j));
           }
-        } else {
-          d[n - 1] = x + p;
-          d[n] = x + p;
-          e[n - 1] = z;
-          e[n] = -z;
         }
 
-        n = n - 2;
-        iter = 0;
+        tred2(n, e, d, V);
+        tql2(n, e, d, V);
       } else {
-        x = H.get(n, n);
-        y = 0;
-        w = 0;
+        let H = new Matrix(n, n);
+        let ort = new Float64Array(n);
 
-        if (l < n) {
-          y = H.get(n - 1, n - 1);
-          w = H.get(n, n - 1) * H.get(n - 1, n);
+        for (j = 0; j < n; j++) {
+          for (i = 0; i < n; i++) {
+            H.set(i, j, value.get(i, j));
+          }
         }
 
-        if (iter === 10) {
-          exshift += x;
+        orthes(n, H, ort, V);
+        hqr2(n, e, d, V, H);
+      }
 
-          for (i = low; i <= n; i++) {
-            H.set(i, i, H.get(i, i) - x);
-          }
+      this.n = n;
+      this.e = e;
+      this.d = d;
+      this.V = V;
+    }
 
-          s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));
-          x = y = 0.75 * s;
-          w = -0.4375 * s * s;
-        }
+    get realEigenvalues() {
+      return Array.from(this.d);
+    }
 
-        if (iter === 30) {
-          s = (y - x) / 2;
-          s = s * s + w;
+    get imaginaryEigenvalues() {
+      return Array.from(this.e);
+    }
 
-          if (s > 0) {
-            s = Math.sqrt(s);
+    get eigenvectorMatrix() {
+      return this.V;
+    }
 
-            if (y < x) {
-              s = -s;
-            }
+    get diagonalMatrix() {
+      let n = this.n;
+      let e = this.e;
+      let d = this.d;
+      let X = new Matrix(n, n);
+      let i, j;
 
-            s = x - w / ((y - x) / 2 + s);
+      for (i = 0; i < n; i++) {
+        for (j = 0; j < n; j++) {
+          X.set(i, j, 0);
+        }
 
-            for (i = low; i <= n; i++) {
-              H.set(i, i, H.get(i, i) - s);
-            }
+        X.set(i, i, d[i]);
 
-            exshift += s;
-            x = y = w = 0.964;
-          }
+        if (e[i] > 0) {
+          X.set(i, i + 1, e[i]);
+        } else if (e[i] < 0) {
+          X.set(i, i - 1, e[i]);
         }
+      }
 
-        iter = iter + 1;
-        m = n - 2;
+      return X;
+    }
 
-        while (m >= l) {
-          z = H.get(m, m);
-          r = x - z;
-          s = y - z;
-          p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);
-          q = H.get(m + 1, m + 1) - z - r - s;
-          r = H.get(m + 2, m + 1);
-          s = Math.abs(p) + Math.abs(q) + Math.abs(r);
-          p = p / s;
-          q = q / s;
-          r = r / s;
+  }
 
-          if (m === l) {
-            break;
-          }
+  function tred2(n, e, d, V) {
+    let f, g, h, i, j, k, hh, scale;
 
-          if (Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) < eps * (Math.abs(p) * (Math.abs(H.get(m - 1, m - 1)) + Math.abs(z) + Math.abs(H.get(m + 1, m + 1))))) {
-            break;
-          }
+    for (j = 0; j < n; j++) {
+      d[j] = V.get(n - 1, j);
+    }
 
-          m--;
+    for (i = n - 1; i > 0; i--) {
+      scale = 0;
+      h = 0;
+
+      for (k = 0; k < i; k++) {
+        scale = scale + Math.abs(d[k]);
+      }
+
+      if (scale === 0) {
+        e[i] = d[i - 1];
+
+        for (j = 0; j < i; j++) {
+          d[j] = V.get(i - 1, j);
+          V.set(i, j, 0);
+          V.set(j, i, 0);
+        }
+      } else {
+        for (k = 0; k < i; k++) {
+          d[k] /= scale;
+          h += d[k] * d[k];
         }
 
-        for (i = m + 2; i <= n; i++) {
-          H.set(i, i - 2, 0);
+        f = d[i - 1];
+        g = Math.sqrt(h);
 
-          if (i > m + 2) {
-            H.set(i, i - 3, 0);
-          }
+        if (f > 0) {
+          g = -g;
         }
 
-        for (k = m; k <= n - 1; k++) {
-          notlast = k !== n - 1;
+        e[i] = scale * g;
+        h = h - f * g;
+        d[i - 1] = f - g;
 
-          if (k !== m) {
-            p = H.get(k, k - 1);
-            q = H.get(k + 1, k - 1);
-            r = notlast ? H.get(k + 2, k - 1) : 0;
-            x = Math.abs(p) + Math.abs(q) + Math.abs(r);
+        for (j = 0; j < i; j++) {
+          e[j] = 0;
+        }
 
-            if (x !== 0) {
-              p = p / x;
-              q = q / x;
-              r = r / x;
-            }
-          }
+        for (j = 0; j < i; j++) {
+          f = d[j];
+          V.set(j, i, f);
+          g = e[j] + V.get(j, j) * f;
 
-          if (x === 0) {
-            break;
+          for (k = j + 1; k <= i - 1; k++) {
+            g += V.get(k, j) * d[k];
+            e[k] += V.get(k, j) * f;
           }
 
-          s = Math.sqrt(p * p + q * q + r * r);
+          e[j] = g;
+        }
 
-          if (p < 0) {
-            s = -s;
-          }
+        f = 0;
 
-          if (s !== 0) {
-            if (k !== m) {
-              H.set(k, k - 1, -s * x);
-            } else if (l !== m) {
-              H.set(k, k - 1, -H.get(k, k - 1));
-            }
+        for (j = 0; j < i; j++) {
+          e[j] /= h;
+          f += e[j] * d[j];
+        }
 
-            p = p + s;
-            x = p / s;
-            y = q / s;
-            z = r / s;
-            q = q / p;
-            r = r / p;
+        hh = f / (h + h);
 
-            for (j = k; j < nn; j++) {
-              p = H.get(k, j) + q * H.get(k + 1, j);
+        for (j = 0; j < i; j++) {
+          e[j] -= hh * d[j];
+        }
 
-              if (notlast) {
-                p = p + r * H.get(k + 2, j);
-                H.set(k + 2, j, H.get(k + 2, j) - p * z);
-              }
+        for (j = 0; j < i; j++) {
+          f = d[j];
+          g = e[j];
 
-              H.set(k, j, H.get(k, j) - p * x);
-              H.set(k + 1, j, H.get(k + 1, j) - p * y);
-            }
+          for (k = j; k <= i - 1; k++) {
+            V.set(k, j, V.get(k, j) - (f * e[k] + g * d[k]));
+          }
 
-            for (i = 0; i <= Math.min(n, k + 3); i++) {
-              p = x * H.get(i, k) + y * H.get(i, k + 1);
+          d[j] = V.get(i - 1, j);
+          V.set(i, j, 0);
+        }
+      }
 
-              if (notlast) {
-                p = p + z * H.get(i, k + 2);
-                H.set(i, k + 2, H.get(i, k + 2) - p * r);
-              }
+      d[i] = h;
+    }
 
-              H.set(i, k, H.get(i, k) - p);
-              H.set(i, k + 1, H.get(i, k + 1) - p * q);
-            }
+    for (i = 0; i < n - 1; i++) {
+      V.set(n - 1, i, V.get(i, i));
+      V.set(i, i, 1);
+      h = d[i + 1];
 
-            for (i = low; i <= high; i++) {
-              p = x * V.get(i, k) + y * V.get(i, k + 1);
+      if (h !== 0) {
+        for (k = 0; k <= i; k++) {
+          d[k] = V.get(k, i + 1) / h;
+        }
 
-              if (notlast) {
-                p = p + z * V.get(i, k + 2);
-                V.set(i, k + 2, V.get(i, k + 2) - p * r);
-              }
+        for (j = 0; j <= i; j++) {
+          g = 0;
 
-              V.set(i, k, V.get(i, k) - p);
-              V.set(i, k + 1, V.get(i, k + 1) - p * q);
-            }
+          for (k = 0; k <= i; k++) {
+            g += V.get(k, i + 1) * V.get(k, j);
+          }
+
+          for (k = 0; k <= i; k++) {
+            V.set(k, j, V.get(k, j) - g * d[k]);
           }
         }
       }
-    }
 
-    if (norm === 0) {
-      return;
+      for (k = 0; k <= i; k++) {
+        V.set(k, i + 1, 0);
+      }
     }
 
-    for (n = nn - 1; n >= 0; n--) {
-      p = d[n];
-      q = e[n];
-
-      if (q === 0) {
-        l = n;
-        H.set(n, n, 1);
+    for (j = 0; j < n; j++) {
+      d[j] = V.get(n - 1, j);
+      V.set(n - 1, j, 0);
+    }
 
-        for (i = n - 1; i >= 0; i--) {
-          w = H.get(i, i) - p;
-          r = 0;
+    V.set(n - 1, n - 1, 1);
+    e[0] = 0;
+  }
 
-          for (j = l; j <= n; j++) {
-            r = r + H.get(i, j) * H.get(j, n);
-          }
+  function tql2(n, e, d, V) {
+    let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2;
 
-          if (e[i] < 0) {
-            z = w;
-            s = r;
-          } else {
-            l = i;
+    for (i = 1; i < n; i++) {
+      e[i - 1] = e[i];
+    }
 
-            if (e[i] === 0) {
-              H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));
-            } else {
-              x = H.get(i, i + 1);
-              y = H.get(i + 1, i);
-              q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
-              t = (x * s - z * r) / q;
-              H.set(i, n, t);
-              H.set(i + 1, n, Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z);
-            }
+    e[n - 1] = 0;
+    let f = 0;
+    let tst1 = 0;
+    let eps = Number.EPSILON;
 
-            t = Math.abs(H.get(i, n));
+    for (l = 0; l < n; l++) {
+      tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));
+      m = l;
 
-            if (eps * t * t > 1) {
-              for (j = i; j <= n; j++) {
-                H.set(j, n, H.get(j, n) / t);
-              }
-            }
-          }
+      while (m < n) {
+        if (Math.abs(e[m]) <= eps * tst1) {
+          break;
         }
-      } else if (q < 0) {
-        l = n - 1;
 
-        if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {
-          H.set(n - 1, n - 1, q / H.get(n, n - 1));
-          H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));
-        } else {
-          cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);
-          H.set(n - 1, n - 1, cdivres[0]);
-          H.set(n - 1, n, cdivres[1]);
-        }
+        m++;
+      }
 
-        H.set(n, n - 1, 0);
-        H.set(n, n, 1);
+      if (m > l) {
 
-        for (i = n - 2; i >= 0; i--) {
-          ra = 0;
-          sa = 0;
+        do {
+          g = d[l];
+          p = (d[l + 1] - g) / (2 * e[l]);
+          r = hypotenuse(p, 1);
 
-          for (j = l; j <= n; j++) {
-            ra = ra + H.get(i, j) * H.get(j, n - 1);
-            sa = sa + H.get(i, j) * H.get(j, n);
+          if (p < 0) {
+            r = -r;
           }
 
-          w = H.get(i, i) - p;
-
-          if (e[i] < 0) {
-            z = w;
-            r = ra;
-            s = sa;
-          } else {
-            l = i;
+          d[l] = e[l] / (p + r);
+          d[l + 1] = e[l] * (p + r);
+          dl1 = d[l + 1];
+          h = g - d[l];
 
-            if (e[i] === 0) {
-              cdivres = cdiv(-ra, -sa, w, q);
-              H.set(i, n - 1, cdivres[0]);
-              H.set(i, n, cdivres[1]);
-            } else {
-              x = H.get(i, i + 1);
-              y = H.get(i + 1, i);
-              vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
-              vi = (d[i] - p) * 2 * q;
+          for (i = l + 2; i < n; i++) {
+            d[i] -= h;
+          }
 
-              if (vr === 0 && vi === 0) {
-                vr = eps * norm * (Math.abs(w) + Math.abs(q) + Math.abs(x) + Math.abs(y) + Math.abs(z));
-              }
+          f = f + h;
+          p = d[m];
+          c = 1;
+          c2 = c;
+          c3 = c;
+          el1 = e[l + 1];
+          s = 0;
+          s2 = 0;
 
-              cdivres = cdiv(x * r - z * ra + q * sa, x * s - z * sa - q * ra, vr, vi);
-              H.set(i, n - 1, cdivres[0]);
-              H.set(i, n, cdivres[1]);
+          for (i = m - 1; i >= l; i--) {
+            c3 = c2;
+            c2 = c;
+            s2 = s;
+            g = c * e[i];
+            h = c * p;
+            r = hypotenuse(p, e[i]);
+            e[i + 1] = s * r;
+            s = e[i] / r;
+            c = p / r;
+            p = c * d[i] - s * g;
+            d[i + 1] = h + s * (c * g + s * d[i]);
 
-              if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {
-                H.set(i + 1, n - 1, (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x);
-                H.set(i + 1, n, (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x);
-              } else {
-                cdivres = cdiv(-r - y * H.get(i, n - 1), -s - y * H.get(i, n), z, q);
-                H.set(i + 1, n - 1, cdivres[0]);
-                H.set(i + 1, n, cdivres[1]);
-              }
+            for (k = 0; k < n; k++) {
+              h = V.get(k, i + 1);
+              V.set(k, i + 1, s * V.get(k, i) + c * h);
+              V.set(k, i, c * V.get(k, i) - s * h);
             }
+          }
 
-            t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));
+          p = -s * s2 * c3 * el1 * e[l] / dl1;
+          e[l] = s * p;
+          d[l] = c * p;
+        } while (Math.abs(e[l]) > eps * tst1);
+      }
 
-            if (eps * t * t > 1) {
-              for (j = i; j <= n; j++) {
-                H.set(j, n - 1, H.get(j, n - 1) / t);
-                H.set(j, n, H.get(j, n) / t);
-              }
-            }
-          }
-        }
-      }
+      d[l] = d[l] + f;
+      e[l] = 0;
     }
 
-    for (i = 0; i < nn; i++) {
-      if (i < low || i > high) {
-        for (j = i; j < nn; j++) {
-          V.set(i, j, H.get(i, j));
+    for (i = 0; i < n - 1; i++) {
+      k = i;
+      p = d[i];
+
+      for (j = i + 1; j < n; j++) {
+        if (d[j] < p) {
+          k = j;
+          p = d[j];
         }
       }
-    }
 
-    for (j = nn - 1; j >= low; j--) {
-      for (i = low; i <= high; i++) {
-        z = 0;
+      if (k !== i) {
+        d[k] = d[i];
+        d[i] = p;
 
-        for (k = low; k <= Math.min(j, high); k++) {
-          z = z + V.get(i, k) * H.get(k, j);
+        for (j = 0; j < n; j++) {
+          p = V.get(j, i);
+          V.set(j, i, V.get(j, k));
+          V.set(j, k, p);
         }
-
-        V.set(i, j, z);
       }
     }
   }
 
-  function cdiv(xr, xi, yr, yi) {
-    let r, d;
-
-    if (Math.abs(yr) > Math.abs(yi)) {
-      r = yi / yr;
-      d = yr + r * yi;
-      return [(xr + r * xi) / d, (xi - r * xr) / d];
-    } else {
-      r = yr / yi;
-      d = yi + r * yr;
-      return [(r * xr + xi) / d, (r * xi - xr) / d];
-    }
-  }
+  function orthes(n, H, ort, V) {
+    let low = 0;
+    let high = n - 1;
+    let f, g, h, i, j, m;
+    let scale;
 
-  class CholeskyDecomposition {
-    constructor(value) {
-      value = WrapperMatrix2D.checkMatrix(value);
+    for (m = low + 1; m <= high - 1; m++) {
+      scale = 0;
 
-      if (!value.isSymmetric()) {
-        throw new Error('Matrix is not symmetric');
+      for (i = m; i <= high; i++) {
+        scale = scale + Math.abs(H.get(i, m - 1));
       }
 
-      let a = value;
-      let dimension = a.rows;
-      let l = new Matrix(dimension, dimension);
-      let positiveDefinite = true;
-      let i, j, k;
+      if (scale !== 0) {
+        h = 0;
 
-      for (j = 0; j < dimension; j++) {
-        let d = 0;
+        for (i = high; i >= m; i--) {
+          ort[i] = H.get(i, m - 1) / scale;
+          h += ort[i] * ort[i];
+        }
 
-        for (k = 0; k < j; k++) {
-          let s = 0;
+        g = Math.sqrt(h);
 
-          for (i = 0; i < k; i++) {
-            s += l.get(k, i) * l.get(j, i);
+        if (ort[m] > 0) {
+          g = -g;
+        }
+
+        h = h - ort[m] * g;
+        ort[m] = ort[m] - g;
+
+        for (j = m; j < n; j++) {
+          f = 0;
+
+          for (i = high; i >= m; i--) {
+            f += ort[i] * H.get(i, j);
           }
 
-          s = (a.get(j, k) - s) / l.get(k, k);
-          l.set(j, k, s);
-          d = d + s * s;
+          f = f / h;
+
+          for (i = m; i <= high; i++) {
+            H.set(i, j, H.get(i, j) - f * ort[i]);
+          }
         }
 
-        d = a.get(j, j) - d;
-        positiveDefinite &= d > 0;
-        l.set(j, j, Math.sqrt(Math.max(d, 0)));
+        for (i = 0; i <= high; i++) {
+          f = 0;
 
-        for (k = j + 1; k < dimension; k++) {
-          l.set(j, k, 0);
+          for (j = high; j >= m; j--) {
+            f += ort[j] * H.get(i, j);
+          }
+
+          f = f / h;
+
+          for (j = m; j <= high; j++) {
+            H.set(i, j, H.get(i, j) - f * ort[j]);
+          }
         }
-      }
 
-      this.L = l;
-      this.positiveDefinite = Boolean(positiveDefinite);
+        ort[m] = scale * ort[m];
+        H.set(m, m - 1, scale * g);
+      }
     }
 
-    isPositiveDefinite() {
-      return this.positiveDefinite;
+    for (i = 0; i < n; i++) {
+      for (j = 0; j < n; j++) {
+        V.set(i, j, i === j ? 1 : 0);
+      }
     }
 
-    solve(value) {
-      value = WrapperMatrix2D.checkMatrix(value);
-      let l = this.L;
-      let dimension = l.rows;
+    for (m = high - 1; m >= low + 1; m--) {
+      if (H.get(m, m - 1) !== 0) {
+        for (i = m + 1; i <= high; i++) {
+          ort[i] = H.get(i, m - 1);
+        }
 
-      if (value.rows !== dimension) {
-        throw new Error('Matrix dimensions do not match');
-      }
+        for (j = m; j <= high; j++) {
+          g = 0;
 
-      if (this.isPositiveDefinite() === false) {
-        throw new Error('Matrix is not positive definite');
-      }
+          for (i = m; i <= high; i++) {
+            g += ort[i] * V.get(i, j);
+          }
 
-      let count = value.columns;
-      let B = value.clone();
-      let i, j, k;
+          g = g / ort[m] / H.get(m, m - 1);
 
-      for (k = 0; k < dimension; k++) {
-        for (j = 0; j < count; j++) {
-          for (i = 0; i < k; i++) {
-            B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));
+          for (i = m; i <= high; i++) {
+            V.set(i, j, V.get(i, j) + g * ort[i]);
           }
-
-          B.set(k, j, B.get(k, j) / l.get(k, k));
         }
       }
+    }
+  }
 
-      for (k = dimension - 1; k >= 0; k--) {
-        for (j = 0; j < count; j++) {
-          for (i = k + 1; i < dimension; i++) {
-            B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));
-          }
+  function hqr2(nn, e, d, V, H) {
+    let n = nn - 1;
+    let low = 0;
+    let high = nn - 1;
+    let eps = Number.EPSILON;
+    let exshift = 0;
+    let norm = 0;
+    let p = 0;
+    let q = 0;
+    let r = 0;
+    let s = 0;
+    let z = 0;
+    let iter = 0;
+    let i, j, k, l, m, t, w, x, y;
+    let ra, sa, vr, vi;
+    let notlast, cdivres;
 
-          B.set(k, j, B.get(k, j) / l.get(k, k));
-        }
+    for (i = 0; i < nn; i++) {
+      if (i < low || i > high) {
+        d[i] = H.get(i, i);
+        e[i] = 0;
       }
 
-      return B;
-    }
-
-    get lowerTriangularMatrix() {
-      return this.L;
+      for (j = Math.max(i - 1, 0); j < nn; j++) {
+        norm = norm + Math.abs(H.get(i, j));
+      }
     }
 
-  }
+    while (n >= low) {
+      l = n;
 
-  class nipals {
-    constructor(X) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-      X = WrapperMatrix2D.checkMatrix(X);
-      let {
-        Y
-      } = options;
-      const {
-        scaleScores = false,
-        maxIterations = 1000,
-        terminationCriteria = 1e-10
-      } = options;
-      let u;
+      while (l > low) {
+        s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));
 
-      if (Y) {
-        if (Array.isArray(Y) && typeof Y[0] === 'number') {
-          Y = Matrix.columnVector(Y);
-        } else {
-          Y = WrapperMatrix2D.checkMatrix(Y);
+        if (s === 0) {
+          s = norm;
         }
 
-        if (!Y.isColumnVector() || Y.rows !== X.rows) {
-          throw new Error('Y must be a column vector of length X.rows');
+        if (Math.abs(H.get(l, l - 1)) < eps * s) {
+          break;
         }
 
-        u = Y;
-      } else {
-        u = X.getColumnVector(0);
+        l--;
       }
 
-      let diff = 1;
-      let t, q, w, tOld;
+      if (l === n) {
+        H.set(n, n, H.get(n, n) + exshift);
+        d[n] = H.get(n, n);
+        e[n] = 0;
+        n--;
+        iter = 0;
+      } else if (l === n - 1) {
+        w = H.get(n, n - 1) * H.get(n - 1, n);
+        p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;
+        q = p * p + w;
+        z = Math.sqrt(Math.abs(q));
+        H.set(n, n, H.get(n, n) + exshift);
+        H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);
+        x = H.get(n, n);
 
-      for (let counter = 0; counter < maxIterations && diff > terminationCriteria; counter++) {
-        w = X.transpose().mmul(u).div(u.transpose().mmul(u).get(0, 0));
-        w = w.div(w.norm());
-        t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0));
+        if (q >= 0) {
+          z = p >= 0 ? p + z : p - z;
+          d[n - 1] = x + z;
+          d[n] = d[n - 1];
 
-        if (counter > 0) {
-          diff = t.clone().sub(tOld).pow(2).sum();
-        }
+          if (z !== 0) {
+            d[n] = x - w / z;
+          }
 
-        tOld = t.clone();
+          e[n - 1] = 0;
+          e[n] = 0;
+          x = H.get(n, n - 1);
+          s = Math.abs(x) + Math.abs(z);
+          p = x / s;
+          q = z / s;
+          r = Math.sqrt(p * p + q * q);
+          p = p / r;
+          q = q / r;
 
-        if (Y) {
-          q = Y.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));
-          q = q.div(q.norm());
-          u = Y.mmul(q).div(q.transpose().mmul(q).get(0, 0));
+          for (j = n - 1; j < nn; j++) {
+            z = H.get(n - 1, j);
+            H.set(n - 1, j, q * z + p * H.get(n, j));
+            H.set(n, j, q * H.get(n, j) - p * z);
+          }
+
+          for (i = 0; i <= n; i++) {
+            z = H.get(i, n - 1);
+            H.set(i, n - 1, q * z + p * H.get(i, n));
+            H.set(i, n, q * H.get(i, n) - p * z);
+          }
+
+          for (i = low; i <= high; i++) {
+            z = V.get(i, n - 1);
+            V.set(i, n - 1, q * z + p * V.get(i, n));
+            V.set(i, n, q * V.get(i, n) - p * z);
+          }
         } else {
-          u = t;
+          d[n - 1] = x + p;
+          d[n] = x + p;
+          e[n - 1] = z;
+          e[n] = -z;
         }
-      }
 
-      if (Y) {
-        let p = X.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));
-        p = p.div(p.norm());
-        let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));
-        let residual = u.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));
-        let yResidual = Y.clone().sub(t.clone().mulS(residual.get(0, 0)).mmul(q.transpose()));
-        this.t = t;
-        this.p = p.transpose();
-        this.w = w.transpose();
-        this.q = q;
-        this.u = u;
-        this.s = t.transpose().mmul(t);
-        this.xResidual = xResidual;
-        this.yResidual = yResidual;
-        this.betas = residual;
+        n = n - 2;
+        iter = 0;
       } else {
-        this.w = w.transpose();
-        this.s = t.transpose().mmul(t).sqrt();
+        x = H.get(n, n);
+        y = 0;
+        w = 0;
 
-        if (scaleScores) {
-          this.t = t.clone().div(this.s.get(0, 0));
-        } else {
-          this.t = t;
+        if (l < n) {
+          y = H.get(n - 1, n - 1);
+          w = H.get(n, n - 1) * H.get(n - 1, n);
         }
 
-        this.xResidual = X.sub(t.mmul(w.transpose()));
-      }
-    }
+        if (iter === 10) {
+          exshift += x;
 
-  }
+          for (i = low; i <= n; i++) {
+            H.set(i, i, H.get(i, i) - x);
+          }
 
+          s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));
+          x = y = 0.75 * s;
+          w = -0.4375 * s * s;
+        }
 
+        if (iter === 30) {
+          s = (y - x) / 2;
+          s = s * s + w;
 
-  var MatrixLib = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    AbstractMatrix: AbstractMatrix,
-    'default': Matrix,
-    Matrix: Matrix,
-    wrap: wrap,
-    WrapperMatrix1D: WrapperMatrix1D,
-    WrapperMatrix2D: WrapperMatrix2D,
-    solve: solve,
-    inverse: inverse,
-    determinant: determinant,
-    linearDependencies: linearDependencies,
-    pseudoInverse: pseudoInverse,
-    covariance: covariance,
-    correlation: correlation,
-    SingularValueDecomposition: SingularValueDecomposition,
-    SVD: SingularValueDecomposition,
-    EigenvalueDecomposition: EigenvalueDecomposition,
-    EVD: EigenvalueDecomposition,
-    CholeskyDecomposition: CholeskyDecomposition,
-    CHO: CholeskyDecomposition,
-    LuDecomposition: LuDecomposition,
-    LU: LuDecomposition,
-    QrDecomposition: QrDecomposition,
-    QR: QrDecomposition,
-    Nipals: nipals,
-    NIPALS: nipals,
-    MatrixColumnView: MatrixColumnView,
-    MatrixColumnSelectionView: MatrixColumnSelectionView,
-    MatrixFlipColumnView: MatrixFlipColumnView,
-    MatrixFlipRowView: MatrixFlipRowView,
-    MatrixRowView: MatrixRowView,
-    MatrixRowSelectionView: MatrixRowSelectionView,
-    MatrixSelectionView: MatrixSelectionView,
-    MatrixSubView: MatrixSubView,
-    MatrixTransposeView: MatrixTransposeView
-  });
+          if (s > 0) {
+            s = Math.sqrt(s);
 
-  /**
-   * Computes the mean of the given values
-   * @param {Array<number>} input
-   * @return {number}
-   */
+            if (y < x) {
+              s = -s;
+            }
 
-  function sum(input) {
-    if (!src(input)) {
-      throw new TypeError('input must be an array');
-    }
+            s = x - w / ((y - x) / 2 + s);
 
-    if (input.length === 0) {
-      throw new TypeError('input must not be empty');
-    }
+            for (i = low; i <= n; i++) {
+              H.set(i, i, H.get(i, i) - s);
+            }
 
-    var sumValue = 0;
+            exshift += s;
+            x = y = w = 0.964;
+          }
+        }
 
-    for (var i = 0; i < input.length; i++) {
-      sumValue += input[i];
-    }
+        iter = iter + 1;
+        m = n - 2;
 
-    return sumValue;
-  }
+        while (m >= l) {
+          z = H.get(m, m);
+          r = x - z;
+          s = y - z;
+          p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);
+          q = H.get(m + 1, m + 1) - z - r - s;
+          r = H.get(m + 2, m + 1);
+          s = Math.abs(p) + Math.abs(q) + Math.abs(r);
+          p = p / s;
+          q = q / s;
+          r = r / s;
 
-  /**
-   * Computes the mean of the given values
-   * @param {Array<number>} input
-   * @return {number}
-   */
+          if (m === l) {
+            break;
+          }
 
-  function mean(input) {
-    return sum(input) / input.length;
-  }
+          if (Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) < eps * (Math.abs(p) * (Math.abs(H.get(m - 1, m - 1)) + Math.abs(z) + Math.abs(H.get(m + 1, m + 1))))) {
+            break;
+          }
 
-  /**
-   * @private
-   * return an array of probabilities of each class
-   * @param {Array} array - contains the classes
-   * @param {number} numberOfClasses
-   * @return {Matrix} - rowVector of probabilities.
-   */
+          m--;
+        }
 
-  function toDiscreteDistribution(array, numberOfClasses) {
-    let counts = new Array(numberOfClasses).fill(0);
+        for (i = m + 2; i <= n; i++) {
+          H.set(i, i - 2, 0);
 
-    for (let i = 0; i < array.length; ++i) {
-      counts[array[i]] += 1 / array.length;
-    }
+          if (i > m + 2) {
+            H.set(i, i - 3, 0);
+          }
+        }
 
-    return Matrix.rowVector(counts);
-  }
-  /**
-   * @private
-   * Retrieves the impurity of array of predictions
-   * @param {Array} array - predictions.
-   * @return {number} Gini impurity
-   */
+        for (k = m; k <= n - 1; k++) {
+          notlast = k !== n - 1;
 
-  function giniImpurity(array) {
-    if (array.length === 0) {
-      return 0;
-    }
+          if (k !== m) {
+            p = H.get(k, k - 1);
+            q = H.get(k + 1, k - 1);
+            r = notlast ? H.get(k + 2, k - 1) : 0;
+            x = Math.abs(p) + Math.abs(q) + Math.abs(r);
 
-    let probabilities = toDiscreteDistribution(array, getNumberOfClasses(array)).getRow(0);
-    let sum = 0.0;
+            if (x !== 0) {
+              p = p / x;
+              q = q / x;
+              r = r / x;
+            }
+          }
 
-    for (let i = 0; i < probabilities.length; ++i) {
-      sum += probabilities[i] * probabilities[i];
-    }
+          if (x === 0) {
+            break;
+          }
 
-    return 1 - sum;
-  }
-  /**
-   * @private
-   * Return the number of classes given the array of predictions.
-   * @param {Array} array - predictions.
-   * @return {number} Number of classes.
-   */
+          s = Math.sqrt(p * p + q * q + r * r);
 
-  function getNumberOfClasses(array) {
-    return array.filter(function (val, i, arr) {
-      return arr.indexOf(val) === i;
-    }).map(val => val + 1).reduce((a, b) => Math.max(a, b));
-  }
-  /**
-   * @private
-   * Calculates the Gini Gain of an array of predictions and those predictions splitted by a feature.
-   * @param {Array} array - Predictions
-   * @param {object} splitted - Object with elements "greater" and "lesser" that contains an array of predictions splitted.
-   * @return {number} - Gini Gain.
-   */
+          if (p < 0) {
+            s = -s;
+          }
 
-  function giniGain(array, splitted) {
-    let splitsImpurity = 0.0;
-    let splits = ['greater', 'lesser'];
+          if (s !== 0) {
+            if (k !== m) {
+              H.set(k, k - 1, -s * x);
+            } else if (l !== m) {
+              H.set(k, k - 1, -H.get(k, k - 1));
+            }
 
-    for (let i = 0; i < splits.length; ++i) {
-      let currentSplit = splitted[splits[i]];
-      splitsImpurity += giniImpurity(currentSplit) * currentSplit.length / array.length;
-    }
+            p = p + s;
+            x = p / s;
+            y = q / s;
+            z = r / s;
+            q = q / p;
+            r = r / p;
 
-    return giniImpurity(array) - splitsImpurity;
-  }
-  /**
-   * @private
-   * Calculates the squared error of a predictions values.
-   * @param {Array} array - predictions values
-   * @return {number} squared error.
-   */
+            for (j = k; j < nn; j++) {
+              p = H.get(k, j) + q * H.get(k + 1, j);
 
-  function squaredError(array) {
-    let l = array.length;
-    let m = mean(array);
-    let error = 0.0;
+              if (notlast) {
+                p = p + r * H.get(k + 2, j);
+                H.set(k + 2, j, H.get(k + 2, j) - p * z);
+              }
 
-    for (let i = 0; i < l; ++i) {
-      let currentElement = array[i];
-      error += (currentElement - m) * (currentElement - m);
-    }
+              H.set(k, j, H.get(k, j) - p * x);
+              H.set(k + 1, j, H.get(k + 1, j) - p * y);
+            }
 
-    return error;
-  }
-  /**
-   * @private
-   * Calculates the sum of squared error of the two arrays that contains the splitted values.
-   * @param {Array} array - this argument is no necessary but is used to fit with the main interface.
-   * @param {object} splitted - Object with elements "greater" and "lesser" that contains an array of predictions splitted.
-   * @return {number} - sum of squared errors.
-   */
+            for (i = 0; i <= Math.min(n, k + 3); i++) {
+              p = x * H.get(i, k) + y * H.get(i, k + 1);
 
-  function regressionError(array, splitted) {
-    let error = 0.0;
-    let splits = ['greater', 'lesser'];
+              if (notlast) {
+                p = p + z * H.get(i, k + 2);
+                H.set(i, k + 2, H.get(i, k + 2) - p * r);
+              }
 
-    for (let i = 0; i < splits.length; ++i) {
-      let currentSplit = splitted[splits[i]];
-      error += squaredError(currentSplit);
-    }
+              H.set(i, k, H.get(i, k) - p);
+              H.set(i, k + 1, H.get(i, k + 1) - p * q);
+            }
 
-    return error;
-  }
-  /**
-   * @private
-   * Split the training set and values from a given column of the training set if is less than a value
-   * @param {Matrix} X - Training set.
-   * @param {Array} y - Training values.
-   * @param {number} column - Column to split.
-   * @param {number} value - value to split the Training set and values.
-   * @return {object} - Object that contains the splitted values.
-   */
+            for (i = low; i <= high; i++) {
+              p = x * V.get(i, k) + y * V.get(i, k + 1);
 
-  function matrixSplitter(X, y, column, value) {
-    let lesserX = [];
-    let greaterX = [];
-    let lesserY = [];
-    let greaterY = [];
+              if (notlast) {
+                p = p + z * V.get(i, k + 2);
+                V.set(i, k + 2, V.get(i, k + 2) - p * r);
+              }
 
-    for (let i = 0; i < X.rows; ++i) {
-      if (X.get(i, column) < value) {
-        lesserX.push(X.getRow(i));
-        lesserY.push(y[i]);
-      } else {
-        greaterX.push(X.getRow(i));
-        greaterY.push(y[i]);
+              V.set(i, k, V.get(i, k) - p);
+              V.set(i, k + 1, V.get(i, k + 1) - p * q);
+            }
+          }
+        }
       }
     }
 
-    return {
-      greaterX: greaterX,
-      greaterY: greaterY,
-      lesserX: lesserX,
-      lesserY: lesserY
-    };
-  }
-  /**
-   * @private
-   * Calculates the mean between two values
-   * @param {number} a
-   * @param {number} b
-   * @return {number}
-   */
-
-  function mean$1(a, b) {
-    return (a + b) / 2;
-  }
-  /**
-   * @private
-   * Returns a list of tuples that contains the i-th element of each array.
-   * @param {Array} a
-   * @param {Array} b
-   * @return {Array} list of tuples.
-   */
-
-  function zip(a, b) {
-    if (a.length !== b.length) {
-      throw new TypeError("Error on zip: the size of a: ".concat(a.length, " is different from b: ").concat(b.length));
+    if (norm === 0) {
+      return;
     }
 
-    let ret = new Array(a.length);
-
-    for (let i = 0; i < a.length; ++i) {
-      ret[i] = [a[i], b[i]];
-    }
+    for (n = nn - 1; n >= 0; n--) {
+      p = d[n];
+      q = e[n];
 
-    return ret;
-  }
+      if (q === 0) {
+        l = n;
+        H.set(n, n, 1);
 
-  const gainFunctions = {
-    gini: giniGain,
-    regression: regressionError
-  };
-  const splitFunctions = {
-    mean: mean$1
-  };
-  class TreeNode {
-    /**
-     * @private
-     * Constructor for a tree node given the options received on the main classes (DecisionTreeClassifier, DecisionTreeRegression)
-     * @param {object|TreeNode} options for loading
-     * @constructor
-     */
-    constructor(options) {
-      // options parameters
-      this.kind = options.kind;
-      this.gainFunction = options.gainFunction;
-      this.splitFunction = options.splitFunction;
-      this.minNumSamples = options.minNumSamples;
-      this.maxDepth = options.maxDepth;
-    }
-    /**
-     * @private
-     * Function that retrieve the best feature to make the split.
-     * @param {Matrix} XTranspose - Training set transposed
-     * @param {Array} y - labels or values (depending of the decision tree)
-     * @return {object} - return tree values, the best gain, column and the split value.
-     */
+        for (i = n - 1; i >= 0; i--) {
+          w = H.get(i, i) - p;
+          r = 0;
 
+          for (j = l; j <= n; j++) {
+            r = r + H.get(i, j) * H.get(j, n);
+          }
 
-    bestSplit(XTranspose, y) {
-      // Depending in the node tree class, we set the variables to check information gain (to classify)
-      // or error (for regression)
-      let bestGain = this.kind === 'classifier' ? -Infinity : Infinity;
-      let check = this.kind === 'classifier' ? (a, b) => a > b : (a, b) => a < b;
-      let maxColumn;
-      let maxValue;
+          if (e[i] < 0) {
+            z = w;
+            s = r;
+          } else {
+            l = i;
 
-      for (let i = 0; i < XTranspose.rows; ++i) {
-        let currentFeature = XTranspose.getRow(i);
-        let splitValues = this.featureSplit(currentFeature, y);
+            if (e[i] === 0) {
+              H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));
+            } else {
+              x = H.get(i, i + 1);
+              y = H.get(i + 1, i);
+              q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
+              t = (x * s - z * r) / q;
+              H.set(i, n, t);
+              H.set(i + 1, n, Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z);
+            }
 
-        for (let j = 0; j < splitValues.length; ++j) {
-          let currentSplitVal = splitValues[j];
-          let splitted = this.split(currentFeature, y, currentSplitVal);
-          let gain = gainFunctions[this.gainFunction](y, splitted);
+            t = Math.abs(H.get(i, n));
 
-          if (check(gain, bestGain)) {
-            maxColumn = i;
-            maxValue = currentSplitVal;
-            bestGain = gain;
+            if (eps * t * t > 1) {
+              for (j = i; j <= n; j++) {
+                H.set(j, n, H.get(j, n) / t);
+              }
+            }
           }
         }
-      }
-
-      return {
-        maxGain: bestGain,
-        maxColumn: maxColumn,
-        maxValue: maxValue
-      };
-    }
-    /**
-     * @private
-     * Makes the split of the training labels or values from the training set feature given a split value.
-     * @param {Array} x - Training set feature
-     * @param {Array} y - Training set value or label
-     * @param {number} splitValue
-     * @return {object}
-     */
-
-
-    split(x, y, splitValue) {
-      let lesser = [];
-      let greater = [];
+      } else if (q < 0) {
+        l = n - 1;
 
-      for (let i = 0; i < x.length; ++i) {
-        if (x[i] < splitValue) {
-          lesser.push(y[i]);
+        if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {
+          H.set(n - 1, n - 1, q / H.get(n, n - 1));
+          H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));
         } else {
-          greater.push(y[i]);
+          cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);
+          H.set(n - 1, n - 1, cdivres[0]);
+          H.set(n - 1, n, cdivres[1]);
         }
-      }
-
-      return {
-        greater: greater,
-        lesser: lesser
-      };
-    }
-    /**
-     * @private
-     * Calculates the possible points to split over the tree given a training set feature and corresponding labels or values.
-     * @param {Array} x - Training set feature
-     * @param {Array} y - Training set value or label
-     * @return {Array} possible split values.
-     */
 
+        H.set(n, n - 1, 0);
+        H.set(n, n, 1);
 
-    featureSplit(x, y) {
-      let splitValues = [];
-      let arr = zip(x, y);
-      arr.sort(function (a, b) {
-        return a[0] - b[0];
-      });
+        for (i = n - 2; i >= 0; i--) {
+          ra = 0;
+          sa = 0;
 
-      for (let i = 1; i < arr.length; ++i) {
-        if (arr[i - 1][1] !== arr[i][1]) {
-          splitValues.push(splitFunctions[this.splitFunction](arr[i - 1][0], arr[i][0]));
-        }
-      }
+          for (j = l; j <= n; j++) {
+            ra = ra + H.get(i, j) * H.get(j, n - 1);
+            sa = sa + H.get(i, j) * H.get(j, n);
+          }
 
-      return splitValues;
-    }
-    /**
-     * @private
-     * Calculate the predictions of a leaf tree node given the training labels or values
-     * @param {Array} y
-     */
+          w = H.get(i, i) - p;
 
+          if (e[i] < 0) {
+            z = w;
+            r = ra;
+            s = sa;
+          } else {
+            l = i;
 
-    calculatePrediction(y) {
-      if (this.kind === 'classifier') {
-        this.distribution = toDiscreteDistribution(y, getNumberOfClasses(y));
+            if (e[i] === 0) {
+              cdivres = cdiv(-ra, -sa, w, q);
+              H.set(i, n - 1, cdivres[0]);
+              H.set(i, n, cdivres[1]);
+            } else {
+              x = H.get(i, i + 1);
+              y = H.get(i + 1, i);
+              vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
+              vi = (d[i] - p) * 2 * q;
 
-        if (this.distribution.columns === 0) {
-          throw new TypeError('Error on calculate the prediction');
-        }
-      } else {
-        this.distribution = mean(y);
-      }
-    }
-    /**
-     * @private
-     * Train a node given the training set and labels, because it trains recursively, it also receive
-     * the current depth of the node, parent gain to avoid infinite recursion and boolean value to check if
-     * the training set is transposed.
-     * @param {Matrix} X - Training set (could be transposed or not given transposed).
-     * @param {Array} y - Training labels or values.
-     * @param {number} currentDepth - Current depth of the node.
-     * @param {number} parentGain - parent node gain or error.
-     */
+              if (vr === 0 && vi === 0) {
+                vr = eps * norm * (Math.abs(w) + Math.abs(q) + Math.abs(x) + Math.abs(y) + Math.abs(z));
+              }
 
+              cdivres = cdiv(x * r - z * ra + q * sa, x * s - z * sa - q * ra, vr, vi);
+              H.set(i, n - 1, cdivres[0]);
+              H.set(i, n, cdivres[1]);
 
-    train(X, y, currentDepth, parentGain) {
-      if (X.rows <= this.minNumSamples) {
-        this.calculatePrediction(y);
-        return;
-      }
+              if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {
+                H.set(i + 1, n - 1, (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x);
+                H.set(i + 1, n, (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x);
+              } else {
+                cdivres = cdiv(-r - y * H.get(i, n - 1), -s - y * H.get(i, n), z, q);
+                H.set(i + 1, n - 1, cdivres[0]);
+                H.set(i + 1, n, cdivres[1]);
+              }
+            }
 
-      if (parentGain === undefined) parentGain = 0.0;
-      let XTranspose = X.transpose();
-      let split = this.bestSplit(XTranspose, y);
-      this.splitValue = split.maxValue;
-      this.splitColumn = split.maxColumn;
-      this.gain = split.maxGain;
-      let splittedMatrix = matrixSplitter(X, y, this.splitColumn, this.splitValue);
+            t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));
 
-      if (currentDepth < this.maxDepth && this.gain > 0.01 && this.gain !== parentGain && splittedMatrix.lesserX.length > 0 && splittedMatrix.greaterX.length > 0) {
-        this.left = new TreeNode(this);
-        this.right = new TreeNode(this);
-        let lesserX = new Matrix(splittedMatrix.lesserX);
-        let greaterX = new Matrix(splittedMatrix.greaterX);
-        this.left.train(lesserX, splittedMatrix.lesserY, currentDepth + 1, this.gain);
-        this.right.train(greaterX, splittedMatrix.greaterY, currentDepth + 1, this.gain);
-      } else {
-        this.calculatePrediction(y);
+            if (eps * t * t > 1) {
+              for (j = i; j <= n; j++) {
+                H.set(j, n - 1, H.get(j, n - 1) / t);
+                H.set(j, n, H.get(j, n) / t);
+              }
+            }
+          }
+        }
       }
     }
-    /**
-     * @private
-     * Calculates the prediction of a given element.
-     * @param {Array} row
-     * @return {number|Array} prediction
-     *          * if a node is a classifier returns an array of probabilities of each class.
-     *          * if a node is for regression returns a number with the prediction.
-     */
 
-
-    classify(row) {
-      if (this.right && this.left) {
-        if (row[this.splitColumn] < this.splitValue) {
-          return this.left.classify(row);
-        } else {
-          return this.right.classify(row);
+    for (i = 0; i < nn; i++) {
+      if (i < low || i > high) {
+        for (j = i; j < nn; j++) {
+          V.set(i, j, H.get(i, j));
         }
       }
-
-      return this.distribution;
     }
-    /**
-     * @private
-     * Set the parameter of the current node and their children.
-     * @param {object} node - parameters of the current node and the children.
-     */
 
+    for (j = nn - 1; j >= low; j--) {
+      for (i = low; i <= high; i++) {
+        z = 0;
 
-    setNodeParameters(node) {
-      if (node.distribution !== undefined) {
-        this.distribution = node.distribution.constructor === Array ? new Matrix(node.distribution) : node.distribution;
-      } else {
-        this.distribution = undefined;
-        this.splitValue = node.splitValue;
-        this.splitColumn = node.splitColumn;
-        this.gain = node.gain;
-        this.left = new TreeNode(this);
-        this.right = new TreeNode(this);
-
-        if (node.left !== {}) {
-          this.left.setNodeParameters(node.left);
+        for (k = low; k <= Math.min(j, high); k++) {
+          z = z + V.get(i, k) * H.get(k, j);
         }
 
-        if (node.right !== {}) {
-          this.right.setNodeParameters(node.right);
-        }
+        V.set(i, j, z);
       }
     }
+  }
+
+  function cdiv(xr, xi, yr, yi) {
+    let r, d;
 
+    if (Math.abs(yr) > Math.abs(yi)) {
+      r = yi / yr;
+      d = yr + r * yi;
+      return [(xr + r * xi) / d, (xi - r * xr) / d];
+    } else {
+      r = yr / yi;
+      d = yi + r * yr;
+      return [(r * xr + xi) / d, (r * xi - xr) / d];
+    }
   }
 
-  const defaultOptions = {
-    gainFunction: 'gini',
-    splitFunction: 'mean',
-    minNumSamples: 3,
-    maxDepth: Infinity
-  };
-  class DecisionTreeClassifier {
-    /**
-     * Create new Decision Tree Classifier with CART implementation with the given options
-     * @param {object} options
-     * @param {string} [options.gainFunction="gini"] - gain function to get the best split, "gini" the only one supported.
-     * @param {string} [options.splitFunction="mean"] - given two integers from a split feature, get the value to split, "mean" the only one supported.
-     * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.
-     * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.
-     * @param {object} model - for load purposes.
-     * @constructor
-     */
-    constructor(options, model) {
-      if (options === true) {
-        this.options = model.options;
-        this.root = new TreeNode(model.options);
-        this.root.setNodeParameters(model.root);
-      } else {
-        this.options = Object.assign({}, defaultOptions, options);
-        this.options.kind = 'classifier';
+  class CholeskyDecomposition {
+    constructor(value) {
+      value = WrapperMatrix2D.checkMatrix(value);
+
+      if (!value.isSymmetric()) {
+        throw new Error('Matrix is not symmetric');
       }
-    }
-    /**
-     * Train the decision tree with the given training set and labels.
-     * @param {Matrix|MatrixTransposeView|Array} trainingSet
-     * @param {Array} trainingLabels
-     */
 
+      let a = value;
+      let dimension = a.rows;
+      let l = new Matrix(dimension, dimension);
+      let positiveDefinite = true;
+      let i, j, k;
 
-    train(trainingSet, trainingLabels) {
-      this.root = new TreeNode(this.options);
-      trainingSet = Matrix.checkMatrix(trainingSet);
-      this.root.train(trainingSet, trainingLabels, 0, null);
-    }
-    /**
-     * Predicts the output given the matrix to predict.
-     * @param {Matrix|MatrixTransposeView|Array} toPredict
-     * @return {Array} predictions
-     */
+      for (j = 0; j < dimension; j++) {
+        let d = 0;
 
+        for (k = 0; k < j; k++) {
+          let s = 0;
 
-    predict(toPredict) {
-      toPredict = Matrix.checkMatrix(toPredict);
-      let predictions = new Array(toPredict.rows);
+          for (i = 0; i < k; i++) {
+            s += l.get(k, i) * l.get(j, i);
+          }
 
-      for (let i = 0; i < toPredict.rows; ++i) {
-        predictions[i] = this.root.classify(toPredict.getRow(i)).maxRowIndex(0)[1];
+          s = (a.get(j, k) - s) / l.get(k, k);
+          l.set(j, k, s);
+          d = d + s * s;
+        }
+
+        d = a.get(j, j) - d;
+        positiveDefinite &= d > 0;
+        l.set(j, j, Math.sqrt(Math.max(d, 0)));
+
+        for (k = j + 1; k < dimension; k++) {
+          l.set(j, k, 0);
+        }
       }
 
-      return predictions;
+      this.L = l;
+      this.positiveDefinite = Boolean(positiveDefinite);
     }
-    /**
-     * Export the current model to JSON.
-     * @return {object} - Current model.
-     */
-
-
-    toJSON() {
-      return {
-        options: this.options,
-        root: this.root,
-        name: 'DTClassifier'
-      };
-    }
-    /**
-     * Load a Decision tree classifier with the given model.
-     * @param {object} model
-     * @return {DecisionTreeClassifier}
-     */
-
-
-    static load(model) {
-      if (model.name !== 'DTClassifier') {
-        throw new RangeError("Invalid model: ".concat(model.name));
-      }
 
-      return new DecisionTreeClassifier(true, model);
+    isPositiveDefinite() {
+      return this.positiveDefinite;
     }
 
-  }
+    solve(value) {
+      value = WrapperMatrix2D.checkMatrix(value);
+      let l = this.L;
+      let dimension = l.rows;
 
-  const defaultOptions$1 = {
-    gainFunction: 'regression',
-    splitFunction: 'mean',
-    minNumSamples: 3,
-    maxDepth: Infinity
-  };
-  class DecisionTreeRegression {
-    /**
-     * Create new Decision Tree Regression with CART implementation with the given options.
-     * @param {object} options
-     * @param {string} [options.gainFunction="regression"] - gain function to get the best split, "regression" the only one supported.
-     * @param {string} [options.splitFunction="mean"] - given two integers from a split feature, get the value to split, "mean" the only one supported.
-     * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.
-     * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.
-     * @param {object} model - for load purposes.
-     */
-    constructor(options, model) {
-      if (options === true) {
-        this.options = model.options;
-        this.root = new TreeNode(model.options);
-        this.root.setNodeParameters(model.root);
-      } else {
-        this.options = Object.assign({}, defaultOptions$1, options);
-        this.options.kind = 'regression';
+      if (value.rows !== dimension) {
+        throw new Error('Matrix dimensions do not match');
       }
-    }
-    /**
-     * Train the decision tree with the given training set and values.
-     * @param {Matrix|MatrixTransposeView|Array} trainingSet
-     * @param {Array} trainingValues
-     */
-
 
-    train(trainingSet, trainingValues) {
-      this.root = new TreeNode(this.options);
-
-      if (typeof trainingSet[0] !== 'undefined' && trainingSet[0].length === undefined) {
-        trainingSet = Matrix.columnVector(trainingSet);
-      } else {
-        trainingSet = Matrix.checkMatrix(trainingSet);
+      if (this.isPositiveDefinite() === false) {
+        throw new Error('Matrix is not positive definite');
       }
 
-      this.root.train(trainingSet, trainingValues, 0);
-    }
-    /**
-     * Predicts the values given the matrix to predict.
-     * @param {Matrix|MatrixTransposeView|Array} toPredict
-     * @return {Array} predictions
-     */
+      let count = value.columns;
+      let B = value.clone();
+      let i, j, k;
 
+      for (k = 0; k < dimension; k++) {
+        for (j = 0; j < count; j++) {
+          for (i = 0; i < k; i++) {
+            B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));
+          }
 
-    predict(toPredict) {
-      if (typeof toPredict[0] !== 'undefined' && toPredict[0].length === undefined) {
-        toPredict = Matrix.columnVector(toPredict);
+          B.set(k, j, B.get(k, j) / l.get(k, k));
+        }
       }
 
-      toPredict = Matrix.checkMatrix(toPredict);
-      let predictions = new Array(toPredict.rows);
+      for (k = dimension - 1; k >= 0; k--) {
+        for (j = 0; j < count; j++) {
+          for (i = k + 1; i < dimension; i++) {
+            B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));
+          }
 
-      for (let i = 0; i < toPredict.rows; ++i) {
-        predictions[i] = this.root.classify(toPredict.getRow(i));
+          B.set(k, j, B.get(k, j) / l.get(k, k));
+        }
       }
 
-      return predictions;
+      return B;
     }
-    /**
-     * Export the current model to JSON.
-     * @return {object} - Current model.
-     */
 
-
-    toJSON() {
-      return {
-        options: this.options,
-        root: this.root,
-        name: 'DTRegression'
-      };
+    get lowerTriangularMatrix() {
+      return this.L;
     }
-    /**
-     * Load a Decision tree regression with the given model.
-     * @param {object} model
-     * @return {DecisionTreeRegression}
-     */
 
+  }
 
-    static load(model) {
-      if (model.name !== 'DTRegression') {
-        throw new RangeError("Invalid model:".concat(model.name));
-      }
+  class nipals {
+    constructor(X, options = {}) {
+      X = WrapperMatrix2D.checkMatrix(X);
+      let {
+        Y
+      } = options;
+      const {
+        scaleScores = false,
+        maxIterations = 1000,
+        terminationCriteria = 1e-10
+      } = options;
+      let u;
 
-      return new DecisionTreeRegression(true, model);
-    }
+      if (Y) {
+        if (Array.isArray(Y) && typeof Y[0] === 'number') {
+          Y = Matrix.columnVector(Y);
+        } else {
+          Y = WrapperMatrix2D.checkMatrix(Y);
+        }
 
-  }
+        if (!Y.isColumnVector() || Y.rows !== X.rows) {
+          throw new Error('Y must be a column vector of length X.rows');
+        }
 
-  const SMALLEST_UNSAFE_INTEGER = 0x20000000000000;
-  const LARGEST_SAFE_INTEGER = SMALLEST_UNSAFE_INTEGER - 1;
-  const UINT32_MAX = -1 >>> 0;
-  const UINT32_SIZE = UINT32_MAX + 1;
-  const INT32_SIZE = UINT32_SIZE / 2;
-  const INT32_MAX = INT32_SIZE - 1;
-  const UINT21_SIZE = 1 << 21;
-  const UINT21_MAX = UINT21_SIZE - 1;
-  /**
-   * Returns a value within [-0x80000000, 0x7fffffff]
-   */
+        u = Y;
+      } else {
+        u = X.getColumnVector(0);
+      }
 
-  function int32(engine) {
-    return engine.next() | 0;
-  }
+      let diff = 1;
+      let t, q, w, tOld;
 
-  function add(distribution, addend) {
-    if (addend === 0) {
-      return distribution;
-    } else {
-      return engine => distribution(engine) + addend;
-    }
-  }
-  /**
-   * Returns a value within [-0x20000000000000, 0x1fffffffffffff]
-   */
+      for (let counter = 0; counter < maxIterations && diff > terminationCriteria; counter++) {
+        w = X.transpose().mmul(u).div(u.transpose().mmul(u).get(0, 0));
+        w = w.div(w.norm());
+        t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0));
 
+        if (counter > 0) {
+          diff = t.clone().sub(tOld).pow(2).sum();
+        }
 
-  function int53(engine) {
-    const high = engine.next() | 0;
-    const low = engine.next() >>> 0;
-    return (high & UINT21_MAX) * UINT32_SIZE + low + (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);
-  }
-  /**
-   * Returns a value within [-0x20000000000000, 0x20000000000000]
-   */
+        tOld = t.clone();
 
+        if (Y) {
+          q = Y.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));
+          q = q.div(q.norm());
+          u = Y.mmul(q).div(q.transpose().mmul(q).get(0, 0));
+        } else {
+          u = t;
+        }
+      }
 
-  function int53Full(engine) {
-    while (true) {
-      const high = engine.next() | 0;
+      if (Y) {
+        let p = X.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));
+        p = p.div(p.norm());
+        let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));
+        let residual = u.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));
+        let yResidual = Y.clone().sub(t.clone().mulS(residual.get(0, 0)).mmul(q.transpose()));
+        this.t = t;
+        this.p = p.transpose();
+        this.w = w.transpose();
+        this.q = q;
+        this.u = u;
+        this.s = t.transpose().mmul(t);
+        this.xResidual = xResidual;
+        this.yResidual = yResidual;
+        this.betas = residual;
+      } else {
+        this.w = w.transpose();
+        this.s = t.transpose().mmul(t).sqrt();
 
-      if (high & 0x400000) {
-        if ((high & 0x7fffff) === 0x400000 && (engine.next() | 0) === 0) {
-          return SMALLEST_UNSAFE_INTEGER;
+        if (scaleScores) {
+          this.t = t.clone().div(this.s.get(0, 0));
+        } else {
+          this.t = t;
         }
-      } else {
-        const low = engine.next() >>> 0;
-        return (high & UINT21_MAX) * UINT32_SIZE + low + (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);
+
+        this.xResidual = X.sub(t.mmul(w.transpose()));
       }
     }
-  }
-  /**
-   * Returns a value within [0, 0xffffffff]
-   */
 
-
-  function uint32(engine) {
-    return engine.next() >>> 0;
   }
-  /**
-   * Returns a value within [0, 0x1fffffffffffff]
-   */
 
+  var MatrixLib = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    AbstractMatrix: AbstractMatrix,
+    'default': Matrix,
+    Matrix: Matrix,
+    wrap: wrap,
+    WrapperMatrix1D: WrapperMatrix1D,
+    WrapperMatrix2D: WrapperMatrix2D,
+    solve: solve,
+    inverse: inverse,
+    determinant: determinant,
+    linearDependencies: linearDependencies,
+    pseudoInverse: pseudoInverse,
+    covariance: covariance,
+    correlation: correlation,
+    SingularValueDecomposition: SingularValueDecomposition,
+    SVD: SingularValueDecomposition,
+    EigenvalueDecomposition: EigenvalueDecomposition,
+    EVD: EigenvalueDecomposition,
+    CholeskyDecomposition: CholeskyDecomposition,
+    CHO: CholeskyDecomposition,
+    LuDecomposition: LuDecomposition,
+    LU: LuDecomposition,
+    QrDecomposition: QrDecomposition,
+    QR: QrDecomposition,
+    Nipals: nipals,
+    NIPALS: nipals,
+    MatrixColumnView: MatrixColumnView,
+    MatrixColumnSelectionView: MatrixColumnSelectionView,
+    MatrixFlipColumnView: MatrixFlipColumnView,
+    MatrixFlipRowView: MatrixFlipRowView,
+    MatrixRowView: MatrixRowView,
+    MatrixRowSelectionView: MatrixRowSelectionView,
+    MatrixSelectionView: MatrixSelectionView,
+    MatrixSubView: MatrixSubView,
+    MatrixTransposeView: MatrixTransposeView
+  });
 
-  function uint53(engine) {
-    const high = engine.next() & UINT21_MAX;
-    const low = engine.next() >>> 0;
-    return high * UINT32_SIZE + low;
-  }
-  /**
-   * Returns a value within [0, 0x20000000000000]
-   */
-
+  const toString$1 = Object.prototype.toString;
+  function isAnyArray$1(object) {
+    return toString$1.call(object).endsWith('Array]');
+  }
 
-  function uint53Full(engine) {
-    while (true) {
-      const high = engine.next() | 0;
+  function sum(input) {
+    if (!isAnyArray$1(input)) {
+      throw new TypeError('input must be an array');
+    }
 
-      if (high & UINT21_SIZE) {
-        if ((high & UINT21_MAX) === 0 && (engine.next() | 0) === 0) {
-          return SMALLEST_UNSAFE_INTEGER;
-        }
-      } else {
-        const low = engine.next() >>> 0;
-        return (high & UINT21_MAX) * UINT32_SIZE + low;
-      }
+    if (input.length === 0) {
+      throw new TypeError('input must not be empty');
     }
-  }
 
-  function isPowerOfTwoMinusOne(value) {
-    return (value + 1 & value) === 0;
-  }
+    var sumValue = 0;
 
-  function bitmask(masking) {
-    return engine => engine.next() & masking;
+    for (var i = 0; i < input.length; i++) {
+      sumValue += input[i];
+    }
+
+    return sumValue;
   }
 
-  function downscaleToLoopCheckedRange(range) {
-    const extendedRange = range + 1;
-    const maximum = extendedRange * Math.floor(UINT32_SIZE / extendedRange);
-    return engine => {
-      let value = 0;
+  function mean(input) {
+    return sum(input) / input.length;
+  }
 
-      do {
-        value = engine.next() >>> 0;
-      } while (value >= maximum);
+  /**
+   * @private
+   * return an array of probabilities of each class
+   * @param {Array} array - contains the classes
+   * @param {number} numberOfClasses
+   * @return {Matrix} - rowVector of probabilities.
+   */
 
-      return value % extendedRange;
-    };
-  }
+  function toDiscreteDistribution(array, numberOfClasses) {
+    let counts = new Array(numberOfClasses).fill(0);
 
-  function downscaleToRange(range) {
-    if (isPowerOfTwoMinusOne(range)) {
-      return bitmask(range);
-    } else {
-      return downscaleToLoopCheckedRange(range);
+    for (let i = 0; i < array.length; ++i) {
+      counts[array[i]] += 1 / array.length;
     }
-  }
 
-  function isEvenlyDivisibleByMaxInt32(value) {
-    return (value | 0) === 0;
+    return Matrix.rowVector(counts);
   }
+  /**
+   * @private
+   * Retrieves the impurity of array of predictions
+   * @param {Array} array - predictions.
+   * @return {number} Gini impurity
+   */
 
-  function upscaleWithHighMasking(masking) {
-    return engine => {
-      const high = engine.next() & masking;
-      const low = engine.next() >>> 0;
-      return high * UINT32_SIZE + low;
-    };
-  }
+  function giniImpurity(array) {
+    if (array.length === 0) {
+      return 0;
+    }
 
-  function upscaleToLoopCheckedRange(extendedRange) {
-    const maximum = extendedRange * Math.floor(SMALLEST_UNSAFE_INTEGER / extendedRange);
-    return engine => {
-      let ret = 0;
+    let probabilities = toDiscreteDistribution(array, getNumberOfClasses(array)).getRow(0);
+    let sum = 0.0;
 
-      do {
-        const high = engine.next() & UINT21_MAX;
-        const low = engine.next() >>> 0;
-        ret = high * UINT32_SIZE + low;
-      } while (ret >= maximum);
+    for (let i = 0; i < probabilities.length; ++i) {
+      sum += probabilities[i] * probabilities[i];
+    }
 
-      return ret % extendedRange;
-    };
+    return 1 - sum;
   }
+  /**
+   * @private
+   * Return the number of classes given the array of predictions.
+   * @param {Array} array - predictions.
+   * @return {number} Number of classes.
+   */
 
-  function upscaleWithinU53(range) {
-    const extendedRange = range + 1;
+  function getNumberOfClasses(array) {
+    return array.filter(function (val, i, arr) {
+      return arr.indexOf(val) === i;
+    }).map(val => val + 1).reduce((a, b) => Math.max(a, b));
+  }
+  /**
+   * @private
+   * Calculates the Gini Gain of an array of predictions and those predictions splitted by a feature.
+   * @param {Array} array - Predictions
+   * @param {object} splitted - Object with elements "greater" and "lesser" that contains an array of predictions splitted.
+   * @return {number} - Gini Gain.
+   */
 
-    if (isEvenlyDivisibleByMaxInt32(extendedRange)) {
-      const highRange = (extendedRange / UINT32_SIZE | 0) - 1;
+  function giniGain(array, splitted) {
+    let splitsImpurity = 0.0;
+    let splits = ['greater', 'lesser'];
 
-      if (isPowerOfTwoMinusOne(highRange)) {
-        return upscaleWithHighMasking(highRange);
-      }
+    for (let i = 0; i < splits.length; ++i) {
+      let currentSplit = splitted[splits[i]];
+      splitsImpurity += giniImpurity(currentSplit) * currentSplit.length / array.length;
     }
 
-    return upscaleToLoopCheckedRange(extendedRange);
+    return giniImpurity(array) - splitsImpurity;
   }
+  /**
+   * @private
+   * Calculates the squared error of a predictions values.
+   * @param {Array} array - predictions values
+   * @return {number} squared error.
+   */
 
-  function upscaleWithinI53AndLoopCheck(min, max) {
-    return engine => {
-      let ret = 0;
+  function squaredError(array) {
+    let l = array.length;
+    let m = mean(array);
+    let error = 0.0;
 
-      do {
-        const high = engine.next() | 0;
-        const low = engine.next() >>> 0;
-        ret = (high & UINT21_MAX) * UINT32_SIZE + low + (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);
-      } while (ret < min || ret > max);
+    for (let i = 0; i < l; ++i) {
+      let currentElement = array[i];
+      error += (currentElement - m) * (currentElement - m);
+    }
 
-      return ret;
-    };
+    return error;
   }
-  /**
-   * Returns a Distribution to return a value within [min, max]
-   * @param min The minimum integer value, inclusive. No less than -0x20000000000000.
-   * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.
+  /**
+   * @private
+   * Calculates the sum of squared error of the two arrays that contains the splitted values.
+   * @param {Array} array - this argument is no necessary but is used to fit with the main interface.
+   * @param {object} splitted - Object with elements "greater" and "lesser" that contains an array of predictions splitted.
+   * @return {number} - sum of squared errors.
    */
 
+  function regressionError(array, splitted) {
+    let error = 0.0;
+    let splits = ['greater', 'lesser'];
 
-  function integer(min, max) {
-    min = Math.floor(min);
-    max = Math.floor(max);
-
-    if (min < -SMALLEST_UNSAFE_INTEGER || !isFinite(min)) {
-      throw new RangeError("Expected min to be at least ".concat(-SMALLEST_UNSAFE_INTEGER));
-    } else if (max > SMALLEST_UNSAFE_INTEGER || !isFinite(max)) {
-      throw new RangeError("Expected max to be at most ".concat(SMALLEST_UNSAFE_INTEGER));
+    for (let i = 0; i < splits.length; ++i) {
+      let currentSplit = splitted[splits[i]];
+      error += squaredError(currentSplit);
     }
 
-    const range = max - min;
+    return error;
+  }
+  /**
+   * @private
+   * Split the training set and values from a given column of the training set if is less than a value
+   * @param {Matrix} X - Training set.
+   * @param {Array} y - Training values.
+   * @param {number} column - Column to split.
+   * @param {number} value - value to split the Training set and values.
+   * @return {object} - Object that contains the splitted values.
+   */
 
-    if (range <= 0 || !isFinite(range)) {
-      return () => min;
-    } else if (range === UINT32_MAX) {
-      if (min === 0) {
-        return uint32;
+  function matrixSplitter(X, y, column, value) {
+    let lesserX = [];
+    let greaterX = [];
+    let lesserY = [];
+    let greaterY = [];
+
+    for (let i = 0; i < X.rows; ++i) {
+      if (X.get(i, column) < value) {
+        lesserX.push(X.getRow(i));
+        lesserY.push(y[i]);
       } else {
-        return add(int32, min + INT32_SIZE);
+        greaterX.push(X.getRow(i));
+        greaterY.push(y[i]);
       }
-    } else if (range < UINT32_MAX) {
-      return add(downscaleToRange(range), min);
-    } else if (range === LARGEST_SAFE_INTEGER) {
-      return add(uint53, min);
-    } else if (range < LARGEST_SAFE_INTEGER) {
-      return add(upscaleWithinU53(range), min);
-    } else if (max - 1 - min === LARGEST_SAFE_INTEGER) {
-      return add(uint53Full, min);
-    } else if (min === -SMALLEST_UNSAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {
-      return int53Full;
-    } else if (min === -SMALLEST_UNSAFE_INTEGER && max === LARGEST_SAFE_INTEGER) {
-      return int53;
-    } else if (min === -LARGEST_SAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {
-      return add(int53, 1);
-    } else if (max === SMALLEST_UNSAFE_INTEGER) {
-      return add(upscaleWithinI53AndLoopCheck(min - 1, max - 1), 1);
-    } else {
-      return upscaleWithinI53AndLoopCheck(min, max);
     }
-  }
-  // has 2**x chars, for faster uniform distribution
-
 
-  const DEFAULT_STRING_POOL = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-";
+    return {
+      greaterX: greaterX,
+      greaterY: greaterY,
+      lesserX: lesserX,
+      lesserY: lesserY
+    };
+  }
+  /**
+   * @private
+   * Calculates the mean between two values
+   * @param {number} a
+   * @param {number} b
+   * @return {number}
+   */
 
-  function string() {
-    let pool = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : DEFAULT_STRING_POOL;
-    const poolLength = pool.length;
+  function mean$1(a, b) {
+    return (a + b) / 2;
+  }
+  /**
+   * @private
+   * Returns a list of tuples that contains the i-th element of each array.
+   * @param {Array} a
+   * @param {Array} b
+   * @return {Array} list of tuples.
+   */
 
-    if (!poolLength) {
-      throw new Error("Expected pool not to be an empty string");
+  function zip(a, b) {
+    if (a.length !== b.length) {
+      throw new TypeError(`Error on zip: the size of a: ${a.length} is different from b: ${b.length}`);
     }
 
-    const distribution = integer(0, poolLength - 1);
-    return (engine, length) => {
-      let result = "";
+    let ret = new Array(a.length);
 
-      for (let i = 0; i < length; ++i) {
-        const j = distribution(engine);
-        result += pool.charAt(j);
-      }
+    for (let i = 0; i < a.length; ++i) {
+      ret[i] = [a[i], b[i]];
+    }
 
-      return result;
-    };
+    return ret;
   }
 
-  const LOWER_HEX_POOL = "0123456789abcdef";
-  const lowerHex = string(LOWER_HEX_POOL);
-  const upperHex = string(LOWER_HEX_POOL.toUpperCase());
-
-  const stringRepeat = (() => {
-    try {
-      if ("x".repeat(3) === "xxx") {
-        return (pattern, count) => pattern.repeat(count);
-      }
-    } catch (_) {// nothing to do here
+  const gainFunctions = {
+    gini: giniGain,
+    regression: regressionError
+  };
+  const splitFunctions = {
+    mean: mean$1
+  };
+  class TreeNode {
+    /**
+     * @private
+     * Constructor for a tree node given the options received on the main classes (DecisionTreeClassifier, DecisionTreeRegression)
+     * @param {object|TreeNode} options for loading
+     * @constructor
+     */
+    constructor(options) {
+      // options parameters
+      this.kind = options.kind;
+      this.gainFunction = options.gainFunction;
+      this.splitFunction = options.splitFunction;
+      this.minNumSamples = options.minNumSamples;
+      this.maxDepth = options.maxDepth;
     }
+    /**
+     * @private
+     * Function that retrieve the best feature to make the split.
+     * @param {Matrix} XTranspose - Training set transposed
+     * @param {Array} y - labels or values (depending of the decision tree)
+     * @return {object} - return tree values, the best gain, column and the split value.
+     */
 
-    return (pattern, count) => {
-      let result = "";
 
-      while (count > 0) {
-        if (count & 1) {
-          result += pattern;
-        }
+    bestSplit(XTranspose, y) {
+      // Depending in the node tree class, we set the variables to check information gain (to classify)
+      // or error (for regression)
+      let bestGain = this.kind === 'classifier' ? -Infinity : Infinity;
+      let check = this.kind === 'classifier' ? (a, b) => a > b : (a, b) => a < b;
+      let maxColumn;
+      let maxValue;
 
-        count >>= 1;
-        pattern += pattern;
-      }
+      for (let i = 0; i < XTranspose.rows; ++i) {
+        let currentFeature = XTranspose.getRow(i);
+        let splitValues = this.featureSplit(currentFeature, y);
 
-      return result;
-    };
-  })();
-  /**
-   * An int32-producing Engine that uses `Math.random()`
-   */
+        for (let j = 0; j < splitValues.length; ++j) {
+          let currentSplitVal = splitValues[j];
+          let splitted = this.split(currentFeature, y, currentSplitVal);
+          let gain = gainFunctions[this.gainFunction](y, splitted);
 
+          if (check(gain, bestGain)) {
+            maxColumn = i;
+            maxValue = currentSplitVal;
+            bestGain = gain;
+          }
+        }
+      }
 
-  const nativeMath = {
-    next() {
-      return Math.random() * UINT32_SIZE | 0;
+      return {
+        maxGain: bestGain,
+        maxColumn: maxColumn,
+        maxValue: maxValue
+      };
     }
-
-  }; // tslint:disable:unified-signatures
-  /**
-   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array
-   */
+    /**
+     * @private
+     * Makes the split of the training labels or values from the training set feature given a split value.
+     * @param {Array} x - Training set feature
+     * @param {Array} y - Training set value or label
+     * @param {number} splitValue
+     * @return {object}
+     */
 
 
-  const I32Array = (() => {
-    try {
-      const buffer = new ArrayBuffer(4);
-      const view = new Int32Array(buffer);
-      view[0] = INT32_SIZE;
+    split(x, y, splitValue) {
+      let lesser = [];
+      let greater = [];
 
-      if (view[0] === -INT32_SIZE) {
-        return Int32Array;
+      for (let i = 0; i < x.length; ++i) {
+        if (x[i] < splitValue) {
+          lesser.push(y[i]);
+        } else {
+          greater.push(y[i]);
+        }
       }
-    } catch (_) {// nothing to do here
+
+      return {
+        greater: greater,
+        lesser: lesser
+      };
     }
+    /**
+     * @private
+     * Calculates the possible points to split over the tree given a training set feature and corresponding labels or values.
+     * @param {Array} x - Training set feature
+     * @param {Array} y - Training set value or label
+     * @return {Array} possible split values.
+     */
 
-    return Array;
-  })();
-  /**
-   * Returns an array of random int32 values, based on current time
-   * and a random number engine
-   *
-   * @param engine an Engine to pull random values from, default `nativeMath`
-   * @param length the length of the Array, minimum 1, default 16
-   */
 
-  function createEntropy() {
-    let engine = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : nativeMath;
-    let length = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 16;
-    const array = [];
-    array.push(new Date().getTime() | 0);
+    featureSplit(x, y) {
+      let splitValues = [];
+      let arr = zip(x, y);
+      arr.sort(function (a, b) {
+        return a[0] - b[0];
+      });
 
-    for (let i = 1; i < length; ++i) {
-      array[i] = engine.next() | 0;
+      for (let i = 1; i < arr.length; ++i) {
+        if (arr[i - 1][1] !== arr[i][1]) {
+          splitValues.push(splitFunctions[this.splitFunction](arr[i - 1][0], arr[i][0]));
+        }
+      }
+
+      return splitValues;
     }
+    /**
+     * @private
+     * Calculate the predictions of a leaf tree node given the training labels or values
+     * @param {Array} y
+     */
 
-    return array;
-  }
-  /**
-   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/imul
-   */
 
+    calculatePrediction(y) {
+      if (this.kind === 'classifier') {
+        this.distribution = toDiscreteDistribution(y, getNumberOfClasses(y));
 
-  const imul = (() => {
-    try {
-      if (Math.imul(UINT32_MAX, 5) === -5) {
-        return Math.imul;
+        if (this.distribution.columns === 0) {
+          throw new TypeError('Error on calculate the prediction');
+        }
+      } else {
+        this.distribution = mean(y);
       }
-    } catch (_) {// nothing to do here
     }
+    /**
+     * @private
+     * Train a node given the training set and labels, because it trains recursively, it also receive
+     * the current depth of the node, parent gain to avoid infinite recursion and boolean value to check if
+     * the training set is transposed.
+     * @param {Matrix} X - Training set (could be transposed or not given transposed).
+     * @param {Array} y - Training labels or values.
+     * @param {number} currentDepth - Current depth of the node.
+     * @param {number} parentGain - parent node gain or error.
+     */
 
-    const UINT16_MAX = 0xffff;
-    return (a, b) => {
-      const ah = a >>> 16 & UINT16_MAX;
-      const al = a & UINT16_MAX;
-      const bh = b >>> 16 & UINT16_MAX;
-      const bl = b & UINT16_MAX; // the shift by 0 fixes the sign on the high part
-      // the final |0 converts the unsigned value into a signed value
 
-      return al * bl + (ah * bl + al * bh << 16 >>> 0) | 0;
-    };
-  })();
+    train(X, y, currentDepth, parentGain) {
+      if (X.rows <= this.minNumSamples) {
+        this.calculatePrediction(y);
+        return;
+      }
 
-  const ARRAY_SIZE = 624;
-  const ARRAY_MAX = ARRAY_SIZE - 1;
-  const M = 397;
-  const ARRAY_SIZE_MINUS_M = ARRAY_SIZE - M;
-  const A = 0x9908b0df;
-  /**
-   * An Engine that is a pseudorandom number generator using the Mersenne
-   * Twister algorithm based on the prime 2**19937 − 1
-   *
-   * See http://en.wikipedia.org/wiki/Mersenne_twister
-   */
+      if (parentGain === undefined) parentGain = 0.0;
+      let XTranspose = X.transpose();
+      let split = this.bestSplit(XTranspose, y);
+      this.splitValue = split.maxValue;
+      this.splitColumn = split.maxColumn;
+      this.gain = split.maxGain;
+      let splittedMatrix = matrixSplitter(X, y, this.splitColumn, this.splitValue);
 
-  class MersenneTwister19937 {
-    /**
-     * MersenneTwister19937 should not be instantiated directly.
-     * Instead, use the static methods `seed`, `seedWithArray`, or `autoSeed`.
+      if (currentDepth < this.maxDepth && this.gain > 0.01 && this.gain !== parentGain && splittedMatrix.lesserX.length > 0 && splittedMatrix.greaterX.length > 0) {
+        this.left = new TreeNode(this);
+        this.right = new TreeNode(this);
+        let lesserX = new Matrix(splittedMatrix.lesserX);
+        let greaterX = new Matrix(splittedMatrix.greaterX);
+        this.left.train(lesserX, splittedMatrix.lesserY, currentDepth + 1, this.gain);
+        this.right.train(greaterX, splittedMatrix.greaterY, currentDepth + 1, this.gain);
+      } else {
+        this.calculatePrediction(y);
+      }
+    }
+    /**
+     * @private
+     * Calculates the prediction of a given element.
+     * @param {Array} row
+     * @return {number|Array} prediction
+     *          * if a node is a classifier returns an array of probabilities of each class.
+     *          * if a node is for regression returns a number with the prediction.
      */
-    constructor() {
-      this.data = new I32Array(ARRAY_SIZE);
-      this.index = 0; // integer within [0, 624]
 
-      this.uses = 0;
+
+    classify(row) {
+      if (this.right && this.left) {
+        if (row[this.splitColumn] < this.splitValue) {
+          return this.left.classify(row);
+        } else {
+          return this.right.classify(row);
+        }
+      }
+
+      return this.distribution;
     }
-    /**
-     * Returns a MersenneTwister19937 seeded with an initial int32 value
-     * @param initial the initial seed value
+    /**
+     * @private
+     * Set the parameter of the current node and their children.
+     * @param {object} node - parameters of the current node and the children.
      */
 
 
-    static seed(initial) {
-      return new MersenneTwister19937().seed(initial);
+    setNodeParameters(node) {
+      if (node.distribution !== undefined) {
+        this.distribution = node.distribution.constructor === Array ? new Matrix(node.distribution) : node.distribution;
+      } else {
+        this.distribution = undefined;
+        this.splitValue = node.splitValue;
+        this.splitColumn = node.splitColumn;
+        this.gain = node.gain;
+        this.left = new TreeNode(this);
+        this.right = new TreeNode(this);
+
+        if (node.left !== {}) {
+          this.left.setNodeParameters(node.left);
+        }
+
+        if (node.right !== {}) {
+          this.right.setNodeParameters(node.right);
+        }
+      }
     }
-    /**
-     * Returns a MersenneTwister19937 seeded with zero or more int32 values
-     * @param source A series of int32 values
-     */
 
+  }
 
-    static seedWithArray(source) {
-      return new MersenneTwister19937().seedWithArray(source);
+  const defaultOptions = {
+    gainFunction: 'gini',
+    splitFunction: 'mean',
+    minNumSamples: 3,
+    maxDepth: Infinity
+  };
+  class DecisionTreeClassifier {
+    /**
+     * Create new Decision Tree Classifier with CART implementation with the given options
+     * @param {object} options
+     * @param {string} [options.gainFunction="gini"] - gain function to get the best split, "gini" the only one supported.
+     * @param {string} [options.splitFunction="mean"] - given two integers from a split feature, get the value to split, "mean" the only one supported.
+     * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.
+     * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.
+     * @param {object} model - for load purposes.
+     * @constructor
+     */
+    constructor(options, model) {
+      if (options === true) {
+        this.options = model.options;
+        this.root = new TreeNode(model.options);
+        this.root.setNodeParameters(model.root);
+      } else {
+        this.options = Object.assign({}, defaultOptions, options);
+        this.options.kind = 'classifier';
+      }
     }
-    /**
-     * Returns a MersenneTwister19937 seeded with the current time and
-     * a series of natively-generated random values
+    /**
+     * Train the decision tree with the given training set and labels.
+     * @param {Matrix|MatrixTransposeView|Array} trainingSet
+     * @param {Array} trainingLabels
      */
 
 
-    static autoSeed() {
-      return MersenneTwister19937.seedWithArray(createEntropy());
+    train(trainingSet, trainingLabels) {
+      this.root = new TreeNode(this.options);
+      trainingSet = Matrix.checkMatrix(trainingSet);
+      this.root.train(trainingSet, trainingLabels, 0, null);
     }
-    /**
-     * Returns the next int32 value of the sequence
+    /**
+     * Predicts the output given the matrix to predict.
+     * @param {Matrix|MatrixTransposeView|Array} toPredict
+     * @return {Array} predictions
      */
 
 
-    next() {
-      if ((this.index | 0) >= ARRAY_SIZE) {
-        refreshData(this.data);
-        this.index = 0;
+    predict(toPredict) {
+      toPredict = Matrix.checkMatrix(toPredict);
+      let predictions = new Array(toPredict.rows);
+
+      for (let i = 0; i < toPredict.rows; ++i) {
+        predictions[i] = this.root.classify(toPredict.getRow(i)).maxRowIndex(0)[1];
       }
 
-      const value = this.data[this.index];
-      this.index = this.index + 1 | 0;
-      this.uses += 1;
-      return temper(value) | 0;
+      return predictions;
     }
-    /**
-     * Returns the number of times that the Engine has been used.
-     *
-     * This can be provided to an unused MersenneTwister19937 with the same
-     * seed, bringing it to the exact point that was left off.
+    /**
+     * Export the current model to JSON.
+     * @return {object} - Current model.
      */
 
 
-    getUseCount() {
-      return this.uses;
+    toJSON() {
+      return {
+        options: this.options,
+        root: this.root,
+        name: 'DTClassifier'
+      };
     }
-    /**
-     * Discards one or more items from the engine
-     * @param count The count of items to discard
+    /**
+     * Load a Decision tree classifier with the given model.
+     * @param {object} model
+     * @return {DecisionTreeClassifier}
      */
 
 
-    discard(count) {
-      if (count <= 0) {
-        return this;
+    static load(model) {
+      if (model.name !== 'DTClassifier') {
+        throw new RangeError(`Invalid model: ${model.name}`);
       }
 
-      this.uses += count;
+      return new DecisionTreeClassifier(true, model);
+    }
 
-      if ((this.index | 0) >= ARRAY_SIZE) {
-        refreshData(this.data);
-        this.index = 0;
-      }
+  }
 
-      while (count + this.index > ARRAY_SIZE) {
-        count -= ARRAY_SIZE - this.index;
-        refreshData(this.data);
-        this.index = 0;
+  const defaultOptions$1 = {
+    gainFunction: 'regression',
+    splitFunction: 'mean',
+    minNumSamples: 3,
+    maxDepth: Infinity
+  };
+  class DecisionTreeRegression {
+    /**
+     * Create new Decision Tree Regression with CART implementation with the given options.
+     * @param {object} options
+     * @param {string} [options.gainFunction="regression"] - gain function to get the best split, "regression" the only one supported.
+     * @param {string} [options.splitFunction="mean"] - given two integers from a split feature, get the value to split, "mean" the only one supported.
+     * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.
+     * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.
+     * @param {object} model - for load purposes.
+     */
+    constructor(options, model) {
+      if (options === true) {
+        this.options = model.options;
+        this.root = new TreeNode(model.options);
+        this.root.setNodeParameters(model.root);
+      } else {
+        this.options = Object.assign({}, defaultOptions$1, options);
+        this.options.kind = 'regression';
       }
-
-      this.index = this.index + count | 0;
-      return this;
     }
+    /**
+     * Train the decision tree with the given training set and values.
+     * @param {Matrix|MatrixTransposeView|Array} trainingSet
+     * @param {Array} trainingValues
+     */
 
-    seed(initial) {
-      let previous = 0;
-      this.data[0] = previous = initial | 0;
 
-      for (let i = 1; i < ARRAY_SIZE; i = i + 1 | 0) {
-        this.data[i] = previous = imul(previous ^ previous >>> 30, 0x6c078965) + i | 0;
+    train(trainingSet, trainingValues) {
+      this.root = new TreeNode(this.options);
+
+      if (typeof trainingSet[0] !== 'undefined' && trainingSet[0].length === undefined) {
+        trainingSet = Matrix.columnVector(trainingSet);
+      } else {
+        trainingSet = Matrix.checkMatrix(trainingSet);
       }
 
-      this.index = ARRAY_SIZE;
-      this.uses = 0;
-      return this;
+      this.root.train(trainingSet, trainingValues, 0);
     }
+    /**
+     * Predicts the values given the matrix to predict.
+     * @param {Matrix|MatrixTransposeView|Array} toPredict
+     * @return {Array} predictions
+     */
 
-    seedWithArray(source) {
-      this.seed(0x012bd6aa);
-      seedWithArray(this.data, source);
-      return this;
-    }
 
-  }
+    predict(toPredict) {
+      if (typeof toPredict[0] !== 'undefined' && toPredict[0].length === undefined) {
+        toPredict = Matrix.columnVector(toPredict);
+      }
 
-  function refreshData(data) {
-    let k = 0;
-    let tmp = 0;
+      toPredict = Matrix.checkMatrix(toPredict);
+      let predictions = new Array(toPredict.rows);
 
-    for (; (k | 0) < ARRAY_SIZE_MINUS_M; k = k + 1 | 0) {
-      tmp = data[k] & INT32_SIZE | data[k + 1 | 0] & INT32_MAX;
-      data[k] = data[k + M | 0] ^ tmp >>> 1 ^ (tmp & 0x1 ? A : 0);
-    }
+      for (let i = 0; i < toPredict.rows; ++i) {
+        predictions[i] = this.root.classify(toPredict.getRow(i));
+      }
 
-    for (; (k | 0) < ARRAY_MAX; k = k + 1 | 0) {
-      tmp = data[k] & INT32_SIZE | data[k + 1 | 0] & INT32_MAX;
-      data[k] = data[k - ARRAY_SIZE_MINUS_M | 0] ^ tmp >>> 1 ^ (tmp & 0x1 ? A : 0);
+      return predictions;
     }
+    /**
+     * Export the current model to JSON.
+     * @return {object} - Current model.
+     */
 
-    tmp = data[ARRAY_MAX] & INT32_SIZE | data[0] & INT32_MAX;
-    data[ARRAY_MAX] = data[M - 1] ^ tmp >>> 1 ^ (tmp & 0x1 ? A : 0);
-  }
-
-  function temper(value) {
-    value ^= value >>> 11;
-    value ^= value << 7 & 0x9d2c5680;
-    value ^= value << 15 & 0xefc60000;
-    return value ^ value >>> 18;
-  }
 
-  function seedWithArray(data, source) {
-    let i = 1;
-    let j = 0;
-    const sourceLength = source.length;
-    let k = Math.max(sourceLength, ARRAY_SIZE) | 0;
-    let previous = data[0] | 0;
+    toJSON() {
+      return {
+        options: this.options,
+        root: this.root,
+        name: 'DTRegression'
+      };
+    }
+    /**
+     * Load a Decision tree regression with the given model.
+     * @param {object} model
+     * @return {DecisionTreeRegression}
+     */
 
-    for (; (k | 0) > 0; --k) {
-      data[i] = previous = (data[i] ^ imul(previous ^ previous >>> 30, 0x0019660d)) + (source[j] | 0) + (j | 0) | 0;
-      i = i + 1 | 0;
-      ++j;
 
-      if ((i | 0) > ARRAY_MAX) {
-        data[0] = data[ARRAY_MAX];
-        i = 1;
+    static load(model) {
+      if (model.name !== 'DTRegression') {
+        throw new RangeError(`Invalid model:${model.name}`);
       }
 
-      if (j >= sourceLength) {
-        j = 0;
-      }
+      return new DecisionTreeRegression(true, model);
     }
 
-    for (k = ARRAY_MAX; (k | 0) > 0; --k) {
-      data[i] = previous = (data[i] ^ imul(previous ^ previous >>> 30, 0x5d588b65)) - i | 0;
-      i = i + 1 | 0;
+  }
 
-      if ((i | 0) > ARRAY_MAX) {
-        data[0] = data[ARRAY_MAX];
-        i = 1;
-      }
-    }
+  const SMALLEST_UNSAFE_INTEGER = 0x20000000000000;
+  const LARGEST_SAFE_INTEGER = SMALLEST_UNSAFE_INTEGER - 1;
+  const UINT32_MAX = -1 >>> 0;
+  const UINT32_SIZE = UINT32_MAX + 1;
+  const INT32_SIZE = UINT32_SIZE / 2;
+  const INT32_MAX = INT32_SIZE - 1;
+  const UINT21_SIZE = 1 << 21;
+  const UINT21_MAX = UINT21_SIZE - 1;
+  /**
+   * Returns a value within [-0x80000000, 0x7fffffff]
+   */
 
-    data[0] = INT32_SIZE;
+  function int32(engine) {
+    return engine.next() | 0;
   }
 
-  function checkFloat(n) {
-    return n > 0.0 && n <= 1.0;
+  function add(distribution, addend) {
+    if (addend === 0) {
+      return distribution;
+    } else {
+      return engine => distribution(engine) + addend;
+    }
   }
-  /**
-   * Select n with replacement elements on the training set and values, where n is the size of the training set.
-   * @ignore
-   * @param {Matrix} trainingSet
-   * @param {Array} trainingValue
-   * @param {number} seed - seed for the random selection, must be a 32-bit integer.
-   * @return {object} with new X and y.
+  /**
+   * Returns a value within [-0x20000000000000, 0x1fffffffffffff]
    */
 
-  function examplesBaggingWithReplacement(trainingSet, trainingValue, seed) {
-    let engine;
-    let distribution = integer(0, trainingSet.rows - 1);
 
-    if (seed === undefined) {
-      engine = MersenneTwister19937.autoSeed();
-    } else if (Number.isInteger(seed)) {
-      engine = MersenneTwister19937.seed(seed);
-    } else {
-      throw new RangeError("Expected seed must be undefined or integer not ".concat(seed));
-    }
+  function int53(engine) {
+    const high = engine.next() | 0;
+    const low = engine.next() >>> 0;
+    return (high & UINT21_MAX) * UINT32_SIZE + low + (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);
+  }
+  /**
+   * Returns a value within [-0x20000000000000, 0x20000000000000]
+   */
 
-    let Xr = new Array(trainingSet.rows);
-    let yr = new Array(trainingSet.rows);
 
-    for (let i = 0; i < trainingSet.rows; ++i) {
-      let index = distribution(engine);
-      Xr[i] = trainingSet.getRow(index);
-      yr[i] = trainingValue[index];
+  function int53Full(engine) {
+    while (true) {
+      const high = engine.next() | 0;
+
+      if (high & 0x400000) {
+        if ((high & 0x7fffff) === 0x400000 && (engine.next() | 0) === 0) {
+          return SMALLEST_UNSAFE_INTEGER;
+        }
+      } else {
+        const low = engine.next() >>> 0;
+        return (high & UINT21_MAX) * UINT32_SIZE + low + (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);
+      }
     }
+  }
+  /**
+   * Returns a value within [0, 0xffffffff]
+   */
 
-    return {
-      X: new Matrix(Xr),
-      y: yr
-    };
+
+  function uint32(engine) {
+    return engine.next() >>> 0;
   }
-  /**
-   * selects n features from the training set with or without replacement, returns the new training set and the indexes used.
-   * @ignore
-   * @param {Matrix} trainingSet
-   * @param {number} n - features.
-   * @param {boolean} replacement
-   * @param {number} seed - seed for the random selection, must be a 32-bit integer.
-   * @return {object}
+  /**
+   * Returns a value within [0, 0x1fffffffffffff]
    */
 
-  function featureBagging(trainingSet, n, replacement, seed) {
-    if (trainingSet.columns < n) {
-      throw new RangeError('N should be less or equal to the number of columns of X');
-    }
 
-    let distribution = integer(0, trainingSet.columns - 1);
-    let engine;
+  function uint53(engine) {
+    const high = engine.next() & UINT21_MAX;
+    const low = engine.next() >>> 0;
+    return high * UINT32_SIZE + low;
+  }
+  /**
+   * Returns a value within [0, 0x20000000000000]
+   */
 
-    if (seed === undefined) {
-      engine = MersenneTwister19937.autoSeed();
-    } else if (Number.isInteger(seed)) {
-      engine = MersenneTwister19937.seed(seed);
-    } else {
-      throw new RangeError("Expected seed must be undefined or integer not ".concat(seed));
+
+  function uint53Full(engine) {
+    while (true) {
+      const high = engine.next() | 0;
+
+      if (high & UINT21_SIZE) {
+        if ((high & UINT21_MAX) === 0 && (engine.next() | 0) === 0) {
+          return SMALLEST_UNSAFE_INTEGER;
+        }
+      } else {
+        const low = engine.next() >>> 0;
+        return (high & UINT21_MAX) * UINT32_SIZE + low;
+      }
     }
+  }
 
-    let toRet = new Matrix(trainingSet.rows, n);
-    let usedIndex;
-    let index;
+  function isPowerOfTwoMinusOne(value) {
+    return (value + 1 & value) === 0;
+  }
 
-    if (replacement) {
-      usedIndex = new Array(n);
+  function bitmask(masking) {
+    return engine => engine.next() & masking;
+  }
 
-      for (let i = 0; i < n; ++i) {
-        index = distribution(engine);
-        usedIndex[i] = index;
-        toRet.setColumn(i, trainingSet.getColumn(index));
-      }
-    } else {
-      usedIndex = new Set();
-      index = distribution(engine);
+  function downscaleToLoopCheckedRange(range) {
+    const extendedRange = range + 1;
+    const maximum = extendedRange * Math.floor(UINT32_SIZE / extendedRange);
+    return engine => {
+      let value = 0;
 
-      for (let i = 0; i < n; ++i) {
-        while (usedIndex.has(index)) {
-          index = distribution(engine);
-        }
+      do {
+        value = engine.next() >>> 0;
+      } while (value >= maximum);
 
-        toRet.setColumn(i, trainingSet.getColumn(index));
-        usedIndex.add(index);
-      }
+      return value % extendedRange;
+    };
+  }
 
-      usedIndex = Array.from(usedIndex);
+  function downscaleToRange(range) {
+    if (isPowerOfTwoMinusOne(range)) {
+      return bitmask(range);
+    } else {
+      return downscaleToLoopCheckedRange(range);
     }
+  }
 
-    return {
-      X: toRet,
-      usedIndex: usedIndex
+  function isEvenlyDivisibleByMaxInt32(value) {
+    return (value | 0) === 0;
+  }
+
+  function upscaleWithHighMasking(masking) {
+    return engine => {
+      const high = engine.next() & masking;
+      const low = engine.next() >>> 0;
+      return high * UINT32_SIZE + low;
     };
   }
 
-  /**
-   * @class RandomForestBase
-   */
+  function upscaleToLoopCheckedRange(extendedRange) {
+    const maximum = extendedRange * Math.floor(SMALLEST_UNSAFE_INTEGER / extendedRange);
+    return engine => {
+      let ret = 0;
 
-  class RandomForestBase {
-    /**
-     * Create a new base random forest for a classifier or regression model.
-     * @constructor
-     * @param {object} options
-     * @param {number|String} [options.maxFeatures] - the number of features used on each estimator.
-     *        * if is an integer it selects maxFeatures elements over the sample features.
-     *        * if is a float between (0, 1), it takes the percentage of features.
-     * @param {boolean} [options.replacement] - use replacement over the sample features.
-     * @param {number} [options.seed] - seed for feature and samples selection, must be a 32-bit integer.
-     * @param {number} [options.nEstimators] - number of estimator to use.
-     * @param {object} [options.treeOptions] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}
-     * @param {boolean} [options.isClassifier] - boolean to check if is a classifier or regression model (used by subclasses).
-     * @param {boolean} [options.useSampleBagging] - use bagging over training samples.
-     * @param {object} model - for load purposes.
-     */
-    constructor(options, model) {
-      if (options === true) {
-        this.replacement = model.replacement;
-        this.maxFeatures = model.maxFeatures;
-        this.nEstimators = model.nEstimators;
-        this.treeOptions = model.treeOptions;
-        this.isClassifier = model.isClassifier;
-        this.seed = model.seed;
-        this.n = model.n;
-        this.indexes = model.indexes;
-        this.useSampleBagging = model.useSampleBagging;
-        let Estimator = this.isClassifier ? DecisionTreeClassifier : DecisionTreeRegression;
-        this.estimators = model.estimators.map(est => Estimator.load(est));
-      } else {
-        this.replacement = options.replacement;
-        this.maxFeatures = options.maxFeatures;
-        this.nEstimators = options.nEstimators;
-        this.treeOptions = options.treeOptions;
-        this.isClassifier = options.isClassifier;
-        this.seed = options.seed;
-        this.useSampleBagging = options.useSampleBagging;
+      do {
+        const high = engine.next() & UINT21_MAX;
+        const low = engine.next() >>> 0;
+        ret = high * UINT32_SIZE + low;
+      } while (ret >= maximum);
+
+      return ret % extendedRange;
+    };
+  }
+
+  function upscaleWithinU53(range) {
+    const extendedRange = range + 1;
+
+    if (isEvenlyDivisibleByMaxInt32(extendedRange)) {
+      const highRange = (extendedRange / UINT32_SIZE | 0) - 1;
+
+      if (isPowerOfTwoMinusOne(highRange)) {
+        return upscaleWithHighMasking(highRange);
       }
     }
-    /**
-     * Train the decision tree with the given training set and labels.
-     * @param {Matrix|Array} trainingSet
-     * @param {Array} trainingValues
-     */
 
+    return upscaleToLoopCheckedRange(extendedRange);
+  }
 
-    train(trainingSet, trainingValues) {
-      trainingSet = Matrix.checkMatrix(trainingSet);
-      this.maxFeatures = this.maxFeatures || trainingSet.columns;
+  function upscaleWithinI53AndLoopCheck(min, max) {
+    return engine => {
+      let ret = 0;
 
-      if (checkFloat(this.maxFeatures)) {
-        this.n = Math.floor(trainingSet.columns * this.maxFeatures);
-      } else if (Number.isInteger(this.maxFeatures)) {
-        if (this.maxFeatures > trainingSet.columns) {
-          throw new RangeError("The maxFeatures parameter should be less than ".concat(trainingSet.columns));
-        } else {
-          this.n = this.maxFeatures;
-        }
-      } else {
-        throw new RangeError("Cannot process the maxFeatures parameter ".concat(this.maxFeatures));
-      }
+      do {
+        const high = engine.next() | 0;
+        const low = engine.next() >>> 0;
+        ret = (high & UINT21_MAX) * UINT32_SIZE + low + (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);
+      } while (ret < min || ret > max);
 
-      let Estimator;
+      return ret;
+    };
+  }
+  /**
+   * Returns a Distribution to return a value within [min, max]
+   * @param min The minimum integer value, inclusive. No less than -0x20000000000000.
+   * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.
+   */
 
-      if (this.isClassifier) {
-        Estimator = DecisionTreeClassifier;
-      } else {
-        Estimator = DecisionTreeRegression;
-      }
 
-      this.estimators = new Array(this.nEstimators);
-      this.indexes = new Array(this.nEstimators);
+  function integer(min, max) {
+    min = Math.floor(min);
+    max = Math.floor(max);
 
-      for (let i = 0; i < this.nEstimators; ++i) {
-        let res = this.useSampleBagging ? examplesBaggingWithReplacement(trainingSet, trainingValues, this.seed) : {
-          X: trainingSet,
-          y: trainingValues
-        };
-        let X = res.X;
-        let y = res.y;
-        res = featureBagging(X, this.n, this.replacement, this.seed);
-        X = res.X;
-        this.indexes[i] = res.usedIndex;
-        this.estimators[i] = new Estimator(this.treeOptions);
-        this.estimators[i].train(X, y);
-      }
+    if (min < -SMALLEST_UNSAFE_INTEGER || !isFinite(min)) {
+      throw new RangeError(`Expected min to be at least ${-SMALLEST_UNSAFE_INTEGER}`);
+    } else if (max > SMALLEST_UNSAFE_INTEGER || !isFinite(max)) {
+      throw new RangeError(`Expected max to be at most ${SMALLEST_UNSAFE_INTEGER}`);
     }
-    /**
-     * Method that returns the way the algorithm generates the predictions, for example, in classification
-     * you can return the mode of all predictions retrieved by the trees, or in case of regression you can
-     * use the mean or the median.
-     * @abstract
-     * @param {Array} values - predictions of the estimators.
-     * @return {number} prediction.
-     */
-    // eslint-disable-next-line no-unused-vars
 
+    const range = max - min;
 
-    selection(values) {
-      throw new Error("Abstract method 'selection' not implemented!");
+    if (range <= 0 || !isFinite(range)) {
+      return () => min;
+    } else if (range === UINT32_MAX) {
+      if (min === 0) {
+        return uint32;
+      } else {
+        return add(int32, min + INT32_SIZE);
+      }
+    } else if (range < UINT32_MAX) {
+      return add(downscaleToRange(range), min);
+    } else if (range === LARGEST_SAFE_INTEGER) {
+      return add(uint53, min);
+    } else if (range < LARGEST_SAFE_INTEGER) {
+      return add(upscaleWithinU53(range), min);
+    } else if (max - 1 - min === LARGEST_SAFE_INTEGER) {
+      return add(uint53Full, min);
+    } else if (min === -SMALLEST_UNSAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {
+      return int53Full;
+    } else if (min === -SMALLEST_UNSAFE_INTEGER && max === LARGEST_SAFE_INTEGER) {
+      return int53;
+    } else if (min === -LARGEST_SAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {
+      return add(int53, 1);
+    } else if (max === SMALLEST_UNSAFE_INTEGER) {
+      return add(upscaleWithinI53AndLoopCheck(min - 1, max - 1), 1);
+    } else {
+      return upscaleWithinI53AndLoopCheck(min, max);
     }
-    /**
-     * Predicts the output given the matrix to predict.
-     * @param {Matrix|Array} toPredict
-     * @return {Array} predictions
-     */
+  }
+  // has 2**x chars, for faster uniform distribution
 
 
-    predict(toPredict) {
-      let predictionValues = new Array(this.nEstimators);
-      toPredict = Matrix.checkMatrix(toPredict);
+  const DEFAULT_STRING_POOL = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-";
 
-      for (let i = 0; i < this.nEstimators; ++i) {
-        let X = new MatrixColumnSelectionView(toPredict, this.indexes[i]); // get features for estimator
+  function string(pool = DEFAULT_STRING_POOL) {
+    const poolLength = pool.length;
 
-        predictionValues[i] = this.estimators[i].predict(X);
-      }
+    if (!poolLength) {
+      throw new Error("Expected pool not to be an empty string");
+    }
 
-      predictionValues = new MatrixTransposeView(new WrapperMatrix2D(predictionValues));
-      let predictions = new Array(predictionValues.rows);
+    const distribution = integer(0, poolLength - 1);
+    return (engine, length) => {
+      let result = "";
 
-      for (let i = 0; i < predictionValues.rows; ++i) {
-        predictions[i] = this.selection(predictionValues.getRow(i));
+      for (let i = 0; i < length; ++i) {
+        const j = distribution(engine);
+        result += pool.charAt(j);
       }
 
-      return predictions;
-    }
-    /**
-     * Export the current model to JSON.
-     * @return {object} - Current model.
-     */
+      return result;
+    };
+  }
 
+  const LOWER_HEX_POOL = "0123456789abcdef";
+  const lowerHex = string(LOWER_HEX_POOL);
+  const upperHex = string(LOWER_HEX_POOL.toUpperCase());
 
-    toJSON() {
-      return {
-        indexes: this.indexes,
-        n: this.n,
-        replacement: this.replacement,
-        maxFeatures: this.maxFeatures,
-        nEstimators: this.nEstimators,
-        treeOptions: this.treeOptions,
-        isClassifier: this.isClassifier,
-        seed: this.seed,
-        estimators: this.estimators.map(est => est.toJSON()),
-        useSampleBagging: this.useSampleBagging
-      };
+  const stringRepeat = (() => {
+    try {
+      if ("x".repeat(3) === "xxx") {
+        return (pattern, count) => pattern.repeat(count);
+      }
+    } catch (_) {// nothing to do here
     }
 
-  }
+    return (pattern, count) => {
+      let result = "";
 
-  const defaultOptions$2 = {
-    maxFeatures: 1.0,
-    replacement: true,
-    nEstimators: 10,
-    seed: 42,
-    useSampleBagging: false
-  };
-  /**
-   * @class RandomForestClassifier
-   * @augments RandomForestBase
-   */
+      while (count > 0) {
+        if (count & 1) {
+          result += pattern;
+        }
 
-  class RandomForestClassifier extends RandomForestBase {
-    /**
-     * Create a new base random forest for a classifier or regression model.
-     * @constructor
-     * @param {object} options
-     * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.
-     *        * if is an integer it selects maxFeatures elements over the sample features.
-     *        * if is a float between (0, 1), it takes the percentage of features.
-     * @param {boolean} [options.replacement=true] - use replacement over the sample features.
-     * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.
-     * @param {number} [options.nEstimators=10] - number of estimator to use.
-     * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}
-     * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.
-     * @param {object} model - for load purposes.
-     */
-    constructor(options, model) {
-      if (options === true) {
-        super(true, model.baseModel);
-      } else {
-        options = Object.assign({}, defaultOptions$2, options);
-        options.isClassifier = true;
-        super(options);
+        count >>= 1;
+        pattern += pattern;
       }
-    }
-    /**
-     * retrieve the prediction given the selection method.
-     * @param {Array} values - predictions of the estimators.
-     * @return {number} prediction
-     */
 
-
-    selection(values) {
-      return mode(values);
-    }
-    /**
-     * Export the current model to JSON.
-     * @return {object} - Current model.
-     */
+      return result;
+    };
+  })();
+  /**
+   * An int32-producing Engine that uses `Math.random()`
+   */
 
 
-    toJSON() {
-      let baseModel = super.toJSON();
-      return {
-        baseModel: baseModel,
-        name: 'RFClassifier'
-      };
+  const nativeMath = {
+    next() {
+      return Math.random() * UINT32_SIZE | 0;
     }
-    /**
-     * Load a Decision tree classifier with the given model.
-     * @param {object} model
-     * @return {RandomForestClassifier}
-     */
 
+  }; // tslint:disable:unified-signatures
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array
+   */
 
-    static load(model) {
-      if (model.name !== 'RFClassifier') {
-        throw new RangeError("Invalid model: ".concat(model.name));
-      }
 
-      return new RandomForestClassifier(true, model);
+  const I32Array = (() => {
+    try {
+      const buffer = new ArrayBuffer(4);
+      const view = new Int32Array(buffer);
+      view[0] = INT32_SIZE;
+
+      if (view[0] === -INT32_SIZE) {
+        return Int32Array;
+      }
+    } catch (_) {// nothing to do here
     }
 
-  }
-  /**
-   * Return the most repeated element on the array.
-   * @param {Array} arr
-   * @return {number} mode
+    return Array;
+  })();
+  /**
+   * Returns an array of random int32 values, based on current time
+   * and a random number engine
+   *
+   * @param engine an Engine to pull random values from, default `nativeMath`
+   * @param length the length of the Array, minimum 1, default 16
    */
 
-  function mode(arr) {
-    return arr.sort((a, b) => arr.filter(v => v === a).length - arr.filter(v => v === b).length).pop();
-  }
+  function createEntropy(engine = nativeMath, length = 16) {
+    const array = [];
+    array.push(new Date().getTime() | 0);
 
-  var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};
+    for (let i = 1; i < length; ++i) {
+      array[i] = engine.next() | 0;
+    }
 
-  function createCommonjsModule(fn, module) {
-  	return module = { exports: {} }, fn(module, module.exports), module.exports;
+    return array;
   }
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/imul
+   */
 
-  var medianQuickselect_min = createCommonjsModule(function (module) {
-    (function () {
-      function a(d) {
-        for (var e = 0, f = d.length - 1, g = void 0, h = void 0, i = void 0, j = c(e, f); !0;) {
-          if (f <= e) return d[j];
-          if (f == e + 1) return d[e] > d[f] && b(d, e, f), d[j];
-
-          for (g = c(e, f), d[g] > d[f] && b(d, g, f), d[e] > d[f] && b(d, e, f), d[g] > d[e] && b(d, g, e), b(d, g, e + 1), h = e + 1, i = f; !0;) {
-            do h++; while (d[e] > d[h]);
-
-            do i--; while (d[i] > d[e]);
-
-            if (i < h) break;
-            b(d, h, i);
-          }
 
-          b(d, e, i), i <= j && (e = h), i >= j && (f = i - 1);
-        }
+  const imul = (() => {
+    try {
+      if (Math.imul(UINT32_MAX, 5) === -5) {
+        return Math.imul;
       }
+    } catch (_) {// nothing to do here
+    }
 
-      var b = function b(d, e, f) {
-        var _ref;
-
-        return _ref = [d[f], d[e]], d[e] = _ref[0], d[f] = _ref[1], _ref;
-      },
-          c = function c(d, e) {
-        return ~~((d + e) / 2);
-      };
+    const UINT16_MAX = 0xffff;
+    return (a, b) => {
+      const ah = a >>> 16 & UINT16_MAX;
+      const al = a & UINT16_MAX;
+      const bh = b >>> 16 & UINT16_MAX;
+      const bl = b & UINT16_MAX; // the shift by 0 fixes the sign on the high part
+      // the final |0 converts the unsigned value into a signed value
 
-       module.exports ? module.exports = a : window.median = a;
-    })();
-  });
+      return al * bl + (ah * bl + al * bh << 16 >>> 0) | 0;
+    };
+  })();
 
-  /**
-   * Computes the median of the given values
-   * @param {Array<number>} input
-   * @return {number}
+  const ARRAY_SIZE = 624;
+  const ARRAY_MAX = ARRAY_SIZE - 1;
+  const M = 397;
+  const ARRAY_SIZE_MINUS_M = ARRAY_SIZE - M;
+  const A = 0x9908b0df;
+  /**
+   * An Engine that is a pseudorandom number generator using the Mersenne
+   * Twister algorithm based on the prime 2**19937 − 1
+   *
+   * See http://en.wikipedia.org/wiki/Mersenne_twister
    */
 
-  function median(input) {
-    if (!src(input)) {
-      throw new TypeError('input must be an array');
-    }
+  class MersenneTwister19937 {
+    /**
+     * MersenneTwister19937 should not be instantiated directly.
+     * Instead, use the static methods `seed`, `seedWithArray`, or `autoSeed`.
+     */
+    constructor() {
+      this.data = new I32Array(ARRAY_SIZE);
+      this.index = 0; // integer within [0, 624]
 
-    if (input.length === 0) {
-      throw new TypeError('input must not be empty');
+      this.uses = 0;
     }
+    /**
+     * Returns a MersenneTwister19937 seeded with an initial int32 value
+     * @param initial the initial seed value
+     */
 
-    return medianQuickselect_min(input.slice());
-  }
 
-  const selectionMethods = {
-    mean: mean,
-    median: median
-  };
-  const defaultOptions$3 = {
-    maxFeatures: 1.0,
-    replacement: false,
-    nEstimators: 10,
-    treeOptions: {},
-    selectionMethod: 'mean',
-    seed: 42,
-    useSampleBagging: false
-  };
-  /**
-   * @class RandomForestRegression
-   * @augments RandomForestBase
-   */
+    static seed(initial) {
+      return new MersenneTwister19937().seed(initial);
+    }
+    /**
+     * Returns a MersenneTwister19937 seeded with zero or more int32 values
+     * @param source A series of int32 values
+     */
 
-  class RandomForestRegression extends RandomForestBase {
-    /**
-     * Create a new base random forest for a classifier or regression model.
-     * @constructor
-     * @param {object} options
-     * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.
-     *        * if is an integer it selects maxFeatures elements over the sample features.
-     *        * if is a float between (0, 1), it takes the percentage of features.
-     * @param {boolean} [options.replacement=true] - use replacement over the sample features.
-     * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.
-     * @param {number} [options.nEstimators=10] - number of estimator to use.
-     * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}
-     * @param {string} [options.selectionMethod="mean"] - the way to calculate the prediction from estimators, "mean" and "median" are supported.
-     * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.
-     * @param {object} model - for load purposes.
+
+    static seedWithArray(source) {
+      return new MersenneTwister19937().seedWithArray(source);
+    }
+    /**
+     * Returns a MersenneTwister19937 seeded with the current time and
+     * a series of natively-generated random values
      */
-    constructor(options, model) {
-      if (options === true) {
-        super(true, model.baseModel);
-        this.selectionMethod = model.selectionMethod;
-      } else {
-        options = Object.assign({}, defaultOptions$3, options);
 
-        if (!(options.selectionMethod === 'mean' || options.selectionMethod === 'median')) {
-          throw new RangeError("Unsupported selection method ".concat(options.selectionMethod));
-        }
 
-        options.isClassifier = false;
-        super(options);
-        this.selectionMethod = options.selectionMethod;
-      }
+    static autoSeed() {
+      return MersenneTwister19937.seedWithArray(createEntropy());
     }
-    /**
-     * retrieve the prediction given the selection method.
-     * @param {Array} values - predictions of the estimators.
-     * @return {number} prediction
+    /**
+     * Returns the next int32 value of the sequence
      */
 
 
-    selection(values) {
-      return selectionMethods[this.selectionMethod](values);
+    next() {
+      if ((this.index | 0) >= ARRAY_SIZE) {
+        refreshData(this.data);
+        this.index = 0;
+      }
+
+      const value = this.data[this.index];
+      this.index = this.index + 1 | 0;
+      this.uses += 1;
+      return temper(value) | 0;
     }
-    /**
-     * Export the current model to JSON.
-     * @return {object} - Current model.
+    /**
+     * Returns the number of times that the Engine has been used.
+     *
+     * This can be provided to an unused MersenneTwister19937 with the same
+     * seed, bringing it to the exact point that was left off.
      */
 
 
-    toJSON() {
-      let baseModel = super.toJSON();
-      return {
-        baseModel: baseModel,
-        selectionMethod: this.selectionMethod,
-        name: 'RFRegression'
-      };
+    getUseCount() {
+      return this.uses;
     }
-    /**
-     * Load a Decision tree classifier with the given model.
-     * @param {object} model
-     * @return {RandomForestRegression}
+    /**
+     * Discards one or more items from the engine
+     * @param count The count of items to discard
      */
 
 
-    static load(model) {
-      if (model.name !== 'RFRegression') {
-        throw new RangeError("Invalid model: ".concat(model.name));
+    discard(count) {
+      if (count <= 0) {
+        return this;
       }
 
-      return new RandomForestRegression(true, model);
-    }
-
-  }
+      this.uses += count;
 
-  /**
-   * Creates new PCA (Principal Component Analysis) from the dataset
-   * @param {Matrix} dataset - dataset or covariance matrix.
-   * @param {Object} [options]
-   * @param {boolean} [options.isCovarianceMatrix=false] - true if the dataset is a covariance matrix.
-   * @param {string} [options.method='SVD'] - select which method to use: SVD (default), covarianceMatrirx or NIPALS.
-   * @param {number} [options.nCompNIPALS=2] - number of components to be computed with NIPALS.
-   * @param {boolean} [options.center=true] - should the data be centered (subtract the mean).
-   * @param {boolean} [options.scale=false] - should the data be scaled (divide by the standard deviation).
-   * @param {boolean} [options.ignoreZeroVariance=false] - ignore columns with zero variance if `scale` is `true`.
-   * */
-
-  class PCA {
-    constructor(dataset) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+      if ((this.index | 0) >= ARRAY_SIZE) {
+        refreshData(this.data);
+        this.index = 0;
+      }
 
-      if (dataset === true) {
-        const model = options;
-        this.center = model.center;
-        this.scale = model.scale;
-        this.means = model.means;
-        this.stdevs = model.stdevs;
-        this.U = Matrix.checkMatrix(model.U);
-        this.S = model.S;
-        this.R = model.R;
-        this.excludedFeatures = model.excludedFeatures || [];
-        return;
+      while (count + this.index > ARRAY_SIZE) {
+        count -= ARRAY_SIZE - this.index;
+        refreshData(this.data);
+        this.index = 0;
       }
 
-      dataset = new Matrix(dataset);
-      const {
-        isCovarianceMatrix = false,
-        method = 'SVD',
-        nCompNIPALS = 2,
-        center = true,
-        scale = false,
-        ignoreZeroVariance = false
-      } = options;
-      this.center = center;
-      this.scale = scale;
-      this.means = null;
-      this.stdevs = null;
-      this.excludedFeatures = [];
+      this.index = this.index + count | 0;
+      return this;
+    }
 
-      if (isCovarianceMatrix) {
-        // User provided a covariance matrix instead of dataset.
-        this._computeFromCovarianceMatrix(dataset);
+    seed(initial) {
+      let previous = 0;
+      this.data[0] = previous = initial | 0;
 
-        return;
+      for (let i = 1; i < ARRAY_SIZE; i = i + 1 | 0) {
+        this.data[i] = previous = imul(previous ^ previous >>> 30, 0x6c078965) + i | 0;
       }
 
-      this._adjust(dataset, ignoreZeroVariance);
-
-      switch (method) {
-        case 'covarianceMatrix':
-          {
-            // User provided a dataset but wants us to compute and use the covariance matrix.
-            const covarianceMatrix = new MatrixTransposeView(dataset).mmul(dataset).div(dataset.rows - 1);
+      this.index = ARRAY_SIZE;
+      this.uses = 0;
+      return this;
+    }
 
-            this._computeFromCovarianceMatrix(covarianceMatrix);
+    seedWithArray(source) {
+      this.seed(0x012bd6aa);
+      seedWithArray(this.data, source);
+      return this;
+    }
 
-            break;
-          }
+  }
 
-        case 'NIPALS':
-          {
-            this._computeWithNIPALS(dataset, nCompNIPALS);
+  function refreshData(data) {
+    let k = 0;
+    let tmp = 0;
 
-            break;
-          }
+    for (; (k | 0) < ARRAY_SIZE_MINUS_M; k = k + 1 | 0) {
+      tmp = data[k] & INT32_SIZE | data[k + 1 | 0] & INT32_MAX;
+      data[k] = data[k + M | 0] ^ tmp >>> 1 ^ (tmp & 0x1 ? A : 0);
+    }
 
-        case 'SVD':
-          {
-            const svd = new SingularValueDecomposition(dataset, {
-              computeLeftSingularVectors: false,
-              computeRightSingularVectors: true,
-              autoTranspose: true
-            });
-            this.U = svd.rightSingularVectors;
-            const singularValues = svd.diagonal;
-            const eigenvalues = [];
+    for (; (k | 0) < ARRAY_MAX; k = k + 1 | 0) {
+      tmp = data[k] & INT32_SIZE | data[k + 1 | 0] & INT32_MAX;
+      data[k] = data[k - ARRAY_SIZE_MINUS_M | 0] ^ tmp >>> 1 ^ (tmp & 0x1 ? A : 0);
+    }
 
-            for (const singularValue of singularValues) {
-              eigenvalues.push(singularValue * singularValue / (dataset.rows - 1));
-            }
+    tmp = data[ARRAY_MAX] & INT32_SIZE | data[0] & INT32_MAX;
+    data[ARRAY_MAX] = data[M - 1] ^ tmp >>> 1 ^ (tmp & 0x1 ? A : 0);
+  }
 
-            this.S = eigenvalues;
-            break;
-          }
+  function temper(value) {
+    value ^= value >>> 11;
+    value ^= value << 7 & 0x9d2c5680;
+    value ^= value << 15 & 0xefc60000;
+    return value ^ value >>> 18;
+  }
 
-        default:
-          {
-            throw new Error("unknown method: ".concat(method));
-          }
-      }
-    }
-    /**
-     * Load a PCA model from JSON
-     * @param {Object} model
-     * @return {PCA}
-     */
+  function seedWithArray(data, source) {
+    let i = 1;
+    let j = 0;
+    const sourceLength = source.length;
+    let k = Math.max(sourceLength, ARRAY_SIZE) | 0;
+    let previous = data[0] | 0;
 
+    for (; (k | 0) > 0; --k) {
+      data[i] = previous = (data[i] ^ imul(previous ^ previous >>> 30, 0x0019660d)) + (source[j] | 0) + (j | 0) | 0;
+      i = i + 1 | 0;
+      ++j;
 
-    static load(model) {
-      if (typeof model.name !== 'string') {
-        throw new TypeError('model must have a name property');
+      if ((i | 0) > ARRAY_MAX) {
+        data[0] = data[ARRAY_MAX];
+        i = 1;
       }
 
-      if (model.name !== 'PCA') {
-        throw new RangeError("invalid model: ".concat(model.name));
+      if (j >= sourceLength) {
+        j = 0;
       }
-
-      return new PCA(true, model);
     }
-    /**
-     * Project the dataset into the PCA space
-     * @param {Matrix} dataset
-     * @param {Object} options
-     * @return {Matrix} dataset projected in the PCA space
-     */
-
-
-    predict(dataset) {
-      let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-      const {
-        nComponents = this.U.columns
-      } = options;
-      dataset = new Matrix(dataset);
-
-      if (this.center) {
-        dataset.subRowVector(this.means);
 
-        if (this.scale) {
-          for (let i of this.excludedFeatures) {
-            dataset.removeColumn(i);
-          }
+    for (k = ARRAY_MAX; (k | 0) > 0; --k) {
+      data[i] = previous = (data[i] ^ imul(previous ^ previous >>> 30, 0x5d588b65)) - i | 0;
+      i = i + 1 | 0;
 
-          dataset.divRowVector(this.stdevs);
-        }
+      if ((i | 0) > ARRAY_MAX) {
+        data[0] = data[ARRAY_MAX];
+        i = 1;
       }
-
-      var predictions = dataset.mmul(this.U);
-      return predictions.subMatrix(0, predictions.rows - 1, 0, nComponents - 1);
     }
-    /**
-     * Calculates the inverse PCA transform
-     * @param {Matrix} dataset
-     * @return {Matrix} dataset projected in the PCA space
-     */
 
+    data[0] = INT32_SIZE;
+  }
 
-    invert(dataset) {
-      dataset = Matrix.checkMatrix(dataset);
-      var inverse = dataset.mmul(this.U.transpose());
-
-      if (this.center) {
-        if (this.scale) {
-          inverse.mulRowVector(this.stdevs);
-        }
+  function checkFloat(n) {
+    return n > 0.0 && n <= 1.0;
+  }
+  /**
+   * Select n with replacement elements on the training set and values, where n is the size of the training set.
+   * @ignore
+   * @param {Matrix} trainingSet
+   * @param {Array} trainingValue
+   * @param {number} seed - seed for the random selection, must be a 32-bit integer.
+   * @return {object} with new X and y.
+   */
 
-        inverse.addRowVector(this.means);
-      }
+  function examplesBaggingWithReplacement(trainingSet, trainingValue, seed) {
+    let engine;
+    let distribution = integer(0, trainingSet.rows - 1);
 
-      return inverse;
+    if (seed === undefined) {
+      engine = MersenneTwister19937.autoSeed();
+    } else if (Number.isInteger(seed)) {
+      engine = MersenneTwister19937.seed(seed);
+    } else {
+      throw new RangeError(`Expected seed must be undefined or integer not ${seed}`);
     }
-    /**
-     * Returns the proportion of variance for each component
-     * @return {[number]}
-     */
 
+    let Xr = new Array(trainingSet.rows);
+    let yr = new Array(trainingSet.rows);
 
-    getExplainedVariance() {
-      var sum = 0;
+    for (let i = 0; i < trainingSet.rows; ++i) {
+      let index = distribution(engine);
+      Xr[i] = trainingSet.getRow(index);
+      yr[i] = trainingValue[index];
+    }
 
-      for (const s of this.S) {
-        sum += s;
-      }
+    return {
+      X: new Matrix(Xr),
+      y: yr
+    };
+  }
+  /**
+   * selects n features from the training set with or without replacement, returns the new training set and the indexes used.
+   * @ignore
+   * @param {Matrix} trainingSet
+   * @param {number} n - features.
+   * @param {boolean} replacement
+   * @param {number} seed - seed for the random selection, must be a 32-bit integer.
+   * @return {object}
+   */
 
-      return this.S.map(value => value / sum);
+  function featureBagging(trainingSet, n, replacement, seed) {
+    if (trainingSet.columns < n) {
+      throw new RangeError('N should be less or equal to the number of columns of X');
     }
-    /**
-     * Returns the cumulative proportion of variance
-     * @return {[number]}
-     */
 
+    let distribution = integer(0, trainingSet.columns - 1);
+    let engine;
 
-    getCumulativeVariance() {
-      var explained = this.getExplainedVariance();
+    if (seed === undefined) {
+      engine = MersenneTwister19937.autoSeed();
+    } else if (Number.isInteger(seed)) {
+      engine = MersenneTwister19937.seed(seed);
+    } else {
+      throw new RangeError(`Expected seed must be undefined or integer not ${seed}`);
+    }
 
-      for (var i = 1; i < explained.length; i++) {
-        explained[i] += explained[i - 1];
+    let toRet = new Matrix(trainingSet.rows, n);
+    let usedIndex;
+    let index;
+
+    if (replacement) {
+      usedIndex = new Array(n);
+
+      for (let i = 0; i < n; ++i) {
+        index = distribution(engine);
+        usedIndex[i] = index;
+        toRet.setColumn(i, trainingSet.getColumn(index));
       }
+    } else {
+      usedIndex = new Set();
+      index = distribution(engine);
 
-      return explained;
-    }
-    /**
-     * Returns the Eigenvectors of the covariance matrix
-     * @returns {Matrix}
-     */
+      for (let i = 0; i < n; ++i) {
+        while (usedIndex.has(index)) {
+          index = distribution(engine);
+        }
 
+        toRet.setColumn(i, trainingSet.getColumn(index));
+        usedIndex.add(index);
+      }
 
-    getEigenvectors() {
-      return this.U;
+      usedIndex = Array.from(usedIndex);
     }
-    /**
-     * Returns the Eigenvalues (on the diagonal)
-     * @returns {[number]}
-     */
 
+    return {
+      X: toRet,
+      usedIndex: usedIndex
+    };
+  }
+
+  /**
+   * @class RandomForestBase
+   */
 
-    getEigenvalues() {
-      return this.S;
+  class RandomForestBase {
+    /**
+     * Create a new base random forest for a classifier or regression model.
+     * @constructor
+     * @param {object} options
+     * @param {number|String} [options.maxFeatures] - the number of features used on each estimator.
+     *        * if is an integer it selects maxFeatures elements over the sample features.
+     *        * if is a float between (0, 1), it takes the percentage of features.
+     * @param {boolean} [options.replacement] - use replacement over the sample features.
+     * @param {number} [options.seed] - seed for feature and samples selection, must be a 32-bit integer.
+     * @param {number} [options.nEstimators] - number of estimator to use.
+     * @param {object} [options.treeOptions] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}
+     * @param {boolean} [options.isClassifier] - boolean to check if is a classifier or regression model (used by subclasses).
+     * @param {boolean} [options.useSampleBagging] - use bagging over training samples.
+     * @param {object} model - for load purposes.
+     */
+    constructor(options, model) {
+      if (options === true) {
+        this.replacement = model.replacement;
+        this.maxFeatures = model.maxFeatures;
+        this.nEstimators = model.nEstimators;
+        this.treeOptions = model.treeOptions;
+        this.isClassifier = model.isClassifier;
+        this.seed = model.seed;
+        this.n = model.n;
+        this.indexes = model.indexes;
+        this.useSampleBagging = model.useSampleBagging;
+        let Estimator = this.isClassifier ? DecisionTreeClassifier : DecisionTreeRegression;
+        this.estimators = model.estimators.map(est => Estimator.load(est));
+      } else {
+        this.replacement = options.replacement;
+        this.maxFeatures = options.maxFeatures;
+        this.nEstimators = options.nEstimators;
+        this.treeOptions = options.treeOptions;
+        this.isClassifier = options.isClassifier;
+        this.seed = options.seed;
+        this.useSampleBagging = options.useSampleBagging;
+      }
     }
     /**
-     * Returns the standard deviations of the principal components
-     * @returns {[number]}
+     * Train the decision tree with the given training set and labels.
+     * @param {Matrix|Array} trainingSet
+     * @param {Array} trainingValues
      */
 
 
-    getStandardDeviations() {
-      return this.S.map(x => Math.sqrt(x));
+    train(trainingSet, trainingValues) {
+      trainingSet = Matrix.checkMatrix(trainingSet);
+      this.maxFeatures = this.maxFeatures || trainingSet.columns;
+
+      if (checkFloat(this.maxFeatures)) {
+        this.n = Math.floor(trainingSet.columns * this.maxFeatures);
+      } else if (Number.isInteger(this.maxFeatures)) {
+        if (this.maxFeatures > trainingSet.columns) {
+          throw new RangeError(`The maxFeatures parameter should be less than ${trainingSet.columns}`);
+        } else {
+          this.n = this.maxFeatures;
+        }
+      } else {
+        throw new RangeError(`Cannot process the maxFeatures parameter ${this.maxFeatures}`);
+      }
+
+      let Estimator;
+
+      if (this.isClassifier) {
+        Estimator = DecisionTreeClassifier;
+      } else {
+        Estimator = DecisionTreeRegression;
+      }
+
+      this.estimators = new Array(this.nEstimators);
+      this.indexes = new Array(this.nEstimators);
+
+      for (let i = 0; i < this.nEstimators; ++i) {
+        let res = this.useSampleBagging ? examplesBaggingWithReplacement(trainingSet, trainingValues, this.seed) : {
+          X: trainingSet,
+          y: trainingValues
+        };
+        let X = res.X;
+        let y = res.y;
+        res = featureBagging(X, this.n, this.replacement, this.seed);
+        X = res.X;
+        this.indexes[i] = res.usedIndex;
+        this.estimators[i] = new Estimator(this.treeOptions);
+        this.estimators[i].train(X, y);
+      }
     }
     /**
-     * Returns the loadings matrix
-     * @return {Matrix}
+     * Method that returns the way the algorithm generates the predictions, for example, in classification
+     * you can return the mode of all predictions retrieved by the trees, or in case of regression you can
+     * use the mean or the median.
+     * @abstract
+     * @param {Array} values - predictions of the estimators.
+     * @return {number} prediction.
      */
+    // eslint-disable-next-line no-unused-vars
 
 
-    getLoadings() {
-      return this.U.transpose();
+    selection(values) {
+      throw new Error("Abstract method 'selection' not implemented!");
     }
     /**
-     * Export the current model to a JSON object
-     * @return {Object} model
+     * Predicts the output given the matrix to predict.
+     * @param {Matrix|Array} toPredict
+     * @return {Array} predictions
      */
 
 
-    toJSON() {
-      return {
-        name: 'PCA',
-        center: this.center,
-        scale: this.scale,
-        means: this.means,
-        stdevs: this.stdevs,
-        U: this.U,
-        S: this.S,
-        excludedFeatures: this.excludedFeatures
-      };
-    }
-
-    _adjust(dataset, ignoreZeroVariance) {
-      if (this.center) {
-        const mean = dataset.mean('column');
-        const stdevs = this.scale ? dataset.standardDeviation('column', {
-          mean
-        }) : null;
-        this.means = mean;
-        dataset.subRowVector(mean);
+    predict(toPredict) {
+      let predictionValues = new Array(this.nEstimators);
+      toPredict = Matrix.checkMatrix(toPredict);
 
-        if (this.scale) {
-          for (let i = 0; i < stdevs.length; i++) {
-            if (stdevs[i] === 0) {
-              if (ignoreZeroVariance) {
-                dataset.removeColumn(i);
-                stdevs.splice(i, 1);
-                this.excludedFeatures.push(i);
-                i--;
-              } else {
-                throw new RangeError("Cannot scale the dataset (standard deviation is zero at index ".concat(i));
-              }
-            }
-          }
+      for (let i = 0; i < this.nEstimators; ++i) {
+        let X = new MatrixColumnSelectionView(toPredict, this.indexes[i]); // get features for estimator
 
-          this.stdevs = stdevs;
-          dataset.divRowVector(stdevs);
-        }
+        predictionValues[i] = this.estimators[i].predict(X);
       }
-    }
-
-    _computeFromCovarianceMatrix(dataset) {
-      const evd = new EigenvalueDecomposition(dataset, {
-        assumeSymmetric: true
-      });
-      this.U = evd.eigenvectorMatrix;
-      this.U.flipRows();
-      this.S = evd.realEigenvalues;
-      this.S.reverse();
-    }
 
-    _computeWithNIPALS(dataset, nCompNIPALS) {
-      this.U = new Matrix(nCompNIPALS, dataset.columns);
-      this.S = [];
-      let x = dataset;
+      predictionValues = new MatrixTransposeView(new WrapperMatrix2D(predictionValues));
+      let predictions = new Array(predictionValues.rows);
 
-      for (let i = 0; i < nCompNIPALS; i++) {
-        let dc = new nipals(x);
-        this.U.setRow(i, dc.w.transpose());
-        this.S.push(Math.pow(dc.s.get(0, 0), 2));
-        x = dc.xResidual;
+      for (let i = 0; i < predictionValues.rows; ++i) {
+        predictions[i] = this.selection(predictionValues.getRow(i));
       }
 
-      this.U = this.U.transpose(); // to be compatible with API
+      return predictions;
     }
+    /**
+     * Export the current model to JSON.
+     * @return {object} - Current model.
+     */
 
-  }
-
-  function squaredEuclidean(p, q) {
-    let d = 0;
 
-    for (let i = 0; i < p.length; i++) {
-      d += (p[i] - q[i]) * (p[i] - q[i]);
+    toJSON() {
+      return {
+        indexes: this.indexes,
+        n: this.n,
+        replacement: this.replacement,
+        maxFeatures: this.maxFeatures,
+        nEstimators: this.nEstimators,
+        treeOptions: this.treeOptions,
+        isClassifier: this.isClassifier,
+        seed: this.seed,
+        estimators: this.estimators.map(est => est.toJSON()),
+        useSampleBagging: this.useSampleBagging
+      };
     }
 
-    return d;
-  }
-  function euclidean(p, q) {
-    return Math.sqrt(squaredEuclidean(p, q));
   }
 
-  var euclidean$1 = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    squaredEuclidean: squaredEuclidean,
-    euclidean: euclidean
-  });
-
+  const defaultOptions$2 = {
+    maxFeatures: 1.0,
+    replacement: true,
+    nEstimators: 10,
+    seed: 42,
+    useSampleBagging: false
+  };
   /**
-   * Computes a distance/similarity matrix given an array of data and a distance/similarity function.
-   * @param {Array} data An array of data
-   * @param {function} distanceFn  A function that accepts two arguments and computes a distance/similarity between them
-   * @return {Array<Array>} The distance/similarity matrix. The matrix is square and has a size equal to the length of
-   * the data array
+   * @class RandomForestClassifier
+   * @augments RandomForestBase
    */
-  function distanceMatrix(data, distanceFn) {
-    const result = getMatrix(data.length); // Compute upper distance matrix
 
-    for (let i = 0; i < data.length; i++) {
-      for (let j = 0; j <= i; j++) {
-        result[i][j] = distanceFn(data[i], data[j]);
-        result[j][i] = result[i][j];
+  class RandomForestClassifier extends RandomForestBase {
+    /**
+     * Create a new base random forest for a classifier or regression model.
+     * @constructor
+     * @param {object} options
+     * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.
+     *        * if is an integer it selects maxFeatures elements over the sample features.
+     *        * if is a float between (0, 1), it takes the percentage of features.
+     * @param {boolean} [options.replacement=true] - use replacement over the sample features.
+     * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.
+     * @param {number} [options.nEstimators=10] - number of estimator to use.
+     * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}
+     * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.
+     * @param {object} model - for load purposes.
+     */
+    constructor(options, model) {
+      if (options === true) {
+        super(true, model.baseModel);
+      } else {
+        options = Object.assign({}, defaultOptions$2, options);
+        options.isClassifier = true;
+        super(options);
       }
     }
+    /**
+     * retrieve the prediction given the selection method.
+     * @param {Array} values - predictions of the estimators.
+     * @return {number} prediction
+     */
 
-    return result;
-  }
 
-  function getMatrix(size) {
-    const matrix = [];
+    selection(values) {
+      return mode(values);
+    }
+    /**
+     * Export the current model to JSON.
+     * @return {object} - Current model.
+     */
 
-    for (let i = 0; i < size; i++) {
-      const row = [];
-      matrix.push(row);
 
-      for (let j = 0; j < size; j++) {
-        row.push(0);
-      }
+    toJSON() {
+      let baseModel = super.toJSON();
+      return {
+        baseModel: baseModel,
+        name: 'RFClassifier'
+      };
     }
+    /**
+     * Load a Decision tree classifier with the given model.
+     * @param {object} model
+     * @return {RandomForestClassifier}
+     */
 
-    return matrix;
-  }
 
-  var heap = createCommonjsModule(function (module, exports) {
-    // Generated by CoffeeScript 1.8.0
-    (function () {
-      var Heap, defaultCmp, floor, heapify, heappop, heappush, heappushpop, heapreplace, insort, min, nlargest, nsmallest, updateItem, _siftdown, _siftup;
+    static load(model) {
+      if (model.name !== 'RFClassifier') {
+        throw new RangeError(`Invalid model: ${model.name}`);
+      }
 
-      floor = Math.floor, min = Math.min;
-      /*
-      Default comparison function to be used
-       */
+      return new RandomForestClassifier(true, model);
+    }
 
-      defaultCmp = function defaultCmp(x, y) {
-        if (x < y) {
-          return -1;
-        }
+  }
+  /**
+   * Return the most repeated element on the array.
+   * @param {Array} arr
+   * @return {number} mode
+   */
 
-        if (x > y) {
-          return 1;
-        }
+  function mode(arr) {
+    return arr.sort((a, b) => arr.filter(v => v === a).length - arr.filter(v => v === b).length).pop();
+  }
 
-        return 0;
-      };
-      /*
-      Insert item x in list a, and keep it sorted assuming a is sorted.
-      
-      If x is already in a, insert it to the right of the rightmost x.
-      
-      Optional args lo (default 0) and hi (default a.length) bound the slice
-      of a to be searched.
-       */
+  const toString$2 = Object.prototype.toString;
+  function isAnyArray$2(object) {
+    return toString$2.call(object).endsWith('Array]');
+  }
 
+  var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};
 
-      insort = function insort(a, x, lo, hi, cmp) {
-        var mid;
+  function createCommonjsModule(fn, basedir, module) {
+  	return module = {
+  		path: basedir,
+  		exports: {},
+  		require: function (path, base) {
+  			return commonjsRequire(path, (base === undefined || base === null) ? module.path : base);
+  		}
+  	}, fn(module, module.exports), module.exports;
+  }
 
-        if (lo == null) {
-          lo = 0;
-        }
+  function getAugmentedNamespace(n) {
+  	if (n.__esModule) return n;
+  	var a = Object.defineProperty({}, '__esModule', {value: true});
+  	Object.keys(n).forEach(function (k) {
+  		var d = Object.getOwnPropertyDescriptor(n, k);
+  		Object.defineProperty(a, k, d.get ? d : {
+  			enumerable: true,
+  			get: function () {
+  				return n[k];
+  			}
+  		});
+  	});
+  	return a;
+  }
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+  function commonjsRequire () {
+  	throw new Error('Dynamic requires are not currently supported by @rollup/plugin-commonjs');
+  }
 
-        if (lo < 0) {
-          throw new Error('lo must be non-negative');
-        }
+  var medianQuickselect_min = createCommonjsModule(function (module) {
+    (function () {
+      function a(d) {
+        for (var e = 0, f = d.length - 1, g = void 0, h = void 0, i = void 0, j = c(e, f); !0;) {
+          if (f <= e) return d[j];
+          if (f == e + 1) return d[e] > d[f] && b(d, e, f), d[j];
 
-        if (hi == null) {
-          hi = a.length;
-        }
+          for (g = c(e, f), d[g] > d[f] && b(d, g, f), d[e] > d[f] && b(d, e, f), d[g] > d[e] && b(d, g, e), b(d, g, e + 1), h = e + 1, i = f; !0;) {
+            do h++; while (d[e] > d[h]);
 
-        while (lo < hi) {
-          mid = floor((lo + hi) / 2);
+            do i--; while (d[i] > d[e]);
 
-          if (cmp(x, a[mid]) < 0) {
-            hi = mid;
-          } else {
-            lo = mid + 1;
+            if (i < h) break;
+            b(d, h, i);
           }
+
+          b(d, e, i), i <= j && (e = h), i >= j && (f = i - 1);
         }
+      }
 
-        return [].splice.apply(a, [lo, lo - lo].concat(x)), x;
+      var b = function b(d, e, f) {
+        var _ref;
+
+        return _ref = [d[f], d[e]], d[e] = _ref[0], d[f] = _ref[1], _ref;
+      },
+          c = function c(d, e) {
+        return ~~((d + e) / 2);
       };
-      /*
-      Push item onto heap, maintaining the heap invariant.
-       */
 
+       module.exports ? module.exports = a : window.median = a;
+    })();
+  });
 
-      heappush = function heappush(array, item, cmp) {
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+  function median(input) {
+    if (!isAnyArray$2(input)) {
+      throw new TypeError('input must be an array');
+    }
 
-        array.push(item);
-        return _siftdown(array, 0, array.length - 1, cmp);
-      };
-      /*
-      Pop the smallest item off the heap, maintaining the heap invariant.
-       */
+    if (input.length === 0) {
+      throw new TypeError('input must not be empty');
+    }
 
+    return medianQuickselect_min(input.slice());
+  }
 
-      heappop = function heappop(array, cmp) {
-        var lastelt, returnitem;
+  const selectionMethods = {
+    mean: mean,
+    median: median
+  };
+  const defaultOptions$3 = {
+    maxFeatures: 1.0,
+    replacement: false,
+    nEstimators: 10,
+    treeOptions: {},
+    selectionMethod: 'mean',
+    seed: 42,
+    useSampleBagging: false
+  };
+  /**
+   * @class RandomForestRegression
+   * @augments RandomForestBase
+   */
 
-        if (cmp == null) {
-          cmp = defaultCmp;
+  class RandomForestRegression extends RandomForestBase {
+    /**
+     * Create a new base random forest for a classifier or regression model.
+     * @constructor
+     * @param {object} options
+     * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.
+     *        * if is an integer it selects maxFeatures elements over the sample features.
+     *        * if is a float between (0, 1), it takes the percentage of features.
+     * @param {boolean} [options.replacement=true] - use replacement over the sample features.
+     * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.
+     * @param {number} [options.nEstimators=10] - number of estimator to use.
+     * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}
+     * @param {string} [options.selectionMethod="mean"] - the way to calculate the prediction from estimators, "mean" and "median" are supported.
+     * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.
+     * @param {object} model - for load purposes.
+     */
+    constructor(options, model) {
+      if (options === true) {
+        super(true, model.baseModel);
+        this.selectionMethod = model.selectionMethod;
+      } else {
+        options = Object.assign({}, defaultOptions$3, options);
+
+        if (!(options.selectionMethod === 'mean' || options.selectionMethod === 'median')) {
+          throw new RangeError(`Unsupported selection method ${options.selectionMethod}`);
         }
 
-        lastelt = array.pop();
+        options.isClassifier = false;
+        super(options);
+        this.selectionMethod = options.selectionMethod;
+      }
+    }
+    /**
+     * retrieve the prediction given the selection method.
+     * @param {Array} values - predictions of the estimators.
+     * @return {number} prediction
+     */
 
-        if (array.length) {
-          returnitem = array[0];
-          array[0] = lastelt;
 
-          _siftup(array, 0, cmp);
-        } else {
-          returnitem = lastelt;
-        }
+    selection(values) {
+      return selectionMethods[this.selectionMethod](values);
+    }
+    /**
+     * Export the current model to JSON.
+     * @return {object} - Current model.
+     */
 
-        return returnitem;
+
+    toJSON() {
+      let baseModel = super.toJSON();
+      return {
+        baseModel: baseModel,
+        selectionMethod: this.selectionMethod,
+        name: 'RFRegression'
       };
-      /*
-      Pop and return the current smallest value, and add the new item.
-      
-      This is more efficient than heappop() followed by heappush(), and can be
-      more appropriate when using a fixed size heap. Note that the value
-      returned may be larger than item! That constrains reasonable use of
-      this routine unless written as part of a conditional replacement:
-          if item > array[0]
-            item = heapreplace(array, item)
-       */
+    }
+    /**
+     * Load a Decision tree classifier with the given model.
+     * @param {object} model
+     * @return {RandomForestRegression}
+     */
 
 
-      heapreplace = function heapreplace(array, item, cmp) {
-        var returnitem;
+    static load(model) {
+      if (model.name !== 'RFRegression') {
+        throw new RangeError(`Invalid model: ${model.name}`);
+      }
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+      return new RandomForestRegression(true, model);
+    }
 
-        returnitem = array[0];
-        array[0] = item;
+  }
 
-        _siftup(array, 0, cmp);
+  /**
+   * Creates new PCA (Principal Component Analysis) from the dataset
+   * @param {Matrix} dataset - dataset or covariance matrix.
+   * @param {Object} [options]
+   * @param {boolean} [options.isCovarianceMatrix=false] - true if the dataset is a covariance matrix.
+   * @param {string} [options.method='SVD'] - select which method to use: SVD (default), covarianceMatrirx or NIPALS.
+   * @param {number} [options.nCompNIPALS=2] - number of components to be computed with NIPALS.
+   * @param {boolean} [options.center=true] - should the data be centered (subtract the mean).
+   * @param {boolean} [options.scale=false] - should the data be scaled (divide by the standard deviation).
+   * @param {boolean} [options.ignoreZeroVariance=false] - ignore columns with zero variance if `scale` is `true`.
+   * */
 
-        return returnitem;
-      };
-      /*
-      Fast version of a heappush followed by a heappop.
-       */
+  class PCA {
+    constructor(dataset, options = {}) {
+      if (dataset === true) {
+        const model = options;
+        this.center = model.center;
+        this.scale = model.scale;
+        this.means = model.means;
+        this.stdevs = model.stdevs;
+        this.U = Matrix.checkMatrix(model.U);
+        this.S = model.S;
+        this.R = model.R;
+        this.excludedFeatures = model.excludedFeatures || [];
+        return;
+      }
 
+      dataset = new Matrix(dataset);
+      const {
+        isCovarianceMatrix = false,
+        method = 'SVD',
+        nCompNIPALS = 2,
+        center = true,
+        scale = false,
+        ignoreZeroVariance = false
+      } = options;
+      this.center = center;
+      this.scale = scale;
+      this.means = null;
+      this.stdevs = null;
+      this.excludedFeatures = [];
 
-      heappushpop = function heappushpop(array, item, cmp) {
-        var _ref;
+      if (isCovarianceMatrix) {
+        // User provided a covariance matrix instead of dataset.
+        this._computeFromCovarianceMatrix(dataset);
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+        return;
+      }
 
-        if (array.length && cmp(array[0], item) < 0) {
-          _ref = [array[0], item], item = _ref[0], array[0] = _ref[1];
+      this._adjust(dataset, ignoreZeroVariance);
 
-          _siftup(array, 0, cmp);
-        }
+      switch (method) {
+        case 'covarianceMatrix':
+          {
+            // User provided a dataset but wants us to compute and use the covariance matrix.
+            const covarianceMatrix = new MatrixTransposeView(dataset).mmul(dataset).div(dataset.rows - 1);
 
-        return item;
-      };
-      /*
-      Transform list into a heap, in-place, in O(array.length) time.
-       */
+            this._computeFromCovarianceMatrix(covarianceMatrix);
 
+            break;
+          }
 
-      heapify = function heapify(array, cmp) {
-        var i, _i, _len, _ref1, _results, _results1;
+        case 'NIPALS':
+          {
+            this._computeWithNIPALS(dataset, nCompNIPALS);
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+            break;
+          }
 
-        _ref1 = function () {
-          _results1 = [];
+        case 'SVD':
+          {
+            const svd = new SingularValueDecomposition(dataset, {
+              computeLeftSingularVectors: false,
+              computeRightSingularVectors: true,
+              autoTranspose: true
+            });
+            this.U = svd.rightSingularVectors;
+            const singularValues = svd.diagonal;
+            const eigenvalues = [];
 
-          for (var _j = 0, _ref = floor(array.length / 2); 0 <= _ref ? _j < _ref : _j > _ref; 0 <= _ref ? _j++ : _j--) {
-            _results1.push(_j);
+            for (const singularValue of singularValues) {
+              eigenvalues.push(singularValue * singularValue / (dataset.rows - 1));
+            }
+
+            this.S = eigenvalues;
+            break;
           }
 
-          return _results1;
-        }.apply(this).reverse();
+        default:
+          {
+            throw new Error(`unknown method: ${method}`);
+          }
+      }
+    }
+    /**
+     * Load a PCA model from JSON
+     * @param {Object} model
+     * @return {PCA}
+     */
 
-        _results = [];
 
-        for (_i = 0, _len = _ref1.length; _i < _len; _i++) {
-          i = _ref1[_i];
+    static load(model) {
+      if (typeof model.name !== 'string') {
+        throw new TypeError('model must have a name property');
+      }
 
-          _results.push(_siftup(array, i, cmp));
-        }
+      if (model.name !== 'PCA') {
+        throw new RangeError(`invalid model: ${model.name}`);
+      }
 
-        return _results;
-      };
-      /*
-      Update the position of the given item in the heap.
-      This function should be called every time the item is being modified.
-       */
+      return new PCA(true, model);
+    }
+    /**
+     * Project the dataset into the PCA space
+     * @param {Matrix} dataset
+     * @param {Object} options
+     * @return {Matrix} dataset projected in the PCA space
+     */
 
 
-      updateItem = function updateItem(array, item, cmp) {
-        var pos;
+    predict(dataset, options = {}) {
+      const {
+        nComponents = this.U.columns
+      } = options;
+      dataset = new Matrix(dataset);
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+      if (this.center) {
+        dataset.subRowVector(this.means);
 
-        pos = array.indexOf(item);
+        if (this.scale) {
+          for (let i of this.excludedFeatures) {
+            dataset.removeColumn(i);
+          }
 
-        if (pos === -1) {
-          return;
+          dataset.divRowVector(this.stdevs);
         }
+      }
 
-        _siftdown(array, 0, pos, cmp);
-
-        return _siftup(array, pos, cmp);
-      };
-      /*
-      Find the n largest elements in a dataset.
-       */
+      var predictions = dataset.mmul(this.U);
+      return predictions.subMatrix(0, predictions.rows - 1, 0, nComponents - 1);
+    }
+    /**
+     * Calculates the inverse PCA transform
+     * @param {Matrix} dataset
+     * @return {Matrix} dataset projected in the PCA space
+     */
 
 
-      nlargest = function nlargest(array, n, cmp) {
-        var elem, result, _i, _len, _ref;
+    invert(dataset) {
+      dataset = Matrix.checkMatrix(dataset);
+      var inverse = dataset.mmul(this.U.transpose());
 
-        if (cmp == null) {
-          cmp = defaultCmp;
+      if (this.center) {
+        if (this.scale) {
+          inverse.mulRowVector(this.stdevs);
         }
 
-        result = array.slice(0, n);
+        inverse.addRowVector(this.means);
+      }
 
-        if (!result.length) {
-          return result;
-        }
+      return inverse;
+    }
+    /**
+     * Returns the proportion of variance for each component
+     * @return {[number]}
+     */
 
-        heapify(result, cmp);
-        _ref = array.slice(n);
 
-        for (_i = 0, _len = _ref.length; _i < _len; _i++) {
-          elem = _ref[_i];
-          heappushpop(result, elem, cmp);
-        }
+    getExplainedVariance() {
+      var sum = 0;
 
-        return result.sort(cmp).reverse();
-      };
-      /*
-      Find the n smallest elements in a dataset.
-       */
+      for (const s of this.S) {
+        sum += s;
+      }
 
+      return this.S.map(value => value / sum);
+    }
+    /**
+     * Returns the cumulative proportion of variance
+     * @return {[number]}
+     */
 
-      nsmallest = function nsmallest(array, n, cmp) {
-        var elem, i, los, result, _i, _j, _len, _ref, _ref1, _results;
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+    getCumulativeVariance() {
+      var explained = this.getExplainedVariance();
 
-        if (n * 10 <= array.length) {
-          result = array.slice(0, n).sort(cmp);
+      for (var i = 1; i < explained.length; i++) {
+        explained[i] += explained[i - 1];
+      }
 
-          if (!result.length) {
-            return result;
-          }
+      return explained;
+    }
+    /**
+     * Returns the Eigenvectors of the covariance matrix
+     * @returns {Matrix}
+     */
 
-          los = result[result.length - 1];
-          _ref = array.slice(n);
 
-          for (_i = 0, _len = _ref.length; _i < _len; _i++) {
-            elem = _ref[_i];
+    getEigenvectors() {
+      return this.U;
+    }
+    /**
+     * Returns the Eigenvalues (on the diagonal)
+     * @returns {[number]}
+     */
 
-            if (cmp(elem, los) < 0) {
-              insort(result, elem, 0, null, cmp);
-              result.pop();
-              los = result[result.length - 1];
-            }
-          }
 
-          return result;
-        }
+    getEigenvalues() {
+      return this.S;
+    }
+    /**
+     * Returns the standard deviations of the principal components
+     * @returns {[number]}
+     */
 
-        heapify(array, cmp);
-        _results = [];
 
-        for (i = _j = 0, _ref1 = min(n, array.length); 0 <= _ref1 ? _j < _ref1 : _j > _ref1; i = 0 <= _ref1 ? ++_j : --_j) {
-          _results.push(heappop(array, cmp));
-        }
+    getStandardDeviations() {
+      return this.S.map(x => Math.sqrt(x));
+    }
+    /**
+     * Returns the loadings matrix
+     * @return {Matrix}
+     */
 
-        return _results;
-      };
 
-      _siftdown = function _siftdown(array, startpos, pos, cmp) {
-        var newitem, parent, parentpos;
+    getLoadings() {
+      return this.U.transpose();
+    }
+    /**
+     * Export the current model to a JSON object
+     * @return {Object} model
+     */
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
 
-        newitem = array[pos];
+    toJSON() {
+      return {
+        name: 'PCA',
+        center: this.center,
+        scale: this.scale,
+        means: this.means,
+        stdevs: this.stdevs,
+        U: this.U,
+        S: this.S,
+        excludedFeatures: this.excludedFeatures
+      };
+    }
 
-        while (pos > startpos) {
-          parentpos = pos - 1 >> 1;
-          parent = array[parentpos];
+    _adjust(dataset, ignoreZeroVariance) {
+      if (this.center) {
+        const mean = dataset.mean('column');
+        const stdevs = this.scale ? dataset.standardDeviation('column', {
+          mean
+        }) : null;
+        this.means = mean;
+        dataset.subRowVector(mean);
 
-          if (cmp(newitem, parent) < 0) {
-            array[pos] = parent;
-            pos = parentpos;
-            continue;
+        if (this.scale) {
+          for (let i = 0; i < stdevs.length; i++) {
+            if (stdevs[i] === 0) {
+              if (ignoreZeroVariance) {
+                dataset.removeColumn(i);
+                stdevs.splice(i, 1);
+                this.excludedFeatures.push(i);
+                i--;
+              } else {
+                throw new RangeError(`Cannot scale the dataset (standard deviation is zero at index ${i}`);
+              }
+            }
           }
 
-          break;
+          this.stdevs = stdevs;
+          dataset.divRowVector(stdevs);
         }
+      }
+    }
 
-        return array[pos] = newitem;
-      };
+    _computeFromCovarianceMatrix(dataset) {
+      const evd = new EigenvalueDecomposition(dataset, {
+        assumeSymmetric: true
+      });
+      this.U = evd.eigenvectorMatrix;
+      this.U.flipRows();
+      this.S = evd.realEigenvalues;
+      this.S.reverse();
+    }
 
-      _siftup = function _siftup(array, pos, cmp) {
-        var childpos, endpos, newitem, rightpos, startpos;
+    _computeWithNIPALS(dataset, nCompNIPALS) {
+      this.U = new Matrix(nCompNIPALS, dataset.columns);
+      this.S = [];
+      let x = dataset;
 
-        if (cmp == null) {
-          cmp = defaultCmp;
-        }
+      for (let i = 0; i < nCompNIPALS; i++) {
+        let dc = new nipals(x);
+        this.U.setRow(i, dc.w.transpose());
+        this.S.push(Math.pow(dc.s.get(0, 0), 2));
+        x = dc.xResidual;
+      }
 
-        endpos = array.length;
-        startpos = pos;
-        newitem = array[pos];
-        childpos = 2 * pos + 1;
+      this.U = this.U.transpose(); // to be compatible with API
+    }
 
-        while (childpos < endpos) {
-          rightpos = childpos + 1;
+  }
 
-          if (rightpos < endpos && !(cmp(array[childpos], array[rightpos]) < 0)) {
-            childpos = rightpos;
-          }
+  function squaredEuclidean(p, q) {
+    let d = 0;
 
-          array[pos] = array[childpos];
-          pos = childpos;
-          childpos = 2 * pos + 1;
-        }
+    for (let i = 0; i < p.length; i++) {
+      d += (p[i] - q[i]) * (p[i] - q[i]);
+    }
 
-        array[pos] = newitem;
-        return _siftdown(array, startpos, pos, cmp);
-      };
+    return d;
+  }
+  function euclidean(p, q) {
+    return Math.sqrt(squaredEuclidean(p, q));
+  }
 
-      Heap = function () {
-        Heap.push = heappush;
-        Heap.pop = heappop;
-        Heap.replace = heapreplace;
-        Heap.pushpop = heappushpop;
-        Heap.heapify = heapify;
-        Heap.updateItem = updateItem;
-        Heap.nlargest = nlargest;
-        Heap.nsmallest = nsmallest;
+  var euclidean$1 = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    squaredEuclidean: squaredEuclidean,
+    euclidean: euclidean
+  });
 
-        function Heap(cmp) {
-          this.cmp = cmp != null ? cmp : defaultCmp;
-          this.nodes = [];
-        }
+  /**
+   * Computes a distance/similarity matrix given an array of data and a distance/similarity function.
+   * @param {Array} data An array of data
+   * @param {function} distanceFn  A function that accepts two arguments and computes a distance/similarity between them
+   * @return {Array<Array>} The distance/similarity matrix. The matrix is square and has a size equal to the length of
+   * the data array
+   */
+  function distanceMatrix(data, distanceFn) {
+    const result = getMatrix(data.length); // Compute upper distance matrix
 
-        Heap.prototype.push = function (x) {
-          return heappush(this.nodes, x, this.cmp);
-        };
+    for (let i = 0; i < data.length; i++) {
+      for (let j = 0; j <= i; j++) {
+        result[i][j] = distanceFn(data[i], data[j]);
+        result[j][i] = result[i][j];
+      }
+    }
 
-        Heap.prototype.pop = function () {
-          return heappop(this.nodes, this.cmp);
-        };
+    return result;
+  }
 
-        Heap.prototype.peek = function () {
-          return this.nodes[0];
-        };
+  function getMatrix(size) {
+    const matrix = [];
 
-        Heap.prototype.contains = function (x) {
-          return this.nodes.indexOf(x) !== -1;
-        };
+    for (let i = 0; i < size; i++) {
+      const row = [];
+      matrix.push(row);
 
-        Heap.prototype.replace = function (x) {
-          return heapreplace(this.nodes, x, this.cmp);
-        };
+      for (let j = 0; j < size; j++) {
+        row.push(0);
+      }
+    }
 
-        Heap.prototype.pushpop = function (x) {
-          return heappushpop(this.nodes, x, this.cmp);
-        };
+    return matrix;
+  }
 
-        Heap.prototype.heapify = function () {
-          return heapify(this.nodes, this.cmp);
-        };
+  var heap = createCommonjsModule(function (module, exports) {
+    // Generated by CoffeeScript 1.8.0
+    (function () {
+      var Heap, defaultCmp, floor, heapify, heappop, heappush, heappushpop, heapreplace, insort, min, nlargest, nsmallest, updateItem, _siftdown, _siftup;
 
-        Heap.prototype.updateItem = function (x) {
-          return updateItem(this.nodes, x, this.cmp);
-        };
+      floor = Math.floor, min = Math.min;
+      /*
+      Default comparison function to be used
+       */
 
-        Heap.prototype.clear = function () {
-          return this.nodes = [];
-        };
+      defaultCmp = function (x, y) {
+        if (x < y) {
+          return -1;
+        }
 
-        Heap.prototype.empty = function () {
-          return this.nodes.length === 0;
-        };
+        if (x > y) {
+          return 1;
+        }
 
-        Heap.prototype.size = function () {
-          return this.nodes.length;
-        };
+        return 0;
+      };
+      /*
+      Insert item x in list a, and keep it sorted assuming a is sorted.
+      
+      If x is already in a, insert it to the right of the rightmost x.
+      
+      Optional args lo (default 0) and hi (default a.length) bound the slice
+      of a to be searched.
+       */
 
-        Heap.prototype.clone = function () {
-          var heap;
-          heap = new Heap();
-          heap.nodes = this.nodes.slice(0);
-          return heap;
-        };
 
-        Heap.prototype.toArray = function () {
-          return this.nodes.slice(0);
-        };
+      insort = function (a, x, lo, hi, cmp) {
+        var mid;
 
-        Heap.prototype.insert = Heap.prototype.push;
-        Heap.prototype.top = Heap.prototype.peek;
-        Heap.prototype.front = Heap.prototype.peek;
-        Heap.prototype.has = Heap.prototype.contains;
-        Heap.prototype.copy = Heap.prototype.clone;
-        return Heap;
-      }();
+        if (lo == null) {
+          lo = 0;
+        }
 
-      (function (root, factory) {
-        {
-          return module.exports = factory();
+        if (cmp == null) {
+          cmp = defaultCmp;
         }
-      })(this, function () {
-        return Heap;
-      });
-    }).call(commonjsGlobal);
-  });
 
-  var heap$1 = heap;
+        if (lo < 0) {
+          throw new Error('lo must be non-negative');
+        }
 
-  class Cluster {
-    constructor() {
-      this.children = [];
-      this.height = 0;
-      this.size = 1;
-      this.index = -1;
-      this.isLeaf = false;
-    }
-    /**
-     * Creates an array of clusters where the maximum height is smaller than the threshold
-     * @param {number} threshold
-     * @return {Array<Cluster>}
-     */
+        if (hi == null) {
+          hi = a.length;
+        }
 
+        while (lo < hi) {
+          mid = floor((lo + hi) / 2);
 
-    cut(threshold) {
-      if (typeof threshold !== 'number') {
-        throw new TypeError('threshold must be a number');
-      }
+          if (cmp(x, a[mid]) < 0) {
+            hi = mid;
+          } else {
+            lo = mid + 1;
+          }
+        }
 
-      if (threshold < 0) {
-        throw new RangeError('threshold must be a positive number');
-      }
+        return [].splice.apply(a, [lo, lo - lo].concat(x)), x;
+      };
+      /*
+      Push item onto heap, maintaining the heap invariant.
+       */
 
-      let list = [this];
-      const ans = [];
 
-      while (list.length > 0) {
-        const aux = list.shift();
+      heappush = function (array, item, cmp) {
+        if (cmp == null) {
+          cmp = defaultCmp;
+        }
 
-        if (threshold >= aux.height) {
-          ans.push(aux);
-        } else {
-          list = list.concat(aux.children);
+        array.push(item);
+        return _siftdown(array, 0, array.length - 1, cmp);
+      };
+      /*
+      Pop the smallest item off the heap, maintaining the heap invariant.
+       */
+
+
+      heappop = function (array, cmp) {
+        var lastelt, returnitem;
+
+        if (cmp == null) {
+          cmp = defaultCmp;
         }
-      }
 
-      return ans;
-    }
-    /**
-     * Merge the leaves in the minimum way to have `groups` number of clusters.
-     * @param {number} groups - Them number of children the first level of the tree should have.
-     * @return {Cluster}
-     */
+        lastelt = array.pop();
 
+        if (array.length) {
+          returnitem = array[0];
+          array[0] = lastelt;
 
-    group(groups) {
-      if (!Number.isInteger(groups) || groups < 1) {
-        throw new RangeError('groups must be a positive integer');
-      }
+          _siftup(array, 0, cmp);
+        } else {
+          returnitem = lastelt;
+        }
 
-      const heap = new heap$1((a, b) => {
-        return b.height - a.height;
-      });
-      heap.push(this);
+        return returnitem;
+      };
+      /*
+      Pop and return the current smallest value, and add the new item.
+      
+      This is more efficient than heappop() followed by heappush(), and can be
+      more appropriate when using a fixed size heap. Note that the value
+      returned may be larger than item! That constrains reasonable use of
+      this routine unless written as part of a conditional replacement:
+          if item > array[0]
+            item = heapreplace(array, item)
+       */
 
-      while (heap.size() < groups) {
-        var first = heap.pop();
 
-        if (first.children.length === 0) {
-          break;
+      heapreplace = function (array, item, cmp) {
+        var returnitem;
+
+        if (cmp == null) {
+          cmp = defaultCmp;
         }
 
-        first.children.forEach(child => heap.push(child));
-      }
+        returnitem = array[0];
+        array[0] = item;
 
-      var root = new Cluster();
-      root.children = heap.toArray();
-      root.height = this.height;
-      return root;
-    }
-    /**
-     * Traverses the tree depth-first and calls the provided callback with each individual node
-     * @param {function} cb - The callback to be called on each node encounter
-     */
+        _siftup(array, 0, cmp);
 
+        return returnitem;
+      };
+      /*
+      Fast version of a heappush followed by a heappop.
+       */
 
-    traverse(cb) {
-      function visit(root, callback) {
-        callback(root);
 
-        if (root.children) {
-          for (const child of root.children) {
-            visit(child, callback);
-          }
+      heappushpop = function (array, item, cmp) {
+        var _ref;
+
+        if (cmp == null) {
+          cmp = defaultCmp;
         }
-      }
 
-      visit(this, cb);
-    }
-    /**
-     * Returns a list of indices for all the leaves of this cluster.
-     * The list is ordered in such a way that a dendrogram could be drawn without crossing branches.
-     * @returns {Array<number>}
-     */
+        if (array.length && cmp(array[0], item) < 0) {
+          _ref = [array[0], item], item = _ref[0], array[0] = _ref[1];
 
+          _siftup(array, 0, cmp);
+        }
 
-    indices() {
-      const result = [];
-      this.traverse(cluster => {
-        if (cluster.isLeaf) {
-          result.push(cluster.index);
+        return item;
+      };
+      /*
+      Transform list into a heap, in-place, in O(array.length) time.
+       */
+
+
+      heapify = function (array, cmp) {
+        var i, _i, _len, _ref1, _results, _results1;
+
+        if (cmp == null) {
+          cmp = defaultCmp;
         }
-      });
-      return result;
-    }
 
-  }
+        _ref1 = function () {
+          _results1 = [];
 
-  function singleLink(dKI, dKJ) {
-    return Math.min(dKI, dKJ);
-  }
+          for (var _j = 0, _ref = floor(array.length / 2); 0 <= _ref ? _j < _ref : _j > _ref; 0 <= _ref ? _j++ : _j--) {
+            _results1.push(_j);
+          }
 
-  function completeLink(dKI, dKJ) {
-    return Math.max(dKI, dKJ);
-  }
+          return _results1;
+        }.apply(this).reverse();
 
-  function averageLink(dKI, dKJ, dIJ, ni, nj) {
-    const ai = ni / (ni + nj);
-    const aj = nj / (ni + nj);
-    return ai * dKI + aj * dKJ;
-  }
+        _results = [];
 
-  function weightedAverageLink(dKI, dKJ) {
-    return (dKI + dKJ) / 2;
-  }
+        for (_i = 0, _len = _ref1.length; _i < _len; _i++) {
+          i = _ref1[_i];
 
-  function centroidLink(dKI, dKJ, dIJ, ni, nj) {
-    const ai = ni / (ni + nj);
-    const aj = nj / (ni + nj);
-    const b = -(ni * nj) / (ni + nj) ** 2;
-    return ai * dKI + aj * dKJ + b * dIJ;
-  }
+          _results.push(_siftup(array, i, cmp));
+        }
 
-  function medianLink(dKI, dKJ, dIJ) {
-    return dKI / 2 + dKJ / 2 - dIJ / 4;
-  }
+        return _results;
+      };
+      /*
+      Update the position of the given item in the heap.
+      This function should be called every time the item is being modified.
+       */
 
-  function wardLink(dKI, dKJ, dIJ, ni, nj, nk) {
-    const ai = (ni + nk) / (ni + nj + nk);
-    const aj = (nj + nk) / (ni + nj + nk);
-    const b = -nk / (ni + nj + nk);
-    return ai * dKI + aj * dKJ + b * dIJ;
-  }
-
-  function wardLink2(dKI, dKJ, dIJ, ni, nj, nk) {
-    const ai = (ni + nk) / (ni + nj + nk);
-    const aj = (nj + nk) / (ni + nj + nk);
-    const b = -nk / (ni + nj + nk);
-    return Math.sqrt(ai * dKI * dKI + aj * dKJ * dKJ + b * dIJ * dIJ);
-  }
-  /**
-   * Continuously merge nodes that have the least dissimilarity
-   * @param {Array<Array<number>>} data - Array of points to be clustered
-   * @param {object} [options]
-   * @param {Function} [options.distanceFunction]
-   * @param {string} [options.method] - Default: `'complete'`
-   * @param {boolean} [options.isDistanceMatrix] - Is the input already a distance matrix?
-   * @constructor
-   */
 
+      updateItem = function (array, item, cmp) {
+        var pos;
 
-  function agnes(data) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-    const {
-      distanceFunction = euclidean,
-      method = 'complete',
-      isDistanceMatrix = false
-    } = options;
-    let updateFunc;
+        if (cmp == null) {
+          cmp = defaultCmp;
+        }
 
-    if (!isDistanceMatrix) {
-      data = distanceMatrix(data, distanceFunction);
-    }
+        pos = array.indexOf(item);
 
-    let distanceMatrix$1 = new Matrix(data);
-    const numLeaves = distanceMatrix$1.rows; // allows to use a string or a given function
+        if (pos === -1) {
+          return;
+        }
 
-    if (typeof method === 'string') {
-      switch (method.toLowerCase()) {
-        case 'single':
-          updateFunc = singleLink;
-          break;
+        _siftdown(array, 0, pos, cmp);
 
-        case 'complete':
-          updateFunc = completeLink;
-          break;
+        return _siftup(array, pos, cmp);
+      };
+      /*
+      Find the n largest elements in a dataset.
+       */
 
-        case 'average':
-        case 'upgma':
-          updateFunc = averageLink;
-          break;
 
-        case 'wpgma':
-          updateFunc = weightedAverageLink;
-          break;
+      nlargest = function (array, n, cmp) {
+        var elem, result, _i, _len, _ref;
 
-        case 'centroid':
-        case 'upgmc':
-          updateFunc = centroidLink;
-          break;
+        if (cmp == null) {
+          cmp = defaultCmp;
+        }
 
-        case 'median':
-        case 'wpgmc':
-          updateFunc = medianLink;
-          break;
+        result = array.slice(0, n);
 
-        case 'ward':
-          updateFunc = wardLink;
-          break;
+        if (!result.length) {
+          return result;
+        }
 
-        case 'ward2':
-          updateFunc = wardLink2;
-          break;
+        heapify(result, cmp);
+        _ref = array.slice(n);
 
-        default:
-          throw new RangeError("unknown clustering method: ".concat(method));
-      }
-    } else if (typeof method !== 'function') {
-      throw new TypeError('method must be a string or function');
-    }
+        for (_i = 0, _len = _ref.length; _i < _len; _i++) {
+          elem = _ref[_i];
+          heappushpop(result, elem, cmp);
+        }
 
-    let clusters = [];
+        return result.sort(cmp).reverse();
+      };
+      /*
+      Find the n smallest elements in a dataset.
+       */
 
-    for (let i = 0; i < numLeaves; i++) {
-      const cluster = new Cluster();
-      cluster.isLeaf = true;
-      cluster.index = i;
-      clusters.push(cluster);
-    }
 
-    for (let n = 0; n < numLeaves - 1; n++) {
-      const [row, column, distance] = getSmallestDistance(distanceMatrix$1);
-      const cluster1 = clusters[row];
-      const cluster2 = clusters[column];
-      const newCluster = new Cluster();
-      newCluster.size = cluster1.size + cluster2.size;
-      newCluster.children.push(cluster1, cluster2);
-      newCluster.height = distance;
-      const newClusters = [newCluster];
-      const newDistanceMatrix = new Matrix(distanceMatrix$1.rows - 1, distanceMatrix$1.rows - 1);
+      nsmallest = function (array, n, cmp) {
+        var elem, i, los, result, _i, _j, _len, _ref, _ref1, _results;
 
-      const previous = newIndex => getPreviousIndex(newIndex, Math.min(row, column), Math.max(row, column));
+        if (cmp == null) {
+          cmp = defaultCmp;
+        }
 
-      for (let i = 1; i < newDistanceMatrix.rows; i++) {
-        const prevI = previous(i);
-        const prevICluster = clusters[prevI];
-        newClusters.push(prevICluster);
+        if (n * 10 <= array.length) {
+          result = array.slice(0, n).sort(cmp);
 
-        for (let j = 0; j < i; j++) {
-          if (j === 0) {
-            const dKI = distanceMatrix$1.get(row, prevI);
-            const dKJ = distanceMatrix$1.get(prevI, column);
-            const val = updateFunc(dKI, dKJ, distance, cluster1.size, cluster2.size, prevICluster.size);
-            newDistanceMatrix.set(i, j, val);
-            newDistanceMatrix.set(j, i, val);
-          } else {
-            // Just copy distance from previous matrix
-            const val = distanceMatrix$1.get(prevI, previous(j));
-            newDistanceMatrix.set(i, j, val);
-            newDistanceMatrix.set(j, i, val);
+          if (!result.length) {
+            return result;
           }
-        }
-      }
 
-      clusters = newClusters;
-      distanceMatrix$1 = newDistanceMatrix;
-    }
+          los = result[result.length - 1];
+          _ref = array.slice(n);
 
-    return clusters[0];
-  }
+          for (_i = 0, _len = _ref.length; _i < _len; _i++) {
+            elem = _ref[_i];
 
-  function getSmallestDistance(distance) {
-    let smallest = Infinity;
-    let smallestI = 0;
-    let smallestJ = 0;
+            if (cmp(elem, los) < 0) {
+              insort(result, elem, 0, null, cmp);
+              result.pop();
+              los = result[result.length - 1];
+            }
+          }
 
-    for (let i = 1; i < distance.rows; i++) {
-      for (let j = 0; j < i; j++) {
-        if (distance.get(i, j) < smallest) {
-          smallest = distance.get(i, j);
-          smallestI = i;
-          smallestJ = j;
+          return result;
         }
-      }
-    }
 
-    return [smallestI, smallestJ, smallest];
-  }
+        heapify(array, cmp);
+        _results = [];
 
-  function getPreviousIndex(newIndex, prev1, prev2) {
-    newIndex -= 1;
-    if (newIndex >= prev1) newIndex++;
-    if (newIndex >= prev2) newIndex++;
-    return newIndex;
-  }
+        for (i = _j = 0, _ref1 = min(n, array.length); 0 <= _ref1 ? _j < _ref1 : _j > _ref1; i = 0 <= _ref1 ? ++_j : --_j) {
+          _results.push(heappop(array, cmp));
+        }
 
-  // export * from './birch';
-  // export * './cure';
-  // export * from './chameleon';
+        return _results;
+      };
 
-  var index = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    agnes: agnes
-  });
+      _siftdown = function (array, startpos, pos, cmp) {
+        var newitem, parent, parentpos;
 
-  const defaultOptions$4 = {
-    distanceFunction: squaredEuclidean
-  };
-  function nearestVector(listVectors, vector) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : defaultOptions$4;
-    const distanceFunction = options.distanceFunction || defaultOptions$4.distanceFunction;
-    const similarityFunction = options.similarityFunction || defaultOptions$4.similarityFunction;
-    let vectorIndex = -1;
+        if (cmp == null) {
+          cmp = defaultCmp;
+        }
 
-    if (typeof similarityFunction === 'function') {
-      // maximum similarity
-      let maxSim = Number.MIN_VALUE;
+        newitem = array[pos];
 
-      for (let j = 0; j < listVectors.length; j++) {
-        const sim = similarityFunction(vector, listVectors[j]);
+        while (pos > startpos) {
+          parentpos = pos - 1 >> 1;
+          parent = array[parentpos];
 
-        if (sim > maxSim) {
-          maxSim = sim;
-          vectorIndex = j;
+          if (cmp(newitem, parent) < 0) {
+            array[pos] = parent;
+            pos = parentpos;
+            continue;
+          }
+
+          break;
         }
-      }
-    } else if (typeof distanceFunction === 'function') {
-      // minimum distance
-      let minDist = Number.MAX_VALUE;
 
-      for (let i = 0; i < listVectors.length; i++) {
-        const dist = distanceFunction(vector, listVectors[i]);
+        return array[pos] = newitem;
+      };
 
-        if (dist < minDist) {
-          minDist = dist;
-          vectorIndex = i;
+      _siftup = function (array, pos, cmp) {
+        var childpos, endpos, newitem, rightpos, startpos;
+
+        if (cmp == null) {
+          cmp = defaultCmp;
         }
-      }
-    } else {
-      throw new Error("A similarity or distance function it's required");
-    }
 
-    return vectorIndex;
-  }
+        endpos = array.length;
+        startpos = pos;
+        newitem = array[pos];
+        childpos = 2 * pos + 1;
 
-  /**
-   * Calculates the distance matrix for a given array of points
-   * @ignore
-   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
-   * @param {function} distance - Distance function to use between the points
-   * @return {Array<Array<number>>} - matrix with the distance values
-   */
+        while (childpos < endpos) {
+          rightpos = childpos + 1;
 
-  function calculateDistanceMatrix(data, distance) {
-    var distanceMatrix = new Array(data.length);
+          if (rightpos < endpos && !(cmp(array[childpos], array[rightpos]) < 0)) {
+            childpos = rightpos;
+          }
 
-    for (var i = 0; i < data.length; ++i) {
-      for (var j = i; j < data.length; ++j) {
-        if (!distanceMatrix[i]) {
-          distanceMatrix[i] = new Array(data.length);
+          array[pos] = array[childpos];
+          pos = childpos;
+          childpos = 2 * pos + 1;
         }
 
-        if (!distanceMatrix[j]) {
-          distanceMatrix[j] = new Array(data.length);
-        }
+        array[pos] = newitem;
+        return _siftdown(array, startpos, pos, cmp);
+      };
 
-        const dist = distance(data[i], data[j]);
-        distanceMatrix[i][j] = dist;
-        distanceMatrix[j][i] = dist;
-      }
-    }
+      Heap = function () {
+        Heap.push = heappush;
+        Heap.pop = heappop;
+        Heap.replace = heapreplace;
+        Heap.pushpop = heappushpop;
+        Heap.heapify = heapify;
+        Heap.updateItem = updateItem;
+        Heap.nlargest = nlargest;
+        Heap.nsmallest = nsmallest;
 
-    return distanceMatrix;
-  }
-  /**
-   * Updates the cluster identifier based in the new data
-   * @ignore
-   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
-   * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]
-   * @param {Array <number>} clusterID - the cluster identifier for each data dot
-   * @param {function} distance - Distance function to use between the points
-   * @return {Array} the cluster identifier for each data dot
-   */
+        function Heap(cmp) {
+          this.cmp = cmp != null ? cmp : defaultCmp;
+          this.nodes = [];
+        }
 
-  function updateClusterID(data, centers, clusterID, distance) {
-    for (var i = 0; i < data.length; i++) {
-      clusterID[i] = nearestVector(centers, data[i], {
-        distanceFunction: distance
-      });
-    }
+        Heap.prototype.push = function (x) {
+          return heappush(this.nodes, x, this.cmp);
+        };
 
-    return clusterID;
-  }
-  /**
-   * Update the center values based in the new configurations of the clusters
-   * @ignore
-   * @param {Array<Array<number>>} prevCenters - Centroids from the previous iteration
-   * @param {Array <Array <number>>} data - the [x,y,z,...] points to cluster
-   * @param {Array <number>} clusterID - the cluster identifier for each data dot
-   * @param {number} K - Number of clusters
-   * @return {Array} he K centers in format [x,y,z,...]
-   */
+        Heap.prototype.pop = function () {
+          return heappop(this.nodes, this.cmp);
+        };
 
-  function updateCenters(prevCenters, data, clusterID, K) {
-    const nDim = data[0].length; // copy previous centers
+        Heap.prototype.peek = function () {
+          return this.nodes[0];
+        };
 
-    var centers = new Array(K);
-    var centersLen = new Array(K);
+        Heap.prototype.contains = function (x) {
+          return this.nodes.indexOf(x) !== -1;
+        };
 
-    for (var i = 0; i < K; i++) {
-      centers[i] = new Array(nDim);
-      centersLen[i] = 0;
+        Heap.prototype.replace = function (x) {
+          return heapreplace(this.nodes, x, this.cmp);
+        };
 
-      for (var j = 0; j < nDim; j++) {
-        centers[i][j] = 0;
-      }
-    } // add the value for all dimensions of the point
+        Heap.prototype.pushpop = function (x) {
+          return heappushpop(this.nodes, x, this.cmp);
+        };
 
+        Heap.prototype.heapify = function () {
+          return heapify(this.nodes, this.cmp);
+        };
 
-    for (var l = 0; l < data.length; l++) {
-      centersLen[clusterID[l]]++;
+        Heap.prototype.updateItem = function (x) {
+          return updateItem(this.nodes, x, this.cmp);
+        };
 
-      for (var dim = 0; dim < nDim; dim++) {
-        centers[clusterID[l]][dim] += data[l][dim];
-      }
-    } // divides by length
+        Heap.prototype.clear = function () {
+          return this.nodes = [];
+        };
 
+        Heap.prototype.empty = function () {
+          return this.nodes.length === 0;
+        };
 
-    for (var id = 0; id < K; id++) {
-      for (var d = 0; d < nDim; d++) {
-        if (centersLen[id]) {
-          centers[id][d] /= centersLen[id];
-        } else {
-          centers[id][d] = prevCenters[id][d];
-        }
-      }
-    }
+        Heap.prototype.size = function () {
+          return this.nodes.length;
+        };
 
-    return centers;
-  }
-  /**
-   * The centers have moved more than the tolerance value?
-   * @ignore
-   * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]
-   * @param {Array<Array<number>>} oldCenters - the K old centers in format [x,y,z,...]
-   * @param {function} distanceFunction - Distance function to use between the points
-   * @param {number} tolerance - Allowed distance for the centroids to move
-   * @return {boolean}
-   */
+        Heap.prototype.clone = function () {
+          var heap;
+          heap = new Heap();
+          heap.nodes = this.nodes.slice(0);
+          return heap;
+        };
 
-  function hasConverged(centers, oldCenters, distanceFunction, tolerance) {
-    for (var i = 0; i < centers.length; i++) {
-      if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {
-        return false;
-      }
-    }
+        Heap.prototype.toArray = function () {
+          return this.nodes.slice(0);
+        };
 
-    return true;
-  }
+        Heap.prototype.insert = Heap.prototype.push;
+        Heap.prototype.top = Heap.prototype.peek;
+        Heap.prototype.front = Heap.prototype.peek;
+        Heap.prototype.has = Heap.prototype.contains;
+        Heap.prototype.copy = Heap.prototype.clone;
+        return Heap;
+      }();
 
-  const LOOP = 8;
-  const FLOAT_MUL = 1 / 16777216;
-  const sh1 = 15;
-  const sh2 = 18;
-  const sh3 = 11;
+      (function (root, factory) {
+        {
+          return module.exports = factory();
+        }
+      })(this, function () {
+        return Heap;
+      });
+    }).call(commonjsGlobal);
+  });
 
-  function multiply_uint32(n, m) {
-    n >>>= 0;
-    m >>>= 0;
-    const nlo = n & 0xffff;
-    const nhi = n - nlo;
-    return (nhi * m >>> 0) + nlo * m >>> 0;
-  }
+  var heap$1 = heap;
 
-  class XSadd {
+  class Cluster {
     constructor() {
-      let seed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : Date.now();
-      this.state = new Uint32Array(4);
-      this.init(seed);
-      this.random = this.getFloat.bind(this);
-    }
-    /**
-     * Returns a 32-bit integer r (0 <= r < 2^32)
-     */
-
-
-    getUint32() {
-      this.nextState();
-      return this.state[3] + this.state[2] >>> 0;
+      this.children = [];
+      this.height = 0;
+      this.size = 1;
+      this.index = -1;
+      this.isLeaf = false;
     }
     /**
-     * Returns a floating point number r (0.0 <= r < 1.0)
+     * Creates an array of clusters where the maximum height is smaller than the threshold
+     * @param {number} threshold
+     * @return {Array<Cluster>}
      */
 
 
-    getFloat() {
-      return (this.getUint32() >>> 8) * FLOAT_MUL;
-    }
-
-    init(seed) {
-      if (!Number.isInteger(seed)) {
-        throw new TypeError('seed must be an integer');
+    cut(threshold) {
+      if (typeof threshold !== 'number') {
+        throw new TypeError('threshold must be a number');
       }
 
-      this.state[0] = seed;
-      this.state[1] = 0;
-      this.state[2] = 0;
-      this.state[3] = 0;
-
-      for (let i = 1; i < LOOP; i++) {
-        this.state[i & 3] ^= i + multiply_uint32(1812433253, this.state[i - 1 & 3] ^ this.state[i - 1 & 3] >>> 30 >>> 0) >>> 0;
+      if (threshold < 0) {
+        throw new RangeError('threshold must be a positive number');
       }
 
-      this.periodCertification();
+      let list = [this];
+      const ans = [];
 
-      for (let i = 0; i < LOOP; i++) {
-        this.nextState();
+      while (list.length > 0) {
+        const aux = list.shift();
+
+        if (threshold >= aux.height) {
+          ans.push(aux);
+        } else {
+          list = list.concat(aux.children);
+        }
       }
+
+      return ans;
     }
+    /**
+     * Merge the leaves in the minimum way to have `groups` number of clusters.
+     * @param {number} groups - Them number of children the first level of the tree should have.
+     * @return {Cluster}
+     */
 
-    periodCertification() {
-      if (this.state[0] === 0 && this.state[1] === 0 && this.state[2] === 0 && this.state[3] === 0) {
-        this.state[0] = 88; // X
 
-        this.state[1] = 83; // S
+    group(groups) {
+      if (!Number.isInteger(groups) || groups < 1) {
+        throw new RangeError('groups must be a positive integer');
+      }
 
-        this.state[2] = 65; // A
+      const heap = new heap$1((a, b) => {
+        return b.height - a.height;
+      });
+      heap.push(this);
 
-        this.state[3] = 68; // D
-      }
-    }
+      while (heap.size() < groups) {
+        var first = heap.pop();
 
-    nextState() {
-      let t = this.state[0];
-      t ^= t << sh1;
-      t ^= t >>> sh2;
-      t ^= this.state[3] << sh3;
-      this.state[0] = this.state[1];
-      this.state[1] = this.state[2];
-      this.state[2] = this.state[3];
-      this.state[3] = t;
-    }
-
-  }
-
-  const PROB_TOLERANCE = 0.00000001;
+        if (first.children.length === 0) {
+          break;
+        }
 
-  function randomChoice(values) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-    let random = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : Math.random;
-    const {
-      size = 1,
-      replace = false,
-      probabilities
-    } = options;
-    let valuesArr;
-    let cumSum;
+        first.children.forEach(child => heap.push(child));
+      }
 
-    if (typeof values === 'number') {
-      valuesArr = getArray(values);
-    } else {
-      valuesArr = values.slice();
+      var root = new Cluster();
+      root.children = heap.toArray();
+      root.height = this.height;
+      return root;
     }
+    /**
+     * Traverses the tree depth-first and calls the provided callback with each individual node
+     * @param {function} cb - The callback to be called on each node encounter
+     */
 
-    if (probabilities) {
-      if (!replace) {
-        throw new Error('choice with probabilities and no replacement is not implemented');
-      } // check input is sane
-
-
-      if (probabilities.length !== valuesArr.length) {
-        throw new Error('the length of probabilities option should be equal to the number of choices');
-      }
-
-      cumSum = [probabilities[0]];
 
-      for (let i = 1; i < probabilities.length; i++) {
-        cumSum[i] = cumSum[i - 1] + probabilities[i];
-      }
+    traverse(cb) {
+      function visit(root, callback) {
+        callback(root);
 
-      if (Math.abs(1 - cumSum[cumSum.length - 1]) > PROB_TOLERANCE) {
-        throw new Error("probabilities should sum to 1, but instead sums to ".concat(cumSum[cumSum.length - 1]));
+        if (root.children) {
+          for (const child of root.children) {
+            visit(child, callback);
+          }
+        }
       }
-    }
 
-    if (replace === false && size > valuesArr.length) {
-      throw new Error('size option is too large');
+      visit(this, cb);
     }
+    /**
+     * Returns a list of indices for all the leaves of this cluster.
+     * The list is ordered in such a way that a dendrogram could be drawn without crossing branches.
+     * @returns {Array<number>}
+     */
 
-    const result = [];
-
-    for (let i = 0; i < size; i++) {
-      const index = randomIndex(valuesArr.length, random, cumSum);
-      result.push(valuesArr[index]);
 
-      if (!replace) {
-        valuesArr.splice(index, 1);
-      }
+    indices() {
+      const result = [];
+      this.traverse(cluster => {
+        if (cluster.isLeaf) {
+          result.push(cluster.index);
+        }
+      });
+      return result;
     }
 
-    return result;
   }
 
-  function getArray(n) {
-    const arr = [];
+  function singleLink(dKI, dKJ) {
+    return Math.min(dKI, dKJ);
+  }
 
-    for (let i = 0; i < n; i++) {
-      arr.push(i);
-    }
+  function completeLink(dKI, dKJ) {
+    return Math.max(dKI, dKJ);
+  }
 
-    return arr;
+  function averageLink(dKI, dKJ, dIJ, ni, nj) {
+    const ai = ni / (ni + nj);
+    const aj = nj / (ni + nj);
+    return ai * dKI + aj * dKJ;
   }
 
-  function randomIndex(n, random, cumSum) {
-    const rand = random();
+  function weightedAverageLink(dKI, dKJ) {
+    return (dKI + dKJ) / 2;
+  }
 
-    if (!cumSum) {
-      return Math.floor(rand * n);
-    } else {
-      let idx = 0;
+  function centroidLink(dKI, dKJ, dIJ, ni, nj) {
+    const ai = ni / (ni + nj);
+    const aj = nj / (ni + nj);
+    const b = -(ni * nj) / (ni + nj) ** 2;
+    return ai * dKI + aj * dKJ + b * dIJ;
+  }
 
-      while (rand > cumSum[idx]) {
-        idx++;
-      }
+  function medianLink(dKI, dKJ, dIJ) {
+    return dKI / 2 + dKJ / 2 - dIJ / 4;
+  }
 
-      return idx;
-    }
+  function wardLink(dKI, dKJ, dIJ, ni, nj, nk) {
+    const ai = (ni + nk) / (ni + nj + nk);
+    const aj = (nj + nk) / (ni + nj + nk);
+    const b = -nk / (ni + nj + nk);
+    return ai * dKI + aj * dKJ + b * dIJ;
   }
 
-  // tslint:disable-next-line
+  function wardLink2(dKI, dKJ, dIJ, ni, nj, nk) {
+    const ai = (ni + nk) / (ni + nj + nk);
+    const aj = (nj + nk) / (ni + nj + nk);
+    const b = -nk / (ni + nj + nk);
+    return Math.sqrt(ai * dKI * dKI + aj * dKJ * dKJ + b * dIJ * dIJ);
+  }
   /**
-   * @classdesc Random class
+   * Continuously merge nodes that have the least dissimilarity
+   * @param {Array<Array<number>>} data - Array of points to be clustered
+   * @param {object} [options]
+   * @param {Function} [options.distanceFunction]
+   * @param {string} [options.method] - Default: `'complete'`
+   * @param {boolean} [options.isDistanceMatrix] - Is the input already a distance matrix?
+   * @constructor
    */
 
-  class Random {
-    /**
-     * @param [seedOrRandom=Math.random] - Control the random number generator used by the Random class instance. Pass a random number generator function with a uniform distribution over the half-open interval [0, 1[. If seed will pass it to ml-xsadd to create a seeded random number generator. If undefined will use Math.random.
-     */
-    constructor() {
-      let seedOrRandom = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : Math.random;
 
-      if (typeof seedOrRandom === 'number') {
-        const xsadd = new XSadd(seedOrRandom);
-        this.randomGenerator = xsadd.random;
-      } else {
-        this.randomGenerator = seedOrRandom;
-      }
+  function agnes(data, options = {}) {
+    const {
+      distanceFunction = euclidean,
+      method = 'complete',
+      isDistanceMatrix = false
+    } = options;
+    let updateFunc;
+
+    if (!isDistanceMatrix) {
+      data = distanceMatrix(data, distanceFunction);
     }
 
-    choice(values, options) {
-      if (typeof values === 'number') {
-        return randomChoice(values, options, this.randomGenerator);
-      }
+    let distanceMatrix$1 = new Matrix(data);
+    const numLeaves = distanceMatrix$1.rows; // allows to use a string or a given function
 
-      return randomChoice(values, options, this.randomGenerator);
-    }
-    /**
-     * Draw a random number from a uniform distribution on [0,1)
-     * @return The random number
-     */
+    if (typeof method === 'string') {
+      switch (method.toLowerCase()) {
+        case 'single':
+          updateFunc = singleLink;
+          break;
 
+        case 'complete':
+          updateFunc = completeLink;
+          break;
 
-    random() {
-      return this.randomGenerator();
-    }
-    /**
-     * Draw a random integer from a uniform distribution on [low, high). If only low is specified, the number is drawn on [0, low)
-     * @param low - The lower bound of the uniform distribution interval.
-     * @param high - The higher bound of the uniform distribution interval.
-     */
+        case 'average':
+        case 'upgma':
+          updateFunc = averageLink;
+          break;
 
+        case 'wpgma':
+          updateFunc = weightedAverageLink;
+          break;
 
-    randInt(low, high) {
-      if (high === undefined) {
-        high = low;
-        low = 0;
-      }
+        case 'centroid':
+        case 'upgmc':
+          updateFunc = centroidLink;
+          break;
 
-      return low + Math.floor(this.randomGenerator() * (high - low));
-    }
-    /**
-     * Draw several random number from a uniform distribution on [0, 1)
-     * @param size - The number of number to draw
-     * @return - The list of drawn numbers.
-     */
+        case 'median':
+        case 'wpgmc':
+          updateFunc = medianLink;
+          break;
 
+        case 'ward':
+          updateFunc = wardLink;
+          break;
 
-    randomSample(size) {
-      const result = [];
+        case 'ward2':
+          updateFunc = wardLink2;
+          break;
 
-      for (let i = 0; i < size; i++) {
-        result.push(this.random());
+        default:
+          throw new RangeError(`unknown clustering method: ${method}`);
       }
-
-      return result;
+    } else if (typeof method !== 'function') {
+      throw new TypeError('method must be a string or function');
     }
 
-  }
+    let clusters = [];
 
-  /**
-   * Choose K different random points from the original data
-   * @ignore
-   * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]
-   * @param {number} K - number of clusters
-   * @param {number} seed - seed for random number generation
-   * @return {Array<Array<number>>} - Initial random points
-   */
+    for (let i = 0; i < numLeaves; i++) {
+      const cluster = new Cluster();
+      cluster.isLeaf = true;
+      cluster.index = i;
+      clusters.push(cluster);
+    }
 
-  function random(data, K, seed) {
-    const random = new Random(seed);
-    return random.choice(data, {
-      size: K
-    });
-  }
-  /**
-   * Chooses the most distant points to a first random pick
-   * @ignore
-   * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]
-   * @param {number} K - number of clusters
-   * @param {Array<Array<number>>} distanceMatrix - matrix with the distance values
-   * @param {number} seed - seed for random number generation
-   * @return {Array<Array<number>>} - Initial random points
-   */
-
-  function mostDistant(data, K, distanceMatrix, seed) {
-    const random = new Random(seed);
-    var ans = new Array(K); // chooses a random point as initial cluster
+    for (let n = 0; n < numLeaves - 1; n++) {
+      const [row, column, distance] = getSmallestDistance(distanceMatrix$1);
+      const cluster1 = clusters[row];
+      const cluster2 = clusters[column];
+      const newCluster = new Cluster();
+      newCluster.size = cluster1.size + cluster2.size;
+      newCluster.children.push(cluster1, cluster2);
+      newCluster.height = distance;
+      const newClusters = [newCluster];
+      const newDistanceMatrix = new Matrix(distanceMatrix$1.rows - 1, distanceMatrix$1.rows - 1);
 
-    ans[0] = Math.floor(random.random() * data.length);
+      const previous = newIndex => getPreviousIndex(newIndex, Math.min(row, column), Math.max(row, column));
 
-    if (K > 1) {
-      // chooses the more distant point
-      var maxDist = {
-        dist: -1,
-        index: -1
-      };
+      for (let i = 1; i < newDistanceMatrix.rows; i++) {
+        const prevI = previous(i);
+        const prevICluster = clusters[prevI];
+        newClusters.push(prevICluster);
 
-      for (var l = 0; l < data.length; ++l) {
-        if (distanceMatrix[ans[0]][l] > maxDist.dist) {
-          maxDist.dist = distanceMatrix[ans[0]][l];
-          maxDist.index = l;
+        for (let j = 0; j < i; j++) {
+          if (j === 0) {
+            const dKI = distanceMatrix$1.get(row, prevI);
+            const dKJ = distanceMatrix$1.get(prevI, column);
+            const val = updateFunc(dKI, dKJ, distance, cluster1.size, cluster2.size, prevICluster.size);
+            newDistanceMatrix.set(i, j, val);
+            newDistanceMatrix.set(j, i, val);
+          } else {
+            // Just copy distance from previous matrix
+            const val = distanceMatrix$1.get(prevI, previous(j));
+            newDistanceMatrix.set(i, j, val);
+            newDistanceMatrix.set(j, i, val);
+          }
         }
       }
 
-      ans[1] = maxDist.index;
-
-      if (K > 2) {
-        // chooses the set of points that maximises the min distance
-        for (var k = 2; k < K; ++k) {
-          var center = {
-            dist: -1,
-            index: -1
-          };
-
-          for (var m = 0; m < data.length; ++m) {
-            // minimum distance to centers
-            var minDistCent = {
-              dist: Number.MAX_VALUE,
-              index: -1
-            };
+      clusters = newClusters;
+      distanceMatrix$1 = newDistanceMatrix;
+    }
 
-            for (var n = 0; n < k; ++n) {
-              if (distanceMatrix[n][m] < minDistCent.dist && ans.indexOf(m) === -1) {
-                minDistCent = {
-                  dist: distanceMatrix[n][m],
-                  index: m
-                };
-              }
-            }
+    return clusters[0];
+  }
 
-            if (minDistCent.dist !== Number.MAX_VALUE && minDistCent.dist > center.dist) {
-              center = Object.assign({}, minDistCent);
-            }
-          }
+  function getSmallestDistance(distance) {
+    let smallest = Infinity;
+    let smallestI = 0;
+    let smallestJ = 0;
 
-          ans[k] = center.index;
+    for (let i = 1; i < distance.rows; i++) {
+      for (let j = 0; j < i; j++) {
+        if (distance.get(i, j) < smallest) {
+          smallest = distance.get(i, j);
+          smallestI = i;
+          smallestJ = j;
         }
       }
     }
 
-    return ans.map(index => data[index]);
-  } // Implementation inspired from scikit
+    return [smallestI, smallestJ, smallest];
+  }
 
-  function kmeanspp(X, K) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-    X = new Matrix(X);
-    const nSamples = X.rows;
-    const random = new Random(options.seed); // Set the number of trials
+  function getPreviousIndex(newIndex, prev1, prev2) {
+    newIndex -= 1;
+    if (newIndex >= prev1) newIndex++;
+    if (newIndex >= prev2) newIndex++;
+    return newIndex;
+  }
 
-    const centers = [];
-    const localTrials = options.localTrials || 2 + Math.floor(Math.log(K)); // Pick the first center at random from the dataset
+  // export * from './birch';
+  // export * './cure';
+  // export * from './chameleon';
 
-    const firstCenterIdx = random.randInt(nSamples);
-    centers.push(X.getRow(firstCenterIdx)); // Init closest distances
+  var index = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    agnes: agnes
+  });
 
-    let closestDistSquared = new Matrix(1, X.rows);
+  const defaultOptions$4 = {
+    distanceFunction: squaredEuclidean
+  };
+  function nearestVector(listVectors, vector, options = defaultOptions$4) {
+    const distanceFunction = options.distanceFunction || defaultOptions$4.distanceFunction;
+    const similarityFunction = options.similarityFunction || defaultOptions$4.similarityFunction;
+    let vectorIndex = -1;
 
-    for (let i = 0; i < X.rows; i++) {
-      closestDistSquared.set(0, i, squaredEuclidean(X.getRow(i), centers[0]));
-    }
+    if (typeof similarityFunction === 'function') {
+      // maximum similarity
+      let maxSim = Number.MIN_VALUE;
 
-    let cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];
-    const factor = 1 / cumSumClosestDistSquared[0][nSamples - 1];
-    let probabilities = Matrix.mul(closestDistSquared, factor); // Iterate over the remaining centers
+      for (let j = 0; j < listVectors.length; j++) {
+        const sim = similarityFunction(vector, listVectors[j]);
 
-    for (let i = 1; i < K; i++) {
-      const candidateIdx = random.choice(nSamples, {
-        replace: true,
-        size: localTrials,
-        probabilities: probabilities[0]
-      });
-      const candidates = X.selection(candidateIdx, range(X.columns));
-      const distanceToCandidates = euclideanDistances(candidates, X);
-      let bestCandidate;
-      let bestPot;
-      let bestDistSquared;
+        if (sim > maxSim) {
+          maxSim = sim;
+          vectorIndex = j;
+        }
+      }
+    } else if (typeof distanceFunction === 'function') {
+      // minimum distance
+      let minDist = Number.MAX_VALUE;
 
-      for (let j = 0; j < localTrials; j++) {
-        const newDistSquared = Matrix.min(closestDistSquared, [distanceToCandidates.getRow(j)]);
-        const newPot = newDistSquared.sum();
+      for (let i = 0; i < listVectors.length; i++) {
+        const dist = distanceFunction(vector, listVectors[i]);
 
-        if (bestCandidate === undefined || newPot < bestPot) {
-          bestCandidate = candidateIdx[j];
-          bestPot = newPot;
-          bestDistSquared = newDistSquared;
+        if (dist < minDist) {
+          minDist = dist;
+          vectorIndex = i;
         }
       }
-
-      centers[i] = X.getRow(bestCandidate);
-      closestDistSquared = bestDistSquared;
-      cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];
-      probabilities = Matrix.mul(closestDistSquared, 1 / cumSumClosestDistSquared[0][nSamples - 1]);
+    } else {
+      throw new Error("A similarity or distance function it's required");
     }
 
-    return centers;
+    return vectorIndex;
   }
 
-  function euclideanDistances(A, B) {
-    const result = new Matrix(A.rows, B.rows);
+  /**
+   * Calculates the distance matrix for a given array of points
+   * @ignore
+   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
+   * @param {function} distance - Distance function to use between the points
+   * @return {Array<Array<number>>} - matrix with the distance values
+   */
 
-    for (let i = 0; i < A.rows; i++) {
-      for (let j = 0; j < B.rows; j++) {
-        result.set(i, j, squaredEuclidean(A.getRow(i), B.getRow(j)));
+  function calculateDistanceMatrix(data, distance) {
+    var distanceMatrix = new Array(data.length);
+
+    for (var i = 0; i < data.length; ++i) {
+      for (var j = i; j < data.length; ++j) {
+        if (!distanceMatrix[i]) {
+          distanceMatrix[i] = new Array(data.length);
+        }
+
+        if (!distanceMatrix[j]) {
+          distanceMatrix[j] = new Array(data.length);
+        }
+
+        const dist = distance(data[i], data[j]);
+        distanceMatrix[i][j] = dist;
+        distanceMatrix[j][i] = dist;
       }
     }
 
-    return result;
+    return distanceMatrix;
   }
+  /**
+   * Updates the cluster identifier based in the new data
+   * @ignore
+   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
+   * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]
+   * @param {Array <number>} clusterID - the cluster identifier for each data dot
+   * @param {function} distance - Distance function to use between the points
+   * @return {Array} the cluster identifier for each data dot
+   */
 
-  function range(l) {
-    let r = [];
-
-    for (let i = 0; i < l; i++) {
-      r.push(i);
+  function updateClusterID(data, centers, clusterID, distance) {
+    for (var i = 0; i < data.length; i++) {
+      clusterID[i] = nearestVector(centers, data[i], {
+        distanceFunction: distance
+      });
     }
 
-    return r;
+    return clusterID;
   }
+  /**
+   * Update the center values based in the new configurations of the clusters
+   * @ignore
+   * @param {Array<Array<number>>} prevCenters - Centroids from the previous iteration
+   * @param {Array <Array <number>>} data - the [x,y,z,...] points to cluster
+   * @param {Array <number>} clusterID - the cluster identifier for each data dot
+   * @param {number} K - Number of clusters
+   * @return {Array} he K centers in format [x,y,z,...]
+   */
 
-  function cumSum(arr) {
-    let cumSum = [arr[0]];
+  function updateCenters(prevCenters, data, clusterID, K) {
+    const nDim = data[0].length; // copy previous centers
 
-    for (let i = 1; i < arr.length; i++) {
-      cumSum[i] = cumSum[i - 1] + arr[i];
-    }
+    var centers = new Array(K);
+    var centersLen = new Array(K);
 
-    return cumSum;
-  }
+    for (var i = 0; i < K; i++) {
+      centers[i] = new Array(nDim);
+      centersLen[i] = 0;
 
-  const distanceSymbol = Symbol('distance');
-  class KMeansResult {
-    /**
-     * Result of the kmeans algorithm
-     * @param {Array<number>} clusters - the cluster identifier for each data dot
-     * @param {Array<Array<object>>} centroids - the K centers in format [x,y,z,...], the error and size of the cluster
-     * @param {boolean} converged - Converge criteria satisfied
-     * @param {number} iterations - Current number of iterations
-     * @param {function} distance - (*Private*) Distance function to use between the points
-     * @constructor
-     */
-    constructor(clusters, centroids, converged, iterations, distance) {
-      this.clusters = clusters;
-      this.centroids = centroids;
-      this.converged = converged;
-      this.iterations = iterations;
-      this[distanceSymbol] = distance;
-    }
-    /**
-     * Allows to compute for a new array of points their cluster id
-     * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
-     * @return {Array<number>} - cluster id for each point
-     */
-
-
-    nearest(data) {
-      const clusterID = new Array(data.length);
-      const centroids = this.centroids.map(function (centroid) {
-        return centroid.centroid;
-      });
-      return updateClusterID(data, centroids, clusterID, this[distanceSymbol]);
-    }
-    /**
-     * Returns a KMeansResult with the error and size of the cluster
-     * @ignore
-     * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
-     * @return {KMeansResult}
-     */
+      for (var j = 0; j < nDim; j++) {
+        centers[i][j] = 0;
+      }
+    } // add the value for all dimensions of the point
 
 
-    computeInformation(data) {
-      var enrichedCentroids = this.centroids.map(function (centroid) {
-        return {
-          centroid: centroid,
-          error: 0,
-          size: 0
-        };
-      });
+    for (var l = 0; l < data.length; l++) {
+      centersLen[clusterID[l]]++;
 
-      for (var i = 0; i < data.length; i++) {
-        enrichedCentroids[this.clusters[i]].error += this[distanceSymbol](data[i], this.centroids[this.clusters[i]]);
-        enrichedCentroids[this.clusters[i]].size++;
+      for (var dim = 0; dim < nDim; dim++) {
+        centers[clusterID[l]][dim] += data[l][dim];
       }
+    } // divides by length
 
-      for (var j = 0; j < this.centroids.length; j++) {
-        if (enrichedCentroids[j].size) {
-          enrichedCentroids[j].error /= enrichedCentroids[j].size;
+
+    for (var id = 0; id < K; id++) {
+      for (var d = 0; d < nDim; d++) {
+        if (centersLen[id]) {
+          centers[id][d] /= centersLen[id];
         } else {
-          enrichedCentroids[j].error = null;
+          centers[id][d] = prevCenters[id][d];
         }
       }
-
-      return new KMeansResult(this.clusters, enrichedCentroids, this.converged, this.iterations, this[distanceSymbol]);
     }
 
+    return centers;
   }
-
-  const defaultOptions$5 = {
-    maxIterations: 100,
-    tolerance: 1e-6,
-    withIterations: false,
-    initialization: 'kmeans++',
-    distanceFunction: squaredEuclidean
-  };
   /**
-   * Each step operation for kmeans
+   * The centers have moved more than the tolerance value?
    * @ignore
-   * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]
-   * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster
-   * @param {Array<number>} clusterID - Cluster identifier for each data dot
-   * @param {number} K - Number of clusters
-   * @param {object} [options] - Option object
-   * @param {number} iterations - Current number of iterations
-   * @return {KMeansResult}
+   * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]
+   * @param {Array<Array<number>>} oldCenters - the K old centers in format [x,y,z,...]
+   * @param {function} distanceFunction - Distance function to use between the points
+   * @param {number} tolerance - Allowed distance for the centroids to move
+   * @return {boolean}
    */
 
-  function step(centers, data, clusterID, K, options, iterations) {
-    clusterID = updateClusterID(data, centers, clusterID, options.distanceFunction);
-    var newCenters = updateCenters(centers, data, clusterID, K);
-    var converged = hasConverged(newCenters, centers, options.distanceFunction, options.tolerance);
-    return new KMeansResult(clusterID, newCenters, converged, iterations, options.distanceFunction);
-  }
-  /**
-   * Generator version for the algorithm
-   * @ignore
-   * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]
-   * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster
-   * @param {Array<number>} clusterID - Cluster identifier for each data dot
-   * @param {number} K - Number of clusters
-   * @param {object} [options] - Option object
-   */
+  function hasConverged(centers, oldCenters, distanceFunction, tolerance) {
+    for (var i = 0; i < centers.length; i++) {
+      if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {
+        return false;
+      }
+    }
 
+    return true;
+  }
 
-  function* kmeansGenerator(centers, data, clusterID, K, options) {
-    var converged = false;
-    var stepNumber = 0;
-    var stepResult;
+  const LOOP = 8;
+  const FLOAT_MUL = 1 / 16777216;
+  const sh1 = 15;
+  const sh2 = 18;
+  const sh3 = 11;
 
-    while (!converged && stepNumber < options.maxIterations) {
-      stepResult = step(centers, data, clusterID, K, options, ++stepNumber);
-      yield stepResult.computeInformation(data);
-      converged = stepResult.converged;
-      centers = stepResult.centroids;
-    }
+  function multiply_uint32(n, m) {
+    n >>>= 0;
+    m >>>= 0;
+    const nlo = n & 0xffff;
+    const nhi = n - nlo;
+    return (nhi * m >>> 0) + nlo * m >>> 0;
   }
-  /**
-   * K-means algorithm
-   * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]
-   * @param {number} K - Number of clusters
-   * @param {object} [options] - Option object
-   * @param {number} [options.maxIterations = 100] - Maximum of iterations allowed
-   * @param {number} [options.tolerance = 1e-6] - Error tolerance
-   * @param {boolean} [options.withIterations = false] - Store clusters and centroids for each iteration
-   * @param {function} [options.distanceFunction = squaredDistance] - Distance function to use between the points
-   * @param {number} [options.seed] - Seed for random initialization.
-   * @param {string|Array<Array<number>>} [options.initialization = 'kmeans++'] - K centers in format [x,y,z,...] or a method for initialize the data:
-   *  * You can either specify your custom start centroids, or select one of the following initialization method:
-   *  * `'kmeans++'` will use the kmeans++ method as described by http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
-   *  * `'random'` will choose K random different values.
-   *  * `'mostDistant'` will choose the more distant points to a first random pick
-   * @return {KMeansResult} - Cluster identifier for each data dot and centroids with the following fields:
-   *  * `'clusters'`: Array of indexes for the clusters.
-   *  * `'centroids'`: Array with the resulting centroids.
-   *  * `'iterations'`: Number of iterations that took to converge
-   */
 
+  class XSadd {
+    constructor(seed = Date.now()) {
+      this.state = new Uint32Array(4);
+      this.init(seed);
+      this.random = this.getFloat.bind(this);
+    }
+    /**
+     * Returns a 32-bit integer r (0 <= r < 2^32)
+     */
 
-  function kmeans(data, K, options) {
-    options = Object.assign({}, defaultOptions$5, options);
 
-    if (K <= 0 || K > data.length || !Number.isInteger(K)) {
-      throw new Error('K should be a positive integer smaller than the number of points');
+    getUint32() {
+      this.nextState();
+      return this.state[3] + this.state[2] >>> 0;
     }
+    /**
+     * Returns a floating point number r (0.0 <= r < 1.0)
+     */
 
-    var centers;
 
-    if (Array.isArray(options.initialization)) {
-      if (options.initialization.length !== K) {
-        throw new Error('The initial centers should have the same length as K');
-      } else {
-        centers = options.initialization;
-      }
-    } else {
-      switch (options.initialization) {
-        case 'kmeans++':
-          centers = kmeanspp(data, K, options);
-          break;
+    getFloat() {
+      return (this.getUint32() >>> 8) * FLOAT_MUL;
+    }
 
-        case 'random':
-          centers = random(data, K, options.seed);
-          break;
+    init(seed) {
+      if (!Number.isInteger(seed)) {
+        throw new TypeError('seed must be an integer');
+      }
 
-        case 'mostDistant':
-          centers = mostDistant(data, K, calculateDistanceMatrix(data, options.distanceFunction), options.seed);
-          break;
+      this.state[0] = seed;
+      this.state[1] = 0;
+      this.state[2] = 0;
+      this.state[3] = 0;
 
-        default:
-          throw new Error("Unknown initialization method: \"".concat(options.initialization, "\""));
+      for (let i = 1; i < LOOP; i++) {
+        this.state[i & 3] ^= i + multiply_uint32(1812433253, this.state[i - 1 & 3] ^ this.state[i - 1 & 3] >>> 30 >>> 0) >>> 0;
       }
-    } // infinite loop until convergence
 
+      this.periodCertification();
 
-    if (options.maxIterations === 0) {
-      options.maxIterations = Number.MAX_VALUE;
+      for (let i = 0; i < LOOP; i++) {
+        this.nextState();
+      }
     }
 
-    var clusterID = new Array(data.length);
+    periodCertification() {
+      if (this.state[0] === 0 && this.state[1] === 0 && this.state[2] === 0 && this.state[3] === 0) {
+        this.state[0] = 88; // X
 
-    if (options.withIterations) {
-      return kmeansGenerator(centers, data, clusterID, K, options);
-    } else {
-      var converged = false;
-      var stepNumber = 0;
-      var stepResult;
+        this.state[1] = 83; // S
 
-      while (!converged && stepNumber < options.maxIterations) {
-        stepResult = step(centers, data, clusterID, K, options, ++stepNumber);
-        converged = stepResult.converged;
-        centers = stepResult.centroids;
+        this.state[2] = 65; // A
+
+        this.state[3] = 68; // D
       }
+    }
 
-      return stepResult.computeInformation(data);
+    nextState() {
+      let t = this.state[0];
+      t ^= t << sh1;
+      t ^= t >>> sh2;
+      t ^= this.state[3] << sh3;
+      this.state[0] = this.state[1];
+      this.state[1] = this.state[2];
+      this.state[2] = this.state[3];
+      this.state[3] = t;
     }
-  }
 
-  /**
-   * @private
-   * Function that retuns an array of matrices of the cases that belong to each class.
-   * @param {Matrix} X - dataset
-   * @param {Array} y - predictions
-   * @return {Array}
-   */
+  }
 
-  function separateClasses(X, y) {
-    var features = X.columns;
-    var classes = 0;
-    var totalPerClasses = new Array(10000); // max upperbound of classes
+  const PROB_TOLERANCE = 0.00000001;
 
-    for (var i = 0; i < y.length; i++) {
-      if (totalPerClasses[y[i]] === undefined) {
-        totalPerClasses[y[i]] = 0;
-        classes++;
-      }
+  function randomChoice(values, options = {}, random = Math.random) {
+    const {
+      size = 1,
+      replace = false,
+      probabilities
+    } = options;
+    let valuesArr;
+    let cumSum;
 
-      totalPerClasses[y[i]]++;
+    if (typeof values === 'number') {
+      valuesArr = getArray(values);
+    } else {
+      valuesArr = values.slice();
     }
 
-    var separatedClasses = new Array(classes);
-    var currentIndex = new Array(classes);
+    if (probabilities) {
+      if (!replace) {
+        throw new Error('choice with probabilities and no replacement is not implemented');
+      } // check input is sane
 
-    for (i = 0; i < classes; ++i) {
-      separatedClasses[i] = new Matrix(totalPerClasses[i], features);
-      currentIndex[i] = 0;
-    }
 
-    for (i = 0; i < X.rows; ++i) {
-      separatedClasses[y[i]].setRow(currentIndex[y[i]], X.getRow(i));
-      currentIndex[y[i]]++;
-    }
+      if (probabilities.length !== valuesArr.length) {
+        throw new Error('the length of probabilities option should be equal to the number of choices');
+      }
 
-    return separatedClasses;
-  }
+      cumSum = [probabilities[0]];
 
-  class GaussianNB {
-    /**
-     * Constructor for the Gaussian Naive Bayes classifier, the parameters here is just for loading purposes.
-     * @constructor
-     * @param {boolean} reload
-     * @param {object} model
-     */
-    constructor(reload, model) {
-      if (reload) {
-        this.means = model.means;
-        this.calculateProbabilities = model.calculateProbabilities;
+      for (let i = 1; i < probabilities.length; i++) {
+        cumSum[i] = cumSum[i - 1] + probabilities[i];
+      }
+
+      if (Math.abs(1 - cumSum[cumSum.length - 1]) > PROB_TOLERANCE) {
+        throw new Error(`probabilities should sum to 1, but instead sums to ${cumSum[cumSum.length - 1]}`);
       }
     }
-    /**
-     * Function that trains the classifier with a matrix that represents the training set and an array that
-     * represents the label of each row in the training set. the labels must be numbers between 0 to n-1 where
-     * n represents the number of classes.
-     *
-     * WARNING: in the case that one class, all the cases in one or more features have the same value, the
-     * Naive Bayes classifier will not work well.
-     * @param {Matrix|Array} trainingSet
-     * @param {Matrix|Array} trainingLabels
-     */
 
+    if (replace === false && size > valuesArr.length) {
+      throw new Error('size option is too large');
+    }
 
-    train(trainingSet, trainingLabels) {
-      var C1 = Math.sqrt(2 * Math.PI); // constant to precalculate the squared root
+    const result = [];
 
-      trainingSet = Matrix.checkMatrix(trainingSet);
+    for (let i = 0; i < size; i++) {
+      const index = randomIndex(valuesArr.length, random, cumSum);
+      result.push(valuesArr[index]);
 
-      if (trainingSet.rows !== trainingLabels.length) {
-        throw new RangeError('the size of the training set and the training labels must be the same.');
+      if (!replace) {
+        valuesArr.splice(index, 1);
       }
+    }
 
-      var separatedClasses = separateClasses(trainingSet, trainingLabels);
-      var calculateProbabilities = new Array(separatedClasses.length);
-      this.means = new Array(separatedClasses.length);
+    return result;
+  }
 
-      for (var i = 0; i < separatedClasses.length; ++i) {
-        var means = separatedClasses[i].mean('column');
-        var std = separatedClasses[i].standardDeviation('column', {
-          mean: means
-        });
-        var logPriorProbability = Math.log(separatedClasses[i].rows / trainingSet.rows);
-        calculateProbabilities[i] = new Array(means.length + 1);
-        calculateProbabilities[i][0] = logPriorProbability;
+  function getArray(n) {
+    const arr = [];
 
-        for (var j = 1; j < means.length + 1; ++j) {
-          var currentStd = std[j - 1];
-          calculateProbabilities[i][j] = [1 / (C1 * currentStd), -2 * currentStd * currentStd];
-        }
+    for (let i = 0; i < n; i++) {
+      arr.push(i);
+    }
 
-        this.means[i] = means;
+    return arr;
+  }
+
+  function randomIndex(n, random, cumSum) {
+    const rand = random();
+
+    if (!cumSum) {
+      return Math.floor(rand * n);
+    } else {
+      let idx = 0;
+
+      while (rand > cumSum[idx]) {
+        idx++;
       }
 
-      this.calculateProbabilities = calculateProbabilities;
+      return idx;
     }
-    /**
-     * function that predicts each row of the dataset (must be a matrix).
-     *
-     * @param {Matrix|Array} dataset
-     * @return {Array}
-     */
-
+  }
 
-    predict(dataset) {
-      dataset = Matrix.checkMatrix(dataset);
+  // tslint:disable-next-line
+  /**
+   * @classdesc Random class
+   */
 
-      if (dataset.rows === this.calculateProbabilities[0].length) {
-        throw new RangeError('the dataset must have the same features as the training set');
+  class Random {
+    /**
+     * @param [seedOrRandom=Math.random] - Control the random number generator used by the Random class instance. Pass a random number generator function with a uniform distribution over the half-open interval [0, 1[. If seed will pass it to ml-xsadd to create a seeded random number generator. If undefined will use Math.random.
+     */
+    constructor(seedOrRandom = Math.random) {
+      if (typeof seedOrRandom === 'number') {
+        const xsadd = new XSadd(seedOrRandom);
+        this.randomGenerator = xsadd.random;
+      } else {
+        this.randomGenerator = seedOrRandom;
       }
+    }
 
-      var predictions = new Array(dataset.rows);
-
-      for (var i = 0; i < predictions.length; ++i) {
-        predictions[i] = getCurrentClass(dataset.getRow(i), this.means, this.calculateProbabilities);
+    choice(values, options) {
+      if (typeof values === 'number') {
+        return randomChoice(values, options, this.randomGenerator);
       }
 
-      return predictions;
+      return randomChoice(values, options, this.randomGenerator);
     }
     /**
-     * Function that export the NaiveBayes model.
-     * @return {object}
+     * Draw a random number from a uniform distribution on [0,1)
+     * @return The random number
      */
 
 
-    toJSON() {
-      return {
-        modelName: 'NaiveBayes',
-        means: this.means,
-        calculateProbabilities: this.calculateProbabilities
-      };
+    random() {
+      return this.randomGenerator();
     }
     /**
-     * Function that create a GaussianNB classifier with the given model.
-     * @param {object} model
-     * @return {GaussianNB}
+     * Draw a random integer from a uniform distribution on [low, high). If only low is specified, the number is drawn on [0, low)
+     * @param low - The lower bound of the uniform distribution interval.
+     * @param high - The higher bound of the uniform distribution interval.
      */
 
 
-    static load(model) {
-      if (model.modelName !== 'NaiveBayes') {
-        throw new RangeError('The current model is not a Multinomial Naive Bayes, current model:', model.name);
+    randInt(low, high) {
+      if (high === undefined) {
+        high = low;
+        low = 0;
       }
 
-      return new GaussianNB(true, model);
+      return low + Math.floor(this.randomGenerator() * (high - low));
     }
+    /**
+     * Draw several random number from a uniform distribution on [0, 1)
+     * @param size - The number of number to draw
+     * @return - The list of drawn numbers.
+     */
 
-  }
-  /**
-   * @private
-   * Function the retrieves a prediction with one case.
-   *
-   * @param {Array} currentCase
-   * @param {Array} mean - Precalculated means of each class trained
-   * @param {Array} classes - Precalculated value of each class (Prior probability and probability function of each feature)
-   * @return {number}
-   */
-
-  function getCurrentClass(currentCase, mean, classes) {
-    var maxProbability = 0;
-    var predictedClass = -1; // going through all precalculated values for the classes
 
-    for (var i = 0; i < classes.length; ++i) {
-      var currentProbability = classes[i][0]; // initialize with the prior probability
+    randomSample(size) {
+      const result = [];
 
-      for (var j = 1; j < classes[0][1].length + 1; ++j) {
-        currentProbability += calculateLogProbability(currentCase[j - 1], mean[i][j - 1], classes[i][j][0], classes[i][j][1]);
+      for (let i = 0; i < size; i++) {
+        result.push(this.random());
       }
 
-      currentProbability = Math.exp(currentProbability);
-
-      if (currentProbability > maxProbability) {
-        maxProbability = currentProbability;
-        predictedClass = i;
-      }
+      return result;
     }
 
-    return predictedClass;
   }
+
   /**
-   * @private
-   * function that retrieves the probability of the feature given the class.
-   * @param {number} value - value of the feature.
-   * @param {number} mean - mean of the feature for the given class.
-   * @param {number} C1 - precalculated value of (1 / (sqrt(2*pi) * std)).
-   * @param {number} C2 - precalculated value of (2 * std^2) for the denominator of the exponential.
-   * @return {number}
+   * Choose K different random points from the original data
+   * @ignore
+   * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]
+   * @param {number} K - number of clusters
+   * @param {number} seed - seed for random number generation
+   * @return {Array<Array<number>>} - Initial random points
    */
 
-
-  function calculateLogProbability(value, mean, C1, C2) {
-    value = value - mean;
-    return Math.log(C1 * Math.exp(value * value / C2));
+  function random(data, K, seed) {
+    const random = new Random(seed);
+    return random.choice(data, {
+      size: K
+    });
   }
+  /**
+   * Chooses the most distant points to a first random pick
+   * @ignore
+   * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]
+   * @param {number} K - number of clusters
+   * @param {Array<Array<number>>} distanceMatrix - matrix with the distance values
+   * @param {number} seed - seed for random number generation
+   * @return {Array<Array<number>>} - Initial random points
+   */
 
-  class MultinomialNB {
-    /**
-     * Constructor for Multinomial Naive Bayes, the model parameter is for load purposes.
-     * @constructor
-     * @param {object} model - for load purposes.
-     */
-    constructor(model) {
-      if (model) {
-        this.conditionalProbability = Matrix.checkMatrix(model.conditionalProbability);
-        this.priorProbability = Matrix.checkMatrix(model.priorProbability);
-      }
-    }
-    /**
-     * Train the classifier with the current training set and labels, the labels must be numbers between 0 and n.
-     * @param {Matrix|Array} trainingSet
-     * @param {Array} trainingLabels
-     */
+  function mostDistant(data, K, distanceMatrix, seed) {
+    const random = new Random(seed);
+    var ans = new Array(K); // chooses a random point as initial cluster
 
+    ans[0] = Math.floor(random.random() * data.length);
 
-    train(trainingSet, trainingLabels) {
-      trainingSet = Matrix.checkMatrix(trainingSet);
+    if (K > 1) {
+      // chooses the more distant point
+      var maxDist = {
+        dist: -1,
+        index: -1
+      };
 
-      if (trainingSet.rows !== trainingLabels.length) {
-        throw new RangeError('the size of the training set and the training labels must be the same.');
+      for (var l = 0; l < data.length; ++l) {
+        if (distanceMatrix[ans[0]][l] > maxDist.dist) {
+          maxDist.dist = distanceMatrix[ans[0]][l];
+          maxDist.index = l;
+        }
       }
 
-      var separateClass = separateClasses(trainingSet, trainingLabels);
-      this.priorProbability = new Matrix(separateClass.length, 1);
+      ans[1] = maxDist.index;
 
-      for (var i = 0; i < separateClass.length; ++i) {
-        this.priorProbability.set(i, 0, Math.log(separateClass[i].rows / trainingSet.rows));
-      }
+      if (K > 2) {
+        // chooses the set of points that maximises the min distance
+        for (var k = 2; k < K; ++k) {
+          var center = {
+            dist: -1,
+            index: -1
+          };
 
-      var features = trainingSet.columns;
-      this.conditionalProbability = new Matrix(separateClass.length, features);
+          for (var m = 0; m < data.length; ++m) {
+            // minimum distance to centers
+            var minDistCent = {
+              dist: Number.MAX_VALUE,
+              index: -1
+            };
 
-      for (i = 0; i < separateClass.length; ++i) {
-        var classValues = Matrix.checkMatrix(separateClass[i]);
-        var total = classValues.sum();
-        var divisor = total + features;
-        this.conditionalProbability.setRow(i, Matrix.rowVector(classValues.sum('column')).add(1).div(divisor).apply(matrixLog));
+            for (var n = 0; n < k; ++n) {
+              if (distanceMatrix[n][m] < minDistCent.dist && ans.indexOf(m) === -1) {
+                minDistCent = {
+                  dist: distanceMatrix[n][m],
+                  index: m
+                };
+              }
+            }
+
+            if (minDistCent.dist !== Number.MAX_VALUE && minDistCent.dist > center.dist) {
+              center = Object.assign({}, minDistCent);
+            }
+          }
+
+          ans[k] = center.index;
+        }
       }
     }
-    /**
-     * Retrieves the predictions for the dataset with the current model.
-     * @param {Matrix|Array} dataset
-     * @return {Array} - predictions from the dataset.
-     */
 
+    return ans.map(index => data[index]);
+  } // Implementation inspired from scikit
 
-    predict(dataset) {
-      dataset = Matrix.checkMatrix(dataset);
-      var predictions = new Array(dataset.rows);
+  function kmeanspp(X, K, options = {}) {
+    X = new Matrix(X);
+    const nSamples = X.rows;
+    const random = new Random(options.seed); // Set the number of trials
 
-      for (var i = 0; i < dataset.rows; ++i) {
-        var currentElement = dataset.getRowVector(i);
-        const v = Matrix.columnVector(this.conditionalProbability.clone().mulRowVector(currentElement).sum('row'));
-        predictions[i] = v.add(this.priorProbability).maxIndex()[0];
-      }
+    const centers = [];
+    const localTrials = options.localTrials || 2 + Math.floor(Math.log(K)); // Pick the first center at random from the dataset
 
-      return predictions;
-    }
-    /**
-     * Function that saves the current model.
-     * @return {object} - model in JSON format.
-     */
+    const firstCenterIdx = random.randInt(nSamples);
+    centers.push(X.getRow(firstCenterIdx)); // Init closest distances
 
+    let closestDistSquared = new Matrix(1, X.rows);
 
-    toJSON() {
-      return {
-        name: 'MultinomialNB',
-        priorProbability: this.priorProbability,
-        conditionalProbability: this.conditionalProbability
-      };
+    for (let i = 0; i < X.rows; i++) {
+      closestDistSquared.set(0, i, squaredEuclidean(X.getRow(i), centers[0]));
     }
-    /**
-     * Creates a new MultinomialNB from the given model
-     * @param {object} model
-     * @return {MultinomialNB}
-     */
 
+    let cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];
+    const factor = 1 / cumSumClosestDistSquared[0][nSamples - 1];
+    let probabilities = Matrix.mul(closestDistSquared, factor); // Iterate over the remaining centers
 
-    static load(model) {
-      if (model.name !== 'MultinomialNB') {
-        throw new RangeError("".concat(model.name, " is not a Multinomial Naive Bayes"));
+    for (let i = 1; i < K; i++) {
+      const candidateIdx = random.choice(nSamples, {
+        replace: true,
+        size: localTrials,
+        probabilities: probabilities[0]
+      });
+      const candidates = X.selection(candidateIdx, range(X.columns));
+      const distanceToCandidates = euclideanDistances(candidates, X);
+      let bestCandidate;
+      let bestPot;
+      let bestDistSquared;
+
+      for (let j = 0; j < localTrials; j++) {
+        const newDistSquared = Matrix.min(closestDistSquared, [distanceToCandidates.getRow(j)]);
+        const newPot = newDistSquared.sum();
+
+        if (bestCandidate === undefined || newPot < bestPot) {
+          bestCandidate = candidateIdx[j];
+          bestPot = newPot;
+          bestDistSquared = newDistSquared;
+        }
       }
 
-      return new MultinomialNB(model);
+      centers[i] = X.getRow(bestCandidate);
+      closestDistSquared = bestDistSquared;
+      cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];
+      probabilities = Matrix.mul(closestDistSquared, 1 / cumSumClosestDistSquared[0][nSamples - 1]);
     }
 
+    return centers;
   }
 
-  function matrixLog(i, j) {
-    this.set(i, j, Math.log(this.get(i, j)));
-  }
+  function euclideanDistances(A, B) {
+    const result = new Matrix(A.rows, B.rows);
 
+    for (let i = 0; i < A.rows; i++) {
+      for (let j = 0; j < B.rows; j++) {
+        result.set(i, j, squaredEuclidean(A.getRow(i), B.getRow(j)));
+      }
+    }
 
+    return result;
+  }
 
-  var index$1 = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    GaussianNB: GaussianNB,
-    MultinomialNB: MultinomialNB
-  });
+  function range(l) {
+    let r = [];
 
-  /*
-   * Original code from:
-   *
-   * k-d Tree JavaScript - V 1.01
-   *
-   * https://github.com/ubilabs/kd-tree-javascript
-   *
-   * @author Mircea Pricop <pricop@ubilabs.net>, 2012
-   * @author Martin Kleppe <kleppe@ubilabs.net>, 2012
-   * @author Ubilabs http://ubilabs.net, 2012
-   * @license MIT License <http://www.opensource.org/licenses/mit-license.php>
-   */
-  function Node(obj, dimension, parent) {
-    this.obj = obj;
-    this.left = null;
-    this.right = null;
-    this.parent = parent;
-    this.dimension = dimension;
+    for (let i = 0; i < l; i++) {
+      r.push(i);
+    }
+
+    return r;
   }
 
-  class KDTree {
-    constructor(points, metric) {
-      // If points is not an array, assume we're loading a pre-built tree
-      if (!Array.isArray(points)) {
-        this.dimensions = points.dimensions;
-        this.root = points;
-        restoreParent(this.root);
-      } else {
-        this.dimensions = new Array(points[0].length);
+  function cumSum(arr) {
+    let cumSum = [arr[0]];
 
-        for (var i = 0; i < this.dimensions.length; i++) {
-          this.dimensions[i] = i;
-        }
+    for (let i = 1; i < arr.length; i++) {
+      cumSum[i] = cumSum[i - 1] + arr[i];
+    }
 
-        this.root = buildTree(points, 0, null, this.dimensions);
-      }
+    return cumSum;
+  }
 
-      this.metric = metric;
-    } // Convert to a JSON serializable structure; this just requires removing
-    // the `parent` property
+  const distanceSymbol = Symbol('distance');
+  class KMeansResult {
+    /**
+     * Result of the kmeans algorithm
+     * @param {Array<number>} clusters - the cluster identifier for each data dot
+     * @param {Array<Array<object>>} centroids - the K centers in format [x,y,z,...], the error and size of the cluster
+     * @param {boolean} converged - Converge criteria satisfied
+     * @param {number} iterations - Current number of iterations
+     * @param {function} distance - (*Private*) Distance function to use between the points
+     * @constructor
+     */
+    constructor(clusters, centroids, converged, iterations, distance) {
+      this.clusters = clusters;
+      this.centroids = centroids;
+      this.converged = converged;
+      this.iterations = iterations;
+      this[distanceSymbol] = distance;
+    }
+    /**
+     * Allows to compute for a new array of points their cluster id
+     * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
+     * @return {Array<number>} - cluster id for each point
+     */
 
 
-    toJSON() {
-      const result = toJSONImpl(this.root);
-      result.dimensions = this.dimensions;
-      return result;
+    nearest(data) {
+      const clusterID = new Array(data.length);
+      const centroids = this.centroids.map(function (centroid) {
+        return centroid.centroid;
+      });
+      return updateClusterID(data, centroids, clusterID, this[distanceSymbol]);
     }
+    /**
+     * Returns a KMeansResult with the error and size of the cluster
+     * @ignore
+     * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster
+     * @return {KMeansResult}
+     */
 
-    nearest(point, maxNodes, maxDistance) {
-      const metric = this.metric;
-      const dimensions = this.dimensions;
-      var i;
-      const bestNodes = new BinaryHeap(function (e) {
-        return -e[1];
-      });
 
-      function nearestSearch(node) {
-        const dimension = dimensions[node.dimension];
-        const ownDistance = metric(point, node.obj);
-        const linearPoint = {};
-        var bestChild, linearDistance, otherChild, i;
+    computeInformation(data) {
+      var enrichedCentroids = this.centroids.map(function (centroid) {
+        return {
+          centroid: centroid,
+          error: 0,
+          size: 0
+        };
+      });
 
-        function saveNode(node, distance) {
-          bestNodes.push([node, distance]);
+      for (var i = 0; i < data.length; i++) {
+        enrichedCentroids[this.clusters[i]].error += this[distanceSymbol](data[i], this.centroids[this.clusters[i]]);
+        enrichedCentroids[this.clusters[i]].size++;
+      }
 
-          if (bestNodes.size() > maxNodes) {
-            bestNodes.pop();
-          }
+      for (var j = 0; j < this.centroids.length; j++) {
+        if (enrichedCentroids[j].size) {
+          enrichedCentroids[j].error /= enrichedCentroids[j].size;
+        } else {
+          enrichedCentroids[j].error = null;
         }
+      }
 
-        for (i = 0; i < dimensions.length; i += 1) {
-          if (i === node.dimension) {
-            linearPoint[dimensions[i]] = point[dimensions[i]];
-          } else {
-            linearPoint[dimensions[i]] = node.obj[dimensions[i]];
-          }
-        }
+      return new KMeansResult(this.clusters, enrichedCentroids, this.converged, this.iterations, this[distanceSymbol]);
+    }
 
-        linearDistance = metric(linearPoint, node.obj);
+  }
 
-        if (node.right === null && node.left === null) {
-          if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {
-            saveNode(node, ownDistance);
-          }
+  const defaultOptions$5 = {
+    maxIterations: 100,
+    tolerance: 1e-6,
+    withIterations: false,
+    initialization: 'kmeans++',
+    distanceFunction: squaredEuclidean
+  };
+  /**
+   * Each step operation for kmeans
+   * @ignore
+   * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]
+   * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster
+   * @param {Array<number>} clusterID - Cluster identifier for each data dot
+   * @param {number} K - Number of clusters
+   * @param {object} [options] - Option object
+   * @param {number} iterations - Current number of iterations
+   * @return {KMeansResult}
+   */
 
-          return;
-        }
+  function step(centers, data, clusterID, K, options, iterations) {
+    clusterID = updateClusterID(data, centers, clusterID, options.distanceFunction);
+    var newCenters = updateCenters(centers, data, clusterID, K);
+    var converged = hasConverged(newCenters, centers, options.distanceFunction, options.tolerance);
+    return new KMeansResult(clusterID, newCenters, converged, iterations, options.distanceFunction);
+  }
+  /**
+   * Generator version for the algorithm
+   * @ignore
+   * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]
+   * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster
+   * @param {Array<number>} clusterID - Cluster identifier for each data dot
+   * @param {number} K - Number of clusters
+   * @param {object} [options] - Option object
+   */
 
-        if (node.right === null) {
-          bestChild = node.left;
-        } else if (node.left === null) {
-          bestChild = node.right;
-        } else {
-          if (point[dimension] < node.obj[dimension]) {
-            bestChild = node.left;
-          } else {
-            bestChild = node.right;
-          }
-        }
 
-        nearestSearch(bestChild);
+  function* kmeansGenerator(centers, data, clusterID, K, options) {
+    var converged = false;
+    var stepNumber = 0;
+    var stepResult;
 
-        if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {
-          saveNode(node, ownDistance);
-        }
+    while (!converged && stepNumber < options.maxIterations) {
+      stepResult = step(centers, data, clusterID, K, options, ++stepNumber);
+      yield stepResult.computeInformation(data);
+      converged = stepResult.converged;
+      centers = stepResult.centroids;
+    }
+  }
+  /**
+   * K-means algorithm
+   * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]
+   * @param {number} K - Number of clusters
+   * @param {object} [options] - Option object
+   * @param {number} [options.maxIterations = 100] - Maximum of iterations allowed
+   * @param {number} [options.tolerance = 1e-6] - Error tolerance
+   * @param {boolean} [options.withIterations = false] - Store clusters and centroids for each iteration
+   * @param {function} [options.distanceFunction = squaredDistance] - Distance function to use between the points
+   * @param {number} [options.seed] - Seed for random initialization.
+   * @param {string|Array<Array<number>>} [options.initialization = 'kmeans++'] - K centers in format [x,y,z,...] or a method for initialize the data:
+   *  * You can either specify your custom start centroids, or select one of the following initialization method:
+   *  * `'kmeans++'` will use the kmeans++ method as described by http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
+   *  * `'random'` will choose K random different values.
+   *  * `'mostDistant'` will choose the more distant points to a first random pick
+   * @return {KMeansResult} - Cluster identifier for each data dot and centroids with the following fields:
+   *  * `'clusters'`: Array of indexes for the clusters.
+   *  * `'centroids'`: Array with the resulting centroids.
+   *  * `'iterations'`: Number of iterations that took to converge
+   */
 
-        if (bestNodes.size() < maxNodes || Math.abs(linearDistance) < bestNodes.peek()[1]) {
-          if (bestChild === node.left) {
-            otherChild = node.right;
-          } else {
-            otherChild = node.left;
-          }
 
-          if (otherChild !== null) {
-            nearestSearch(otherChild);
-          }
-        }
-      }
+  function kmeans(data, K, options) {
+    options = Object.assign({}, defaultOptions$5, options);
 
-      if (maxDistance) {
-        for (i = 0; i < maxNodes; i += 1) {
-          bestNodes.push([null, maxDistance]);
-        }
-      }
+    if (K <= 0 || K > data.length || !Number.isInteger(K)) {
+      throw new Error('K should be a positive integer smaller than the number of points');
+    }
 
-      if (this.root) {
-        nearestSearch(this.root);
+    var centers;
+
+    if (Array.isArray(options.initialization)) {
+      if (options.initialization.length !== K) {
+        throw new Error('The initial centers should have the same length as K');
+      } else {
+        centers = options.initialization;
       }
+    } else {
+      switch (options.initialization) {
+        case 'kmeans++':
+          centers = kmeanspp(data, K, options);
+          break;
 
-      const result = [];
+        case 'random':
+          centers = random(data, K, options.seed);
+          break;
 
-      for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {
-        if (bestNodes.content[i][0]) {
-          result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);
-        }
+        case 'mostDistant':
+          centers = mostDistant(data, K, calculateDistanceMatrix(data, options.distanceFunction), options.seed);
+          break;
+
+        default:
+          throw new Error(`Unknown initialization method: "${options.initialization}"`);
       }
+    } // infinite loop until convergence
 
-      return result;
+
+    if (options.maxIterations === 0) {
+      options.maxIterations = Number.MAX_VALUE;
     }
 
-  }
+    var clusterID = new Array(data.length);
 
-  function toJSONImpl(src) {
-    const dest = new Node(src.obj, src.dimension, null);
-    if (src.left) dest.left = toJSONImpl(src.left);
-    if (src.right) dest.right = toJSONImpl(src.right);
-    return dest;
-  }
+    if (options.withIterations) {
+      return kmeansGenerator(centers, data, clusterID, K, options);
+    } else {
+      var converged = false;
+      var stepNumber = 0;
+      var stepResult;
 
-  function buildTree(points, depth, parent, dimensions) {
-    const dim = depth % dimensions.length;
+      while (!converged && stepNumber < options.maxIterations) {
+        stepResult = step(centers, data, clusterID, K, options, ++stepNumber);
+        converged = stepResult.converged;
+        centers = stepResult.centroids;
+      }
 
-    if (points.length === 0) {
-      return null;
+      return stepResult.computeInformation(data);
     }
+  }
 
-    if (points.length === 1) {
-      return new Node(points[0], dim, parent);
-    }
+  /**
+   * @private
+   * Function that retuns an array of matrices of the cases that belong to each class.
+   * @param {Matrix} X - dataset
+   * @param {Array} y - predictions
+   * @return {Array}
+   */
 
-    points.sort((a, b) => a[dimensions[dim]] - b[dimensions[dim]]);
-    const median = Math.floor(points.length / 2);
-    const node = new Node(points[median], dim, parent);
-    node.left = buildTree(points.slice(0, median), depth + 1, node, dimensions);
-    node.right = buildTree(points.slice(median + 1), depth + 1, node, dimensions);
-    return node;
-  }
+  function separateClasses(X, y) {
+    var features = X.columns;
+    var classes = 0;
+    var totalPerClasses = new Array(10000); // max upperbound of classes
 
-  function restoreParent(root) {
-    if (root.left) {
-      root.left.parent = root;
-      restoreParent(root.left);
-    }
+    for (var i = 0; i < y.length; i++) {
+      if (totalPerClasses[y[i]] === undefined) {
+        totalPerClasses[y[i]] = 0;
+        classes++;
+      }
 
-    if (root.right) {
-      root.right.parent = root;
-      restoreParent(root.right);
+      totalPerClasses[y[i]]++;
     }
-  } // Binary heap implementation from:
-  // http://eloquentjavascript.net/appendix2.html
 
+    var separatedClasses = new Array(classes);
+    var currentIndex = new Array(classes);
 
-  class BinaryHeap {
-    constructor(scoreFunction) {
-      this.content = [];
-      this.scoreFunction = scoreFunction;
+    for (i = 0; i < classes; ++i) {
+      separatedClasses[i] = new Matrix(totalPerClasses[i], features);
+      currentIndex[i] = 0;
     }
 
-    push(element) {
-      // Add the new element to the end of the array.
-      this.content.push(element); // Allow it to bubble up.
-
-      this.bubbleUp(this.content.length - 1);
+    for (i = 0; i < X.rows; ++i) {
+      separatedClasses[y[i]].setRow(currentIndex[y[i]], X.getRow(i));
+      currentIndex[y[i]]++;
     }
 
-    pop() {
-      // Store the first element so we can return it later.
-      var result = this.content[0]; // Get the element at the end of the array.
+    return separatedClasses;
+  }
 
-      var end = this.content.pop(); // If there are any elements left, put the end element at the
-      // start, and let it sink down.
-
-      if (this.content.length > 0) {
-        this.content[0] = end;
-        this.sinkDown(0);
+  class GaussianNB {
+    /**
+     * Constructor for the Gaussian Naive Bayes classifier, the parameters here is just for loading purposes.
+     * @constructor
+     * @param {boolean} reload
+     * @param {object} model
+     */
+    constructor(reload, model) {
+      if (reload) {
+        this.means = model.means;
+        this.calculateProbabilities = model.calculateProbabilities;
       }
-
-      return result;
-    }
-
-    peek() {
-      return this.content[0];
-    }
-
-    size() {
-      return this.content.length;
     }
+    /**
+     * Function that trains the classifier with a matrix that represents the training set and an array that
+     * represents the label of each row in the training set. the labels must be numbers between 0 to n-1 where
+     * n represents the number of classes.
+     *
+     * WARNING: in the case that one class, all the cases in one or more features have the same value, the
+     * Naive Bayes classifier will not work well.
+     * @param {Matrix|Array} trainingSet
+     * @param {Matrix|Array} trainingLabels
+     */
 
-    bubbleUp(n) {
-      // Fetch the element that has to be moved.
-      var element = this.content[n]; // When at 0, an element can not go up any further.
 
-      while (n > 0) {
-        // Compute the parent element's index, and fetch it.
-        const parentN = Math.floor((n + 1) / 2) - 1;
-        const parent = this.content[parentN]; // Swap the elements if the parent is greater.
+    train(trainingSet, trainingLabels) {
+      var C1 = Math.sqrt(2 * Math.PI); // constant to precalculate the squared root
 
-        if (this.scoreFunction(element) < this.scoreFunction(parent)) {
-          this.content[parentN] = element;
-          this.content[n] = parent; // Update 'n' to continue at the new position.
+      trainingSet = Matrix.checkMatrix(trainingSet);
 
-          n = parentN;
-        } else {
-          // Found a parent that is less, no need to move it further.
-          break;
-        }
+      if (trainingSet.rows !== trainingLabels.length) {
+        throw new RangeError('the size of the training set and the training labels must be the same.');
       }
-    }
-
-    sinkDown(n) {
-      // Look up the target element and its score.
-      var length = this.content.length;
-      var element = this.content[n];
-      var elemScore = this.scoreFunction(element);
 
-      while (true) {
-        // Compute the indices of the child elements.
-        var child2N = (n + 1) * 2;
-        var child1N = child2N - 1; // This is used to store the new position of the element,
-        // if any.
-
-        var swap = null; // If the first child exists (is inside the array)...
-
-        if (child1N < length) {
-          // Look it up and compute its score.
-          var child1 = this.content[child1N];
-          var child1Score = this.scoreFunction(child1); // If the score is less than our element's, we need to swap.
-
-          if (child1Score < elemScore) {
-            swap = child1N;
-          }
-        } // Do the same checks for the other child.
-
-
-        if (child2N < length) {
-          var child2 = this.content[child2N];
-          var child2Score = this.scoreFunction(child2);
-
-          if (child2Score < (swap === null ? elemScore : child1Score)) {
-            swap = child2N;
-          }
-        } // If the element needs to be moved, swap it, and continue.
+      var separatedClasses = separateClasses(trainingSet, trainingLabels);
+      var calculateProbabilities = new Array(separatedClasses.length);
+      this.means = new Array(separatedClasses.length);
 
+      for (var i = 0; i < separatedClasses.length; ++i) {
+        var means = separatedClasses[i].mean('column');
+        var std = separatedClasses[i].standardDeviation('column', {
+          mean: means
+        });
+        var logPriorProbability = Math.log(separatedClasses[i].rows / trainingSet.rows);
+        calculateProbabilities[i] = new Array(means.length + 1);
+        calculateProbabilities[i][0] = logPriorProbability;
 
-        if (swap !== null) {
-          this.content[n] = this.content[swap];
-          this.content[swap] = element;
-          n = swap;
-        } else {
-          // Otherwise, we are done.
-          break;
+        for (var j = 1; j < means.length + 1; ++j) {
+          var currentStd = std[j - 1];
+          calculateProbabilities[i][j] = [1 / (C1 * currentStd), -2 * currentStd * currentStd];
         }
-      }
-    }
-
-  }
-
-  class KNN {
-    /**
-     * @param {Array} dataset
-     * @param {Array} labels
-     * @param {object} options
-     * @param {number} [options.k=numberOfClasses + 1] - Number of neighbors to classify.
-     * @param {function} [options.distance=euclideanDistance] - Distance function that takes two parameters.
-     */
-    constructor(dataset, labels) {
-      let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-
-      if (dataset === true) {
-        const model = labels;
-        this.kdTree = new KDTree(model.kdTree, options);
-        this.k = model.k;
-        this.classes = new Set(model.classes);
-        this.isEuclidean = model.isEuclidean;
-        return;
-      }
-
-      const classes = new Set(labels);
-      const {
-        distance = euclidean,
-        k = classes.size + 1
-      } = options;
-      const points = new Array(dataset.length);
-
-      for (var i = 0; i < points.length; ++i) {
-        points[i] = dataset[i].slice();
-      }
 
-      for (i = 0; i < labels.length; ++i) {
-        points[i].push(labels[i]);
+        this.means[i] = means;
       }
 
-      this.kdTree = new KDTree(points, distance);
-      this.k = k;
-      this.classes = classes;
-      this.isEuclidean = distance === euclidean;
+      this.calculateProbabilities = calculateProbabilities;
     }
     /**
-     * Create a new KNN instance with the given model.
-     * @param {object} model
-     * @param {function} distance=euclideanDistance - distance function must be provided if the model wasn't trained with euclidean distance.
-     * @return {KNN}
+     * function that predicts each row of the dataset (must be a matrix).
+     *
+     * @param {Matrix|Array} dataset
+     * @return {Array}
      */
 
 
-    static load(model) {
-      let distance = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : euclidean;
+    predict(dataset) {
+      dataset = Matrix.checkMatrix(dataset);
 
-      if (model.name !== 'KNN') {
-        throw new Error("invalid model: ".concat(model.name));
+      if (dataset.rows === this.calculateProbabilities[0].length) {
+        throw new RangeError('the dataset must have the same features as the training set');
       }
 
-      if (!model.isEuclidean && distance === euclidean) {
-        throw new Error('a custom distance function was used to create the model. Please provide it again');
-      }
+      var predictions = new Array(dataset.rows);
 
-      if (model.isEuclidean && distance !== euclidean) {
-        throw new Error('the model was created with the default distance function. Do not load it with another one');
+      for (var i = 0; i < predictions.length; ++i) {
+        predictions[i] = getCurrentClass(dataset.getRow(i), this.means, this.calculateProbabilities);
       }
 
-      return new KNN(true, model, distance);
+      return predictions;
     }
     /**
-     * Return a JSON containing the kd-tree model.
-     * @return {object} JSON KNN model.
+     * Function that export the NaiveBayes model.
+     * @return {object}
      */
 
 
     toJSON() {
       return {
-        name: 'KNN',
-        kdTree: this.kdTree,
-        k: this.k,
-        classes: Array.from(this.classes),
-        isEuclidean: this.isEuclidean
+        modelName: 'NaiveBayes',
+        means: this.means,
+        calculateProbabilities: this.calculateProbabilities
       };
     }
     /**
-     * Predicts the output given the matrix to predict.
-     * @param {Array} dataset
-     * @return {Array} predictions
+     * Function that create a GaussianNB classifier with the given model.
+     * @param {object} model
+     * @return {GaussianNB}
      */
 
 
-    predict(dataset) {
-      if (Array.isArray(dataset)) {
-        if (typeof dataset[0] === 'number') {
-          return getSinglePrediction(this, dataset);
-        } else if (Array.isArray(dataset[0]) && typeof dataset[0][0] === 'number') {
-          const predictions = new Array(dataset.length);
-
-          for (var i = 0; i < dataset.length; i++) {
-            predictions[i] = getSinglePrediction(this, dataset[i]);
-          }
-
-          return predictions;
-        }
+    static load(model) {
+      if (model.modelName !== 'NaiveBayes') {
+        throw new RangeError('The current model is not a Multinomial Naive Bayes, current model:', model.name);
       }
 
-      throw new TypeError('dataset to predict must be an array or a matrix');
+      return new GaussianNB(true, model);
     }
 
   }
+  /**
+   * @private
+   * Function the retrieves a prediction with one case.
+   *
+   * @param {Array} currentCase
+   * @param {Array} mean - Precalculated means of each class trained
+   * @param {Array} classes - Precalculated value of each class (Prior probability and probability function of each feature)
+   * @return {number}
+   */
 
-  function getSinglePrediction(knn, currentCase) {
-    var nearestPoints = knn.kdTree.nearest(currentCase, knn.k);
-    var pointsPerClass = {};
-    var predictedClass = -1;
-    var maxPoints = -1;
-    var lastElement = nearestPoints[0][0].length - 1;
+  function getCurrentClass(currentCase, mean, classes) {
+    var maxProbability = 0;
+    var predictedClass = -1; // going through all precalculated values for the classes
 
-    for (var element of knn.classes) {
-      pointsPerClass[element] = 0;
-    }
+    for (var i = 0; i < classes.length; ++i) {
+      var currentProbability = classes[i][0]; // initialize with the prior probability
 
-    for (var i = 0; i < nearestPoints.length; ++i) {
-      var currentClass = nearestPoints[i][0][lastElement];
-      var currentPoints = ++pointsPerClass[currentClass];
+      for (var j = 1; j < classes[0][1].length + 1; ++j) {
+        currentProbability += calculateLogProbability(currentCase[j - 1], mean[i][j - 1], classes[i][j][0], classes[i][j][1]);
+      }
 
-      if (currentPoints > maxPoints) {
-        predictedClass = currentClass;
-        maxPoints = currentPoints;
+      currentProbability = Math.exp(currentProbability);
+
+      if (currentProbability > maxProbability) {
+        maxProbability = currentProbability;
+        predictedClass = i;
       }
     }
 
     return predictedClass;
   }
-
   /**
    * @private
-   * Function that given vector, returns its norm
-   * @param {Vector} X
-   * @return {number} Norm of the vector
-   */
-
-  function norm(X) {
-    return Math.sqrt(X.clone().apply(pow2array).sum());
-  }
-  /**
-   * @private
-   * Function that pow 2 each element of a Matrix or a Vector,
-   * used in the apply method of the Matrix object
-   * @param {number} i - index i.
-   * @param {number} j - index j.
-   * @return {Matrix} The Matrix object modified at the index i, j.
-   * */
-
-  function pow2array(i, j) {
-    this.set(i, j, this.get(i, j) ** 2);
-  }
-  /**
-   * @private
-   * Function that initialize an array of matrices.
-   * @param {Array} array
-   * @param {boolean} isMatrix
-   * @return {Array} array with the matrices initialized.
+   * function that retrieves the probability of the feature given the class.
+   * @param {number} value - value of the feature.
+   * @param {number} mean - mean of the feature for the given class.
+   * @param {number} C1 - precalculated value of (1 / (sqrt(2*pi) * std)).
+   * @param {number} C2 - precalculated value of (2 * std^2) for the denominator of the exponential.
+   * @return {number}
    */
 
-  function initializeMatrices(array, isMatrix) {
-    if (isMatrix) {
-      for (var i = 0; i < array.length; ++i) {
-        for (var j = 0; j < array[i].length; ++j) {
-          var elem = array[i][j];
-          array[i][j] = elem !== null ? new Matrix(array[i][j]) : undefined;
-        }
-      }
-    } else {
-      for (i = 0; i < array.length; ++i) {
-        array[i] = new Matrix(array[i]);
-      }
-    }
 
-    return array;
+  function calculateLogProbability(value, mean, C1, C2) {
+    value = value - mean;
+    return Math.log(C1 * Math.exp(value * value / C2));
   }
 
-  /**
-   * @class PLS
-   */
-
-  class PLS {
+  class MultinomialNB {
     /**
-     * Constructor for Partial Least Squares (PLS)
-     * @param {object} options
-     * @param {number} [options.latentVectors] - Number of latent vector to get (if the algorithm doesn't find a good model below the tolerance)
-     * @param {number} [options.tolerance=1e-5]
-     * @param {boolean} [options.scale=true] - rescale dataset using mean.
+     * Constructor for Multinomial Naive Bayes, the model parameter is for load purposes.
+     * @constructor
      * @param {object} model - for load purposes.
      */
-    constructor(options, model) {
-      if (options === true) {
-        this.meanX = model.meanX;
-        this.stdDevX = model.stdDevX;
-        this.meanY = model.meanY;
-        this.stdDevY = model.stdDevY;
-        this.PBQ = Matrix.checkMatrix(model.PBQ);
-        this.R2X = model.R2X;
-        this.scale = model.scale;
-        this.scaleMethod = model.scaleMethod;
-        this.tolerance = model.tolerance;
-      } else {
-        var {
-          tolerance = 1e-5,
-          scale = true
-        } = options;
-        this.tolerance = tolerance;
-        this.scale = scale;
-        this.latentVectors = options.latentVectors;
+    constructor(model) {
+      if (model) {
+        this.conditionalProbability = Matrix.checkMatrix(model.conditionalProbability);
+        this.priorProbability = Matrix.checkMatrix(model.priorProbability);
       }
     }
     /**
-     * Fits the model with the given data and predictions, in this function is calculated the
-     * following outputs:
-     *
-     * T - Score matrix of X
-     * P - Loading matrix of X
-     * U - Score matrix of Y
-     * Q - Loading matrix of Y
-     * B - Matrix of regression coefficient
-     * W - Weight matrix of X
-     *
+     * Train the classifier with the current training set and labels, the labels must be numbers between 0 and n.
      * @param {Matrix|Array} trainingSet
-     * @param {Matrix|Array} trainingValues
+     * @param {Array} trainingLabels
      */
 
 
-    train(trainingSet, trainingValues) {
+    train(trainingSet, trainingLabels) {
       trainingSet = Matrix.checkMatrix(trainingSet);
-      trainingValues = Matrix.checkMatrix(trainingValues);
 
-      if (trainingSet.length !== trainingValues.length) {
-        throw new RangeError('The number of X rows must be equal to the number of Y rows');
+      if (trainingSet.rows !== trainingLabels.length) {
+        throw new RangeError('the size of the training set and the training labels must be the same.');
       }
 
-      this.meanX = trainingSet.mean('column');
-      this.stdDevX = trainingSet.standardDeviation('column', {
-        mean: this.meanX,
-        unbiased: true
-      });
-      this.meanY = trainingValues.mean('column');
-      this.stdDevY = trainingValues.standardDeviation('column', {
-        mean: this.meanY,
-        unbiased: true
-      });
-
-      if (this.scale) {
-        trainingSet = trainingSet.clone().subRowVector(this.meanX).divRowVector(this.stdDevX);
-        trainingValues = trainingValues.clone().subRowVector(this.meanY).divRowVector(this.stdDevY);
-      }
+      var separateClass = separateClasses(trainingSet, trainingLabels);
+      this.priorProbability = new Matrix(separateClass.length, 1);
 
-      if (this.latentVectors === undefined) {
-        this.latentVectors = Math.min(trainingSet.rows - 1, trainingSet.columns);
+      for (var i = 0; i < separateClass.length; ++i) {
+        this.priorProbability.set(i, 0, Math.log(separateClass[i].rows / trainingSet.rows));
       }
 
-      var rx = trainingSet.rows;
-      var cx = trainingSet.columns;
-      var ry = trainingValues.rows;
-      var cy = trainingValues.columns;
-      var ssqXcal = trainingSet.clone().mul(trainingSet).sum(); // for the r²
-
-      var sumOfSquaresY = trainingValues.clone().mul(trainingValues).sum();
-      var tolerance = this.tolerance;
-      var n = this.latentVectors;
-      var T = Matrix.zeros(rx, n);
-      var P = Matrix.zeros(cx, n);
-      var U = Matrix.zeros(ry, n);
-      var Q = Matrix.zeros(cy, n);
-      var B = Matrix.zeros(n, n);
-      var W = P.clone();
-      var k = 0;
-
-      while (norm(trainingValues) > tolerance && k < n) {
-        var transposeX = trainingSet.transpose();
-        var transposeY = trainingValues.transpose();
-        var tIndex = maxSumColIndex(trainingSet.clone().mul(trainingSet));
-        var uIndex = maxSumColIndex(trainingValues.clone().mul(trainingValues));
-        var t1 = trainingSet.getColumnVector(tIndex);
-        var u = trainingValues.getColumnVector(uIndex);
-        var t = Matrix.zeros(rx, 1);
-
-        while (norm(t1.clone().sub(t)) > tolerance) {
-          var w = transposeX.mmul(u);
-          w.div(norm(w));
-          t = t1;
-          t1 = trainingSet.mmul(w);
-          var q = transposeY.mmul(t1);
-          q.div(norm(q));
-          u = trainingValues.mmul(q);
-        }
+      var features = trainingSet.columns;
+      this.conditionalProbability = new Matrix(separateClass.length, features);
 
-        t = t1;
-        var num = transposeX.mmul(t);
-        var den = t.transpose().mmul(t).get(0, 0);
-        var p = num.div(den);
-        var pnorm = norm(p);
-        p.div(pnorm);
-        t.mul(pnorm);
-        w.mul(pnorm);
-        num = u.transpose().mmul(t);
-        den = t.transpose().mmul(t).get(0, 0);
-        var b = num.div(den).get(0, 0);
-        trainingSet.sub(t.mmul(p.transpose()));
-        trainingValues.sub(t.clone().mul(b).mmul(q.transpose()));
-        T.setColumn(k, t);
-        P.setColumn(k, p);
-        U.setColumn(k, u);
-        Q.setColumn(k, q);
-        W.setColumn(k, w);
-        B.set(k, k, b);
-        k++;
+      for (i = 0; i < separateClass.length; ++i) {
+        var classValues = Matrix.checkMatrix(separateClass[i]);
+        var total = classValues.sum();
+        var divisor = total + features;
+        this.conditionalProbability.setRow(i, Matrix.rowVector(classValues.sum('column')).add(1).div(divisor).apply(matrixLog));
       }
-
-      k--;
-      T = T.subMatrix(0, T.rows - 1, 0, k);
-      P = P.subMatrix(0, P.rows - 1, 0, k);
-      U = U.subMatrix(0, U.rows - 1, 0, k);
-      Q = Q.subMatrix(0, Q.rows - 1, 0, k);
-      W = W.subMatrix(0, W.rows - 1, 0, k);
-      B = B.subMatrix(0, k, 0, k); // TODO: review of R2Y
-      // this.R2Y = t.transpose().mmul(t).mul(q[k][0]*q[k][0]).divS(ssqYcal)[0][0];
-      //
-
-      this.ssqYcal = sumOfSquaresY;
-      this.E = trainingSet;
-      this.F = trainingValues;
-      this.T = T;
-      this.P = P;
-      this.U = U;
-      this.Q = Q;
-      this.W = W;
-      this.B = B;
-      this.PBQ = P.mmul(B).mmul(Q.transpose());
-      this.R2X = t.transpose().mmul(t).mmul(p.transpose().mmul(p)).div(ssqXcal).get(0, 0);
     }
     /**
-     * Predicts the behavior of the given dataset.
-     * @param {Matrix|Array} dataset - data to be predicted.
-     * @return {Matrix} - predictions of each element of the dataset.
+     * Retrieves the predictions for the dataset with the current model.
+     * @param {Matrix|Array} dataset
+     * @return {Array} - predictions from the dataset.
      */
 
 
     predict(dataset) {
-      var X = Matrix.checkMatrix(dataset);
+      dataset = Matrix.checkMatrix(dataset);
+      var predictions = new Array(dataset.rows);
 
-      if (this.scale) {
-        X = X.subRowVector(this.meanX).divRowVector(this.stdDevX);
+      for (var i = 0; i < dataset.rows; ++i) {
+        var currentElement = dataset.getRowVector(i);
+        const v = Matrix.columnVector(this.conditionalProbability.clone().mulRowVector(currentElement).sum('row'));
+        predictions[i] = v.add(this.priorProbability).maxIndex()[0];
       }
 
-      var Y = X.mmul(this.PBQ);
-      Y = Y.mulRowVector(this.stdDevY).addRowVector(this.meanY);
-      return Y;
-    }
-    /**
-     * Returns the explained variance on training of the PLS model
-     * @return {number}
-     */
-
-
-    getExplainedVariance() {
-      return this.R2X;
+      return predictions;
     }
     /**
-     * Export the current model to JSON.
-     * @return {object} - Current model.
+     * Function that saves the current model.
+     * @return {object} - model in JSON format.
      */
 
 
     toJSON() {
       return {
-        name: 'PLS',
-        R2X: this.R2X,
-        meanX: this.meanX,
-        stdDevX: this.stdDevX,
-        meanY: this.meanY,
-        stdDevY: this.stdDevY,
-        PBQ: this.PBQ,
-        tolerance: this.tolerance,
-        scale: this.scale
+        name: 'MultinomialNB',
+        priorProbability: this.priorProbability,
+        conditionalProbability: this.conditionalProbability
       };
     }
     /**
-     * Load a PLS model from a JSON Object
+     * Creates a new MultinomialNB from the given model
      * @param {object} model
-     * @return {PLS} - PLS object from the given model
+     * @return {MultinomialNB}
      */
 
 
     static load(model) {
-      if (model.name !== 'PLS') {
-        throw new RangeError("Invalid model: ".concat(model.name));
+      if (model.name !== 'MultinomialNB') {
+        throw new RangeError(`${model.name} is not a Multinomial Naive Bayes`);
       }
 
-      return new PLS(true, model);
+      return new MultinomialNB(model);
     }
 
   }
-  /**
-   * @private
-   * Function that returns the index where the sum of each
-   * column vector is maximum.
-   * @param {Matrix} data
-   * @return {number} index of the maximum
-   */
 
-  function maxSumColIndex(data) {
-    return Matrix.rowVector(data.sum('column')).maxIndex()[0];
+  function matrixLog(i, j) {
+    this.set(i, j, Math.log(this.get(i, j)));
   }
 
-  /**
-   * @class KOPLS
+  var index$1 = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    GaussianNB: GaussianNB,
+    MultinomialNB: MultinomialNB
+  });
+
+  /*
+   * Original code from:
+   *
+   * k-d Tree JavaScript - V 1.01
+   *
+   * https://github.com/ubilabs/kd-tree-javascript
+   *
+   * @author Mircea Pricop <pricop@ubilabs.net>, 2012
+   * @author Martin Kleppe <kleppe@ubilabs.net>, 2012
+   * @author Ubilabs http://ubilabs.net, 2012
+   * @license MIT License <http://www.opensource.org/licenses/mit-license.php>
    */
+  function Node(obj, dimension, parent) {
+    this.obj = obj;
+    this.left = null;
+    this.right = null;
+    this.parent = parent;
+    this.dimension = dimension;
+  }
 
-  class KOPLS {
-    /**
-       * Constructor for Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)
-       * @param {object} options
-       * @param {number} [options.predictiveComponents] - Number of predictive components to use.
-       * @param {number} [options.orthogonalComponents] - Number of Y-Orthogonal components.
-       * @param {Kernel} [options.kernel] - Kernel object to apply, see [ml-kernel](https://github.com/mljs/kernel).
-       * @param {object} model - for load purposes.
-       */
-    constructor(options, model) {
-      if (options === true) {
-        this.trainingSet = new Matrix(model.trainingSet);
-        this.YLoadingMat = new Matrix(model.YLoadingMat);
-        this.SigmaPow = new Matrix(model.SigmaPow);
-        this.YScoreMat = new Matrix(model.YScoreMat);
-        this.predScoreMat = initializeMatrices(model.predScoreMat, false);
-        this.YOrthLoadingVec = initializeMatrices(model.YOrthLoadingVec, false);
-        this.YOrthEigen = model.YOrthEigen;
-        this.YOrthScoreMat = initializeMatrices(model.YOrthScoreMat, false);
-        this.toNorm = initializeMatrices(model.toNorm, false);
-        this.TURegressionCoeff = initializeMatrices(model.TURegressionCoeff, false);
-        this.kernelX = initializeMatrices(model.kernelX, true);
-        this.kernel = model.kernel;
-        this.orthogonalComp = model.orthogonalComp;
-        this.predictiveComp = model.predictiveComp;
+  class KDTree {
+    constructor(points, metric) {
+      // If points is not an array, assume we're loading a pre-built tree
+      if (!Array.isArray(points)) {
+        this.dimensions = points.dimensions;
+        this.root = points;
+        restoreParent(this.root);
       } else {
-        if (options.predictiveComponents === undefined) {
-          throw new RangeError('no predictive components found!');
-        }
-
-        if (options.orthogonalComponents === undefined) {
-          throw new RangeError('no orthogonal components found!');
-        }
+        this.dimensions = new Array(points[0].length);
 
-        if (options.kernel === undefined) {
-          throw new RangeError('no kernel found!');
+        for (var i = 0; i < this.dimensions.length; i++) {
+          this.dimensions[i] = i;
         }
 
-        this.orthogonalComp = options.orthogonalComponents;
-        this.predictiveComp = options.predictiveComponents;
-        this.kernel = options.kernel;
+        this.root = buildTree(points, 0, null, this.dimensions);
       }
-    }
-    /**
-       * Train the K-OPLS model with the given training set and labels.
-       * @param {Matrix|Array} trainingSet
-       * @param {Matrix|Array} trainingValues
-       */
-
 
-    train(trainingSet, trainingValues) {
-      trainingSet = Matrix.checkMatrix(trainingSet);
-      trainingValues = Matrix.checkMatrix(trainingValues); // to save and compute kernel with the prediction dataset.
+      this.metric = metric;
+    } // Convert to a JSON serializable structure; this just requires removing
+    // the `parent` property
 
-      this.trainingSet = trainingSet.clone();
-      var kernelX = this.kernel.compute(trainingSet);
-      var Identity = Matrix.eye(kernelX.rows, kernelX.rows, 1);
-      var temp = kernelX;
-      kernelX = new Array(this.orthogonalComp + 1);
 
-      for (let i = 0; i < this.orthogonalComp + 1; i++) {
-        kernelX[i] = new Array(this.orthogonalComp + 1);
-      }
+    toJSON() {
+      const result = toJSONImpl(this.root);
+      result.dimensions = this.dimensions;
+      return result;
+    }
 
-      kernelX[0][0] = temp;
-      var result = new SingularValueDecomposition(trainingValues.transpose().mmul(kernelX[0][0]).mmul(trainingValues), {
-        computeLeftSingularVectors: true,
-        computeRightSingularVectors: false
+    nearest(point, maxNodes, maxDistance) {
+      const metric = this.metric;
+      const dimensions = this.dimensions;
+      var i;
+      const bestNodes = new BinaryHeap(function (e) {
+        return -e[1];
       });
-      var YLoadingMat = result.leftSingularVectors;
-      var Sigma = result.diagonalMatrix;
-      YLoadingMat = YLoadingMat.subMatrix(0, YLoadingMat.rows - 1, 0, this.predictiveComp - 1);
-      Sigma = Sigma.subMatrix(0, this.predictiveComp - 1, 0, this.predictiveComp - 1);
-      var YScoreMat = trainingValues.mmul(YLoadingMat);
-      var predScoreMat = new Array(this.orthogonalComp + 1);
-      var TURegressionCoeff = new Array(this.orthogonalComp + 1);
-      var YOrthScoreMat = new Array(this.orthogonalComp);
-      var YOrthLoadingVec = new Array(this.orthogonalComp);
-      var YOrthEigen = new Array(this.orthogonalComp);
-      var YOrthScoreNorm = new Array(this.orthogonalComp);
-      var SigmaPow = Matrix.pow(Sigma, -0.5); // to avoid errors, check infinity
 
-      SigmaPow.apply(function (i, j) {
-        if (this.get(i, j) === Infinity) {
-          this.set(i, j, 0);
-        }
-      });
+      function nearestSearch(node) {
+        const dimension = dimensions[node.dimension];
+        const ownDistance = metric(point, node.obj);
+        const linearPoint = {};
+        var bestChild, linearDistance, otherChild, i;
 
-      for (var i = 0; i < this.orthogonalComp; ++i) {
-        predScoreMat[i] = kernelX[0][i].transpose().mmul(YScoreMat).mmul(SigmaPow);
-        var TpiPrime = predScoreMat[i].transpose();
-        TURegressionCoeff[i] = inverse(TpiPrime.mmul(predScoreMat[i])).mmul(TpiPrime).mmul(YScoreMat);
-        result = new SingularValueDecomposition(TpiPrime.mmul(Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime))).mmul(predScoreMat[i]), {
-          computeLeftSingularVectors: true,
-          computeRightSingularVectors: false
-        });
-        var CoTemp = result.leftSingularVectors;
-        var SoTemp = result.diagonalMatrix;
-        YOrthLoadingVec[i] = CoTemp.subMatrix(0, CoTemp.rows - 1, 0, 0);
-        YOrthEigen[i] = SoTemp.get(0, 0);
-        YOrthScoreMat[i] = Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime)).mmul(predScoreMat[i]).mmul(YOrthLoadingVec[i]).mul(Math.pow(YOrthEigen[i], -0.5));
-        var toiPrime = YOrthScoreMat[i].transpose();
-        YOrthScoreNorm[i] = Matrix.sqrt(toiPrime.mmul(YOrthScoreMat[i]));
-        YOrthScoreMat[i] = YOrthScoreMat[i].divRowVector(YOrthScoreNorm[i]);
-        var ITo = Matrix.sub(Identity, YOrthScoreMat[i].mmul(YOrthScoreMat[i].transpose()));
-        kernelX[0][i + 1] = kernelX[0][i].mmul(ITo);
-        kernelX[i + 1][i + 1] = ITo.mmul(kernelX[i][i]).mmul(ITo);
-      }
+        function saveNode(node, distance) {
+          bestNodes.push([node, distance]);
 
-      var lastScoreMat = predScoreMat[this.orthogonalComp] = kernelX[0][this.orthogonalComp].transpose().mmul(YScoreMat).mmul(SigmaPow);
-      var lastTpPrime = lastScoreMat.transpose();
-      TURegressionCoeff[this.orthogonalComp] = inverse(lastTpPrime.mmul(lastScoreMat)).mmul(lastTpPrime).mmul(YScoreMat);
-      this.YLoadingMat = YLoadingMat;
-      this.SigmaPow = SigmaPow;
-      this.YScoreMat = YScoreMat;
-      this.predScoreMat = predScoreMat;
-      this.YOrthLoadingVec = YOrthLoadingVec;
-      this.YOrthEigen = YOrthEigen;
-      this.YOrthScoreMat = YOrthScoreMat;
-      this.toNorm = YOrthScoreNorm;
-      this.TURegressionCoeff = TURegressionCoeff;
-      this.kernelX = kernelX;
-    }
-    /**
-       * Predicts the output given the matrix to predict.
-       * @param {Matrix|Array} toPredict
-       * @return {{y: Matrix, predScoreMat: Array<Matrix>, predYOrthVectors: Array<Matrix>}} predictions
-       */
+          if (bestNodes.size() > maxNodes) {
+            bestNodes.pop();
+          }
+        }
 
+        for (i = 0; i < dimensions.length; i += 1) {
+          if (i === node.dimension) {
+            linearPoint[dimensions[i]] = point[dimensions[i]];
+          } else {
+            linearPoint[dimensions[i]] = node.obj[dimensions[i]];
+          }
+        }
 
-    predict(toPredict) {
-      var KTestTrain = this.kernel.compute(toPredict, this.trainingSet);
-      var temp = KTestTrain;
-      KTestTrain = new Array(this.orthogonalComp + 1);
+        linearDistance = metric(linearPoint, node.obj);
 
-      for (let i = 0; i < this.orthogonalComp + 1; i++) {
-        KTestTrain[i] = new Array(this.orthogonalComp + 1);
-      }
+        if (node.right === null && node.left === null) {
+          if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {
+            saveNode(node, ownDistance);
+          }
 
-      KTestTrain[0][0] = temp;
-      var YOrthScoreVector = new Array(this.orthogonalComp);
-      var predScoreMat = new Array(this.orthogonalComp);
-      var i;
+          return;
+        }
 
-      for (i = 0; i < this.orthogonalComp; ++i) {
-        predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);
-        YOrthScoreVector[i] = Matrix.sub(KTestTrain[i][i], predScoreMat[i].mmul(this.predScoreMat[i].transpose())).mmul(this.predScoreMat[i]).mmul(this.YOrthLoadingVec[i]).mul(Math.pow(this.YOrthEigen[i], -0.5));
-        YOrthScoreVector[i] = YOrthScoreVector[i].divRowVector(this.toNorm[i]);
-        var scoreMatPrime = this.YOrthScoreMat[i].transpose();
-        KTestTrain[i + 1][0] = Matrix.sub(KTestTrain[i][0], YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[0][i].transpose()));
-        var p1 = Matrix.sub(KTestTrain[i][0], KTestTrain[i][i].mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime));
-        var p2 = YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[i][i]);
-        var p3 = p2.mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime);
-        KTestTrain[i + 1][i + 1] = p1.sub(p2).add(p3);
-      }
+        if (node.right === null) {
+          bestChild = node.left;
+        } else if (node.left === null) {
+          bestChild = node.right;
+        } else {
+          if (point[dimension] < node.obj[dimension]) {
+            bestChild = node.left;
+          } else {
+            bestChild = node.right;
+          }
+        }
 
-      predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);
-      var prediction = predScoreMat[i].mmul(this.TURegressionCoeff[i]).mmul(this.YLoadingMat.transpose());
-      return {
-        prediction: prediction,
-        predScoreMat: predScoreMat,
-        predYOrthVectors: YOrthScoreVector
-      };
-    }
-    /**
-       * Export the current model to JSON.
-       * @return {object} - Current model.
-       */
+        nearestSearch(bestChild);
 
+        if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {
+          saveNode(node, ownDistance);
+        }
 
-    toJSON() {
-      return {
-        name: 'K-OPLS',
-        YLoadingMat: this.YLoadingMat,
-        SigmaPow: this.SigmaPow,
-        YScoreMat: this.YScoreMat,
-        predScoreMat: this.predScoreMat,
-        YOrthLoadingVec: this.YOrthLoadingVec,
-        YOrthEigen: this.YOrthEigen,
-        YOrthScoreMat: this.YOrthScoreMat,
-        toNorm: this.toNorm,
-        TURegressionCoeff: this.TURegressionCoeff,
-        kernelX: this.kernelX,
-        trainingSet: this.trainingSet,
-        orthogonalComp: this.orthogonalComp,
-        predictiveComp: this.predictiveComp
-      };
-    }
-    /**
-       * Load a K-OPLS with the given model.
-       * @param {object} model
-       * @param {Kernel} kernel - kernel used on the model, see [ml-kernel](https://github.com/mljs/kernel).
-       * @return {KOPLS}
-       */
+        if (bestNodes.size() < maxNodes || Math.abs(linearDistance) < bestNodes.peek()[1]) {
+          if (bestChild === node.left) {
+            otherChild = node.right;
+          } else {
+            otherChild = node.left;
+          }
 
+          if (otherChild !== null) {
+            nearestSearch(otherChild);
+          }
+        }
+      }
 
-    static load(model, kernel) {
-      if (model.name !== 'K-OPLS') {
-        throw new RangeError("Invalid model: ".concat(model.name));
+      if (maxDistance) {
+        for (i = 0; i < maxNodes; i += 1) {
+          bestNodes.push([null, maxDistance]);
+        }
       }
 
-      if (!kernel) {
-        throw new RangeError('You must provide a kernel for the model!');
+      if (this.root) {
+        nearestSearch(this.root);
       }
 
-      model.kernel = kernel;
-      return new KOPLS(true, model);
+      const result = [];
+
+      for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {
+        if (bestNodes.content[i][0]) {
+          result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);
+        }
+      }
+
+      return result;
     }
 
   }
 
-  /**
-   *  Constructs a confusion matrix
-   * @class ConfusionMatrix
-   * @example
-   * const CM = new ConfusionMatrix([[13, 2], [10, 5]], ['cat', 'dog'])
-   * @param {Array<Array<number>>} matrix - The confusion matrix, a 2D Array. Rows represent the actual label and columns
-   *     the predicted label.
-   * @param {Array<any>} labels - Labels of the confusion matrix, a 1D Array
-   */
-  class ConfusionMatrix {
-    constructor(matrix, labels) {
-      if (matrix.length !== matrix[0].length) {
-        throw new Error('Confusion matrix must be square');
-      }
+  function toJSONImpl(src) {
+    const dest = new Node(src.obj, src.dimension, null);
+    if (src.left) dest.left = toJSONImpl(src.left);
+    if (src.right) dest.right = toJSONImpl(src.right);
+    return dest;
+  }
 
-      if (labels.length !== matrix.length) {
-        throw new Error('Confusion matrix and labels should have the same length');
-      }
+  function buildTree(points, depth, parent, dimensions) {
+    const dim = depth % dimensions.length;
 
-      this.labels = labels;
-      this.matrix = matrix;
+    if (points.length === 0) {
+      return null;
     }
-    /**
-     * Construct confusion matrix from the predicted and actual labels (classes). Be sure to provide the arguments in
-     * the correct order!
-     * @param {Array<any>} actual  - The predicted labels of the classification
-     * @param {Array<any>} predicted     - The actual labels of the classification. Has to be of same length as
-     *     predicted.
-     * @param {object} [options] - Additional options
-     * @param {Array<any>} [options.labels] - The list of labels that should be used. If not provided the distinct set
-     *     of labels present in predicted and actual is used. Labels are compared using the strict equality operator
-     *     '==='
-     * @return {ConfusionMatrix} - Confusion matrix
-     */
-
 
-    static fromLabels(actual, predicted) {
-      let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+    if (points.length === 1) {
+      return new Node(points[0], dim, parent);
+    }
 
-      if (predicted.length !== actual.length) {
-        throw new Error('predicted and actual must have the same length');
-      }
+    points.sort((a, b) => a[dimensions[dim]] - b[dimensions[dim]]);
+    const median = Math.floor(points.length / 2);
+    const node = new Node(points[median], dim, parent);
+    node.left = buildTree(points.slice(0, median), depth + 1, node, dimensions);
+    node.right = buildTree(points.slice(median + 1), depth + 1, node, dimensions);
+    return node;
+  }
 
-      let distinctLabels;
+  function restoreParent(root) {
+    if (root.left) {
+      root.left.parent = root;
+      restoreParent(root.left);
+    }
 
-      if (options.labels) {
-        distinctLabels = new Set(options.labels);
-      } else {
-        distinctLabels = new Set([...actual, ...predicted]);
-      }
+    if (root.right) {
+      root.right.parent = root;
+      restoreParent(root.right);
+    }
+  } // Binary heap implementation from:
+  // http://eloquentjavascript.net/appendix2.html
 
-      distinctLabels = Array.from(distinctLabels);
 
-      if (options.sort) {
-        distinctLabels.sort(options.sort);
-      } // Create confusion matrix and fill with 0's
+  class BinaryHeap {
+    constructor(scoreFunction) {
+      this.content = [];
+      this.scoreFunction = scoreFunction;
+    }
 
+    push(element) {
+      // Add the new element to the end of the array.
+      this.content.push(element); // Allow it to bubble up.
 
-      const matrix = Array.from({
-        length: distinctLabels.length
-      });
+      this.bubbleUp(this.content.length - 1);
+    }
 
-      for (let i = 0; i < matrix.length; i++) {
-        matrix[i] = new Array(matrix.length);
-        matrix[i].fill(0);
-      }
+    pop() {
+      // Store the first element so we can return it later.
+      var result = this.content[0]; // Get the element at the end of the array.
 
-      for (let i = 0; i < predicted.length; i++) {
-        const actualIdx = distinctLabels.indexOf(actual[i]);
-        const predictedIdx = distinctLabels.indexOf(predicted[i]);
+      var end = this.content.pop(); // If there are any elements left, put the end element at the
+      // start, and let it sink down.
 
-        if (actualIdx >= 0 && predictedIdx >= 0) {
-          matrix[actualIdx][predictedIdx]++;
-        }
+      if (this.content.length > 0) {
+        this.content[0] = end;
+        this.sinkDown(0);
       }
 
-      return new ConfusionMatrix(matrix, distinctLabels);
+      return result;
     }
-    /**
-     * Get the confusion matrix
-     * @return {Array<Array<number> >}
-     */
-
 
-    getMatrix() {
-      return this.matrix;
+    peek() {
+      return this.content[0];
     }
 
-    getLabels() {
-      return this.labels;
+    size() {
+      return this.content.length;
     }
-    /**
-     * Get the total number of samples
-     * @return {number}
-     */
 
+    bubbleUp(n) {
+      // Fetch the element that has to be moved.
+      var element = this.content[n]; // When at 0, an element can not go up any further.
 
-    getTotalCount() {
-      let predicted = 0;
+      while (n > 0) {
+        // Compute the parent element's index, and fetch it.
+        const parentN = Math.floor((n + 1) / 2) - 1;
+        const parent = this.content[parentN]; // Swap the elements if the parent is greater.
 
-      for (var i = 0; i < this.matrix.length; i++) {
-        for (var j = 0; j < this.matrix.length; j++) {
-          predicted += this.matrix[i][j];
+        if (this.scoreFunction(element) < this.scoreFunction(parent)) {
+          this.content[parentN] = element;
+          this.content[n] = parent; // Update 'n' to continue at the new position.
+
+          n = parentN;
+        } else {
+          // Found a parent that is less, no need to move it further.
+          break;
         }
       }
-
-      return predicted;
     }
-    /**
-     * Get the total number of true predictions
-     * @return {number}
-     */
 
+    sinkDown(n) {
+      // Look up the target element and its score.
+      var length = this.content.length;
+      var element = this.content[n];
+      var elemScore = this.scoreFunction(element);
 
-    getTrueCount() {
-      var count = 0;
-
-      for (var i = 0; i < this.matrix.length; i++) {
-        count += this.matrix[i][i];
-      }
+      while (true) {
+        // Compute the indices of the child elements.
+        var child2N = (n + 1) * 2;
+        var child1N = child2N - 1; // This is used to store the new position of the element,
+        // if any.
 
-      return count;
-    }
-    /**
-     * Get the total number of false predictions.
-     * @return {number}
-     */
+        var swap = null; // If the first child exists (is inside the array)...
 
+        if (child1N < length) {
+          // Look it up and compute its score.
+          var child1 = this.content[child1N];
+          var child1Score = this.scoreFunction(child1); // If the score is less than our element's, we need to swap.
 
-    getFalseCount() {
-      return this.getTotalCount() - this.getTrueCount();
-    }
-    /**
-     * Get the number of true positive predictions.
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
+          if (child1Score < elemScore) {
+            swap = child1N;
+          }
+        } // Do the same checks for the other child.
 
 
-    getTruePositiveCount(label) {
-      const index = this.getIndex(label);
-      return this.matrix[index][index];
-    }
-    /**
-     * Get the number of true negative predictions
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
+        if (child2N < length) {
+          var child2 = this.content[child2N];
+          var child2Score = this.scoreFunction(child2);
 
+          if (child2Score < (swap === null ? elemScore : child1Score)) {
+            swap = child2N;
+          }
+        } // If the element needs to be moved, swap it, and continue.
 
-    getTrueNegativeCount(label) {
-      const index = this.getIndex(label);
-      var count = 0;
 
-      for (var i = 0; i < this.matrix.length; i++) {
-        for (var j = 0; j < this.matrix.length; j++) {
-          if (i !== index && j !== index) {
-            count += this.matrix[i][j];
-          }
+        if (swap !== null) {
+          this.content[n] = this.content[swap];
+          this.content[swap] = element;
+          n = swap;
+        } else {
+          // Otherwise, we are done.
+          break;
         }
       }
-
-      return count;
     }
+
+  }
+
+  class KNN {
     /**
-     * Get the number of false positive predictions.
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
+     * @param {Array} dataset
+     * @param {Array} labels
+     * @param {object} options
+     * @param {number} [options.k=numberOfClasses + 1] - Number of neighbors to classify.
+     * @param {function} [options.distance=euclideanDistance] - Distance function that takes two parameters.
      */
+    constructor(dataset, labels, options = {}) {
+      if (dataset === true) {
+        const model = labels;
+        this.kdTree = new KDTree(model.kdTree, options);
+        this.k = model.k;
+        this.classes = new Set(model.classes);
+        this.isEuclidean = model.isEuclidean;
+        return;
+      }
 
+      const classes = new Set(labels);
+      const {
+        distance = euclidean,
+        k = classes.size + 1
+      } = options;
+      const points = new Array(dataset.length);
 
-    getFalsePositiveCount(label) {
-      const index = this.getIndex(label);
-      var count = 0;
+      for (var i = 0; i < points.length; ++i) {
+        points[i] = dataset[i].slice();
+      }
 
-      for (var i = 0; i < this.matrix.length; i++) {
-        if (i !== index) {
-          count += this.matrix[i][index];
-        }
+      for (i = 0; i < labels.length; ++i) {
+        points[i].push(labels[i]);
       }
 
-      return count;
+      this.kdTree = new KDTree(points, distance);
+      this.k = k;
+      this.classes = classes;
+      this.isEuclidean = distance === euclidean;
     }
     /**
-     * Get the number of false negative predictions.
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
+     * Create a new KNN instance with the given model.
+     * @param {object} model
+     * @param {function} distance=euclideanDistance - distance function must be provided if the model wasn't trained with euclidean distance.
+     * @return {KNN}
      */
 
 
-    getFalseNegativeCount(label) {
-      const index = this.getIndex(label);
-      var count = 0;
+    static load(model, distance = euclidean) {
+      if (model.name !== 'KNN') {
+        throw new Error(`invalid model: ${model.name}`);
+      }
 
-      for (var i = 0; i < this.matrix.length; i++) {
-        if (i !== index) {
-          count += this.matrix[index][i];
-        }
+      if (!model.isEuclidean && distance === euclidean) {
+        throw new Error('a custom distance function was used to create the model. Please provide it again');
       }
 
-      return count;
+      if (model.isEuclidean && distance !== euclidean) {
+        throw new Error('the model was created with the default distance function. Do not load it with another one');
+      }
+
+      return new KNN(true, model, distance);
     }
     /**
-     * Get the number of real positive samples.
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
+     * Return a JSON containing the kd-tree model.
+     * @return {object} JSON KNN model.
      */
 
 
-    getPositiveCount(label) {
-      return this.getTruePositiveCount(label) + this.getFalseNegativeCount(label);
-    }
-    /**
-     * Get the number of real negative samples.
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
-
-
-    getNegativeCount(label) {
-      return this.getTrueNegativeCount(label) + this.getFalsePositiveCount(label);
+    toJSON() {
+      return {
+        name: 'KNN',
+        kdTree: this.kdTree,
+        k: this.k,
+        classes: Array.from(this.classes),
+        isEuclidean: this.isEuclidean
+      };
     }
     /**
-     * Get the index in the confusion matrix that corresponds to the given label
-     * @param {any} label - The label to search for
-     * @throws if the label is not found
-     * @return {number}
+     * Predicts the output given the matrix to predict.
+     * @param {Array} dataset
+     * @return {Array} predictions
      */
 
 
-    getIndex(label) {
-      const index = this.labels.indexOf(label);
-      if (index === -1) throw new Error('The label does not exist');
-      return index;
-    }
-    /**
-     * Get the true positive rate a.k.a. sensitivity. Computes the ratio between the number of true positive predictions and the total number of positive samples.
-     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number} - The true positive rate [0-1]
-     */
-
+    predict(dataset) {
+      if (Array.isArray(dataset)) {
+        if (typeof dataset[0] === 'number') {
+          return getSinglePrediction(this, dataset);
+        } else if (Array.isArray(dataset[0]) && typeof dataset[0][0] === 'number') {
+          const predictions = new Array(dataset.length);
 
-    getTruePositiveRate(label) {
-      return this.getTruePositiveCount(label) / this.getPositiveCount(label);
-    }
-    /**
-     * Get the true negative rate a.k.a. specificity. Computes the ration between the number of true negative predictions and the total number of negative samples.
-     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
+          for (var i = 0; i < dataset.length; i++) {
+            predictions[i] = getSinglePrediction(this, dataset[i]);
+          }
 
+          return predictions;
+        }
+      }
 
-    getTrueNegativeRate(label) {
-      return this.getTrueNegativeCount(label) / this.getNegativeCount(label);
+      throw new TypeError('dataset to predict must be an array or a matrix');
     }
-    /**
-     * Get the positive predictive value a.k.a. precision. Computes TP / (TP + FP)
-     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
 
+  }
 
-    getPositivePredictiveValue(label) {
-      const TP = this.getTruePositiveCount(label);
-      return TP / (TP + this.getFalsePositiveCount(label));
-    }
-    /**
-     * Negative predictive value
-     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
-
+  function getSinglePrediction(knn, currentCase) {
+    var nearestPoints = knn.kdTree.nearest(currentCase, knn.k);
+    var pointsPerClass = {};
+    var predictedClass = -1;
+    var maxPoints = -1;
+    var lastElement = nearestPoints[0][0].length - 1;
 
-    getNegativePredictiveValue(label) {
-      const TN = this.getTrueNegativeCount(label);
-      return TN / (TN + this.getFalseNegativeCount(label));
+    for (var element of knn.classes) {
+      pointsPerClass[element] = 0;
     }
-    /**
-     * False negative rate a.k.a. miss rate.
-     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
 
+    for (var i = 0; i < nearestPoints.length; ++i) {
+      var currentClass = nearestPoints[i][0][lastElement];
+      var currentPoints = ++pointsPerClass[currentClass];
 
-    getFalseNegativeRate(label) {
-      return 1 - this.getTruePositiveRate(label);
+      if (currentPoints > maxPoints) {
+        predictedClass = currentClass;
+        maxPoints = currentPoints;
+      }
     }
-    /**
-     * False positive rate a.k.a. fall-out rate.
-     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
 
+    return predictedClass;
+  }
 
-    getFalsePositiveRate(label) {
-      return 1 - this.getTrueNegativeRate(label);
-    }
-    /**
-     * False discovery rate (FDR)
-     * {@link https://en.wikipedia.org/wiki/False_discovery_rate}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
-
+  /**
+   * @private
+   * Function that given vector, returns its norm
+   * @param {Vector} X
+   * @return {number} Norm of the vector
+   */
 
-    getFalseDiscoveryRate(label) {
-      const FP = this.getFalsePositiveCount(label);
-      return FP / (FP + this.getTruePositiveCount(label));
-    }
-    /**
-     * False omission rate (FOR)
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
+  function norm(X) {
+    return Math.sqrt(X.clone().apply(pow2array).sum());
+  }
+  /**
+   * @private
+   * Function that pow 2 each element of a Matrix or a Vector,
+   * used in the apply method of the Matrix object
+   * @param {number} i - index i.
+   * @param {number} j - index j.
+   * @return {Matrix} The Matrix object modified at the index i, j.
+   * */
 
+  function pow2array(i, j) {
+    this.set(i, j, this.get(i, j) ** 2);
+  }
+  /**
+   * @private
+   * Function that initialize an array of matrices.
+   * @param {Array} array
+   * @param {boolean} isMatrix
+   * @return {Array} array with the matrices initialized.
+   */
 
-    getFalseOmissionRate(label) {
-      const FN = this.getFalseNegativeCount(label);
-      return FN / (FN + this.getTruePositiveCount(label));
+  function initializeMatrices(array, isMatrix) {
+    if (isMatrix) {
+      for (let i = 0; i < array.length; ++i) {
+        for (let j = 0; j < array[i].length; ++j) {
+          let elem = array[i][j];
+          array[i][j] = elem !== null ? new Matrix(array[i][j]) : undefined;
+        }
+      }
+    } else {
+      for (let i = 0; i < array.length; ++i) {
+        array[i] = new Matrix(array[i]);
+      }
     }
-    /**
-     * F1 score
-     * {@link https://en.wikipedia.org/wiki/F1_score}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
-
 
-    getF1Score(label) {
-      const TP = this.getTruePositiveCount(label);
-      return 2 * TP / (2 * TP + this.getFalsePositiveCount(label) + this.getFalseNegativeCount(label));
-    }
-    /**
-     * Matthews correlation coefficient (MCC)
-     * {@link https://en.wikipedia.org/wiki/Matthews_correlation_coefficient}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
-     */
+    return array;
+  }
 
+  /**
+   * @class PLS
+   */
 
-    getMatthewsCorrelationCoefficient(label) {
-      const TP = this.getTruePositiveCount(label);
-      const TN = this.getTrueNegativeCount(label);
-      const FP = this.getFalsePositiveCount(label);
-      const FN = this.getFalseNegativeCount(label);
-      return (TP * TN - FP * FN) / Math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN));
-    }
+  class PLS {
     /**
-     * Informedness
-     * {@link https://en.wikipedia.org/wiki/Youden%27s_J_statistic}
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
+     * Constructor for Partial Least Squares (PLS)
+     * @param {object} options
+     * @param {number} [options.latentVectors] - Number of latent vector to get (if the algorithm doesn't find a good model below the tolerance)
+     * @param {number} [options.tolerance=1e-5]
+     * @param {boolean} [options.scale=true] - rescale dataset using mean.
+     * @param {object} model - for load purposes.
      */
-
-
-    getInformedness(label) {
-      return this.getTruePositiveRate(label) + this.getTrueNegativeRate(label) - 1;
+    constructor(options, model) {
+      if (options === true) {
+        this.meanX = model.meanX;
+        this.stdDevX = model.stdDevX;
+        this.meanY = model.meanY;
+        this.stdDevY = model.stdDevY;
+        this.PBQ = Matrix.checkMatrix(model.PBQ);
+        this.R2X = model.R2X;
+        this.scale = model.scale;
+        this.scaleMethod = model.scaleMethod;
+        this.tolerance = model.tolerance;
+      } else {
+        let {
+          tolerance = 1e-5,
+          scale = true
+        } = options;
+        this.tolerance = tolerance;
+        this.scale = scale;
+        this.latentVectors = options.latentVectors;
+      }
     }
     /**
-     * Markedness
-     * @param {any} label - The label that should be considered "positive"
-     * @return {number}
+     * Fits the model with the given data and predictions, in this function is calculated the
+     * following outputs:
+     *
+     * T - Score matrix of X
+     * P - Loading matrix of X
+     * U - Score matrix of Y
+     * Q - Loading matrix of Y
+     * B - Matrix of regression coefficient
+     * W - Weight matrix of X
+     *
+     * @param {Matrix|Array} trainingSet
+     * @param {Matrix|Array} trainingValues
      */
 
 
-    getMarkedness(label) {
-      return this.getPositivePredictiveValue(label) + this.getNegativePredictiveValue(label) - 1;
-    }
-    /**
-     * Get the confusion table.
-     * @param {any} label - The label that should be considered "positive"
-     * @return {Array<Array<number> >} - The 2x2 confusion table. [[TP, FN], [FP, TN]]
-     */
+    train(trainingSet, trainingValues) {
+      trainingSet = Matrix.checkMatrix(trainingSet);
+      trainingValues = Matrix.checkMatrix(trainingValues);
 
+      if (trainingSet.length !== trainingValues.length) {
+        throw new RangeError('The number of X rows must be equal to the number of Y rows');
+      }
 
-    getConfusionTable(label) {
-      return [[this.getTruePositiveCount(label), this.getFalseNegativeCount(label)], [this.getFalsePositiveCount(label), this.getTrueNegativeCount(label)]];
+      this.meanX = trainingSet.mean('column');
+      this.stdDevX = trainingSet.standardDeviation('column', {
+        mean: this.meanX,
+        unbiased: true
+      });
+      this.meanY = trainingValues.mean('column');
+      this.stdDevY = trainingValues.standardDeviation('column', {
+        mean: this.meanY,
+        unbiased: true
+      });
+
+      if (this.scale) {
+        trainingSet = trainingSet.clone().subRowVector(this.meanX).divRowVector(this.stdDevX);
+        trainingValues = trainingValues.clone().subRowVector(this.meanY).divRowVector(this.stdDevY);
+      }
+
+      if (this.latentVectors === undefined) {
+        this.latentVectors = Math.min(trainingSet.rows - 1, trainingSet.columns);
+      }
+
+      let rx = trainingSet.rows;
+      let cx = trainingSet.columns;
+      let ry = trainingValues.rows;
+      let cy = trainingValues.columns;
+      let ssqXcal = trainingSet.clone().mul(trainingSet).sum(); // for the r²
+
+      let sumOfSquaresY = trainingValues.clone().mul(trainingValues).sum();
+      let tolerance = this.tolerance;
+      let n = this.latentVectors;
+      let T = Matrix.zeros(rx, n);
+      let P = Matrix.zeros(cx, n);
+      let U = Matrix.zeros(ry, n);
+      let Q = Matrix.zeros(cy, n);
+      let B = Matrix.zeros(n, n);
+      let W = P.clone();
+      let k = 0;
+      let t;
+      let w;
+      let q;
+      let p;
+
+      while (norm(trainingValues) > tolerance && k < n) {
+        let transposeX = trainingSet.transpose();
+        let transposeY = trainingValues.transpose();
+        let tIndex = maxSumColIndex(trainingSet.clone().mul(trainingSet));
+        let uIndex = maxSumColIndex(trainingValues.clone().mul(trainingValues));
+        let t1 = trainingSet.getColumnVector(tIndex);
+        let u = trainingValues.getColumnVector(uIndex);
+        t = Matrix.zeros(rx, 1);
+
+        while (norm(t1.clone().sub(t)) > tolerance) {
+          w = transposeX.mmul(u);
+          w.div(norm(w));
+          t = t1;
+          t1 = trainingSet.mmul(w);
+          q = transposeY.mmul(t1);
+          q.div(norm(q));
+          u = trainingValues.mmul(q);
+        }
+
+        t = t1;
+        let num = transposeX.mmul(t);
+        let den = t.transpose().mmul(t).get(0, 0);
+        p = num.div(den);
+        let pnorm = norm(p);
+        p.div(pnorm);
+        t.mul(pnorm);
+        w.mul(pnorm);
+        num = u.transpose().mmul(t);
+        den = t.transpose().mmul(t).get(0, 0);
+        let b = num.div(den).get(0, 0);
+        trainingSet.sub(t.mmul(p.transpose()));
+        trainingValues.sub(t.clone().mul(b).mmul(q.transpose()));
+        T.setColumn(k, t);
+        P.setColumn(k, p);
+        U.setColumn(k, u);
+        Q.setColumn(k, q);
+        W.setColumn(k, w);
+        B.set(k, k, b);
+        k++;
+      }
+
+      k--;
+      T = T.subMatrix(0, T.rows - 1, 0, k);
+      P = P.subMatrix(0, P.rows - 1, 0, k);
+      U = U.subMatrix(0, U.rows - 1, 0, k);
+      Q = Q.subMatrix(0, Q.rows - 1, 0, k);
+      W = W.subMatrix(0, W.rows - 1, 0, k);
+      B = B.subMatrix(0, k, 0, k);
+      this.ssqYcal = sumOfSquaresY;
+      this.E = trainingSet;
+      this.F = trainingValues;
+      this.T = T;
+      this.P = P;
+      this.U = U;
+      this.Q = Q;
+      this.W = W;
+      this.B = B;
+      this.PBQ = P.mmul(B).mmul(Q.transpose());
+      this.R2X = t.transpose().mmul(t).mmul(p.transpose().mmul(p)).div(ssqXcal).get(0, 0);
     }
     /**
-     * Get total accuracy.
-     * @return {number} - The ratio between the number of true predictions and total number of classifications ([0-1])
+     * Predicts the behavior of the given dataset.
+     * @param {Matrix|Array} dataset - data to be predicted.
+     * @return {Matrix} - predictions of each element of the dataset.
      */
 
 
-    getAccuracy() {
-      let correct = 0;
-      let incorrect = 0;
+    predict(dataset) {
+      let X = Matrix.checkMatrix(dataset);
 
-      for (var i = 0; i < this.matrix.length; i++) {
-        for (var j = 0; j < this.matrix.length; j++) {
-          if (i === j) correct += this.matrix[i][j];else incorrect += this.matrix[i][j];
-        }
+      if (this.scale) {
+        X = X.subRowVector(this.meanX).divRowVector(this.stdDevX);
       }
 
-      return correct / (correct + incorrect);
+      let Y = X.mmul(this.PBQ);
+      Y = Y.mulRowVector(this.stdDevY).addRowVector(this.meanY);
+      return Y;
     }
     /**
-     * Returns the element in the confusion matrix that corresponds to the given actual and predicted labels.
-     * @param {any} actual - The true label
-     * @param {any} predicted - The predicted label
-     * @return {number} - The element in the confusion matrix
+     * Returns the explained variance on training of the PLS model
+     * @return {number}
      */
 
 
-    getCount(actual, predicted) {
-      const actualIndex = this.getIndex(actual);
-      const predictedIndex = this.getIndex(predicted);
-      return this.matrix[actualIndex][predictedIndex];
+    getExplainedVariance() {
+      return this.R2X;
     }
     /**
-     * Compute the general prediction accuracy
-     * @deprecated Use getAccuracy
-     * @return {number} - The prediction accuracy ([0-1]
+     * Export the current model to JSON.
+     * @return {object} - Current model.
      */
 
 
-    get accuracy() {
-      return this.getAccuracy();
+    toJSON() {
+      return {
+        name: 'PLS',
+        R2X: this.R2X,
+        meanX: this.meanX,
+        stdDevX: this.stdDevX,
+        meanY: this.meanY,
+        stdDevY: this.stdDevY,
+        PBQ: this.PBQ,
+        tolerance: this.tolerance,
+        scale: this.scale
+      };
     }
     /**
-     * Compute the number of predicted observations
-     * @deprecated Use getTotalCount
-     * @return {number}
+     * Load a PLS model from a JSON Object
+     * @param {object} model
+     * @return {PLS} - PLS object from the given model
      */
 
 
-    get total() {
-      return this.getTotalCount();
+    static load(model) {
+      if (model.name !== 'PLS') {
+        throw new RangeError(`Invalid model: ${model.name}`);
+      }
+
+      return new PLS(true, model);
     }
 
   }
+  /**
+   * @private
+   * Function that returns the index where the sum of each
+   * column vector is maximum.
+   * @param {Matrix} data
+   * @return {number} index of the maximum
+   */
 
-  var src$1 = ConfusionMatrix;
-
-  const defaultOptions$6 = {
-    mode: 'index'
-  };
-
-  var src$2 = function* src(M, N, options) {
-    options = Object.assign({}, defaultOptions$6, options);
-    var a = new Array(N);
-    var c = new Array(M);
-    var b = new Array(N);
-    var p = new Array(N + 2);
-    var x, y, z; // init a and b
+  function maxSumColIndex(data) {
+    return Matrix.rowVector(data.sum('column')).maxIndex()[0];
+  }
 
-    for (var i = 0; i < N; i++) {
-      a[i] = i;
-      if (i < N - M) b[i] = 0;else b[i] = 1;
-    } // init c
+  /**
+   * @class KOPLS
+   */
 
+  class KOPLS {
+    /**
+     * Constructor for Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)
+     * @param {object} options
+     * @param {number} [options.predictiveComponents] - Number of predictive components to use.
+     * @param {number} [options.orthogonalComponents] - Number of Y-Orthogonal components.
+     * @param {Kernel} [options.kernel] - Kernel object to apply, see [ml-kernel](https://github.com/mljs/kernel).
+     * @param {object} model - for load purposes.
+     */
+    constructor(options, model) {
+      if (options === true) {
+        this.trainingSet = new Matrix(model.trainingSet);
+        this.YLoadingMat = new Matrix(model.YLoadingMat);
+        this.SigmaPow = new Matrix(model.SigmaPow);
+        this.YScoreMat = new Matrix(model.YScoreMat);
+        this.predScoreMat = initializeMatrices(model.predScoreMat, false);
+        this.YOrthLoadingVec = initializeMatrices(model.YOrthLoadingVec, false);
+        this.YOrthEigen = model.YOrthEigen;
+        this.YOrthScoreMat = initializeMatrices(model.YOrthScoreMat, false);
+        this.toNorm = initializeMatrices(model.toNorm, false);
+        this.TURegressionCoeff = initializeMatrices(model.TURegressionCoeff, false);
+        this.kernelX = initializeMatrices(model.kernelX, true);
+        this.kernel = model.kernel;
+        this.orthogonalComp = model.orthogonalComp;
+        this.predictiveComp = model.predictiveComp;
+      } else {
+        if (options.predictiveComponents === undefined) {
+          throw new RangeError('no predictive components found!');
+        }
 
-    for (i = 0; i < M; i++) {
-      c[i] = N - M + i;
-    } // init p
+        if (options.orthogonalComponents === undefined) {
+          throw new RangeError('no orthogonal components found!');
+        }
 
+        if (options.kernel === undefined) {
+          throw new RangeError('no kernel found!');
+        }
 
-    for (i = 0; i < p.length; i++) {
-      if (i === 0) p[i] = N + 1;else if (i <= N - M) p[i] = 0;else if (i <= N) p[i] = i - N + M;else p[i] = -2;
+        this.orthogonalComp = options.orthogonalComponents;
+        this.predictiveComp = options.predictiveComponents;
+        this.kernel = options.kernel;
+      }
     }
+    /**
+     * Train the K-OPLS model with the given training set and labels.
+     * @param {Matrix|Array} trainingSet
+     * @param {Matrix|Array} trainingValues
+     */
 
-    function twiddle() {
-      var i, j, k;
-      j = 1;
-
-      while (p[j] <= 0) {
-        j++;
-      }
 
-      if (p[j - 1] === 0) {
-        for (i = j - 1; i !== 1; i--) {
-          p[i] = -1;
-        }
+    train(trainingSet, trainingValues) {
+      trainingSet = Matrix.checkMatrix(trainingSet);
+      trainingValues = Matrix.checkMatrix(trainingValues); // to save and compute kernel with the prediction dataset.
 
-        p[j] = 0;
-        x = z = 0;
-        p[1] = 1;
-        y = j - 1;
-      } else {
-        if (j > 1) {
-          p[j - 1] = 0;
-        }
+      this.trainingSet = trainingSet.clone();
+      let kernelX = this.kernel.compute(trainingSet);
+      let Identity = Matrix.eye(kernelX.rows, kernelX.rows, 1);
+      let temp = kernelX;
+      kernelX = new Array(this.orthogonalComp + 1);
 
-        do {
-          j++;
-        } while (p[j] > 0);
+      for (let i = 0; i < this.orthogonalComp + 1; i++) {
+        kernelX[i] = new Array(this.orthogonalComp + 1);
+      }
 
-        k = j - 1;
-        i = j;
+      kernelX[0][0] = temp;
+      let result = new SingularValueDecomposition(trainingValues.transpose().mmul(kernelX[0][0]).mmul(trainingValues), {
+        computeLeftSingularVectors: true,
+        computeRightSingularVectors: false
+      });
+      let YLoadingMat = result.leftSingularVectors;
+      let Sigma = result.diagonalMatrix;
+      YLoadingMat = YLoadingMat.subMatrix(0, YLoadingMat.rows - 1, 0, this.predictiveComp - 1);
+      Sigma = Sigma.subMatrix(0, this.predictiveComp - 1, 0, this.predictiveComp - 1);
+      let YScoreMat = trainingValues.mmul(YLoadingMat);
+      let predScoreMat = new Array(this.orthogonalComp + 1);
+      let TURegressionCoeff = new Array(this.orthogonalComp + 1);
+      let YOrthScoreMat = new Array(this.orthogonalComp);
+      let YOrthLoadingVec = new Array(this.orthogonalComp);
+      let YOrthEigen = new Array(this.orthogonalComp);
+      let YOrthScoreNorm = new Array(this.orthogonalComp);
+      let SigmaPow = Matrix.pow(Sigma, -0.5); // to avoid errors, check infinity
 
-        while (p[i] === 0) {
-          p[i++] = -1;
+      SigmaPow.apply(function (i, j) {
+        if (this.get(i, j) === Infinity) {
+          this.set(i, j, 0);
         }
+      });
 
-        if (p[i] === -1) {
-          p[i] = p[k];
-          z = p[k] - 1;
-          x = i - 1;
-          y = k - 1;
-          p[k] = -1;
-        } else {
-          if (i === p[0]) {
-            return 0;
-          } else {
-            p[j] = p[i];
-            z = p[i] - 1;
-            p[i] = 0;
-            x = j - 1;
-            y = i - 1;
-          }
-        }
+      for (let i = 0; i < this.orthogonalComp; ++i) {
+        predScoreMat[i] = kernelX[0][i].transpose().mmul(YScoreMat).mmul(SigmaPow);
+        let TpiPrime = predScoreMat[i].transpose();
+        TURegressionCoeff[i] = inverse(TpiPrime.mmul(predScoreMat[i])).mmul(TpiPrime).mmul(YScoreMat);
+        result = new SingularValueDecomposition(TpiPrime.mmul(Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime))).mmul(predScoreMat[i]), {
+          computeLeftSingularVectors: true,
+          computeRightSingularVectors: false
+        });
+        let CoTemp = result.leftSingularVectors;
+        let SoTemp = result.diagonalMatrix;
+        YOrthLoadingVec[i] = CoTemp.subMatrix(0, CoTemp.rows - 1, 0, 0);
+        YOrthEigen[i] = SoTemp.get(0, 0);
+        YOrthScoreMat[i] = Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime)).mmul(predScoreMat[i]).mmul(YOrthLoadingVec[i]).mul(Math.pow(YOrthEigen[i], -0.5));
+        let toiPrime = YOrthScoreMat[i].transpose();
+        YOrthScoreNorm[i] = Matrix.sqrt(toiPrime.mmul(YOrthScoreMat[i]));
+        YOrthScoreMat[i] = YOrthScoreMat[i].divRowVector(YOrthScoreNorm[i]);
+        let ITo = Matrix.sub(Identity, YOrthScoreMat[i].mmul(YOrthScoreMat[i].transpose()));
+        kernelX[0][i + 1] = kernelX[0][i].mmul(ITo);
+        kernelX[i + 1][i + 1] = ITo.mmul(kernelX[i][i]).mmul(ITo);
       }
 
-      return 1;
+      let lastScoreMat = predScoreMat[this.orthogonalComp] = kernelX[0][this.orthogonalComp].transpose().mmul(YScoreMat).mmul(SigmaPow);
+      let lastTpPrime = lastScoreMat.transpose();
+      TURegressionCoeff[this.orthogonalComp] = inverse(lastTpPrime.mmul(lastScoreMat)).mmul(lastTpPrime).mmul(YScoreMat);
+      this.YLoadingMat = YLoadingMat;
+      this.SigmaPow = SigmaPow;
+      this.YScoreMat = YScoreMat;
+      this.predScoreMat = predScoreMat;
+      this.YOrthLoadingVec = YOrthLoadingVec;
+      this.YOrthEigen = YOrthEigen;
+      this.YOrthScoreMat = YOrthScoreMat;
+      this.toNorm = YOrthScoreNorm;
+      this.TURegressionCoeff = TURegressionCoeff;
+      this.kernelX = kernelX;
     }
+    /**
+     * Predicts the output given the matrix to predict.
+     * @param {Matrix|Array} toPredict
+     * @return {{y: Matrix, predScoreMat: Array<Matrix>, predYOrthVectors: Array<Matrix>}} predictions
+     */
 
-    if (options.mode === 'index') {
-      yield c.slice();
 
-      while (twiddle()) {
-        c[z] = a[x];
-        yield c.slice();
-      }
-    } else if (options.mode === 'mask') {
-      yield b.slice();
+    predict(toPredict) {
+      let KTestTrain = this.kernel.compute(toPredict, this.trainingSet);
+      let temp = KTestTrain;
+      KTestTrain = new Array(this.orthogonalComp + 1);
 
-      while (twiddle()) {
-        b[x] = 1;
-        b[y] = 0;
-        yield b.slice();
+      for (let i = 0; i < this.orthogonalComp + 1; i++) {
+        KTestTrain[i] = new Array(this.orthogonalComp + 1);
       }
-    } else {
-      throw new Error('Invalid mode');
-    }
-  };
 
-  const CV = {};
-  /**
-   * Performs a leave-one-out cross-validation (LOO-CV) of the given samples. In LOO-CV, 1 observation is used as the
-   * validation set while the rest is used as the training set. This is repeated once for each observation. LOO-CV is a
-   * special case of LPO-CV. @see leavePout
-   * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier
-   *     api.
-   * @param {Array} features - The features for all samples of the data-set
-   * @param {Array} labels - The classification class of all samples of the data-set
-   * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.
-   * @return {ConfusionMatrix} - The cross-validation confusion matrix
-   */
-
-  CV.leaveOneOut = function (Classifier, features, labels, classifierOptions) {
-    if (typeof labels === 'function') {
-      var callback = labels;
-      labels = features;
-      features = Classifier;
-      return CV.leavePOut(features, labels, 1, callback);
-    }
-
-    return CV.leavePOut(Classifier, features, labels, classifierOptions, 1);
-  };
-  /**
-   * Performs a leave-p-out cross-validation (LPO-CV) of the given samples. In LPO-CV, p observations are used as the
-   * validation set while the rest is used as the training set. This is repeated as many times as there are possible
-   * ways to combine p observations from the set (unordered without replacement). Be aware that for relatively small
-   * data-set size this can require a very large number of training and testing to do!
-   * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier
-   *     api.
-   * @param {Array} features - The features for all samples of the data-set
-   * @param {Array} labels - The classification class of all samples of the data-set
-   * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.
-   * @param {number} p - The size of the validation sub-samples' set
-   * @return {ConfusionMatrix} - The cross-validation confusion matrix
-   */
+      KTestTrain[0][0] = temp;
+      let YOrthScoreVector = new Array(this.orthogonalComp);
+      let predScoreMat = new Array(this.orthogonalComp);
+      let i;
 
+      for (i = 0; i < this.orthogonalComp; ++i) {
+        predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);
+        YOrthScoreVector[i] = Matrix.sub(KTestTrain[i][i], predScoreMat[i].mmul(this.predScoreMat[i].transpose())).mmul(this.predScoreMat[i]).mmul(this.YOrthLoadingVec[i]).mul(Math.pow(this.YOrthEigen[i], -0.5));
+        YOrthScoreVector[i] = YOrthScoreVector[i].divRowVector(this.toNorm[i]);
+        let scoreMatPrime = this.YOrthScoreMat[i].transpose();
+        KTestTrain[i + 1][0] = Matrix.sub(KTestTrain[i][0], YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[0][i].transpose()));
+        let p1 = Matrix.sub(KTestTrain[i][0], KTestTrain[i][i].mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime));
+        let p2 = YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[i][i]);
+        let p3 = p2.mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime);
+        KTestTrain[i + 1][i + 1] = p1.sub(p2).add(p3);
+      }
 
-  CV.leavePOut = function (Classifier, features, labels, classifierOptions, p) {
-    if (typeof classifierOptions === 'function') {
-      var callback = classifierOptions;
-      p = labels;
-      labels = features;
-      features = Classifier;
+      predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);
+      let prediction = predScoreMat[i].mmul(this.TURegressionCoeff[i]).mmul(this.YLoadingMat.transpose());
+      return {
+        prediction: prediction,
+        predScoreMat: predScoreMat,
+        predYOrthVectors: YOrthScoreVector
+      };
     }
+    /**
+     * Export the current model to JSON.
+     * @return {object} - Current model.
+     */
 
-    check(features, labels);
-    const distinct = getDistinct(labels);
-    const confusionMatrix = initMatrix(distinct.length, distinct.length);
-    var N = features.length;
-    var gen = src$2(p, N);
-    var allIdx = new Array(N);
 
-    for (let i = 0; i < N; i++) {
-      allIdx[i] = i;
+    toJSON() {
+      return {
+        name: 'K-OPLS',
+        YLoadingMat: this.YLoadingMat,
+        SigmaPow: this.SigmaPow,
+        YScoreMat: this.YScoreMat,
+        predScoreMat: this.predScoreMat,
+        YOrthLoadingVec: this.YOrthLoadingVec,
+        YOrthEigen: this.YOrthEigen,
+        YOrthScoreMat: this.YOrthScoreMat,
+        toNorm: this.toNorm,
+        TURegressionCoeff: this.TURegressionCoeff,
+        kernelX: this.kernelX,
+        trainingSet: this.trainingSet,
+        orthogonalComp: this.orthogonalComp,
+        predictiveComp: this.predictiveComp
+      };
     }
+    /**
+     * Load a K-OPLS with the given model.
+     * @param {object} model
+     * @param {Kernel} kernel - kernel used on the model, see [ml-kernel](https://github.com/mljs/kernel).
+     * @return {KOPLS}
+     */
 
-    for (const testIdx of gen) {
-      var trainIdx = allIdx.slice();
 
-      for (let i = testIdx.length - 1; i >= 0; i--) {
-        trainIdx.splice(testIdx[i], 1);
+    static load(model, kernel) {
+      if (model.name !== 'K-OPLS') {
+        throw new RangeError(`Invalid model: ${model.name}`);
       }
 
-      if (callback) {
-        validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);
-      } else {
-        validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);
+      if (!kernel) {
+        throw new RangeError('You must provide a kernel for the model!');
       }
+
+      model.kernel = kernel;
+      return new KOPLS(true, model);
     }
 
-    return new src$1(confusionMatrix, distinct);
-  };
+  }
+
   /**
-   * Performs k-fold cross-validation (KF-CV). KF-CV separates the data-set into k random equally sized partitions, and
-   * uses each as a validation set, with all other partitions used in the training set. Observations left over from if k
-   * does not divide the number of observations are left out of the cross-validation process.
-   * @param {function} Classifier - The classifier's to use for the cross validation. Expect ml-classifier api.
-   * @param {Array} features - The features for all samples of the data-set
-   * @param {Array} labels - The classification class of all samples of the data-set
-   * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.
-   * @param {number} k - The number of partitions to create
-   * @return {ConfusionMatrix} - The cross-validation confusion matrix
+   *  Constructs a confusion matrix
+   * @class ConfusionMatrix
+   * @example
+   * const CM = new ConfusionMatrix([[13, 2], [10, 5]], ['cat', 'dog'])
+   * @param {Array<Array<number>>} matrix - The confusion matrix, a 2D Array. Rows represent the actual label and columns
+   *     the predicted label.
+   * @param {Array<any>} labels - Labels of the confusion matrix, a 1D Array
    */
+  class ConfusionMatrix {
+    constructor(matrix, labels) {
+      if (matrix.length !== matrix[0].length) {
+        throw new Error('Confusion matrix must be square');
+      }
 
+      if (labels.length !== matrix.length) {
+        throw new Error('Confusion matrix and labels should have the same length');
+      }
 
-  CV.kFold = function (Classifier, features, labels, classifierOptions, k) {
-    if (typeof classifierOptions === 'function') {
-      var callback = classifierOptions;
-      k = labels;
-      labels = features;
-      features = Classifier;
+      this.labels = labels;
+      this.matrix = matrix;
     }
+    /**
+     * Construct confusion matrix from the predicted and actual labels (classes). Be sure to provide the arguments in
+     * the correct order!
+     * @param {Array<any>} actual  - The predicted labels of the classification
+     * @param {Array<any>} predicted     - The actual labels of the classification. Has to be of same length as
+     *     predicted.
+     * @param {object} [options] - Additional options
+     * @param {Array<any>} [options.labels] - The list of labels that should be used. If not provided the distinct set
+     *     of labels present in predicted and actual is used. Labels are compared using the strict equality operator
+     *     '==='
+     * @return {ConfusionMatrix} - Confusion matrix
+     */
 
-    check(features, labels);
-    const distinct = getDistinct(labels);
-    const confusionMatrix = initMatrix(distinct.length, distinct.length);
-    var N = features.length;
-    var allIdx = new Array(N);
 
-    for (var i = 0; i < N; i++) {
-      allIdx[i] = i;
-    }
+    static fromLabels(actual, predicted, options = {}) {
+      if (predicted.length !== actual.length) {
+        throw new Error('predicted and actual must have the same length');
+      }
 
-    var l = Math.floor(N / k); // create random k-folds
+      let distinctLabels;
 
-    var current = [];
-    var folds = [];
+      if (options.labels) {
+        distinctLabels = new Set(options.labels);
+      } else {
+        distinctLabels = new Set([...actual, ...predicted]);
+      }
 
-    while (allIdx.length) {
-      var randi = Math.floor(Math.random() * allIdx.length);
-      current.push(allIdx[randi]);
-      allIdx.splice(randi, 1);
+      distinctLabels = Array.from(distinctLabels);
 
-      if (current.length === l) {
-        folds.push(current);
-        current = [];
-      }
-    }
+      if (options.sort) {
+        distinctLabels.sort(options.sort);
+      } // Create confusion matrix and fill with 0's
 
-    if (current.length) folds.push(current);
-    folds = folds.slice(0, k);
 
-    for (i = 0; i < folds.length; i++) {
-      var testIdx = folds[i];
-      var trainIdx = [];
+      const matrix = Array.from({
+        length: distinctLabels.length
+      });
 
-      for (var j = 0; j < folds.length; j++) {
-        if (j !== i) trainIdx = trainIdx.concat(folds[j]);
+      for (let i = 0; i < matrix.length; i++) {
+        matrix[i] = new Array(matrix.length);
+        matrix[i].fill(0);
       }
 
-      if (callback) {
-        validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);
-      } else {
-        validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);
-      }
-    }
+      for (let i = 0; i < predicted.length; i++) {
+        const actualIdx = distinctLabels.indexOf(actual[i]);
+        const predictedIdx = distinctLabels.indexOf(predicted[i]);
 
-    return new src$1(confusionMatrix, distinct);
-  };
+        if (actualIdx >= 0 && predictedIdx >= 0) {
+          matrix[actualIdx][predictedIdx]++;
+        }
+      }
 
-  function check(features, labels) {
-    if (features.length !== labels.length) {
-      throw new Error('features and labels should have the same length');
+      return new ConfusionMatrix(matrix, distinctLabels);
     }
-  }
+    /**
+     * Get the confusion matrix
+     * @return {Array<Array<number> >}
+     */
 
-  function initMatrix(rows, columns) {
-    return new Array(rows).fill(0).map(() => new Array(columns).fill(0));
-  }
 
-  function getDistinct(arr) {
-    var s = new Set();
+    getMatrix() {
+      return this.matrix;
+    }
 
-    for (let i = 0; i < arr.length; i++) {
-      s.add(arr[i]);
+    getLabels() {
+      return this.labels;
     }
+    /**
+     * Get the total number of samples
+     * @return {number}
+     */
 
-    return Array.from(s);
-  }
 
-  function validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct) {
-    const {
-      testFeatures,
-      trainFeatures,
-      testLabels,
-      trainLabels
-    } = getTrainTest(features, labels, testIdx, trainIdx);
-    var classifier;
+    getTotalCount() {
+      let predicted = 0;
 
-    if (Classifier.prototype.train) {
-      classifier = new Classifier(classifierOptions);
-      classifier.train(trainFeatures, trainLabels);
-    } else {
-      classifier = new Classifier(trainFeatures, trainLabels, classifierOptions);
-    }
+      for (let i = 0; i < this.matrix.length; i++) {
+        for (let j = 0; j < this.matrix.length; j++) {
+          predicted += this.matrix[i][j];
+        }
+      }
 
-    var predictedLabels = classifier.predict(testFeatures);
-    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);
-  }
+      return predicted;
+    }
+    /**
+     * Get the total number of true predictions
+     * @return {number}
+     */
 
-  function validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback) {
-    const {
-      testFeatures,
-      trainFeatures,
-      testLabels,
-      trainLabels
-    } = getTrainTest(features, labels, testIdx, trainIdx);
-    const predictedLabels = callback(trainFeatures, trainLabels, testFeatures);
-    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);
-  }
 
-  function updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct) {
-    for (var i = 0; i < predictedLabels.length; i++) {
-      const actualIdx = distinct.indexOf(testLabels[i]);
-      const predictedIdx = distinct.indexOf(predictedLabels[i]);
+    getTrueCount() {
+      let count = 0;
 
-      if (actualIdx < 0 || predictedIdx < 0) {
-        // eslint-disable-next-line no-console
-        console.warn("ignore unknown predicted label ".concat(predictedLabels[i]));
+      for (let i = 0; i < this.matrix.length; i++) {
+        count += this.matrix[i][i];
       }
 
-      confusionMatrix[actualIdx][predictedIdx]++;
+      return count;
     }
-  }
+    /**
+     * Get the total number of false predictions.
+     * @return {number}
+     */
 
-  function getTrainTest(features, labels, testIdx, trainIdx) {
-    return {
-      testFeatures: testIdx.map(function (index) {
-        return features[index];
-      }),
-      trainFeatures: trainIdx.map(function (index) {
-        return features[index];
-      }),
-      testLabels: testIdx.map(function (index) {
-        return labels[index];
-      }),
-      trainLabels: trainIdx.map(function (index) {
-        return labels[index];
-      })
-    };
-  }
 
-  var src$3 = CV;
-
-  function logistic(val) {
-    return 1 / (1 + Math.exp(-val));
-  }
+    getFalseCount() {
+      return this.getTotalCount() - this.getTrueCount();
+    }
+    /**
+     * Get the number of true positive predictions.
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
 
-  function expELU(val, param) {
-    return val < 0 ? param * (Math.exp(val) - 1) : val;
-  }
 
-  function softExponential(val, param) {
-    if (param < 0) {
-      return -Math.log(1 - param * (val + param)) / param;
+    getTruePositiveCount(label) {
+      const index = this.getIndex(label);
+      return this.matrix[index][index];
     }
+    /**
+     * Get the number of true negative predictions
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
 
-    if (param > 0) {
-      return (Math.exp(param * val) - 1) / param + param;
-    }
 
-    return val;
-  }
+    getTrueNegativeCount(label) {
+      const index = this.getIndex(label);
+      let count = 0;
 
-  function softExponentialPrime(val, param) {
-    if (param < 0) {
-      return 1 / (1 - param * (param + val));
-    } else {
-      return Math.exp(param * val);
-    }
-  }
+      for (let i = 0; i < this.matrix.length; i++) {
+        for (let j = 0; j < this.matrix.length; j++) {
+          if (i !== index && j !== index) {
+            count += this.matrix[i][j];
+          }
+        }
+      }
 
-  const ACTIVATION_FUNCTIONS = {
-    tanh: {
-      activation: Math.tanh,
-      derivate: val => 1 - val * val
-    },
-    identity: {
-      activation: val => val,
-      derivate: () => 1
-    },
-    logistic: {
-      activation: logistic,
-      derivate: val => logistic(val) * (1 - logistic(val))
-    },
-    arctan: {
-      activation: Math.atan,
-      derivate: val => 1 / (val * val + 1)
-    },
-    softsign: {
-      activation: val => val / (1 + Math.abs(val)),
-      derivate: val => 1 / ((1 + Math.abs(val)) * (1 + Math.abs(val)))
-    },
-    relu: {
-      activation: val => val < 0 ? 0 : val,
-      derivate: val => val < 0 ? 0 : 1
-    },
-    softplus: {
-      activation: val => Math.log(1 + Math.exp(val)),
-      derivate: val => 1 / (1 + Math.exp(-val))
-    },
-    bent: {
-      activation: val => (Math.sqrt(val * val + 1) - 1) / 2 + val,
-      derivate: val => val / (2 * Math.sqrt(val * val + 1)) + 1
-    },
-    sinusoid: {
-      activation: Math.sin,
-      derivate: Math.cos
-    },
-    sinc: {
-      activation: val => val === 0 ? 1 : Math.sin(val) / val,
-      derivate: val => val === 0 ? 0 : Math.cos(val) / val - Math.sin(val) / (val * val)
-    },
-    gaussian: {
-      activation: val => Math.exp(-(val * val)),
-      derivate: val => -2 * val * Math.exp(-(val * val))
-    },
-    'parametric-relu': {
-      activation: (val, param) => val < 0 ? param * val : val,
-      derivate: (val, param) => val < 0 ? param : 1
-    },
-    'exponential-elu': {
-      activation: expELU,
-      derivate: (val, param) => val < 0 ? expELU(val, param) + param : 1
-    },
-    'soft-exponential': {
-      activation: softExponential,
-      derivate: softExponentialPrime
+      return count;
     }
-  };
+    /**
+     * Get the number of false positive predictions.
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
 
-  class Layer {
+
+    getFalsePositiveCount(label) {
+      const index = this.getIndex(label);
+      let count = 0;
+
+      for (let i = 0; i < this.matrix.length; i++) {
+        if (i !== index) {
+          count += this.matrix[i][index];
+        }
+      }
+
+      return count;
+    }
     /**
-       * @private
-       * Create a new layer with the given options
-       * @param {object} options
-       * @param {number} [options.inputSize] - Number of conections that enter the neurons.
-       * @param {number} [options.outputSize] - Number of conections that leave the neurons.
-       * @param {number} [options.regularization] - Regularization parameter.
-       * @param {number} [options.epsilon] - Learning rate parameter.
-       * @param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.
-       * @param {number} [options.activationParam] - Activation parameter if needed.
-       */
-    constructor(options) {
-      this.inputSize = options.inputSize;
-      this.outputSize = options.outputSize;
-      this.regularization = options.regularization;
-      this.epsilon = options.epsilon;
-      this.activation = options.activation;
-      this.activationParam = options.activationParam;
-      var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];
-      var params = selectedFunction.activation.length;
-      var actFunction = params > 1 ? val => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;
-      var derFunction = params > 1 ? val => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;
+     * Get the number of false negative predictions.
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
 
-      this.activationFunction = function (i, j) {
-        this.set(i, j, actFunction(this.get(i, j)));
-      };
 
-      this.derivate = function (i, j) {
-        this.set(i, j, derFunction(this.get(i, j)));
-      };
+    getFalseNegativeCount(label) {
+      const index = this.getIndex(label);
+      let count = 0;
 
-      if (options.model) {
-        // load model
-        this.W = Matrix.Matrix.checkMatrix(options.W);
-        this.b = Matrix.Matrix.checkMatrix(options.b);
-      } else {
-        // default constructor
-        this.W = Matrix.Matrix.rand(this.inputSize, this.outputSize);
-        this.b = Matrix.Matrix.zeros(1, this.outputSize);
-        this.W.apply(function (i, j) {
-          this.set(i, j, this.get(i, j) / Math.sqrt(options.inputSize));
-        });
+      for (let i = 0; i < this.matrix.length; i++) {
+        if (i !== index) {
+          count += this.matrix[index][i];
+        }
       }
+
+      return count;
     }
     /**
-       * @private
-       * propagate the given input through the current layer.
-       * @param {Matrix} X - input.
-       * @return {Matrix} output at the current layer.
-       */
+     * Get the number of real positive samples.
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
 
 
-    forward(X) {
-      var z = X.mmul(this.W).addRowVector(this.b);
-      z.apply(this.activationFunction);
-      this.a = z.clone();
-      return z;
+    getPositiveCount(label) {
+      return this.getTruePositiveCount(label) + this.getFalseNegativeCount(label);
     }
     /**
-       * @private
-       * apply backpropagation algorithm at the current layer
-       * @param {Matrix} delta - delta values estimated at the following layer.
-       * @param {Matrix} a - 'a' values from the following layer.
-       * @return {Matrix} the new delta values for the next layer.
-       */
+     * Get the number of real negative samples.
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
 
 
-    backpropagation(delta, a) {
-      this.dW = a.transpose().mmul(delta);
-      this.db = Matrix.Matrix.rowVector(delta.sum('column'));
-      var aCopy = a.clone();
-      return delta.mmul(this.W.transpose()).mul(aCopy.apply(this.derivate));
+    getNegativeCount(label) {
+      return this.getTrueNegativeCount(label) + this.getFalsePositiveCount(label);
     }
     /**
-       * @private
-       * Function that updates the weights at the current layer with the derivatives.
-       */
+     * Get the index in the confusion matrix that corresponds to the given label
+     * @param {any} label - The label to search for
+     * @throws if the label is not found
+     * @return {number}
+     */
 
 
-    update() {
-      this.dW.add(this.W.clone().mul(this.regularization));
-      this.W.add(this.dW.mul(-this.epsilon));
-      this.b.add(this.db.mul(-this.epsilon));
+    getIndex(label) {
+      const index = this.labels.indexOf(label);
+      if (index === -1) throw new Error('The label does not exist');
+      return index;
     }
     /**
-       * @private
-       * Export the current layer to JSON.
-       * @return {object} model
-       */
+     * Get the true positive rate a.k.a. sensitivity. Computes the ratio between the number of true positive predictions and the total number of positive samples.
+     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number} - The true positive rate [0-1]
+     */
 
 
-    toJSON() {
-      return {
-        model: 'Layer',
-        inputSize: this.inputSize,
-        outputSize: this.outputSize,
-        regularization: this.regularization,
-        epsilon: this.epsilon,
-        activation: this.activation,
-        W: this.W,
-        b: this.b
-      };
+    getTruePositiveRate(label) {
+      return this.getTruePositiveCount(label) / this.getPositiveCount(label);
     }
     /**
-       * @private
-       * Creates a new Layer with the given model.
-       * @param {object} model
-       * @return {Layer}
-       */
+     * Get the true negative rate a.k.a. specificity. Computes the ration between the number of true negative predictions and the total number of negative samples.
+     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getTrueNegativeRate(label) {
+      return this.getTrueNegativeCount(label) / this.getNegativeCount(label);
+    }
+    /**
+     * Get the positive predictive value a.k.a. precision. Computes TP / (TP + FP)
+     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getPositivePredictiveValue(label) {
+      const TP = this.getTruePositiveCount(label);
+      return TP / (TP + this.getFalsePositiveCount(label));
+    }
+    /**
+     * Negative predictive value
+     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getNegativePredictiveValue(label) {
+      const TN = this.getTrueNegativeCount(label);
+      return TN / (TN + this.getFalseNegativeCount(label));
+    }
+    /**
+     * False negative rate a.k.a. miss rate.
+     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getFalseNegativeRate(label) {
+      return 1 - this.getTruePositiveRate(label);
+    }
+    /**
+     * False positive rate a.k.a. fall-out rate.
+     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getFalsePositiveRate(label) {
+      return 1 - this.getTrueNegativeRate(label);
+    }
+    /**
+     * False discovery rate (FDR)
+     * {@link https://en.wikipedia.org/wiki/False_discovery_rate}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getFalseDiscoveryRate(label) {
+      const FP = this.getFalsePositiveCount(label);
+      return FP / (FP + this.getTruePositiveCount(label));
+    }
+    /**
+     * False omission rate (FOR)
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getFalseOmissionRate(label) {
+      const FN = this.getFalseNegativeCount(label);
+      return FN / (FN + this.getTruePositiveCount(label));
+    }
+    /**
+     * F1 score
+     * {@link https://en.wikipedia.org/wiki/F1_score}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getF1Score(label) {
+      const TP = this.getTruePositiveCount(label);
+      return 2 * TP / (2 * TP + this.getFalsePositiveCount(label) + this.getFalseNegativeCount(label));
+    }
+    /**
+     * Matthews correlation coefficient (MCC)
+     * {@link https://en.wikipedia.org/wiki/Matthews_correlation_coefficient}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getMatthewsCorrelationCoefficient(label) {
+      const TP = this.getTruePositiveCount(label);
+      const TN = this.getTrueNegativeCount(label);
+      const FP = this.getFalsePositiveCount(label);
+      const FN = this.getFalseNegativeCount(label);
+      return (TP * TN - FP * FN) / Math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN));
+    }
+    /**
+     * Informedness
+     * {@link https://en.wikipedia.org/wiki/Youden%27s_J_statistic}
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getInformedness(label) {
+      return this.getTruePositiveRate(label) + this.getTrueNegativeRate(label) - 1;
+    }
+    /**
+     * Markedness
+     * @param {any} label - The label that should be considered "positive"
+     * @return {number}
+     */
+
+
+    getMarkedness(label) {
+      return this.getPositivePredictiveValue(label) + this.getNegativePredictiveValue(label) - 1;
+    }
+    /**
+     * Get the confusion table.
+     * @param {any} label - The label that should be considered "positive"
+     * @return {Array<Array<number> >} - The 2x2 confusion table. [[TP, FN], [FP, TN]]
+     */
+
+
+    getConfusionTable(label) {
+      return [[this.getTruePositiveCount(label), this.getFalseNegativeCount(label)], [this.getFalsePositiveCount(label), this.getTrueNegativeCount(label)]];
+    }
+    /**
+     * Get total accuracy.
+     * @return {number} - The ratio between the number of true predictions and total number of classifications ([0-1])
+     */
+
+
+    getAccuracy() {
+      let correct = 0;
+      let incorrect = 0;
+
+      for (let i = 0; i < this.matrix.length; i++) {
+        for (let j = 0; j < this.matrix.length; j++) {
+          if (i === j) correct += this.matrix[i][j];else incorrect += this.matrix[i][j];
+        }
+      }
+
+      return correct / (correct + incorrect);
+    }
+    /**
+     * Returns the element in the confusion matrix that corresponds to the given actual and predicted labels.
+     * @param {any} actual - The true label
+     * @param {any} predicted - The predicted label
+     * @return {number} - The element in the confusion matrix
+     */
+
+
+    getCount(actual, predicted) {
+      const actualIndex = this.getIndex(actual);
+      const predictedIndex = this.getIndex(predicted);
+      return this.matrix[actualIndex][predictedIndex];
+    }
+    /**
+     * Compute the general prediction accuracy
+     * @deprecated Use getAccuracy
+     * @return {number} - The prediction accuracy ([0-1]
+     */
+
+
+    get accuracy() {
+      return this.getAccuracy();
+    }
+    /**
+     * Compute the number of predicted observations
+     * @deprecated Use getTotalCount
+     * @return {number}
+     */
+
+
+    get total() {
+      return this.getTotalCount();
+    }
+
+  }
+
+  var lib = createCommonjsModule(function (module, exports) {
+    (function (global, factory) {
+       factory() ;
+    })(commonjsGlobal, function () {
+
+      function createCommonjsModule(fn, module) {
+        return module = {
+          exports: {}
+        }, fn(module, module.exports), module.exports;
+      }
+
+      var runtime = createCommonjsModule(function (module) {
+        /**
+         * Copyright (c) 2014-present, Facebook, Inc.
+         *
+         * This source code is licensed under the MIT license found in the
+         * LICENSE file in the root directory of this source tree.
+         */
+        !function (global) {
+          var Op = Object.prototype;
+          var hasOwn = Op.hasOwnProperty;
+          var undefined$1; // More compressible than void 0.
+
+          var $Symbol = typeof Symbol === "function" ? Symbol : {};
+          var iteratorSymbol = $Symbol.iterator || "@@iterator";
+          var asyncIteratorSymbol = $Symbol.asyncIterator || "@@asyncIterator";
+          var toStringTagSymbol = $Symbol.toStringTag || "@@toStringTag";
+          var runtime = global.regeneratorRuntime;
+
+          if (runtime) {
+            {
+              // If regeneratorRuntime is defined globally and we're in a module,
+              // make the exports object identical to regeneratorRuntime.
+              module.exports = runtime;
+            } // Don't bother evaluating the rest of this file if the runtime was
+            // already defined globally.
+
+            return;
+          } // Define the runtime globally (as expected by generated code) as either
+          // module.exports (if we're in a module) or a new, empty object.
+
+
+          runtime = global.regeneratorRuntime = module.exports;
+
+          function wrap(innerFn, outerFn, self, tryLocsList) {
+            // If outerFn provided and outerFn.prototype is a Generator, then outerFn.prototype instanceof Generator.
+            var protoGenerator = outerFn && outerFn.prototype instanceof Generator ? outerFn : Generator;
+            var generator = Object.create(protoGenerator.prototype);
+            var context = new Context(tryLocsList || []); // The ._invoke method unifies the implementations of the .next,
+            // .throw, and .return methods.
+
+            generator._invoke = makeInvokeMethod(innerFn, self, context);
+            return generator;
+          }
+
+          runtime.wrap = wrap; // Try/catch helper to minimize deoptimizations. Returns a completion
+          // record like context.tryEntries[i].completion. This interface could
+          // have been (and was previously) designed to take a closure to be
+          // invoked without arguments, but in all the cases we care about we
+          // already have an existing method we want to call, so there's no need
+          // to create a new function object. We can even get away with assuming
+          // the method takes exactly one argument, since that happens to be true
+          // in every case, so we don't have to touch the arguments object. The
+          // only additional allocation required is the completion record, which
+          // has a stable shape and so hopefully should be cheap to allocate.
+
+          function tryCatch(fn, obj, arg) {
+            try {
+              return {
+                type: "normal",
+                arg: fn.call(obj, arg)
+              };
+            } catch (err) {
+              return {
+                type: "throw",
+                arg: err
+              };
+            }
+          }
+
+          var GenStateSuspendedStart = "suspendedStart";
+          var GenStateSuspendedYield = "suspendedYield";
+          var GenStateExecuting = "executing";
+          var GenStateCompleted = "completed"; // Returning this object from the innerFn has the same effect as
+          // breaking out of the dispatch switch statement.
+
+          var ContinueSentinel = {}; // Dummy constructor functions that we use as the .constructor and
+          // .constructor.prototype properties for functions that return Generator
+          // objects. For full spec compliance, you may wish to configure your
+          // minifier not to mangle the names of these two functions.
+
+          function Generator() {}
+
+          function GeneratorFunction() {}
+
+          function GeneratorFunctionPrototype() {} // This is a polyfill for %IteratorPrototype% for environments that
+          // don't natively support it.
+
+
+          var IteratorPrototype = {};
+
+          IteratorPrototype[iteratorSymbol] = function () {
+            return this;
+          };
+
+          var getProto = Object.getPrototypeOf;
+          var NativeIteratorPrototype = getProto && getProto(getProto(values([])));
+
+          if (NativeIteratorPrototype && NativeIteratorPrototype !== Op && hasOwn.call(NativeIteratorPrototype, iteratorSymbol)) {
+            // This environment has a native %IteratorPrototype%; use it instead
+            // of the polyfill.
+            IteratorPrototype = NativeIteratorPrototype;
+          }
+
+          var Gp = GeneratorFunctionPrototype.prototype = Generator.prototype = Object.create(IteratorPrototype);
+          GeneratorFunction.prototype = Gp.constructor = GeneratorFunctionPrototype;
+          GeneratorFunctionPrototype.constructor = GeneratorFunction;
+          GeneratorFunctionPrototype[toStringTagSymbol] = GeneratorFunction.displayName = "GeneratorFunction"; // Helper for defining the .next, .throw, and .return methods of the
+          // Iterator interface in terms of a single ._invoke method.
+
+          function defineIteratorMethods(prototype) {
+            ["next", "throw", "return"].forEach(function (method) {
+              prototype[method] = function (arg) {
+                return this._invoke(method, arg);
+              };
+            });
+          }
+
+          runtime.isGeneratorFunction = function (genFun) {
+            var ctor = typeof genFun === "function" && genFun.constructor;
+            return ctor ? ctor === GeneratorFunction || // For the native GeneratorFunction constructor, the best we can
+            // do is to check its .name property.
+            (ctor.displayName || ctor.name) === "GeneratorFunction" : false;
+          };
+
+          runtime.mark = function (genFun) {
+            if (Object.setPrototypeOf) {
+              Object.setPrototypeOf(genFun, GeneratorFunctionPrototype);
+            } else {
+              genFun.__proto__ = GeneratorFunctionPrototype;
+
+              if (!(toStringTagSymbol in genFun)) {
+                genFun[toStringTagSymbol] = "GeneratorFunction";
+              }
+            }
+
+            genFun.prototype = Object.create(Gp);
+            return genFun;
+          }; // Within the body of any async function, `await x` is transformed to
+          // `yield regeneratorRuntime.awrap(x)`, so that the runtime can test
+          // `hasOwn.call(value, "__await")` to determine if the yielded value is
+          // meant to be awaited.
+
+
+          runtime.awrap = function (arg) {
+            return {
+              __await: arg
+            };
+          };
+
+          function AsyncIterator(generator) {
+            function invoke(method, arg, resolve, reject) {
+              var record = tryCatch(generator[method], generator, arg);
+
+              if (record.type === "throw") {
+                reject(record.arg);
+              } else {
+                var result = record.arg;
+                var value = result.value;
+
+                if (value && typeof value === "object" && hasOwn.call(value, "__await")) {
+                  return Promise.resolve(value.__await).then(function (value) {
+                    invoke("next", value, resolve, reject);
+                  }, function (err) {
+                    invoke("throw", err, resolve, reject);
+                  });
+                }
+
+                return Promise.resolve(value).then(function (unwrapped) {
+                  // When a yielded Promise is resolved, its final value becomes
+                  // the .value of the Promise<{value,done}> result for the
+                  // current iteration. If the Promise is rejected, however, the
+                  // result for this iteration will be rejected with the same
+                  // reason. Note that rejections of yielded Promises are not
+                  // thrown back into the generator function, as is the case
+                  // when an awaited Promise is rejected. This difference in
+                  // behavior between yield and await is important, because it
+                  // allows the consumer to decide what to do with the yielded
+                  // rejection (swallow it and continue, manually .throw it back
+                  // into the generator, abandon iteration, whatever). With
+                  // await, by contrast, there is no opportunity to examine the
+                  // rejection reason outside the generator function, so the
+                  // only option is to throw it from the await expression, and
+                  // let the generator function handle the exception.
+                  result.value = unwrapped;
+                  resolve(result);
+                }, reject);
+              }
+            }
+
+            var previousPromise;
+
+            function enqueue(method, arg) {
+              function callInvokeWithMethodAndArg() {
+                return new Promise(function (resolve, reject) {
+                  invoke(method, arg, resolve, reject);
+                });
+              }
+
+              return previousPromise = // If enqueue has been called before, then we want to wait until
+              // all previous Promises have been resolved before calling invoke,
+              // so that results are always delivered in the correct order. If
+              // enqueue has not been called before, then it is important to
+              // call invoke immediately, without waiting on a callback to fire,
+              // so that the async generator function has the opportunity to do
+              // any necessary setup in a predictable way. This predictability
+              // is why the Promise constructor synchronously invokes its
+              // executor callback, and why async functions synchronously
+              // execute code before the first await. Since we implement simple
+              // async functions in terms of async generators, it is especially
+              // important to get this right, even though it requires care.
+              previousPromise ? previousPromise.then(callInvokeWithMethodAndArg, // Avoid propagating failures to Promises returned by later
+              // invocations of the iterator.
+              callInvokeWithMethodAndArg) : callInvokeWithMethodAndArg();
+            } // Define the unified helper method that is used to implement .next,
+            // .throw, and .return (see defineIteratorMethods).
+
+
+            this._invoke = enqueue;
+          }
+
+          defineIteratorMethods(AsyncIterator.prototype);
+
+          AsyncIterator.prototype[asyncIteratorSymbol] = function () {
+            return this;
+          };
+
+          runtime.AsyncIterator = AsyncIterator; // Note that simple async functions are implemented on top of
+          // AsyncIterator objects; they just return a Promise for the value of
+          // the final result produced by the iterator.
+
+          runtime.async = function (innerFn, outerFn, self, tryLocsList) {
+            var iter = new AsyncIterator(wrap(innerFn, outerFn, self, tryLocsList));
+            return runtime.isGeneratorFunction(outerFn) ? iter // If outerFn is a generator, return the full iterator.
+            : iter.next().then(function (result) {
+              return result.done ? result.value : iter.next();
+            });
+          };
+
+          function makeInvokeMethod(innerFn, self, context) {
+            var state = GenStateSuspendedStart;
+            return function invoke(method, arg) {
+              if (state === GenStateExecuting) {
+                throw new Error("Generator is already running");
+              }
+
+              if (state === GenStateCompleted) {
+                if (method === "throw") {
+                  throw arg;
+                } // Be forgiving, per 25.3.3.3.3 of the spec:
+                // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-generatorresume
+
+
+                return doneResult();
+              }
+
+              context.method = method;
+              context.arg = arg;
+
+              while (true) {
+                var delegate = context.delegate;
+
+                if (delegate) {
+                  var delegateResult = maybeInvokeDelegate(delegate, context);
+
+                  if (delegateResult) {
+                    if (delegateResult === ContinueSentinel) continue;
+                    return delegateResult;
+                  }
+                }
+
+                if (context.method === "next") {
+                  // Setting context._sent for legacy support of Babel's
+                  // function.sent implementation.
+                  context.sent = context._sent = context.arg;
+                } else if (context.method === "throw") {
+                  if (state === GenStateSuspendedStart) {
+                    state = GenStateCompleted;
+                    throw context.arg;
+                  }
+
+                  context.dispatchException(context.arg);
+                } else if (context.method === "return") {
+                  context.abrupt("return", context.arg);
+                }
+
+                state = GenStateExecuting;
+                var record = tryCatch(innerFn, self, context);
+
+                if (record.type === "normal") {
+                  // If an exception is thrown from innerFn, we leave state ===
+                  // GenStateExecuting and loop back for another invocation.
+                  state = context.done ? GenStateCompleted : GenStateSuspendedYield;
+
+                  if (record.arg === ContinueSentinel) {
+                    continue;
+                  }
+
+                  return {
+                    value: record.arg,
+                    done: context.done
+                  };
+                } else if (record.type === "throw") {
+                  state = GenStateCompleted; // Dispatch the exception by looping back around to the
+                  // context.dispatchException(context.arg) call above.
+
+                  context.method = "throw";
+                  context.arg = record.arg;
+                }
+              }
+            };
+          } // Call delegate.iterator[context.method](context.arg) and handle the
+          // result, either by returning a { value, done } result from the
+          // delegate iterator, or by modifying context.method and context.arg,
+          // setting context.delegate to null, and returning the ContinueSentinel.
+
+
+          function maybeInvokeDelegate(delegate, context) {
+            var method = delegate.iterator[context.method];
+
+            if (method === undefined$1) {
+              // A .throw or .return when the delegate iterator has no .throw
+              // method always terminates the yield* loop.
+              context.delegate = null;
+
+              if (context.method === "throw") {
+                if (delegate.iterator.return) {
+                  // If the delegate iterator has a return method, give it a
+                  // chance to clean up.
+                  context.method = "return";
+                  context.arg = undefined$1;
+                  maybeInvokeDelegate(delegate, context);
+
+                  if (context.method === "throw") {
+                    // If maybeInvokeDelegate(context) changed context.method from
+                    // "return" to "throw", let that override the TypeError below.
+                    return ContinueSentinel;
+                  }
+                }
+
+                context.method = "throw";
+                context.arg = new TypeError("The iterator does not provide a 'throw' method");
+              }
+
+              return ContinueSentinel;
+            }
+
+            var record = tryCatch(method, delegate.iterator, context.arg);
+
+            if (record.type === "throw") {
+              context.method = "throw";
+              context.arg = record.arg;
+              context.delegate = null;
+              return ContinueSentinel;
+            }
+
+            var info = record.arg;
+
+            if (!info) {
+              context.method = "throw";
+              context.arg = new TypeError("iterator result is not an object");
+              context.delegate = null;
+              return ContinueSentinel;
+            }
+
+            if (info.done) {
+              // Assign the result of the finished delegate to the temporary
+              // variable specified by delegate.resultName (see delegateYield).
+              context[delegate.resultName] = info.value; // Resume execution at the desired location (see delegateYield).
+
+              context.next = delegate.nextLoc; // If context.method was "throw" but the delegate handled the
+              // exception, let the outer generator proceed normally. If
+              // context.method was "next", forget context.arg since it has been
+              // "consumed" by the delegate iterator. If context.method was
+              // "return", allow the original .return call to continue in the
+              // outer generator.
+
+              if (context.method !== "return") {
+                context.method = "next";
+                context.arg = undefined$1;
+              }
+            } else {
+              // Re-yield the result returned by the delegate method.
+              return info;
+            } // The delegate iterator is finished, so forget it and continue with
+            // the outer generator.
+
+
+            context.delegate = null;
+            return ContinueSentinel;
+          } // Define Generator.prototype.{next,throw,return} in terms of the
+          // unified ._invoke helper method.
+
+
+          defineIteratorMethods(Gp);
+          Gp[toStringTagSymbol] = "Generator"; // A Generator should always return itself as the iterator object when the
+          // @@iterator function is called on it. Some browsers' implementations of the
+          // iterator prototype chain incorrectly implement this, causing the Generator
+          // object to not be returned from this call. This ensures that doesn't happen.
+          // See https://github.com/facebook/regenerator/issues/274 for more details.
+
+          Gp[iteratorSymbol] = function () {
+            return this;
+          };
+
+          Gp.toString = function () {
+            return "[object Generator]";
+          };
+
+          function pushTryEntry(locs) {
+            var entry = {
+              tryLoc: locs[0]
+            };
+
+            if (1 in locs) {
+              entry.catchLoc = locs[1];
+            }
+
+            if (2 in locs) {
+              entry.finallyLoc = locs[2];
+              entry.afterLoc = locs[3];
+            }
+
+            this.tryEntries.push(entry);
+          }
+
+          function resetTryEntry(entry) {
+            var record = entry.completion || {};
+            record.type = "normal";
+            delete record.arg;
+            entry.completion = record;
+          }
+
+          function Context(tryLocsList) {
+            // The root entry object (effectively a try statement without a catch
+            // or a finally block) gives us a place to store values thrown from
+            // locations where there is no enclosing try statement.
+            this.tryEntries = [{
+              tryLoc: "root"
+            }];
+            tryLocsList.forEach(pushTryEntry, this);
+            this.reset(true);
+          }
+
+          runtime.keys = function (object) {
+            var keys = [];
+
+            for (var key in object) {
+              keys.push(key);
+            }
+
+            keys.reverse(); // Rather than returning an object with a next method, we keep
+            // things simple and return the next function itself.
+
+            return function next() {
+              while (keys.length) {
+                var key = keys.pop();
+
+                if (key in object) {
+                  next.value = key;
+                  next.done = false;
+                  return next;
+                }
+              } // To avoid creating an additional object, we just hang the .value
+              // and .done properties off the next function object itself. This
+              // also ensures that the minifier will not anonymize the function.
+
+
+              next.done = true;
+              return next;
+            };
+          };
+
+          function values(iterable) {
+            if (iterable) {
+              var iteratorMethod = iterable[iteratorSymbol];
+
+              if (iteratorMethod) {
+                return iteratorMethod.call(iterable);
+              }
+
+              if (typeof iterable.next === "function") {
+                return iterable;
+              }
+
+              if (!isNaN(iterable.length)) {
+                var i = -1,
+                    next = function next() {
+                  while (++i < iterable.length) {
+                    if (hasOwn.call(iterable, i)) {
+                      next.value = iterable[i];
+                      next.done = false;
+                      return next;
+                    }
+                  }
+
+                  next.value = undefined$1;
+                  next.done = true;
+                  return next;
+                };
+
+                return next.next = next;
+              }
+            } // Return an iterator with no values.
+
+
+            return {
+              next: doneResult
+            };
+          }
+
+          runtime.values = values;
+
+          function doneResult() {
+            return {
+              value: undefined$1,
+              done: true
+            };
+          }
+
+          Context.prototype = {
+            constructor: Context,
+            reset: function (skipTempReset) {
+              this.prev = 0;
+              this.next = 0; // Resetting context._sent for legacy support of Babel's
+              // function.sent implementation.
+
+              this.sent = this._sent = undefined$1;
+              this.done = false;
+              this.delegate = null;
+              this.method = "next";
+              this.arg = undefined$1;
+              this.tryEntries.forEach(resetTryEntry);
+
+              if (!skipTempReset) {
+                for (var name in this) {
+                  // Not sure about the optimal order of these conditions:
+                  if (name.charAt(0) === "t" && hasOwn.call(this, name) && !isNaN(+name.slice(1))) {
+                    this[name] = undefined$1;
+                  }
+                }
+              }
+            },
+            stop: function () {
+              this.done = true;
+              var rootEntry = this.tryEntries[0];
+              var rootRecord = rootEntry.completion;
+
+              if (rootRecord.type === "throw") {
+                throw rootRecord.arg;
+              }
+
+              return this.rval;
+            },
+            dispatchException: function (exception) {
+              if (this.done) {
+                throw exception;
+              }
+
+              var context = this;
+
+              function handle(loc, caught) {
+                record.type = "throw";
+                record.arg = exception;
+                context.next = loc;
+
+                if (caught) {
+                  // If the dispatched exception was caught by a catch block,
+                  // then let that catch block handle the exception normally.
+                  context.method = "next";
+                  context.arg = undefined$1;
+                }
+
+                return !!caught;
+              }
+
+              for (var i = this.tryEntries.length - 1; i >= 0; --i) {
+                var entry = this.tryEntries[i];
+                var record = entry.completion;
+
+                if (entry.tryLoc === "root") {
+                  // Exception thrown outside of any try block that could handle
+                  // it, so set the completion value of the entire function to
+                  // throw the exception.
+                  return handle("end");
+                }
+
+                if (entry.tryLoc <= this.prev) {
+                  var hasCatch = hasOwn.call(entry, "catchLoc");
+                  var hasFinally = hasOwn.call(entry, "finallyLoc");
+
+                  if (hasCatch && hasFinally) {
+                    if (this.prev < entry.catchLoc) {
+                      return handle(entry.catchLoc, true);
+                    } else if (this.prev < entry.finallyLoc) {
+                      return handle(entry.finallyLoc);
+                    }
+                  } else if (hasCatch) {
+                    if (this.prev < entry.catchLoc) {
+                      return handle(entry.catchLoc, true);
+                    }
+                  } else if (hasFinally) {
+                    if (this.prev < entry.finallyLoc) {
+                      return handle(entry.finallyLoc);
+                    }
+                  } else {
+                    throw new Error("try statement without catch or finally");
+                  }
+                }
+              }
+            },
+            abrupt: function (type, arg) {
+              for (var i = this.tryEntries.length - 1; i >= 0; --i) {
+                var entry = this.tryEntries[i];
+
+                if (entry.tryLoc <= this.prev && hasOwn.call(entry, "finallyLoc") && this.prev < entry.finallyLoc) {
+                  var finallyEntry = entry;
+                  break;
+                }
+              }
+
+              if (finallyEntry && (type === "break" || type === "continue") && finallyEntry.tryLoc <= arg && arg <= finallyEntry.finallyLoc) {
+                // Ignore the finally entry if control is not jumping to a
+                // location outside the try/catch block.
+                finallyEntry = null;
+              }
+
+              var record = finallyEntry ? finallyEntry.completion : {};
+              record.type = type;
+              record.arg = arg;
+
+              if (finallyEntry) {
+                this.method = "next";
+                this.next = finallyEntry.finallyLoc;
+                return ContinueSentinel;
+              }
+
+              return this.complete(record);
+            },
+            complete: function (record, afterLoc) {
+              if (record.type === "throw") {
+                throw record.arg;
+              }
+
+              if (record.type === "break" || record.type === "continue") {
+                this.next = record.arg;
+              } else if (record.type === "return") {
+                this.rval = this.arg = record.arg;
+                this.method = "return";
+                this.next = "end";
+              } else if (record.type === "normal" && afterLoc) {
+                this.next = afterLoc;
+              }
+
+              return ContinueSentinel;
+            },
+            finish: function (finallyLoc) {
+              for (var i = this.tryEntries.length - 1; i >= 0; --i) {
+                var entry = this.tryEntries[i];
+
+                if (entry.finallyLoc === finallyLoc) {
+                  this.complete(entry.completion, entry.afterLoc);
+                  resetTryEntry(entry);
+                  return ContinueSentinel;
+                }
+              }
+            },
+            "catch": function (tryLoc) {
+              for (var i = this.tryEntries.length - 1; i >= 0; --i) {
+                var entry = this.tryEntries[i];
+
+                if (entry.tryLoc === tryLoc) {
+                  var record = entry.completion;
+
+                  if (record.type === "throw") {
+                    var thrown = record.arg;
+                    resetTryEntry(entry);
+                  }
+
+                  return thrown;
+                }
+              } // The context.catch method must only be called with a location
+              // argument that corresponds to a known catch block.
+
+
+              throw new Error("illegal catch attempt");
+            },
+            delegateYield: function (iterable, resultName, nextLoc) {
+              this.delegate = {
+                iterator: values(iterable),
+                resultName: resultName,
+                nextLoc: nextLoc
+              };
+
+              if (this.method === "next") {
+                // Deliberately forget the last sent value so that we don't
+                // accidentally pass it on to the delegate.
+                this.arg = undefined$1;
+              }
+
+              return ContinueSentinel;
+            }
+          };
+        }( // In sloppy mode, unbound `this` refers to the global object, fallback to
+        // Function constructor if we're in global strict mode. That is sadly a form
+        // of indirect eval which violates Content Security Policy.
+        function () {
+          return this;
+        }() || Function("return this")());
+      });
+      /**
+       * Copyright (c) 2014-present, Facebook, Inc.
+       *
+       * This source code is licensed under the MIT license found in the
+       * LICENSE file in the root directory of this source tree.
+       */
+      // This method of obtaining a reference to the global object needs to be
+      // kept identical to the way it is obtained in runtime.js
+
+      var g = function () {
+        return this;
+      }() || Function("return this")(); // Use `getOwnPropertyNames` because not all browsers support calling
+      // `hasOwnProperty` on the global `self` object in a worker. See #183.
+
+
+      var hadRuntime = g.regeneratorRuntime && Object.getOwnPropertyNames(g).indexOf("regeneratorRuntime") >= 0; // Save the old regeneratorRuntime in case it needs to be restored later.
+
+      var oldRuntime = hadRuntime && g.regeneratorRuntime; // Force reevalutation of runtime.js.
+
+      g.regeneratorRuntime = undefined;
+      var runtimeModule = runtime;
+
+      if (hadRuntime) {
+        // Restore the original runtime.
+        g.regeneratorRuntime = oldRuntime;
+      } else {
+        // Remove the global property added by runtime.js.
+        try {
+          delete g.regeneratorRuntime;
+        } catch (e) {
+          g.regeneratorRuntime = undefined;
+        }
+      }
+
+      var regenerator = runtimeModule;
+      var defaultOptions = {
+        mode: 'index'
+      };
+      module.exports = /*#__PURE__*/regenerator.mark(function _callee(M, N, options) {
+        var a, c, b, p, x, y, z, i, twiddle;
+        return regenerator.wrap(function _callee$(_context) {
+          while (1) {
+            switch (_context.prev = _context.next) {
+              case 0:
+                twiddle = function twiddle() {
+                  var i, j, k;
+                  j = 1;
+
+                  while (p[j] <= 0) {
+                    j++;
+                  }
+
+                  if (p[j - 1] === 0) {
+                    for (i = j - 1; i !== 1; i--) {
+                      p[i] = -1;
+                    }
+
+                    p[j] = 0;
+                    x = z = 0;
+                    p[1] = 1;
+                    y = j - 1;
+                  } else {
+                    if (j > 1) {
+                      p[j - 1] = 0;
+                    }
+
+                    do {
+                      j++;
+                    } while (p[j] > 0);
+
+                    k = j - 1;
+                    i = j;
+
+                    while (p[i] === 0) {
+                      p[i++] = -1;
+                    }
+
+                    if (p[i] === -1) {
+                      p[i] = p[k];
+                      z = p[k] - 1;
+                      x = i - 1;
+                      y = k - 1;
+                      p[k] = -1;
+                    } else {
+                      if (i === p[0]) {
+                        return 0;
+                      } else {
+                        p[j] = p[i];
+                        z = p[i] - 1;
+                        p[i] = 0;
+                        x = j - 1;
+                        y = i - 1;
+                      }
+                    }
+                  }
+
+                  return 1;
+                };
+
+                options = Object.assign({}, defaultOptions, options);
+                a = new Array(N);
+                c = new Array(M);
+                b = new Array(N);
+                p = new Array(N + 2); // init a and b
+
+                for (i = 0; i < N; i++) {
+                  a[i] = i;
+                  if (i < N - M) b[i] = 0;else b[i] = 1;
+                } // init c
+
+
+                for (i = 0; i < M; i++) {
+                  c[i] = N - M + i;
+                } // init p
+
+
+                for (i = 0; i < p.length; i++) {
+                  if (i === 0) p[i] = N + 1;else if (i <= N - M) p[i] = 0;else if (i <= N) p[i] = i - N + M;else p[i] = -2;
+                }
+
+                if (!(options.mode === 'index')) {
+                  _context.next = 20;
+                  break;
+                }
+
+                _context.next = 12;
+                return c.slice();
+
+              case 12:
+                if (!twiddle()) {
+                  _context.next = 18;
+                  break;
+                }
+
+                c[z] = a[x];
+                _context.next = 16;
+                return c.slice();
+
+              case 16:
+                _context.next = 12;
+                break;
+
+              case 18:
+                _context.next = 33;
+                break;
+
+              case 20:
+                if (!(options.mode === 'mask')) {
+                  _context.next = 32;
+                  break;
+                }
+
+                _context.next = 23;
+                return b.slice();
+
+              case 23:
+                if (!twiddle()) {
+                  _context.next = 30;
+                  break;
+                }
+
+                b[x] = 1;
+                b[y] = 0;
+                _context.next = 28;
+                return b.slice();
+
+              case 28:
+                _context.next = 23;
+                break;
+
+              case 30:
+                _context.next = 33;
+                break;
+
+              case 32:
+                throw new Error('Invalid mode');
+
+              case 33:
+              case 'end':
+                return _context.stop();
+            }
+          }
+        }, _callee, this);
+      });
+    });
+  });
+
+  /**
+   * get folds indexes
+   * @param {Array} features
+   * @param {Number} k - number of folds, a
+   */
+  function getFolds(features, k = 5) {
+    let N = features.length;
+    let allIdx = new Array(N);
+
+    for (let i = 0; i < N; i++) {
+      allIdx[i] = i;
+    }
+
+    let l = Math.floor(N / k); // create random k-folds
+
+    let current = [];
+    let folds = [];
+
+    while (allIdx.length) {
+      let randi = Math.floor(Math.random() * allIdx.length);
+      current.push(allIdx[randi]);
+      allIdx.splice(randi, 1);
+
+      if (current.length === l) {
+        folds.push(current);
+        current = [];
+      }
+    } // we push the remaining to the last fold so that the total length is
+    // preserved. Otherwise the Q2 will fail.
+
+
+    if (current.length) current.forEach(e => folds[k - 1].push(e));
+    folds = folds.slice(0, k);
+    let foldsIndex = folds.map((x, idx) => ({
+      testIndex: x,
+      trainIndex: [].concat(...folds.filter((el, idx2) => idx2 !== idx))
+    }));
+    return foldsIndex;
+  }
+
+  /**
+   * A function to sample a dataset maintaining classes equilibrated
+   * @param {Array} classVector - an array containing class or group information
+   * @param {Number} fraction - a fraction of the class to sample
+   * @return {Object} - an object with indexes
+   */
+  function sampleAClass(classVector, fraction) {
+    // sort the vector
+    let classVectorSorted = JSON.parse(JSON.stringify(classVector));
+    let result = Array.from(Array(classVectorSorted.length).keys()).sort((a, b) => classVectorSorted[a] < classVectorSorted[b] ? -1 : classVectorSorted[b] < classVectorSorted[a] | 0);
+    classVectorSorted.sort((a, b) => a < b ? -1 : b < a | 0); // counts the class elements
+
+    let counts = {};
+    classVectorSorted.forEach(x => counts[x] = (counts[x] || 0) + 1); // pick a few per class
+
+    let indexOfSelected = [];
+    Object.keys(counts).forEach((e, i) => {
+      let shift = [];
+      Object.values(counts).reduce((a, c, item) => shift[item] = a + c, 0);
+      let arr = [...Array(counts[e]).keys()];
+      let r = [];
+
+      for (let j = 0; j < Math.floor(counts[e] * fraction); j++) {
+        let n = arr[Math.floor(Math.random() * arr.length)];
+        r.push(n);
+        let ind = arr.indexOf(n);
+        arr.splice(ind, 1);
+      }
+
+      if (i === 0) {
+        indexOfSelected = indexOfSelected.concat(r);
+      } else {
+        indexOfSelected = indexOfSelected.concat(r.map(x => x + shift[i - 1]));
+      }
+    }); // sort back the index
+
+    let trainIndex = [];
+    indexOfSelected.forEach(e => trainIndex.push(result[e]));
+    let testIndex = [];
+    let mask = [];
+    classVector.forEach((el, idx) => {
+      if (trainIndex.includes(idx)) {
+        mask.push(true);
+      } else {
+        mask.push(false);
+        testIndex.push(idx);
+      }
+    });
+    return {
+      trainIndex,
+      testIndex,
+      mask
+    };
+  }
+
+  /**
+   * Performs a leave-one-out cross-validation (LOO-CV) of the given samples. In LOO-CV, 1 observation is used as the
+   * validation set while the rest is used as the training set. This is repeated once for each observation. LOO-CV is a
+   * special case of LPO-CV. @see leavePout
+   * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier
+   *     api.
+   * @param {Array} features - The features for all samples of the data-set
+   * @param {Array} labels - The classification class of all samples of the data-set
+   * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.
+   * @return {ConfusionMatrix} - The cross-validation confusion matrix
+   */
+
+  function leaveOneOut(Classifier, features, labels, classifierOptions) {
+    if (typeof labels === 'function') {
+      let callback = labels;
+      labels = features;
+      features = Classifier;
+      return leavePOut(features, labels, 1, callback);
+    }
+
+    return leavePOut(Classifier, features, labels, classifierOptions, 1);
+  }
+  /**
+   * Performs a leave-p-out cross-validation (LPO-CV) of the given samples. In LPO-CV, p observations are used as the
+   * validation set while the rest is used as the training set. This is repeated as many times as there are possible
+   * ways to combine p observations from the set (unordered without replacement). Be aware that for relatively small
+   * data-set size this can require a very large number of training and testing to do!
+   * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier
+   *     api.
+   * @param {Array} features - The features for all samples of the data-set
+   * @param {Array} labels - The classification class of all samples of the data-set
+   * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.
+   * @param {number} p - The size of the validation sub-samples' set
+   * @return {ConfusionMatrix} - The cross-validation confusion matrix
+   */
+
+  function leavePOut(Classifier, features, labels, classifierOptions, p) {
+    let callback;
+
+    if (typeof classifierOptions === 'function') {
+      callback = classifierOptions;
+      p = labels;
+      labels = features;
+      features = Classifier;
+    }
+
+    check(features, labels);
+    const distinct = getDistinct(labels);
+    const confusionMatrix = initMatrix(distinct.length, distinct.length);
+    let N = features.length;
+    let gen = lib(p, N);
+    let allIdx = new Array(N);
+
+    for (let i = 0; i < N; i++) {
+      allIdx[i] = i;
+    }
+
+    for (const testIdx of gen) {
+      let trainIdx = allIdx.slice();
+
+      for (let i = testIdx.length - 1; i >= 0; i--) {
+        trainIdx.splice(testIdx[i], 1);
+      }
+
+      if (callback) {
+        validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);
+      } else {
+        validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);
+      }
+    }
+
+    return new ConfusionMatrix(confusionMatrix, distinct);
+  }
+  /**
+   * Performs k-fold cross-validation (KF-CV). KF-CV separates the data-set into k random equally sized partitions, and
+   * uses each as a validation set, with all other partitions used in the training set. Observations left over from if k
+   * does not divide the number of observations are left out of the cross-validation process.
+   * @param {function} Classifier - The classifier's to use for the cross validation. Expect ml-classifier api.
+   * @param {Array} features - The features for all samples of the data-set
+   * @param {Array} labels - The classification class of all samples of the data-set
+   * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.
+   * @param {number} k - The number of partitions to create
+   * @return {ConfusionMatrix} - The cross-validation confusion matrix
+   */
+
+  function kFold(Classifier, features, labels, classifierOptions, k) {
+    let callback;
+
+    if (typeof classifierOptions === 'function') {
+      callback = classifierOptions;
+      k = labels;
+      labels = features;
+      features = Classifier;
+    }
+
+    check(features, labels);
+    const distinct = getDistinct(labels);
+    const confusionMatrix = initMatrix(distinct.length, distinct.length);
+    let folds = getFolds(features, k);
+
+    for (let i = 0; i < folds.length; i++) {
+      let testIdx = folds[i].testIndex;
+      let trainIdx = folds[i].trainIndex;
+
+      if (callback) {
+        validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);
+      } else {
+        validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);
+      }
+    }
+
+    return new ConfusionMatrix(confusionMatrix, distinct);
+  }
+
+  function check(features, labels) {
+    if (features.length !== labels.length) {
+      throw new Error('features and labels should have the same length');
+    }
+  }
+
+  function initMatrix(rows, columns) {
+    return new Array(rows).fill(0).map(() => new Array(columns).fill(0));
+  }
+
+  function getDistinct(arr) {
+    let s = new Set();
+
+    for (let i = 0; i < arr.length; i++) {
+      s.add(arr[i]);
+    }
+
+    return Array.from(s);
+  }
+
+  function validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct) {
+    const {
+      testFeatures,
+      trainFeatures,
+      testLabels,
+      trainLabels
+    } = getTrainTest(features, labels, testIdx, trainIdx);
+    let classifier;
+
+    if (Classifier.prototype.train) {
+      classifier = new Classifier(classifierOptions);
+      classifier.train(trainFeatures, trainLabels);
+    } else {
+      classifier = new Classifier(trainFeatures, trainLabels, classifierOptions);
+    }
+
+    let predictedLabels = classifier.predict(testFeatures);
+    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);
+  }
+
+  function validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback) {
+    const {
+      testFeatures,
+      trainFeatures,
+      testLabels,
+      trainLabels
+    } = getTrainTest(features, labels, testIdx, trainIdx);
+    const predictedLabels = callback(trainFeatures, trainLabels, testFeatures);
+    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);
+  }
+
+  function updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct) {
+    for (let i = 0; i < predictedLabels.length; i++) {
+      const actualIdx = distinct.indexOf(testLabels[i]);
+      const predictedIdx = distinct.indexOf(predictedLabels[i]);
+
+      if (actualIdx < 0 || predictedIdx < 0) {
+        // eslint-disable-next-line no-console
+        console.warn(`ignore unknown predicted label ${predictedLabels[i]}`);
+      }
+
+      confusionMatrix[actualIdx][predictedIdx]++;
+    }
+  }
+
+  function getTrainTest(features, labels, testIdx, trainIdx) {
+    return {
+      testFeatures: testIdx.map(function (index) {
+        return features[index];
+      }),
+      trainFeatures: trainIdx.map(function (index) {
+        return features[index];
+      }),
+      testLabels: testIdx.map(function (index) {
+        return labels[index];
+      }),
+      trainLabels: trainIdx.map(function (index) {
+        return labels[index];
+      })
+    };
+  }
+
+  var index$2 = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    leaveOneOut: leaveOneOut,
+    leavePOut: leavePOut,
+    kFold: kFold,
+    getTrainTest: getTrainTest,
+    sampleAClass: sampleAClass,
+    getFolds: getFolds
+  });
+
+  /**
+   * OPLS loop
+   * @param {Array} x a matrix with features
+   * @param {Array} y an array of labels (dependent variable)
+   * @param {Object} options an object with options
+   * @return {Object} an object with model (filteredX: err,
+      loadingsXOrtho: pOrtho,
+      scoresXOrtho: tOrtho,
+      weightsXOrtho: wOrtho,
+      weightsPred: w,
+      loadingsXpred: p,
+      scoresXpred: t,
+      loadingsY:)
+   */
+
+  function OPLSNipals(x, y, options = {}) {
+    const {
+      numberOSC = 100
+    } = options;
+    let X = Matrix.checkMatrix(x);
+    let Y = Matrix.checkMatrix(y);
+    let u = Y.getColumnVector(0);
+    let diff = 1;
+    let t, c, w, uNew;
+
+    for (let i = 0; i < numberOSC && diff > 1e-10; i++) {
+      w = u.transpose().mmul(X).div(u.transpose().mmul(u).get(0, 0));
+      w = w.transpose().div(norm(w));
+      t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0)); // t_h paso 3
+      // calc loading
+
+      c = t.transpose().mmul(Y).div(t.transpose().mmul(t).get(0, 0)); // calc new u and compare with one in previus iteration (stop criterion)
+
+      uNew = Y.mmul(c.transpose());
+      uNew = uNew.div(c.transpose().mmul(c).get(0, 0));
+
+      if (i > 0) {
+        diff = uNew.clone().sub(u).pow(2).sum() / uNew.clone().pow(2).sum();
+      }
+
+      u = uNew.clone();
+    } // calc loadings
+
+
+    let p = t.transpose().mmul(X).div(t.transpose().mmul(t).get(0, 0));
+    let wOrtho = p.clone().sub(w.transpose().mmul(p.transpose()).div(w.transpose().mmul(w).get(0, 0)).mmul(w.transpose()));
+    wOrtho.div(norm(wOrtho)); // orthogonal scores
+
+    let tOrtho = X.mmul(wOrtho.transpose()).div(wOrtho.mmul(wOrtho.transpose()).get(0, 0)); // orthogonal loadings
+
+    let pOrtho = tOrtho.transpose().mmul(X).div(tOrtho.transpose().mmul(tOrtho).get(0, 0)); // filtered data
+
+    let err = X.clone().sub(tOrtho.mmul(pOrtho));
+    return {
+      filteredX: err,
+      weightsXOrtho: wOrtho,
+      loadingsXOrtho: pOrtho,
+      scoresXOrtho: tOrtho,
+      weightsXPred: w,
+      loadingsXpred: p,
+      scoresXpred: t,
+      loadingsY: c
+    };
+  }
+
+  /**
+   * Get total sum of square
+   * @param {Array} x an array
+   * @return {Number} - the sum of the squares
+   */
+
+  function tss(x) {
+    return Matrix.mul(x, x).sum();
+  }
+
+  /**
+   * Creates new OPLS (orthogonal partial latent structures) from features and labels.
+   * @param {Matrix} data - matrix containing data (X).
+   * @param {Array} labels - 1D Array containing metadata (Y).
+   * @param {Object} [options]
+   * @param {number} [options.nComp = 3] - number of latent structures computed.
+   * @param {boolean} [options.center = true] - should the data be centered (subtract the mean).
+   * @param {boolean} [options.scale = false] - should the data be scaled (divide by the standard deviation).
+   * @param {Array} [options.cvFolds = []] - allows to provide folds as 2D array for testing purpose.
+   * */
+
+  class OPLS {
+    constructor(data, labels, options = {}) {
+      if (data === true) {
+        const opls = options;
+        this.center = opls.center;
+        this.scale = opls.scale;
+        this.means = opls.means;
+        this.meansY = opls.meansY;
+        this.stdevs = opls.stdevs;
+        this.stdevs = opls.stdevsY;
+        this.model = opls.model;
+        this.tCV = opls.tCV;
+        this.tOrthCV = opls.tOrthCV;
+        this.yHatCV = opls.yHatCV;
+        this.mode = opls.mode;
+        return;
+      }
+
+      let features = data.clone(); // set default values
+      // cvFolds allows to define folds for testing purpose
+
+      const {
+        nComp = 3,
+        center = true,
+        scale = true,
+        cvFolds = []
+      } = options;
+      let group;
+
+      if (typeof labels[0] === 'number') {
+        // numeric labels: OPLS regression is used
+        this.mode = 'regression';
+        group = Matrix.from1DArray(labels.length, 1, labels);
+      } else if (typeof labels[0] === 'string') {
+        // non-numeric labels: OPLS-DA is used
+        this.mode = 'discriminantAnalysis';
+        group = labels;
+        throw new Error('discriminant analysis is not yet supported');
+      } // check types of features and labels
+
+
+      if (features.constructor.name !== 'Matrix') {
+        throw new TypeError('features must be of class Matrix');
+      } // getting center and scale the features (all)
+
+
+      this.center = center;
+
+      if (this.center) {
+        this.means = features.mean('column');
+        this.meansY = group.mean('column');
+      } else {
+        this.stdevs = null;
+      }
+
+      this.scale = scale;
+
+      if (this.scale) {
+        this.stdevs = features.standardDeviation('column');
+        this.stdevsY = group.standardDeviation('column');
+      } else {
+        this.means = null;
+      } // check and remove for features with sd = 0 TODO here
+      // check opls.R line 70
+
+
+      let folds;
+
+      if (cvFolds.length > 0) {
+        folds = cvFolds;
+      } else {
+        folds = getFolds(labels, 5);
+      }
+
+      let Q2 = [];
+      this.model = [];
+      this.tCV = [];
+      this.tOrthCV = [];
+      this.yHatCV = [];
+      let oplsCV = [];
+      let modelNC = []; // this code could be made more efficient by reverting the order of the loops
+      // this is a legacy loop to be consistent with R code from MetaboMate package
+      // this allows for having statistic (R2) from CV to decide wether to continue
+      // with more latent structures
+
+      let nc;
+
+      for (nc = 0; nc < nComp; nc++) {
+        let yHatk = new Matrix(group.rows, 1);
+        let tPredk = new Matrix(group.rows, 1);
+        let tOrthk = new Matrix(group.rows, 1);
+        let oplsk = [];
+        let f = 0;
+
+        for (let fold of folds) {
+          let trainTest = this._getTrainTest(features, group, fold);
+
+          let testXk = trainTest.testFeatures;
+          let Xk = trainTest.trainFeatures;
+          let Yk = trainTest.trainLabels; // determine center and scale of training set
+
+          let dataCenter = Xk.mean('column');
+          let dataSD = Xk.standardDeviation('column'); // center and scale training set
+
+          if (center) {
+            Xk.center('column');
+            Yk.center('column');
+          }
+
+          if (scale) {
+            Xk.scale('column');
+            Yk.scale('column');
+          } // perform opls
+
+
+          if (nc === 0) {
+            oplsk[f] = OPLSNipals(Xk, Yk);
+          } else {
+            oplsk[f] = OPLSNipals(oplsCV[nc - 1][f].filteredX, Yk);
+          } // store model for next component
+
+
+          oplsCV[nc] = oplsk;
+          let plsCV = new nipals(oplsk[f].filteredX, {
+            Y: Yk
+          }); // scaling the test dataset with respect to the train
+
+          testXk.center('column', {
+            center: dataCenter
+          });
+          testXk.scale('column', {
+            scale: dataSD
+          });
+          let Eh = testXk; // removing the orthogonal components from PLS
+
+          let scores;
+
+          for (let idx = 0; idx < nc + 1; idx++) {
+            scores = Eh.mmul(oplsCV[idx][f].weightsXOrtho.transpose()); // ok
+
+            Eh.sub(scores.mmul(oplsCV[idx][f].loadingsXOrtho));
+          } // prediction
+
+
+          let tPred = Eh.mmul(plsCV.w.transpose()); // this should be summed over ncomp (pls_prediction.R line 23)
+
+          let yHat = tPred.mmul(plsCV.betas); // ok
+          // adding all prediction from all folds
+
+          for (let i = 0; i < fold.testIndex.length; i++) {
+            yHatk.setRow(fold.testIndex[i], [yHat.get(i, 0)]);
+            tPredk.setRow(fold.testIndex[i], [tPred.get(i, 0)]);
+            tOrthk.setRow(fold.testIndex[i], [scores.get(i, 0)]);
+          }
+
+          f++;
+        } // end of loop over folds
+
+
+        this.tCV.push(tPredk);
+        this.tOrthCV.push(tOrthk);
+        this.yHatCV.push(yHatk); // calculate Q2y for all the prediction (all folds)
+        // ROC for DA is not implemented (check opls.R line 183) TODO
+
+        if (this.mode === 'regression') {
+          let tssy = tss(group.center('column').scale('column'));
+          let press = tss(group.clone().sub(yHatk));
+          let Q2y = 1 - press / tssy;
+          Q2.push(Q2y);
+        } else if (this.mode === 'discriminantAnalysis') {
+          throw new Error('discriminant analysis is not yet supported');
+        } // calculate the R2y for the complete data
+
+
+        if (nc === 0) {
+          modelNC = this._predictAll(features, group);
+        } else {
+          modelNC = this._predictAll(modelNC.xRes, group, options = {
+            scale: false,
+            center: false
+          });
+        } // adding the predictive statistics from CV
+
+
+        modelNC.Q2y = Q2; // store the model for each component
+
+        this.model.push(modelNC); // console.warn(`OPLS iteration over # of Components: ${nc + 1}`);
+      } // end of loop over nc
+      // store scores from CV
+
+
+      let tCV = this.tCV;
+      let tOrthCV = this.tOrthCV;
+      let m = this.model[nc - 1];
+      let XOrth = m.XOrth;
+      let FeaturesCS = features.center('column').scale('column');
+      let labelsCS = group.center('column').scale('column');
+      let Xres = FeaturesCS.clone().sub(XOrth);
+      let plsCall = new nipals(Xres, {
+        Y: labelsCS
+      });
+      let E = Xres.clone().sub(plsCall.t.mmul(plsCall.p));
+      let R2x = this.model.map(x => x.R2x);
+      let R2y = this.model.map(x => x.R2y);
+      this.output = {
+        Q2y: Q2,
+        R2x,
+        R2y,
+        tPred: m.plsC.t,
+        pPred: m.plsC.p,
+        wPred: m.plsC.w,
+        betasPred: m.plsC.betas,
+        Qpc: m.plsC.q,
+        tCV,
+        tOrthCV,
+        tOrth: m.tOrth,
+        pOrth: m.pOrth,
+        wOrth: m.wOrth,
+        XOrth,
+        yHat: m.totalPred,
+        Yres: m.plsC.yResidual,
+        E
+      };
+    }
+    /**
+     * get access to all the computed elements
+     * Mainly for debug and testing
+     * @return {Object} output object
+     */
+
+
+    getLogs() {
+      return this.output;
+    }
+
+    getScores() {
+      let scoresX = this.tCV.map(x => x.to1DArray());
+      let scoresY = this.tOrthCV.map(x => x.to1DArray());
+      return {
+        scoresX,
+        scoresY
+      };
+    }
+    /**
+     * Load an OPLS model from JSON
+     * @param {Object} model
+     * @return {OPLS}
+     */
+
+
+    static load(model) {
+      if (typeof model.name !== 'string') {
+        throw new TypeError('model must have a name property');
+      }
+
+      if (model.name !== 'OPLS') {
+        throw new RangeError(`invalid model: ${model.name}`);
+      }
+
+      return new OPLS(true, [], model);
+    }
+    /**
+     * Export the current model to a JSON object
+     * @return {Object} model
+     */
+
+
+    toJSON() {
+      return {
+        name: 'OPLS',
+        center: this.center,
+        scale: this.scale,
+        means: this.means,
+        stdevs: this.stdevs,
+        model: this.model,
+        tCV: this.tCV,
+        tOrthCV: this.tOrthCV,
+        yHatCV: this.yHatCV
+      };
+    }
+    /**
+     * Predict scores for new data
+     * @param {Matrix} features - a matrix containing new data
+     * @param {Object} [options]
+     * @param {Array} [options.trueLabel] - an array with true values to compute confusion matrix
+     * @param {Number} [options.nc] - the number of components to be used
+     * @return {Object} - predictions
+     */
+
+
+    predict(newData, options = {}) {
+      let {
+        trueLabels = [],
+        nc = 1
+      } = options;
+      let labels = [];
+
+      if (trueLabels.length > 0) {
+        trueLabels = Matrix.from1DArray(trueLabels.length, 1, trueLabels);
+        labels = trueLabels.clone();
+      }
+
+      let features = newData.clone(); // scaling the test dataset with respect to the train
+
+      if (this.center) {
+        features.center('column', {
+          center: this.means
+        });
+
+        if (labels.rows > 0 && this.mode === 'regression') {
+          labels.center('column', {
+            center: this.meansY
+          });
+        }
+      }
+
+      if (this.scale) {
+        features.scale('column', {
+          scale: this.stdevs
+        });
+
+        if (labels.rows > 0 && this.mode === 'regression') {
+          labels.scale('column', {
+            scale: this.stdevsY
+          });
+        }
+      }
+
+      let Eh = features.clone(); // removing the orthogonal components from PLS
+
+      let tOrth;
+      let wOrth;
+      let pOrth;
+      let yHat;
+      let tPred;
+
+      for (let idx = 0; idx < nc; idx++) {
+        wOrth = this.model[idx].wOrth.transpose();
+        pOrth = this.model[idx].pOrth;
+        tOrth = Eh.mmul(wOrth);
+        Eh.sub(tOrth.mmul(pOrth)); // prediction
+
+        tPred = Eh.mmul(this.model[idx].plsC.w.transpose()); // this should be summed over ncomp (pls_prediction.R line 23)
+
+        yHat = tPred.mmul(this.model[idx].plsC.betas);
+      }
+
+      if (labels.rows > 0) {
+        if (this.mode === 'regression') {
+          let tssy = tss(labels);
+          let press = tss(labels.clone().sub(yHat));
+          let Q2y = 1 - press / tssy;
+          return {
+            tPred,
+            tOrth,
+            yHat,
+            Q2y
+          };
+        } else if (this.mode === 'discriminantAnalysis') {
+          let confusionMatrix = [];
+          confusionMatrix = ConfusionMatrix.fromLabels(trueLabels.to1DArray(), yHat.to1DArray());
+          return {
+            tPred,
+            tOrth,
+            yHat,
+            confusionMatrix
+          };
+        }
+      } else {
+        return {
+          tPred,
+          tOrth,
+          yHat
+        };
+      }
+    }
+
+    _predictAll(features, labels, options = {}) {
+      // cannot use the global this.center here
+      // since it is used in the NC loop and
+      // centering and scaling should only be
+      // performed once
+      const {
+        center = true,
+        scale = true
+      } = options;
+
+      if (center) {
+        features.center('column');
+        labels.center('column');
+      }
+
+      if (scale) {
+        features.scale('column');
+        labels.scale('column'); // reevaluate tssy and tssx after scaling
+        // must be global because re-used for next nc iteration
+        // tssx is only evaluate the first time
+
+        this.tssy = tss(labels);
+        this.tssx = tss(features);
+      }
+
+      let oplsC = OPLSNipals(features, labels);
+      let plsC = new nipals(oplsC.filteredX, {
+        Y: labels
+      });
+      let tPred = oplsC.filteredX.mmul(plsC.w.transpose());
+      let yHat = tPred.mmul(plsC.betas);
+      let rss = tss(labels.clone().sub(yHat));
+      let R2y = 1 - rss / this.tssy;
+      let xEx = plsC.t.mmul(plsC.p);
+      let rssx = tss(xEx);
+      let R2x = rssx / this.tssx;
+      return {
+        R2y,
+        R2x,
+        xRes: oplsC.filteredX,
+        tOrth: oplsC.scoresXOrtho,
+        pOrth: oplsC.loadingsXOrtho,
+        wOrth: oplsC.weightsXOrtho,
+        tPred: tPred,
+        totalPred: yHat,
+        XOrth: oplsC.scoresXOrtho.mmul(oplsC.loadingsXOrtho),
+        oplsC,
+        plsC
+      };
+    }
+    /**
+     *
+     * @param {*} X - dataset matrix object
+     * @param {*} group - labels matrix object
+     * @param {*} index - train and test index (output from getFold())
+     */
+
+
+    _getTrainTest(X, group, index) {
+      let testFeatures = new Matrix(index.testIndex.length, X.columns);
+      let testLabels = new Matrix(index.testIndex.length, 1);
+      index.testIndex.forEach((el, idx) => {
+        testFeatures.setRow(idx, X.getRow(el));
+        testLabels.setRow(idx, group.getRow(el));
+      });
+      let trainFeatures = new Matrix(index.trainIndex.length, X.columns);
+      let trainLabels = new Matrix(index.trainIndex.length, 1);
+      index.trainIndex.forEach((el, idx) => {
+        trainFeatures.setRow(idx, X.getRow(el));
+        trainLabels.setRow(idx, group.getRow(el));
+      });
+      return {
+        trainFeatures,
+        testFeatures,
+        trainLabels,
+        testLabels
+      };
+    }
+
+  }
+
+  var require$$0 = /*@__PURE__*/getAugmentedNamespace(MatrixLib);
+
+  function logistic(val) {
+    return 1 / (1 + Math.exp(-val));
+  }
+
+  function expELU(val, param) {
+    return val < 0 ? param * (Math.exp(val) - 1) : val;
+  }
+
+  function softExponential(val, param) {
+    if (param < 0) {
+      return -Math.log(1 - param * (val + param)) / param;
+    }
+
+    if (param > 0) {
+      return (Math.exp(param * val) - 1) / param + param;
+    }
+
+    return val;
+  }
+
+  function softExponentialPrime(val, param) {
+    if (param < 0) {
+      return 1 / (1 - param * (param + val));
+    } else {
+      return Math.exp(param * val);
+    }
+  }
+
+  const ACTIVATION_FUNCTIONS = {
+    tanh: {
+      activation: Math.tanh,
+      derivate: val => 1 - val * val
+    },
+    identity: {
+      activation: val => val,
+      derivate: () => 1
+    },
+    logistic: {
+      activation: logistic,
+      derivate: val => logistic(val) * (1 - logistic(val))
+    },
+    arctan: {
+      activation: Math.atan,
+      derivate: val => 1 / (val * val + 1)
+    },
+    softsign: {
+      activation: val => val / (1 + Math.abs(val)),
+      derivate: val => 1 / ((1 + Math.abs(val)) * (1 + Math.abs(val)))
+    },
+    relu: {
+      activation: val => val < 0 ? 0 : val,
+      derivate: val => val < 0 ? 0 : 1
+    },
+    softplus: {
+      activation: val => Math.log(1 + Math.exp(val)),
+      derivate: val => 1 / (1 + Math.exp(-val))
+    },
+    bent: {
+      activation: val => (Math.sqrt(val * val + 1) - 1) / 2 + val,
+      derivate: val => val / (2 * Math.sqrt(val * val + 1)) + 1
+    },
+    sinusoid: {
+      activation: Math.sin,
+      derivate: Math.cos
+    },
+    sinc: {
+      activation: val => val === 0 ? 1 : Math.sin(val) / val,
+      derivate: val => val === 0 ? 0 : Math.cos(val) / val - Math.sin(val) / (val * val)
+    },
+    gaussian: {
+      activation: val => Math.exp(-(val * val)),
+      derivate: val => -2 * val * Math.exp(-(val * val))
+    },
+    'parametric-relu': {
+      activation: (val, param) => val < 0 ? param * val : val,
+      derivate: (val, param) => val < 0 ? param : 1
+    },
+    'exponential-elu': {
+      activation: expELU,
+      derivate: (val, param) => val < 0 ? expELU(val, param) + param : 1
+    },
+    'soft-exponential': {
+      activation: softExponential,
+      derivate: softExponentialPrime
+    }
+  };
+
+  class Layer {
+    /**
+       * @private
+       * Create a new layer with the given options
+       * @param {object} options
+       * @param {number} [options.inputSize] - Number of conections that enter the neurons.
+       * @param {number} [options.outputSize] - Number of conections that leave the neurons.
+       * @param {number} [options.regularization] - Regularization parameter.
+       * @param {number} [options.epsilon] - Learning rate parameter.
+       * @param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.
+       * @param {number} [options.activationParam] - Activation parameter if needed.
+       */
+    constructor(options) {
+      this.inputSize = options.inputSize;
+      this.outputSize = options.outputSize;
+      this.regularization = options.regularization;
+      this.epsilon = options.epsilon;
+      this.activation = options.activation;
+      this.activationParam = options.activationParam;
+      var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];
+      var params = selectedFunction.activation.length;
+      var actFunction = params > 1 ? val => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;
+      var derFunction = params > 1 ? val => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;
+
+      this.activationFunction = function (i, j) {
+        this.set(i, j, actFunction(this.get(i, j)));
+      };
+
+      this.derivate = function (i, j) {
+        this.set(i, j, derFunction(this.get(i, j)));
+      };
+
+      if (options.model) {
+        // load model
+        this.W = require$$0.Matrix.checkMatrix(options.W);
+        this.b = require$$0.Matrix.checkMatrix(options.b);
+      } else {
+        // default constructor
+        this.W = require$$0.Matrix.rand(this.inputSize, this.outputSize);
+        this.b = require$$0.Matrix.zeros(1, this.outputSize);
+        this.W.apply(function (i, j) {
+          this.set(i, j, this.get(i, j) / Math.sqrt(options.inputSize));
+        });
+      }
+    }
+    /**
+       * @private
+       * propagate the given input through the current layer.
+       * @param {Matrix} X - input.
+       * @return {Matrix} output at the current layer.
+       */
+
+
+    forward(X) {
+      var z = X.mmul(this.W).addRowVector(this.b);
+      z.apply(this.activationFunction);
+      this.a = z.clone();
+      return z;
+    }
+    /**
+       * @private
+       * apply backpropagation algorithm at the current layer
+       * @param {Matrix} delta - delta values estimated at the following layer.
+       * @param {Matrix} a - 'a' values from the following layer.
+       * @return {Matrix} the new delta values for the next layer.
+       */
+
+
+    backpropagation(delta, a) {
+      this.dW = a.transpose().mmul(delta);
+      this.db = require$$0.Matrix.rowVector(delta.sum('column'));
+      var aCopy = a.clone();
+      return delta.mmul(this.W.transpose()).mul(aCopy.apply(this.derivate));
+    }
+    /**
+       * @private
+       * Function that updates the weights at the current layer with the derivatives.
+       */
+
+
+    update() {
+      this.dW.add(this.W.clone().mul(this.regularization));
+      this.W.add(this.dW.mul(-this.epsilon));
+      this.b.add(this.db.mul(-this.epsilon));
+    }
+    /**
+       * @private
+       * Export the current layer to JSON.
+       * @return {object} model
+       */
+
+
+    toJSON() {
+      return {
+        model: 'Layer',
+        inputSize: this.inputSize,
+        outputSize: this.outputSize,
+        regularization: this.regularization,
+        epsilon: this.epsilon,
+        activation: this.activation,
+        W: this.W,
+        b: this.b
+      };
+    }
+    /**
+       * @private
+       * Creates a new Layer with the given model.
+       * @param {object} model
+       * @return {Layer}
+       */
+
+
+    static load(model) {
+      if (model.model !== 'Layer') {
+        throw new RangeError('the current model is not a Layer model');
+      }
+
+      return new Layer(model);
+    }
+
+  }
+
+  class OutputLayer extends Layer {
+    constructor(options) {
+      super(options);
+
+      this.activationFunction = function (i, j) {
+        this.set(i, j, Math.exp(this.get(i, j)));
+      };
+    }
+
+    static load(model) {
+      if (model.model !== 'Layer') {
+        throw new RangeError('the current model is not a Layer model');
+      }
+
+      return new OutputLayer(model);
+    }
+
+  }
+
+  class FeedForwardNeuralNetworks {
+    /**
+     * Create a new Feedforward neural network model.
+     * @class FeedForwardNeuralNetworks
+     * @param {object} [options]
+     * @param {Array} [options.hiddenLayers=[10]] - Array that contains the sizes of the hidden layers.
+     * @param {number} [options.iterations=50] - Number of iterations at the training step.
+     * @param {number} [options.learningRate=0.01] - Learning rate of the neural net (also known as epsilon).
+     * @param {number} [options.regularization=0.01] - Regularization parameter af the neural net.
+     * @param {string} [options.activation='tanh'] - activation function to be used. (options: 'tanh'(default),
+     * 'identity', 'logistic', 'arctan', 'softsign', 'relu', 'softplus', 'bent', 'sinusoid', 'sinc', 'gaussian').
+     * (single-parametric options: 'parametric-relu', 'exponential-relu', 'soft-exponential').
+     * @param {number} [options.activationParam=1] - if the selected activation function needs a parameter.
+     */
+    constructor(options) {
+      options = options || {};
+
+      if (options.model) {
+        // load network
+        this.hiddenLayers = options.hiddenLayers;
+        this.iterations = options.iterations;
+        this.learningRate = options.learningRate;
+        this.regularization = options.regularization;
+        this.dicts = options.dicts;
+        this.activation = options.activation;
+        this.activationParam = options.activationParam;
+        this.model = new Array(options.layers.length);
+
+        for (var i = 0; i < this.model.length - 1; ++i) {
+          this.model[i] = Layer.load(options.layers[i]);
+        }
+
+        this.model[this.model.length - 1] = OutputLayer.load(options.layers[this.model.length - 1]);
+      } else {
+        // default constructor
+        this.hiddenLayers = options.hiddenLayers || [10];
+        this.iterations = options.iterations || 50;
+        this.learningRate = options.learningRate || 0.01;
+        this.regularization = options.regularization || 0.01;
+        this.activation = options.activation || 'tanh';
+        this.activationParam = options.activationParam || 1;
+
+        if (!(this.activation in Object.keys(ACTIVATION_FUNCTIONS))) {
+          this.activation = 'tanh';
+        }
+      }
+    }
+    /**
+     * @private
+     * Function that build and initialize the neural net.
+     * @param {number} inputSize - total of features to fit.
+     * @param {number} outputSize - total of labels of the prediction set.
+     */
+
+
+    buildNetwork(inputSize, outputSize) {
+      var size = 2 + (this.hiddenLayers.length - 1);
+      this.model = new Array(size); // input layer
+
+      this.model[0] = new Layer({
+        inputSize: inputSize,
+        outputSize: this.hiddenLayers[0],
+        activation: this.activation,
+        activationParam: this.activationParam,
+        regularization: this.regularization,
+        epsilon: this.learningRate
+      }); // hidden layers
+
+      for (var i = 1; i < this.hiddenLayers.length; ++i) {
+        this.model[i] = new Layer({
+          inputSize: this.hiddenLayers[i - 1],
+          outputSize: this.hiddenLayers[i],
+          activation: this.activation,
+          activationParam: this.activationParam,
+          regularization: this.regularization,
+          epsilon: this.learningRate
+        });
+      } // output layer
+
+
+      this.model[size - 1] = new OutputLayer({
+        inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],
+        outputSize: outputSize,
+        activation: this.activation,
+        activationParam: this.activationParam,
+        regularization: this.regularization,
+        epsilon: this.learningRate
+      });
+    }
+    /**
+     * Train the neural net with the given features and labels.
+     * @param {Matrix|Array} features
+     * @param {Matrix|Array} labels
+     */
+
+
+    train(features, labels) {
+      features = require$$0.Matrix.checkMatrix(features);
+      this.dicts = dictOutputs(labels);
+      var inputSize = features.columns;
+      var outputSize = Object.keys(this.dicts.inputs).length;
+
+      if (!this.model) {
+        this.buildNetwork(inputSize, outputSize);
+      }
+
+      for (var i = 0; i < this.iterations; ++i) {
+        var probabilities = this.propagate(features);
+        this.backpropagation(features, labels, probabilities);
+      }
+    }
+    /**
+     * @private
+     * Propagate the input(training set) and retrives the probabilities of each class.
+     * @param {Matrix} X
+     * @return {Matrix} probabilities of each class.
+     */
+
+
+    propagate(X) {
+      var input = X;
+
+      for (var i = 0; i < this.model.length; ++i) {
+        input = this.model[i].forward(input);
+      } // get probabilities
+
+
+      return input.divColumnVector(input.sum('row'));
+    }
+    /**
+     * @private
+     * Function that applies the backpropagation algorithm on each layer of the network
+     * in order to fit the features and labels.
+     * @param {Matrix} features
+     * @param {Array} labels
+     * @param {Matrix} probabilities - probabilities of each class of the feature set.
+     */
+
+
+    backpropagation(features, labels, probabilities) {
+      for (var i = 0; i < probabilities.rows; ++i) {
+        probabilities.set(i, this.dicts.inputs[labels[i]], probabilities.get(i, this.dicts.inputs[labels[i]]) - 1);
+      } // remember, the last delta doesn't matter
+
+
+      var delta = probabilities;
+
+      for (i = this.model.length - 1; i >= 0; --i) {
+        var a = i > 0 ? this.model[i - 1].a : features;
+        delta = this.model[i].backpropagation(delta, a);
+      }
+
+      for (i = 0; i < this.model.length; ++i) {
+        this.model[i].update();
+      }
+    }
+    /**
+     * Predict the output given the feature set.
+     * @param {Array|Matrix} features
+     * @return {Array}
+     */
+
+
+    predict(features) {
+      features = require$$0.Matrix.checkMatrix(features);
+      var outputs = new Array(features.rows);
+      var probabilities = this.propagate(features);
+
+      for (var i = 0; i < features.rows; ++i) {
+        outputs[i] = this.dicts.outputs[probabilities.maxRowIndex(i)[1]];
+      }
+
+      return outputs;
+    }
+    /**
+     * Export the current model to JSON.
+     * @return {object} model
+     */
+
+
+    toJSON() {
+      var model = {
+        model: 'FNN',
+        hiddenLayers: this.hiddenLayers,
+        iterations: this.iterations,
+        learningRate: this.learningRate,
+        regularization: this.regularization,
+        activation: this.activation,
+        activationParam: this.activationParam,
+        dicts: this.dicts,
+        layers: new Array(this.model.length)
+      };
+
+      for (var i = 0; i < this.model.length; ++i) {
+        model.layers[i] = this.model[i].toJSON();
+      }
+
+      return model;
+    }
+    /**
+     * Load a Feedforward Neural Network with the current model.
+     * @param {object} model
+     * @return {FeedForwardNeuralNetworks}
+     */
+
+
+    static load(model) {
+      if (model.model !== 'FNN') {
+        throw new RangeError('the current model is not a feed forward network');
+      }
+
+      return new FeedForwardNeuralNetworks(model);
+    }
+
+  }
+  /**
+   * @private
+   * Method that given an array of labels(predictions), returns two dictionaries, one to transform from labels to
+   * numbers and other in the reverse way
+   * @param {Array} array
+   * @return {object}
+   */
+
+
+  function dictOutputs(array) {
+    var inputs = {};
+    var outputs = {};
+    var index = 0;
+
+    for (var i = 0; i < array.length; i += 1) {
+      if (inputs[array[i]] === undefined) {
+        inputs[array[i]] = index;
+        outputs[index] = array[i];
+        index++;
+      }
+    }
+
+    return {
+      inputs: inputs,
+      outputs: outputs
+    };
+  }
+
+  var FeedForwardNeuralNetwork = FeedForwardNeuralNetworks;
+
+  function NodeSquare(x, y, weights, som) {
+    this.x = x;
+    this.y = y;
+    this.weights = weights;
+    this.som = som;
+    this.neighbors = {};
+  }
+
+  NodeSquare.prototype.adjustWeights = function adjustWeights(target, learningRate, influence) {
+    for (var i = 0, ii = this.weights.length; i < ii; i++) {
+      this.weights[i] += learningRate * influence * (target[i] - this.weights[i]);
+    }
+  };
+
+  NodeSquare.prototype.getDistance = function getDistance(otherNode) {
+    return Math.max(Math.abs(this.x - otherNode.x), Math.abs(this.y - otherNode.y));
+  };
+
+  NodeSquare.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {
+    var distX = Math.abs(this.x - otherNode.x),
+        distY = Math.abs(this.y - otherNode.y);
+    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY));
+  };
+
+  NodeSquare.prototype.getNeighbors = function getNeighbors(xy) {
+    if (!this.neighbors[xy]) {
+      this.neighbors[xy] = new Array(2); // left or bottom neighbor
+
+      var v;
+
+      if (this[xy] > 0) {
+        v = this[xy] - 1;
+      } else if (this.som.torus) {
+        v = this.som.gridDim[xy] - 1;
+      }
+
+      if (typeof v !== 'undefined') {
+        var x, y;
+
+        if (xy === 'x') {
+          x = v;
+          y = this.y;
+        } else {
+          x = this.x;
+          y = v;
+        }
+
+        this.neighbors[xy][0] = this.som.nodes[x][y];
+      } // top or right neighbor
+
+
+      var w;
+
+      if (this[xy] < this.som.gridDim[xy] - 1) {
+        w = this[xy] + 1;
+      } else if (this.som.torus) {
+        w = 0;
+      }
+
+      if (typeof w !== 'undefined') {
+        if (xy === 'x') {
+          x = w;
+          y = this.y;
+        } else {
+          x = this.x;
+          y = w;
+        }
+
+        this.neighbors[xy][1] = this.som.nodes[x][y];
+      }
+    }
+
+    return this.neighbors[xy];
+  };
+
+  NodeSquare.prototype.getPos = function getPos(xy, element) {
+    var neighbors = this.getNeighbors(xy),
+        distance = this.som.distance,
+        bestNeighbor,
+        direction;
+
+    if (neighbors[0]) {
+      if (neighbors[1]) {
+        var dist1 = distance(element, neighbors[0].weights),
+            dist2 = distance(element, neighbors[1].weights);
+
+        if (dist1 < dist2) {
+          bestNeighbor = neighbors[0];
+          direction = -1;
+        } else {
+          bestNeighbor = neighbors[1];
+          direction = 1;
+        }
+      } else {
+        bestNeighbor = neighbors[0];
+        direction = -1;
+      }
+    } else {
+      bestNeighbor = neighbors[1];
+      direction = 1;
+    }
+
+    var simA = 1 - distance(element, this.weights),
+        simB = 1 - distance(element, bestNeighbor.weights);
+    var factor = (simA - simB) / (2 - simA - simB);
+    return 0.5 + 0.5 * factor * direction;
+  };
+
+  NodeSquare.prototype.getPosition = function getPosition(element) {
+    return [this.getPos('x', element), this.getPos('y', element)];
+  };
+
+  var nodeSquare = NodeSquare;
+
+  function NodeHexagonal(x, y, weights, som) {
+    nodeSquare.call(this, x, y, weights, som);
+    this.hX = x - Math.floor(y / 2);
+    this.z = 0 - this.hX - y;
+  }
+
+  NodeHexagonal.prototype = new nodeSquare();
+  NodeHexagonal.prototype.constructor = NodeHexagonal;
+
+  NodeHexagonal.prototype.getDistance = function getDistanceHexagonal(otherNode) {
+    return Math.max(Math.abs(this.hX - otherNode.hX), Math.abs(this.y - otherNode.y), Math.abs(this.z - otherNode.z));
+  };
+
+  NodeHexagonal.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {
+    var distX = Math.abs(this.hX - otherNode.hX),
+        distY = Math.abs(this.y - otherNode.y),
+        distZ = Math.abs(this.z - otherNode.z);
+    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY), Math.min(distZ, this.som.gridDim.z - distZ));
+  };
+
+  NodeHexagonal.prototype.getPosition = function getPosition() {
+    throw new Error('Unimplemented : cannot get position of the points for hexagonal grid');
+  };
 
+  var nodeHexagonal = NodeHexagonal;
 
-    static load(model) {
-      if (model.model !== 'Layer') {
-        throw new RangeError('the current model is not a Layer model');
-      }
+  var defaultOptions$6 = {
+    fields: 3,
+    randomizer: Math.random,
+    distance: squareEuclidean,
+    iterations: 10,
+    learningRate: 0.1,
+    gridType: 'rect',
+    torus: true,
+    method: 'random'
+  };
 
-      return new Layer(model);
-    }
+  function SOM(x, y, options, reload) {
+    this.x = x;
+    this.y = y;
+    options = options || {};
+    this.options = {};
 
-  }
+    for (var i in defaultOptions$6) {
+      if (options.hasOwnProperty(i)) {
+        this.options[i] = options[i];
+      } else {
+        this.options[i] = defaultOptions$6[i];
+      }
+    }
 
-  class OutputLayer extends Layer {
-    constructor(options) {
-      super(options);
+    if (typeof this.options.fields === 'number') {
+      this.numWeights = this.options.fields;
+    } else if (Array.isArray(this.options.fields)) {
+      this.numWeights = this.options.fields.length;
+      var converters = getConverters(this.options.fields);
+      this.extractor = converters.extractor;
+      this.creator = converters.creator;
+    } else {
+      throw new Error('Invalid fields definition');
+    }
 
-      this.activationFunction = function (i, j) {
-        this.set(i, j, Math.exp(this.get(i, j)));
+    if (this.options.gridType === 'rect') {
+      this.nodeType = nodeSquare;
+      this.gridDim = {
+        x: x,
+        y: y
+      };
+    } else {
+      this.nodeType = nodeHexagonal;
+      var hx = this.x - Math.floor(this.y / 2);
+      this.gridDim = {
+        x: hx,
+        y: this.y,
+        z: -(0 - hx - this.y)
       };
     }
 
-    static load(model) {
-      if (model.model !== 'Layer') {
-        throw new RangeError('the current model is not a Layer model');
-      }
+    this.torus = this.options.torus;
+    this.distanceMethod = this.torus ? 'getDistanceTorus' : 'getDistance';
+    this.distance = this.options.distance;
+    this.maxDistance = getMaxDistance(this.distance, this.numWeights);
 
-      return new OutputLayer(model);
+    if (reload === true) {
+      // For model loading
+      this.done = true;
+      return;
     }
 
-  }
+    if (!(x > 0 && y > 0)) {
+      throw new Error('x and y must be positive');
+    }
 
-  class FeedForwardNeuralNetworks {
-    /**
-     * Create a new Feedforward neural network model.
-     * @class FeedForwardNeuralNetworks
-     * @param {object} [options]
-     * @param {Array} [options.hiddenLayers=[10]] - Array that contains the sizes of the hidden layers.
-     * @param {number} [options.iterations=50] - Number of iterations at the training step.
-     * @param {number} [options.learningRate=0.01] - Learning rate of the neural net (also known as epsilon).
-     * @param {number} [options.regularization=0.01] - Regularization parameter af the neural net.
-     * @param {string} [options.activation='tanh'] - activation function to be used. (options: 'tanh'(default),
-     * 'identity', 'logistic', 'arctan', 'softsign', 'relu', 'softplus', 'bent', 'sinusoid', 'sinc', 'gaussian').
-     * (single-parametric options: 'parametric-relu', 'exponential-relu', 'soft-exponential').
-     * @param {number} [options.activationParam=1] - if the selected activation function needs a parameter.
-     */
-    constructor(options) {
-      options = options || {};
+    this.times = {
+      findBMU: 0,
+      adjust: 0
+    };
+    this.randomizer = this.options.randomizer;
+    this.iterationCount = 0;
+    this.iterations = this.options.iterations;
+    this.startLearningRate = this.learningRate = this.options.learningRate;
+    this.mapRadius = Math.floor(Math.max(x, y) / 2);
+    this.algorithmMethod = this.options.method;
 
-      if (options.model) {
-        // load network
-        this.hiddenLayers = options.hiddenLayers;
-        this.iterations = options.iterations;
-        this.learningRate = options.learningRate;
-        this.regularization = options.regularization;
-        this.dicts = options.dicts;
-        this.activation = options.activation;
-        this.activationParam = options.activationParam;
-        this.model = new Array(options.layers.length);
+    this._initNodes();
 
-        for (var i = 0; i < this.model.length - 1; ++i) {
-          this.model[i] = Layer.load(options.layers[i]);
-        }
+    this.done = false;
+  }
 
-        this.model[this.model.length - 1] = OutputLayer.load(options.layers[this.model.length - 1]);
-      } else {
-        // default constructor
-        this.hiddenLayers = options.hiddenLayers || [10];
-        this.iterations = options.iterations || 50;
-        this.learningRate = options.learningRate || 0.01;
-        this.regularization = options.regularization || 0.01;
-        this.activation = options.activation || 'tanh';
-        this.activationParam = options.activationParam || 1;
+  SOM.load = function loadModel(model, distance) {
+    if (model.name === 'SOM') {
+      var x = model.data.length,
+          y = model.data[0].length;
 
-        if (!(this.activation in Object.keys(ACTIVATION_FUNCTIONS))) {
-          this.activation = 'tanh';
-        }
+      if (distance) {
+        model.options.distance = distance;
+      } else if (model.options.distance) {
+        model.options.distance = eval('(' + model.options.distance + ')');
       }
-    }
-    /**
-     * @private
-     * Function that build and initialize the neural net.
-     * @param {number} inputSize - total of features to fit.
-     * @param {number} outputSize - total of labels of the prediction set.
-     */
-
 
-    buildNetwork(inputSize, outputSize) {
-      var size = 2 + (this.hiddenLayers.length - 1);
-      this.model = new Array(size); // input layer
-
-      this.model[0] = new Layer({
-        inputSize: inputSize,
-        outputSize: this.hiddenLayers[0],
-        activation: this.activation,
-        activationParam: this.activationParam,
-        regularization: this.regularization,
-        epsilon: this.learningRate
-      }); // hidden layers
+      var som = new SOM(x, y, model.options, true);
+      som.nodes = new Array(x);
 
-      for (var i = 1; i < this.hiddenLayers.length; ++i) {
-        this.model[i] = new Layer({
-          inputSize: this.hiddenLayers[i - 1],
-          outputSize: this.hiddenLayers[i],
-          activation: this.activation,
-          activationParam: this.activationParam,
-          regularization: this.regularization,
-          epsilon: this.learningRate
-        });
-      } // output layer
+      for (var i = 0; i < x; i++) {
+        som.nodes[i] = new Array(y);
 
+        for (var j = 0; j < y; j++) {
+          som.nodes[i][j] = new som.nodeType(i, j, model.data[i][j], som);
+        }
+      }
 
-      this.model[size - 1] = new OutputLayer({
-        inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],
-        outputSize: outputSize,
-        activation: this.activation,
-        activationParam: this.activationParam,
-        regularization: this.regularization,
-        epsilon: this.learningRate
-      });
+      return som;
+    } else {
+      throw new Error('expecting a SOM model');
     }
-    /**
-     * Train the neural net with the given features and labels.
-     * @param {Matrix|Array} features
-     * @param {Matrix|Array} labels
-     */
+  };
 
+  SOM.prototype.export = function exportModel(includeDistance) {
+    if (!this.done) {
+      throw new Error('model is not ready yet');
+    }
 
-    train(features, labels) {
-      features = Matrix.Matrix.checkMatrix(features);
-      this.dicts = dictOutputs(labels);
-      var inputSize = features.columns;
-      var outputSize = Object.keys(this.dicts.inputs).length;
+    var model = {
+      name: 'SOM'
+    };
+    model.options = {
+      fields: this.options.fields,
+      gridType: this.options.gridType,
+      torus: this.options.torus
+    };
+    model.data = new Array(this.x);
 
-      if (!this.model) {
-        this.buildNetwork(inputSize, outputSize);
-      }
+    for (var i = 0; i < this.x; i++) {
+      model.data[i] = new Array(this.y);
 
-      for (var i = 0; i < this.iterations; ++i) {
-        var probabilities = this.propagate(features);
-        this.backpropagation(features, labels, probabilities);
+      for (var j = 0; j < this.y; j++) {
+        model.data[i][j] = this.nodes[i][j].weights;
       }
     }
-    /**
-     * @private
-     * Propagate the input(training set) and retrives the probabilities of each class.
-     * @param {Matrix} X
-     * @return {Matrix} probabilities of each class.
-     */
 
-
-    propagate(X) {
-      var input = X;
-
-      for (var i = 0; i < this.model.length; ++i) {
-        input = this.model[i].forward(input);
-      } // get probabilities
-
-
-      return input.divColumnVector(input.sum('row'));
+    if (includeDistance) {
+      model.options.distance = this.distance.toString();
     }
-    /**
-     * @private
-     * Function that applies the backpropagation algorithm on each layer of the network
-     * in order to fit the features and labels.
-     * @param {Matrix} features
-     * @param {Array} labels
-     * @param {Matrix} probabilities - probabilities of each class of the feature set.
-     */
 
+    return model;
+  };
 
-    backpropagation(features, labels, probabilities) {
-      for (var i = 0; i < probabilities.rows; ++i) {
-        probabilities.set(i, this.dicts.inputs[labels[i]], probabilities.get(i, this.dicts.inputs[labels[i]]) - 1);
-      } // remember, the last delta doesn't matter
+  SOM.prototype._initNodes = function initNodes() {
+    var now = Date.now(),
+        i,
+        j,
+        k;
+    this.nodes = new Array(this.x);
 
+    for (i = 0; i < this.x; i++) {
+      this.nodes[i] = new Array(this.y);
 
-      var delta = probabilities;
+      for (j = 0; j < this.y; j++) {
+        var weights = new Array(this.numWeights);
 
-      for (i = this.model.length - 1; i >= 0; --i) {
-        var a = i > 0 ? this.model[i - 1].a : features;
-        delta = this.model[i].backpropagation(delta, a);
-      }
+        for (k = 0; k < this.numWeights; k++) {
+          weights[k] = this.randomizer();
+        }
 
-      for (i = 0; i < this.model.length; ++i) {
-        this.model[i].update();
+        this.nodes[i][j] = new this.nodeType(i, j, weights, this);
       }
     }
-    /**
-     * Predict the output given the feature set.
-     * @param {Array|Matrix} features
-     * @return {Array}
-     */
-
-
-    predict(features) {
-      features = Matrix.Matrix.checkMatrix(features);
-      var outputs = new Array(features.rows);
-      var probabilities = this.propagate(features);
 
-      for (var i = 0; i < features.rows; ++i) {
-        outputs[i] = this.dicts.outputs[probabilities.maxRowIndex(i)[1]];
-      }
+    this.times.initNodes = Date.now() - now;
+  };
 
-      return outputs;
+  SOM.prototype.setTraining = function setTraining(trainingSet) {
+    if (this.trainingSet) {
+      throw new Error('training set has already been set');
     }
-    /**
-     * Export the current model to JSON.
-     * @return {object} model
-     */
 
+    var now = Date.now();
+    var convertedSet = trainingSet;
+    var i,
+        l = trainingSet.length;
 
-    toJSON() {
-      var model = {
-        model: 'FNN',
-        hiddenLayers: this.hiddenLayers,
-        iterations: this.iterations,
-        learningRate: this.learningRate,
-        regularization: this.regularization,
-        activation: this.activation,
-        activationParam: this.activationParam,
-        dicts: this.dicts,
-        layers: new Array(this.model.length)
-      };
+    if (this.extractor) {
+      convertedSet = new Array(l);
 
-      for (var i = 0; i < this.model.length; ++i) {
-        model.layers[i] = this.model[i].toJSON();
+      for (i = 0; i < l; i++) {
+        convertedSet[i] = this.extractor(trainingSet[i]);
       }
-
-      return model;
     }
-    /**
-     * Load a Feedforward Neural Network with the current model.
-     * @param {object} model
-     * @return {FeedForwardNeuralNetworks}
-     */
-
 
-    static load(model) {
-      if (model.model !== 'FNN') {
-        throw new RangeError('the current model is not a feed forward network');
-      }
+    this.numIterations = this.iterations * l;
 
-      return new FeedForwardNeuralNetworks(model);
+    if (this.algorithmMethod === 'random') {
+      this.timeConstant = this.numIterations / Math.log(this.mapRadius);
+    } else {
+      this.timeConstant = l / Math.log(this.mapRadius);
     }
 
-  }
-  /**
-   * @private
-   * Method that given an array of labels(predictions), returns two dictionaries, one to transform from labels to
-   * numbers and other in the reverse way
-   * @param {Array} array
-   * @return {object}
-   */
+    this.trainingSet = convertedSet;
+    this.times.setTraining = Date.now() - now;
+  };
 
+  SOM.prototype.trainOne = function trainOne() {
+    if (this.done) {
+      return false;
+    } else if (this.numIterations-- > 0) {
+      var neighbourhoodRadius, trainingValue, trainingSetFactor;
 
-  function dictOutputs(array) {
-    var inputs = {};
-    var outputs = {};
-    var index = 0;
+      if (this.algorithmMethod === 'random') {
+        // Pick a random value of the training set at each step
+        neighbourhoodRadius = this.mapRadius * Math.exp(-this.iterationCount / this.timeConstant);
+        trainingValue = getRandomValue(this.trainingSet, this.randomizer);
 
-    for (var i = 0; i < array.length; i += 1) {
-      if (inputs[array[i]] === undefined) {
-        inputs[array[i]] = index;
-        outputs[index] = array[i];
-        index++;
-      }
-    }
+        this._adjust(trainingValue, neighbourhoodRadius);
 
-    return {
-      inputs: inputs,
-      outputs: outputs
-    };
-  }
+        this.learningRate = this.startLearningRate * Math.exp(-this.iterationCount / this.numIterations);
+      } else {
+        // Get next input vector
+        trainingSetFactor = -Math.floor(this.iterationCount / this.trainingSet.length);
+        neighbourhoodRadius = this.mapRadius * Math.exp(trainingSetFactor / this.timeConstant);
+        trainingValue = this.trainingSet[this.iterationCount % this.trainingSet.length];
 
-  var FeedForwardNeuralNetwork = FeedForwardNeuralNetworks;
+        this._adjust(trainingValue, neighbourhoodRadius);
 
-  function NodeSquare(x, y, weights, som) {
-    this.x = x;
-    this.y = y;
-    this.weights = weights;
-    this.som = som;
-    this.neighbors = {};
-  }
+        if ((this.iterationCount + 1) % this.trainingSet.length === 0) {
+          this.learningRate = this.startLearningRate * Math.exp(trainingSetFactor / Math.floor(this.numIterations / this.trainingSet.length));
+        }
+      }
 
-  NodeSquare.prototype.adjustWeights = function adjustWeights(target, learningRate, influence) {
-    for (var i = 0, ii = this.weights.length; i < ii; i++) {
-      this.weights[i] += learningRate * influence * (target[i] - this.weights[i]);
+      this.iterationCount++;
+      return true;
+    } else {
+      this.done = true;
+      return false;
     }
   };
 
-  NodeSquare.prototype.getDistance = function getDistance(otherNode) {
-    return Math.max(Math.abs(this.x - otherNode.x), Math.abs(this.y - otherNode.y));
-  };
+  SOM.prototype._adjust = function adjust(trainingValue, neighbourhoodRadius) {
+    var now = Date.now(),
+        x,
+        y,
+        dist,
+        influence;
 
-  NodeSquare.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {
-    var distX = Math.abs(this.x - otherNode.x),
-        distY = Math.abs(this.y - otherNode.y);
-    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY));
-  };
+    var bmu = this._findBestMatchingUnit(trainingValue);
 
-  NodeSquare.prototype.getNeighbors = function getNeighbors(xy) {
-    if (!this.neighbors[xy]) {
-      this.neighbors[xy] = new Array(2); // left or bottom neighbor
+    var now2 = Date.now();
+    this.times.findBMU += now2 - now;
+    var radiusLimit = Math.floor(neighbourhoodRadius);
+    var xMin = bmu.x - radiusLimit,
+        xMax = bmu.x + radiusLimit,
+        yMin = bmu.y - radiusLimit,
+        yMax = bmu.y + radiusLimit;
 
-      var v;
+    for (x = xMin; x <= xMax; x++) {
+      var theX = x;
 
-      if (this[xy] > 0) {
-        v = this[xy] - 1;
-      } else if (this.som.torus) {
-        v = this.som.gridDim[xy] - 1;
+      if (x < 0) {
+        theX += this.x;
+      } else if (x >= this.x) {
+        theX -= this.x;
       }
 
-      if (typeof v !== 'undefined') {
-        var x, y;
+      for (y = yMin; y <= yMax; y++) {
+        var theY = y;
 
-        if (xy === 'x') {
-          x = v;
-          y = this.y;
-        } else {
-          x = this.x;
-          y = v;
+        if (y < 0) {
+          theY += this.y;
+        } else if (y >= this.y) {
+          theY -= this.y;
         }
 
-        this.neighbors[xy][0] = this.som.nodes[x][y];
-      } // top or right neighbor
+        dist = bmu[this.distanceMethod](this.nodes[theX][theY]);
 
+        if (dist < neighbourhoodRadius) {
+          influence = Math.exp(-dist / (2 * neighbourhoodRadius));
+          this.nodes[theX][theY].adjustWeights(trainingValue, this.learningRate, influence);
+        }
+      }
+    }
 
-      var w;
+    this.times.adjust += Date.now() - now2;
+  };
 
-      if (this[xy] < this.som.gridDim[xy] - 1) {
-        w = this[xy] + 1;
-      } else if (this.som.torus) {
-        w = 0;
-      }
+  SOM.prototype.train = function train(trainingSet) {
+    if (!this.done) {
+      this.setTraining(trainingSet);
 
-      if (typeof w !== 'undefined') {
-        if (xy === 'x') {
-          x = w;
-          y = this.y;
-        } else {
-          x = this.x;
-          y = w;
-        }
+      while (this.trainOne()) {}
+    }
+  };
 
-        this.neighbors[xy][1] = this.som.nodes[x][y];
+  SOM.prototype.getConvertedNodes = function getConvertedNodes() {
+    var result = new Array(this.x);
+
+    for (var i = 0; i < this.x; i++) {
+      result[i] = new Array(this.y);
+
+      for (var j = 0; j < this.y; j++) {
+        var node = this.nodes[i][j];
+        result[i][j] = this.creator ? this.creator(node.weights) : node.weights;
       }
     }
 
-    return this.neighbors[xy];
+    return result;
   };
 
-  NodeSquare.prototype.getPos = function getPos(xy, element) {
-    var neighbors = this.getNeighbors(xy),
-        distance = this.som.distance,
-        bestNeighbor,
-        direction;
-
-    if (neighbors[0]) {
-      if (neighbors[1]) {
-        var dist1 = distance(element, neighbors[0].weights),
-            dist2 = distance(element, neighbors[1].weights);
+  SOM.prototype._findBestMatchingUnit = function findBestMatchingUnit(candidate) {
+    var bmu,
+        lowest = Infinity,
+        dist;
 
-        if (dist1 < dist2) {
-          bestNeighbor = neighbors[0];
-          direction = -1;
-        } else {
-          bestNeighbor = neighbors[1];
-          direction = 1;
+    for (var i = 0; i < this.x; i++) {
+      for (var j = 0; j < this.y; j++) {
+        dist = this.distance(this.nodes[i][j].weights, candidate);
+
+        if (dist < lowest) {
+          lowest = dist;
+          bmu = this.nodes[i][j];
         }
-      } else {
-        bestNeighbor = neighbors[0];
-        direction = -1;
       }
-    } else {
-      bestNeighbor = neighbors[1];
-      direction = 1;
     }
 
-    var simA = 1 - distance(element, this.weights),
-        simB = 1 - distance(element, bestNeighbor.weights);
-    var factor = (simA - simB) / (2 - simA - simB);
-    return 0.5 + 0.5 * factor * direction;
+    return bmu;
   };
 
-  NodeSquare.prototype.getPosition = function getPosition(element) {
-    return [this.getPos('x', element), this.getPos('y', element)];
-  };
+  SOM.prototype.predict = function predict(data, computePosition) {
+    if (typeof data === 'boolean') {
+      computePosition = data;
+      data = null;
+    }
 
-  var nodeSquare = NodeSquare;
+    if (!data) {
+      data = this.trainingSet;
+    }
 
-  function NodeHexagonal(x, y, weights, som) {
-    nodeSquare.call(this, x, y, weights, som);
-    this.hX = x - Math.floor(y / 2);
-    this.z = 0 - this.hX - y;
-  }
+    if (Array.isArray(data) && (Array.isArray(data[0]) || typeof data[0] === 'object')) {
+      // predict a dataset
+      var self = this;
+      return data.map(function (element) {
+        return self._predict(element, computePosition);
+      });
+    } else {
+      // predict a single element
+      return this._predict(data, computePosition);
+    }
+  };
 
-  NodeHexagonal.prototype = new nodeSquare();
-  NodeHexagonal.prototype.constructor = NodeHexagonal;
+  SOM.prototype._predict = function _predict(element, computePosition) {
+    if (!Array.isArray(element)) {
+      element = this.extractor(element);
+    }
 
-  NodeHexagonal.prototype.getDistance = function getDistanceHexagonal(otherNode) {
-    return Math.max(Math.abs(this.hX - otherNode.hX), Math.abs(this.y - otherNode.y), Math.abs(this.z - otherNode.z));
-  };
+    var bmu = this._findBestMatchingUnit(element);
 
-  NodeHexagonal.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {
-    var distX = Math.abs(this.hX - otherNode.hX),
-        distY = Math.abs(this.y - otherNode.y),
-        distZ = Math.abs(this.z - otherNode.z);
-    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY), Math.min(distZ, this.som.gridDim.z - distZ));
-  };
+    var result = [bmu.x, bmu.y];
 
-  NodeHexagonal.prototype.getPosition = function getPosition() {
-    throw new Error('Unimplemented : cannot get position of the points for hexagonal grid');
-  };
+    if (computePosition) {
+      result[2] = bmu.getPosition(element);
+    }
 
-  var nodeHexagonal = NodeHexagonal;
+    return result;
+  }; // As seen in http://www.scholarpedia.org/article/Kohonen_network
 
-  var defaultOptions$7 = {
-    fields: 3,
-    randomizer: Math.random,
-    distance: squareEuclidean,
-    iterations: 10,
-    learningRate: 0.1,
-    gridType: 'rect',
-    torus: true,
-    method: 'random'
-  };
 
-  function SOM(x, y, options, reload) {
-    this.x = x;
-    this.y = y;
-    options = options || {};
-    this.options = {};
+  SOM.prototype.getQuantizationError = function getQuantizationError() {
+    var fit = this.getFit(),
+        l = fit.length,
+        sum = 0;
 
-    for (var i in defaultOptions$7) {
-      if (options.hasOwnProperty(i)) {
-        this.options[i] = options[i];
-      } else {
-        this.options[i] = defaultOptions$7[i];
-      }
+    for (var i = 0; i < l; i++) {
+      sum += fit[i];
     }
 
-    if (typeof this.options.fields === 'number') {
-      this.numWeights = this.options.fields;
-    } else if (Array.isArray(this.options.fields)) {
-      this.numWeights = this.options.fields.length;
-      var converters = getConverters(this.options.fields);
-      this.extractor = converters.extractor;
-      this.creator = converters.creator;
-    } else {
-      throw new Error('Invalid fields definition');
-    }
+    return sum / l;
+  };
 
-    if (this.options.gridType === 'rect') {
-      this.nodeType = nodeSquare;
-      this.gridDim = {
-        x: x,
-        y: y
-      };
-    } else {
-      this.nodeType = nodeHexagonal;
-      var hx = this.x - Math.floor(this.y / 2);
-      this.gridDim = {
-        x: hx,
-        y: this.y,
-        z: -(0 - hx - this.y)
-      };
+  SOM.prototype.getFit = function getFit(dataset) {
+    if (!dataset) {
+      dataset = this.trainingSet;
     }
 
-    this.torus = this.options.torus;
-    this.distanceMethod = this.torus ? 'getDistanceTorus' : 'getDistance';
-    this.distance = this.options.distance;
-    this.maxDistance = getMaxDistance(this.distance, this.numWeights);
+    var l = dataset.length,
+        bmu,
+        result = new Array(l);
 
-    if (reload === true) {
-      // For model loading
-      this.done = true;
-      return;
+    for (var i = 0; i < l; i++) {
+      bmu = this._findBestMatchingUnit(dataset[i]);
+      result[i] = Math.sqrt(this.distance(dataset[i], bmu.weights));
     }
 
-    if (!(x > 0 && y > 0)) {
-      throw new Error('x and y must be positive');
+    return result;
+  };
+
+  function getConverters(fields) {
+    var l = fields.length,
+        normalizers = new Array(l),
+        denormalizers = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      normalizers[i] = getNormalizer(fields[i].range);
+      denormalizers[i] = getDenormalizer(fields[i].range);
     }
 
-    this.times = {
-      findBMU: 0,
-      adjust: 0
-    };
-    this.randomizer = this.options.randomizer;
-    this.iterationCount = 0;
-    this.iterations = this.options.iterations;
-    this.startLearningRate = this.learningRate = this.options.learningRate;
-    this.mapRadius = Math.floor(Math.max(x, y) / 2);
-    this.algorithmMethod = this.options.method;
+    return {
+      extractor: function extractor(value) {
+        var result = new Array(l);
 
-    this._initNodes();
+        for (var i = 0; i < l; i++) {
+          result[i] = normalizers[i](value[fields[i].name]);
+        }
 
-    this.done = false;
-  }
+        return result;
+      },
+      creator: function creator(value) {
+        var result = {};
 
-  SOM.load = function loadModel(model, distance) {
-    if (model.name === 'SOM') {
-      var x = model.data.length,
-          y = model.data[0].length;
+        for (var i = 0; i < l; i++) {
+          result[fields[i].name] = denormalizers[i](value[i]);
+        }
 
-      if (distance) {
-        model.options.distance = distance;
-      } else if (model.options.distance) {
-        model.options.distance = eval('(' + model.options.distance + ')');
+        return result;
       }
+    };
+  }
 
-      var som = new SOM(x, y, model.options, true);
-      som.nodes = new Array(x);
+  function getNormalizer(minMax) {
+    return function normalizer(value) {
+      return (value - minMax[0]) / (minMax[1] - minMax[0]);
+    };
+  }
 
-      for (var i = 0; i < x; i++) {
-        som.nodes[i] = new Array(y);
+  function getDenormalizer(minMax) {
+    return function denormalizer(value) {
+      return minMax[0] + value * (minMax[1] - minMax[0]);
+    };
+  }
 
-        for (var j = 0; j < y; j++) {
-          som.nodes[i][j] = new som.nodeType(i, j, model.data[i][j], som);
-        }
-      }
+  function squareEuclidean(a, b) {
+    var d = 0;
 
-      return som;
-    } else {
-      throw new Error('expecting a SOM model');
+    for (var i = 0, ii = a.length; i < ii; i++) {
+      d += (a[i] - b[i]) * (a[i] - b[i]);
     }
-  };
 
-  SOM.prototype.export = function exportModel(includeDistance) {
-    if (!this.done) {
-      throw new Error('model is not ready yet');
+    return d;
+  }
+
+  function getRandomValue(arr, randomizer) {
+    return arr[Math.floor(randomizer() * arr.length)];
+  }
+
+  function getMaxDistance(distance, numWeights) {
+    var zero = new Array(numWeights),
+        one = new Array(numWeights);
+
+    for (var i = 0; i < numWeights; i++) {
+      zero[i] = 0;
+      one[i] = 1;
     }
 
-    var model = {
-      name: 'SOM'
-    };
-    model.options = {
-      fields: this.options.fields,
-      gridType: this.options.gridType,
-      torus: this.options.torus
-    };
-    model.data = new Array(this.x);
+    return distance(zero, one);
+  }
 
-    for (var i = 0; i < this.x; i++) {
-      model.data[i] = new Array(this.y);
+  var src = SOM;
 
-      for (var j = 0; j < this.y; j++) {
-        model.data[i][j] = this.nodes[i][j].weights;
+  function maybeToPrecision(value, digits) {
+    if (value < 0) {
+      value = 0 - value;
+
+      if (typeof digits === 'number') {
+        return `- ${value.toPrecision(digits)}`;
+      } else {
+        return `- ${value.toString()}`;
+      }
+    } else {
+      if (typeof digits === 'number') {
+        return value.toPrecision(digits);
+      } else {
+        return value.toString();
       }
     }
+  }
 
-    if (includeDistance) {
-      model.options.distance = this.distance.toString();
+  function checkArraySize(x, y) {
+    if (!Array.isArray(x) || !Array.isArray(y)) {
+      throw new TypeError('x and y must be arrays');
     }
 
-    return model;
-  };
-
-  SOM.prototype._initNodes = function initNodes() {
-    var now = Date.now(),
-        i,
-        j,
-        k;
-    this.nodes = new Array(this.x);
+    if (x.length !== y.length) {
+      throw new RangeError('x and y arrays must have the same length');
+    }
+  }
 
-    for (i = 0; i < this.x; i++) {
-      this.nodes[i] = new Array(this.y);
+  class BaseRegression {
+    constructor() {
+      if (new.target === BaseRegression) {
+        throw new Error('BaseRegression must be subclassed');
+      }
+    }
 
-      for (j = 0; j < this.y; j++) {
-        var weights = new Array(this.numWeights);
+    predict(x) {
+      if (typeof x === 'number') {
+        return this._predict(x);
+      } else if (Array.isArray(x)) {
+        const y = [];
 
-        for (k = 0; k < this.numWeights; k++) {
-          weights[k] = this.randomizer();
+        for (let i = 0; i < x.length; i++) {
+          y.push(this._predict(x[i]));
         }
 
-        this.nodes[i][j] = new this.nodeType(i, j, weights, this);
+        return y;
+      } else {
+        throw new TypeError('x must be a number or array');
       }
     }
 
-    this.times.initNodes = Date.now() - now;
-  };
-
-  SOM.prototype.setTraining = function setTraining(trainingSet) {
-    if (this.trainingSet) {
-      throw new Error('training set has already been set');
+    _predict() {
+      throw new Error('_predict must be implemented');
     }
 
-    var now = Date.now();
-    var convertedSet = trainingSet;
-    var i,
-        l = trainingSet.length;
-
-    if (this.extractor) {
-      convertedSet = new Array(l);
-
-      for (i = 0; i < l; i++) {
-        convertedSet[i] = this.extractor(trainingSet[i]);
-      }
+    train() {// Do nothing for this package
     }
 
-    this.numIterations = this.iterations * l;
+    toString() {
+      return '';
+    }
 
-    if (this.algorithmMethod === 'random') {
-      this.timeConstant = this.numIterations / Math.log(this.mapRadius);
-    } else {
-      this.timeConstant = l / Math.log(this.mapRadius);
+    toLaTeX() {
+      return '';
     }
+    /**
+     * Return the correlation coefficient of determination (r) and chi-square.
+     * @param {Array<number>} x
+     * @param {Array<number>} y
+     * @return {object}
+     */
 
-    this.trainingSet = convertedSet;
-    this.times.setTraining = Date.now() - now;
-  };
 
-  SOM.prototype.trainOne = function trainOne() {
-    if (this.done) {
-      return false;
-    } else if (this.numIterations-- > 0) {
-      var neighbourhoodRadius, trainingValue, trainingSetFactor;
+    score(x, y) {
+      if (!Array.isArray(x) || !Array.isArray(y) || x.length !== y.length) {
+        throw new Error('x and y must be arrays of the same length');
+      }
 
-      if (this.algorithmMethod === 'random') {
-        // Pick a random value of the training set at each step
-        neighbourhoodRadius = this.mapRadius * Math.exp(-this.iterationCount / this.timeConstant);
-        trainingValue = getRandomValue(this.trainingSet, this.randomizer);
+      const n = x.length;
+      const y2 = new Array(n);
 
-        this._adjust(trainingValue, neighbourhoodRadius);
+      for (let i = 0; i < n; i++) {
+        y2[i] = this._predict(x[i]);
+      }
 
-        this.learningRate = this.startLearningRate * Math.exp(-this.iterationCount / this.numIterations);
-      } else {
-        // Get next input vector
-        trainingSetFactor = -Math.floor(this.iterationCount / this.trainingSet.length);
-        neighbourhoodRadius = this.mapRadius * Math.exp(trainingSetFactor / this.timeConstant);
-        trainingValue = this.trainingSet[this.iterationCount % this.trainingSet.length];
+      let xSum = 0;
+      let ySum = 0;
+      let chi2 = 0;
+      let rmsd = 0;
+      let xSquared = 0;
+      let ySquared = 0;
+      let xY = 0;
 
-        this._adjust(trainingValue, neighbourhoodRadius);
+      for (let i = 0; i < n; i++) {
+        xSum += y2[i];
+        ySum += y[i];
+        xSquared += y2[i] * y2[i];
+        ySquared += y[i] * y[i];
+        xY += y2[i] * y[i];
 
-        if ((this.iterationCount + 1) % this.trainingSet.length === 0) {
-          this.learningRate = this.startLearningRate * Math.exp(trainingSetFactor / Math.floor(this.numIterations / this.trainingSet.length));
+        if (y[i] !== 0) {
+          chi2 += (y[i] - y2[i]) * (y[i] - y2[i]) / y[i];
         }
+
+        rmsd += (y[i] - y2[i]) * (y[i] - y2[i]);
       }
 
-      this.iterationCount++;
-      return true;
-    } else {
-      this.done = true;
-      return false;
+      const r = (n * xY - xSum * ySum) / Math.sqrt((n * xSquared - xSum * xSum) * (n * ySquared - ySum * ySum));
+      return {
+        r: r,
+        r2: r * r,
+        chi2: chi2,
+        rmsd: Math.sqrt(rmsd / n)
+      };
     }
-  };
-
-  SOM.prototype._adjust = function adjust(trainingValue, neighbourhoodRadius) {
-    var now = Date.now(),
-        x,
-        y,
-        dist,
-        influence;
 
-    var bmu = this._findBestMatchingUnit(trainingValue);
-
-    var now2 = Date.now();
-    this.times.findBMU += now2 - now;
-    var radiusLimit = Math.floor(neighbourhoodRadius);
-    var xMin = bmu.x - radiusLimit,
-        xMax = bmu.x + radiusLimit,
-        yMin = bmu.y - radiusLimit,
-        yMax = bmu.y + radiusLimit;
+  }
 
-    for (x = xMin; x <= xMax; x++) {
-      var theX = x;
+  class PolynomialRegression extends BaseRegression {
+    constructor(x, y, degree) {
+      super();
 
-      if (x < 0) {
-        theX += this.x;
-      } else if (x >= this.x) {
-        theX -= this.x;
+      if (x === true) {
+        this.degree = y.degree;
+        this.powers = y.powers;
+        this.coefficients = y.coefficients;
+      } else {
+        checkArraySize(x, y);
+        regress(this, x, y, degree);
       }
+    }
 
-      for (y = yMin; y <= yMax; y++) {
-        var theY = y;
-
-        if (y < 0) {
-          theY += this.y;
-        } else if (y >= this.y) {
-          theY -= this.y;
-        }
-
-        dist = bmu[this.distanceMethod](this.nodes[theX][theY]);
+    _predict(x) {
+      let y = 0;
 
-        if (dist < neighbourhoodRadius) {
-          influence = Math.exp(-dist / (2 * neighbourhoodRadius));
-          this.nodes[theX][theY].adjustWeights(trainingValue, this.learningRate, influence);
-        }
+      for (let k = 0; k < this.powers.length; k++) {
+        y += this.coefficients[k] * Math.pow(x, this.powers[k]);
       }
-    }
 
-    this.times.adjust += Date.now() - now2;
-  };
+      return y;
+    }
 
-  SOM.prototype.train = function train(trainingSet) {
-    if (!this.done) {
-      this.setTraining(trainingSet);
+    toJSON() {
+      return {
+        name: 'polynomialRegression',
+        degree: this.degree,
+        powers: this.powers,
+        coefficients: this.coefficients
+      };
+    }
 
-      while (this.trainOne()) {}
+    toString(precision) {
+      return this._toFormula(precision, false);
     }
-  };
 
-  SOM.prototype.getConvertedNodes = function getConvertedNodes() {
-    var result = new Array(this.x);
+    toLaTeX(precision) {
+      return this._toFormula(precision, true);
+    }
 
-    for (var i = 0; i < this.x; i++) {
-      result[i] = new Array(this.y);
+    _toFormula(precision, isLaTeX) {
+      let sup = '^';
+      let closeSup = '';
+      let times = ' * ';
 
-      for (var j = 0; j < this.y; j++) {
-        var node = this.nodes[i][j];
-        result[i][j] = this.creator ? this.creator(node.weights) : node.weights;
+      if (isLaTeX) {
+        sup = '^{';
+        closeSup = '}';
+        times = '';
       }
-    }
 
-    return result;
-  };
+      let fn = '';
+      let str = '';
 
-  SOM.prototype._findBestMatchingUnit = function findBestMatchingUnit(candidate) {
-    var bmu,
-        lowest = Infinity,
-        dist;
+      for (let k = 0; k < this.coefficients.length; k++) {
+        str = '';
 
-    for (var i = 0; i < this.x; i++) {
-      for (var j = 0; j < this.y; j++) {
-        dist = this.distance(this.nodes[i][j].weights, candidate);
+        if (this.coefficients[k] !== 0) {
+          if (this.powers[k] === 0) {
+            str = maybeToPrecision(this.coefficients[k], precision);
+          } else {
+            if (this.powers[k] === 1) {
+              str = `${maybeToPrecision(this.coefficients[k], precision) + times}x`;
+            } else {
+              str = `${maybeToPrecision(this.coefficients[k], precision) + times}x${sup}${this.powers[k]}${closeSup}`;
+            }
+          }
 
-        if (dist < lowest) {
-          lowest = dist;
-          bmu = this.nodes[i][j];
+          if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {
+            str = ` + ${str}`;
+          } else if (k !== this.coefficients.length - 1) {
+            str = ` ${str}`;
+          }
         }
+
+        fn = str + fn;
       }
-    }
 
-    return bmu;
-  };
+      if (fn.charAt(0) === '+') {
+        fn = fn.slice(1);
+      }
 
-  SOM.prototype.predict = function predict(data, computePosition) {
-    if (typeof data === 'boolean') {
-      computePosition = data;
-      data = null;
+      return `f(x) = ${fn}`;
     }
 
-    if (!data) {
-      data = this.trainingSet;
-    }
+    static load(json) {
+      if (json.name !== 'polynomialRegression') {
+        throw new TypeError('not a polynomial regression model');
+      }
 
-    if (Array.isArray(data) && (Array.isArray(data[0]) || typeof data[0] === 'object')) {
-      // predict a dataset
-      var self = this;
-      return data.map(function (element) {
-        return self._predict(element, computePosition);
-      });
-    } else {
-      // predict a single element
-      return this._predict(data, computePosition);
+      return new PolynomialRegression(true, json);
     }
-  };
 
-  SOM.prototype._predict = function _predict(element, computePosition) {
-    if (!Array.isArray(element)) {
-      element = this.extractor(element);
-    }
+  }
 
-    var bmu = this._findBestMatchingUnit(element);
+  function regress(pr, x, y, degree) {
+    const n = x.length;
+    let powers;
 
-    var result = [bmu.x, bmu.y];
+    if (Array.isArray(degree)) {
+      powers = degree;
+      degree = powers.length;
+    } else {
+      degree++;
+      powers = new Array(degree);
 
-    if (computePosition) {
-      result[2] = bmu.getPosition(element);
+      for (let k = 0; k < degree; k++) {
+        powers[k] = k;
+      }
     }
 
-    return result;
-  }; // As seen in http://www.scholarpedia.org/article/Kohonen_network
-
-
-  SOM.prototype.getQuantizationError = function getQuantizationError() {
-    var fit = this.getFit(),
-        l = fit.length,
-        sum = 0;
+    const F = new Matrix(n, degree);
+    const Y = new Matrix([y]);
 
-    for (var i = 0; i < l; i++) {
-      sum += fit[i];
+    for (let k = 0; k < degree; k++) {
+      for (let i = 0; i < n; i++) {
+        if (powers[k] === 0) {
+          F.set(i, k, 1);
+        } else {
+          F.set(i, k, Math.pow(x[i], powers[k]));
+        }
+      }
     }
 
-    return sum / l;
-  };
-
-  SOM.prototype.getFit = function getFit(dataset) {
-    if (!dataset) {
-      dataset = this.trainingSet;
-    }
+    const FT = new MatrixTransposeView(F);
+    const A = FT.mmul(F);
+    const B = FT.mmul(new MatrixTransposeView(Y));
+    pr.degree = degree - 1;
+    pr.powers = powers;
+    pr.coefficients = solve(A, B).to1DArray();
+  }
 
-    var l = dataset.length,
-        bmu,
-        result = new Array(l);
+  class SimpleLinearRegression extends BaseRegression {
+    constructor(x, y) {
+      super();
 
-    for (var i = 0; i < l; i++) {
-      bmu = this._findBestMatchingUnit(dataset[i]);
-      result[i] = Math.sqrt(this.distance(dataset[i], bmu.weights));
+      if (x === true) {
+        this.slope = y.slope;
+        this.intercept = y.intercept;
+        this.coefficients = [y.intercept, y.slope];
+      } else {
+        checkArraySize(x, y);
+        regress$1(this, x, y);
+      }
     }
 
-    return result;
-  };
+    toJSON() {
+      return {
+        name: 'simpleLinearRegression',
+        slope: this.slope,
+        intercept: this.intercept
+      };
+    }
 
-  function getConverters(fields) {
-    var l = fields.length,
-        normalizers = new Array(l),
-        denormalizers = new Array(l);
+    _predict(x) {
+      return this.slope * x + this.intercept;
+    }
 
-    for (var i = 0; i < l; i++) {
-      normalizers[i] = getNormalizer(fields[i].range);
-      denormalizers[i] = getDenormalizer(fields[i].range);
+    computeX(y) {
+      return (y - this.intercept) / this.slope;
     }
 
-    return {
-      extractor: function extractor(value) {
-        var result = new Array(l);
+    toString(precision) {
+      let result = 'f(x) = ';
 
-        for (var i = 0; i < l; i++) {
-          result[i] = normalizers[i](value[fields[i].name]);
+      if (this.slope !== 0) {
+        const xFactor = maybeToPrecision(this.slope, precision);
+        result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;
+
+        if (this.intercept !== 0) {
+          const absIntercept = Math.abs(this.intercept);
+          const operator = absIntercept === this.intercept ? '+' : '-';
+          result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;
         }
+      } else {
+        result += maybeToPrecision(this.intercept, precision);
+      }
 
-        return result;
-      },
-      creator: function creator(value) {
-        var result = {};
+      return result;
+    }
 
-        for (var i = 0; i < l; i++) {
-          result[fields[i].name] = denormalizers[i](value[i]);
-        }
+    toLaTeX(precision) {
+      return this.toString(precision);
+    }
 
-        return result;
+    static load(json) {
+      if (json.name !== 'simpleLinearRegression') {
+        throw new TypeError('not a SLR model');
       }
-    };
-  }
 
-  function getNormalizer(minMax) {
-    return function normalizer(value) {
-      return (value - minMax[0]) / (minMax[1] - minMax[0]);
-    };
-  }
+      return new SimpleLinearRegression(true, json);
+    }
 
-  function getDenormalizer(minMax) {
-    return function denormalizer(value) {
-      return minMax[0] + value * (minMax[1] - minMax[0]);
-    };
   }
 
-  function squareEuclidean(a, b) {
-    var d = 0;
+  function regress$1(slr, x, y) {
+    const n = x.length;
+    let xSum = 0;
+    let ySum = 0;
+    let xSquared = 0;
+    let xY = 0;
 
-    for (var i = 0, ii = a.length; i < ii; i++) {
-      d += (a[i] - b[i]) * (a[i] - b[i]);
+    for (let i = 0; i < n; i++) {
+      xSum += x[i];
+      ySum += y[i];
+      xSquared += x[i] * x[i];
+      xY += x[i] * y[i];
     }
 
-    return d;
-  }
-
-  function getRandomValue(arr, randomizer) {
-    return arr[Math.floor(randomizer() * arr.length)];
+    const numerator = n * xY - xSum * ySum;
+    slr.slope = numerator / (n * xSquared - xSum * xSum);
+    slr.intercept = 1 / n * ySum - slr.slope * (1 / n) * xSum;
+    slr.coefficients = [slr.intercept, slr.slope];
   }
 
-  function getMaxDistance(distance, numWeights) {
-    var zero = new Array(numWeights),
-        one = new Array(numWeights);
+  class ExponentialRegression extends BaseRegression {
+    constructor(x, y) {
+      super();
 
-    for (var i = 0; i < numWeights; i++) {
-      zero[i] = 0;
-      one[i] = 1;
+      if (x === true) {
+        this.A = y.A;
+        this.B = y.B;
+      } else {
+        checkArraySize(x, y);
+        regress$2(this, x, y);
+      }
     }
 
-    return distance(zero, one);
-  }
+    _predict(input) {
+      return this.B * Math.exp(input * this.A);
+    }
 
-  var src$4 = SOM;
+    toJSON() {
+      return {
+        name: 'exponentialRegression',
+        A: this.A,
+        B: this.B
+      };
+    }
 
-  function maybeToPrecision(value, digits) {
-    if (value < 0) {
-      value = 0 - value;
+    toString(precision) {
+      return `f(x) = ${maybeToPrecision(this.B, precision)} * e^(${maybeToPrecision(this.A, precision)} * x)`;
+    }
 
-      if (typeof digits === 'number') {
-        return "- ".concat(value.toPrecision(digits));
-      } else {
-        return "- ".concat(value.toString());
-      }
-    } else {
-      if (typeof digits === 'number') {
-        return value.toPrecision(digits);
+    toLaTeX(precision) {
+      if (this.A >= 0) {
+        return `f(x) = ${maybeToPrecision(this.B, precision)}e^{${maybeToPrecision(this.A, precision)}x}`;
       } else {
-        return value.toString();
+        return `f(x) = \\frac{${maybeToPrecision(this.B, precision)}}{e^{${maybeToPrecision(-this.A, precision)}x}}`;
       }
     }
-  }
 
-  function checkArraySize(x, y) {
-    if (!Array.isArray(x) || !Array.isArray(y)) {
-      throw new TypeError('x and y must be arrays');
-    }
+    static load(json) {
+      if (json.name !== 'exponentialRegression') {
+        throw new TypeError('not a exponential regression model');
+      }
 
-    if (x.length !== y.length) {
-      throw new RangeError('x and y arrays must have the same length');
+      return new ExponentialRegression(true, json);
     }
+
   }
 
-  class BaseRegression {
-    constructor() {
-      if (new.target === BaseRegression) {
-        throw new Error('BaseRegression must be subclassed');
-      }
+  function regress$2(er, x, y) {
+    const n = x.length;
+    const yl = new Array(n);
+
+    for (let i = 0; i < n; i++) {
+      yl[i] = Math.log(y[i]);
     }
 
-    predict(x) {
-      if (typeof x === 'number') {
-        return this._predict(x);
-      } else if (Array.isArray(x)) {
-        const y = [];
+    const linear = new SimpleLinearRegression(x, yl);
+    er.A = linear.slope;
+    er.B = Math.exp(linear.intercept);
+  }
 
-        for (let i = 0; i < x.length; i++) {
-          y.push(this._predict(x[i]));
-        }
+  class PowerRegression extends BaseRegression {
+    constructor(x, y) {
+      super();
 
-        return y;
+      if (x === true) {
+        // reloading model
+        this.A = y.A;
+        this.B = y.B;
       } else {
-        throw new TypeError('x must be a number or array');
+        checkArraySize(x, y);
+        regress$3(this, x, y);
       }
     }
 
-    _predict() {
-      throw new Error('_predict must be implemented');
-    }
-
-    train() {// Do nothing for this package
+    _predict(newInputs) {
+      return this.A * Math.pow(newInputs, this.B);
     }
 
-    toString() {
-      return '';
+    toJSON() {
+      return {
+        name: 'powerRegression',
+        A: this.A,
+        B: this.B
+      };
     }
 
-    toLaTeX() {
-      return '';
+    toString(precision) {
+      return `f(x) = ${maybeToPrecision(this.A, precision)} * x^${maybeToPrecision(this.B, precision)}`;
     }
-    /**
-     * Return the correlation coefficient of determination (r) and chi-square.
-     * @param {Array<number>} x
-     * @param {Array<number>} y
-     * @return {object}
-     */
 
+    toLaTeX(precision) {
+      let latex = '';
 
-    score(x, y) {
-      if (!Array.isArray(x) || !Array.isArray(y) || x.length !== y.length) {
-        throw new Error('x and y must be arrays of the same length');
+      if (this.B >= 0) {
+        latex = `f(x) = ${maybeToPrecision(this.A, precision)}x^{${maybeToPrecision(this.B, precision)}}`;
+      } else {
+        latex = `f(x) = \\frac{${maybeToPrecision(this.A, precision)}}{x^{${maybeToPrecision(-this.B, precision)}}}`;
       }
 
-      const n = x.length;
-      const y2 = new Array(n);
+      latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');
+      return latex;
+    }
 
-      for (let i = 0; i < n; i++) {
-        y2[i] = this._predict(x[i]);
+    static load(json) {
+      if (json.name !== 'powerRegression') {
+        throw new TypeError('not a power regression model');
       }
 
-      let xSum = 0;
-      let ySum = 0;
-      let chi2 = 0;
-      let rmsd = 0;
-      let xSquared = 0;
-      let ySquared = 0;
-      let xY = 0;
-
-      for (let i = 0; i < n; i++) {
-        xSum += y2[i];
-        ySum += y[i];
-        xSquared += y2[i] * y2[i];
-        ySquared += y[i] * y[i];
-        xY += y2[i] * y[i];
+      return new PowerRegression(true, json);
+    }
 
-        if (y[i] !== 0) {
-          chi2 += (y[i] - y2[i]) * (y[i] - y2[i]) / y[i];
-        }
+  }
 
-        rmsd += (y[i] - y2[i]) * (y[i] - y2[i]);
-      }
+  function regress$3(pr, x, y) {
+    const n = x.length;
+    const xl = new Array(n);
+    const yl = new Array(n);
 
-      const r = (n * xY - xSum * ySum) / Math.sqrt((n * xSquared - xSum * xSum) * (n * ySquared - ySum * ySum));
-      return {
-        r: r,
-        r2: r * r,
-        chi2: chi2,
-        rmsd: Math.sqrt(rmsd / n)
-      };
+    for (let i = 0; i < n; i++) {
+      xl[i] = Math.log(x[i]);
+      yl[i] = Math.log(y[i]);
     }
 
+    const linear = new SimpleLinearRegression(xl, yl);
+    pr.A = Math.exp(linear.intercept);
+    pr.B = linear.slope;
   }
 
-  class PolynomialRegression extends BaseRegression {
-    constructor(x, y, degree) {
-      super();
+  class MultivariateLinearRegression {
+    constructor(x, y, options = {}) {
+      const {
+        intercept = true,
+        statistics = true
+      } = options;
+      this.statistics = statistics;
 
       if (x === true) {
-        this.degree = y.degree;
-        this.powers = y.powers;
-        this.coefficients = y.coefficients;
+        this.weights = y.weights;
+        this.inputs = y.inputs;
+        this.outputs = y.outputs;
+        this.intercept = y.intercept;
       } else {
-        checkArraySize(x, y);
-        regress(this, x, y, degree);
-      }
-    }
+        x = new Matrix(x);
+        y = new Matrix(y);
 
-    _predict(x) {
-      let y = 0;
+        if (intercept) {
+          x.addColumn(new Array(x.rows).fill(1));
+        }
 
-      for (let k = 0; k < this.powers.length; k++) {
-        y += this.coefficients[k] * Math.pow(x, this.powers[k]);
-      }
+        let xt = x.transpose();
+        const xx = xt.mmul(x);
+        const xy = xt.mmul(y);
+        const invxx = new SingularValueDecomposition(xx).inverse();
+        const beta = xy.transpose().mmul(invxx).transpose();
+        this.weights = beta.to2DArray();
+        this.inputs = x.columns;
+        this.outputs = y.columns;
+        if (intercept) this.inputs--;
+        this.intercept = intercept;
 
-      return y;
+        if (statistics) {
+          /*
+           * Let's add some basic statistics about the beta's to be able to interpret them.
+           * source: http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat401/Notes/401-multreg.pdf
+           * validated against Excel Regression AddIn
+           * test: "datamining statistics test"
+           */
+          const fittedValues = x.mmul(beta);
+          const residuals = y.clone().addM(fittedValues.neg());
+          const variance = residuals.to2DArray().map(ri => Math.pow(ri[0], 2)).reduce((a, b) => a + b) / (y.rows - x.columns);
+          this.stdError = Math.sqrt(variance);
+          this.stdErrorMatrix = pseudoInverse(xx).mul(variance);
+          this.stdErrors = this.stdErrorMatrix.diagonal().map(d => Math.sqrt(d));
+          this.tStats = this.weights.map((d, i) => this.stdErrors[i] === 0 ? 0 : d[0] / this.stdErrors[i]);
+        }
+      }
     }
 
-    toJSON() {
-      return {
-        name: 'polynomialRegression',
-        degree: this.degree,
-        powers: this.powers,
-        coefficients: this.coefficients
-      };
-    }
+    predict(x) {
+      if (Array.isArray(x)) {
+        if (typeof x[0] === 'number') {
+          return this._predict(x);
+        } else if (Array.isArray(x[0])) {
+          const y = new Array(x.length);
 
-    toString(precision) {
-      return this._toFormula(precision, false);
-    }
+          for (let i = 0; i < x.length; i++) {
+            y[i] = this._predict(x[i]);
+          }
 
-    toLaTeX(precision) {
-      return this._toFormula(precision, true);
-    }
+          return y;
+        }
+      } else if (Matrix.isMatrix(x)) {
+        const y = new Matrix(x.rows, this.outputs);
 
-    _toFormula(precision, isLaTeX) {
-      let sup = '^';
-      let closeSup = '';
-      let times = ' * ';
+        for (let i = 0; i < x.rows; i++) {
+          y.setRow(i, this._predict(x.getRow(i)));
+        }
 
-      if (isLaTeX) {
-        sup = '^{';
-        closeSup = '}';
-        times = '';
+        return y;
       }
 
-      let fn = '';
-      let str = '';
-
-      for (let k = 0; k < this.coefficients.length; k++) {
-        str = '';
+      throw new TypeError('x must be a matrix or array of numbers');
+    }
 
-        if (this.coefficients[k] !== 0) {
-          if (this.powers[k] === 0) {
-            str = maybeToPrecision(this.coefficients[k], precision);
-          } else {
-            if (this.powers[k] === 1) {
-              str = "".concat(maybeToPrecision(this.coefficients[k], precision) + times, "x");
-            } else {
-              str = "".concat(maybeToPrecision(this.coefficients[k], precision) + times, "x").concat(sup).concat(this.powers[k]).concat(closeSup);
-            }
-          }
+    _predict(x) {
+      const result = new Array(this.outputs);
 
-          if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {
-            str = " + ".concat(str);
-          } else if (k !== this.coefficients.length - 1) {
-            str = " ".concat(str);
-          }
+      if (this.intercept) {
+        for (let i = 0; i < this.outputs; i++) {
+          result[i] = this.weights[this.inputs][i];
         }
+      } else {
+        result.fill(0);
+      }
 
-        fn = str + fn;
+      for (let i = 0; i < this.inputs; i++) {
+        for (let j = 0; j < this.outputs; j++) {
+          result[j] += this.weights[i][j] * x[i];
+        }
       }
 
-      if (fn.charAt(0) === '+') {
-        fn = fn.slice(1);
+      return result;
+    }
+
+    score() {
+      throw new Error('score method is not implemented yet');
+    }
+
+    toJSON() {
+      return {
+        name: 'multivariateLinearRegression',
+        weights: this.weights,
+        inputs: this.inputs,
+        outputs: this.outputs,
+        intercept: this.intercept,
+        summary: this.statistics ? {
+          regressionStatistics: {
+            standardError: this.stdError,
+            observations: this.outputs
+          },
+          variables: this.weights.map((d, i) => {
+            return {
+              label: i === this.weights.length - 1 ? 'Intercept' : `X Variable ${i + 1}`,
+              coefficients: d,
+              standardError: this.stdErrors[i],
+              tStat: this.tStats[i]
+            };
+          })
+        } : undefined
+      };
+    }
+
+    static load(model) {
+      if (model.name !== 'multivariateLinearRegression') {
+        throw new Error('not a MLR model');
       }
 
-      return "f(x) = ".concat(fn);
+      return new MultivariateLinearRegression(true, model);
     }
 
-    static load(json) {
-      if (json.name !== 'polynomialRegression') {
-        throw new TypeError('not a polynomial regression model');
-      }
+  }
 
-      return new PolynomialRegression(true, json);
+  var require$$0$1 = /*@__PURE__*/getAugmentedNamespace(euclidean$1);
+
+  const {
+    squaredEuclidean: squaredEuclidean$1
+  } = require$$0$1;
+  const defaultOptions$7 = {
+    sigma: 1
+  };
+
+  class GaussianKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$7, options);
+      this.sigma = options.sigma;
+      this.divisor = 2 * options.sigma * options.sigma;
+    }
+
+    compute(x, y) {
+      const distance = squaredEuclidean$1(x, y);
+      return Math.exp(-distance / this.divisor);
     }
 
   }
 
-  function regress(pr, x, y, degree) {
-    const n = x.length;
-    let powers;
+  var gaussianKernel = GaussianKernel;
 
-    if (Array.isArray(degree)) {
-      powers = degree;
-      degree = powers.length;
-    } else {
-      degree++;
-      powers = new Array(degree);
+  const defaultOptions$8 = {
+    degree: 1,
+    constant: 1,
+    scale: 1
+  };
 
-      for (let k = 0; k < degree; k++) {
-        powers[k] = k;
-      }
+  class PolynomialKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$8, options);
+      this.degree = options.degree;
+      this.constant = options.constant;
+      this.scale = options.scale;
     }
 
-    const F = new Matrix(n, degree);
-    const Y = new Matrix([y]);
+    compute(x, y) {
+      var sum = 0;
 
-    for (let k = 0; k < degree; k++) {
-      for (let i = 0; i < n; i++) {
-        if (powers[k] === 0) {
-          F.set(i, k, 1);
-        } else {
-          F.set(i, k, Math.pow(x[i], powers[k]));
-        }
+      for (var i = 0; i < x.length; i++) {
+        sum += x[i] * y[i];
       }
+
+      return Math.pow(this.scale * sum + this.constant, this.degree);
     }
 
-    const FT = new MatrixTransposeView(F);
-    const A = FT.mmul(F);
-    const B = FT.mmul(new MatrixTransposeView(Y));
-    pr.degree = degree - 1;
-    pr.powers = powers;
-    pr.coefficients = solve(A, B).to1DArray();
   }
 
-  class SimpleLinearRegression extends BaseRegression {
-    constructor(x, y) {
-      super();
+  var polynomialKernel = PolynomialKernel;
 
-      if (x === true) {
-        this.slope = y.slope;
-        this.intercept = y.intercept;
-        this.coefficients = [y.intercept, y.slope];
-      } else {
-        checkArraySize(x, y);
-        regress$1(this, x, y);
-      }
-    }
+  const defaultOptions$9 = {
+    alpha: 0.01,
+    constant: -Math.E
+  };
 
-    toJSON() {
-      return {
-        name: 'simpleLinearRegression',
-        slope: this.slope,
-        intercept: this.intercept
-      };
+  class SigmoidKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$9, options);
+      this.alpha = options.alpha;
+      this.constant = options.constant;
     }
 
-    _predict(x) {
-      return this.slope * x + this.intercept;
-    }
+    compute(x, y) {
+      var sum = 0;
 
-    computeX(y) {
-      return (y - this.intercept) / this.slope;
+      for (var i = 0; i < x.length; i++) {
+        sum += x[i] * y[i];
+      }
+
+      return Math.tanh(this.alpha * sum + this.constant);
     }
 
-    toString(precision) {
-      let result = 'f(x) = ';
+  }
 
-      if (this.slope !== 0) {
-        const xFactor = maybeToPrecision(this.slope, precision);
-        result += "".concat(xFactor === '1' ? '' : "".concat(xFactor, " * "), "x");
+  var sigmoidKernel = SigmoidKernel;
 
-        if (this.intercept !== 0) {
-          const absIntercept = Math.abs(this.intercept);
-          const operator = absIntercept === this.intercept ? '+' : '-';
-          result += " ".concat(operator, " ").concat(maybeToPrecision(absIntercept, precision));
-        }
-      } else {
-        result += maybeToPrecision(this.intercept, precision);
-      }
+  const defaultOptions$a = {
+    sigma: 1,
+    degree: 1
+  };
 
-      return result;
+  class ANOVAKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$a, options);
+      this.sigma = options.sigma;
+      this.degree = options.degree;
     }
 
-    toLaTeX(precision) {
-      return this.toString(precision);
-    }
+    compute(x, y) {
+      var sum = 0;
+      var len = Math.min(x.length, y.length);
 
-    static load(json) {
-      if (json.name !== 'simpleLinearRegression') {
-        throw new TypeError('not a SLR model');
+      for (var i = 1; i <= len; ++i) {
+        sum += Math.pow(Math.exp(-this.sigma * Math.pow(Math.pow(x[i - 1], i) - Math.pow(y[i - 1], i), 2)), this.degree);
       }
 
-      return new SimpleLinearRegression(true, json);
+      return sum;
     }
 
   }
 
-  function regress$1(slr, x, y) {
-    const n = x.length;
-    let xSum = 0;
-    let ySum = 0;
-    let xSquared = 0;
-    let xY = 0;
+  var anovaKernel = ANOVAKernel;
 
-    for (let i = 0; i < n; i++) {
-      xSum += x[i];
-      ySum += y[i];
-      xSquared += x[i] * x[i];
-      xY += x[i] * y[i];
+  const {
+    squaredEuclidean: squaredEuclidean$2
+  } = require$$0$1;
+  const defaultOptions$b = {
+    sigma: 1
+  };
+
+  class CauchyKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$b, options);
+      this.sigma = options.sigma;
+    }
+
+    compute(x, y) {
+      return 1 / (1 + squaredEuclidean$2(x, y) / (this.sigma * this.sigma));
     }
 
-    const numerator = n * xY - xSum * ySum;
-    slr.slope = numerator / (n * xSquared - xSum * xSum);
-    slr.intercept = 1 / n * ySum - slr.slope * (1 / n) * xSum;
-    slr.coefficients = [slr.intercept, slr.slope];
   }
 
-  class ExponentialRegression extends BaseRegression {
-    constructor(x, y) {
-      super();
+  var cauchyKernel = CauchyKernel;
 
-      if (x === true) {
-        this.A = y.A;
-        this.B = y.B;
-      } else {
-        checkArraySize(x, y);
-        regress$2(this, x, y);
-      }
-    }
+  const {
+    euclidean: euclidean$2
+  } = require$$0$1;
+  const defaultOptions$c = {
+    sigma: 1
+  };
 
-    _predict(input) {
-      return this.B * Math.exp(input * this.A);
+  class ExponentialKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$c, options);
+      this.sigma = options.sigma;
+      this.divisor = 2 * options.sigma * options.sigma;
     }
 
-    toJSON() {
-      return {
-        name: 'exponentialRegression',
-        A: this.A,
-        B: this.B
-      };
+    compute(x, y) {
+      const distance = euclidean$2(x, y);
+      return Math.exp(-distance / this.divisor);
     }
 
-    toString(precision) {
-      return "f(x) = ".concat(maybeToPrecision(this.B, precision), " * e^(").concat(maybeToPrecision(this.A, precision), " * x)");
-    }
+  }
 
-    toLaTeX(precision) {
-      if (this.A >= 0) {
-        return "f(x) = ".concat(maybeToPrecision(this.B, precision), "e^{").concat(maybeToPrecision(this.A, precision), "x}");
-      } else {
-        return "f(x) = \\frac{".concat(maybeToPrecision(this.B, precision), "}{e^{").concat(maybeToPrecision(-this.A, precision), "x}}");
+  var exponentialKernel = ExponentialKernel;
+
+  class HistogramIntersectionKernel {
+    compute(x, y) {
+      var min = Math.min(x.length, y.length);
+      var sum = 0;
+
+      for (var i = 0; i < min; ++i) {
+        sum += Math.min(x[i], y[i]);
       }
+
+      return sum;
     }
 
-    static load(json) {
-      if (json.name !== 'exponentialRegression') {
-        throw new TypeError('not a exponential regression model');
-      }
+  }
+
+  var histogramIntersectionKernel = HistogramIntersectionKernel;
+
+  const {
+    euclidean: euclidean$3
+  } = require$$0$1;
+  const defaultOptions$d = {
+    sigma: 1
+  };
+
+  class LaplacianKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$d, options);
+      this.sigma = options.sigma;
+    }
 
-      return new ExponentialRegression(true, json);
+    compute(x, y) {
+      const distance = euclidean$3(x, y);
+      return Math.exp(-distance / this.sigma);
     }
 
   }
 
-  function regress$2(er, x, y) {
-    const n = x.length;
-    const yl = new Array(n);
+  var laplacianKernel = LaplacianKernel;
 
-    for (let i = 0; i < n; i++) {
-      yl[i] = Math.log(y[i]);
+  const {
+    squaredEuclidean: squaredEuclidean$3
+  } = require$$0$1;
+  const defaultOptions$e = {
+    constant: 1
+  };
+
+  class MultiquadraticKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$e, options);
+      this.constant = options.constant;
+    }
+
+    compute(x, y) {
+      return Math.sqrt(squaredEuclidean$3(x, y) + this.constant * this.constant);
     }
 
-    const linear = new SimpleLinearRegression(x, yl);
-    er.A = linear.slope;
-    er.B = Math.exp(linear.intercept);
   }
 
-  class PowerRegression extends BaseRegression {
-    constructor(x, y) {
-      super();
+  var multiquadraticKernel = MultiquadraticKernel;
 
-      if (x === true) {
-        // reloading model
-        this.A = y.A;
-        this.B = y.B;
-      } else {
-        checkArraySize(x, y);
-        regress$3(this, x, y);
-      }
-    }
+  const {
+    squaredEuclidean: squaredEuclidean$4
+  } = require$$0$1;
+  const defaultOptions$f = {
+    constant: 1
+  };
 
-    _predict(newInputs) {
-      return this.A * Math.pow(newInputs, this.B);
+  class RationalQuadraticKernel {
+    constructor(options) {
+      options = Object.assign({}, defaultOptions$f, options);
+      this.constant = options.constant;
     }
 
-    toJSON() {
-      return {
-        name: 'powerRegression',
-        A: this.A,
-        B: this.B
-      };
+    compute(x, y) {
+      const distance = squaredEuclidean$4(x, y);
+      return 1 - distance / (distance + this.constant);
     }
 
-    toString(precision) {
-      return "f(x) = ".concat(maybeToPrecision(this.A, precision), " * x^").concat(maybeToPrecision(this.B, precision));
-    }
+  }
 
-    toLaTeX(precision) {
-      let latex = '';
+  var rationalQuadraticKernel = RationalQuadraticKernel;
 
-      if (this.B >= 0) {
-        latex = "f(x) = ".concat(maybeToPrecision(this.A, precision), "x^{").concat(maybeToPrecision(this.B, precision), "}");
+  const {
+    Matrix: Matrix$1,
+    MatrixTransposeView: MatrixTransposeView$1
+  } = require$$0;
+  const kernelType = {
+    gaussian: gaussianKernel,
+    rbf: gaussianKernel,
+    polynomial: polynomialKernel,
+    poly: polynomialKernel,
+    anova: anovaKernel,
+    cauchy: cauchyKernel,
+    exponential: exponentialKernel,
+    histogram: histogramIntersectionKernel,
+    min: histogramIntersectionKernel,
+    laplacian: laplacianKernel,
+    multiquadratic: multiquadraticKernel,
+    rational: rationalQuadraticKernel,
+    sigmoid: sigmoidKernel,
+    mlp: sigmoidKernel
+  };
+
+  class Kernel {
+    constructor(type, options) {
+      this.kernelType = type;
+      if (type === 'linear') return;
+
+      if (typeof type === 'string') {
+        type = type.toLowerCase();
+        var KernelConstructor = kernelType[type];
+
+        if (KernelConstructor) {
+          this.kernelFunction = new KernelConstructor(options);
+        } else {
+          throw new Error(`unsupported kernel type: ${type}`);
+        }
+      } else if (typeof type === 'object' && typeof type.compute === 'function') {
+        this.kernelFunction = type;
       } else {
-        latex = "f(x) = \\frac{".concat(maybeToPrecision(this.A, precision), "}{x^{").concat(maybeToPrecision(-this.B, precision), "}}");
+        throw new TypeError('first argument must be a valid kernel type or instance');
       }
-
-      latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');
-      return latex;
     }
 
-    static load(json) {
-      if (json.name !== 'powerRegression') {
-        throw new TypeError('not a power regression model');
+    compute(inputs, landmarks) {
+      inputs = Matrix$1.checkMatrix(inputs);
+
+      if (landmarks === undefined) {
+        landmarks = inputs;
+      } else {
+        landmarks = Matrix$1.checkMatrix(landmarks);
       }
 
-      return new PowerRegression(true, json);
-    }
+      if (this.kernelType === 'linear') {
+        return inputs.mmul(new MatrixTransposeView$1(landmarks));
+      }
 
-  }
+      const kernelMatrix = new Matrix$1(inputs.rows, landmarks.rows);
 
-  function regress$3(pr, x, y) {
-    const n = x.length;
-    const xl = new Array(n);
-    const yl = new Array(n);
+      if (inputs === landmarks) {
+        // fast path, matrix is symmetric
+        for (let i = 0; i < inputs.rows; i++) {
+          for (let j = i; j < inputs.rows; j++) {
+            const value = this.kernelFunction.compute(inputs.getRow(i), inputs.getRow(j));
+            kernelMatrix.set(i, j, value);
+            kernelMatrix.set(j, i, value);
+          }
+        }
+      } else {
+        for (let i = 0; i < inputs.rows; i++) {
+          for (let j = 0; j < landmarks.rows; j++) {
+            kernelMatrix.set(i, j, this.kernelFunction.compute(inputs.getRow(i), landmarks.getRow(j)));
+          }
+        }
+      }
 
-    for (let i = 0; i < n; i++) {
-      xl[i] = Math.log(x[i]);
-      yl[i] = Math.log(y[i]);
+      return kernelMatrix;
     }
 
-    const linear = new SimpleLinearRegression(xl, yl);
-    pr.A = Math.exp(linear.intercept);
-    pr.B = linear.slope;
   }
 
-  class MultivariateLinearRegression {
+  var kernel = Kernel;
+
+  class TheilSenRegression extends BaseRegression {
+    /**
+     * Theil–Sen estimator
+     * https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator
+     * @param {Array<number>|boolean} x
+     * @param {Array<number>|object} y
+     * @constructor
+     */
     constructor(x, y) {
-      let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-      const {
-        intercept = true,
-        statistics = true
-      } = options;
-      this.statistics = statistics;
+      super();
 
       if (x === true) {
-        this.weights = y.weights;
-        this.inputs = y.inputs;
-        this.outputs = y.outputs;
+        // loads the model
+        this.slope = y.slope;
         this.intercept = y.intercept;
+        this.coefficients = y.coefficients;
       } else {
-        x = new Matrix(x);
-        y = new Matrix(y);
+        // creates the model
+        checkArraySize(x, y);
+        theilSen(this, x, y);
+      }
+    }
 
-        if (intercept) {
-          x.addColumn(new Array(x.rows).fill(1));
-        }
+    toJSON() {
+      return {
+        name: 'TheilSenRegression',
+        slope: this.slope,
+        intercept: this.intercept
+      };
+    }
 
-        let xt = x.transpose();
-        const xx = xt.mmul(x);
-        const xy = xt.mmul(y);
-        const invxx = new SingularValueDecomposition(xx).inverse();
-        const beta = xy.transpose().mmul(invxx).transpose();
-        this.weights = beta.to2DArray();
-        this.inputs = x.columns;
-        this.outputs = y.columns;
-        if (intercept) this.inputs--;
-        this.intercept = intercept;
+    _predict(input) {
+      return this.slope * input + this.intercept;
+    }
 
-        if (statistics) {
-          /*
-           * Let's add some basic statistics about the beta's to be able to interpret them.
-           * source: http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat401/Notes/401-multreg.pdf
-           * validated against Excel Regression AddIn
-           * test: "datamining statistics test"
-           */
-          const fittedValues = x.mmul(beta);
-          const residuals = y.clone().addM(fittedValues.neg());
-          const variance = residuals.to2DArray().map(ri => Math.pow(ri[0], 2)).reduce((a, b) => a + b) / (y.rows - x.columns);
-          this.stdError = Math.sqrt(variance);
-          this.stdErrorMatrix = pseudoInverse(xx).mul(variance);
-          this.stdErrors = this.stdErrorMatrix.diagonal().map(d => Math.sqrt(d));
-          this.tStats = this.weights.map((d, i) => this.stdErrors[i] === 0 ? 0 : d[0] / this.stdErrors[i]);
-        }
-      }
+    computeX(input) {
+      return (input - this.intercept) / this.slope;
     }
 
-    predict(x) {
-      if (Array.isArray(x)) {
-        if (typeof x[0] === 'number') {
-          return this._predict(x);
-        } else if (Array.isArray(x[0])) {
-          const y = new Array(x.length);
+    toString(precision) {
+      var result = 'f(x) = ';
 
-          for (let i = 0; i < x.length; i++) {
-            y[i] = this._predict(x[i]);
-          }
+      if (this.slope) {
+        var xFactor = maybeToPrecision(this.slope, precision);
+        result += `${Math.abs(xFactor - 1) < 1e-5 ? '' : `${xFactor} * `}x`;
 
-          return y;
+        if (this.intercept) {
+          var absIntercept = Math.abs(this.intercept);
+          var operator = absIntercept === this.intercept ? '+' : '-';
+          result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;
         }
-      } else if (Matrix.isMatrix(x)) {
-        const y = new Matrix(x.rows, this.outputs);
+      } else {
+        result += maybeToPrecision(this.intercept, precision);
+      }
 
-        for (let i = 0; i < x.rows; i++) {
-          y.setRow(i, this._predict(x.getRow(i)));
-        }
+      return result;
+    }
 
-        return y;
+    toLaTeX(precision) {
+      return this.toString(precision);
+    }
+
+    static load(json) {
+      if (json.name !== 'TheilSenRegression') {
+        throw new TypeError('not a Theil-Sen model');
       }
 
-      throw new TypeError('x must be a matrix or array of numbers');
+      return new TheilSenRegression(true, json);
     }
 
-    _predict(x) {
-      const result = new Array(this.outputs);
+  }
 
-      if (this.intercept) {
-        for (let i = 0; i < this.outputs; i++) {
-          result[i] = this.weights[this.inputs][i];
-        }
-      } else {
-        result.fill(0);
-      }
+  function theilSen(regression, x, y) {
+    let len = x.length;
+    let slopes = new Array(len * len);
+    let count = 0;
 
-      for (let i = 0; i < this.inputs; i++) {
-        for (let j = 0; j < this.outputs; j++) {
-          result[j] += this.weights[i][j] * x[i];
+    for (let i = 0; i < len; ++i) {
+      for (let j = i + 1; j < len; ++j) {
+        if (x[i] !== x[j]) {
+          slopes[count++] = (y[j] - y[i]) / (x[j] - x[i]);
         }
       }
+    }
 
-      return result;
+    slopes.length = count;
+    let medianSlope = median(slopes);
+    let cuts = new Array(len);
+
+    for (let i = 0; i < len; ++i) {
+      cuts[i] = y[i] - medianSlope * x[i];
     }
 
-    score() {
-      throw new Error('score method is not implemented yet');
+    regression.slope = medianSlope;
+    regression.intercept = median(cuts);
+    regression.coefficients = [regression.intercept, regression.slope];
+  }
+
+  /**
+   * @class RobustPolynomialRegression
+   * @param {Array<number>} x
+   * @param {Array<number>} y
+   * @param {number} degree - polynomial degree
+   */
+
+  class RobustPolynomialRegression extends BaseRegression {
+    constructor(x, y, degree) {
+      super();
+
+      if (x === true) {
+        this.degree = y.degree;
+        this.powers = y.powers;
+        this.coefficients = y.coefficients;
+      } else {
+        checkArraySize(x, y);
+        robustPolynomial(this, x, y, degree);
+      }
     }
 
     toJSON() {
       return {
-        name: 'multivariateLinearRegression',
-        weights: this.weights,
-        inputs: this.inputs,
-        outputs: this.outputs,
-        intercept: this.intercept,
-        summary: this.statistics ? {
-          regressionStatistics: {
-            standardError: this.stdError,
-            observations: this.outputs
-          },
-          variables: this.weights.map((d, i) => {
-            return {
-              label: i === this.weights.length - 1 ? 'Intercept' : "X Variable ".concat(i + 1),
-              coefficients: d,
-              standardError: this.stdErrors[i],
-              tStat: this.tStats[i]
-            };
-          })
-        } : undefined
+        name: 'robustPolynomialRegression',
+        degree: this.degree,
+        powers: this.powers,
+        coefficients: this.coefficients
       };
     }
 
-    static load(model) {
-      if (model.name !== 'multivariateLinearRegression') {
-        throw new Error('not a MLR model');
-      }
+    _predict(x) {
+      return predict(x, this.powers, this.coefficients);
+    }
+    /**
+     * Display the formula
+     * @param {number} precision - precision for the numbers
+     * @return {string}
+     */
 
-      return new MultivariateLinearRegression(true, model);
+
+    toString(precision) {
+      return this._toFormula(precision, false);
+    }
+    /**
+     * Display the formula in LaTeX format
+     * @param {number} precision - precision for the numbers
+     * @return {string}
+     */
+
+
+    toLaTeX(precision) {
+      return this._toFormula(precision, true);
     }
 
-  }
+    _toFormula(precision, isLaTeX) {
+      let sup = '^';
+      let closeSup = '';
+      let times = ' * ';
 
-  const {
-    squaredEuclidean: squaredEuclidean$1
-  } = euclidean$1;
-  const defaultOptions$8 = {
-    sigma: 1
-  };
+      if (isLaTeX) {
+        sup = '^{';
+        closeSup = '}';
+        times = '';
+      }
 
-  class GaussianKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$8, options);
-      this.sigma = options.sigma;
-      this.divisor = 2 * options.sigma * options.sigma;
+      let fn = '';
+      let str = '';
+
+      for (let k = 0; k < this.coefficients.length; k++) {
+        str = '';
+
+        if (this.coefficients[k] !== 0) {
+          if (this.powers[k] === 0) {
+            str = maybeToPrecision(this.coefficients[k], precision);
+          } else {
+            if (this.powers[k] === 1) {
+              str = `${maybeToPrecision(this.coefficients[k], precision) + times}x`;
+            } else {
+              str = `${maybeToPrecision(this.coefficients[k], precision) + times}x${sup}${this.powers[k]}${closeSup}`;
+            }
+          }
+
+          if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {
+            str = ` + ${str}`;
+          } else if (k !== this.coefficients.length - 1) {
+            str = ` ${str}`;
+          }
+        }
+
+        fn = str + fn;
+      }
+
+      if (fn.charAt(0) === '+') {
+        fn = fn.slice(1);
+      }
+
+      return `f(x) = ${fn}`;
     }
 
-    compute(x, y) {
-      const distance = squaredEuclidean$1(x, y);
-      return Math.exp(-distance / this.divisor);
+    static load(json) {
+      if (json.name !== 'robustPolynomialRegression') {
+        throw new TypeError('not a RobustPolynomialRegression model');
+      }
+
+      return new RobustPolynomialRegression(true, json);
     }
 
   }
 
-  var gaussianKernel = GaussianKernel;
+  function robustPolynomial(regression, x, y, degree) {
+    let powers = Array(degree).fill(0).map((_, index) => index);
+    const tuples = getRandomTuples(x, y, degree);
+    var min;
 
-  const defaultOptions$9 = {
-    degree: 1,
-    constant: 1,
-    scale: 1
-  };
+    for (var i = 0; i < tuples.length; i++) {
+      var tuple = tuples[i];
+      var coefficients = calcCoefficients(tuple, powers);
+      var residuals = x.slice();
 
-  class PolynomialKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$9, options);
-      this.degree = options.degree;
-      this.constant = options.constant;
-      this.scale = options.scale;
-    }
+      for (var j = 0; j < x.length; j++) {
+        residuals[j] = y[j] - predict(x[j], powers, coefficients);
+        residuals[j] = {
+          residual: residuals[j] * residuals[j],
+          coefficients
+        };
+      }
 
-    compute(x, y) {
-      var sum = 0;
+      var median = residualsMedian(residuals);
 
-      for (var i = 0; i < x.length; i++) {
-        sum += x[i] * y[i];
+      if (!min || median.residual < min.residual) {
+        min = median;
       }
-
-      return Math.pow(this.scale * sum + this.constant, this.degree);
     }
 
+    regression.degree = degree;
+    regression.powers = powers;
+    regression.coefficients = min.coefficients;
   }
+  /**
+   * @ignore
+   * @param {Array<number>} x
+   * @param {Array<number>} y
+   * @param {number} degree
+   * @return {Array<{x:number,y:number}>}
+   */
 
-  var polynomialKernel = PolynomialKernel;
 
-  const defaultOptions$a = {
-    alpha: 0.01,
-    constant: -Math.E
-  };
+  function getRandomTuples(x, y, degree) {
+    var len = Math.floor(x.length / degree);
+    var tuples = new Array(len);
 
-  class SigmoidKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$a, options);
-      this.alpha = options.alpha;
-      this.constant = options.constant;
+    for (var i = 0; i < x.length; i++) {
+      var pos = Math.floor(Math.random() * len);
+      var counter = 0;
+
+      while (counter < x.length) {
+        if (!tuples[pos]) {
+          tuples[pos] = [{
+            x: x[i],
+            y: y[i]
+          }];
+          break;
+        } else if (tuples[pos].length < degree) {
+          tuples[pos].push({
+            x: x[i],
+            y: y[i]
+          });
+          break;
+        } else {
+          counter++;
+          pos = (pos + 1) % len;
+        }
+      }
+
+      if (counter === x.length) {
+        return tuples;
+      }
     }
 
-    compute(x, y) {
-      var sum = 0;
+    return tuples;
+  }
+  /**
+   * @ignore
+   * @param {{x:number,y:number}} tuple
+   * @param {Array<number>} powers
+   * @return {Array<number>}
+   */
+
 
-      for (var i = 0; i < x.length; i++) {
-        sum += x[i] * y[i];
-      }
+  function calcCoefficients(tuple, powers) {
+    var X = tuple.slice();
+    var Y = tuple.slice();
 
-      return Math.tanh(this.alpha * sum + this.constant);
+    for (var i = 0; i < X.length; i++) {
+      Y[i] = [tuple[i].y];
+      X[i] = new Array(powers.length);
+
+      for (var j = 0; j < powers.length; j++) {
+        X[i][j] = Math.pow(tuple[i].x, powers[j]);
+      }
     }
 
+    return solve(X, Y).to1DArray();
   }
 
-  var sigmoidKernel = SigmoidKernel;
-
-  const defaultOptions$b = {
-    sigma: 1,
-    degree: 1
-  };
+  function predict(x, powers, coefficients) {
+    let y = 0;
 
-  class ANOVAKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$b, options);
-      this.sigma = options.sigma;
-      this.degree = options.degree;
+    for (let k = 0; k < powers.length; k++) {
+      y += coefficients[k] * Math.pow(x, powers[k]);
     }
 
-    compute(x, y) {
-      var sum = 0;
-      var len = Math.min(x.length, y.length);
+    return y;
+  }
 
-      for (var i = 1; i <= len; ++i) {
-        sum += Math.pow(Math.exp(-this.sigma * Math.pow(Math.pow(x[i - 1], i) - Math.pow(y[i - 1], i), 2)), this.degree);
-      }
+  function residualsMedian(residuals) {
+    residuals.sort((a, b) => a.residual - b.residual);
+    var l = residuals.length;
+    var half = Math.floor(l / 2);
+    return l % 2 === 0 ? residuals[half - 1] : residuals[half];
+  }
 
-      return sum;
+  const toString$3 = Object.prototype.toString;
+  function isAnyArray$3(object) {
+    return toString$3.call(object).endsWith('Array]');
+  }
+
+  /**
+   * Calculate current error
+   * @ignore
+   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
+   * @param {Array<number>} parameters - Array of current parameter values
+   * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
+   * @return {number}
+   */
+  function errorCalculation(data, parameters, parameterizedFunction) {
+    let error = 0;
+    const func = parameterizedFunction(parameters);
+
+    for (let i = 0; i < data.x.length; i++) {
+      error += Math.abs(data.y[i] - func(data.x[i]));
     }
 
+    return error;
   }
 
-  var anovaKernel = ANOVAKernel;
+  /**
+   * Difference of the matrix function over the parameters
+   * @ignore
+   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
+   * @param {Array<number>} evaluatedData - Array of previous evaluated function values
+   * @param {Array<number>} params - Array of previous parameter values
+   * @param {number} gradientDifference - Adjustment for decrease the damping parameter
+   * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter
+   * @return {Matrix}
+   */
 
-  const {
-    squaredEuclidean: squaredEuclidean$2
-  } = euclidean$1;
-  const defaultOptions$c = {
-    sigma: 1
-  };
+  function gradientFunction(data, evaluatedData, params, gradientDifference, paramFunction) {
+    const n = params.length;
+    const m = data.x.length;
+    let ans = new Array(n);
 
-  class CauchyKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$c, options);
-      this.sigma = options.sigma;
-    }
+    for (let param = 0; param < n; param++) {
+      ans[param] = new Array(m);
+      let auxParams = params.slice();
+      auxParams[param] += gradientDifference;
+      let funcParam = paramFunction(auxParams);
 
-    compute(x, y) {
-      return 1 / (1 + squaredEuclidean$2(x, y) / (this.sigma * this.sigma));
+      for (let point = 0; point < m; point++) {
+        ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);
+      }
     }
 
+    return new Matrix(ans);
   }
+  /**
+   * Matrix function over the samples
+   * @ignore
+   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
+   * @param {Array<number>} evaluatedData - Array of previous evaluated function values
+   * @return {Matrix}
+   */
 
-  var cauchyKernel = CauchyKernel;
-
-  const {
-    euclidean: euclidean$2
-  } = euclidean$1;
-  const defaultOptions$d = {
-    sigma: 1
-  };
 
-  class ExponentialKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$d, options);
-      this.sigma = options.sigma;
-      this.divisor = 2 * options.sigma * options.sigma;
-    }
+  function matrixFunction(data, evaluatedData) {
+    const m = data.x.length;
+    let ans = new Array(m);
 
-    compute(x, y) {
-      const distance = euclidean$2(x, y);
-      return Math.exp(-distance / this.divisor);
+    for (let point = 0; point < m; point++) {
+      ans[point] = [data.y[point] - evaluatedData[point]];
     }
 
+    return new Matrix(ans);
   }
+  /**
+   * Iteration for Levenberg-Marquardt
+   * @ignore
+   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
+   * @param {Array<number>} params - Array of previous parameter values
+   * @param {number} damping - Levenberg-Marquardt parameter
+   * @param {number} gradientDifference - Adjustment for decrease the damping parameter
+   * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
+   * @return {Array<number>}
+   */
 
-  var exponentialKernel = ExponentialKernel;
-
-  class HistogramIntersectionKernel {
-    compute(x, y) {
-      var min = Math.min(x.length, y.length);
-      var sum = 0;
 
-      for (var i = 0; i < min; ++i) {
-        sum += Math.min(x[i], y[i]);
-      }
+  function step$1(data, params, damping, gradientDifference, parameterizedFunction) {
+    let value = damping * gradientDifference * gradientDifference;
+    let identity = Matrix.eye(params.length, params.length, value);
+    const func = parameterizedFunction(params);
+    let evaluatedData = new Float64Array(data.x.length);
 
-      return sum;
+    for (let i = 0; i < data.x.length; i++) {
+      evaluatedData[i] = func(data.x[i]);
     }
 
+    let gradientFunc = gradientFunction(data, evaluatedData, params, gradientDifference, parameterizedFunction);
+    let matrixFunc = matrixFunction(data, evaluatedData);
+    let inverseMatrix = inverse(identity.add(gradientFunc.mmul(gradientFunc.transpose())));
+    params = new Matrix([params]);
+    params = params.sub(inverseMatrix.mmul(gradientFunc).mmul(matrixFunc).mul(gradientDifference).transpose());
+    return params.to1DArray();
   }
 
-  var histogramIntersectionKernel = HistogramIntersectionKernel;
+  /**
+   * Curve fitting algorithm
+   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
+   * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
+   * @param {object} [options] - Options object
+   * @param {number} [options.damping] - Levenberg-Marquardt parameter
+   * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter
+   * @param {Array<number>} [options.minValues] - Minimum allowed values for parameters
+   * @param {Array<number>} [options.maxValues] - Maximum allowed values for parameters
+   * @param {Array<number>} [options.initialValues] - Array of initial parameter values
+   * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations
+   * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point
+   * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}
+   */
 
-  const {
-    euclidean: euclidean$3
-  } = euclidean$1;
-  const defaultOptions$e = {
-    sigma: 1
-  };
+  function levenbergMarquardt(data, parameterizedFunction, options = {}) {
+    let {
+      maxIterations = 100,
+      gradientDifference = 10e-2,
+      damping = 0,
+      errorTolerance = 10e-3,
+      minValues,
+      maxValues,
+      initialValues
+    } = options;
 
-  class LaplacianKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$e, options);
-      this.sigma = options.sigma;
+    if (damping <= 0) {
+      throw new Error('The damping option must be a positive number');
+    } else if (!data.x || !data.y) {
+      throw new Error('The data parameter must have x and y elements');
+    } else if (!isAnyArray$3(data.x) || data.x.length < 2 || !isAnyArray$3(data.y) || data.y.length < 2) {
+      throw new Error('The data parameter elements must be an array with more than 2 points');
+    } else if (data.x.length !== data.y.length) {
+      throw new Error('The data parameter elements must have the same size');
     }
 
-    compute(x, y) {
-      const distance = euclidean$3(x, y);
-      return Math.exp(-distance / this.sigma);
+    let parameters = initialValues || new Array(parameterizedFunction.length).fill(1);
+    let parLen = parameters.length;
+    maxValues = maxValues || new Array(parLen).fill(Number.MAX_SAFE_INTEGER);
+    minValues = minValues || new Array(parLen).fill(Number.MIN_SAFE_INTEGER);
+
+    if (maxValues.length !== minValues.length) {
+      throw new Error('minValues and maxValues must be the same size');
     }
 
-  }
+    if (!isAnyArray$3(parameters)) {
+      throw new Error('initialValues must be an array');
+    }
 
-  var laplacianKernel = LaplacianKernel;
+    let error = errorCalculation(data, parameters, parameterizedFunction);
+    let converged = error <= errorTolerance;
+    let iteration;
 
-  const {
-    squaredEuclidean: squaredEuclidean$3
-  } = euclidean$1;
-  const defaultOptions$f = {
-    constant: 1
-  };
+    for (iteration = 0; iteration < maxIterations && !converged; iteration++) {
+      parameters = step$1(data, parameters, damping, gradientDifference, parameterizedFunction);
 
-  class MultiquadraticKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$f, options);
-      this.constant = options.constant;
-    }
+      for (let k = 0; k < parLen; k++) {
+        parameters[k] = Math.min(Math.max(minValues[k], parameters[k]), maxValues[k]);
+      }
 
-    compute(x, y) {
-      return Math.sqrt(squaredEuclidean$3(x, y) + this.constant * this.constant);
+      error = errorCalculation(data, parameters, parameterizedFunction);
+      if (isNaN(error)) break;
+      converged = error <= errorTolerance;
     }
 
+    return {
+      parameterValues: parameters,
+      parameterError: error,
+      iterations: iteration
+    };
   }
 
-  var multiquadraticKernel = MultiquadraticKernel;
+  /**
+   * Returns a new array based on extraction of specific indices of an array
+   * @private
+   * @param {Array} vector
+   * @param {Array} indices
+   */
+  function selection(vector, indices) {
+    let u = []; //new Float64Array(indices.length);
 
-  const {
-    squaredEuclidean: squaredEuclidean$4
-  } = euclidean$1;
-  const defaultOptions$g = {
-    constant: 1
-  };
+    for (let i = 0; i < indices.length; i++) {
+      u[i] = vector[indices[i]];
+    }
+
+    return u;
+  }
+
+  /**
+   *
+   * @private
+   * @param {Array of arrays} collection
+   */
+  function sortCollectionSet(collection) {
+    let objectCollection = collection.map((value, index) => {
+      let key = BigInt(0);
+      value.forEach(item => key |= BigInt(1) << BigInt(item));
+      return {
+        value,
+        index,
+        key
+      };
+    }).sort((a, b) => {
+      if (a.key - b.key < 0) return -1;
+      return 1;
+    });
+    let sorted = [];
+    let indices = [];
+    let key;
 
-  class RationalQuadraticKernel {
-    constructor(options) {
-      options = Object.assign({}, defaultOptions$g, options);
-      this.constant = options.constant;
-    }
+    for (let set of objectCollection) {
+      if (set.key !== key) {
+        key = set.key;
+        indices.push([]);
+        sorted.push(set.value);
+      }
 
-    compute(x, y) {
-      const distance = squaredEuclidean$4(x, y);
-      return 1 - distance / (distance + this.constant);
+      indices[indices.length - 1].push(set.index);
     }
 
+    let result = {
+      values: sorted,
+      indices: indices
+    };
+    return result;
   }
 
-  var rationalQuadraticKernel = RationalQuadraticKernel;
+  /**
+   * (Combinatorial Subspace Least Squares) - subfunction for the FC-NNLS
+   * @private
+   * @param {Matrix} XtX
+   * @param {Matrix} XtY
+   * @param {Array} Pset
+   * @param {Numbers} l
+   * @param {Numbers} p
+   */
 
-  const {
-    Matrix: Matrix$1,
-    MatrixTransposeView: MatrixTransposeView$1
-  } = Matrix;
-  const kernelType = {
-    gaussian: gaussianKernel,
-    rbf: gaussianKernel,
-    polynomial: polynomialKernel,
-    poly: polynomialKernel,
-    anova: anovaKernel,
-    cauchy: cauchyKernel,
-    exponential: exponentialKernel,
-    histogram: histogramIntersectionKernel,
-    min: histogramIntersectionKernel,
-    laplacian: laplacianKernel,
-    multiquadratic: multiquadraticKernel,
-    rational: rationalQuadraticKernel,
-    sigmoid: sigmoidKernel,
-    mlp: sigmoidKernel
-  };
+  function cssls(XtX, XtY, Pset, l, p) {
+    // Solves the set of equation XtX*K = XtY for the variables in Pset
+    // if XtX (or XtX(vars,vars)) is singular, performs the svd and find pseudoinverse, otherwise (even if ill-conditioned) finds inverse with LU decomposition and solves the set of equation
+    // it is consistent with matlab results for ill-conditioned matrices (at least consistent with test 'ill-conditionned square X rank 2, Y 3x1' in cssls.test)
+    let K = Matrix.zeros(l, p);
 
-  class Kernel {
-    constructor(type, options) {
-      this.kernelType = type;
-      if (type === 'linear') return;
+    if (Pset === null) {
+      let choXtX = new CholeskyDecomposition(XtX);
 
-      if (typeof type === 'string') {
-        type = type.toLowerCase();
-        var KernelConstructor = kernelType[type];
+      if (choXtX.isPositiveDefinite() === true) {
+        K = choXtX.solve(XtY);
+      } else {
+        let luXtX = new LuDecomposition(XtX);
 
-        if (KernelConstructor) {
-          this.kernelFunction = new KernelConstructor(options);
+        if (luXtX.isSingular() === false) {
+          K = luXtX.solve(Matrix.eye(l)).mmul(XtY);
         } else {
-          throw new Error("unsupported kernel type: ".concat(type));
+          K = solve(XtX, XtY, {
+            useSVD: true
+          });
         }
-      } else if (typeof type === 'object' && typeof type.compute === 'function') {
-        this.kernelFunction = type;
-      } else {
-        throw new TypeError('first argument must be a valid kernel type or instance');
-      }
-    }
-
-    compute(inputs, landmarks) {
-      inputs = Matrix$1.checkMatrix(inputs);
-
-      if (landmarks === undefined) {
-        landmarks = inputs;
-      } else {
-        landmarks = Matrix$1.checkMatrix(landmarks);
       }
+    } else {
+      let sortedPset = sortCollectionSet(Pset).values;
+      let sortedEset = sortCollectionSet(Pset).indices;
 
-      if (this.kernelType === 'linear') {
-        return inputs.mmul(new MatrixTransposeView$1(landmarks));
-      }
+      if (sortedPset.length === 1 && sortedPset[0].length === 0 && sortedEset[0].length === p) {
+        return K;
+      } else if (sortedPset.length === 1 && sortedPset[0].length === l && sortedEset[0].length === p) {
+        let choXtX = new CholeskyDecomposition(XtX);
 
-      const kernelMatrix = new Matrix$1(inputs.rows, landmarks.rows);
+        if (choXtX.isPositiveDefinite() === true) {
+          K = choXtX.solve(XtY);
+        } else {
+          let luXtX = new LuDecomposition(XtX);
 
-      if (inputs === landmarks) {
-        // fast path, matrix is symmetric
-        for (let i = 0; i < inputs.rows; i++) {
-          for (let j = i; j < inputs.rows; j++) {
-            const value = this.kernelFunction.compute(inputs.getRow(i), inputs.getRow(j));
-            kernelMatrix.set(i, j, value);
-            kernelMatrix.set(j, i, value);
+          if (luXtX.isSingular() === false) {
+            K = luXtX.solve(Matrix.eye(l)).mmul(XtY);
+          } else {
+            K = solve(XtX, XtY, {
+              useSVD: true
+            });
           }
         }
       } else {
-        for (let i = 0; i < inputs.rows; i++) {
-          for (let j = 0; j < landmarks.rows; j++) {
-            kernelMatrix.set(i, j, this.kernelFunction.compute(inputs.getRow(i), landmarks.getRow(j)));
+        for (let k = 0; k < sortedPset.length; k++) {
+          let cols2Solve = sortedEset[k];
+          let vars = sortedPset[k];
+          let L;
+          let choXtX = new CholeskyDecomposition(XtX.selection(vars, vars));
+
+          if (choXtX.isPositiveDefinite() === true) {
+            L = choXtX.solve(XtY.selection(vars, cols2Solve));
+          } else {
+            let luXtX = new LuDecomposition(XtX.selection(vars, vars));
+
+            if (luXtX.isSingular() === false) {
+              L = luXtX.solve(Matrix.eye(vars.length)).mmul(XtY.selection(vars, cols2Solve));
+            } else {
+              L = solve(XtX.selection(vars, vars), XtY.selection(vars, cols2Solve), {
+                useSVD: true
+              });
+            }
+          }
+
+          for (let i = 0; i < L.rows; i++) {
+            for (let j = 0; j < L.columns; j++) {
+              K.set(vars[i], cols2Solve[j], L.get(i, j));
+            }
           }
         }
       }
-
-      return kernelMatrix;
     }
 
+    return K;
   }
 
-  var kernel = Kernel;
+  function initialisation(X, Y) {
+    let n = X.rows;
+    let l = X.columns;
+    let p = Y.columns;
+    let iter = 0;
+    if (Y.rows !== n) throw new Error('ERROR: matrix size not compatible');
+    let W = Matrix.zeros(l, p); // precomputes part of pseudoinverse
 
-  class TheilSenRegression extends BaseRegression {
-    /**
-     * Theil–Sen estimator
-     * https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator
-     * @param {Array<number>|boolean} x
-     * @param {Array<number>|object} y
-     * @constructor
-     */
-    constructor(x, y) {
-      super();
+    let XtX = X.transpose().mmul(X);
+    let XtY = X.transpose().mmul(Y);
+    let K = cssls(XtX, XtY, null, l, p); // K is lxp
+
+    let Pset = [];
+
+    for (let j = 0; j < p; j++) {
+      Pset[j] = [];
+
+      for (let i = 0; i < l; i++) {
+        if (K.get(i, j) > 0) {
+          Pset[j].push(i);
+        } else {
+          K.set(i, j, 0);
+        } //This is our initial solution, it's the solution found by overwriting the unconstrained least square solution
 
-      if (x === true) {
-        // loads the model
-        this.slope = y.slope;
-        this.intercept = y.intercept;
-        this.coefficients = y.coefficients;
-      } else {
-        // creates the model
-        checkArraySize(x, y);
-        theilSen(this, x, y);
       }
     }
 
-    toJSON() {
-      return {
-        name: 'TheilSenRegression',
-        slope: this.slope,
-        intercept: this.intercept
-      };
-    }
+    let Fset = [];
 
-    _predict(input) {
-      return this.slope * input + this.intercept;
+    for (let j = 0; j < p; j++) {
+      if (Pset[j].length !== l) {
+        Fset.push(j);
+      }
     }
 
-    computeX(input) {
-      return (input - this.intercept) / this.slope;
+    let D = K.clone();
+    return {
+      n,
+      l,
+      p,
+      iter,
+      W,
+      XtX,
+      XtY,
+      K,
+      Pset,
+      Fset,
+      D
+    };
+  }
+
+  /**
+   * Computes the set difference A\B
+   * @private
+   * @param {A} set A as an array
+   * @param {B} set B as an array
+   */
+  function setDifference(A, B) {
+    let C = [];
+
+    for (let i of A) {
+      if (!B.includes(i)) C.push(i);
     }
 
-    toString(precision) {
-      var result = 'f(x) = ';
+    return C;
+  }
 
-      if (this.slope) {
-        var xFactor = maybeToPrecision(this.slope, precision);
-        result += "".concat(Math.abs(xFactor - 1) < 1e-5 ? '' : "".concat(xFactor, " * "), "x");
+  function optimality(iter, maxIter, XtX, XtY, Fset, Pset, W, K, l, p, D) {
+    if (iter === maxIter) {
+      throw new Error('Maximum number of iterations exceeded');
+    } // Check solution for optimality
 
-        if (this.intercept) {
-          var absIntercept = Math.abs(this.intercept);
-          var operator = absIntercept === this.intercept ? '+' : '-';
-          result += " ".concat(operator, " ").concat(maybeToPrecision(absIntercept, precision));
-        }
-      } else {
-        result += maybeToPrecision(this.intercept, precision);
-      }
 
-      return result;
+    let V = XtY.subMatrixColumn(Fset).subtract(XtX.mmul(K.subMatrixColumn(Fset)));
+
+    for (let j = 0; j < Fset.length; j++) {
+      W.setColumn(Fset[j], V.subMatrixColumn([j]));
     }
 
-    toLaTeX(precision) {
-      return this.toString(precision);
+    let Jset = [];
+    let fullSet = [];
+
+    for (let i = 0; i < l; i++) {
+      fullSet.push(i);
     }
 
-    static load(json) {
-      if (json.name !== 'TheilSenRegression') {
-        throw new TypeError('not a Theil-Sen model');
-      }
+    for (let j = 0; j < Fset.length; j++) {
+      let notPset = setDifference(fullSet, Pset[Fset[j]]);
 
-      return new TheilSenRegression(true, json);
+      if (notPset.length === 0) {
+        Jset.push(Fset[j]);
+      } else if (W.selection(notPset, [Fset[j]]).max() <= 0) {
+        Jset.push(Fset[j]);
+      }
     }
 
-  }
-
-  function theilSen(regression, x, y) {
-    let len = x.length;
-    let slopes = new Array(len * len);
-    let count = 0;
+    Fset = setDifference(Fset, Jset); // For non-optimal solutions, add the appropriate variables to Pset
 
-    for (let i = 0; i < len; ++i) {
-      for (let j = i + 1; j < len; ++j) {
-        if (x[i] !== x[j]) {
-          slopes[count++] = (y[j] - y[i]) / (x[j] - x[i]);
+    if (Fset.length !== 0) {
+      for (let j = 0; j < Fset.length; j++) {
+        for (let i = 0; i < l; i++) {
+          if (Pset[Fset[j]].includes(i)) W.set(i, Fset[j], -Infinity);
         }
+
+        Pset[Fset[j]].push(W.subMatrixColumn(Fset).maxColumnIndex(j)[0]);
       }
-    }
 
-    slopes.length = count;
-    let medianSlope = median(slopes);
-    let cuts = new Array(len);
+      for (let j = 0; j < Fset.length; j++) {
+        D.setColumn(Fset[j], K.getColumn(Fset[j]));
+      }
+    }
 
-    for (let i = 0; i < len; ++i) {
-      cuts[i] = y[i] - medianSlope * x[i];
+    for (let j = 0; j < p; j++) {
+      Pset[j].sort((a, b) => a - b);
     }
 
-    regression.slope = medianSlope;
-    regression.intercept = median(cuts);
-    regression.coefficients = [regression.intercept, regression.slope];
+    return {
+      Pset,
+      Fset,
+      W
+    };
   }
 
   /**
-   * @class RobustPolynomialRegression
-   * @param {Array<number>} x
-   * @param {Array<number>} y
-   * @param {number} degree - polynomial degree
+   * Fast Combinatorial Non-negative Least Squares with multiple Right Hand Side
+   * @param {Matrix|number[][]} X
+   * @param {Matrix|number[][]} Y
+   * @param {object} [options={}]
+   * @param {number} [options.maxIterations] if empty maxIterations is set at 3 times the number of columns of X
+   * @returns {Matrix} K
    */
 
-  class RobustPolynomialRegression extends BaseRegression {
-    constructor(x, y, degree) {
-      super();
+  function fcnnls(X, Y, options = {}) {
+    X = Matrix.checkMatrix(X);
+    Y = Matrix.checkMatrix(Y);
+    let {
+      l,
+      p,
+      iter,
+      W,
+      XtX,
+      XtY,
+      K,
+      Pset,
+      Fset,
+      D
+    } = initialisation(X, Y);
+    const {
+      maxIterations = X.columns * 3
+    } = options; // Active set algorithm for NNLS main loop
 
-      if (x === true) {
-        this.degree = y.degree;
-        this.powers = y.powers;
-        this.coefficients = y.coefficients;
-      } else {
-        checkArraySize(x, y);
-        robustPolynomial(this, x, y, degree);
-      }
-    }
+    while (Fset.length > 0) {
+      // Solves for the passive variables (uses subroutine below)
+      let L = cssls(XtX, XtY.subMatrixColumn(Fset), selection(Pset, Fset), l, Fset.length);
 
-    toJSON() {
-      return {
-        name: 'robustPolynomialRegression',
-        degree: this.degree,
-        powers: this.powers,
-        coefficients: this.coefficients
-      };
-    }
+      for (let i = 0; i < l; i++) {
+        for (let j = 0; j < Fset.length; j++) {
+          K.set(i, Fset[j], L.get(i, j));
+        }
+      } // Finds any infeasible solutions
 
-    _predict(x) {
-      return predict(x, this.powers, this.coefficients);
-    }
-    /**
-     * Display the formula
-     * @param {number} precision - precision for the numbers
-     * @return {string}
-     */
 
+      let infeasIndex = [];
 
-    toString(precision) {
-      return this._toFormula(precision, false);
-    }
-    /**
-     * Display the formula in LaTeX format
-     * @param {number} precision - precision for the numbers
-     * @return {string}
-     */
+      for (let j = 0; j < Fset.length; j++) {
+        for (let i = 0; i < l; i++) {
+          if (L.get(i, j) < 0) {
+            infeasIndex.push(j);
+            break;
+          }
+        }
+      }
 
+      let Hset = selection(Fset, infeasIndex); // Makes infeasible solutions feasible (standard NNLS inner loop)
 
-    toLaTeX(precision) {
-      return this._toFormula(precision, true);
-    }
+      if (Hset.length > 0) {
+        let m = Hset.length;
+        let alpha = Matrix.ones(l, m);
 
-    _toFormula(precision, isLaTeX) {
-      let sup = '^';
-      let closeSup = '';
-      let times = ' * ';
+        while (m > 0 && iter < maxIterations) {
+          iter++;
+          alpha.mul(Infinity); // Finds indices of negative variables in passive set
 
-      if (isLaTeX) {
-        sup = '^{';
-        closeSup = '}';
-        times = '';
-      }
+          let hRowColIdx = [[], []]; // Indexes work in pairs, each pair reprensents a single element, first array is row index, second array is column index
 
-      let fn = '';
-      let str = '';
+          let negRowColIdx = [[], []]; // Same as before
 
-      for (let k = 0; k < this.coefficients.length; k++) {
-        str = '';
+          for (let j = 0; j < m; j++) {
+            for (let i = 0; i < Pset[Hset[j]].length; i++) {
+              if (K.get(Pset[Hset[j]][i], Hset[j]) < 0) {
+                hRowColIdx[0].push(Pset[Hset[j]][i]); // i
+
+                hRowColIdx[1].push(j);
+                negRowColIdx[0].push(Pset[Hset[j]][i]); // i
+
+                negRowColIdx[1].push(Hset[j]);
+              } // Compared to matlab, here we keep the row/column indexing (we are not taking the linear indexing)
 
-        if (this.coefficients[k] !== 0) {
-          if (this.powers[k] === 0) {
-            str = maybeToPrecision(this.coefficients[k], precision);
-          } else {
-            if (this.powers[k] === 1) {
-              str = "".concat(maybeToPrecision(this.coefficients[k], precision) + times, "x");
-            } else {
-              str = "".concat(maybeToPrecision(this.coefficients[k], precision) + times, "x").concat(sup).concat(this.powers[k]).concat(closeSup);
             }
           }
 
-          if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {
-            str = " + ".concat(str);
-          } else if (k !== this.coefficients.length - 1) {
-            str = " ".concat(str);
+          for (let k = 0; k < hRowColIdx[0].length; k++) {
+            // could be hRowColIdx[1].length as well
+            alpha.set(hRowColIdx[0][k], hRowColIdx[1][k], D.get(negRowColIdx[0][k], negRowColIdx[1][k]) / (D.get(negRowColIdx[0][k], negRowColIdx[1][k]) - K.get(negRowColIdx[0][k], negRowColIdx[1][k])));
           }
-        }
-
-        fn = str + fn;
-      }
-
-      if (fn.charAt(0) === '+') {
-        fn = fn.slice(1);
-      }
 
-      return "f(x) = ".concat(fn);
-    }
+          let alphaMin = [];
+          let minIdx = [];
 
-    static load(json) {
-      if (json.name !== 'robustPolynomialRegression') {
-        throw new TypeError('not a RobustPolynomialRegression model');
-      }
+          for (let j = 0; j < m; j++) {
+            alphaMin[j] = alpha.minColumn(j);
+            minIdx[j] = alpha.minColumnIndex(j)[0];
+          }
 
-      return new RobustPolynomialRegression(true, json);
-    }
+          alphaMin = Matrix.rowVector(alphaMin);
 
-  }
+          for (let i = 0; i < l; i++) {
+            alpha.setSubMatrix(alphaMin, i, 0);
+          }
 
-  function robustPolynomial(regression, x, y, degree) {
-    let powers = Array(degree).fill(0).map((_, index) => index);
-    const tuples = getRandomTuples(x, y, degree);
-    var min;
+          let E = new Matrix(l, m);
+          E = D.subMatrixColumn(Hset).subtract(alpha.subMatrix(0, l - 1, 0, m - 1).mul(D.subMatrixColumn(Hset).subtract(K.subMatrixColumn(Hset))));
 
-    for (var i = 0; i < tuples.length; i++) {
-      var tuple = tuples[i];
-      var coefficients = calcCoefficients(tuple, powers);
-      var residuals = x.slice();
+          for (let j = 0; j < m; j++) {
+            D.setColumn(Hset[j], E.subMatrixColumn([j]));
+          }
 
-      for (var j = 0; j < x.length; j++) {
-        residuals[j] = y[j] - predict(x[j], powers, coefficients);
-        residuals[j] = {
-          residual: residuals[j] * residuals[j],
-          coefficients
-        };
-      }
+          let idx2zero = [minIdx, Hset];
 
-      var median = residualsMedian(residuals);
+          for (let k = 0; k < m; k++) {
+            D.set(idx2zero[0][k], idx2zero[1][k], 0);
+          }
 
-      if (!min || median.residual < min.residual) {
-        min = median;
-      }
-    }
+          for (let j = 0; j < m; j++) {
+            Pset[Hset[j]].splice(Pset[Hset[j]].findIndex(item => item === minIdx[j]), 1);
+          }
 
-    regression.degree = degree;
-    regression.powers = powers;
-    regression.coefficients = min.coefficients;
-  }
-  /**
-   * @ignore
-   * @param {Array<number>} x
-   * @param {Array<number>} y
-   * @param {number} degree
-   * @return {Array<{x:number,y:number}>}
-   */
+          L = cssls(XtX, XtY.subMatrixColumn(Hset), selection(Pset, Hset), l, m);
 
+          for (let j = 0; j < m; j++) {
+            K.setColumn(Hset[j], L.subMatrixColumn([j]));
+          }
 
-  function getRandomTuples(x, y, degree) {
-    var len = Math.floor(x.length / degree);
-    var tuples = new Array(len);
+          Hset = [];
 
-    for (var i = 0; i < x.length; i++) {
-      var pos = Math.floor(Math.random() * len);
-      var counter = 0;
+          for (let j = 0; j < K.columns; j++) {
+            for (let i = 0; i < l; i++) {
+              if (K.get(i, j) < 0) {
+                Hset.push(j);
+                break;
+              }
+            }
+          }
 
-      while (counter < x.length) {
-        if (!tuples[pos]) {
-          tuples[pos] = [{
-            x: x[i],
-            y: y[i]
-          }];
-          break;
-        } else if (tuples[pos].length < degree) {
-          tuples[pos].push({
-            x: x[i],
-            y: y[i]
-          });
-          break;
-        } else {
-          counter++;
-          pos = (pos + 1) % len;
+          m = Hset.length;
         }
       }
 
-      if (counter === x.length) {
-        return tuples;
-      }
+      let newParam = optimality(iter, maxIterations, XtX, XtY, Fset, Pset, W, K, l, p, D);
+      Pset = newParam.Pset;
+      Fset = newParam.Fset;
+      W = newParam.W;
     }
 
-    return tuples;
+    return K;
   }
+
   /**
-   * @ignore
-   * @param {{x:number,y:number}} tuple
-   * @param {Array<number>} powers
-   * @return {Array<number>}
+   * Fast Combinatorial Non-negative Least Squares with single Right Hand Side
+   * @param {Matrix|number[][]} X
+   * @param {number[]} y
+   * @param {object} [options={}]
+   * @param {boolean} [maxIterations] if true or empty maxIterations is set at 3 times the number of columns of X
+   * @returns {Array} k
    */
 
+  function fcnnlsVector(X, y, options = {}) {
+    if (Array.isArray(y) === false) {
+      throw new TypeError('y must be a 1D Array');
+    }
 
-  function calcCoefficients(tuple, powers) {
-    var X = tuple.slice();
-    var Y = tuple.slice();
+    let Y = Matrix.columnVector(y);
+    let K = fcnnls(X, Y, options);
+    let k = K.to1DArray();
+    return k;
+  }
 
-    for (var i = 0; i < X.length; i++) {
-      Y[i] = [tuple[i].y];
-      X[i] = new Array(powers.length);
+  var index$3 = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    fcnnls: fcnnls,
+    fcnnlsVector: fcnnlsVector
+  });
 
-      for (var j = 0; j < powers.length; j++) {
-        X[i][j] = Math.pow(tuple[i].x, powers[j]);
-      }
+  var binarySearch = function (haystack, needle, comparator, low, high) {
+    var mid, cmp;
+    if (low === undefined) low = 0;else {
+      low = low | 0;
+      if (low < 0 || low >= haystack.length) throw new RangeError("invalid lower bound");
+    }
+    if (high === undefined) high = haystack.length - 1;else {
+      high = high | 0;
+      if (high < low || high >= haystack.length) throw new RangeError("invalid upper bound");
     }
 
-    return solve(X, Y).to1DArray();
-  }
+    while (low <= high) {
+      // The naive `low + high >>> 1` could fail for array lengths > 2**31
+      // because `>>>` converts its operands to int32. `low + (high - low >>> 1)`
+      // works for array lengths <= 2**32-1 which is also Javascript's max array
+      // length.
+      mid = low + (high - low >>> 1);
+      cmp = +comparator(haystack[mid], needle, mid, haystack); // Too low.
 
-  function predict(x, powers, coefficients) {
-    let y = 0;
+      if (cmp < 0.0) low = mid + 1; // Too high.
+      else if (cmp > 0.0) high = mid - 1; // Key found.
+        else return mid;
+    } // Key not found.
 
-    for (let k = 0; k < powers.length; k++) {
-      y += coefficients[k] * Math.pow(x, powers[k]);
-    }
 
-    return y;
-  }
+    return ~low;
+  };
 
-  function residualsMedian(residuals) {
-    residuals.sort((a, b) => a.residual - b.residual);
-    var l = residuals.length;
-    var half = Math.floor(l / 2);
-    return l % 2 === 0 ? residuals[half - 1] : residuals[half];
+  function assertNumber(number) {
+    if (typeof number !== 'number') {
+      throw new TypeError('Expected a number');
+    }
   }
 
-  /**
-   * Calculate current error
-   * @ignore
-   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
-   * @param {Array<number>} parameters - Array of current parameter values
-   * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
-   * @return {number}
-   */
-  function errorCalculation(data, parameters, parameterizedFunction) {
-    var error = 0;
-    const func = parameterizedFunction(parameters);
+  var ascending = (left, right) => {
+    assertNumber(left);
+    assertNumber(right);
 
-    for (var i = 0; i < data.x.length; i++) {
-      error += Math.abs(data.y[i] - func(data.x[i]));
+    if (Number.isNaN(left)) {
+      return -1;
     }
 
-    return error;
-  }
+    if (Number.isNaN(right)) {
+      return 1;
+    }
 
-  /**
-   * Difference of the matrix function over the parameters
-   * @ignore
-   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
-   * @param {Array<number>} evaluatedData - Array of previous evaluated function values
-   * @param {Array<number>} params - Array of previous parameter values
-   * @param {number} gradientDifference - Adjustment for decrease the damping parameter
-   * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter
-   * @return {Matrix}
-   */
+    return left - right;
+  };
 
-  function gradientFunction(data, evaluatedData, params, gradientDifference, paramFunction) {
-    const n = params.length;
-    const m = data.x.length;
-    var ans = new Array(n);
+  var descending = (left, right) => {
+    assertNumber(left);
+    assertNumber(right);
 
-    for (var param = 0; param < n; param++) {
-      ans[param] = new Array(m);
-      var auxParams = params.concat();
-      auxParams[param] += gradientDifference;
-      var funcParam = paramFunction(auxParams);
+    if (Number.isNaN(left)) {
+      return 1;
+    }
 
-      for (var point = 0; point < m; point++) {
-        ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);
-      }
+    if (Number.isNaN(right)) {
+      return -1;
     }
 
-    return new Matrix(ans);
-  }
-  /**
-   * Matrix function over the samples
-   * @ignore
-   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
-   * @param {Array<number>} evaluatedData - Array of previous evaluated function values
-   * @return {Matrix}
-   */
+    return right - left;
+  };
 
+  var numSort = {
+    ascending: ascending,
+    descending: descending
+  };
 
-  function matrixFunction(data, evaluatedData) {
-    const m = data.x.length;
-    var ans = new Array(m);
+  var index$4 = /*#__PURE__*/Object.freeze(/*#__PURE__*/Object.assign(/*#__PURE__*/Object.create(null), numSort, {
+    'default': numSort,
+    ascending: ascending,
+    descending: descending
+  }));
 
-    for (var point = 0; point < m; point++) {
-      ans[point] = [data.y[point] - evaluatedData[point]];
+  const largestPrime = 0x7fffffff;
+  const primeNumbers = [// chunk #0
+  largestPrime, // 2^31-1
+  // chunk #1
+  5, 11, 23, 47, 97, 197, 397, 797, 1597, 3203, 6421, 12853, 25717, 51437, 102877, 205759, 411527, 823117, 1646237, 3292489, 6584983, 13169977, 26339969, 52679969, 105359939, 210719881, 421439783, 842879579, 1685759167, // chunk #2
+  433, 877, 1759, 3527, 7057, 14143, 28289, 56591, 113189, 226379, 452759, 905551, 1811107, 3622219, 7244441, 14488931, 28977863, 57955739, 115911563, 231823147, 463646329, 927292699, 1854585413, // chunk #3
+  953, 1907, 3821, 7643, 15287, 30577, 61169, 122347, 244703, 489407, 978821, 1957651, 3915341, 7830701, 15661423, 31322867, 62645741, 125291483, 250582987, 501165979, 1002331963, 2004663929, // chunk #4
+  1039, 2081, 4177, 8363, 16729, 33461, 66923, 133853, 267713, 535481, 1070981, 2141977, 4283963, 8567929, 17135863, 34271747, 68543509, 137087021, 274174111, 548348231, 1096696463, // chunk #5
+  31, 67, 137, 277, 557, 1117, 2237, 4481, 8963, 17929, 35863, 71741, 143483, 286973, 573953, 1147921, 2295859, 4591721, 9183457, 18366923, 36733847, 73467739, 146935499, 293871013, 587742049, 1175484103, // chunk #6
+  599, 1201, 2411, 4831, 9677, 19373, 38747, 77509, 155027, 310081, 620171, 1240361, 2480729, 4961459, 9922933, 19845871, 39691759, 79383533, 158767069, 317534141, 635068283, 1270136683, // chunk #7
+  311, 631, 1277, 2557, 5119, 10243, 20507, 41017, 82037, 164089, 328213, 656429, 1312867, 2625761, 5251529, 10503061, 21006137, 42012281, 84024581, 168049163, 336098327, 672196673, 1344393353, // chunk #8
+  3, 7, 17, 37, 79, 163, 331, 673, 1361, 2729, 5471, 10949, 21911, 43853, 87719, 175447, 350899, 701819, 1403641, 2807303, 5614657, 11229331, 22458671, 44917381, 89834777, 179669557, 359339171, 718678369, 1437356741, // chunk #9
+  43, 89, 179, 359, 719, 1439, 2879, 5779, 11579, 23159, 46327, 92657, 185323, 370661, 741337, 1482707, 2965421, 5930887, 11861791, 23723597, 47447201, 94894427, 189788857, 379577741, 759155483, 1518310967, // chunk #10
+  379, 761, 1523, 3049, 6101, 12203, 24407, 48817, 97649, 195311, 390647, 781301, 1562611, 3125257, 6250537, 12501169, 25002389, 50004791, 100009607, 200019221, 400038451, 800076929, 1600153859, // chunk #11
+  13, 29, 59, 127, 257, 521, 1049, 2099, 4201, 8419, 16843, 33703, 67409, 134837, 269683, 539389, 1078787, 2157587, 4315183, 8630387, 17260781, 34521589, 69043189, 138086407, 276172823, 552345671, 1104691373, // chunk #12
+  19, 41, 83, 167, 337, 677, 1361, 2729, 5471, 10949, 21911, 43853, 87719, 175447, 350899, 701819, 1403641, 2807303, 5614657, 11229331, 22458671, 44917381, 89834777, 179669557, 359339171, 718678369, 1437356741, // chunk #13
+  53, 107, 223, 449, 907, 1823, 3659, 7321, 14653, 29311, 58631, 117269, 234539, 469099, 938207, 1876417, 3752839, 7505681, 15011389, 30022781, 60045577, 120091177, 240182359, 480364727, 960729461, 1921458943];
+  primeNumbers.sort(ascending);
+  function nextPrime(value) {
+    let index = binarySearch(primeNumbers, value, ascending);
+
+    if (index < 0) {
+      index = ~index;
     }
 
-    return new Matrix(ans);
+    return primeNumbers[index];
   }
-  /**
-   * Iteration for Levenberg-Marquardt
-   * @ignore
-   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
-   * @param {Array<number>} params - Array of previous parameter values
-   * @param {number} damping - Levenberg-Marquardt parameter
-   * @param {number} gradientDifference - Adjustment for decrease the damping parameter
-   * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
-   * @return {Array<number>}
-   */
-
 
-  function step$1(data, params, damping, gradientDifference, parameterizedFunction) {
-    var value = damping * gradientDifference * gradientDifference;
-    var identity = Matrix.eye(params.length, params.length, value);
-    const func = parameterizedFunction(params);
-    var evaluatedData = data.x.map(e => func(e));
-    var gradientFunc = gradientFunction(data, evaluatedData, params, gradientDifference, parameterizedFunction);
-    var matrixFunc = matrixFunction(data, evaluatedData);
-    var inverseMatrix = inverse(identity.add(gradientFunc.mmul(gradientFunc.transpose())));
-    params = new Matrix([params]);
-    params = params.sub(inverseMatrix.mmul(gradientFunc).mmul(matrixFunc).mul(gradientDifference).transpose());
-    return params.to1DArray();
-  }
+  const FREE = 0;
+  const FULL = 1;
+  const REMOVED = 2;
+  const defaultInitialCapacity = 150;
+  const defaultMinLoadFactor = 1 / 6;
+  const defaultMaxLoadFactor = 2 / 3;
+  class HashTable {
+    constructor(options = {}) {
+      if (options instanceof HashTable) {
+        this.table = options.table.slice();
+        this.values = options.values.slice();
+        this.state = options.state.slice();
+        this.minLoadFactor = options.minLoadFactor;
+        this.maxLoadFactor = options.maxLoadFactor;
+        this.distinct = options.distinct;
+        this.freeEntries = options.freeEntries;
+        this.lowWaterMark = options.lowWaterMark;
+        this.highWaterMark = options.maxLoadFactor;
+        return;
+      }
 
-  /**
-   * Curve fitting algorithm
-   * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
-   * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
-   * @param {object} [options] - Options object
-   * @param {number} [options.damping] - Levenberg-Marquardt parameter
-   * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter
-   * @param {Array<number>} [options.minValues] - Minimum allowed values for parameters
-   * @param {Array<number>} [options.maxValues] - Maximum allowed values for parameters
-   * @param {Array<number>} [options.initialValues] - Array of initial parameter values
-   * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations
-   * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point
-   * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}
-   */
+      const initialCapacity = options.initialCapacity === undefined ? defaultInitialCapacity : options.initialCapacity;
 
-  function levenbergMarquardt(data, parameterizedFunction) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-    let {
-      maxIterations = 100,
-      gradientDifference = 10e-2,
-      damping = 0,
-      errorTolerance = 10e-3,
-      minValues,
-      maxValues,
-      initialValues
-    } = options;
+      if (initialCapacity < 0) {
+        throw new RangeError(`initial capacity must not be less than zero: ${initialCapacity}`);
+      }
 
-    if (damping <= 0) {
-      throw new Error('The damping option must be a positive number');
-    } else if (!data.x || !data.y) {
-      throw new Error('The data parameter must have x and y elements');
-    } else if (!Array.isArray(data.x) || data.x.length < 2 || !Array.isArray(data.y) || data.y.length < 2) {
-      throw new Error('The data parameter elements must be an array with more than 2 points');
-    } else if (data.x.length !== data.y.length) {
-      throw new Error('The data parameter elements must have the same size');
-    }
+      const minLoadFactor = options.minLoadFactor === undefined ? defaultMinLoadFactor : options.minLoadFactor;
+      const maxLoadFactor = options.maxLoadFactor === undefined ? defaultMaxLoadFactor : options.maxLoadFactor;
 
-    var parameters = initialValues || new Array(parameterizedFunction.length).fill(1);
-    let parLen = parameters.length;
-    maxValues = maxValues || new Array(parLen).fill(Number.MAX_SAFE_INTEGER);
-    minValues = minValues || new Array(parLen).fill(Number.MIN_SAFE_INTEGER);
+      if (minLoadFactor < 0 || minLoadFactor >= 1) {
+        throw new RangeError(`invalid minLoadFactor: ${minLoadFactor}`);
+      }
 
-    if (maxValues.length !== minValues.length) {
-      throw new Error('minValues and maxValues must be the same size');
-    }
+      if (maxLoadFactor <= 0 || maxLoadFactor >= 1) {
+        throw new RangeError(`invalid maxLoadFactor: ${maxLoadFactor}`);
+      }
 
-    if (!Array.isArray(parameters)) {
-      throw new Error('initialValues must be an array');
-    }
+      if (minLoadFactor >= maxLoadFactor) {
+        throw new RangeError(`minLoadFactor (${minLoadFactor}) must be smaller than maxLoadFactor (${maxLoadFactor})`);
+      }
 
-    var error = errorCalculation(data, parameters, parameterizedFunction);
-    var converged = error <= errorTolerance;
+      let capacity = initialCapacity; // User wants to put at least capacity elements. We need to choose the size based on the maxLoadFactor to
+      // avoid the need to rehash before this capacity is reached.
+      // actualCapacity * maxLoadFactor >= capacity
 
-    for (var iteration = 0; iteration < maxIterations && !converged; iteration++) {
-      parameters = step$1(data, parameters, damping, gradientDifference, parameterizedFunction);
+      capacity = capacity / maxLoadFactor | 0;
+      capacity = nextPrime(capacity);
+      if (capacity === 0) capacity = 1;
+      this.table = newArray$1(capacity);
+      this.values = newArray$1(capacity);
+      this.state = newArray$1(capacity);
+      this.minLoadFactor = minLoadFactor;
 
-      for (let k = 0; k < parLen; k++) {
-        parameters[k] = Math.min(Math.max(minValues[k], parameters[k]), maxValues[k]);
+      if (capacity === largestPrime) {
+        this.maxLoadFactor = 1;
+      } else {
+        this.maxLoadFactor = maxLoadFactor;
       }
 
-      error = errorCalculation(data, parameters, parameterizedFunction);
-      if (isNaN(error)) break;
-      converged = error <= errorTolerance;
+      this.distinct = 0;
+      this.freeEntries = capacity;
+      this.lowWaterMark = 0;
+      this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);
     }
 
-    return {
-      parameterValues: parameters,
-      parameterError: error,
-      iterations: iteration
-    };
-  }
-
-  /**
-   * Returns a new array based on extraction of specific indices of an array
-   * @private
-   * @param {Array} vector
-   * @param {Array} indices
-   */
-  function selection(vector, indices) {
-    let u = []; //new Float64Array(indices.length);
+    clone() {
+      return new HashTable(this);
+    }
 
-    for (let i = 0; i < indices.length; i++) {
-      u[i] = vector[indices[i]];
+    get size() {
+      return this.distinct;
     }
 
-    return u;
-  }
+    get(key) {
+      const i = this.indexOfKey(key);
+      if (i < 0) return 0;
+      return this.values[i];
+    }
 
-  /**
-   *
-   * @private
-   * @param {Array of arrays} collection
-   */
-  function sortCollectionSet(collection) {
-    let objectCollection = collection.map((value, index) => {
-      let key = BigInt(0);
-      value.forEach(item => key |= BigInt(1) << BigInt(item));
-      return {
-        value,
-        index,
-        key
-      };
-    }).sort((a, b) => {
-      if (a.key - b.key < 0) return -1;
-      return 1;
-    });
-    let sorted = [];
-    let indices = [];
-    let key;
+    set(key, value) {
+      let i = this.indexOfInsertion(key);
 
-    for (let set of objectCollection) {
-      if (set.key !== key) {
-        key = set.key;
-        indices.push([]);
-        sorted.push(set.value);
+      if (i < 0) {
+        i = -i - 1;
+        this.values[i] = value;
+        return false;
       }
 
-      indices[indices.length - 1].push(set.index);
-    }
+      if (this.distinct > this.highWaterMark) {
+        const newCapacity = chooseGrowCapacity(this.distinct + 1, this.minLoadFactor, this.maxLoadFactor);
+        this.rehash(newCapacity);
+        return this.set(key, value);
+      }
 
-    let result = {
-      values: sorted,
-      indices: indices
-    };
-    return result;
-  }
+      this.table[i] = key;
+      this.values[i] = value;
+      if (this.state[i] === FREE) this.freeEntries--;
+      this.state[i] = FULL;
+      this.distinct++;
 
-  /**
-   * (Combinatorial Subspace Least Squares) - subfunction for the FC-NNLS
-   * @private
-   * @param {Matrix} XtX
-   * @param {Matrix} XtY
-   * @param {Array} Pset
-   * @param {Numbers} l
-   * @param {Numbers} p
-   */
+      if (this.freeEntries < 1) {
+        const newCapacity = chooseGrowCapacity(this.distinct + 1, this.minLoadFactor, this.maxLoadFactor);
+        this.rehash(newCapacity);
+      }
 
-  function cssls(XtX, XtY, Pset, l, p) {
-    // Solves the set of equation XtX*K = XtY for the variables in Pset
-    // if XtX (or XtX(vars,vars)) is singular, performs the svd and find pseudoinverse, otherwise (even if ill-conditioned) finds inverse with LU decomposition and solves the set of equation
-    // it is consistent with matlab results for ill-conditioned matrices (at least consistent with test 'ill-conditionned square X rank 2, Y 3x1' in cssls.test)
-    let K = Matrix.zeros(l, p);
+      return true;
+    }
 
-    if (Pset === null) {
-      let choXtX = new CholeskyDecomposition(XtX);
+    remove(key, noRehash) {
+      const i = this.indexOfKey(key);
+      if (i < 0) return false;
+      this.state[i] = REMOVED;
+      this.distinct--;
+      if (!noRehash) this.maybeShrinkCapacity();
+      return true;
+    }
 
-      if (choXtX.isPositiveDefinite() === true) {
-        K = choXtX.solve(XtY);
-      } else {
-        let luXtX = new LuDecomposition(XtX);
+    delete(key, noRehash) {
+      const i = this.indexOfKey(key);
+      if (i < 0) return false;
+      this.state[i] = FREE;
+      this.distinct--;
+      if (!noRehash) this.maybeShrinkCapacity();
+      return true;
+    }
 
-        if (luXtX.isSingular() === false) {
-          K = luXtX.solve(Matrix.eye(l)).mmul(XtY);
-        } else {
-          K = solve(XtX, XtY, {
-            useSVD: true
-          });
-        }
+    maybeShrinkCapacity() {
+      if (this.distinct < this.lowWaterMark) {
+        const newCapacity = chooseShrinkCapacity(this.distinct, this.minLoadFactor, this.maxLoadFactor);
+        this.rehash(newCapacity);
       }
-    } else {
-      let sortedPset = sortCollectionSet(Pset).values;
-      let sortedEset = sortCollectionSet(Pset).indices;
+    }
 
-      if (sortedPset.length === 1 && sortedPset[0].length === 0 && sortedEset[0].length === p) {
-        return K;
-      } else if (sortedPset.length === 1 && sortedPset[0].length === l && sortedEset[0].length === p) {
-        let choXtX = new CholeskyDecomposition(XtX);
+    containsKey(key) {
+      return this.indexOfKey(key) >= 0;
+    }
 
-        if (choXtX.isPositiveDefinite() === true) {
-          K = choXtX.solve(XtY);
-        } else {
-          let luXtX = new LuDecomposition(XtX);
+    indexOfKey(key) {
+      const table = this.table;
+      const state = this.state;
+      const length = this.table.length;
+      const hash = key & 0x7fffffff;
+      let i = hash % length;
+      let decrement = hash % (length - 2);
+      if (decrement === 0) decrement = 1;
 
-          if (luXtX.isSingular() === false) {
-            K = luXtX.solve(Matrix.eye(l)).mmul(XtY);
-          } else {
-            K = solve(XtX, XtY, {
-              useSVD: true
-            });
-          }
-        }
-      } else {
-        for (let k = 0; k < sortedPset.length; k++) {
-          let cols2Solve = sortedEset[k];
-          let vars = sortedPset[k];
-          let L;
-          let choXtX = new CholeskyDecomposition(XtX.selection(vars, vars));
+      while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {
+        i -= decrement;
+        if (i < 0) i += length;
+      }
 
-          if (choXtX.isPositiveDefinite() === true) {
-            L = choXtX.solve(XtY.selection(vars, cols2Solve));
-          } else {
-            let luXtX = new LuDecomposition(XtX.selection(vars, vars));
+      if (state[i] === FREE) return -1;
+      return i;
+    }
 
-            if (luXtX.isSingular() === false) {
-              L = luXtX.solve(Matrix.eye(vars.length)).mmul(XtY.selection(vars, cols2Solve));
-            } else {
-              L = solve(XtX.selection(vars, vars), XtY.selection(vars, cols2Solve), {
-                useSVD: true
-              });
-            }
-          }
+    containsValue(value) {
+      return this.indexOfValue(value) >= 0;
+    }
 
-          for (let i = 0; i < L.rows; i++) {
-            for (let j = 0; j < L.columns; j++) {
-              K.set(vars[i], cols2Solve[j], L.get(i, j));
-            }
-          }
+    indexOfValue(value) {
+      const values = this.values;
+      const state = this.state;
+
+      for (var i = 0; i < state.length; i++) {
+        if (state[i] === FULL && values[i] === value) {
+          return i;
         }
       }
+
+      return -1;
     }
 
-    return K;
-  }
+    indexOfInsertion(key) {
+      const table = this.table;
+      const state = this.state;
+      const length = table.length;
+      const hash = key & 0x7fffffff;
+      let i = hash % length;
+      let decrement = hash % (length - 2);
+      if (decrement === 0) decrement = 1;
 
-  function initialisation(X, Y) {
-    let n = X.rows;
-    let l = X.columns;
-    let p = Y.columns;
-    let iter = 0;
-    if (Y.rows !== n) throw new Error('ERROR: matrix size not compatible');
-    let W = Matrix.zeros(l, p); // precomputes part of pseudoinverse
+      while (state[i] === FULL && table[i] !== key) {
+        i -= decrement;
+        if (i < 0) i += length;
+      }
 
-    let XtX = X.transpose().mmul(X);
-    let XtY = X.transpose().mmul(Y);
-    let K = cssls(XtX, XtY, null, l, p); // K is lxp
+      if (state[i] === REMOVED) {
+        const j = i;
 
-    let Pset = [];
+        while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {
+          i -= decrement;
+          if (i < 0) i += length;
+        }
 
-    for (let j = 0; j < p; j++) {
-      Pset[j] = [];
+        if (state[i] === FREE) i = j;
+      }
 
-      for (let i = 0; i < l; i++) {
-        if (K.get(i, j) > 0) {
-          Pset[j].push(i);
-        } else {
-          K.set(i, j, 0);
-        } //This is our initial solution, it's the solution found by overwriting the unconstrained least square solution
+      if (state[i] === FULL) {
+        return -i - 1;
+      }
 
+      return i;
+    }
+
+    ensureCapacity(minCapacity) {
+      if (this.table.length < minCapacity) {
+        const newCapacity = nextPrime(minCapacity);
+        this.rehash(newCapacity);
       }
     }
 
-    let Fset = [];
+    rehash(newCapacity) {
+      const oldCapacity = this.table.length;
+      if (newCapacity <= this.distinct) throw new Error('Unexpected');
+      const oldTable = this.table;
+      const oldValues = this.values;
+      const oldState = this.state;
+      const newTable = newArray$1(newCapacity);
+      const newValues = newArray$1(newCapacity);
+      const newState = newArray$1(newCapacity);
+      this.lowWaterMark = chooseLowWaterMark(newCapacity, this.minLoadFactor);
+      this.highWaterMark = chooseHighWaterMark(newCapacity, this.maxLoadFactor);
+      this.table = newTable;
+      this.values = newValues;
+      this.state = newState;
+      this.freeEntries = newCapacity - this.distinct;
 
-    for (let j = 0; j < p; j++) {
-      if (Pset[j].length !== l) {
-        Fset.push(j);
+      for (var i = 0; i < oldCapacity; i++) {
+        if (oldState[i] === FULL) {
+          var element = oldTable[i];
+          var index = this.indexOfInsertion(element);
+          newTable[index] = element;
+          newValues[index] = oldValues[i];
+          newState[index] = FULL;
+        }
       }
     }
 
-    let D = K.clone();
-    return {
-      n,
-      l,
-      p,
-      iter,
-      W,
-      XtX,
-      XtY,
-      K,
-      Pset,
-      Fset,
-      D
-    };
-  }
+    forEachKey(callback) {
+      for (var i = 0; i < this.state.length; i++) {
+        if (this.state[i] === FULL) {
+          if (!callback(this.table[i])) return false;
+        }
+      }
 
-  /**
-   * Computes the set difference A\B
-   * @private
-   * @param {A} set A as an array
-   * @param {B} set B as an array
-   */
-  function setDifference(A, B) {
-    let C = [];
+      return true;
+    }
 
-    for (let i of A) {
-      if (!B.includes(i)) C.push(i);
+    forEachValue(callback) {
+      for (var i = 0; i < this.state.length; i++) {
+        if (this.state[i] === FULL) {
+          if (!callback(this.values[i])) return false;
+        }
+      }
+
+      return true;
+    }
+
+    forEachPair(callback) {
+      for (var i = 0; i < this.state.length; i++) {
+        if (this.state[i] === FULL) {
+          if (!callback(this.table[i], this.values[i])) return false;
+        }
+      }
+
+      return true;
     }
 
-    return C;
   }
 
-  function optimality(iter, maxIter, XtX, XtY, Fset, Pset, W, K, l, p, D) {
-    if (iter === maxIter) {
-      throw new Error('Maximum number of iterations exceeded');
-    } // Check solution for optimality
+  function chooseLowWaterMark(capacity, minLoad) {
+    return capacity * minLoad | 0;
+  }
 
+  function chooseHighWaterMark(capacity, maxLoad) {
+    return Math.min(capacity - 2, capacity * maxLoad | 0);
+  }
 
-    let V = XtY.subMatrixColumn(Fset).subtract(XtX.mmul(K.subMatrixColumn(Fset)));
+  function chooseGrowCapacity(size, minLoad, maxLoad) {
+    return nextPrime(Math.max(size + 1, 4 * size / (3 * minLoad + maxLoad) | 0));
+  }
 
-    for (let j = 0; j < Fset.length; j++) {
-      W.setColumn(Fset[j], V.subMatrixColumn([j]));
-    }
+  function chooseShrinkCapacity(size, minLoad, maxLoad) {
+    return nextPrime(Math.max(size + 1, 4 * size / (minLoad + 3 * maxLoad) | 0));
+  }
 
-    let Jset = [];
-    let fullSet = [];
+  function newArray$1(size) {
+    return Array(size).fill(0);
+  }
 
-    for (let i = 0; i < l; i++) {
-      fullSet.push(i);
-    }
+  class SparseMatrix {
+    constructor(rows, columns, options = {}) {
+      if (rows instanceof SparseMatrix) {
+        // clone
+        const other = rows;
 
-    for (let j = 0; j < Fset.length; j++) {
-      let notPset = setDifference(fullSet, Pset[Fset[j]]);
+        this._init(other.rows, other.columns, other.elements.clone(), other.threshold);
 
-      if (notPset.length === 0) {
-        Jset.push(Fset[j]);
-      } else if (W.selection(notPset, [Fset[j]]).max() <= 0) {
-        Jset.push(Fset[j]);
+        return;
       }
-    }
 
-    Fset = setDifference(Fset, Jset); // For non-optimal solutions, add the appropriate variables to Pset
+      if (Array.isArray(rows)) {
+        const matrix = rows;
+        rows = matrix.length;
+        options = columns || {};
+        columns = matrix[0].length;
 
-    if (Fset.length !== 0) {
-      for (let j = 0; j < Fset.length; j++) {
-        for (let i = 0; i < l; i++) {
-          if (Pset[Fset[j]].includes(i)) W.set(i, Fset[j], -Infinity);
-        }
+        this._init(rows, columns, new HashTable(options), options.threshold);
 
-        Pset[Fset[j]].push(W.subMatrixColumn(Fset).maxColumnIndex(j)[0]);
-      }
+        for (var i = 0; i < rows; i++) {
+          for (var j = 0; j < columns; j++) {
+            var value = matrix[i][j];
+            if (this.threshold && Math.abs(value) < this.threshold) value = 0;
 
-      for (let j = 0; j < Fset.length; j++) {
-        D.setColumn(Fset[j], K.getColumn(Fset[j]));
+            if (value !== 0) {
+              this.elements.set(i * columns + j, matrix[i][j]);
+            }
+          }
+        }
+      } else {
+        this._init(rows, columns, new HashTable(options), options.threshold);
       }
     }
 
-    for (let j = 0; j < p; j++) {
-      Pset[j].sort((a, b) => a - b);
+    _init(rows, columns, elements, threshold) {
+      this.rows = rows;
+      this.columns = columns;
+      this.elements = elements;
+      this.threshold = threshold || 0;
     }
 
-    return {
-      Pset,
-      Fset,
-      W
-    };
-  }
-
-  /**
-   * Fast Combinatorial Non-negative Least Squares with multiple Right Hand Side
-   * @param {Matrix|number[][]} X
-   * @param {Matrix|number[][]} Y
-   * @param {object} [options={}]
-   * @param {number} [options.maxIterations] if empty maxIterations is set at 3 times the number of columns of X
-   * @returns {Matrix} K
-   */
-
-  function fcnnls(X, Y) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-    X = Matrix.checkMatrix(X);
-    Y = Matrix.checkMatrix(Y);
-    let {
-      l,
-      p,
-      iter,
-      W,
-      XtX,
-      XtY,
-      K,
-      Pset,
-      Fset,
-      D
-    } = initialisation(X, Y);
-    const {
-      maxIterations = X.columns * 3
-    } = options; // Active set algorithm for NNLS main loop
+    static eye(rows = 1, columns = rows) {
+      const min = Math.min(rows, columns);
+      const matrix = new SparseMatrix(rows, columns, {
+        initialCapacity: min
+      });
 
-    while (Fset.length > 0) {
-      // Solves for the passive variables (uses subroutine below)
-      let L = cssls(XtX, XtY.subMatrixColumn(Fset), selection(Pset, Fset), l, Fset.length);
+      for (var i = 0; i < min; i++) {
+        matrix.set(i, i, 1);
+      }
 
-      for (let i = 0; i < l; i++) {
-        for (let j = 0; j < Fset.length; j++) {
-          K.set(i, Fset[j], L.get(i, j));
-        }
-      } // Finds any infeasible solutions
+      return matrix;
+    }
 
+    clone() {
+      return new SparseMatrix(this);
+    }
 
-      let infeasIndex = [];
+    to2DArray() {
+      const copy = new Array(this.rows);
 
-      for (let j = 0; j < Fset.length; j++) {
-        for (let i = 0; i < l; i++) {
-          if (L.get(i, j) < 0) {
-            infeasIndex.push(j);
-            break;
-          }
+      for (var i = 0; i < this.rows; i++) {
+        copy[i] = new Array(this.columns);
+
+        for (var j = 0; j < this.columns; j++) {
+          copy[i][j] = this.get(i, j);
         }
       }
 
-      let Hset = selection(Fset, infeasIndex); // Makes infeasible solutions feasible (standard NNLS inner loop)
+      return copy;
+    }
 
-      if (Hset.length > 0) {
-        let m = Hset.length;
-        let alpha = Matrix.ones(l, m);
+    isSquare() {
+      return this.rows === this.columns;
+    }
 
-        while (m > 0 && iter < maxIterations) {
-          iter++;
-          alpha.mul(Infinity); // Finds indices of negative variables in passive set
+    isSymmetric() {
+      if (!this.isSquare()) return false;
+      var symmetric = true;
+      this.forEachNonZero((i, j, v) => {
+        if (this.get(j, i) !== v) {
+          symmetric = false;
+          return false;
+        }
 
-          let hRowColIdx = [[], []]; // Indexes work in pairs, each pair reprensents a single element, first array is row index, second array is column index
+        return v;
+      });
+      return symmetric;
+    }
+    /**
+     * Search for the wither band in the main diagonals
+     * @return {number}
+     */
 
-          let negRowColIdx = [[], []]; // Same as before
 
-          for (let j = 0; j < m; j++) {
-            for (let i = 0; i < Pset[Hset[j]].length; i++) {
-              if (K.get(Pset[Hset[j]][i], Hset[j]) < 0) {
-                hRowColIdx[0].push(Pset[Hset[j]][i]); // i
+    bandWidth() {
+      let min = this.columns;
+      let max = -1;
+      this.forEachNonZero((i, j, v) => {
+        let diff = i - j;
+        min = Math.min(min, diff);
+        max = Math.max(max, diff);
+        return v;
+      });
+      return max - min;
+    }
+    /**
+     * Test if a matrix is consider banded using a threshold
+     * @param {number} width
+     * @return {boolean}
+     */
 
-                hRowColIdx[1].push(j);
-                negRowColIdx[0].push(Pset[Hset[j]][i]); // i
 
-                negRowColIdx[1].push(Hset[j]);
-              } // Compared to matlab, here we keep the row/column indexing (we are not taking the linear indexing)
+    isBanded(width) {
+      let bandWidth = this.bandWidth();
+      return bandWidth <= width;
+    }
 
-            }
-          }
+    get cardinality() {
+      return this.elements.size;
+    }
 
-          for (let k = 0; k < hRowColIdx[0].length; k++) {
-            // could be hRowColIdx[1].length as well
-            alpha.set(hRowColIdx[0][k], hRowColIdx[1][k], D.get(negRowColIdx[0][k], negRowColIdx[1][k]) / (D.get(negRowColIdx[0][k], negRowColIdx[1][k]) - K.get(negRowColIdx[0][k], negRowColIdx[1][k])));
-          }
+    get size() {
+      return this.rows * this.columns;
+    }
 
-          let alphaMin = [];
-          let minIdx = [];
+    get(row, column) {
+      return this.elements.get(row * this.columns + column);
+    }
 
-          for (let j = 0; j < m; j++) {
-            alphaMin[j] = alpha.minColumn(j);
-            minIdx[j] = alpha.minColumnIndex(j)[0];
-          }
+    set(row, column, value) {
+      if (this.threshold && Math.abs(value) < this.threshold) value = 0;
 
-          alphaMin = Matrix.rowVector(alphaMin);
+      if (value === 0) {
+        this.elements.remove(row * this.columns + column);
+      } else {
+        this.elements.set(row * this.columns + column, value);
+      }
 
-          for (let i = 0; i < l; i++) {
-            alpha.setSubMatrix(alphaMin, i, 0);
-          }
+      return this;
+    }
 
-          let E = new Matrix(l, m);
-          E = D.subMatrixColumn(Hset).subtract(alpha.subMatrix(0, l - 1, 0, m - 1).mul(D.subMatrixColumn(Hset).subtract(K.subMatrixColumn(Hset))));
+    mmul(other) {
+      if (this.columns !== other.rows) {
+        // eslint-disable-next-line no-console
+        console.warn('Number of columns of left matrix are not equal to number of rows of right matrix.');
+      }
 
-          for (let j = 0; j < m; j++) {
-            D.setColumn(Hset[j], E.subMatrixColumn([j]));
+      const m = this.rows;
+      const p = other.columns;
+      const result = new SparseMatrix(m, p);
+      this.forEachNonZero((i, j, v1) => {
+        other.forEachNonZero((k, l, v2) => {
+          if (j === k) {
+            result.set(i, l, result.get(i, l) + v1 * v2);
           }
 
-          let idx2zero = [minIdx, Hset];
+          return v2;
+        });
+        return v1;
+      });
+      return result;
+    }
 
-          for (let k = 0; k < m; k++) {
-            D.set(idx2zero[0][k], idx2zero[1][k], 0);
-          }
+    kroneckerProduct(other) {
+      const m = this.rows;
+      const n = this.columns;
+      const p = other.rows;
+      const q = other.columns;
+      const result = new SparseMatrix(m * p, n * q, {
+        initialCapacity: this.cardinality * other.cardinality
+      });
+      this.forEachNonZero((i, j, v1) => {
+        other.forEachNonZero((k, l, v2) => {
+          result.set(p * i + k, q * j + l, v1 * v2);
+          return v2;
+        });
+        return v1;
+      });
+      return result;
+    }
 
-          for (let j = 0; j < m; j++) {
-            Pset[Hset[j]].splice(Pset[Hset[j]].findIndex(item => item === minIdx[j]), 1);
-          }
+    forEachNonZero(callback) {
+      this.elements.forEachPair((key, value) => {
+        const i = key / this.columns | 0;
+        const j = key % this.columns;
+        let r = callback(i, j, value);
+        if (r === false) return false; // stop iteration
 
-          L = cssls(XtX, XtY.subMatrixColumn(Hset), selection(Pset, Hset), l, m);
+        if (this.threshold && Math.abs(r) < this.threshold) r = 0;
 
-          for (let j = 0; j < m; j++) {
-            K.setColumn(Hset[j], L.subMatrixColumn([j]));
+        if (r !== value) {
+          if (r === 0) {
+            this.elements.remove(key, true);
+          } else {
+            this.elements.set(key, r);
           }
+        }
 
-          Hset = [];
+        return true;
+      });
+      this.elements.maybeShrinkCapacity();
+      return this;
+    }
 
-          for (let j = 0; j < K.columns; j++) {
-            for (let i = 0; i < l; i++) {
-              if (K.get(i, j) < 0) {
-                Hset.push(j);
-                break;
-              }
-            }
-          }
+    getNonZeros() {
+      const cardinality = this.cardinality;
+      const rows = new Array(cardinality);
+      const columns = new Array(cardinality);
+      const values = new Array(cardinality);
+      var idx = 0;
+      this.forEachNonZero((i, j, value) => {
+        rows[idx] = i;
+        columns[idx] = j;
+        values[idx] = value;
+        idx++;
+        return value;
+      });
+      return {
+        rows,
+        columns,
+        values
+      };
+    }
 
-          m = Hset.length;
-        }
+    setThreshold(newThreshold) {
+      if (newThreshold !== 0 && newThreshold !== this.threshold) {
+        this.threshold = newThreshold;
+        this.forEachNonZero((i, j, v) => v);
       }
 
-      let newParam = optimality(iter, maxIterations, XtX, XtY, Fset, Pset, W, K, l, p, D);
-      Pset = newParam.Pset;
-      Fset = newParam.Fset;
-      W = newParam.W;
+      return this;
+    }
+    /**
+     * @return {SparseMatrix} - New transposed sparse matrix
+     */
+
+
+    transpose() {
+      let trans = new SparseMatrix(this.columns, this.rows, {
+        initialCapacity: this.cardinality
+      });
+      this.forEachNonZero((i, j, value) => {
+        trans.set(j, i, value);
+        return value;
+      });
+      return trans;
+    }
+
+  }
+  SparseMatrix.prototype.klass = 'Matrix';
+  SparseMatrix.identity = SparseMatrix.eye;
+  SparseMatrix.prototype.tensorProduct = SparseMatrix.prototype.kroneckerProduct;
+  /*
+   Add dynamically instance and static methods for mathematical operations
+   */
+
+  var inplaceOperator = `
+(function %name%(value) {
+    if (typeof value === 'number') return this.%name%S(value);
+    return this.%name%M(value);
+})
+`;
+  var inplaceOperatorScalar = `
+(function %name%S(value) {
+    this.forEachNonZero((i, j, v) => v %op% value);
+    return this;
+})
+`;
+  var inplaceOperatorMatrix = `
+(function %name%M(matrix) {
+    matrix.forEachNonZero((i, j, v) => {
+        this.set(i, j, this.get(i, j) %op% v);
+        return v;
+    });
+    return this;
+})
+`;
+  var staticOperator = `
+(function %name%(matrix, value) {
+    var newMatrix = new SparseMatrix(matrix);
+    return newMatrix.%name%(value);
+})
+`;
+  var inplaceMethod = `
+(function %name%() {
+    this.forEachNonZero((i, j, v) => %method%(v));
+    return this;
+})
+`;
+  var staticMethod = `
+(function %name%(matrix) {
+    var newMatrix = new SparseMatrix(matrix);
+    return newMatrix.%name%();
+})
+`;
+  const operators = [// Arithmetic operators
+  ['+', 'add'], ['-', 'sub', 'subtract'], ['*', 'mul', 'multiply'], ['/', 'div', 'divide'], ['%', 'mod', 'modulus'], // Bitwise operators
+  ['&', 'and'], ['|', 'or'], ['^', 'xor'], ['<<', 'leftShift'], ['>>', 'signPropagatingRightShift'], ['>>>', 'rightShift', 'zeroFillRightShift']];
+
+  for (const operator of operators) {
+    for (let i = 1; i < operator.length; i++) {
+      SparseMatrix.prototype[operator[i]] = eval(fillTemplateFunction(inplaceOperator, {
+        name: operator[i],
+        op: operator[0]
+      }));
+      SparseMatrix.prototype[`${operator[i]}S`] = eval(fillTemplateFunction(inplaceOperatorScalar, {
+        name: `${operator[i]}S`,
+        op: operator[0]
+      }));
+      SparseMatrix.prototype[`${operator[i]}M`] = eval(fillTemplateFunction(inplaceOperatorMatrix, {
+        name: `${operator[i]}M`,
+        op: operator[0]
+      }));
+      SparseMatrix[operator[i]] = eval(fillTemplateFunction(staticOperator, {
+        name: operator[i]
+      }));
     }
-
-    return K;
   }
 
-  /**
-   * Fast Combinatorial Non-negative Least Squares with single Right Hand Side
-   * @param {Matrix|number[][]} X
-   * @param {number[]} y
-   * @param {object} [options={}]
-   * @param {boolean} [maxIterations] if true or empty maxIterations is set at 3 times the number of columns of X
-   * @returns {Array} k
-   */
+  var methods = [['~', 'not']];
+  ['abs', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cbrt', 'ceil', 'clz32', 'cos', 'cosh', 'exp', 'expm1', 'floor', 'fround', 'log', 'log1p', 'log10', 'log2', 'round', 'sign', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc'].forEach(function (mathMethod) {
+    methods.push([`Math.${mathMethod}`, mathMethod]);
+  });
 
-  function fcnnlsVector(X, y) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+  for (const method of methods) {
+    for (let i = 1; i < method.length; i++) {
+      SparseMatrix.prototype[method[i]] = eval(fillTemplateFunction(inplaceMethod, {
+        name: method[i],
+        method: method[0]
+      }));
+      SparseMatrix[method[i]] = eval(fillTemplateFunction(staticMethod, {
+        name: method[i]
+      }));
+    }
+  }
 
-    if (Array.isArray(y) === false) {
-      throw new TypeError('y must be a 1D Array');
+  function fillTemplateFunction(template, values) {
+    for (const i in values) {
+      template = template.replace(new RegExp(`%${i}%`, 'g'), values[i]);
     }
 
-    let Y = Matrix.columnVector(y);
-    let K = fcnnls(X, Y, options);
-    let k = K.to1DArray();
-    return k;
+    return template;
   }
 
+  function additiveSymmetric(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
+    for (; i < ii; i++) {
+      d += (a[i] - b[i]) * (a[i] - b[i]) * (a[i] + b[i]) / (a[i] * b[i]);
+    }
 
-  var index$2 = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    fcnnls: fcnnls,
-    fcnnlsVector: fcnnlsVector
-  });
+    return 2 * d;
+  }
 
-  var binarySearch = function binarySearch(haystack, needle, comparator, low, high) {
-    var mid, cmp;
-    if (low === undefined) low = 0;else {
-      low = low | 0;
-      if (low < 0 || low >= haystack.length) throw new RangeError("invalid lower bound");
-    }
-    if (high === undefined) high = haystack.length - 1;else {
-      high = high | 0;
-      if (high < low || high >= haystack.length) throw new RangeError("invalid upper bound");
-    }
+  function avg(a, b) {
+    var ii = a.length;
+    var max = 0;
+    var ans = 0;
+    var aux = 0;
 
-    while (low <= high) {
-      // The naive `low + high >>> 1` could fail for array lengths > 2**31
-      // because `>>>` converts its operands to int32. `low + (high - low >>> 1)`
-      // works for array lengths <= 2**32-1 which is also Javascript's max array
-      // length.
-      mid = low + (high - low >>> 1);
-      cmp = +comparator(haystack[mid], needle, mid, haystack); // Too low.
+    for (var i = 0; i < ii; i++) {
+      aux = Math.abs(a[i] - b[i]);
+      ans += aux;
 
-      if (cmp < 0.0) low = mid + 1; // Too high.
-      else if (cmp > 0.0) high = mid - 1; // Key found.
-        else return mid;
-    } // Key not found.
+      if (max < aux) {
+        max = aux;
+      }
+    }
 
+    return (max + ans) / 2;
+  }
 
-    return ~low;
-  };
+  function bhattacharyya(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-  function assertNumber(number) {
-    if (typeof number !== 'number' || Number.isNaN(number)) {
-      throw new TypeError('Expected a number');
+    for (var i = 0; i < ii; i++) {
+      ans += Math.sqrt(a[i] * b[i]);
     }
+
+    return -Math.log(ans);
   }
 
-  var ascending = (left, right) => {
-    assertNumber(left);
-    assertNumber(right);
-    return left - right;
-  };
+  function canberra(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-  var descending = (left, right) => {
-    assertNumber(left);
-    assertNumber(right);
-    return right - left;
-  };
+    for (var i = 0; i < ii; i++) {
+      ans += Math.abs(a[i] - b[i]) / (a[i] + b[i]);
+    }
 
-  var numSort = {
-    ascending: ascending,
-    descending: descending
-  };
+    return ans;
+  }
 
-  var index$3 = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    'default': numSort,
-    __moduleExports: numSort,
-    ascending: ascending,
-    descending: descending
-  });
+  function chebyshev(a, b) {
+    var ii = a.length;
+    var max = 0;
+    var aux = 0;
 
-  const largestPrime = 0x7fffffff;
-  const primeNumbers = [// chunk #0
-  largestPrime, // 2^31-1
-  // chunk #1
-  5, 11, 23, 47, 97, 197, 397, 797, 1597, 3203, 6421, 12853, 25717, 51437, 102877, 205759, 411527, 823117, 1646237, 3292489, 6584983, 13169977, 26339969, 52679969, 105359939, 210719881, 421439783, 842879579, 1685759167, // chunk #2
-  433, 877, 1759, 3527, 7057, 14143, 28289, 56591, 113189, 226379, 452759, 905551, 1811107, 3622219, 7244441, 14488931, 28977863, 57955739, 115911563, 231823147, 463646329, 927292699, 1854585413, // chunk #3
-  953, 1907, 3821, 7643, 15287, 30577, 61169, 122347, 244703, 489407, 978821, 1957651, 3915341, 7830701, 15661423, 31322867, 62645741, 125291483, 250582987, 501165979, 1002331963, 2004663929, // chunk #4
-  1039, 2081, 4177, 8363, 16729, 33461, 66923, 133853, 267713, 535481, 1070981, 2141977, 4283963, 8567929, 17135863, 34271747, 68543509, 137087021, 274174111, 548348231, 1096696463, // chunk #5
-  31, 67, 137, 277, 557, 1117, 2237, 4481, 8963, 17929, 35863, 71741, 143483, 286973, 573953, 1147921, 2295859, 4591721, 9183457, 18366923, 36733847, 73467739, 146935499, 293871013, 587742049, 1175484103, // chunk #6
-  599, 1201, 2411, 4831, 9677, 19373, 38747, 77509, 155027, 310081, 620171, 1240361, 2480729, 4961459, 9922933, 19845871, 39691759, 79383533, 158767069, 317534141, 635068283, 1270136683, // chunk #7
-  311, 631, 1277, 2557, 5119, 10243, 20507, 41017, 82037, 164089, 328213, 656429, 1312867, 2625761, 5251529, 10503061, 21006137, 42012281, 84024581, 168049163, 336098327, 672196673, 1344393353, // chunk #8
-  3, 7, 17, 37, 79, 163, 331, 673, 1361, 2729, 5471, 10949, 21911, 43853, 87719, 175447, 350899, 701819, 1403641, 2807303, 5614657, 11229331, 22458671, 44917381, 89834777, 179669557, 359339171, 718678369, 1437356741, // chunk #9
-  43, 89, 179, 359, 719, 1439, 2879, 5779, 11579, 23159, 46327, 92657, 185323, 370661, 741337, 1482707, 2965421, 5930887, 11861791, 23723597, 47447201, 94894427, 189788857, 379577741, 759155483, 1518310967, // chunk #10
-  379, 761, 1523, 3049, 6101, 12203, 24407, 48817, 97649, 195311, 390647, 781301, 1562611, 3125257, 6250537, 12501169, 25002389, 50004791, 100009607, 200019221, 400038451, 800076929, 1600153859, // chunk #11
-  13, 29, 59, 127, 257, 521, 1049, 2099, 4201, 8419, 16843, 33703, 67409, 134837, 269683, 539389, 1078787, 2157587, 4315183, 8630387, 17260781, 34521589, 69043189, 138086407, 276172823, 552345671, 1104691373, // chunk #12
-  19, 41, 83, 167, 337, 677, 1361, 2729, 5471, 10949, 21911, 43853, 87719, 175447, 350899, 701819, 1403641, 2807303, 5614657, 11229331, 22458671, 44917381, 89834777, 179669557, 359339171, 718678369, 1437356741, // chunk #13
-  53, 107, 223, 449, 907, 1823, 3659, 7321, 14653, 29311, 58631, 117269, 234539, 469099, 938207, 1876417, 3752839, 7505681, 15011389, 30022781, 60045577, 120091177, 240182359, 480364727, 960729461, 1921458943];
-  primeNumbers.sort(ascending);
-  function nextPrime(value) {
-    let index = binarySearch(primeNumbers, value, ascending);
+    for (var i = 0; i < ii; i++) {
+      aux = Math.abs(a[i] - b[i]);
 
-    if (index < 0) {
-      index = ~index;
+      if (max < aux) {
+        max = aux;
+      }
     }
 
-    return primeNumbers[index];
+    return max;
   }
 
-  const FREE = 0;
-  const FULL = 1;
-  const REMOVED = 2;
-  const defaultInitialCapacity = 150;
-  const defaultMinLoadFactor = 1 / 6;
-  const defaultMaxLoadFactor = 2 / 3;
-  class HashTable {
-    constructor() {
-      let options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
+  function clark(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
-      if (options instanceof HashTable) {
-        this.table = options.table.slice();
-        this.values = options.values.slice();
-        this.state = options.state.slice();
-        this.minLoadFactor = options.minLoadFactor;
-        this.maxLoadFactor = options.maxLoadFactor;
-        this.distinct = options.distinct;
-        this.freeEntries = options.freeEntries;
-        this.lowWaterMark = options.lowWaterMark;
-        this.highWaterMark = options.maxLoadFactor;
-        return;
-      }
+    for (; i < ii; i++) {
+      d += Math.sqrt((a[i] - b[i]) * (a[i] - b[i]) / ((a[i] + b[i]) * (a[i] + b[i])));
+    }
 
-      const initialCapacity = options.initialCapacity === undefined ? defaultInitialCapacity : options.initialCapacity;
+    return 2 * d;
+  }
 
-      if (initialCapacity < 0) {
-        throw new RangeError("initial capacity must not be less than zero: ".concat(initialCapacity));
-      }
+  function czekanowskiSimilarity(a, b) {
+    var up = 0;
+    var down = 0;
 
-      const minLoadFactor = options.minLoadFactor === undefined ? defaultMinLoadFactor : options.minLoadFactor;
-      const maxLoadFactor = options.maxLoadFactor === undefined ? defaultMaxLoadFactor : options.maxLoadFactor;
+    for (var i = 0; i < a.length; i++) {
+      up += Math.min(a[i], b[i]);
+      down += a[i] + b[i];
+    }
 
-      if (minLoadFactor < 0 || minLoadFactor >= 1) {
-        throw new RangeError("invalid minLoadFactor: ".concat(minLoadFactor));
-      }
+    return 2 * up / down;
+  }
 
-      if (maxLoadFactor <= 0 || maxLoadFactor >= 1) {
-        throw new RangeError("invalid maxLoadFactor: ".concat(maxLoadFactor));
-      }
+  function czekanowskiDistance(a, b) {
+    return 1 - czekanowskiSimilarity(a, b);
+  }
 
-      if (minLoadFactor >= maxLoadFactor) {
-        throw new RangeError("minLoadFactor (".concat(minLoadFactor, ") must be smaller than maxLoadFactor (").concat(maxLoadFactor, ")"));
-      }
+  function dice(a, b) {
+    var ii = a.length;
+    var p = 0;
+    var q1 = 0;
+    var q2 = 0;
 
-      let capacity = initialCapacity; // User wants to put at least capacity elements. We need to choose the size based on the maxLoadFactor to
-      // avoid the need to rehash before this capacity is reached.
-      // actualCapacity * maxLoadFactor >= capacity
+    for (var i = 0; i < ii; i++) {
+      p += a[i] * a[i];
+      q1 += b[i] * b[i];
+      q2 += (a[i] - b[i]) * (a[i] - b[i]);
+    }
 
-      capacity = capacity / maxLoadFactor | 0;
-      capacity = nextPrime(capacity);
-      if (capacity === 0) capacity = 1;
-      this.table = newArray$1(capacity);
-      this.values = newArray$1(capacity);
-      this.state = newArray$1(capacity);
-      this.minLoadFactor = minLoadFactor;
+    return q2 / (p + q1);
+  }
 
-      if (capacity === largestPrime) {
-        this.maxLoadFactor = 1;
-      } else {
-        this.maxLoadFactor = maxLoadFactor;
-      }
+  function divergence(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
-      this.distinct = 0;
-      this.freeEntries = capacity;
-      this.lowWaterMark = 0;
-      this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);
+    for (; i < ii; i++) {
+      d += (a[i] - b[i]) * (a[i] - b[i]) / ((a[i] + b[i]) * (a[i] + b[i]));
     }
 
-    clone() {
-      return new HashTable(this);
+    return 2 * d;
+  }
+
+  function fidelity(a, b) {
+    var ii = a.length;
+    var ans = 0;
+
+    for (var i = 0; i < ii; i++) {
+      ans += Math.sqrt(a[i] * b[i]);
     }
 
-    get size() {
-      return this.distinct;
-    }
+    return ans;
+  }
 
-    get(key) {
-      const i = this.indexOfKey(key);
-      if (i < 0) return 0;
-      return this.values[i];
+  function gower(a, b) {
+    var ii = a.length;
+    var ans = 0;
+
+    for (var i = 0; i < ii; i++) {
+      ans += Math.abs(a[i] - b[i]);
     }
 
-    set(key, value) {
-      let i = this.indexOfInsertion(key);
+    return ans / ii;
+  }
 
-      if (i < 0) {
-        i = -i - 1;
-        this.values[i] = value;
-        return false;
-      }
+  function harmonicMean(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-      if (this.distinct > this.highWaterMark) {
-        const newCapacity = chooseGrowCapacity(this.distinct + 1, this.minLoadFactor, this.maxLoadFactor);
-        this.rehash(newCapacity);
-        return this.set(key, value);
-      }
+    for (var i = 0; i < ii; i++) {
+      ans += a[i] * b[i] / (a[i] + b[i]);
+    }
 
-      this.table[i] = key;
-      this.values[i] = value;
-      if (this.state[i] === FREE) this.freeEntries--;
-      this.state[i] = FULL;
-      this.distinct++;
+    return 2 * ans;
+  }
 
-      if (this.freeEntries < 1) {
-        const newCapacity = chooseGrowCapacity(this.distinct + 1, this.minLoadFactor, this.maxLoadFactor);
-        this.rehash(newCapacity);
-      }
+  function hellinger(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-      return true;
+    for (var i = 0; i < ii; i++) {
+      ans += Math.sqrt(a[i] * b[i]);
     }
 
-    remove(key, noRehash) {
-      const i = this.indexOfKey(key);
-      if (i < 0) return false;
-      this.state[i] = REMOVED;
-      this.distinct--;
-      if (!noRehash) this.maybeShrinkCapacity();
-      return true;
-    }
+    return 2 * Math.sqrt(1 - ans);
+  }
 
-    delete(key, noRehash) {
-      const i = this.indexOfKey(key);
-      if (i < 0) return false;
-      this.state[i] = FREE;
-      this.distinct--;
-      if (!noRehash) this.maybeShrinkCapacity();
-      return true;
-    }
+  function innerProduct(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-    maybeShrinkCapacity() {
-      if (this.distinct < this.lowWaterMark) {
-        const newCapacity = chooseShrinkCapacity(this.distinct, this.minLoadFactor, this.maxLoadFactor);
-        this.rehash(newCapacity);
-      }
+    for (var i = 0; i < ii; i++) {
+      ans += a[i] * b[i];
     }
 
-    containsKey(key) {
-      return this.indexOfKey(key) >= 0;
+    return ans;
+  }
+
+  function intersection(a, b) {
+    var ii = a.length;
+    var ans = 0;
+
+    for (var i = 0; i < ii; i++) {
+      ans += Math.min(a[i], b[i]);
     }
 
-    indexOfKey(key) {
-      const table = this.table;
-      const state = this.state;
-      const length = this.table.length;
-      const hash = key & 0x7fffffff;
-      let i = hash % length;
-      let decrement = hash % (length - 2);
-      if (decrement === 0) decrement = 1;
+    return 1 - ans;
+  }
 
-      while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {
-        i -= decrement;
-        if (i < 0) i += length;
-      }
+  function jaccard(a, b) {
+    var ii = a.length;
+    var p1 = 0;
+    var p2 = 0;
+    var q1 = 0;
+    var q2 = 0;
 
-      if (state[i] === FREE) return -1;
-      return i;
+    for (var i = 0; i < ii; i++) {
+      p1 += a[i] * b[i];
+      p2 += a[i] * a[i];
+      q1 += b[i] * b[i];
+      q2 += (a[i] - b[i]) * (a[i] - b[i]);
     }
 
-    containsValue(value) {
-      return this.indexOfValue(value) >= 0;
+    return q2 / (p2 + q1 - p1);
+  }
+
+  function jeffreys(a, b) {
+    var ii = a.length;
+    var ans = 0;
+
+    for (var i = 0; i < ii; i++) {
+      ans += (a[i] - b[i]) * Math.log(a[i] / b[i]);
     }
 
-    indexOfValue(value) {
-      const values = this.values;
-      const state = this.state;
+    return ans;
+  }
 
-      for (var i = 0; i < state.length; i++) {
-        if (state[i] === FULL && values[i] === value) {
-          return i;
-        }
-      }
+  function jensenDifference(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-      return -1;
+    for (var i = 0; i < ii; i++) {
+      ans += (a[i] * Math.log(a[i]) + b[i] * Math.log(b[i])) / 2 - (a[i] + b[i]) / 2 * Math.log((a[i] + b[i]) / 2);
     }
 
-    indexOfInsertion(key) {
-      const table = this.table;
-      const state = this.state;
-      const length = table.length;
-      const hash = key & 0x7fffffff;
-      let i = hash % length;
-      let decrement = hash % (length - 2);
-      if (decrement === 0) decrement = 1;
-
-      while (state[i] === FULL && table[i] !== key) {
-        i -= decrement;
-        if (i < 0) i += length;
-      }
+    return ans;
+  }
 
-      if (state[i] === REMOVED) {
-        const j = i;
+  function jensenShannon(a, b) {
+    var ii = a.length;
+    var p = 0;
+    var q = 0;
 
-        while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {
-          i -= decrement;
-          if (i < 0) i += length;
-        }
+    for (var i = 0; i < ii; i++) {
+      p += a[i] * Math.log(2 * a[i] / (a[i] + b[i]));
+      q += b[i] * Math.log(2 * b[i] / (a[i] + b[i]));
+    }
 
-        if (state[i] === FREE) i = j;
-      }
+    return (p + q) / 2;
+  }
 
-      if (state[i] === FULL) {
-        return -i - 1;
-      }
+  function kdivergence(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-      return i;
+    for (var i = 0; i < ii; i++) {
+      ans += a[i] * Math.log(2 * a[i] / (a[i] + b[i]));
     }
 
-    ensureCapacity(minCapacity) {
-      if (this.table.length < minCapacity) {
-        const newCapacity = nextPrime(minCapacity);
-        this.rehash(newCapacity);
-      }
-    }
+    return ans;
+  }
 
-    rehash(newCapacity) {
-      const oldCapacity = this.table.length;
-      if (newCapacity <= this.distinct) throw new Error('Unexpected');
-      const oldTable = this.table;
-      const oldValues = this.values;
-      const oldState = this.state;
-      const newTable = newArray$1(newCapacity);
-      const newValues = newArray$1(newCapacity);
-      const newState = newArray$1(newCapacity);
-      this.lowWaterMark = chooseLowWaterMark(newCapacity, this.minLoadFactor);
-      this.highWaterMark = chooseHighWaterMark(newCapacity, this.maxLoadFactor);
-      this.table = newTable;
-      this.values = newValues;
-      this.state = newState;
-      this.freeEntries = newCapacity - this.distinct;
+  function kulczynski(a, b) {
+    var ii = a.length;
+    var up = 0;
+    var down = 0;
 
-      for (var i = 0; i < oldCapacity; i++) {
-        if (oldState[i] === FULL) {
-          var element = oldTable[i];
-          var index = this.indexOfInsertion(element);
-          newTable[index] = element;
-          newValues[index] = oldValues[i];
-          newState[index] = FULL;
-        }
-      }
+    for (var i = 0; i < ii; i++) {
+      up += Math.abs(a[i] - b[i]);
+      down += Math.min(a[i], b[i]);
     }
 
-    forEachKey(callback) {
-      for (var i = 0; i < this.state.length; i++) {
-        if (this.state[i] === FULL) {
-          if (!callback(this.table[i])) return false;
-        }
-      }
+    return up / down;
+  }
 
-      return true;
+  function kullbackLeibler(a, b) {
+    var ii = a.length;
+    var ans = 0;
+
+    for (var i = 0; i < ii; i++) {
+      ans += a[i] * Math.log(a[i] / b[i]);
     }
 
-    forEachValue(callback) {
-      for (var i = 0; i < this.state.length; i++) {
-        if (this.state[i] === FULL) {
-          if (!callback(this.values[i])) return false;
-        }
-      }
+    return ans;
+  }
 
-      return true;
+  function kumarHassebrook(a, b) {
+    var ii = a.length;
+    var p = 0;
+    var p2 = 0;
+    var q2 = 0;
+
+    for (var i = 0; i < ii; i++) {
+      p += a[i] * b[i];
+      p2 += a[i] * a[i];
+      q2 += b[i] * b[i];
     }
 
-    forEachPair(callback) {
-      for (var i = 0; i < this.state.length; i++) {
-        if (this.state[i] === FULL) {
-          if (!callback(this.table[i], this.values[i])) return false;
-        }
-      }
+    return p / (p2 + q2 - p);
+  }
 
-      return true;
+  function kumarJohnson(a, b) {
+    var ii = a.length;
+    var ans = 0;
+
+    for (var i = 0; i < ii; i++) {
+      ans += Math.pow(a[i] * a[i] - b[i] * b[i], 2) / (2 * Math.pow(a[i] * b[i], 1.5));
     }
 
+    return ans;
   }
 
-  function chooseLowWaterMark(capacity, minLoad) {
-    return capacity * minLoad | 0;
-  }
+  function lorentzian(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-  function chooseHighWaterMark(capacity, maxLoad) {
-    return Math.min(capacity - 2, capacity * maxLoad | 0);
-  }
+    for (var i = 0; i < ii; i++) {
+      ans += Math.log(Math.abs(a[i] - b[i]) + 1);
+    }
 
-  function chooseGrowCapacity(size, minLoad, maxLoad) {
-    return nextPrime(Math.max(size + 1, 4 * size / (3 * minLoad + maxLoad) | 0));
+    return ans;
   }
 
-  function chooseShrinkCapacity(size, minLoad, maxLoad) {
-    return nextPrime(Math.max(size + 1, 4 * size / (minLoad + 3 * maxLoad) | 0));
-  }
+  function manhattan(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
-  function newArray$1(size) {
-    return Array(size).fill(0);
+    for (; i < ii; i++) {
+      d += Math.abs(a[i] - b[i]);
+    }
+
+    return d;
   }
 
-  class SparseMatrix {
-    constructor(rows, columns) {
-      let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+  function matusita(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-      if (rows instanceof SparseMatrix) {
-        // clone
-        const other = rows;
+    for (var i = 0; i < ii; i++) {
+      ans += Math.sqrt(a[i] * b[i]);
+    }
 
-        this._init(other.rows, other.columns, other.elements.clone(), other.threshold);
+    return Math.sqrt(2 - 2 * ans);
+  }
 
-        return;
-      }
+  function minkowski(a, b, p) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
-      if (Array.isArray(rows)) {
-        const matrix = rows;
-        rows = matrix.length;
-        options = columns || {};
-        columns = matrix[0].length;
+    for (; i < ii; i++) {
+      d += Math.pow(Math.abs(a[i] - b[i]), p);
+    }
 
-        this._init(rows, columns, new HashTable(options), options.threshold);
+    return Math.pow(d, 1 / p);
+  }
 
-        for (var i = 0; i < rows; i++) {
-          for (var j = 0; j < columns; j++) {
-            var value = matrix[i][j];
-            if (this.threshold && Math.abs(value) < this.threshold) value = 0;
+  function motyka(a, b) {
+    var ii = a.length;
+    var up = 0;
+    var down = 0;
 
-            if (value !== 0) {
-              this.elements.set(i * columns + j, matrix[i][j]);
-            }
-          }
-        }
-      } else {
-        this._init(rows, columns, new HashTable(options), options.threshold);
-      }
+    for (var i = 0; i < ii; i++) {
+      up += Math.min(a[i], b[i]);
+      down += a[i] + b[i];
     }
 
-    _init(rows, columns, elements, threshold) {
-      this.rows = rows;
-      this.columns = columns;
-      this.elements = elements;
-      this.threshold = threshold || 0;
+    return 1 - up / down;
+  }
+
+  function neyman(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
+
+    for (; i < ii; i++) {
+      d += (a[i] - b[i]) * (a[i] - b[i]) / a[i];
     }
 
-    static eye() {
-      let rows = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 1;
-      let columns = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : rows;
-      const min = Math.min(rows, columns);
-      const matrix = new SparseMatrix(rows, columns, {
-        initialCapacity: min
-      });
+    return d;
+  }
 
-      for (var i = 0; i < min; i++) {
-        matrix.set(i, i, 1);
-      }
+  function pearson(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
-      return matrix;
+    for (; i < ii; i++) {
+      d += (a[i] - b[i]) * (a[i] - b[i]) / b[i];
     }
 
-    clone() {
-      return new SparseMatrix(this);
-    }
+    return d;
+  }
 
-    to2DArray() {
-      const copy = new Array(this.rows);
+  function probabilisticSymmetric(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
-      for (var i = 0; i < this.rows; i++) {
-        copy[i] = new Array(this.columns);
+    for (; i < ii; i++) {
+      d += (a[i] - b[i]) * (a[i] - b[i]) / (a[i] + b[i]);
+    }
 
-        for (var j = 0; j < this.columns; j++) {
-          copy[i][j] = this.get(i, j);
-        }
-      }
+    return 2 * d;
+  }
 
-      return copy;
-    }
+  function ruzicka(a, b) {
+    var ii = a.length;
+    var up = 0;
+    var down = 0;
 
-    isSquare() {
-      return this.rows === this.columns;
+    for (var i = 0; i < ii; i++) {
+      up += Math.min(a[i], b[i]);
+      down += Math.max(a[i], b[i]);
     }
 
-    isSymmetric() {
-      if (!this.isSquare()) return false;
-      var symmetric = true;
-      this.forEachNonZero((i, j, v) => {
-        if (this.get(j, i) !== v) {
-          symmetric = false;
-          return false;
-        }
+    return up / down;
+  }
 
-        return v;
-      });
-      return symmetric;
+  function soergel(a, b) {
+    var ii = a.length;
+    var up = 0;
+    var down = 0;
+
+    for (var i = 0; i < ii; i++) {
+      up += Math.abs(a[i] - b[i]);
+      down += Math.max(a[i], b[i]);
     }
-    /**
-     * Search for the wither band in the main diagonals
-     * @return {number}
-     */
 
+    return up / down;
+  }
 
-    bandWidth() {
-      let min = this.columns;
-      let max = -1;
-      this.forEachNonZero((i, j, v) => {
-        let diff = i - j;
-        min = Math.min(min, diff);
-        max = Math.max(max, diff);
-        return v;
-      });
-      return max - min;
+  function sorensen(a, b) {
+    var ii = a.length;
+    var up = 0;
+    var down = 0;
+
+    for (var i = 0; i < ii; i++) {
+      up += Math.abs(a[i] - b[i]);
+      down += a[i] + b[i];
     }
-    /**
-     * Test if a matrix is consider banded using a threshold
-     * @param {number} width
-     * @return {boolean}
-     */
 
+    return up / down;
+  }
 
-    isBanded(width) {
-      let bandWidth = this.bandWidth();
-      return bandWidth <= width;
-    }
+  function squared(a, b) {
+    var i = 0;
+    var ii = a.length;
+    var d = 0;
 
-    get cardinality() {
-      return this.elements.size;
+    for (; i < ii; i++) {
+      d += (a[i] - b[i]) * (a[i] - b[i]) / (a[i] + b[i]);
     }
 
-    get size() {
-      return this.rows * this.columns;
-    }
+    return d;
+  }
 
-    get(row, column) {
-      return this.elements.get(row * this.columns + column);
+  function squaredChord(a, b) {
+    var ii = a.length;
+    var ans = 0;
+
+    for (var i = 0; i < ii; i++) {
+      ans += (Math.sqrt(a[i]) - Math.sqrt(b[i])) * (Math.sqrt(a[i]) - Math.sqrt(b[i]));
     }
 
-    set(row, column, value) {
-      if (this.threshold && Math.abs(value) < this.threshold) value = 0;
+    return ans;
+  }
 
-      if (value === 0) {
-        this.elements.remove(row * this.columns + column);
-      } else {
-        this.elements.set(row * this.columns + column, value);
-      }
+  function taneja(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-      return this;
+    for (var i = 0; i < ii; i++) {
+      ans += (a[i] + b[i]) / 2 * Math.log((a[i] + b[i]) / (2 * Math.sqrt(a[i] * b[i])));
     }
 
-    mmul(other) {
-      if (this.columns !== other.rows) {
-        // eslint-disable-next-line no-console
-        console.warn('Number of columns of left matrix are not equal to number of rows of right matrix.');
-      }
-
-      const m = this.rows;
-      const p = other.columns;
-      const result = new SparseMatrix(m, p);
-      this.forEachNonZero((i, j, v1) => {
-        other.forEachNonZero((k, l, v2) => {
-          if (j === k) {
-            result.set(i, l, result.get(i, l) + v1 * v2);
-          }
+    return ans;
+  }
 
-          return v2;
-        });
-        return v1;
-      });
-      return result;
-    }
+  function tanimoto(a, b, bitvector) {
+    if (bitvector) {
+      var inter = 0;
+      var union = 0;
 
-    kroneckerProduct(other) {
-      const m = this.rows;
-      const n = this.columns;
-      const p = other.rows;
-      const q = other.columns;
-      const result = new SparseMatrix(m * p, n * q, {
-        initialCapacity: this.cardinality * other.cardinality
-      });
-      this.forEachNonZero((i, j, v1) => {
-        other.forEachNonZero((k, l, v2) => {
-          result.set(p * i + k, q * j + l, v1 * v2);
-          return v2;
-        });
-        return v1;
-      });
-      return result;
-    }
+      for (var j = 0; j < a.length; j++) {
+        inter += a[j] && b[j];
+        union += a[j] || b[j];
+      }
 
-    forEachNonZero(callback) {
-      this.elements.forEachPair((key, value) => {
-        const i = key / this.columns | 0;
-        const j = key % this.columns;
-        let r = callback(i, j, value);
-        if (r === false) return false; // stop iteration
+      if (union === 0) {
+        return 1;
+      }
 
-        if (this.threshold && Math.abs(r) < this.threshold) r = 0;
+      return inter / union;
+    } else {
+      var ii = a.length;
+      var p = 0;
+      var q = 0;
+      var m = 0;
 
-        if (r !== value) {
-          if (r === 0) {
-            this.elements.remove(key, true);
-          } else {
-            this.elements.set(key, r);
-          }
-        }
+      for (var i = 0; i < ii; i++) {
+        p += a[i];
+        q += b[i];
+        m += Math.min(a[i], b[i]);
+      }
 
-        return true;
-      });
-      this.elements.maybeShrinkCapacity();
-      return this;
+      return 1 - (p + q - 2 * m) / (p + q - m);
     }
+  }
 
-    getNonZeros() {
-      const cardinality = this.cardinality;
-      const rows = new Array(cardinality);
-      const columns = new Array(cardinality);
-      const values = new Array(cardinality);
-      var idx = 0;
-      this.forEachNonZero((i, j, value) => {
-        rows[idx] = i;
-        columns[idx] = j;
-        values[idx] = value;
-        idx++;
-        return value;
-      });
-      return {
-        rows,
-        columns,
-        values
-      };
-    }
+  function tanimoto$1(a, b, bitvector) {
+    if (bitvector) {
+      return 1 - tanimoto(a, b, bitvector);
+    } else {
+      var ii = a.length;
+      var p = 0;
+      var q = 0;
+      var m = 0;
 
-    setThreshold(newThreshold) {
-      if (newThreshold !== 0 && newThreshold !== this.threshold) {
-        this.threshold = newThreshold;
-        this.forEachNonZero((i, j, v) => v);
+      for (var i = 0; i < ii; i++) {
+        p += a[i];
+        q += b[i];
+        m += Math.min(a[i], b[i]);
       }
 
-      return this;
+      return (p + q - 2 * m) / (p + q - m);
     }
-    /**
-     * @return {SparseMatrix} - New transposed sparse matrix
-     */
+  }
 
+  function topsoe(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-    transpose() {
-      let trans = new SparseMatrix(this.columns, this.rows, {
-        initialCapacity: this.cardinality
-      });
-      this.forEachNonZero((i, j, value) => {
-        trans.set(j, i, value);
-        return value;
-      });
-      return trans;
+    for (var i = 0; i < ii; i++) {
+      ans += a[i] * Math.log(2 * a[i] / (a[i] + b[i])) + b[i] * Math.log(2 * b[i] / (a[i] + b[i]));
     }
 
+    return ans;
   }
-  SparseMatrix.prototype.klass = 'Matrix';
-  SparseMatrix.identity = SparseMatrix.eye;
-  SparseMatrix.prototype.tensorProduct = SparseMatrix.prototype.kroneckerProduct;
-  /*
-   Add dynamically instance and static methods for mathematical operations
-   */
 
-  var inplaceOperator = "\n(function %name%(value) {\n    if (typeof value === 'number') return this.%name%S(value);\n    return this.%name%M(value);\n})\n";
-  var inplaceOperatorScalar = "\n(function %name%S(value) {\n    this.forEachNonZero((i, j, v) => v %op% value);\n    return this;\n})\n";
-  var inplaceOperatorMatrix = "\n(function %name%M(matrix) {\n    matrix.forEachNonZero((i, j, v) => {\n        this.set(i, j, this.get(i, j) %op% v);\n        return v;\n    });\n    return this;\n})\n";
-  var staticOperator = "\n(function %name%(matrix, value) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%(value);\n})\n";
-  var inplaceMethod = "\n(function %name%() {\n    this.forEachNonZero((i, j, v) => %method%(v));\n    return this;\n})\n";
-  var staticMethod = "\n(function %name%(matrix) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%();\n})\n";
-  const operators = [// Arithmetic operators
-  ['+', 'add'], ['-', 'sub', 'subtract'], ['*', 'mul', 'multiply'], ['/', 'div', 'divide'], ['%', 'mod', 'modulus'], // Bitwise operators
-  ['&', 'and'], ['|', 'or'], ['^', 'xor'], ['<<', 'leftShift'], ['>>', 'signPropagatingRightShift'], ['>>>', 'rightShift', 'zeroFillRightShift']];
+  function waveHedges(a, b) {
+    var ii = a.length;
+    var ans = 0;
 
-  for (const operator of operators) {
-    for (let i = 1; i < operator.length; i++) {
-      SparseMatrix.prototype[operator[i]] = eval(fillTemplateFunction(inplaceOperator, {
-        name: operator[i],
-        op: operator[0]
-      }));
-      SparseMatrix.prototype["".concat(operator[i], "S")] = eval(fillTemplateFunction(inplaceOperatorScalar, {
-        name: "".concat(operator[i], "S"),
-        op: operator[0]
-      }));
-      SparseMatrix.prototype["".concat(operator[i], "M")] = eval(fillTemplateFunction(inplaceOperatorMatrix, {
-        name: "".concat(operator[i], "M"),
-        op: operator[0]
-      }));
-      SparseMatrix[operator[i]] = eval(fillTemplateFunction(staticOperator, {
-        name: operator[i]
-      }));
+    for (var i = 0; i < ii; i++) {
+      ans += 1 - Math.min(a[i], b[i]) / Math.max(a[i], b[i]);
     }
+
+    return ans;
   }
 
-  var methods = [['~', 'not']];
-  ['abs', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cbrt', 'ceil', 'clz32', 'cos', 'cosh', 'exp', 'expm1', 'floor', 'fround', 'log', 'log1p', 'log10', 'log2', 'round', 'sign', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc'].forEach(function (mathMethod) {
-    methods.push(["Math.".concat(mathMethod), mathMethod]);
+  var distances = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    euclidean: euclidean,
+    squaredEuclidean: squaredEuclidean,
+    additiveSymmetric: additiveSymmetric,
+    avg: avg,
+    bhattacharyya: bhattacharyya,
+    canberra: canberra,
+    chebyshev: chebyshev,
+    clark: clark,
+    czekanowski: czekanowskiDistance,
+    dice: dice,
+    divergence: divergence,
+    fidelity: fidelity,
+    gower: gower,
+    harmonicMean: harmonicMean,
+    hellinger: hellinger,
+    innerProduct: innerProduct,
+    intersection: intersection,
+    jaccard: jaccard,
+    jeffreys: jeffreys,
+    jensenDifference: jensenDifference,
+    jensenShannon: jensenShannon,
+    kdivergence: kdivergence,
+    kulczynski: kulczynski,
+    kullbackLeibler: kullbackLeibler,
+    kumarHassebrook: kumarHassebrook,
+    kumarJohnson: kumarJohnson,
+    lorentzian: lorentzian,
+    manhattan: manhattan,
+    matusita: matusita,
+    minkowski: minkowski,
+    motyka: motyka,
+    neyman: neyman,
+    pearson: pearson,
+    probabilisticSymmetric: probabilisticSymmetric,
+    ruzicka: ruzicka,
+    soergel: soergel,
+    sorensen: sorensen,
+    squared: squared,
+    squaredChord: squaredChord,
+    taneja: taneja,
+    tanimoto: tanimoto$1,
+    topsoe: topsoe,
+    waveHedges: waveHedges
   });
 
-  for (const method of methods) {
-    for (let i = 1; i < method.length; i++) {
-      SparseMatrix.prototype[method[i]] = eval(fillTemplateFunction(inplaceMethod, {
-        name: method[i],
-        method: method[0]
-      }));
-      SparseMatrix[method[i]] = eval(fillTemplateFunction(staticMethod, {
-        name: method[i]
-      }));
-    }
-  }
-
-  function fillTemplateFunction(template, values) {
-    for (const i in values) {
-      template = template.replace(new RegExp("%".concat(i, "%"), 'g'), values[i]);
-    }
+  /**
+   * Function that creates the tree
+   * @param {Array<Array<number>>} spectrum
+   * @param {object} [options]
+   * @return {Tree|null}
+   * left and right have the same structure than the parent,
+   * or are null if they are leaves
+   */
 
-    return template;
+  function createTree(spectrum, options = {}) {
+    var X = spectrum[0];
+    const {
+      minWindow = 0.16,
+      threshold = 0.01,
+      from = X[0],
+      to = X[X.length - 1]
+    } = options;
+    return mainCreateTree(spectrum[0], spectrum[1], from, to, minWindow, threshold);
   }
 
-  function additiveSymmetric(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
+  function mainCreateTree(X, Y, from, to, minWindow, threshold) {
+    if (to - from < minWindow) {
+      return null;
+    } // search first point
 
-    for (; i < ii; i++) {
-      d += (a[i] - b[i]) * (a[i] - b[i]) * (a[i] + b[i]) / (a[i] * b[i]);
-    }
 
-    return 2 * d;
-  }
+    var start = binarySearch(X, from, ascending);
 
-  function avg(a, b) {
-    var ii = a.length;
-    var max = 0;
-    var ans = 0;
-    var aux = 0;
+    if (start < 0) {
+      start = ~start;
+    } // stop at last point
 
-    for (var i = 0; i < ii; i++) {
-      aux = Math.abs(a[i] - b[i]);
-      ans += aux;
 
-      if (max < aux) {
-        max = aux;
+    var sum = 0;
+    var center = 0;
+
+    for (var i = start; i < X.length; i++) {
+      if (X[i] >= to) {
+        break;
       }
+
+      sum += Y[i];
+      center += X[i] * Y[i];
     }
 
-    return (max + ans) / 2;
-  }
+    if (sum < threshold) {
+      return null;
+    }
 
-  function bhattacharyya(a, b) {
-    var ii = a.length;
-    var ans = 0;
+    center /= sum;
 
-    for (var i = 0; i < ii; i++) {
-      ans += Math.sqrt(a[i] * b[i]);
+    if (center - from < 1e-6 || to - center < 1e-6) {
+      return null;
     }
 
-    return -Math.log(ans);
+    if (center - from < minWindow / 4) {
+      return mainCreateTree(X, Y, center, to, minWindow, threshold);
+    } else {
+      if (to - center < minWindow / 4) {
+        return mainCreateTree(X, Y, from, center, minWindow, threshold);
+      } else {
+        return new Tree(sum, center, mainCreateTree(X, Y, from, center, minWindow, threshold), mainCreateTree(X, Y, center, to, minWindow, threshold));
+      }
+    }
   }
 
-  function canberra(a, b) {
-    var ii = a.length;
-    var ans = 0;
-
-    for (var i = 0; i < ii; i++) {
-      ans += Math.abs(a[i] - b[i]) / (a[i] + b[i]);
+  class Tree {
+    constructor(sum, center, left, right) {
+      this.sum = sum;
+      this.center = center;
+      this.left = left;
+      this.right = right;
     }
 
-    return ans;
   }
 
-  function chebyshev(a, b) {
-    var ii = a.length;
-    var max = 0;
-    var aux = 0;
+  /**
+   * Similarity between two nodes
+   * @param {Tree|Array<Array<number>>} a - tree A node
+   * @param {Tree|Array<Array<number>>} b - tree B node
+   * @param {object} [options]
+   * @return {number} similarity measure between tree nodes
+   */
 
-    for (var i = 0; i < ii; i++) {
-      aux = Math.abs(a[i] - b[i]);
+  function getSimilarity(a, b, options = {}) {
+    const {
+      alpha = 0.1,
+      beta = 0.33,
+      gamma = 0.001
+    } = options;
 
-      if (max < aux) {
-        max = aux;
-      }
+    if (a === null || b === null) {
+      return 0;
     }
 
-    return max;
-  }
-
-  function clark(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
-
-    for (; i < ii; i++) {
-      d += Math.sqrt((a[i] - b[i]) * (a[i] - b[i]) / ((a[i] + b[i]) * (a[i] + b[i])));
+    if (Array.isArray(a)) {
+      a = createTree(a);
     }
 
-    return 2 * d;
-  }
-
-  function czekanowskiSimilarity(a, b) {
-    var up = 0;
-    var down = 0;
-
-    for (var i = 0; i < a.length; i++) {
-      up += Math.min(a[i], b[i]);
-      down += a[i] + b[i];
+    if (Array.isArray(b)) {
+      b = createTree(b);
     }
 
-    return 2 * up / down;
+    var C = alpha * Math.min(a.sum, b.sum) / Math.max(a.sum, b.sum) + (1 - alpha) * Math.exp(-gamma * Math.abs(a.center - b.center));
+    return beta * C + (1 - beta) * (getSimilarity(a.left, b.left, options) + getSimilarity(a.right, b.right, options)) / 2;
   }
 
-  function czekanowskiDistance(a, b) {
-    return 1 - czekanowskiSimilarity(a, b);
+  function treeSimilarity(A, B, options = {}) {
+    return getSimilarity(A, B, options);
+  }
+  function getFunction(options = {}) {
+    return (A, B) => getSimilarity(A, B, options);
   }
 
-  function dice(a, b) {
+  var index$5 = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    treeSimilarity: treeSimilarity,
+    getFunction: getFunction,
+    createTree: createTree
+  });
+
+  function cosine(a, b) {
     var ii = a.length;
     var p = 0;
-    var q1 = 0;
+    var p2 = 0;
     var q2 = 0;
 
     for (var i = 0; i < ii; i++) {
-      p += a[i] * a[i];
-      q1 += b[i] * b[i];
-      q2 += (a[i] - b[i]) * (a[i] - b[i]);
+      p += a[i] * b[i];
+      p2 += a[i] * a[i];
+      q2 += b[i] * b[i];
     }
 
-    return q2 / (p + q1);
+    return p / (Math.sqrt(p2) * Math.sqrt(q2));
   }
 
-  function divergence(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
-
-    for (; i < ii; i++) {
-      d += (a[i] - b[i]) * (a[i] - b[i]) / ((a[i] + b[i]) * (a[i] + b[i]));
-    }
-
-    return 2 * d;
+  function dice$1(a, b) {
+    return 1 - dice(a, b);
   }
 
-  function fidelity(a, b) {
-    var ii = a.length;
-    var ans = 0;
-
-    for (var i = 0; i < ii; i++) {
-      ans += Math.sqrt(a[i] * b[i]);
-    }
-
-    return ans;
+  function intersection$1(a, b) {
+    return 1 - intersection(a, b);
   }
 
-  function gower(a, b) {
-    var ii = a.length;
-    var ans = 0;
+  function jaccard$1(a, b) {
+    return 1 - jaccard(a, b);
+  }
 
-    for (var i = 0; i < ii; i++) {
-      ans += Math.abs(a[i] - b[i]);
-    }
+  function kulczynski$1(a, b) {
+    return 1 / kulczynski(a, b);
+  }
 
-    return ans / ii;
+  function motyka$1(a, b) {
+    return 1 - motyka(a, b);
   }
 
-  function harmonicMean(a, b) {
-    var ii = a.length;
-    var ans = 0;
+  function pearson$1(a, b) {
+    var avgA = mean(a);
+    var avgB = mean(b);
+    var newA = new Array(a.length);
+    var newB = new Array(b.length);
 
-    for (var i = 0; i < ii; i++) {
-      ans += a[i] * b[i] / (a[i] + b[i]);
+    for (var i = 0; i < newA.length; i++) {
+      newA[i] = a[i] - avgA;
+      newB[i] = b[i] - avgB;
     }
 
-    return 2 * ans;
+    return cosine(newA, newB);
   }
 
-  function hellinger(a, b) {
-    var ii = a.length;
-    var ans = 0;
-
-    for (var i = 0; i < ii; i++) {
-      ans += Math.sqrt(a[i] * b[i]);
-    }
-
-    return 2 * Math.sqrt(1 - ans);
+  function squaredChord$1(a, b) {
+    return 1 - squaredChord(a, b);
   }
 
-  function innerProduct(a, b) {
-    var ii = a.length;
-    var ans = 0;
+  var similarities = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    tree: index$5,
+    cosine: cosine,
+    czekanowski: czekanowskiSimilarity,
+    dice: dice$1,
+    intersection: intersection$1,
+    jaccard: jaccard$1,
+    kulczynski: kulczynski$1,
+    motyka: motyka$1,
+    pearson: pearson$1,
+    squaredChord: squaredChord$1,
+    tanimoto: tanimoto
+  });
 
-    for (var i = 0; i < ii; i++) {
-      ans += a[i] * b[i];
+  var acc = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = (pred.tn[i] + pred.tp[i]) / (l - 1);
     }
 
-    return ans;
-  }
+    return result;
+  }; // Error rate
 
-  function intersection(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += Math.min(a[i], b[i]);
+  var err = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.fn[i] + pred.fp[i] / (l - 1);
     }
 
-    return 1 - ans;
-  }
+    return result;
+  }; // False positive rate
 
-  function jaccard(a, b) {
-    var ii = a.length;
-    var p1 = 0;
-    var p2 = 0;
-    var q1 = 0;
-    var q2 = 0;
 
-    for (var i = 0; i < ii; i++) {
-      p1 += a[i] * b[i];
-      p2 += a[i] * a[i];
-      q1 += b[i] * b[i];
-      q2 += (a[i] - b[i]) * (a[i] - b[i]);
+  var fpr = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.fp[i] / pred.nNeg;
     }
 
-    return q2 / (p2 + q1 - p1);
-  }
+    return result;
+  }; // True positive rate
 
-  function jeffreys(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += (a[i] - b[i]) * Math.log(a[i] / b[i]);
+  var tpr = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.tp[i] / pred.nPos;
     }
 
-    return ans;
-  }
+    return result;
+  }; // False negative rate
 
-  function jensenDifference(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += (a[i] * Math.log(a[i]) + b[i] * Math.log(b[i])) / 2 - (a[i] + b[i]) / 2 * Math.log((a[i] + b[i]) / 2);
+  var fnr = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.fn[i] / pred.nPos;
     }
 
-    return ans;
-  }
+    return result;
+  }; // True negative rate
 
-  function jensenShannon(a, b) {
-    var ii = a.length;
-    var p = 0;
-    var q = 0;
 
-    for (var i = 0; i < ii; i++) {
-      p += a[i] * Math.log(2 * a[i] / (a[i] + b[i]));
-      q += b[i] * Math.log(2 * b[i] / (a[i] + b[i]));
+  var tnr = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.tn[i] / pred.nNeg;
     }
 
-    return (p + q) / 2;
-  }
+    return result;
+  }; // Positive predictive value
 
-  function kdivergence(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += a[i] * Math.log(2 * a[i] / (a[i] + b[i]));
+  var ppv = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.fp[i] + pred.tp[i] !== 0 ? pred.tp[i] / (pred.fp[i] + pred.tp[i]) : 0;
     }
 
-    return ans;
-  }
+    return result;
+  }; // Negative predictive value
 
-  function kulczynski(a, b) {
-    var ii = a.length;
-    var up = 0;
-    var down = 0;
 
-    for (var i = 0; i < ii; i++) {
-      up += Math.abs(a[i] - b[i]);
-      down += Math.min(a[i], b[i]);
+  var npv = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.fn[i] + pred.tn[i] !== 0 ? pred.tn[i] / (pred.fn[i] + pred.tn[i]) : 0;
     }
 
-    return up / down;
-  }
+    return result;
+  }; // Prediction conditioned fallout
 
-  function kullbackLeibler(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += a[i] * Math.log(a[i] / b[i]);
+  var pcfall = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.fp[i] + pred.tp[i] !== 0 ? 1 - pred.tp[i] / (pred.fp[i] + pred.tp[i]) : 1;
     }
 
-    return ans;
-  }
+    return result;
+  }; // Prediction conditioned miss
 
-  function kumarHassebrook(a, b) {
-    var ii = a.length;
-    var p = 0;
-    var p2 = 0;
-    var q2 = 0;
 
-    for (var i = 0; i < ii; i++) {
-      p += a[i] * b[i];
-      p2 += a[i] * a[i];
-      q2 += b[i] * b[i];
+  var pcmiss = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.fn[i] + pred.tn[i] !== 0 ? 1 - pred.tn[i] / (pred.fn[i] + pred.tn[i]) : 1;
     }
 
-    return p / (p2 + q2 - p);
-  }
+    return result;
+  }; // Lift value
 
-  function kumarJohnson(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += Math.pow(a[i] * a[i] - b[i] * b[i], 2) / (2 * Math.pow(a[i] * b[i], 1.5));
+  var lift = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.nPosPred[i] !== 0 ? pred.tp[i] / pred.nPos / (pred.nPosPred[i] / pred.nSamples) : 0;
     }
 
-    return ans;
-  }
+    return result;
+  }; // Rate of positive predictions
 
-  function lorentzian(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += Math.log(Math.abs(a[i] - b[i]) + 1);
+  var rpp = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.nPosPred[i] / pred.nSamples;
     }
 
-    return ans;
-  }
+    return result;
+  }; // Rate of negative predictions
 
-  function manhattan(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
 
-    for (; i < ii; i++) {
-      d += Math.abs(a[i] - b[i]);
+  var rnp = pred => {
+    const l = pred.cutoffs.length;
+    const result = new Array(l);
+
+    for (var i = 0; i < l; i++) {
+      result[i] = pred.nNegPred[i] / pred.nSamples;
     }
 
-    return d;
-  }
+    return result;
+  }; // Threshold
 
-  function matusita(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += Math.sqrt(a[i] * b[i]);
-    }
+  var threshold = pred => {
+    const clone = pred.cutoffs.slice();
+    clone[0] = clone[1]; // Remove the infinite value
 
-    return Math.sqrt(2 - 2 * ans);
-  }
+    return clone;
+  };
 
-  function minkowski(a, b, p) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
+  var measures = {
+    acc: acc,
+    err: err,
+    fpr: fpr,
+    tpr: tpr,
+    fnr: fnr,
+    tnr: tnr,
+    ppv: ppv,
+    npv: npv,
+    pcfall: pcfall,
+    pcmiss: pcmiss,
+    lift: lift,
+    rpp: rpp,
+    rnp: rnp,
+    threshold: threshold
+  };
 
-    for (; i < ii; i++) {
-      d += Math.pow(Math.abs(a[i] - b[i]), p);
-    }
+  class Performance {
+    /**
+     *
+     * @param prediction - The prediction matrix
+     * @param target - The target matrix (values: truthy for same class, falsy for different class)
+     * @param options
+     *
+     * @option    all    True if the entire matrix must be used. False to ignore the diagonal and lower part (default is false, for similarity/distance matrices)
+     * @option    max    True if the max value corresponds to a perfect match (like in similarity matrices), false if it is the min value (default is false, like in distance matrices. All values will be multiplied by -1)
+     */
+    constructor(prediction, target, options) {
+      options = options || {};
 
-    return Math.pow(d, 1 / p);
-  }
+      if (prediction.length !== target.length || prediction[0].length !== target[0].length) {
+        throw new Error('dimensions of prediction and target do not match');
+      }
 
-  function motyka(a, b) {
-    var ii = a.length;
-    var up = 0;
-    var down = 0;
+      const rows = prediction.length;
+      const columns = prediction[0].length;
+      const isDistance = !options.max;
+      const predP = [];
 
-    for (var i = 0; i < ii; i++) {
-      up += Math.min(a[i], b[i]);
-      down += a[i] + b[i];
-    }
+      if (options.all) {
+        for (var i = 0; i < rows; i++) {
+          for (var j = 0; j < columns; j++) {
+            predP.push({
+              pred: prediction[i][j],
+              targ: target[i][j]
+            });
+          }
+        }
+      } else {
+        if (rows < 3 || rows !== columns) {
+          throw new Error('When "all" option is false, the prediction matrix must be square and have at least 3 columns');
+        }
+
+        for (var i = 0; i < rows - 1; i++) {
+          for (var j = i + 1; j < columns; j++) {
+            predP.push({
+              pred: prediction[i][j],
+              targ: target[i][j]
+            });
+          }
+        }
+      }
 
-    return 1 - up / down;
-  }
+      if (isDistance) {
+        predP.sort((a, b) => a.pred - b.pred);
+      } else {
+        predP.sort((a, b) => b.pred - a.pred);
+      }
 
-  function neyman(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
+      const cutoffs = this.cutoffs = [isDistance ? Number.MIN_VALUE : Number.MAX_VALUE];
+      const fp = this.fp = [0];
+      const tp = this.tp = [0];
+      var nPos = 0;
+      var nNeg = 0;
+      var currentPred = predP[0].pred;
+      var nTp = 0;
+      var nFp = 0;
 
-    for (; i < ii; i++) {
-      d += (a[i] - b[i]) * (a[i] - b[i]) / a[i];
-    }
+      for (var i = 0; i < predP.length; i++) {
+        if (predP[i].pred !== currentPred) {
+          cutoffs.push(currentPred);
+          fp.push(nFp);
+          tp.push(nTp);
+          currentPred = predP[i].pred;
+        }
 
-    return d;
-  }
+        if (predP[i].targ) {
+          nPos++;
+          nTp++;
+        } else {
+          nNeg++;
+          nFp++;
+        }
+      }
 
-  function pearson(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
+      cutoffs.push(currentPred);
+      fp.push(nFp);
+      tp.push(nTp);
+      const l = cutoffs.length;
+      const fn = this.fn = new Array(l);
+      const tn = this.tn = new Array(l);
+      const nPosPred = this.nPosPred = new Array(l);
+      const nNegPred = this.nNegPred = new Array(l);
 
-    for (; i < ii; i++) {
-      d += (a[i] - b[i]) * (a[i] - b[i]) / b[i];
+      for (var i = 0; i < l; i++) {
+        fn[i] = nPos - tp[i];
+        tn[i] = nNeg - fp[i];
+        nPosPred[i] = tp[i] + fp[i];
+        nNegPred[i] = tn[i] + fn[i];
+      }
+
+      this.nPos = nPos;
+      this.nNeg = nNeg;
+      this.nSamples = nPos + nNeg;
     }
+    /**
+     * Computes a measure from the prediction object.
+     *
+     * Many measures are available and can be combined :
+     * To create a ROC curve, you need fpr and tpr
+     * To create a DET curve, you need fnr and fpr
+     * To create a Lift chart, you need rpp and lift
+     *
+     * Possible measures are : threshold (Threshold), acc (Accuracy), err (Error rate),
+     * fpr (False positive rate), tpr (True positive rate), fnr (False negative rate), tnr (True negative rate), ppv (Positive predictive value),
+     * npv (Negative predictive value), pcfall (Prediction-conditioned fallout), pcmiss (Prediction-conditioned miss), lift (Lift value), rpp (Rate of positive predictions), rnp (Rate of negative predictions)
+     *
+     * @param measure - The short name of the measure
+     *
+     * @return [number]
+     */
 
-    return d;
-  }
 
-  function probabilisticSymmetric(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
+    getMeasure(measure) {
+      if (typeof measure !== 'string') {
+        throw new Error('No measure specified');
+      }
 
-    for (; i < ii; i++) {
-      d += (a[i] - b[i]) * (a[i] - b[i]) / (a[i] + b[i]);
+      if (!measures[measure]) {
+        throw new Error(`The specified measure (${measure}) does not exist`);
+      }
+
+      return measures[measure](this);
     }
+    /**
+     * Returns the area under the ROC curve
+     */
 
-    return 2 * d;
-  }
 
-  function ruzicka(a, b) {
-    var ii = a.length;
-    var up = 0;
-    var down = 0;
+    getAURC() {
+      const l = this.cutoffs.length;
+      const x = new Array(l);
+      const y = new Array(l);
 
-    for (var i = 0; i < ii; i++) {
-      up += Math.min(a[i], b[i]);
-      down += Math.max(a[i], b[i]);
-    }
+      for (var i = 0; i < l; i++) {
+        x[i] = this.fp[i] / this.nNeg;
+        y[i] = this.tp[i] / this.nPos;
+      }
 
-    return up / down;
-  }
+      var auc = 0;
 
-  function soergel(a, b) {
-    var ii = a.length;
-    var up = 0;
-    var down = 0;
+      for (i = 1; i < l; i++) {
+        auc += 0.5 * (x[i] - x[i - 1]) * (y[i] + y[i - 1]);
+      }
 
-    for (var i = 0; i < ii; i++) {
-      up += Math.abs(a[i] - b[i]);
-      down += Math.max(a[i], b[i]);
+      return auc;
     }
+    /**
+     * Returns the area under the DET curve
+     */
 
-    return up / down;
-  }
 
-  function sorensen(a, b) {
-    var ii = a.length;
-    var up = 0;
-    var down = 0;
+    getAUDC() {
+      const l = this.cutoffs.length;
+      const x = new Array(l);
+      const y = new Array(l);
 
-    for (var i = 0; i < ii; i++) {
-      up += Math.abs(a[i] - b[i]);
-      down += a[i] + b[i];
-    }
+      for (var i = 0; i < l; i++) {
+        x[i] = this.fn[i] / this.nPos;
+        y[i] = this.fp[i] / this.nNeg;
+      }
 
-    return up / down;
-  }
+      var auc = 0;
 
-  function squared(a, b) {
-    var i = 0;
-    var ii = a.length;
-    var d = 0;
+      for (i = 1; i < l; i++) {
+        auc += 0.5 * (x[i] + x[i - 1]) * (y[i] - y[i - 1]);
+      }
 
-    for (; i < ii; i++) {
-      d += (a[i] - b[i]) * (a[i] - b[i]) / (a[i] + b[i]);
+      return auc;
     }
 
-    return d;
-  }
-
-  function squaredChord(a, b) {
-    var ii = a.length;
-    var ans = 0;
+    getDistribution(options) {
+      options = options || {};
+      var cutLength = this.cutoffs.length;
+      var cutLow = options.xMin || Math.floor(this.cutoffs[cutLength - 1] * 100) / 100;
+      var cutHigh = options.xMax || Math.ceil(this.cutoffs[1] * 100) / 100;
+      var interval = options.interval || Math.floor((cutHigh - cutLow) / 20 * 10000000 - 1) / 10000000; // Trick to avoid the precision problem of float numbers
 
-    for (var i = 0; i < ii; i++) {
-      ans += (Math.sqrt(a[i]) - Math.sqrt(b[i])) * (Math.sqrt(a[i]) - Math.sqrt(b[i]));
-    }
+      var xLabels = [];
+      var interValues = [];
+      var intraValues = [];
+      var interCumPercent = [];
+      var intraCumPercent = [];
+      var nTP = this.tp[cutLength - 1],
+          currentTP = 0;
+      var nFP = this.fp[cutLength - 1],
+          currentFP = 0;
 
-    return ans;
-  }
+      for (var i = cutLow, j = cutLength - 1; i <= cutHigh; i += interval) {
+        while (this.cutoffs[j] < i) j--;
 
-  function taneja(a, b) {
-    var ii = a.length;
-    var ans = 0;
+        xLabels.push(i);
+        var thisTP = nTP - currentTP - this.tp[j];
+        var thisFP = nFP - currentFP - this.fp[j];
+        currentTP += thisTP;
+        currentFP += thisFP;
+        interValues.push(thisFP);
+        intraValues.push(thisTP);
+        interCumPercent.push(100 - (nFP - this.fp[j]) / nFP * 100);
+        intraCumPercent.push(100 - (nTP - this.tp[j]) / nTP * 100);
+      }
 
-    for (var i = 0; i < ii; i++) {
-      ans += (a[i] + b[i]) / 2 * Math.log((a[i] + b[i]) / (2 * Math.sqrt(a[i] * b[i])));
+      return {
+        xLabels: xLabels,
+        interValues: interValues,
+        intraValues: intraValues,
+        interCumPercent: interCumPercent,
+        intraCumPercent: intraCumPercent
+      };
     }
 
-    return ans;
   }
 
-  function tanimoto(a, b, bitvector) {
-    if (bitvector) {
-      var inter = 0;
-      var union = 0;
+  Performance.names = {
+    acc: 'Accuracy',
+    err: 'Error rate',
+    fpr: 'False positive rate',
+    tpr: 'True positive rate',
+    fnr: 'False negative rate',
+    tnr: 'True negative rate',
+    ppv: 'Positive predictive value',
+    npv: 'Negative predictive value',
+    pcfall: 'Prediction-conditioned fallout',
+    pcmiss: 'Prediction-conditioned miss',
+    lift: 'Lift value',
+    rpp: 'Rate of positive predictions',
+    rnp: 'Rate of negative predictions',
+    threshold: 'Threshold'
+  };
+  var src$1 = Performance;
 
-      for (var j = 0; j < a.length; j++) {
-        inter += a[j] && b[j];
-        union += a[j] || b[j];
-      }
+  var defaultOptions$g = {
+    size: 1,
+    value: 0
+  };
+  /**
+   * Case when the entry is an array
+   * @param data
+   * @param options
+   * @returns {Array}
+   */
 
-      if (union === 0) {
-        return 1;
-      }
+  function arrayCase(data, options) {
+    var len = data.length;
+
+    if (typeof options.size === 'number') {
+      options.size = [options.size, options.size];
+    }
 
-      return inter / union;
-    } else {
-      var ii = a.length;
-      var p = 0;
-      var q = 0;
-      var m = 0;
+    var cond = len + options.size[0] + options.size[1];
+    var output;
 
-      for (var i = 0; i < ii; i++) {
-        p += a[i];
-        q += b[i];
-        m += Math.min(a[i], b[i]);
+    if (options.output) {
+      if (options.output.length !== cond) {
+        throw new RangeError('Wrong output size');
       }
 
-      return 1 - (p + q - 2 * m) / (p + q - m);
+      output = options.output;
+    } else {
+      output = new Array(cond);
     }
-  }
 
-  function tanimoto$1(a, b, bitvector) {
-    if (bitvector) {
-      return 1 - tanimoto(a, b, bitvector);
-    } else {
-      var ii = a.length;
-      var p = 0;
-      var q = 0;
-      var m = 0;
+    var i;
 
-      for (var i = 0; i < ii; i++) {
-        p += a[i];
-        q += b[i];
-        m += Math.min(a[i], b[i]);
+    if (options.value === 'circular') {
+      for (i = 0; i < cond; i++) {
+        if (i < options.size[0]) {
+          output[i] = data[(len - options.size[0] % len + i) % len];
+        } else if (i < options.size[0] + len) {
+          output[i] = data[i - options.size[0]];
+        } else {
+          output[i] = data[(i - options.size[0]) % len];
+        }
+      }
+    } else if (options.value === 'replicate') {
+      for (i = 0; i < cond; i++) {
+        if (i < options.size[0]) output[i] = data[0];else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];else output[i] = data[len - 1];
+      }
+    } else if (options.value === 'symmetric') {
+      if (options.size[0] > len || options.size[1] > len) {
+        throw new RangeError('expanded value should not be bigger than the data length');
       }
 
-      return (p + q - 2 * m) / (p + q - m);
+      for (i = 0; i < cond; i++) {
+        if (i < options.size[0]) output[i] = data[options.size[0] - 1 - i];else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];else output[i] = data[2 * len + options.size[0] - i - 1];
+      }
+    } else {
+      for (i = 0; i < cond; i++) {
+        if (i < options.size[0]) output[i] = options.value;else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];else output[i] = options.value;
+      }
     }
+
+    return output;
   }
+  /**
+   * Case when the entry is a matrix
+   * @param data
+   * @param options
+   * @returns {Array}
+   */
 
-  function topsoe(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += a[i] * Math.log(2 * a[i] / (a[i] + b[i])) + b[i] * Math.log(2 * b[i] / (a[i] + b[i]));
+  function matrixCase(data, options) {
+    // var row = data.length;
+    // var col = data[0].length;
+    if (options.size[0] === undefined) {
+      options.size = [options.size, options.size, options.size, options.size];
     }
 
-    return ans;
+    throw new Error('matrix not supported yet, sorry');
   }
+  /**
+   * Pads and array
+   * @param {Array <number>} data
+   * @param {object} options
+   */
 
-  function waveHedges(a, b) {
-    var ii = a.length;
-    var ans = 0;
 
-    for (var i = 0; i < ii; i++) {
-      ans += 1 - Math.min(a[i], b[i]) / Math.max(a[i], b[i]);
-    }
+  function padArray(data, options) {
+    options = Object.assign({}, defaultOptions$g, options);
 
-    return ans;
+    if (Array.isArray(data)) {
+      if (Array.isArray(data[0])) return matrixCase(data, options);else return arrayCase(data, options);
+    } else {
+      throw new TypeError('data should be an array');
+    }
   }
 
+  var src$2 = padArray;
 
+  /**
+   * Factorial of a number
+   * @ignore
+   * @param n
+   * @return {number}
+   */
 
-  var distances = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    euclidean: euclidean,
-    squaredEuclidean: squaredEuclidean,
-    additiveSymmetric: additiveSymmetric,
-    avg: avg,
-    bhattacharyya: bhattacharyya,
-    canberra: canberra,
-    chebyshev: chebyshev,
-    clark: clark,
-    czekanowski: czekanowskiDistance,
-    dice: dice,
-    divergence: divergence,
-    fidelity: fidelity,
-    gower: gower,
-    harmonicMean: harmonicMean,
-    hellinger: hellinger,
-    innerProduct: innerProduct,
-    intersection: intersection,
-    jaccard: jaccard,
-    jeffreys: jeffreys,
-    jensenDifference: jensenDifference,
-    jensenShannon: jensenShannon,
-    kdivergence: kdivergence,
-    kulczynski: kulczynski,
-    kullbackLeibler: kullbackLeibler,
-    kumarHassebrook: kumarHassebrook,
-    kumarJohnson: kumarJohnson,
-    lorentzian: lorentzian,
-    manhattan: manhattan,
-    matusita: matusita,
-    minkowski: minkowski,
-    motyka: motyka,
-    neyman: neyman,
-    pearson: pearson,
-    probabilisticSymmetric: probabilisticSymmetric,
-    ruzicka: ruzicka,
-    soergel: soergel,
-    sorensen: sorensen,
-    squared: squared,
-    squaredChord: squaredChord,
-    taneja: taneja,
-    tanimoto: tanimoto$1,
-    topsoe: topsoe,
-    waveHedges: waveHedges
-  });
+  function factorial(n) {
+    let r = 1;
+
+    while (n > 0) r *= n--;
+
+    return r;
+  }
 
+  const defaultOptions$h = {
+    windowSize: 5,
+    derivative: 1,
+    polynomial: 2,
+    pad: 'none',
+    padValue: 'replicate'
+  };
   /**
-   * Function that creates the tree
-   * @param {Array<Array<number>>} spectrum
-   * @param {object} [options]
-   * @return {Tree|null}
-   * left and right have the same structure than the parent,
-   * or are null if they are leaves
+   * Savitzky-Golay filter
+   * @param {Array <number>} data
+   * @param {number} h
+   * @param {Object} options
+   * @returns {Array}
    */
 
-  function createTree(spectrum) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-    var X = spectrum[0];
-    const {
-      minWindow = 0.16,
-      threshold = 0.01,
-      from = X[0],
-      to = X[X.length - 1]
-    } = options;
-    return mainCreateTree(spectrum[0], spectrum[1], from, to, minWindow, threshold);
-  }
+  function savitzkyGolay(data, h, options) {
+    options = Object.assign({}, defaultOptions$h, options);
 
-  function mainCreateTree(X, Y, from, to, minWindow, threshold) {
-    if (to - from < minWindow) {
-      return null;
-    } // search first point
+    if (options.windowSize % 2 === 0 || options.windowSize < 5 || !Number.isInteger(options.windowSize)) {
+      throw new RangeError('Invalid window size (should be odd and at least 5 integer number)');
+    }
 
+    if (options.derivative < 0 || !Number.isInteger(options.derivative)) {
+      throw new RangeError('Derivative should be a positive integer');
+    }
 
-    var start = binarySearch(X, from, ascending);
+    if (options.polynomial < 1 || !Number.isInteger(options.polynomial)) {
+      throw new RangeError('Polynomial should be a positive integer');
+    }
 
-    if (start < 0) {
-      start = ~start;
-    } // stop at last point
+    let C, norm;
+    let step = Math.floor(options.windowSize / 2);
 
+    if (options.pad === 'pre') {
+      data = src$2(data, {
+        size: step,
+        value: options.padValue
+      });
+    }
 
-    var sum = 0;
-    var center = 0;
+    let ans = new Array(data.length - 2 * step);
 
-    for (var i = start; i < X.length; i++) {
-      if (X[i] >= to) {
-        break;
+    if (options.windowSize === 5 && options.polynomial === 2 && (options.derivative === 1 || options.derivative === 2)) {
+      if (options.derivative === 1) {
+        C = [-2, -1, 0, 1, 2];
+        norm = 10;
+      } else {
+        C = [2, -1, -2, -1, 2];
+        norm = 7;
       }
+    } else {
+      let J = Matrix.ones(options.windowSize, options.polynomial + 1);
+      let inic = -(options.windowSize - 1) / 2;
 
-      sum += Y[i];
-      center += X[i] * Y[i];
-    }
+      for (let i = 0; i < J.rows; i++) {
+        for (let j = 0; j < J.columns; j++) {
+          if (inic + 1 !== 0 || j !== 0) J.set(i, j, Math.pow(inic + i, j));
+        }
+      }
 
-    if (sum < threshold) {
-      return null;
+      let Jtranspose = new MatrixTransposeView(J);
+      let Jinv = inverse(Jtranspose.mmul(J));
+      C = Jinv.mmul(Jtranspose);
+      C = C.getRow(options.derivative);
+      norm = 1 / factorial(options.derivative);
     }
 
-    center /= sum;
+    let det = norm * Math.pow(h, options.derivative);
 
-    if (center - from < 1e-6 || to - center < 1e-6) {
-      return null;
+    for (let k = step; k < data.length - step; k++) {
+      let d = 0;
+
+      for (let l = 0; l < C.length; l++) d += C[l] * data[l + k - step] / det;
+
+      ans[k - step] = d;
     }
 
-    if (center - from < minWindow / 4) {
-      return mainCreateTree(X, Y, center, to, minWindow, threshold);
-    } else {
-      if (to - center < minWindow / 4) {
-        return mainCreateTree(X, Y, from, center, minWindow, threshold);
-      } else {
-        return new Tree(sum, center, mainCreateTree(X, Y, from, center, minWindow, threshold), mainCreateTree(X, Y, center, to, minWindow, threshold));
-      }
+    if (options.pad === 'post') {
+      ans = src$2(ans, {
+        size: step,
+        value: options.padValue
+      });
     }
+
+    return ans;
   }
 
-  class Tree {
-    constructor(sum, center, left, right) {
-      this.sum = sum;
-      this.center = center;
-      this.left = left;
-      this.right = right;
+  // auxiliary file to create the 256 look at table elements
+  var ans = new Array(256);
+
+  for (var i = 0; i < 256; i++) {
+    var num = i;
+    var c = 0;
+
+    while (num) {
+      num = num & num - 1;
+      c++;
     }
 
+    ans[i] = c;
   }
 
+  var creator = ans;
+
   /**
-   * Similarity between two nodes
-   * @param {Tree|Array<Array<number>>} a - tree A node
-   * @param {Tree|Array<Array<number>>} b - tree B node
-   * @param {object} [options]
-   * @return {number} similarity measure between tree nodes
+   * Count the number of true values in an array
+   * @param {Array} arr
+   * @return {number}
    */
 
-  function getSimilarity(a, b) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-    const {
-      alpha = 0.1,
-      beta = 0.33,
-      gamma = 0.001
-    } = options;
-
-    if (a === null || b === null) {
-      return 0;
-    }
 
-    if (Array.isArray(a)) {
-      a = createTree(a);
-    }
+  function count(arr) {
+    var c = 0;
 
-    if (Array.isArray(b)) {
-      b = createTree(b);
+    for (var i = 0; i < arr.length; i++) {
+      c += creator[arr[i] & 0xff] + creator[arr[i] >> 8 & 0xff] + creator[arr[i] >> 16 & 0xff] + creator[arr[i] >> 24 & 0xff];
     }
 
-    var C = alpha * Math.min(a.sum, b.sum) / Math.max(a.sum, b.sum) + (1 - alpha) * Math.exp(-gamma * Math.abs(a.center - b.center));
-    return beta * C + (1 - beta) * (getSimilarity(a.left, b.left, options) + getSimilarity(a.right, b.right, options)) / 2;
+    return c;
   }
+  /**
+   * Logical AND operation
+   * @param {Array} arr1
+   * @param {Array} arr2
+   * @return {Array}
+   */
 
-  function treeSimilarity(A, B) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
-    return getSimilarity(A, B, options);
-  }
-  function getFunction() {
-    let options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
-    return (A, B) => getSimilarity(A, B, options);
+
+  function and(arr1, arr2) {
+    var ans = new Array(arr1.length);
+
+    for (var i = 0; i < arr1.length; i++) ans[i] = arr1[i] & arr2[i];
+
+    return ans;
   }
+  /**
+   * Logical OR operation
+   * @param {Array} arr1
+   * @param {Array} arr2
+   * @return {Array}
+   */
 
-  var index$4 = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    treeSimilarity: treeSimilarity,
-    getFunction: getFunction,
-    createTree: createTree
-  });
 
-  function cosine(a, b) {
-    var ii = a.length;
-    var p = 0;
-    var p2 = 0;
-    var q2 = 0;
+  function or(arr1, arr2) {
+    var ans = new Array(arr1.length);
 
-    for (var i = 0; i < ii; i++) {
-      p += a[i] * b[i];
-      p2 += a[i] * a[i];
-      q2 += b[i] * b[i];
-    }
+    for (var i = 0; i < arr1.length; i++) ans[i] = arr1[i] | arr2[i];
 
-    return p / (Math.sqrt(p2) * Math.sqrt(q2));
+    return ans;
   }
+  /**
+   * Logical XOR operation
+   * @param {Array} arr1
+   * @param {Array} arr2
+   * @return {Array}
+   */
 
-  function dice$1(a, b) {
-    return 1 - dice(a, b);
-  }
 
-  function intersection$1(a, b) {
-    return 1 - intersection(a, b);
+  function xor(arr1, arr2) {
+    var ans = new Array(arr1.length);
+
+    for (var i = 0; i < arr1.length; i++) ans[i] = arr1[i] ^ arr2[i];
+
+    return ans;
   }
+  /**
+   * Logical NOT operation
+   * @param {Array} arr
+   * @return {Array}
+   */
 
-  function jaccard$1(a, b) {
-    return 1 - jaccard(a, b);
+
+  function not(arr) {
+    var ans = new Array(arr.length);
+
+    for (var i = 0; i < ans.length; i++) ans[i] = ~arr[i];
+
+    return ans;
   }
+  /**
+   * Gets the n value of array arr
+   * @param {Array} arr
+   * @param {number} n
+   * @return {boolean}
+   */
 
-  function kulczynski$1(a, b) {
-    return 1 / kulczynski(a, b);
+
+  function getBit(arr, n) {
+    var index = n >> 5; // Same as Math.floor(n/32)
+
+    var mask = 1 << 31 - n % 32;
+    return Boolean(arr[index] & mask);
   }
+  /**
+   * Sets the n value of array arr to the value val
+   * @param {Array} arr
+   * @param {number} n
+   * @param {boolean} val
+   * @return {Array}
+   */
 
-  function motyka$1(a, b) {
-    return 1 - motyka(a, b);
+
+  function setBit(arr, n, val) {
+    var index = n >> 5; // Same as Math.floor(n/32)
+
+    var mask = 1 << 31 - n % 32;
+    if (val) arr[index] = mask | arr[index];else arr[index] = ~mask & arr[index];
+    return arr;
   }
+  /**
+   * Translates an array of numbers to a string of bits
+   * @param {Array} arr
+   * @returns {string}
+   */
 
-  function pearson$1(a, b) {
-    var avgA = mean(a);
-    var avgB = mean(b);
-    var newA = new Array(a.length);
-    var newB = new Array(b.length);
 
-    for (var i = 0; i < newA.length; i++) {
-      newA[i] = a[i] - avgA;
-      newB[i] = b[i] - avgB;
+  function toBinaryString(arr) {
+    var str = '';
+
+    for (var i = 0; i < arr.length; i++) {
+      var obj = (arr[i] >>> 0).toString(2);
+      str += '00000000000000000000000000000000'.substr(obj.length) + obj;
     }
 
-    return cosine(newA, newB);
+    return str;
   }
+  /**
+   * Creates an array of numbers based on a string of bits
+   * @param {string} str
+   * @returns {Array}
+   */
 
-  function squaredChord$1(a, b) {
-    return 1 - squaredChord(a, b);
-  }
 
+  function parseBinaryString(str) {
+    var len = str.length / 32;
+    var ans = new Array(len);
 
+    for (var i = 0; i < len; i++) {
+      ans[i] = parseInt(str.substr(i * 32, 32), 2) | 0;
+    }
 
-  var similarities = /*#__PURE__*/Object.freeze({
-    __proto__: null,
-    tree: index$4,
-    cosine: cosine,
-    czekanowski: czekanowskiSimilarity,
-    dice: dice$1,
-    intersection: intersection$1,
-    jaccard: jaccard$1,
-    kulczynski: kulczynski$1,
-    motyka: motyka$1,
-    pearson: pearson$1,
-    squaredChord: squaredChord$1,
-    tanimoto: tanimoto
-  });
+    return ans;
+  }
+  /**
+   * Translates an array of numbers to a hex string
+   * @param {Array} arr
+   * @returns {string}
+   */
 
-  var acc = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
 
-    for (var i = 0; i < l; i++) {
-      result[i] = (pred.tn[i] + pred.tp[i]) / (l - 1);
+  function toHexString(arr) {
+    var str = '';
+
+    for (var i = 0; i < arr.length; i++) {
+      var obj = (arr[i] >>> 0).toString(16);
+      str += '00000000'.substr(obj.length) + obj;
     }
 
-    return result;
-  }; // Error rate
+    return str;
+  }
+  /**
+   * Creates an array of numbers based on a hex string
+   * @param {string} str
+   * @returns {Array}
+   */
 
 
-  var err = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function parseHexString(str) {
+    var len = str.length / 8;
+    var ans = new Array(len);
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.fn[i] + pred.fp[i] / (l - 1);
+    for (var i = 0; i < len; i++) {
+      ans[i] = parseInt(str.substr(i * 8, 8), 16) | 0;
     }
 
-    return result;
-  }; // False positive rate
+    return ans;
+  }
+  /**
+   * Creates a human readable string of the array
+   * @param {Array} arr
+   * @returns {string}
+   */
 
 
-  var fpr = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function toDebug(arr) {
+    var binary = toBinaryString(arr);
+    var str = '';
+
+    for (var i = 0; i < arr.length; i++) {
+      str += '0000'.substr((i * 32).toString(16).length) + (i * 32).toString(16) + ':';
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.fp[i] / pred.nNeg;
+      for (var j = 0; j < 32; j += 4) {
+        str += ' ' + binary.substr(i * 32 + j, 4);
+      }
+
+      if (i < arr.length - 1) str += '\n';
     }
 
-    return result;
-  }; // True positive rate
+    return str;
+  }
 
+  var src$3 = {
+    count: count,
+    and: and,
+    or: or,
+    xor: xor,
+    not: not,
+    getBit: getBit,
+    setBit: setBit,
+    toBinaryString: toBinaryString,
+    parseBinaryString: parseBinaryString,
+    toHexString: toHexString,
+    parseHexString: parseHexString,
+    toDebug: toDebug
+  };
 
-  var tpr = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function SavitzkyGolay(data, h, options = {}) {
+    let {
+      windowSize = 9,
+      derivative = 0,
+      polynomial = 3
+    } = options;
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.tp[i] / pred.nPos;
+    if (windowSize % 2 === 0 || windowSize < 5 || !Number.isInteger(windowSize)) {
+      throw new RangeError('Invalid window size (should be odd and at least 5 integer number)');
     }
 
-    return result;
-  }; // False negative rate
-
+    if (windowSize > data.length) {
+      throw new RangeError(`Window size is higher than the data length ${windowSize}>${data.length}`);
+    }
 
-  var fnr = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+    if (derivative < 0 || !Number.isInteger(derivative)) {
+      throw new RangeError('Derivative should be a positive integer');
+    }
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.fn[i] / pred.nPos;
+    if (polynomial < 1 || !Number.isInteger(polynomial)) {
+      throw new RangeError('Polynomial should be a positive integer');
     }
 
-    return result;
-  }; // True negative rate
+    if (polynomial >= 6) {
+      // eslint-disable-next-line no-console
+      console.warn('You should not use polynomial grade higher than 5 if you are' + ' not sure that your data arises from such a model. Possible polynomial oscillation problems');
+    }
 
+    let half = Math.floor(windowSize / 2);
+    let np = data.length;
+    let ans = new Array(np);
+    let weights = fullWeights(windowSize, polynomial, derivative);
+    let hs = 0;
+    let constantH = true;
 
-  var tnr = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+    if (Array.isArray(h)) {
+      constantH = false;
+    } else {
+      hs = Math.pow(h, derivative);
+    } //For the borders
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.tn[i] / pred.nNeg;
-    }
 
-    return result;
-  }; // Positive predictive value
+    for (let i = 0; i < half; i++) {
+      let wg1 = weights[half - i - 1];
+      let wg2 = weights[half + i + 1];
+      let d1 = 0;
+      let d2 = 0;
 
+      for (let l = 0; l < windowSize; l++) {
+        d1 += wg1[l] * data[l];
+        d2 += wg2[l] * data[np - windowSize + l];
+      }
 
-  var ppv = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+      if (constantH) {
+        ans[half - i - 1] = d1 / hs;
+        ans[np - half + i] = d2 / hs;
+      } else {
+        hs = getHs(h, half - i - 1, half, derivative);
+        ans[half - i - 1] = d1 / hs;
+        hs = getHs(h, np - half + i, half, derivative);
+        ans[np - half + i] = d2 / hs;
+      }
+    } //For the internal points
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.fp[i] + pred.tp[i] !== 0 ? pred.tp[i] / (pred.fp[i] + pred.tp[i]) : 0;
-    }
 
-    return result;
-  }; // Negative predictive value
+    let wg = weights[half];
 
+    for (let i = windowSize; i <= np; i++) {
+      let d = 0;
 
-  var npv = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+      for (let l = 0; l < windowSize; l++) d += wg[l] * data[l + i - windowSize];
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.fn[i] + pred.tn[i] !== 0 ? pred.tn[i] / (pred.fn[i] + pred.tn[i]) : 0;
+      if (!constantH) hs = getHs(h, i - half - 1, half, derivative);
+      ans[i - half - 1] = d / hs;
     }
 
-    return result;
-  }; // Prediction conditioned fallout
-
+    return ans;
+  }
 
-  var pcfall = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function getHs(h, center, half, derivative) {
+    let hs = 0;
+    let count = 0;
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.fp[i] + pred.tp[i] !== 0 ? 1 - pred.tp[i] / (pred.fp[i] + pred.tp[i]) : 1;
+    for (let i = center - half; i < center + half; i++) {
+      if (i >= 0 && i < h.length - 1) {
+        hs += h[i + 1] - h[i];
+        count++;
+      }
     }
 
-    return result;
-  }; // Prediction conditioned miss
-
+    return Math.pow(hs / count, derivative);
+  }
 
-  var pcmiss = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function GramPoly(i, m, k, s) {
+    let Grampoly = 0;
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.fn[i] + pred.tn[i] !== 0 ? 1 - pred.tn[i] / (pred.fn[i] + pred.tn[i]) : 1;
+    if (k > 0) {
+      Grampoly = (4 * k - 2) / (k * (2 * m - k + 1)) * (i * GramPoly(i, m, k - 1, s) + s * GramPoly(i, m, k - 1, s - 1)) - (k - 1) * (2 * m + k) / (k * (2 * m - k + 1)) * GramPoly(i, m, k - 2, s);
+    } else {
+      if (k === 0 && s === 0) {
+        Grampoly = 1;
+      } else {
+        Grampoly = 0;
+      }
     }
 
-    return result;
-  }; // Lift value
-
+    return Grampoly;
+  }
 
-  var lift = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function GenFact(a, b) {
+    let gf = 1;
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.nPosPred[i] !== 0 ? pred.tp[i] / pred.nPos / (pred.nPosPred[i] / pred.nSamples) : 0;
+    if (a >= b) {
+      for (let j = a - b + 1; j <= a; j++) {
+        gf *= j;
+      }
     }
 
-    return result;
-  }; // Rate of positive predictions
-
+    return gf;
+  }
 
-  var rpp = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function Weight(i, t, m, n, s) {
+    let sum = 0;
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.nPosPred[i] / pred.nSamples;
+    for (let k = 0; k <= n; k++) {
+      //console.log(k);
+      sum += (2 * k + 1) * (GenFact(2 * m, k) / GenFact(2 * m + k + 1, k + 1)) * GramPoly(i, m, k, 0) * GramPoly(t, m, k, s);
     }
 
-    return result;
-  }; // Rate of negative predictions
+    return sum;
+  }
+  /**
+   *
+   * @param m  Number of points
+   * @param n  Polynomial grade
+   * @param s  Derivative
+   */
 
 
-  var rnp = pred => {
-    const l = pred.cutoffs.length;
-    const result = new Array(l);
+  function fullWeights(m, n, s) {
+    let weights = new Array(m);
+    let np = Math.floor(m / 2);
 
-    for (var i = 0; i < l; i++) {
-      result[i] = pred.nNegPred[i] / pred.nSamples;
-    }
+    for (let t = -np; t <= np; t++) {
+      weights[t + np] = new Array(m);
 
-    return result;
-  }; // Threshold
+      for (let j = -np; j <= np; j++) {
+        weights[t + np][j + np] = Weight(j, t, np, n, s);
+      }
+    }
 
+    return weights;
+  }
+  /*function entropy(data,h,options){
+      var trend = SavitzkyGolay(data,h,trendOptions);
+      var copy = new Array(data.length);
+      var sum = 0;
+      var max = 0;
+      for(var i=0;i<data.length;i++){
+          copy[i] = data[i]-trend[i];
+      }
 
-  var threshold = pred => {
-    const clone = pred.cutoffs.slice();
-    clone[0] = clone[1]; // Remove the infinite value
+      sum/=data.length;
+      console.log(sum+" "+max);
+      console.log(stat.array.standardDeviation(copy));
+      console.log(Math.abs(stat.array.mean(copy))/stat.array.standardDeviation(copy));
+      return sum;
 
-    return clone;
-  };
+  }
 
-  var measures = {
-    acc: acc,
-    err: err,
-    fpr: fpr,
-    tpr: tpr,
-    fnr: fnr,
-    tnr: tnr,
-    ppv: ppv,
-    npv: npv,
-    pcfall: pcfall,
-    pcmiss: pcmiss,
-    lift: lift,
-    rpp: rpp,
-    rnp: rnp,
-    threshold: threshold
-  };
 
-  class Performance {
-    /**
-     *
-     * @param prediction - The prediction matrix
-     * @param target - The target matrix (values: truthy for same class, falsy for different class)
-     * @param options
-     *
-     * @option    all    True if the entire matrix must be used. False to ignore the diagonal and lower part (default is false, for similarity/distance matrices)
-     * @option    max    True if the max value corresponds to a perfect match (like in similarity matrices), false if it is the min value (default is false, like in distance matrices. All values will be multiplied by -1)
-     */
-    constructor(prediction, target, options) {
-      options = options || {};
 
-      if (prediction.length !== target.length || prediction[0].length !== target[0].length) {
-        throw new Error('dimensions of prediction and target do not match');
-      }
+  function guessWindowSize(data, h){
+      console.log("entropy "+entropy(data,h,trendOptions));
+      return 5;
+  }
+  */
 
-      const rows = prediction.length;
-      const columns = prediction[0].length;
-      const isDistance = !options.max;
-      const predP = [];
+  /**
+   * Global spectra deconvolution
+   * @param {Array<number>} x - Independent variable
+   * @param {Array<number>} yIn - Dependent variable
+   * @param {object} [options] - Options object
+   * @param {object} [options.sgOptions] - Options object for Savitzky-Golay filter. See https://github.com/mljs/savitzky-golay-generalized#options
+   * @param {number} [options.sgOptions.windowSize = 9] - points to use in the approximations
+   * @param {number} [options.sgOptions.polynomial = 3] - degree of the polynomial to use in the approximations
+   * @param {number} [options.minMaxRatio = 0.00025] - Threshold to determine if a given peak should be considered as a noise
+   * @param {number} [options.broadRatio = 0.00] - If `broadRatio` is higher than 0, then all the peaks which second derivative
+   * smaller than `broadRatio * maxAbsSecondDerivative` will be marked with the soft mask equal to true.
+   * @param {number} [options.noiseLevel = 0] - Noise threshold in spectrum units
+   * @param {boolean} [options.maxCriteria = true] - Peaks are local maximum(true) or minimum(false)
+   * @param {boolean} [options.smoothY = true] - Select the peak intensities from a smoothed version of the independent variables
+   * @param {boolean} [options.realTopDetection = false] - Use a quadratic optimizations with the peak and its 3 closest neighbors
+   * to determine the true x,y values of the peak?
+   * @param {number} [options.heightFactor = 0] - Factor to multiply the calculated height (usually 2)
+   * @param {number} [options.derivativeThreshold = -1] - Filters based on the amplitude of the first derivative
+   * @return {Array<object>}
+   */
 
-      if (options.all) {
-        for (var i = 0; i < rows; i++) {
-          for (var j = 0; j < columns; j++) {
-            predP.push({
-              pred: prediction[i][j],
-              targ: target[i][j]
-            });
-          }
-        }
-      } else {
-        if (rows < 3 || rows !== columns) {
-          throw new Error('When "all" option is false, the prediction matrix must be square and have at least 3 columns');
-        }
+  function gsd(x, yIn, options = {}) {
+    let {
+      noiseLevel,
+      sgOptions = {
+        windowSize: 9,
+        polynomial: 3
+      },
+      smoothY = true,
+      heightFactor = 0,
+      broadRatio = 0.0,
+      maxCriteria = true,
+      minMaxRatio = 0.00025,
+      derivativeThreshold = -1,
+      realTopDetection = false
+    } = options;
+    const y = yIn.slice();
+    let equalSpaced = isEqualSpaced(x);
 
-        for (var i = 0; i < rows - 1; i++) {
-          for (var j = i + 1; j < columns; j++) {
-            predP.push({
-              pred: prediction[i][j],
-              targ: target[i][j]
-            });
-          }
-        }
-      }
+    if (noiseLevel === undefined) {
+      noiseLevel = equalSpaced ? getNoiseLevel(y) : 0;
+    }
 
-      if (isDistance) {
-        predP.sort((a, b) => a.pred - b.pred);
-      } else {
-        predP.sort((a, b) => b.pred - a.pred);
-      }
+    const yCorrection = {
+      m: 1,
+      b: noiseLevel
+    };
 
-      const cutoffs = this.cutoffs = [isDistance ? Number.MIN_VALUE : Number.MAX_VALUE];
-      const fp = this.fp = [0];
-      const tp = this.tp = [0];
-      var nPos = 0;
-      var nNeg = 0;
-      var currentPred = predP[0].pred;
-      var nTp = 0;
-      var nFp = 0;
+    if (!maxCriteria) {
+      yCorrection.m = -1;
+      yCorrection.b *= -1;
+    }
 
-      for (var i = 0; i < predP.length; i++) {
-        if (predP[i].pred !== currentPred) {
-          cutoffs.push(currentPred);
-          fp.push(nFp);
-          tp.push(nTp);
-          currentPred = predP[i].pred;
-        }
+    for (let i = 0; i < y.length; i++) {
+      y[i] = yCorrection.m * y[i] - yCorrection.b;
+    }
 
-        if (predP[i].targ) {
-          nPos++;
-          nTp++;
-        } else {
-          nNeg++;
-          nFp++;
-        }
+    for (let i = 0; i < y.length; i++) {
+      if (y[i] < 0) {
+        y[i] = 0;
       }
+    } // If the max difference between delta x is less than 5%, then,
+    // we can assume it to be equally spaced variable
 
-      cutoffs.push(currentPred);
-      fp.push(nFp);
-      tp.push(nTp);
-      const l = cutoffs.length;
-      const fn = this.fn = new Array(l);
-      const tn = this.tn = new Array(l);
-      const nPosPred = this.nPosPred = new Array(l);
-      const nNegPred = this.nNegPred = new Array(l);
 
-      for (var i = 0; i < l; i++) {
-        fn[i] = nPos - tp[i];
-        tn[i] = nNeg - fp[i];
-        nPosPred[i] = tp[i] + fp[i];
-        nNegPred[i] = tn[i] + fn[i];
+    let yData = y;
+    let dY, ddY;
+    const {
+      windowSize,
+      polynomial
+    } = sgOptions;
+
+    if (equalSpaced) {
+      if (smoothY) {
+        yData = SavitzkyGolay(y, x[1] - x[0], {
+          windowSize,
+          polynomial,
+          derivative: 0
+        });
       }
 
-      this.nPos = nPos;
-      this.nNeg = nNeg;
-      this.nSamples = nPos + nNeg;
+      dY = SavitzkyGolay(y, x[1] - x[0], {
+        windowSize,
+        polynomial,
+        derivative: 1
+      });
+      ddY = SavitzkyGolay(y, x[1] - x[0], {
+        windowSize,
+        polynomial,
+        derivative: 2
+      });
+    } else {
+      if (smoothY) {
+        yData = SavitzkyGolay(y, x, {
+          windowSize,
+          polynomial,
+          derivative: 0
+        });
+      }
+
+      dY = SavitzkyGolay(y, x, {
+        windowSize,
+        polynomial,
+        derivative: 1
+      });
+      ddY = SavitzkyGolay(y, x, {
+        windowSize,
+        polynomial,
+        derivative: 2
+      });
     }
-    /**
-     * Computes a measure from the prediction object.
-     *
-     * Many measures are available and can be combined :
-     * To create a ROC curve, you need fpr and tpr
-     * To create a DET curve, you need fnr and fpr
-     * To create a Lift chart, you need rpp and lift
-     *
-     * Possible measures are : threshold (Threshold), acc (Accuracy), err (Error rate),
-     * fpr (False positive rate), tpr (True positive rate), fnr (False negative rate), tnr (True negative rate), ppv (Positive predictive value),
-     * npv (Negative predictive value), pcfall (Prediction-conditioned fallout), pcmiss (Prediction-conditioned miss), lift (Lift value), rpp (Rate of positive predictions), rnp (Rate of negative predictions)
-     *
-     * @param measure - The short name of the measure
-     *
-     * @return [number]
-     */
 
+    const xData = x;
+    const dX = x[1] - x[0];
+    let maxDdy = 0;
+    let maxY = 0;
 
-    getMeasure(measure) {
-      if (typeof measure !== 'string') {
-        throw new Error('No measure specified');
+    for (let i = 0; i < yData.length; i++) {
+      if (Math.abs(ddY[i]) > maxDdy) {
+        maxDdy = Math.abs(ddY[i]);
       }
 
-      if (!measures[measure]) {
-        throw new Error("The specified measure (".concat(measure, ") does not exist"));
+      if (Math.abs(yData[i]) > maxY) {
+        maxY = Math.abs(yData[i]);
       }
-
-      return measures[measure](this);
     }
-    /**
-     * Returns the area under the ROC curve
-     */
 
+    let lastMax = null;
+    let lastMin = null;
+    let minddY = new Array(yData.length - 2);
+    let intervalL = new Array(yData.length);
+    let intervalR = new Array(yData.length);
+    let broadMask = new Array(yData.length - 2);
+    let minddYLen = 0;
+    let intervalLLen = 0;
+    let intervalRLen = 0;
+    let broadMaskLen = 0; // By the intermediate value theorem We cannot find 2 consecutive maximum or minimum
 
-    getAURC() {
-      const l = this.cutoffs.length;
-      const x = new Array(l);
-      const y = new Array(l);
+    for (let i = 1; i < yData.length - 1; ++i) {
+      // filter based on derivativeThreshold
+      // console.log('pasa', y[i], dY[i], ddY[i]);
+      if (Math.abs(dY[i]) > derivativeThreshold) {
+        // Minimum in first derivative
+        if (dY[i] < dY[i - 1] && dY[i] <= dY[i + 1] || dY[i] <= dY[i - 1] && dY[i] < dY[i + 1]) {
+          lastMin = {
+            x: xData[i],
+            index: i
+          };
 
-      for (var i = 0; i < l; i++) {
-        x[i] = this.fp[i] / this.nNeg;
-        y[i] = this.tp[i] / this.nPos;
-      }
+          if (dX > 0 && lastMax !== null) {
+            intervalL[intervalLLen++] = lastMax;
+            intervalR[intervalRLen++] = lastMin;
+          }
+        } // Maximum in first derivative
 
-      var auc = 0;
 
-      for (i = 1; i < l; i++) {
-        auc += 0.5 * (x[i] - x[i - 1]) * (y[i] + y[i - 1]);
-      }
+        if (dY[i] >= dY[i - 1] && dY[i] > dY[i + 1] || dY[i] > dY[i - 1] && dY[i] >= dY[i + 1]) {
+          lastMax = {
+            x: xData[i],
+            index: i
+          };
 
-      return auc;
-    }
-    /**
-     * Returns the area under the DET curve
-     */
+          if (dX < 0 && lastMin !== null) {
+            intervalL[intervalLLen++] = lastMax;
+            intervalR[intervalRLen++] = lastMin;
+          }
+        }
+      } // Minimum in second derivative
 
 
-    getAUDC() {
-      const l = this.cutoffs.length;
-      const x = new Array(l);
-      const y = new Array(l);
+      if (ddY[i] < ddY[i - 1] && ddY[i] < ddY[i + 1]) {
+        // TODO should we change this to have 3 arrays ? Huge overhead creating arrays
+        minddY[minddYLen++] = i; // ( [xData[i], yData[i], i] );
 
-      for (var i = 0; i < l; i++) {
-        x[i] = this.fn[i] / this.nPos;
-        y[i] = this.fp[i] / this.nNeg;
+        broadMask[broadMaskLen++] = Math.abs(ddY[i]) <= broadRatio * maxDdy;
       }
+    }
 
-      var auc = 0;
+    minddY.length = minddYLen;
+    intervalL.length = intervalLLen;
+    intervalR.length = intervalRLen;
+    broadMask.length = broadMaskLen;
+    let signals = new Array(minddY.length);
+    let signalsLen = 0;
+    let lastK = -1;
+    let possible, frequency, distanceJ, minDistance, gettingCloser;
 
-      for (i = 1; i < l; i++) {
-        auc += 0.5 * (x[i] + x[i - 1]) * (y[i] - y[i - 1]);
-      }
+    for (let j = 0; j < minddY.length; ++j) {
+      frequency = xData[minddY[j]];
+      possible = -1;
+      let k = lastK + 1;
+      minDistance = Number.MAX_VALUE;
+      distanceJ = 0;
+      gettingCloser = true;
 
-      return auc;
-    }
+      while (possible === -1 && k < intervalL.length && gettingCloser) {
+        distanceJ = Math.abs(frequency - (intervalL[k].x + intervalR[k].x) / 2); // Still getting closer?
 
-    getDistribution(options) {
-      options = options || {};
-      var cutLength = this.cutoffs.length;
-      var cutLow = options.xMin || Math.floor(this.cutoffs[cutLength - 1] * 100) / 100;
-      var cutHigh = options.xMax || Math.ceil(this.cutoffs[1] * 100) / 100;
-      var interval = options.interval || Math.floor((cutHigh - cutLow) / 20 * 10000000 - 1) / 10000000; // Trick to avoid the precision problem of float numbers
+        if (distanceJ < minDistance) {
+          minDistance = distanceJ;
+        } else {
+          gettingCloser = false;
+        }
 
-      var xLabels = [];
-      var interValues = [];
-      var intraValues = [];
-      var interCumPercent = [];
-      var intraCumPercent = [];
-      var nTP = this.tp[cutLength - 1],
-          currentTP = 0;
-      var nFP = this.fp[cutLength - 1],
-          currentFP = 0;
+        if (distanceJ < Math.abs(intervalL[k].x - intervalR[k].x) / 2) {
+          possible = k;
+          lastK = k;
+        }
 
-      for (var i = cutLow, j = cutLength - 1; i <= cutHigh; i += interval) {
-        while (this.cutoffs[j] < i) j--;
+        ++k;
+      }
 
-        xLabels.push(i);
-        var thisTP = nTP - currentTP - this.tp[j];
-        var thisFP = nFP - currentFP - this.fp[j];
-        currentTP += thisTP;
-        currentFP += thisFP;
-        interValues.push(thisFP);
-        intraValues.push(thisTP);
-        interCumPercent.push(100 - (nFP - this.fp[j]) / nFP * 100);
-        intraCumPercent.push(100 - (nTP - this.tp[j]) / nTP * 100);
+      if (possible !== -1) {
+        if (Math.abs(yData[minddY[j]]) > minMaxRatio * maxY) {
+          signals[signalsLen++] = {
+            index: minddY[j],
+            x: frequency,
+            y: (yData[minddY[j]] + yCorrection.b) / yCorrection.m,
+            width: Math.abs(intervalR[possible].x - intervalL[possible].x),
+            // widthCorrection
+            soft: broadMask[j]
+          };
+          signals[signalsLen - 1].left = intervalL[possible];
+          signals[signalsLen - 1].right = intervalR[possible];
+
+          if (heightFactor) {
+            let yLeft = yData[intervalL[possible].index];
+            let yRight = yData[intervalR[possible].index];
+            signals[signalsLen - 1].height = heightFactor * (signals[signalsLen - 1].y - (yLeft + yRight) / 2);
+          }
+        }
       }
+    }
 
-      return {
-        xLabels: xLabels,
-        interValues: interValues,
-        intraValues: intraValues,
-        interCumPercent: interCumPercent,
-        intraCumPercent: intraCumPercent
-      };
+    signals.length = signalsLen;
+
+    if (realTopDetection) {
+      determineRealTop(signals, xData, yData);
+    } // Correct the values to fit the original spectra data
+
+
+    for (let j = 0; j < signals.length; j++) {
+      signals[j].base = noiseLevel;
     }
 
+    signals.sort(function (a, b) {
+      return a.x - b.x;
+    });
+    return signals;
   }
 
-  Performance.names = {
-    acc: 'Accuracy',
-    err: 'Error rate',
-    fpr: 'False positive rate',
-    tpr: 'True positive rate',
-    fnr: 'False negative rate',
-    tnr: 'True negative rate',
-    ppv: 'Positive predictive value',
-    npv: 'Negative predictive value',
-    pcfall: 'Prediction-conditioned fallout',
-    pcmiss: 'Prediction-conditioned miss',
-    lift: 'Lift value',
-    rpp: 'Rate of positive predictions',
-    rnp: 'Rate of negative predictions',
-    threshold: 'Threshold'
-  };
-  var src$5 = Performance;
+  const isEqualSpaced = x => {
+    let tmp;
+    let maxDx = 0;
+    let minDx = Number.MAX_SAFE_INTEGER;
 
-  var defaultOptions$h = {
-    size: 1,
-    value: 0
+    for (let i = 0; i < x.length - 1; ++i) {
+      tmp = Math.abs(x[i + 1] - x[i]);
+
+      if (tmp < minDx) {
+        minDx = tmp;
+      }
+
+      if (tmp > maxDx) {
+        maxDx = tmp;
+      }
+    }
+
+    return (maxDx - minDx) / maxDx < 0.05;
   };
-  /**
-   * Case when the entry is an array
-   * @param data
-   * @param options
-   * @returns {Array}
-   */
 
-  function arrayCase(data, options) {
-    var len = data.length;
+  const getNoiseLevel = y => {
+    let mean = 0;
+    let stddev = 0;
+    let length = y.length;
 
-    if (typeof options.size === 'number') {
-      options.size = [options.size, options.size];
+    for (let i = 0; i < length; ++i) {
+      mean += y[i];
     }
 
-    var cond = len + options.size[0] + options.size[1];
-    var output;
+    mean /= length;
+    let averageDeviations = new Array(length);
 
-    if (options.output) {
-      if (options.output.length !== cond) {
-        throw new RangeError('Wrong output size');
-      }
+    for (let i = 0; i < length; ++i) {
+      averageDeviations[i] = Math.abs(y[i] - mean);
+    }
 
-      output = options.output;
+    averageDeviations.sort((a, b) => a - b);
+
+    if (length % 2 === 1) {
+      stddev = averageDeviations[(length - 1) / 2] / 0.6745;
     } else {
-      output = new Array(cond);
+      stddev = 0.5 * (averageDeviations[length / 2] + averageDeviations[length / 2 - 1]) / 0.6745;
     }
 
-    var i;
+    return stddev;
+  };
 
-    if (options.value === 'circular') {
-      for (i = 0; i < cond; i++) {
-        if (i < options.size[0]) {
-          output[i] = data[(len - options.size[0] % len + i) % len];
-        } else if (i < options.size[0] + len) {
-          output[i] = data[i - options.size[0]];
+  const determineRealTop = (peakList, x, y) => {
+    let alpha, beta, gamma, p, currentPoint;
+
+    for (let j = 0; j < peakList.length; j++) {
+      currentPoint = peakList[j].index; // peakList[j][2];
+      // The detected peak could be moved 1 or 2 units to left or right.
+
+      if (y[currentPoint - 1] >= y[currentPoint - 2] && y[currentPoint - 1] >= y[currentPoint]) {
+        currentPoint--;
+      } else {
+        if (y[currentPoint + 1] >= y[currentPoint] && y[currentPoint + 1] >= y[currentPoint + 2]) {
+          currentPoint++;
         } else {
-          output[i] = data[(i - options.size[0]) % len];
+          if (y[currentPoint - 2] >= y[currentPoint - 3] && y[currentPoint - 2] >= y[currentPoint - 1]) {
+            currentPoint -= 2;
+          } else {
+            if (y[currentPoint + 2] >= y[currentPoint + 1] && y[currentPoint + 2] >= y[currentPoint + 3]) {
+              currentPoint += 2;
+            }
+          }
         }
-      }
-    } else if (options.value === 'replicate') {
-      for (i = 0; i < cond; i++) {
-        if (i < options.size[0]) output[i] = data[0];else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];else output[i] = data[len - 1];
-      }
-    } else if (options.value === 'symmetric') {
-      if (options.size[0] > len || options.size[1] > len) {
-        throw new RangeError('expanded value should not be bigger than the data length');
-      }
+      } // interpolation to a sin() function
 
-      for (i = 0; i < cond; i++) {
-        if (i < options.size[0]) output[i] = data[options.size[0] - 1 - i];else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];else output[i] = data[2 * len + options.size[0] - i - 1];
-      }
-    } else {
-      for (i = 0; i < cond; i++) {
-        if (i < options.size[0]) output[i] = options.value;else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];else output[i] = options.value;
+
+      if (y[currentPoint - 1] > 0 && y[currentPoint + 1] > 0 && y[currentPoint] >= y[currentPoint - 1] && y[currentPoint] >= y[currentPoint + 1] && (y[currentPoint] !== y[currentPoint - 1] || y[currentPoint] !== y[currentPoint + 1])) {
+        alpha = 20 * Math.log10(y[currentPoint - 1]);
+        beta = 20 * Math.log10(y[currentPoint]);
+        gamma = 20 * Math.log10(y[currentPoint + 1]);
+        p = 0.5 * (alpha - gamma) / (alpha - 2 * beta + gamma); // console.log(alpha, beta, gamma, `p: ${p}`);
+        // console.log(x[currentPoint]+" "+tmp+" "+currentPoint);
+
+        peakList[j].x = x[currentPoint] + (x[currentPoint] - x[currentPoint - 1]) * p;
+        peakList[j].y = y[currentPoint] - 0.25 * (y[currentPoint - 1] - y[currentPoint + 1]) * p;
       }
     }
+  };
 
-    return output;
+  /**
+   * This function calculates the spectrum as a sum of gaussian functions. The Gaussian
+   * parameters are divided in 3 batches. 1st: centers; 2nd: height; 3th: std's;
+   * @param t Ordinate values
+   * @param p Gaussian parameters
+   * @param c Constant parameters(Not used)
+   * @returns {*}
+   */
+  function sumOfGaussians(p) {
+    return function (t) {
+      let nL = p.length / 3;
+      let factor;
+      let rows = t.length;
+      let result = rows === undefined ? 0 : new Float64Array(rows).fill(0);
+
+      for (let i = 0; i < nL; i++) {
+        factor = Math.pow(p[i + nL * 2], 2) * 2;
+
+        if (rows === undefined) {
+          result += p[i + nL] * Math.exp(-Math.pow(t - p[i], 2) / factor);
+        } else {
+          for (let j = 0; j < rows; j++) {
+            result[j] += p[i + nL] * Math.exp(-Math.pow(t[j] - p[i], 2) / factor);
+          }
+        }
+      }
+
+      return result;
+    };
   }
+
   /**
-   * Case when the entry is a matrix
-   * @param data
-   * @param options
-   * @returns {Array}
+   *
+   * @param xy A two column matrix containing the x and y data to be fitted
+   * @param group A set of initial lorentzian parameters to be optimized [center, heigth, half_width_at_half_height]
+   * @returns {Array} A set of final lorentzian parameters [center, heigth, hwhh*2]
    */
 
+  function optimizeGaussianSum(xy, group, opts = {}) {
+    let t = xy[0];
+    let yData = xy[1];
+    let maxY = Math.max(...yData);
+    yData.forEach((x, i, arr) => arr[i] /= maxY);
+    let nL = group.length;
+    let pInit = new Float64Array(nL * 3);
+    let pMin = new Float64Array(nL * 3);
+    let pMax = new Float64Array(nL * 3);
+    let dt = Math.abs(t[0] - t[1]);
+
+    for (let i = 0; i < nL; i++) {
+      pInit[i] = group[i].x;
+      pInit[i + nL] = group[i].y / maxY;
+      pInit[i + 2 * nL] = group[i].width;
+      pMin[i] = group[i].x - dt;
+      pMin[i + nL] = 0;
+      pMin[i + 2 * nL] = group[i].width / 4;
+      pMax[i] = group[i].x + dt;
+      pMax[i + nL] = group[i].y * 1.2 / maxY;
+      pMax[i + 2 * nL] = group[i].width * 4;
+    }
+
+    let data = {
+      x: t,
+      y: yData
+    };
+    let result = new Array(nL);
+    let lmOptions = {
+      damping: 1.5,
+      initialValues: pInit,
+      minValues: pMin,
+      maxValues: pMax,
+      gradientDifference: dt / 10000,
+      maxIterations: 100,
+      errorTolerance: 10e-5
+    };
+    opts = Object.assign({}, lmOptions, opts);
+    let pFit = levenbergMarquardt(data, sumOfGaussians, opts);
 
-  function matrixCase(data, options) {
-    // var row = data.length;
-    // var col = data[0].length;
-    if (options.size[0] === undefined) {
-      options.size = [options.size, options.size, options.size, options.size];
+    for (let i = 0; i < nL; i++) {
+      result[i] = {
+        parameters: [pFit.parameterValues[i], pFit.parameterValues[i + nL] * maxY, pFit.parameterValues[i + nL * 2]],
+        error: pFit.parameterError
+      };
     }
 
-    throw new Error('matrix not supported yet, sorry');
+    return result;
   }
+
   /**
-   * Pads and array
-   * @param {Array <number>} data
-   * @param {object} options
+   * Single 3 parameter gaussian function
+   * @param t Ordinate values
+   * @param p Gaussian parameters [mean, height, std]
+   * @param c Constant parameters(Not used)
+   * @returns {*}
    */
+  function singleGaussian(p) {
+    return function (t) {
+      let factor2 = p[2] * p[2] / 2;
+      let rows = t.length;
+      if (!rows) return p[1] * Math.exp(-(t - p[0]) * (t - p[0]) / factor2);
+      let result = new Float64Array(t.length);
 
+      for (let i = 0; i < t.length; i++) {
+        result[i] = p[1] * Math.exp(-(t[i] - p[0]) * (t[i] - p[0]) / factor2);
+      }
 
-  function padArray(data, options) {
-    options = Object.assign({}, defaultOptions$h, options);
-
-    if (Array.isArray(data)) {
-      if (Array.isArray(data[0])) return matrixCase(data, options);else return arrayCase(data, options);
-    } else {
-      throw new TypeError('data should be an array');
-    }
+      return result;
+    };
   }
 
-  var src$6 = padArray;
+  /**
+   * Fits a set of points to a gaussian bell. Returns the mean of the peak, the std and the height of the signal.
+   * @param data,[y]
+   * @returns {*[]}
+   */
+
+  function optimizeSingleGaussian(xy, peak, opts = {}) {
+    let t = xy[0];
+    let yData = xy[1];
+    let maxY = Math.max(...yData);
+    yData.forEach((x, i, arr) => arr[i] /= maxY);
+    let dt = Math.abs(t[0] - t[1]);
+    let pInit = new Float64Array([peak.x, 1, peak.width]);
+    let pMin = new Float64Array([peak.x - dt, 0, peak.width / 4]);
+    let pMax = new Float64Array([peak.x + dt, 1.25, peak.width * 4]);
+    let data = {
+      x: t,
+      y: yData
+    };
+    let lmOptions = {
+      damping: 1.5,
+      initialValues: pInit,
+      minValues: pMin,
+      maxValues: pMax,
+      gradientDifference: dt / 10000,
+      maxIterations: 100,
+      errorTolerance: 10e-5
+    };
+    opts = Object.assign({}, lmOptions, opts);
+    let pFit = levenbergMarquardt(data, singleGaussian, opts);
+    return {
+      parameters: [pFit.parameterValues[0], pFit.parameterValues[1] * maxY, pFit.parameterValues[2]],
+      error: pFit.parameterError
+    };
+  }
+
+  /**
+   * This function calculates the spectrum as a sum of lorentzian functions. The Lorentzian
+   * parameters are divided in 3 batches. 1st: centers; 2nd: heights; 3th: widths;
+   * @param t Ordinate values
+   * @param p Lorentzian parameters
+   * @returns {*}
+   */
+  function sumOfLorentzians(p) {
+    return function (t) {
+      let nL = p.length / 3;
+      let factor;
+      let p2;
+      let rows = t.length;
+      let result = rows === undefined ? 0 : new Float64Array(rows).fill(0);
+
+      for (let i = 0; i < nL; i++) {
+        p2 = Math.pow(p[i + nL * 2] / 2, 2);
+        factor = p[i + nL] * p2;
+
+        if (rows === undefined) {
+          result += factor / (Math.pow(t - p[i], 2) + p2);
+        } else {
+          for (let j = 0; j < rows; j++) {
+            result[j] += factor / (Math.pow(t[j] - p[i], 2) + p2);
+          }
+        }
+      }
+
+      return result;
+    };
+  }
 
-  const defaultOptions$i = {
-    windowSize: 5,
-    derivative: 1,
-    polynomial: 2,
-    pad: 'none',
-    padValue: 'replicate'
-  };
   /**
-   * Savitzky-Golay filter
-   * @param {Array <number>} data
-   * @param {number} h
-   * @param {Object} options
-   * @returns {Array}
+   *
+   * @param xy A two column matrix containing the x and y data to be fitted
+   * @param group A set of initial lorentzian parameters to be optimized [center, heigth, half_width_at_half_height]
+   * @returns {Array} A set of final lorentzian parameters [center, heigth, hwhh*2]
    */
 
-  function savitzkyGolay(data, h, options) {
-    options = Object.assign({}, defaultOptions$i, options);
+  function optimizeLorentzianSum(xy, group, opts = {}) {
+    let t = xy[0];
+    let yData = xy[1];
+    let maxY = Math.max(...yData);
+    yData.forEach((x, i, arr) => arr[i] /= maxY);
+    let nL = group.length;
+    let pInit = new Float64Array(nL * 3);
+    let pMin = new Float64Array(nL * 3);
+    let pMax = new Float64Array(nL * 3);
+    let dt = Math.abs(t[0] - t[1]);
+
+    for (let i = 0; i < nL; i++) {
+      pInit[i] = group[i].x;
+      pInit[i + nL] = 1;
+      pInit[i + 2 * nL] = group[i].width;
+      pMin[i] = group[i].x - dt;
+      pMin[i + nL] = 0;
+      pMin[i + 2 * nL] = group[i].width / 4;
+      pMax[i] = group[i].x + dt;
+      pMax[i + nL] = 1.5;
+      pMax[i + 2 * nL] = group[i].width * 4;
+    }
+
+    let data = {
+      x: t,
+      y: yData
+    };
+    let result = new Array(nL);
+    let lmOptions = {
+      damping: 1.5,
+      initialValues: pInit,
+      minValues: pMin,
+      maxValues: pMax,
+      gradientDifference: dt / 10000,
+      maxIterations: 100,
+      errorTolerance: 10e-5
+    };
+    opts = Object.assign({}, lmOptions, opts);
+    let pFit = levenbergMarquardt(data, sumOfLorentzians, opts);
 
-    if (options.windowSize % 2 === 0 || options.windowSize < 5 || !Number.isInteger(options.windowSize)) {
-      throw new RangeError('Invalid window size (should be odd and at least 5 integer number)');
+    for (let i = 0; i < nL; i++) {
+      result[i] = {
+        parameters: [pFit.parameterValues[i], pFit.parameterValues[i + nL] * maxY, pFit.parameterValues[i + nL * 2]],
+        error: pFit.parameterError
+      };
     }
 
-    if (options.derivative < 0 || !Number.isInteger(options.derivative)) {
-      throw new RangeError('Derivative should be a positive integer');
-    }
+    return result;
+  }
 
-    if (options.polynomial < 1 || !Number.isInteger(options.polynomial)) {
-      throw new RangeError('Polynomial should be a positive integer');
-    }
+  /**
+   * Single 4 parameter lorentzian function
+   * @param t Ordinate values
+   * @param p Lorentzian parameters
+   * @param c Constant parameters(Not used)
+   * @returns {*}
+   */
+  function singleLorentzian(p) {
+    return function (t) {
+      let factor = p[1] * Math.pow(p[2] / 2, 2);
+      let rows = t.length;
+      if (!rows) return factor / (Math.pow(t - p[0], 2) + Math.pow(p[2] / 2, 2));
+      let result = new Float64Array(rows);
 
-    let C, norm;
-    let step = Math.floor(options.windowSize / 2);
+      for (let i = 0; i < rows; i++) {
+        result[i] = factor / (Math.pow(t[i] - p[0], 2) + Math.pow(p[2] / 2, 2));
+      }
 
-    if (options.pad === 'pre') {
-      data = src$6(data, {
-        size: step,
-        value: options.padValue
-      });
-    }
+      return result;
+    };
+  }
 
-    let ans = new Array(data.length - 2 * step);
+  /**
+   * * Fits a set of points to a Lorentzian function. Returns the center of the peak, the width at half height, and the height of the signal.
+   * @param data,[y]
+   * @returns {*[]}
+   */
 
-    if (options.windowSize === 5 && options.polynomial === 2 && (options.derivative === 1 || options.derivative === 2)) {
-      if (options.derivative === 1) {
-        C = [-2, -1, 0, 1, 2];
-        norm = 10;
-      } else {
-        C = [2, -1, -2, -1, 2];
-        norm = 7;
-      }
-    } else {
-      let J = Matrix.ones(options.windowSize, options.polynomial + 1);
-      let inic = -(options.windowSize - 1) / 2;
+  function optimizeSingleLorentzian(xy, peak, opts = {}) {
+    let t = xy[0];
+    let yData = xy[1];
+    let maxY = Math.max(...yData);
+    yData.forEach((x, i, arr) => arr[i] /= maxY);
+    let dt = Math.abs(t[0] - t[1]);
+    let pInit = new Float64Array([peak.x, 1, peak.width]);
+    let pMin = new Float64Array([peak.x - dt, 0.75, peak.width / 4]);
+    let pMax = new Float64Array([peak.x + dt, 1.25, peak.width * 4]);
+    let data = {
+      x: t,
+      y: yData
+    };
+    let lmOptions = {
+      damping: 1.5,
+      initialValues: pInit,
+      minValues: pMin,
+      maxValues: pMax,
+      gradientDifference: dt / 10000,
+      maxIterations: 100,
+      errorTolerance: 10e-5
+    };
+    opts = Object.assign({}, lmOptions, opts);
+    let pFit = levenbergMarquardt(data, singleLorentzian, opts);
+    return {
+      parameters: [pFit.parameterValues[0], pFit.parameterValues[1] * maxY, pFit.parameterValues[2]],
+      error: pFit.parameterError
+    };
+  }
 
-      for (let i = 0; i < J.rows; i++) {
-        for (let j = 0; j < J.columns; j++) {
-          if (inic + 1 !== 0 || j !== 0) J.set(i, j, Math.pow(inic + i, j));
-        }
+  function optimizePeaks(peakList, x, y, options = {}) {
+    const {
+      functionName = 'gaussian',
+      factorWidth = 4,
+      optimizationOptions = {
+        damping: 1.5,
+        maxIterations: 100,
+        errorTolerance: 10e-5
       }
+    } = options;
+    let lastIndex = [0];
+    let groups = groupPeaks(peakList, factorWidth);
+    let result = [];
+    let factor = 1;
 
-      let Jtranspose = new MatrixTransposeView(J);
-      let Jinv = inverse(Jtranspose.mmul(J));
-      C = Jinv.mmul(Jtranspose);
-      C = C.getRow(options.derivative);
-      norm = 1;
-    }
+    if (functionName === 'gaussian') {
+      factor = 1.17741;
+    } // From https://en.wikipedia.org/wiki/Gaussian_function#Properties
 
-    let det = norm * Math.pow(h, options.derivative);
 
-    for (let k = step; k < data.length - step; k++) {
-      let d = 0;
+    let sampling;
 
-      for (let l = 0; l < C.length; l++) d += C[l] * data[l + k - step] / det;
+    for (let i = 0; i < groups.length; i++) {
+      let peaks = groups[i].group;
 
-      ans[k - step] = d;
-    }
+      if (peaks.length > 1) {
+        // Multiple peaks
+        sampling = sampleFunction(groups[i].limits[0] - groups[i].limits[1], groups[i].limits[0] + groups[i].limits[1], x, y, lastIndex);
 
-    if (options.pad === 'post') {
-      ans = src$6(ans, {
-        size: step,
-        value: options.padValue
-      });
-    }
+        if (sampling[0].length > 5) {
+          let optPeaks = [];
 
-    return ans;
-  }
+          if (functionName === 'gaussian') {
+            optPeaks = optimizeGaussianSum(sampling, peaks, optimizationOptions);
+          } else {
+            if (functionName === 'lorentzian') {
+              optPeaks = optimizeLorentzianSum(sampling, peaks, optimizationOptions);
+            }
+          }
 
-  // auxiliary file to create the 256 look at table elements
-  var ans = new Array(256);
+          for (let j = 0; j < optPeaks.length; j++) {
+            let {
+              parameters
+            } = optPeaks[j];
+            result.push({
+              x: parameters[0],
+              y: parameters[1],
+              width: parameters[2] * factor,
+              index: peaks[j].index
+            });
+          }
+        }
+      } else {
+        // Single peak
+        peaks = peaks[0];
+        sampling = sampleFunction(peaks.x - factorWidth * peaks.width, peaks.x + factorWidth * peaks.width, x, y, lastIndex);
 
-  for (var i = 0; i < 256; i++) {
-    var num = i;
-    var c = 0;
+        if (sampling[0].length > 5) {
+          let fitResult = [];
 
-    while (num) {
-      num = num & num - 1;
-      c++;
+          if (functionName === 'gaussian') {
+            fitResult = optimizeSingleGaussian([sampling[0], sampling[1]], peaks, optimizationOptions);
+          } else {
+            if (functionName === 'lorentzian') {
+              fitResult = optimizeSingleLorentzian([sampling[0], sampling[1]], peaks, optimizationOptions);
+            }
+          }
+
+          let {
+            parameters
+          } = fitResult;
+          result.push({
+            x: parameters[0],
+            y: parameters[1],
+            width: parameters[2] * factor,
+            index: peaks.index
+          }); // From https://en.wikipedia.org/wiki/Gaussian_function#Properties}
+        }
+      }
     }
 
-    ans[i] = c;
+    return result;
   }
 
-  var creator = ans;
-
-  /**
-   * Count the number of true values in an array
-   * @param {Array} arr
-   * @return {number}
-   */
+  function sampleFunction(from, to, x, y, lastIndex) {
+    let nbPoints = x.length;
+    let sampleX = [];
+    let sampleY = [];
+    let direction = Math.sign(x[1] - x[0]); // Direction of the derivative
 
+    if (direction === -1) {
+      lastIndex[0] = x.length - 1;
+    }
 
-  function count(arr) {
-    var c = 0;
+    let delta = Math.abs(to - from) / 2;
+    let mid = (from + to) / 2;
+    let stop = false;
+    let index = lastIndex[0];
 
-    for (var i = 0; i < arr.length; i++) {
-      c += creator[arr[i] & 0xff] + creator[arr[i] >> 8 & 0xff] + creator[arr[i] >> 16 & 0xff] + creator[arr[i] >> 24 & 0xff];
+    while (!stop && index < nbPoints && index >= 0) {
+      if (Math.abs(x[index] - mid) <= delta) {
+        sampleX.push(x[index]);
+        sampleY.push(y[index]);
+        index += direction;
+      } else {
+        // It is outside the range.
+        if (Math.sign(mid - x[index]) === 1) {
+          // We'll reach the mid going in the current direction
+          index += direction;
+        } else {
+          // There is not more peaks in the current range
+          stop = true;
+        }
+      }
     }
 
-    return c;
+    lastIndex[0] = index;
+    return [sampleX, sampleY];
   }
-  /**
-   * Logical AND operation
-   * @param {Array} arr1
-   * @param {Array} arr2
-   * @return {Array}
-   */
 
+  function groupPeaks(peakList, nL) {
+    let group = [];
+    let groups = [];
+    let limits = [peakList[0].x, nL * peakList[0].width];
+    let upperLimit, lowerLimit; // Merge forward
 
-  function and(arr1, arr2) {
-    var ans = new Array(arr1.length);
+    for (let i = 0; i < peakList.length; i++) {
+      // If the 2 things overlaps
+      if (Math.abs(peakList[i].x - limits[0]) < nL * peakList[i].width + limits[1]) {
+        // Add the peak to the group
+        group.push(peakList[i]); // Update the group limits
 
-    for (var i = 0; i < arr1.length; i++) ans[i] = arr1[i] & arr2[i];
+        upperLimit = limits[0] + limits[1];
 
-    return ans;
-  }
-  /**
-   * Logical OR operation
-   * @param {Array} arr1
-   * @param {Array} arr2
-   * @return {Array}
-   */
+        if (peakList[i].x + nL * peakList[i].width > upperLimit) {
+          upperLimit = peakList[i].x + nL * peakList[i].width;
+        }
 
+        lowerLimit = limits[0] - limits[1];
 
-  function or(arr1, arr2) {
-    var ans = new Array(arr1.length);
+        if (peakList[i].x - nL * peakList[i].width < lowerLimit) {
+          lowerLimit = peakList[i].x - nL * peakList[i].width;
+        }
 
-    for (var i = 0; i < arr1.length; i++) ans[i] = arr1[i] | arr2[i];
+        limits = [(upperLimit + lowerLimit) / 2, Math.abs(upperLimit - lowerLimit) / 2];
+      } else {
+        groups.push({
+          limits: limits,
+          group: group
+        }); // var optmimalPeak = fitSpectrum(group,limits,spectrum);
 
-    return ans;
-  }
-  /**
-   * Logical XOR operation
-   * @param {Array} arr1
-   * @param {Array} arr2
-   * @return {Array}
-   */
+        group = [peakList[i]];
+        limits = [peakList[i].x, nL * peakList[i].width];
+      }
+    }
 
+    groups.push({
+      limits: limits,
+      group: group
+    }); // Merge backward
 
-  function xor(arr1, arr2) {
-    var ans = new Array(arr1.length);
+    for (let i = groups.length - 2; i >= 0; i--) {
+      // The groups overlaps
+      if (Math.abs(groups[i].limits[0] - groups[i + 1].limits[0]) < (groups[i].limits[1] + groups[i + 1].limits[1]) / 2) {
+        for (let j = 0; j < groups[i + 1].group.length; j++) {
+          groups[i].group.push(groups[i + 1].group[j]);
+        }
 
-    for (var i = 0; i < arr1.length; i++) ans[i] = arr1[i] ^ arr2[i];
+        upperLimit = groups[i].limits[0] + groups[i].limits[1];
 
-    return ans;
-  }
-  /**
-   * Logical NOT operation
-   * @param {Array} arr
-   * @return {Array}
-   */
+        if (groups[i + 1].limits[0] + groups[i + 1].limits[1] > upperLimit) {
+          upperLimit = groups[i + 1].limits[0] + groups[i + 1].limits[1];
+        }
 
+        lowerLimit = groups[i].limits[0] - groups[i].limits[1];
 
-  function not(arr) {
-    var ans = new Array(arr.length);
+        if (groups[i + 1].limits[0] - groups[i + 1].limits[1] < lowerLimit) {
+          lowerLimit = groups[i + 1].limits[0] - groups[i + 1].limits[1];
+        }
 
-    for (var i = 0; i < ans.length; i++) ans[i] = ~arr[i];
+        groups[i].limits = [(upperLimit + lowerLimit) / 2, Math.abs(upperLimit - lowerLimit) / 2];
+        groups.splice(i + 1, 1);
+      }
+    }
 
-    return ans;
+    return groups;
   }
+
   /**
-   * Gets the n value of array arr
-   * @param {Array} arr
-   * @param {number} n
-   * @return {boolean}
+   * This function try to join the peaks that seems to belong to a broad signal in a single broad peak.
+   * @param peakList
+   * @param options
    */
 
+  function joinBroadPeaks(peakList, options = {}) {
+    let width = options.width;
+    let broadLines = []; // Optimize the possible broad lines
 
-  function getBit(arr, n) {
-    var index = n >> 5; // Same as Math.floor(n/32)
+    let max = 0;
+    let maxI = 0;
+    let count = 1;
 
-    var mask = 1 << 31 - n % 32;
-    return Boolean(arr[index] & mask);
-  }
-  /**
-   * Sets the n value of array arr to the value val
-   * @param {Array} arr
-   * @param {number} n
-   * @param {boolean} val
-   * @return {Array}
-   */
+    for (let i = peakList.length - 1; i >= 0; i--) {
+      if (peakList[i].soft) {
+        broadLines.push(peakList.splice(i, 1)[0]);
+      }
+    } // Push a feke peak
 
 
-  function setBit(arr, n, val) {
-    var index = n >> 5; // Same as Math.floor(n/32)
+    broadLines.push({
+      x: Number.MAX_VALUE
+    });
+    let candidates = [[broadLines[0].x, broadLines[0].y]];
+    let indexes = [broadLines[0].index];
 
-    var mask = 1 << 31 - n % 32;
-    if (val) arr[index] = mask | arr[index];else arr[index] = ~mask & arr[index];
-    return arr;
-  }
-  /**
-   * Translates an array of numbers to a string of bits
-   * @param {Array} arr
-   * @returns {string}
-   */
+    for (let i = 1; i < broadLines.length; i++) {
+      // console.log(broadLines[i-1].x+" "+broadLines[i].x);
+      if (Math.abs(broadLines[i - 1].x - broadLines[i].x) < width) {
+        candidates.push([broadLines[i].x, broadLines[i].y]);
 
+        if (broadLines[i].y > max) {
+          max = broadLines[i].y;
+          maxI = i;
+        }
 
-  function toBinaryString(arr) {
-    var str = '';
+        indexes.push(broadLines[i].index);
+        count++;
+      } else {
+        if (count > 2) {
+          let fitted = optimizeSingleLorentzian(candidates, {
+            x: broadLines[maxI].x,
+            y: max,
+            width: Math.abs(candidates[0][0] - candidates[candidates.length - 1][0])
+          });
+          let {
+            parameters
+          } = fitted;
+          peakList.push({
+            x: parameters[0],
+            y: parameters[1],
+            width: parameters[2],
+            index: Math.floor(indexes.reduce((a, b) => a + b, 0) / indexes.length),
+            soft: false
+          });
+        } else {
+          // Put back the candidates to the signals list
+          indexes.forEach(index => {
+            peakList.push(broadLines[index]);
+          });
+        }
 
-    for (var i = 0; i < arr.length; i++) {
-      var obj = (arr[i] >>> 0).toString(2);
-      str += '00000000000000000000000000000000'.substr(obj.length) + obj;
+        candidates = [[broadLines[i].x, broadLines[i].y]];
+        indexes = [i];
+        max = broadLines[i].y;
+        maxI = i;
+        count = 1;
+      }
     }
 
-    return str;
+    peakList.sort(function (a, b) {
+      return a.x - b.x;
+    });
+    return peakList;
   }
+
   /**
-   * Creates an array of numbers based on a string of bits
-   * @param {string} str
-   * @returns {Array}
+   * This method will allow to enlarge peaks and prevent overlap between peaks
+   * Because peaks may not be symmetric after we add 2 properties, from and to.
+   * @param {Array} peakList
+   * @param {object} [options={}]
+   * @param {number} [factor=2]
+   * @param {boolean} [overlap=false] by default we don't allow overlap
+   * @return {Array} peakList
    */
+  function broadenPeaks(peakList, options = {}) {
+    const {
+      factor = 2,
+      overlap = false
+    } = options;
 
+    for (let peak of peakList) {
+      if (!peak.right || !peak.left) {
+        peak.from = peak.x - peak.width / 2 * factor;
+        peak.to = peak.x + peak.width / 2 * factor;
+      } else {
+        peak.from = peak.x - (peak.x - peak.left.x) * factor;
+        peak.to = peak.x + (peak.right.x - peak.x) * factor;
+      }
+    }
 
-  function parseBinaryString(str) {
-    var len = str.length / 32;
-    var ans = new Array(len);
+    if (!overlap) {
+      for (let i = 0; i < peakList.length - 1; i++) {
+        let peak = peakList[i];
+        let nextPeak = peakList[i + 1];
 
-    for (var i = 0; i < len; i++) {
-      ans[i] = parseInt(str.substr(i * 32, 32), 2) | 0;
+        if (peak.to > nextPeak.from) {
+          peak.to = nextPeak.from = (peak.to + nextPeak.from) / 2;
+        }
+      }
     }
 
-    return ans;
+    for (let peak of peakList) {
+      peak.width = peak.to - peak.from;
+    }
+
+    return peakList;
   }
-  /**
-   * Translates an array of numbers to a hex string
-   * @param {Array} arr
-   * @returns {string}
-   */
 
+  var index$6 = /*#__PURE__*/Object.freeze({
+    __proto__: null,
+    gsd: gsd,
+    optimizePeaks: optimizePeaks,
+    joinBroadPeaks: joinBroadPeaks,
+    broadenPeaks: broadenPeaks
+  });
+
+  const toString$4 = Object.prototype.toString;
+  function isAnyArray$4(object) {
+    return toString$4.call(object).endsWith('Array]');
+  }
+
+  function min$1(input) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+    if (!isAnyArray$4(input)) {
+      throw new TypeError('input must be an array');
+    }
+
+    if (input.length === 0) {
+      throw new TypeError('input must not be empty');
+    }
 
-  function toHexString(arr) {
-    var str = '';
+    var _options$fromIndex = options.fromIndex,
+        fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,
+        _options$toIndex = options.toIndex,
+        toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;
 
-    for (var i = 0; i < arr.length; i++) {
-      var obj = (arr[i] >>> 0).toString(16);
-      str += '00000000'.substr(obj.length) + obj;
+    if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {
+      throw new Error('fromIndex must be a positive integer smaller than length');
     }
 
-    return str;
-  }
-  /**
-   * Creates an array of numbers based on a hex string
-   * @param {string} str
-   * @returns {Array}
-   */
-
+    if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {
+      throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');
+    }
 
-  function parseHexString(str) {
-    var len = str.length / 8;
-    var ans = new Array(len);
+    var minValue = input[fromIndex];
 
-    for (var i = 0; i < len; i++) {
-      ans[i] = parseInt(str.substr(i * 8, 8), 16) | 0;
+    for (var i = fromIndex + 1; i < toIndex; i++) {
+      if (input[i] < minValue) minValue = input[i];
     }
 
-    return ans;
+    return minValue;
   }
-  /**
-   * Creates a human readable string of the array
-   * @param {Array} arr
-   * @returns {string}
-   */
 
+  function max$1(input) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
 
-  function toDebug(arr) {
-    var binary = toBinaryString(arr);
-    var str = '';
+    if (!isAnyArray$4(input)) {
+      throw new TypeError('input must be an array');
+    }
 
-    for (var i = 0; i < arr.length; i++) {
-      str += '0000'.substr((i * 32).toString(16).length) + (i * 32).toString(16) + ':';
+    if (input.length === 0) {
+      throw new TypeError('input must not be empty');
+    }
 
-      for (var j = 0; j < 32; j += 4) {
-        str += ' ' + binary.substr(i * 32 + j, 4);
-      }
+    var _options$fromIndex = options.fromIndex,
+        fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,
+        _options$toIndex = options.toIndex,
+        toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;
 
-      if (i < arr.length - 1) str += '\n';
+    if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {
+      throw new Error('fromIndex must be a positive integer smaller than length');
     }
 
-    return str;
-  }
+    if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {
+      throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');
+    }
 
-  var src$7 = {
-    count: count,
-    and: and,
-    or: or,
-    xor: xor,
-    not: not,
-    getBit: getBit,
-    setBit: setBit,
-    toBinaryString: toBinaryString,
-    parseBinaryString: parseBinaryString,
-    toHexString: toHexString,
-    parseHexString: parseHexString,
-    toDebug: toDebug
-  };
+    var maxValue = input[fromIndex];
 
-  /**
-   * Computes the mode of the given values
-   * @param {Array<number>} input
-   * @return {number}
-   */
+    for (var i = fromIndex + 1; i < toIndex; i++) {
+      if (input[i] > maxValue) maxValue = input[i];
+    }
+
+    return maxValue;
+  }
 
   function mode$1(input) {
-    if (!src(input)) {
+    if (!isAnyArray$4(input)) {
       throw new TypeError('input must be an array');
     }
 
@@ -16320,23 +18975,69 @@
     return maxValue;
   }
 
-  /**
-   * Computes the norm of the given values
-   * @param {Array<number>} input
-   * @param {object} [options={}]
-   * @param {string} [options.algorithm='absolute'] absolute, sum or max
-   * @return {number}
-   */
+  const toString$5 = Object.prototype.toString;
+  function isAnyArray$5(object) {
+    return toString$5.call(object).endsWith('Array]');
+  }
+
+  function max$2(input) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+    if (!isAnyArray$5(input)) {
+      throw new TypeError('input must be an array');
+    }
+
+    if (input.length === 0) {
+      throw new TypeError('input must not be empty');
+    }
+
+    var _options$fromIndex = options.fromIndex,
+        fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,
+        _options$toIndex = options.toIndex,
+        toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;
+
+    if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {
+      throw new Error('fromIndex must be a positive integer smaller than length');
+    }
+
+    if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {
+      throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');
+    }
+
+    var maxValue = input[fromIndex];
+
+    for (var i = fromIndex + 1; i < toIndex; i++) {
+      if (input[i] > maxValue) maxValue = input[i];
+    }
+
+    return maxValue;
+  }
 
   function norm$1(input) {
     var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
     var _options$algorithm = options.algorithm,
-        algorithm = _options$algorithm === void 0 ? 'absolute' : _options$algorithm;
+        algorithm = _options$algorithm === void 0 ? 'absolute' : _options$algorithm,
+        _options$sumValue = options.sumValue,
+        sumValue = _options$sumValue === void 0 ? 1 : _options$sumValue,
+        _options$maxValue = options.maxValue,
+        maxValue = _options$maxValue === void 0 ? 1 : _options$maxValue;
 
-    if (!Array.isArray(input)) {
+    if (!isAnyArray$5(input)) {
       throw new Error('input must be an array');
     }
 
+    var output;
+
+    if (options.output !== undefined) {
+      if (!isAnyArray$5(options.output)) {
+        throw new TypeError('output option must be an array if specified');
+      }
+
+      output = options.output;
+    } else {
+      output = new Array(input.length);
+    }
+
     if (input.length === 0) {
       throw new Error('input must not be empty');
     }
@@ -16344,29 +19045,39 @@
     switch (algorithm.toLowerCase()) {
       case 'absolute':
         {
-          var absoluteSumValue = absoluteSum(input);
+          var absoluteSumValue = absoluteSum(input) / sumValue;
           if (absoluteSumValue === 0) return input.slice(0);
-          return input.map(function (element) {
-            return element / absoluteSumValue;
-          });
+
+          for (var i = 0; i < input.length; i++) {
+            output[i] = input[i] / absoluteSumValue;
+          }
+
+          return output;
         }
 
       case 'max':
         {
-          var maxValue = max(input);
-          if (maxValue === 0) return input.slice(0);
-          return input.map(function (element) {
-            return element / maxValue;
-          });
+          var currentMaxValue = max$2(input);
+          if (currentMaxValue === 0) return input.slice(0);
+          var factor = maxValue / currentMaxValue;
+
+          for (var _i = 0; _i < input.length; _i++) {
+            output[_i] = input[_i] * factor;
+          }
+
+          return output;
         }
 
       case 'sum':
         {
-          var sumValue = sum(input);
-          if (sumValue === 0) return input.slice(0);
-          return input.map(function (element) {
-            return element / sumValue;
-          });
+          var sumFactor = sum(input) / sumValue;
+          if (sumFactor === 0) return input.slice(0);
+
+          for (var _i2 = 0; _i2 < input.length; _i2++) {
+            output[_i2] = input[_i2] / sumFactor;
+          }
+
+          return output;
         }
 
       default:
@@ -16385,27 +19096,29 @@
   }
 
   function _typeof(obj) {
+    "@babel/helpers - typeof";
+
     if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") {
-      _typeof = function _typeof(obj) {
+      _typeof = function (obj) {
         return typeof obj;
       };
     } else {
-      _typeof = function _typeof(obj) {
+      _typeof = function (obj) {
         return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj;
       };
     }
 
     return _typeof(obj);
   }
-  /**
-   * Fill an array with sequential numbers
-   * @param {Array<number>} [input] - optional destination array (if not provided a new array will be created)
-   * @param {object} [options={}]
-   * @param {number} [options.from=0] - first value in the array
-   * @param {number} [options.to=10] - last value in the array
-   * @param {number} [options.size=input.length] - size of the array (if not provided calculated from step)
-   * @param {number} [options.step] - if not provided calculated from size
-   * @return {Array<number>}
+  /**
+   * Fill an array with sequential numbers
+   * @param {Array<number>} [input] - optional destination array (if not provided a new array will be created)
+   * @param {object} [options={}]
+   * @param {number} [options.from=0] - first value in the array
+   * @param {number} [options.to=10] - last value in the array
+   * @param {number} [options.size=input.length] - size of the array (if not provided calculated from step)
+   * @param {number} [options.step] - if not provided calculated from size
+   * @return {Array<number>}
    */
 
 
@@ -16413,12 +19126,12 @@
     var input = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];
     var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
 
-    if (_typeof(input) === 'object' && !src(input)) {
+    if (_typeof(input) === 'object' && !isAnyArray$4(input)) {
       options = input;
       input = [];
     }
 
-    if (!src(input)) {
+    if (!isAnyArray$4(input)) {
       throw new TypeError('input must be an array');
     }
 
@@ -16431,7 +19144,7 @@
         size = _options$size === void 0 ? input.length : _options$size,
         step = _options.step;
 
-    if (size && step) {
+    if (size !== 0 && step) {
       throw new Error('step is defined by the array size');
     }
 
@@ -16448,7 +19161,8 @@
     }
 
     if (Array.isArray(input)) {
-      input.length = 0; // only works with normal array
+      // only works with normal array
+      input.length = 0;
 
       for (var i = 0; i < size; i++) {
         input.push(from);
@@ -16468,19 +19182,15 @@
     return input;
   }
 
-  /**
-   * Computes the variance of the given values
-   * @param {Array} values
-   * @param {object} [options]
-   * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.
-   * @param {number} [options.mean = arrayMean] - precalculated mean, if any.
-   * @return {number}
-   */
+  const toString$6 = Object.prototype.toString;
+  function isAnyArray$6(object) {
+    return toString$6.call(object).endsWith('Array]');
+  }
 
   function variance(values) {
     var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
 
-    if (!src(values)) {
+    if (!isAnyArray$6(values)) {
       throw new TypeError('input must be an array');
     }
 
@@ -16502,15 +19212,6 @@
     }
   }
 
-  /**
-   * Computes the standard deviation of the given values
-   * @param {Array} values
-   * @param {object} [options]
-   * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.
-   * @param {number} [options.mean = arrayMean] - precalculated mean, if any.
-   * @return {number}
-   */
-
   function standardDeviation(values) {
     var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
     return Math.sqrt(variance(values, options));
@@ -16526,8 +19227,7 @@
    * @param {number} [options.window = 0.01] - has to be a positive number
    * @return {{x: Array<number>, y: Array<number>}}
    */
-  function mergeByCentroids(originalPoints, centroids) {
-    let options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+  function mergeByCentroids(originalPoints, centroids, options = {}) {
     const {
       window = 0.01
     } = options;
@@ -16611,8 +19311,7 @@
    * @return {number}
    */
 
-  function covariance$1(points) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+  function covariance$1(points, options = {}) {
     const {
       x,
       y
@@ -16644,8 +19343,7 @@
    * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge
    * @return {{x: Array<number>, y: Array<number>}}
    */
-  function maxMerge(points) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+  function maxMerge(points, options = {}) {
     const {
       x,
       y
@@ -16699,8 +19397,7 @@
    * @return {{index: number, value: number}}
    */
 
-  function maxY(points) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+  function maxY(points, options = {}) {
     const {
       x,
       y
@@ -16755,14 +19452,13 @@
     }
 
     if (index < 0) {
-      throw new Error("the value ".concat(value, " doesn't belongs to the abscissa value"));
+      throw new Error(`the value ${value} doesn't belongs to the abscissa value`);
     }
 
     return index;
   }
 
-  function sortX(points) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+  function sortX(points, options = {}) {
     const {
       x,
       y
@@ -16800,8 +19496,7 @@
    * @param {object} [points={}] : Object of points contains property x (an array) and y (an array)
    * @return points
    */
-  function uniqueX() {
-    let points = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
+  function uniqueX(points = {}) {
     const {
       x,
       y
@@ -16844,8 +19539,7 @@
    * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge
    * @return {{x: Array<number>, y: Array<number>}}
    */
-  function weightedMerge(points) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+  function weightedMerge(points, options = {}) {
     const {
       x,
       y
@@ -16888,6 +19582,140 @@
     return merged;
   }
 
+  /**
+   * Normalize an array of zones:
+   * - ensure than from < to
+   * - merge overlapping zones
+   *
+   * The method will always check if from if lower than to and will swap if required.
+   * @param {Array} [zones=[]]
+   * @param {object} [options={}]
+   * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of a zone
+   * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of a zone
+   */
+  function normalize(zones = [], options = {}) {
+    if (zones.length === 0) return [];
+    let {
+      from = Number.NEGATIVE_INFINITY,
+      to = Number.POSITIVE_INFINITY
+    } = options;
+    if (from > to) [from, to] = [to, from];
+    zones = JSON.parse(JSON.stringify(zones)).map(zone => zone.from > zone.to ? {
+      from: zone.to,
+      to: zone.from
+    } : zone);
+    zones = zones.sort((a, b) => {
+      if (a.from !== b.from) return a.from - b.from;
+      return a.to - b.to;
+    });
+    zones.forEach(zone => {
+      if (from > zone.from) zone.from = from;
+      if (to < zone.to) zone.to = to;
+    });
+    zones = zones.filter(zone => zone.from <= zone.to);
+    if (zones.length === 0) return [];
+    let currentZone = zones[0];
+    let result = [currentZone];
+
+    for (let i = 1; i < zones.length; i++) {
+      let zone = zones[i];
+
+      if (zone.from <= currentZone.to) {
+        currentZone.to = zone.to;
+      } else {
+        currentZone = zone;
+        result.push(currentZone);
+      }
+    }
+
+    return result;
+  }
+
+  /**
+   * Convert an array of exclusions and keep only from / to
+   *
+   * The method will always check if from if lower than to and will swap if required.
+   * @param {Array} [exclusions=[]]
+   * @param {object} [options={}]
+   * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of zones (after inversion)
+   * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of zones (after inversion)
+   */
+
+  function invert(exclusions = [], options = {}) {
+    let {
+      from = Number.NEGATIVE_INFINITY,
+      to = Number.POSITIVE_INFINITY
+    } = options;
+    if (from > to) [from, to] = [to, from];
+    exclusions = normalize(exclusions, {
+      from,
+      to
+    });
+    if (exclusions.length === 0) return [{
+      from,
+      to
+    }];
+    let zones = [];
+
+    for (let i = 0; i < exclusions.length; i++) {
+      let exclusion = exclusions[i];
+      let nextExclusion = exclusions[i + 1];
+
+      if (i === 0) {
+        if (exclusion.from > from) {
+          zones.push({
+            from,
+            to: exclusion.from
+          });
+        }
+      }
+
+      if (i === exclusions.length - 1) {
+        if (exclusion.to < to) {
+          zones.push({
+            from: exclusion.to,
+            to
+          });
+        }
+      } else {
+        zones.push({
+          from: exclusion.to,
+          to: nextExclusion.from
+        });
+      }
+    }
+
+    return zones;
+  }
+
+  /**
+   * Add the number of points per zone to reach a specified total
+   * @param {Array} [zones=[]]
+   * @param {number} [numberOfPoints] Total number of points to distribute between zones
+   * @param {object} [options={}]
+   * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of a zone
+   * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of a zone
+   */
+
+  function zonesWithPoints(zones, numberOfPoints, options = {}) {
+    if (zones.length === 0) return zones;
+    zones = normalize(zones, options);
+    const totalSize = zones.reduce((previous, current) => {
+      return previous + (current.to - current.from);
+    }, 0);
+    let unitsPerPoint = totalSize / numberOfPoints;
+    let currentTotal = 0;
+
+    for (let i = 0; i < zones.length - 1; i++) {
+      let zone = zones[i];
+      zone.numberOfPoints = Math.min(Math.round((zone.to - zone.from) / unitsPerPoint), numberOfPoints - currentTotal);
+      currentTotal += zone.numberOfPoints;
+    }
+
+    zones[zones.length - 1].numberOfPoints = numberOfPoints - currentTotal;
+    return zones;
+  }
+
   /**
    * Function that calculates the integral of the line between two
    * x-coordinates, given the slope and intercept of the line.
@@ -16913,32 +19741,34 @@
    */
 
   function equallySpacedSmooth(x, y, from, to, numberOfPoints) {
-    var xLength = x.length;
-    var step = (to - from) / (numberOfPoints - 1);
-    var halfStep = step / 2;
-    var output = new Array(numberOfPoints);
-    var initialOriginalStep = x[1] - x[0];
-    var lastOriginalStep = x[xLength - 1] - x[xLength - 2]; // Init main variables
-
-    var min = from - halfStep;
-    var max = from + halfStep;
-    var previousX = Number.MIN_VALUE;
-    var previousY = 0;
-    var nextX = x[0] - initialOriginalStep;
-    var nextY = 0;
-    var currentValue = 0;
-    var slope = 0;
-    var intercept = 0;
-    var sumAtMin = 0;
-    var sumAtMax = 0;
-    var i = 0; // index of input
-
-    var j = 0; // index of output
+    let xLength = x.length;
+    let step = (to - from) / (numberOfPoints - 1);
+    let halfStep = step / 2;
+    let output = new Array(numberOfPoints);
+    let initialOriginalStep = x[1] - x[0];
+    let lastOriginalStep = x[xLength - 1] - x[xLength - 2]; // Init main variables
+
+    let min = from - halfStep;
+    let max = from + halfStep;
+    let previousX = Number.MIN_VALUE;
+    let previousY = 0;
+    let nextX = x[0] - initialOriginalStep;
+    let nextY = 0;
+    let currentValue = 0;
+    let slope = 0;
+    let intercept = 0;
+    let sumAtMin = 0;
+    let sumAtMax = 0;
+    let i = 0; // index of input
+
+    let j = 0; // index of output
 
     function getSlope(x0, y0, x1, y1) {
       return (y1 - y0) / (x1 - x0);
     }
 
+    let add = 0;
+
     main: while (true) {
       if (previousX <= min && min <= nextX) {
         add = integral(0, min - previousX, slope, previousY);
@@ -16947,7 +19777,7 @@
 
       while (nextX - max >= 0) {
         // no overlap with original point, just consume current value
-        var add = integral(0, max - previousX, slope, previousY);
+        add = integral(0, max - previousX, slope, previousY);
         sumAtMax = currentValue + add;
         output[j++] = (sumAtMax - sumAtMin) / step;
 
@@ -16991,27 +19821,27 @@
    * @return {Array} - Array of y's equally spaced with the variant "slot"
    */
   function equallySpacedSlot(x, y, from, to, numberOfPoints) {
-    var xLength = x.length;
-    var step = (to - from) / (numberOfPoints - 1);
-    var halfStep = step / 2;
-    var lastStep = x[x.length - 1] - x[x.length - 2];
-    var start = from - halfStep;
-    var output = new Array(numberOfPoints); // Init main variables
-
-    var min = start;
-    var max = start + step;
-    var previousX = -Number.MAX_VALUE;
-    var previousY = 0;
-    var nextX = x[0];
-    var nextY = y[0];
-    var frontOutsideSpectra = 0;
-    var backOutsideSpectra = true;
-    var currentValue = 0; // for slot algorithm
-
-    var currentPoints = 0;
-    var i = 1; // index of input
-
-    var j = 0; // index of output
+    let xLength = x.length;
+    let step = (to - from) / (numberOfPoints - 1);
+    let halfStep = step / 2;
+    let lastStep = x[x.length - 1] - x[x.length - 2];
+    let start = from - halfStep;
+    let output = new Array(numberOfPoints); // Init main variables
+
+    let min = start;
+    let max = start + step;
+    let previousX = -Number.MAX_VALUE;
+    let previousY = 0;
+    let nextX = x[0];
+    let nextY = y[0];
+    let frontOutsideSpectra = 0;
+    let backOutsideSpectra = true;
+    let currentValue = 0; // for slot algorithm
+
+    let currentPoints = 0;
+    let i = 1; // index of input
+
+    let j = 0; // index of output
 
     main: while (true) {
       if (previousX >= nextX) throw new Error('x must be an increasing serie');
@@ -17062,79 +19892,6 @@
     return output;
   }
 
-  function getZones(from, to, numberOfPoints) {
-    let exclusions = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : [];
-
-    if (from > to) {
-      [from, to] = [to, from];
-    } // in exclusions from and to have to be defined
-
-
-    exclusions = exclusions.filter(exclusion => exclusion.from !== undefined && exclusion.to !== undefined);
-    exclusions = JSON.parse(JSON.stringify(exclusions)); // we ensure that from before to
-
-    exclusions.forEach(exclusion => {
-      if (exclusion.from > exclusion.to) {
-        [exclusion.to, exclusion.from] = [exclusion.from, exclusion.to];
-      }
-    });
-    exclusions.sort((a, b) => a.from - b.from); // we will rework the exclusions in order to remove overlap and outside range (from / to)
-
-    exclusions.forEach(exclusion => {
-      if (exclusion.from < from) exclusion.from = from;
-      if (exclusion.to > to) exclusion.to = to;
-    });
-
-    for (let i = 0; i < exclusions.length - 1; i++) {
-      if (exclusions[i].to > exclusions[i + 1].from) {
-        exclusions[i].to = exclusions[i + 1].from;
-      }
-    }
-
-    exclusions = exclusions.filter(exclusion => exclusion.from < exclusion.to);
-
-    if (!exclusions || exclusions.length === 0) {
-      return [{
-        from,
-        to,
-        numberOfPoints
-      }];
-    } // need to deal with overlapping exclusions and out of bound exclusions
-
-
-    let toRemove = exclusions.reduce((previous, exclusion) => previous += exclusion.to - exclusion.from, 0);
-    let total = to - from;
-    let unitsPerPoint = (total - toRemove) / numberOfPoints;
-    let zones = [];
-    let currentFrom = from;
-    let totalPoints = 0;
-
-    for (let exclusion of exclusions) {
-      let currentNbPoints = Math.round((exclusion.from - currentFrom) / unitsPerPoint);
-      totalPoints += currentNbPoints;
-
-      if (currentNbPoints > 0) {
-        zones.push({
-          from: currentFrom,
-          to: exclusion.from,
-          numberOfPoints: currentNbPoints
-        });
-      }
-
-      currentFrom = exclusion.to;
-    }
-
-    if (numberOfPoints - totalPoints > 0) {
-      zones.push({
-        from: currentFrom,
-        to: to,
-        numberOfPoints: numberOfPoints - totalPoints
-      });
-    }
-
-    return zones;
-  }
-
   /**
    * Function that returns a Number array of equally spaced numberOfPoints
    * containing a representation of intensities of the spectra arguments x
@@ -17151,6 +19908,7 @@
    * value. The smooth variant is the same but takes the integral of the range
    * of the slot and divide by the step size between two points in the new array.
    *
+   * If exclusions zone are present, zones are ignored !
    * @param {object} [arrayXY={}] - object containing 2 properties x and y (both an array)
    * @param {object} [options={}]
    * @param {number} [options.from=x[0]]
@@ -17158,12 +19916,11 @@
    * @param {string} [options.variant='smooth']
    * @param {number} [options.numberOfPoints=100]
    * @param {Array} [options.exclusions=[]] array of from / to that should be skipped for the generation of the points
+   * @param {Array} [options.zones=[]] array of from / to that should be kept
    * @return {object<x: Array, y:Array>} new object with x / y array with the equally spaced data.
    */
 
-  function equallySpaced() {
-    let arrayXY = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+  function equallySpaced(arrayXY = {}, options = {}) {
     let {
       x,
       y
@@ -17182,7 +19939,8 @@
       to = x[xLength - 1],
       variant = 'smooth',
       numberOfPoints = 100,
-      exclusions = []
+      exclusions = [],
+      zones = []
     } = options;
 
     if (xLength !== y.length) {
@@ -17205,7 +19963,17 @@
       throw new RangeError("'numberOfPoints' option must be greater than 1");
     }
 
-    let zones = getZones(from, to, numberOfPoints, exclusions);
+    if (zones.length === 0) {
+      zones = invert(exclusions, {
+        from,
+        to
+      });
+    }
+
+    zones = zonesWithPoints(zones, numberOfPoints, {
+      from,
+      to
+    });
     let xResult = [];
     let yResult = [];
 
@@ -17247,7 +20015,7 @@
       throw new RangeError('the number of points must be at least 1');
     }
 
-    var output = variant === 'slot' ? equallySpacedSlot(x, y, from, to, numberOfPoints) : equallySpacedSmooth(x, y, from, to, numberOfPoints);
+    let output = variant === 'slot' ? equallySpacedSlot(x, y, from, to, numberOfPoints) : equallySpacedSmooth(x, y, from, to, numberOfPoints);
     return {
       x: sequentialFill({
         from,
@@ -17258,9 +20026,7 @@
     };
   }
 
-  function getZones$1(from, to) {
-    let exclusions = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : [];
-
+  function getZones(from, to, exclusions = []) {
     if (from > to) {
       [from, to] = [to, from];
     } // in exclusions from and to have to be defined
@@ -17332,8 +20098,7 @@
    * @return {{x: Array<number>, y: Array<number>}}
    */
 
-  function filterX(points) {
-    let options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+  function filterX(points, options = {}) {
     const {
       x,
       y
@@ -17343,7 +20108,7 @@
       to = x[x.length - 1],
       exclusions = []
     } = options;
-    let zones = getZones$1(from, to, exclusions);
+    let zones = getZones(from, to, exclusions);
     let currentZoneIndex = 0;
     let newX = [];
     let newY = [];
@@ -17378,8 +20143,8 @@
     QrDecomposition: QrDecomposition$1
   } = MatrixLib;
   const Array$1 = {
-    min,
-    max,
+    min: min$1,
+    max: max$1,
     median,
     mean,
     mode: mode$1,
@@ -17405,17 +20170,18 @@
 
   exports.Array = Array$1;
   exports.ArrayXY = ArrayXY;
-  exports.BitArray = src$7;
+  exports.BitArray = src$3;
   exports.CholeskyDecomposition = CholeskyDecomposition$1;
-  exports.ConfusionMatrix = src$1;
-  exports.CrossValidation = src$3;
+  exports.ConfusionMatrix = ConfusionMatrix;
+  exports.CrossValidation = index$2;
   exports.DecisionTreeClassifier = DecisionTreeClassifier;
   exports.DecisionTreeRegression = DecisionTreeRegression;
   exports.Distance = distances;
   exports.EVD = EVD;
   exports.ExponentialRegression = ExponentialRegression;
-  exports.FCNNLS = index$2;
+  exports.FCNNLS = index$3;
   exports.FNN = FeedForwardNeuralNetwork;
+  exports.GSD = index$6;
   exports.HClust = index;
   exports.HashTable = HashTable;
   exports.KMeans = kmeans;
@@ -17427,9 +20193,11 @@
   exports.MatrixLib = MatrixLib;
   exports.MultivariateLinearRegression = MultivariateLinearRegression;
   exports.NaiveBayes = index$1;
+  exports.OPLS = OPLS;
+  exports.OPLSNipals = OPLSNipals;
   exports.PCA = PCA;
   exports.PLS = PLS;
-  exports.Performance = src$5;
+  exports.Performance = src$1;
   exports.PolynomialRegression = PolynomialRegression;
   exports.PowerRegression = PowerRegression;
   exports.QrDecomposition = QrDecomposition$1;
@@ -17437,7 +20205,7 @@
   exports.RandomForestClassifier = RandomForestClassifier;
   exports.RandomForestRegression = RandomForestRegression;
   exports.RobustPolynomialRegression = RobustPolynomialRegression;
-  exports.SOM = src$4;
+  exports.SOM = src;
   exports.SVD = SVD;
   exports.Similarity = similarities;
   exports.SimpleLinearRegression = SimpleLinearRegression;
@@ -17447,11 +20215,11 @@
   exports.binarySearch = binarySearch;
   exports.distanceMatrix = distanceMatrix;
   exports.levenbergMarquardt = levenbergMarquardt;
-  exports.numSort = index$3;
-  exports.padArray = src$6;
+  exports.numSort = index$4;
+  exports.padArray = src$2;
   exports.savitzkyGolay = savitzkyGolay;
 
   Object.defineProperty(exports, '__esModule', { value: true });
 
-}));
+})));
 //# sourceMappingURL=ml.js.map
diff --git a/dist/ml.js.map b/dist/ml.js.map
index fab0bc2..964f990 100644
--- a/dist/ml.js.map
+++ b/dist/ml.js.map
@@ -1 +1 @@
-{"version":3,"file":"ml.js","sources":["../node_modules/is-any-array/src/index.js","../node_modules/ml-array-max/lib-es6/index.js","../node_modules/ml-array-min/lib-es6/index.js","../node_modules/ml-array-rescale/lib-es6/index.js","../node_modules/ml-matrix/src/util.js","../node_modules/ml-matrix/src/stat.js","../node_modules/ml-matrix/src/inspect.js","../node_modules/ml-matrix/src/mathOperations.js","../node_modules/ml-matrix/src/matrix.js","../node_modules/ml-matrix/src/views/base.js","../node_modules/ml-matrix/src/views/column.js","../node_modules/ml-matrix/src/views/columnSelection.js","../node_modules/ml-matrix/src/views/flipColumn.js","../node_modules/ml-matrix/src/views/flipRow.js","../node_modules/ml-matrix/src/views/row.js","../node_modules/ml-matrix/src/views/rowSelection.js","../node_modules/ml-matrix/src/views/selection.js","../node_modules/ml-matrix/src/views/sub.js","../node_modules/ml-matrix/src/views/transpose.js","../node_modules/ml-matrix/src/wrap/WrapperMatrix1D.js","../node_modules/ml-matrix/src/wrap/WrapperMatrix2D.js","../node_modules/ml-matrix/src/wrap/wrap.js","../node_modules/ml-matrix/src/dc/lu.js","../node_modules/ml-matrix/src/dc/util.js","../node_modules/ml-matrix/src/dc/qr.js","../node_modules/ml-matrix/src/dc/svd.js","../node_modules/ml-matrix/src/decompositions.js","../node_modules/ml-matrix/src/determinant.js","../node_modules/ml-matrix/src/linearDependencies.js","../node_modules/ml-matrix/src/pseudoInverse.js","../node_modules/ml-matrix/src/covariance.js","../node_modules/ml-matrix/src/correlation.js","../node_modules/ml-matrix/src/dc/evd.js","../node_modules/ml-matrix/src/dc/cholesky.js","../node_modules/ml-matrix/src/dc/nipals.js","../node_modules/ml-array-sum/lib-es6/index.js","../node_modules/ml-array-mean/lib-es6/index.js","../node_modules/ml-cart/src/utils.js","../node_modules/ml-cart/src/TreeNode.js","../node_modules/ml-cart/src/DecisionTreeClassifier.js","../node_modules/ml-cart/src/DecisionTreeRegression.js","../node_modules/random-js/dist/random-js.esm.js","../node_modules/ml-random-forest/src/utils.js","../node_modules/ml-random-forest/src/RandomForestBase.js","../node_modules/ml-random-forest/src/RandomForestClassifier.js","../node_modules/median-quickselect/lib/median-quickselect.min.js","../node_modules/ml-array-median/lib-es6/index.js","../node_modules/ml-random-forest/src/RandomForestRegression.js","../node_modules/ml-pca/src/pca.js","../node_modules/ml-distance-euclidean/lib-es6/euclidean.js","../node_modules/ml-distance-matrix/src/index.js","../node_modules/heap/lib/heap.js","../node_modules/heap/index.js","../node_modules/ml-hclust/src/Cluster.js","../node_modules/ml-hclust/src/agnes.js","../node_modules/ml-hclust/src/index.js","../node_modules/ml-nearest-vector/lib-es6/index.js","../node_modules/ml-kmeans/src/utils.js","../node_modules/ml-xsadd/lib-es6/xsadd.js","../node_modules/ml-random/lib-es6/choice.js","../node_modules/ml-random/lib-es6/index.js","../node_modules/ml-kmeans/src/initialization.js","../node_modules/ml-kmeans/src/KMeansResult.js","../node_modules/ml-kmeans/src/kmeans.js","../node_modules/ml-naivebayes/src/utils.js","../node_modules/ml-naivebayes/src/GaussianNB.js","../node_modules/ml-naivebayes/src/MultinomialNB.js","../node_modules/ml-knn/src/KDTree.js","../node_modules/ml-knn/src/index.js","../node_modules/ml-pls/src/utils.js","../node_modules/ml-pls/src/pls.js","../node_modules/ml-pls/src/kopls.js","../node_modules/ml-confusion-matrix/src/index.js","../node_modules/ml-combinations/src/index.js","../node_modules/ml-cross-validation/src/index.js","../node_modules/ml-fnn/FeedForwardNeuralNetwork.js","../node_modules/ml-som/src/node-square.js","../node_modules/ml-som/src/node-hexagonal.js","../node_modules/ml-som/src/index.js","../node_modules/ml-regression-base/src/maybeToPrecision.js","../node_modules/ml-regression-base/src/checkArrayLength.js","../node_modules/ml-regression-base/src/index.js","../node_modules/ml-regression-polynomial/src/index.js","../node_modules/ml-regression-simple-linear/src/index.js","../node_modules/ml-regression-exponential/src/index.js","../node_modules/ml-regression-power/src/index.js","../node_modules/ml-regression-multivariate-linear/src/index.js","../node_modules/ml-kernel-gaussian/gaussian-kernel.js","../node_modules/ml-kernel-polynomial/polynomial-kernel.js","../node_modules/ml-kernel-sigmoid/sigmoid-kernel.js","../node_modules/ml-kernel/src/kernels/anova-kernel.js","../node_modules/ml-kernel/src/kernels/cauchy-kernel.js","../node_modules/ml-kernel/src/kernels/exponential-kernel.js","../node_modules/ml-kernel/src/kernels/histogram-intersection-kernel.js","../node_modules/ml-kernel/src/kernels/laplacian-kernel.js","../node_modules/ml-kernel/src/kernels/multiquadratic-kernel.js","../node_modules/ml-kernel/src/kernels/rational-quadratic-kernel.js","../node_modules/ml-kernel/src/kernel.js","../node_modules/ml-regression-theil-sen/src/index.js","../node_modules/ml-regression-robust-polynomial/src/index.js","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strict';\n\nconst toString = Object.prototype.toString;\n\nfunction isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n\nmodule.exports = isAnyArray;\n","import isArray from 'is-any-array';\n\n/**\n * Computes the maximum of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction max(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var maxValue = input[0];\n\n  for (var i = 1; i < input.length; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\n\n/**\n * Computes the minimum of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction min(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var minValue = input[0];\n\n  for (var i = 1; i < input.length; i++) {\n    if (input[i] < minValue) minValue = input[i];\n  }\n\n  return minValue;\n}\n\nexport default min;\n","import max from 'ml-array-max';\nimport min from 'ml-array-min';\nimport isArray from 'is-any-array';\n\nfunction rescale(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  } else if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var output;\n\n  if (options.output !== undefined) {\n    if (!isArray(options.output)) {\n      throw new TypeError('output option must be an array if specified');\n    }\n\n    output = options.output;\n  } else {\n    output = new Array(input.length);\n  }\n\n  var currentMin = min(input);\n  var currentMax = max(input);\n\n  if (currentMin === currentMax) {\n    throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');\n  }\n\n  var _options$min = options.min,\n      minValue = _options$min === void 0 ? options.autoMinMax ? currentMin : 0 : _options$min,\n      _options$max = options.max,\n      maxValue = _options$max === void 0 ? options.autoMinMax ? currentMax : 1 : _options$max;\n\n  if (minValue >= maxValue) {\n    throw new RangeError('min option must be smaller than max option');\n  }\n\n  var factor = (maxValue - minValue) / (currentMax - currentMin);\n\n  for (var i = 0; i < input.length; i++) {\n    output[i] = (input[i] - currentMin) * factor + minValue;\n  }\n\n  return output;\n}\n\nexport default rescale;\n","/**\n * @private\n * Check that a row index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkRowIndex(matrix, index, outer) {\n  let max = outer ? matrix.rows : matrix.rows - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Row index out of range');\n  }\n}\n\n/**\n * @private\n * Check that a column index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkColumnIndex(matrix, index, outer) {\n  let max = outer ? matrix.columns : matrix.columns - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Column index out of range');\n  }\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkRowVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.columns) {\n    throw new RangeError(\n      'vector size must be the same as the number of columns',\n    );\n  }\n  return vector;\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkColumnVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.rows) {\n    throw new RangeError('vector size must be the same as the number of rows');\n  }\n  return vector;\n}\n\nexport function checkIndices(matrix, rowIndices, columnIndices) {\n  return {\n    row: checkRowIndices(matrix, rowIndices),\n    column: checkColumnIndices(matrix, columnIndices),\n  };\n}\n\nexport function checkRowIndices(matrix, rowIndices) {\n  if (typeof rowIndices !== 'object') {\n    throw new TypeError('unexpected type for row indices');\n  }\n\n  let rowOut = rowIndices.some((r) => {\n    return r < 0 || r >= matrix.rows;\n  });\n\n  if (rowOut) {\n    throw new RangeError('row indices are out of range');\n  }\n\n  if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);\n\n  return rowIndices;\n}\n\nexport function checkColumnIndices(matrix, columnIndices) {\n  if (typeof columnIndices !== 'object') {\n    throw new TypeError('unexpected type for column indices');\n  }\n\n  let columnOut = columnIndices.some((c) => {\n    return c < 0 || c >= matrix.columns;\n  });\n\n  if (columnOut) {\n    throw new RangeError('column indices are out of range');\n  }\n  if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);\n\n  return columnIndices;\n}\n\nexport function checkRange(matrix, startRow, endRow, startColumn, endColumn) {\n  if (arguments.length !== 5) {\n    throw new RangeError('expected 4 arguments');\n  }\n  checkNumber('startRow', startRow);\n  checkNumber('endRow', endRow);\n  checkNumber('startColumn', startColumn);\n  checkNumber('endColumn', endColumn);\n  if (\n    startRow > endRow ||\n    startColumn > endColumn ||\n    startRow < 0 ||\n    startRow >= matrix.rows ||\n    endRow < 0 ||\n    endRow >= matrix.rows ||\n    startColumn < 0 ||\n    startColumn >= matrix.columns ||\n    endColumn < 0 ||\n    endColumn >= matrix.columns\n  ) {\n    throw new RangeError('Submatrix indices are out of range');\n  }\n}\n\nexport function newArray(length, value = 0) {\n  let array = [];\n  for (let i = 0; i < length; i++) {\n    array.push(value);\n  }\n  return array;\n}\n\nfunction checkNumber(name, value) {\n  if (typeof value !== 'number') {\n    throw new TypeError(`${name} must be a number`);\n  }\n}\n","import { newArray } from './util';\n\nexport function sumByRow(matrix) {\n  let sum = newArray(matrix.rows);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumByColumn(matrix) {\n  let sum = newArray(matrix.columns);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumAll(matrix) {\n  let v = 0;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v += matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function productByRow(matrix) {\n  let sum = newArray(matrix.rows, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productByColumn(matrix) {\n  let sum = newArray(matrix.columns, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productAll(matrix) {\n  let v = 1;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v *= matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function varianceByRow(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let i = 0; i < rows; i++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean[i];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / cols) / (cols - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / cols) / cols);\n    }\n  }\n  return variance;\n}\n\nexport function varianceByColumn(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let j = 0; j < cols; j++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let i = 0; i < rows; i++) {\n      x = matrix.get(i, j) - mean[j];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / rows) / (rows - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / rows) / rows);\n    }\n  }\n  return variance;\n}\n\nexport function varianceAll(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const size = rows * cols;\n\n  let sum1 = 0;\n  let sum2 = 0;\n  let x = 0;\n  for (let i = 0; i < rows; i++) {\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean;\n      sum1 += x;\n      sum2 += x * x;\n    }\n  }\n  if (unbiased) {\n    return (sum2 - (sum1 * sum1) / size) / (size - 1);\n  } else {\n    return (sum2 - (sum1 * sum1) / size) / size;\n  }\n}\n\nexport function centerByRow(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[i]);\n    }\n  }\n}\n\nexport function centerByColumn(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[j]);\n    }\n  }\n}\n\nexport function centerAll(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean);\n    }\n  }\n}\n\nexport function getScaleByRow(matrix) {\n  const scale = [];\n  for (let i = 0; i < matrix.rows; i++) {\n    let sum = 0;\n    for (let j = 0; j < matrix.columns; j++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.columns - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByRow(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[i]);\n    }\n  }\n}\n\nexport function getScaleByColumn(matrix) {\n  const scale = [];\n  for (let j = 0; j < matrix.columns; j++) {\n    let sum = 0;\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.rows - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByColumn(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[j]);\n    }\n  }\n}\n\nexport function getScaleAll(matrix) {\n  const divider = matrix.size - 1;\n  let sum = 0;\n  for (let j = 0; j < matrix.columns; j++) {\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / divider;\n    }\n  }\n  return Math.sqrt(sum);\n}\n\nexport function scaleAll(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale);\n    }\n  }\n}\n","export function inspectMatrix() {\n  const indent = ' '.repeat(2);\n  const indentData = ' '.repeat(4);\n  return `${this.constructor.name} {\n${indent}[\n${indentData}${inspectData(this, indentData)}\n${indent}]\n${indent}rows: ${this.rows}\n${indent}columns: ${this.columns}\n}`;\n}\n\nconst maxRows = 15;\nconst maxColumns = 10;\nconst maxNumSize = 8;\n\nfunction inspectData(matrix, indent) {\n  const { rows, columns } = matrix;\n  const maxI = Math.min(rows, maxRows);\n  const maxJ = Math.min(columns, maxColumns);\n  const result = [];\n  for (let i = 0; i < maxI; i++) {\n    let line = [];\n    for (let j = 0; j < maxJ; j++) {\n      line.push(formatNumber(matrix.get(i, j)));\n    }\n    result.push(`${line.join(' ')}`);\n  }\n  if (maxJ !== columns) {\n    result[result.length - 1] += ` ... ${columns - maxColumns} more columns`;\n  }\n  if (maxI !== rows) {\n    result.push(`... ${rows - maxRows} more rows`);\n  }\n  return result.join(`\\n${indent}`);\n}\n\nfunction formatNumber(num) {\n  const numStr = String(num);\n  if (numStr.length <= maxNumSize) {\n    return numStr.padEnd(maxNumSize, ' ');\n  }\n  const precise = num.toPrecision(maxNumSize - 2);\n  if (precise.length <= maxNumSize) {\n    return precise;\n  }\n  const exponential = num.toExponential(maxNumSize - 2);\n  const eIndex = exponential.indexOf('e');\n  const e = exponential.substring(eIndex);\n  return exponential.substring(0, maxNumSize - e.length) + e;\n}\n","export function installMathOperations(AbstractMatrix, Matrix) {\n  AbstractMatrix.prototype.add = function add(value) {\n    if (typeof value === 'number') return this.addS(value);\n    return this.addM(value);\n  };\n\n  AbstractMatrix.prototype.addS = function addS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.addM = function addM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.add = function add(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.add(value);\n  };\n\n  AbstractMatrix.prototype.sub = function sub(value) {\n    if (typeof value === 'number') return this.subS(value);\n    return this.subM(value);\n  };\n\n  AbstractMatrix.prototype.subS = function subS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.subM = function subM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sub = function sub(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sub(value);\n  };\n  AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;\n  AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;\n  AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;\n  AbstractMatrix.subtract = AbstractMatrix.sub;\n\n  AbstractMatrix.prototype.mul = function mul(value) {\n    if (typeof value === 'number') return this.mulS(value);\n    return this.mulM(value);\n  };\n\n  AbstractMatrix.prototype.mulS = function mulS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.mulM = function mulM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.mul = function mul(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.mul(value);\n  };\n  AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;\n  AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;\n  AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;\n  AbstractMatrix.multiply = AbstractMatrix.mul;\n\n  AbstractMatrix.prototype.div = function div(value) {\n    if (typeof value === 'number') return this.divS(value);\n    return this.divM(value);\n  };\n\n  AbstractMatrix.prototype.divS = function divS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.divM = function divM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.div = function div(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.div(value);\n  };\n  AbstractMatrix.prototype.divide = AbstractMatrix.prototype.div;\n  AbstractMatrix.prototype.divideS = AbstractMatrix.prototype.divS;\n  AbstractMatrix.prototype.divideM = AbstractMatrix.prototype.divM;\n  AbstractMatrix.divide = AbstractMatrix.div;\n\n  AbstractMatrix.prototype.mod = function mod(value) {\n    if (typeof value === 'number') return this.modS(value);\n    return this.modM(value);\n  };\n\n  AbstractMatrix.prototype.modS = function modS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) % value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.modM = function modM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) % matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.mod = function mod(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.mod(value);\n  };\n  AbstractMatrix.prototype.modulus = AbstractMatrix.prototype.mod;\n  AbstractMatrix.prototype.modulusS = AbstractMatrix.prototype.modS;\n  AbstractMatrix.prototype.modulusM = AbstractMatrix.prototype.modM;\n  AbstractMatrix.modulus = AbstractMatrix.mod;\n\n  AbstractMatrix.prototype.and = function and(value) {\n    if (typeof value === 'number') return this.andS(value);\n    return this.andM(value);\n  };\n\n  AbstractMatrix.prototype.andS = function andS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) & value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.andM = function andM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) & matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.and = function and(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.and(value);\n  };\n\n  AbstractMatrix.prototype.or = function or(value) {\n    if (typeof value === 'number') return this.orS(value);\n    return this.orM(value);\n  };\n\n  AbstractMatrix.prototype.orS = function orS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) | value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.orM = function orM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) | matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.or = function or(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.or(value);\n  };\n\n  AbstractMatrix.prototype.xor = function xor(value) {\n    if (typeof value === 'number') return this.xorS(value);\n    return this.xorM(value);\n  };\n\n  AbstractMatrix.prototype.xorS = function xorS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ^ value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.xorM = function xorM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ^ matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.xor = function xor(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.xor(value);\n  };\n\n  AbstractMatrix.prototype.leftShift = function leftShift(value) {\n    if (typeof value === 'number') return this.leftShiftS(value);\n    return this.leftShiftM(value);\n  };\n\n  AbstractMatrix.prototype.leftShiftS = function leftShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) << value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.leftShiftM = function leftShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) << matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.leftShift = function leftShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.leftShift(value);\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShift = function signPropagatingRightShift(value) {\n    if (typeof value === 'number') return this.signPropagatingRightShiftS(value);\n    return this.signPropagatingRightShiftM(value);\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShiftS = function signPropagatingRightShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >> value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShiftM = function signPropagatingRightShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >> matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.signPropagatingRightShift = function signPropagatingRightShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.signPropagatingRightShift(value);\n  };\n\n  AbstractMatrix.prototype.rightShift = function rightShift(value) {\n    if (typeof value === 'number') return this.rightShiftS(value);\n    return this.rightShiftM(value);\n  };\n\n  AbstractMatrix.prototype.rightShiftS = function rightShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >>> value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.rightShiftM = function rightShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >>> matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.rightShift = function rightShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.rightShift(value);\n  };\n  AbstractMatrix.prototype.zeroFillRightShift = AbstractMatrix.prototype.rightShift;\n  AbstractMatrix.prototype.zeroFillRightShiftS = AbstractMatrix.prototype.rightShiftS;\n  AbstractMatrix.prototype.zeroFillRightShiftM = AbstractMatrix.prototype.rightShiftM;\n  AbstractMatrix.zeroFillRightShift = AbstractMatrix.rightShift;\n\n  AbstractMatrix.prototype.not = function not() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, ~(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.not = function not(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.not();\n  };\n\n  AbstractMatrix.prototype.abs = function abs() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.abs(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.abs = function abs(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.abs();\n  };\n\n  AbstractMatrix.prototype.acos = function acos() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.acos(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.acos = function acos(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.acos();\n  };\n\n  AbstractMatrix.prototype.acosh = function acosh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.acosh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.acosh = function acosh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.acosh();\n  };\n\n  AbstractMatrix.prototype.asin = function asin() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.asin(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.asin = function asin(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.asin();\n  };\n\n  AbstractMatrix.prototype.asinh = function asinh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.asinh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.asinh = function asinh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.asinh();\n  };\n\n  AbstractMatrix.prototype.atan = function atan() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.atan(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.atan = function atan(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.atan();\n  };\n\n  AbstractMatrix.prototype.atanh = function atanh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.atanh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.atanh = function atanh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.atanh();\n  };\n\n  AbstractMatrix.prototype.cbrt = function cbrt() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cbrt(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cbrt = function cbrt(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cbrt();\n  };\n\n  AbstractMatrix.prototype.ceil = function ceil() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.ceil(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.ceil = function ceil(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.ceil();\n  };\n\n  AbstractMatrix.prototype.clz32 = function clz32() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.clz32(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.clz32 = function clz32(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.clz32();\n  };\n\n  AbstractMatrix.prototype.cos = function cos() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cos(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cos = function cos(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cos();\n  };\n\n  AbstractMatrix.prototype.cosh = function cosh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cosh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cosh = function cosh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cosh();\n  };\n\n  AbstractMatrix.prototype.exp = function exp() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.exp(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.exp = function exp(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.exp();\n  };\n\n  AbstractMatrix.prototype.expm1 = function expm1() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.expm1(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.expm1 = function expm1(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.expm1();\n  };\n\n  AbstractMatrix.prototype.floor = function floor() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.floor(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.floor = function floor(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.floor();\n  };\n\n  AbstractMatrix.prototype.fround = function fround() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.fround(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.fround = function fround(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.fround();\n  };\n\n  AbstractMatrix.prototype.log = function log() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log = function log(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log();\n  };\n\n  AbstractMatrix.prototype.log1p = function log1p() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log1p(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log1p = function log1p(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log1p();\n  };\n\n  AbstractMatrix.prototype.log10 = function log10() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log10(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log10 = function log10(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log10();\n  };\n\n  AbstractMatrix.prototype.log2 = function log2() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log2(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log2 = function log2(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log2();\n  };\n\n  AbstractMatrix.prototype.round = function round() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.round(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.round = function round(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.round();\n  };\n\n  AbstractMatrix.prototype.sign = function sign() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sign(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sign = function sign(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sign();\n  };\n\n  AbstractMatrix.prototype.sin = function sin() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sin(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sin = function sin(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sin();\n  };\n\n  AbstractMatrix.prototype.sinh = function sinh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sinh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sinh = function sinh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sinh();\n  };\n\n  AbstractMatrix.prototype.sqrt = function sqrt() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sqrt(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sqrt = function sqrt(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sqrt();\n  };\n\n  AbstractMatrix.prototype.tan = function tan() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.tan(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.tan = function tan(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.tan();\n  };\n\n  AbstractMatrix.prototype.tanh = function tanh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.tanh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.tanh = function tanh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.tanh();\n  };\n\n  AbstractMatrix.prototype.trunc = function trunc() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.trunc(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.trunc = function trunc(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.trunc();\n  };\n\n  AbstractMatrix.pow = function pow(matrix, arg0) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.pow(arg0);\n  };\n\n  AbstractMatrix.prototype.pow = function pow(value) {\n    if (typeof value === 'number') return this.powS(value);\n    return this.powM(value);\n  };\n\n  AbstractMatrix.prototype.powS = function powS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.pow(this.get(i, j), value));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.powM = function powM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.pow(this.get(i, j), matrix.get(i, j)));\n      }\n    }\n    return this;\n  };\n}\n","import rescale from 'ml-array-rescale';\n\nimport {\n  checkRowVector,\n  checkRowIndex,\n  checkColumnIndex,\n  checkColumnVector,\n  checkRange,\n  checkIndices,\n} from './util';\nimport {\n  sumByRow,\n  sumByColumn,\n  sumAll,\n  productByRow,\n  productByColumn,\n  productAll,\n  varianceByRow,\n  varianceByColumn,\n  varianceAll,\n  centerByRow,\n  centerByColumn,\n  centerAll,\n  scaleByRow,\n  scaleByColumn,\n  scaleAll,\n  getScaleByRow,\n  getScaleByColumn,\n  getScaleAll,\n} from './stat';\nimport { inspectMatrix } from './inspect';\nimport { installMathOperations } from './mathOperations';\n\nexport class AbstractMatrix {\n  static from1DArray(newRows, newColumns, newData) {\n    let length = newRows * newColumns;\n    if (length !== newData.length) {\n      throw new RangeError('data length does not match given dimensions');\n    }\n    let newMatrix = new Matrix(newRows, newColumns);\n    for (let row = 0; row < newRows; row++) {\n      for (let column = 0; column < newColumns; column++) {\n        newMatrix.set(row, column, newData[row * newColumns + column]);\n      }\n    }\n    return newMatrix;\n  }\n\n  static rowVector(newData) {\n    let vector = new Matrix(1, newData.length);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(0, i, newData[i]);\n    }\n    return vector;\n  }\n\n  static columnVector(newData) {\n    let vector = new Matrix(newData.length, 1);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(i, 0, newData[i]);\n    }\n    return vector;\n  }\n\n  static zeros(rows, columns) {\n    return new Matrix(rows, columns);\n  }\n\n  static ones(rows, columns) {\n    return new Matrix(rows, columns).fill(1);\n  }\n\n  static rand(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { random = Math.random } = options;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.set(i, j, random());\n      }\n    }\n    return matrix;\n  }\n\n  static randInt(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1000, random = Math.random } = options;\n    if (!Number.isInteger(min)) throw new TypeError('min must be an integer');\n    if (!Number.isInteger(max)) throw new TypeError('max must be an integer');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let interval = max - min;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        let value = min + Math.round(random() * interval);\n        matrix.set(i, j, value);\n      }\n    }\n    return matrix;\n  }\n\n  static eye(rows, columns, value) {\n    if (columns === undefined) columns = rows;\n    if (value === undefined) value = 1;\n    let min = Math.min(rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, value);\n    }\n    return matrix;\n  }\n\n  static diag(data, rows, columns) {\n    let l = data.length;\n    if (rows === undefined) rows = l;\n    if (columns === undefined) columns = rows;\n    let min = Math.min(l, rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, data[i]);\n    }\n    return matrix;\n  }\n\n  static min(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static max(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new this(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static checkMatrix(value) {\n    return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);\n  }\n\n  static isMatrix(value) {\n    return value != null && value.klass === 'Matrix';\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  apply(callback) {\n    if (typeof callback !== 'function') {\n      throw new TypeError('callback must be a function');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        callback.call(this, i, j);\n      }\n    }\n    return this;\n  }\n\n  to1DArray() {\n    let array = [];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        array.push(this.get(i, j));\n      }\n    }\n    return array;\n  }\n\n  to2DArray() {\n    let copy = [];\n    for (let i = 0; i < this.rows; i++) {\n      copy.push([]);\n      for (let j = 0; j < this.columns; j++) {\n        copy[i].push(this.get(i, j));\n      }\n    }\n    return copy;\n  }\n\n  toJSON() {\n    return this.to2DArray();\n  }\n\n  isRowVector() {\n    return this.rows === 1;\n  }\n\n  isColumnVector() {\n    return this.columns === 1;\n  }\n\n  isVector() {\n    return this.rows === 1 || this.columns === 1;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (this.isSquare()) {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j <= i; j++) {\n          if (this.get(i, j) !== this.get(j, i)) {\n            return false;\n          }\n        }\n      }\n      return true;\n    }\n    return false;\n  }\n\n  isEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isEchelonForm = false;\n          checked = true;\n        }\n      }\n      i++;\n    }\n    return isEchelonForm;\n  }\n\n  isReducedEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isReducedEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isReducedEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isReducedEchelonForm = false;\n          checked = true;\n        }\n      }\n      for (let k = j + 1; k < this.rows; k++) {\n        if (this.get(i, k) !== 0) {\n          isReducedEchelonForm = false;\n        }\n      }\n      i++;\n    }\n    return isReducedEchelonForm;\n  }\n\n  echelonForm() {\n    let result = this.clone();\n    let h = 0;\n    let k = 0;\n    while (h < result.rows && k < result.columns) {\n      let iMax = h;\n      for (let i = h; i < result.rows; i++) {\n        if (result.get(i, k) > result.get(iMax, k)) {\n          iMax = i;\n        }\n      }\n      if (result.get(iMax, k) === 0) {\n        k++;\n      } else {\n        result.swapRows(h, iMax);\n        let tmp = result.get(h, k);\n        for (let j = k; j < result.columns; j++) {\n          result.set(h, j, result.get(h, j) / tmp);\n        }\n        for (let i = h + 1; i < result.rows; i++) {\n          let factor = result.get(i, k) / result.get(h, k);\n          result.set(i, k, 0);\n          for (let j = k + 1; j < result.columns; j++) {\n            result.set(i, j, result.get(i, j) - result.get(h, j) * factor);\n          }\n        }\n        h++;\n        k++;\n      }\n    }\n    return result;\n  }\n\n  reducedEchelonForm() {\n    let result = this.echelonForm();\n    let m = result.columns;\n    let n = result.rows;\n    let h = n - 1;\n    while (h >= 0) {\n      if (result.maxRow(h) === 0) {\n        h--;\n      } else {\n        let p = 0;\n        let pivot = false;\n        while (p < n && pivot === false) {\n          if (result.get(h, p) === 1) {\n            pivot = true;\n          } else {\n            p++;\n          }\n        }\n        for (let i = 0; i < h; i++) {\n          let factor = result.get(i, p);\n          for (let j = p; j < m; j++) {\n            let tmp = result.get(i, j) - factor * result.get(h, j);\n            result.set(i, j, tmp);\n          }\n        }\n        h--;\n      }\n    }\n    return result;\n  }\n\n  set() {\n    throw new Error('set method is unimplemented');\n  }\n\n  get() {\n    throw new Error('get method is unimplemented');\n  }\n\n  repeat(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { rows = 1, columns = 1 } = options;\n    if (!Number.isInteger(rows) || rows <= 0) {\n      throw new TypeError('rows must be a positive integer');\n    }\n    if (!Number.isInteger(columns) || columns <= 0) {\n      throw new TypeError('columns must be a positive integer');\n    }\n    let matrix = new Matrix(this.rows * rows, this.columns * columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.setSubMatrix(this, this.rows * i, this.columns * j);\n      }\n    }\n    return matrix;\n  }\n\n  fill(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, value);\n      }\n    }\n    return this;\n  }\n\n  neg() {\n    return this.mulS(-1);\n  }\n\n  getRow(index) {\n    checkRowIndex(this, index);\n    let row = [];\n    for (let i = 0; i < this.columns; i++) {\n      row.push(this.get(index, i));\n    }\n    return row;\n  }\n\n  getRowVector(index) {\n    return Matrix.rowVector(this.getRow(index));\n  }\n\n  setRow(index, array) {\n    checkRowIndex(this, index);\n    array = checkRowVector(this, array);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, array[i]);\n    }\n    return this;\n  }\n\n  swapRows(row1, row2) {\n    checkRowIndex(this, row1);\n    checkRowIndex(this, row2);\n    for (let i = 0; i < this.columns; i++) {\n      let temp = this.get(row1, i);\n      this.set(row1, i, this.get(row2, i));\n      this.set(row2, i, temp);\n    }\n    return this;\n  }\n\n  getColumn(index) {\n    checkColumnIndex(this, index);\n    let column = [];\n    for (let i = 0; i < this.rows; i++) {\n      column.push(this.get(i, index));\n    }\n    return column;\n  }\n\n  getColumnVector(index) {\n    return Matrix.columnVector(this.getColumn(index));\n  }\n\n  setColumn(index, array) {\n    checkColumnIndex(this, index);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, array[i]);\n    }\n    return this;\n  }\n\n  swapColumns(column1, column2) {\n    checkColumnIndex(this, column1);\n    checkColumnIndex(this, column2);\n    for (let i = 0; i < this.rows; i++) {\n      let temp = this.get(i, column1);\n      this.set(i, column1, this.get(i, column2));\n      this.set(i, column2, temp);\n    }\n    return this;\n  }\n\n  addRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[j]);\n      }\n    }\n    return this;\n  }\n\n  subRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[j]);\n      }\n    }\n    return this;\n  }\n\n  mulRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[j]);\n      }\n    }\n    return this;\n  }\n\n  divRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[j]);\n      }\n    }\n    return this;\n  }\n\n  addColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[i]);\n      }\n    }\n    return this;\n  }\n\n  subColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[i]);\n      }\n    }\n    return this;\n  }\n\n  divColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulRow(index, value) {\n    checkRowIndex(this, index);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, this.get(index, i) * value);\n    }\n    return this;\n  }\n\n  mulColumn(index, value) {\n    checkColumnIndex(this, index);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, this.get(i, index) * value);\n    }\n    return this;\n  }\n\n  max() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  maxIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  min() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  minIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  maxRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  maxRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  minRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  minRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  maxColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  maxColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  minColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  minColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  diag() {\n    let min = Math.min(this.rows, this.columns);\n    let diag = [];\n    for (let i = 0; i < min; i++) {\n      diag.push(this.get(i, i));\n    }\n    return diag;\n  }\n\n  norm(type = 'frobenius') {\n    let result = 0;\n    if (type === 'max') {\n      return this.max();\n    } else if (type === 'frobenius') {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j < this.columns; j++) {\n          result = result + this.get(i, j) * this.get(i, j);\n        }\n      }\n      return Math.sqrt(result);\n    } else {\n      throw new RangeError(`unknown norm type: ${type}`);\n    }\n  }\n\n  cumulativeSum() {\n    let sum = 0;\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        sum += this.get(i, j);\n        this.set(i, j, sum);\n      }\n    }\n    return this;\n  }\n\n  dot(vector2) {\n    if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();\n    let vector1 = this.to1DArray();\n    if (vector1.length !== vector2.length) {\n      throw new RangeError('vectors do not have the same size');\n    }\n    let dot = 0;\n    for (let i = 0; i < vector1.length; i++) {\n      dot += vector1[i] * vector2[i];\n    }\n    return dot;\n  }\n\n  mmul(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.columns;\n\n    let result = new Matrix(m, p);\n\n    let Bcolj = new Float64Array(n);\n    for (let j = 0; j < p; j++) {\n      for (let k = 0; k < n; k++) {\n        Bcolj[k] = other.get(k, j);\n      }\n\n      for (let i = 0; i < m; i++) {\n        let s = 0;\n        for (let k = 0; k < n; k++) {\n          s += this.get(i, k) * Bcolj[k];\n        }\n\n        result.set(i, j, s);\n      }\n    }\n    return result;\n  }\n\n  strassen2x2(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(2, 2);\n    const a11 = this.get(0, 0);\n    const b11 = other.get(0, 0);\n    const a12 = this.get(0, 1);\n    const b12 = other.get(0, 1);\n    const a21 = this.get(1, 0);\n    const b21 = other.get(1, 0);\n    const a22 = this.get(1, 1);\n    const b22 = other.get(1, 1);\n\n    // Compute intermediate values.\n    const m1 = (a11 + a22) * (b11 + b22);\n    const m2 = (a21 + a22) * b11;\n    const m3 = a11 * (b12 - b22);\n    const m4 = a22 * (b21 - b11);\n    const m5 = (a11 + a12) * b22;\n    const m6 = (a21 - a11) * (b11 + b12);\n    const m7 = (a12 - a22) * (b21 + b22);\n\n    // Combine intermediate values into the output.\n    const c00 = m1 + m4 - m5 + m7;\n    const c01 = m3 + m5;\n    const c10 = m2 + m4;\n    const c11 = m1 - m2 + m3 + m6;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    return result;\n  }\n\n  strassen3x3(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(3, 3);\n\n    const a00 = this.get(0, 0);\n    const a01 = this.get(0, 1);\n    const a02 = this.get(0, 2);\n    const a10 = this.get(1, 0);\n    const a11 = this.get(1, 1);\n    const a12 = this.get(1, 2);\n    const a20 = this.get(2, 0);\n    const a21 = this.get(2, 1);\n    const a22 = this.get(2, 2);\n\n    const b00 = other.get(0, 0);\n    const b01 = other.get(0, 1);\n    const b02 = other.get(0, 2);\n    const b10 = other.get(1, 0);\n    const b11 = other.get(1, 1);\n    const b12 = other.get(1, 2);\n    const b20 = other.get(2, 0);\n    const b21 = other.get(2, 1);\n    const b22 = other.get(2, 2);\n\n    const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;\n    const m2 = (a00 - a10) * (-b01 + b11);\n    const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);\n    const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);\n    const m5 = (a10 + a11) * (-b00 + b01);\n    const m6 = a00 * b00;\n    const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);\n    const m8 = (-a00 + a20) * (b02 - b12);\n    const m9 = (a20 + a21) * (-b00 + b02);\n    const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;\n    const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);\n    const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);\n    const m13 = (a02 - a22) * (b11 - b21);\n    const m14 = a02 * b20;\n    const m15 = (a21 + a22) * (-b20 + b21);\n    const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);\n    const m17 = (a02 - a12) * (b12 - b22);\n    const m18 = (a11 + a12) * (-b20 + b22);\n    const m19 = a01 * b10;\n    const m20 = a12 * b21;\n    const m21 = a10 * b02;\n    const m22 = a20 * b01;\n    const m23 = a22 * b22;\n\n    const c00 = m6 + m14 + m19;\n    const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;\n    const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;\n    const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;\n    const c11 = m2 + m4 + m5 + m6 + m20;\n    const c12 = m14 + m16 + m17 + m18 + m21;\n    const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;\n    const c21 = m12 + m13 + m14 + m15 + m22;\n    const c22 = m6 + m7 + m8 + m9 + m23;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(0, 2, c02);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    result.set(1, 2, c12);\n    result.set(2, 0, c20);\n    result.set(2, 1, c21);\n    result.set(2, 2, c22);\n    return result;\n  }\n\n  mmulStrassen(y) {\n    y = Matrix.checkMatrix(y);\n    let x = this.clone();\n    let r1 = x.rows;\n    let c1 = x.columns;\n    let r2 = y.rows;\n    let c2 = y.columns;\n    if (c1 !== r2) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        `Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`,\n      );\n    }\n\n    // Put a matrix into the top left of a matrix of zeros.\n    // `rows` and `cols` are the dimensions of the output matrix.\n    function embed(mat, rows, cols) {\n      let r = mat.rows;\n      let c = mat.columns;\n      if (r === rows && c === cols) {\n        return mat;\n      } else {\n        let resultat = AbstractMatrix.zeros(rows, cols);\n        resultat = resultat.setSubMatrix(mat, 0, 0);\n        return resultat;\n      }\n    }\n\n    // Make sure both matrices are the same size.\n    // This is exclusively for simplicity:\n    // this algorithm can be implemented with matrices of different sizes.\n\n    let r = Math.max(r1, r2);\n    let c = Math.max(c1, c2);\n    x = embed(x, r, c);\n    y = embed(y, r, c);\n\n    // Our recursive multiplication function.\n    function blockMult(a, b, rows, cols) {\n      // For small matrices, resort to naive multiplication.\n      if (rows <= 512 || cols <= 512) {\n        return a.mmul(b); // a is equivalent to this\n      }\n\n      // Apply dynamic padding.\n      if (rows % 2 === 1 && cols % 2 === 1) {\n        a = embed(a, rows + 1, cols + 1);\n        b = embed(b, rows + 1, cols + 1);\n      } else if (rows % 2 === 1) {\n        a = embed(a, rows + 1, cols);\n        b = embed(b, rows + 1, cols);\n      } else if (cols % 2 === 1) {\n        a = embed(a, rows, cols + 1);\n        b = embed(b, rows, cols + 1);\n      }\n\n      let halfRows = parseInt(a.rows / 2, 10);\n      let halfCols = parseInt(a.columns / 2, 10);\n      // Subdivide input matrices.\n      let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n      let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n\n      let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);\n      let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);\n\n      let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);\n      let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);\n\n      let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);\n      let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);\n\n      // Compute intermediate values.\n      let m1 = blockMult(\n        AbstractMatrix.add(a11, a22),\n        AbstractMatrix.add(b11, b22),\n        halfRows,\n        halfCols,\n      );\n      let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);\n      let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);\n      let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);\n      let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);\n      let m6 = blockMult(\n        AbstractMatrix.sub(a21, a11),\n        AbstractMatrix.add(b11, b12),\n        halfRows,\n        halfCols,\n      );\n      let m7 = blockMult(\n        AbstractMatrix.sub(a12, a22),\n        AbstractMatrix.add(b21, b22),\n        halfRows,\n        halfCols,\n      );\n\n      // Combine intermediate values into the output.\n      let c11 = AbstractMatrix.add(m1, m4);\n      c11.sub(m5);\n      c11.add(m7);\n      let c12 = AbstractMatrix.add(m3, m5);\n      let c21 = AbstractMatrix.add(m2, m4);\n      let c22 = AbstractMatrix.sub(m1, m2);\n      c22.add(m3);\n      c22.add(m6);\n\n      // Crop output to the desired size (undo dynamic padding).\n      let resultat = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);\n      resultat = resultat.setSubMatrix(c11, 0, 0);\n      resultat = resultat.setSubMatrix(c12, c11.rows, 0);\n      resultat = resultat.setSubMatrix(c21, 0, c11.columns);\n      resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);\n      return resultat.subMatrix(0, rows - 1, 0, cols - 1);\n    }\n    return blockMult(x, y, r, c);\n  }\n\n  scaleRows(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.rows; i++) {\n      const row = this.getRow(i);\n      rescale(row, { min, max, output: row });\n      newMatrix.setRow(i, row);\n    }\n    return newMatrix;\n  }\n\n  scaleColumns(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.columns; i++) {\n      const column = this.getColumn(i);\n      rescale(column, {\n        min: min,\n        max: max,\n        output: column,\n      });\n      newMatrix.setColumn(i, column);\n    }\n    return newMatrix;\n  }\n\n  flipRows() {\n    const middle = Math.ceil(this.columns / 2);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < middle; j++) {\n        let first = this.get(i, j);\n        let last = this.get(i, this.columns - 1 - j);\n        this.set(i, j, last);\n        this.set(i, this.columns - 1 - j, first);\n      }\n    }\n    return this;\n  }\n\n  flipColumns() {\n    const middle = Math.ceil(this.rows / 2);\n    for (let j = 0; j < this.columns; j++) {\n      for (let i = 0; i < middle; i++) {\n        let first = this.get(i, j);\n        let last = this.get(this.rows - 1 - i, j);\n        this.set(i, j, last);\n        this.set(this.rows - 1 - i, j, first);\n      }\n    }\n    return this;\n  }\n\n  kroneckerProduct(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.rows;\n    let q = other.columns;\n\n    let result = new Matrix(m * p, n * q);\n    for (let i = 0; i < m; i++) {\n      for (let j = 0; j < n; j++) {\n        for (let k = 0; k < p; k++) {\n          for (let l = 0; l < q; l++) {\n            result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));\n          }\n        }\n      }\n    }\n    return result;\n  }\n\n  transpose() {\n    let result = new Matrix(this.columns, this.rows);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        result.set(j, i, this.get(i, j));\n      }\n    }\n    return result;\n  }\n\n  sortRows(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.rows; i++) {\n      this.setRow(i, this.getRow(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  sortColumns(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.columns; i++) {\n      this.setColumn(i, this.getColumn(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  subMatrix(startRow, endRow, startColumn, endColumn) {\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    let newMatrix = new Matrix(\n      endRow - startRow + 1,\n      endColumn - startColumn + 1,\n    );\n    for (let i = startRow; i <= endRow; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        newMatrix.set(i - startRow, j - startColumn, this.get(i, j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixRow(indices, startColumn, endColumn) {\n    if (startColumn === undefined) startColumn = 0;\n    if (endColumn === undefined) endColumn = this.columns - 1;\n    if (\n      startColumn > endColumn ||\n      startColumn < 0 ||\n      startColumn >= this.columns ||\n      endColumn < 0 ||\n      endColumn >= this.columns\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        if (indices[i] < 0 || indices[i] >= this.rows) {\n          throw new RangeError(`Row index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(i, j - startColumn, this.get(indices[i], j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixColumn(indices, startRow, endRow) {\n    if (startRow === undefined) startRow = 0;\n    if (endRow === undefined) endRow = this.rows - 1;\n    if (\n      startRow > endRow ||\n      startRow < 0 ||\n      startRow >= this.rows ||\n      endRow < 0 ||\n      endRow >= this.rows\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(endRow - startRow + 1, indices.length);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startRow; j <= endRow; j++) {\n        if (indices[i] < 0 || indices[i] >= this.columns) {\n          throw new RangeError(`Column index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(j - startRow, i, this.get(j, indices[i]));\n      }\n    }\n    return newMatrix;\n  }\n\n  setSubMatrix(matrix, startRow, startColumn) {\n    matrix = Matrix.checkMatrix(matrix);\n    let endRow = startRow + matrix.rows - 1;\n    let endColumn = startColumn + matrix.columns - 1;\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    for (let i = 0; i < matrix.rows; i++) {\n      for (let j = 0; j < matrix.columns; j++) {\n        this.set(startRow + i, startColumn + j, matrix.get(i, j));\n      }\n    }\n    return this;\n  }\n\n  selection(rowIndices, columnIndices) {\n    let indices = checkIndices(this, rowIndices, columnIndices);\n    let newMatrix = new Matrix(rowIndices.length, columnIndices.length);\n    for (let i = 0; i < indices.row.length; i++) {\n      let rowIndex = indices.row[i];\n      for (let j = 0; j < indices.column.length; j++) {\n        let columnIndex = indices.column[j];\n        newMatrix.set(i, j, this.get(rowIndex, columnIndex));\n      }\n    }\n    return newMatrix;\n  }\n\n  trace() {\n    let min = Math.min(this.rows, this.columns);\n    let trace = 0;\n    for (let i = 0; i < min; i++) {\n      trace += this.get(i, i);\n    }\n    return trace;\n  }\n\n  clone() {\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let row = 0; row < this.rows; row++) {\n      for (let column = 0; column < this.columns; column++) {\n        newMatrix.set(row, column, this.get(row, column));\n      }\n    }\n    return newMatrix;\n  }\n\n  sum(by) {\n    switch (by) {\n      case 'row':\n        return sumByRow(this);\n      case 'column':\n        return sumByColumn(this);\n      case undefined:\n        return sumAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  product(by) {\n    switch (by) {\n      case 'row':\n        return productByRow(this);\n      case 'column':\n        return productByColumn(this);\n      case undefined:\n        return productAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  mean(by) {\n    const sum = this.sum(by);\n    switch (by) {\n      case 'row': {\n        for (let i = 0; i < this.rows; i++) {\n          sum[i] /= this.columns;\n        }\n        return sum;\n      }\n      case 'column': {\n        for (let i = 0; i < this.columns; i++) {\n          sum[i] /= this.rows;\n        }\n        return sum;\n      }\n      case undefined:\n        return sum / this.size;\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  variance(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { unbiased = true, mean = this.mean(by) } = options;\n    if (typeof unbiased !== 'boolean') {\n      throw new TypeError('unbiased must be a boolean');\n    }\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByRow(this, unbiased, mean);\n      }\n      case 'column': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByColumn(this, unbiased, mean);\n      }\n      case undefined: {\n        if (typeof mean !== 'number') {\n          throw new TypeError('mean must be a number');\n        }\n        return varianceAll(this, unbiased, mean);\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  standardDeviation(by, options) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    const variance = this.variance(by, options);\n    if (by === undefined) {\n      return Math.sqrt(variance);\n    } else {\n      for (let i = 0; i < variance.length; i++) {\n        variance[i] = Math.sqrt(variance[i]);\n      }\n      return variance;\n    }\n  }\n\n  center(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { center = this.mean(by) } = options;\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByRow(this, center);\n        return this;\n      }\n      case 'column': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByColumn(this, center);\n        return this;\n      }\n      case undefined: {\n        if (typeof center !== 'number') {\n          throw new TypeError('center must be a number');\n        }\n        centerAll(this, center);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  scale(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    let scale = options.scale;\n    switch (by) {\n      case 'row': {\n        if (scale === undefined) {\n          scale = getScaleByRow(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByRow(this, scale);\n        return this;\n      }\n      case 'column': {\n        if (scale === undefined) {\n          scale = getScaleByColumn(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByColumn(this, scale);\n        return this;\n      }\n      case undefined: {\n        if (scale === undefined) {\n          scale = getScaleAll(this);\n        } else if (typeof scale !== 'number') {\n          throw new TypeError('scale must be a number');\n        }\n        scaleAll(this, scale);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n}\n\nAbstractMatrix.prototype.klass = 'Matrix';\nif (typeof Symbol !== 'undefined') {\n  AbstractMatrix.prototype[\n    Symbol.for('nodejs.util.inspect.custom')\n  ] = inspectMatrix;\n}\n\nfunction compareNumbers(a, b) {\n  return a - b;\n}\n\n// Synonyms\nAbstractMatrix.random = AbstractMatrix.rand;\nAbstractMatrix.randomInt = AbstractMatrix.randInt;\nAbstractMatrix.diagonal = AbstractMatrix.diag;\nAbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;\nAbstractMatrix.identity = AbstractMatrix.eye;\nAbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;\nAbstractMatrix.prototype.tensorProduct =\n  AbstractMatrix.prototype.kroneckerProduct;\n\nexport default class Matrix extends AbstractMatrix {\n  constructor(nRows, nColumns) {\n    super();\n    if (Matrix.isMatrix(nRows)) {\n      return nRows.clone();\n    } else if (Number.isInteger(nRows) && nRows > 0) {\n      // Create an empty matrix\n      this.data = [];\n      if (Number.isInteger(nColumns) && nColumns > 0) {\n        for (let i = 0; i < nRows; i++) {\n          this.data.push(new Float64Array(nColumns));\n        }\n      } else {\n        throw new TypeError('nColumns must be a positive integer');\n      }\n    } else if (Array.isArray(nRows)) {\n      // Copy the values from the 2D array\n      const arrayData = nRows;\n      nRows = arrayData.length;\n      nColumns = arrayData[0].length;\n      if (typeof nColumns !== 'number' || nColumns === 0) {\n        throw new TypeError(\n          'Data must be a 2D array with at least one element',\n        );\n      }\n      this.data = [];\n      for (let i = 0; i < nRows; i++) {\n        if (arrayData[i].length !== nColumns) {\n          throw new RangeError('Inconsistent array dimensions');\n        }\n        this.data.push(Float64Array.from(arrayData[i]));\n      }\n    } else {\n      throw new TypeError(\n        'First argument must be a positive number or an array',\n      );\n    }\n    this.rows = nRows;\n    this.columns = nColumns;\n    return this;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n\n  removeRow(index) {\n    checkRowIndex(this, index);\n    if (this.rows === 1) {\n      throw new RangeError('A matrix cannot have less than one row');\n    }\n    this.data.splice(index, 1);\n    this.rows -= 1;\n    return this;\n  }\n\n  addRow(index, array) {\n    if (array === undefined) {\n      array = index;\n      index = this.rows;\n    }\n    checkRowIndex(this, index, true);\n    array = Float64Array.from(checkRowVector(this, array, true));\n    this.data.splice(index, 0, array);\n    this.rows += 1;\n    return this;\n  }\n\n  removeColumn(index) {\n    checkColumnIndex(this, index);\n    if (this.columns === 1) {\n      throw new RangeError('A matrix cannot have less than one column');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns - 1);\n      for (let j = 0; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      for (let j = index + 1; j < this.columns; j++) {\n        newRow[j - 1] = this.data[i][j];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns -= 1;\n    return this;\n  }\n\n  addColumn(index, array) {\n    if (typeof array === 'undefined') {\n      array = index;\n      index = this.columns;\n    }\n    checkColumnIndex(this, index, true);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns + 1);\n      let j = 0;\n      for (; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      newRow[j++] = array[i];\n      for (; j < this.columns + 1; j++) {\n        newRow[j] = this.data[i][j - 1];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns += 1;\n    return this;\n  }\n}\n\ninstallMathOperations(AbstractMatrix, Matrix);\n","import { AbstractMatrix } from '../matrix';\n\nexport default class BaseView extends AbstractMatrix {\n  constructor(matrix, rows, columns) {\n    super();\n    this.matrix = matrix;\n    this.rows = rows;\n    this.columns = columns;\n  }\n}\n","import { checkColumnIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixColumnView extends BaseView {\n  constructor(matrix, column) {\n    checkColumnIndex(matrix, column);\n    super(matrix, matrix.rows, 1);\n    this.column = column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.column, value);\n    return this;\n  }\n\n  get(rowIndex) {\n    return this.matrix.get(rowIndex, this.column);\n  }\n}\n","import { checkColumnIndices } from '../util';\r\n\r\nimport BaseView from './base';\r\n\r\nexport default class MatrixColumnSelectionView extends BaseView {\r\n  constructor(matrix, columnIndices) {\r\n    columnIndices = checkColumnIndices(matrix, columnIndices);\r\n    super(matrix, matrix.rows, columnIndices.length);\r\n    this.columnIndices = columnIndices;\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);\r\n  }\r\n}\r\n","import BaseView from './base';\r\n\r\nexport default class MatrixFlipColumnView extends BaseView {\r\n  constructor(matrix) {\r\n    super(matrix, matrix.rows, matrix.columns);\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(rowIndex, this.columns - columnIndex - 1);\r\n  }\r\n}\r\n","import BaseView from './base';\r\n\r\nexport default class MatrixFlipRowView extends BaseView {\r\n  constructor(matrix) {\r\n    super(matrix, matrix.rows, matrix.columns);\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(this.rows - rowIndex - 1, columnIndex);\r\n  }\r\n}\r\n","import { checkRowIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixRowView extends BaseView {\n  constructor(matrix, row) {\n    checkRowIndex(matrix, row);\n    super(matrix, 1, matrix.columns);\n    this.row = row;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.row, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.row, columnIndex);\n  }\n}\n","import { checkRowIndices } from '../util';\r\n\r\nimport BaseView from './base';\r\n\r\nexport default class MatrixRowSelectionView extends BaseView {\r\n  constructor(matrix, rowIndices) {\r\n    rowIndices = checkRowIndices(matrix, rowIndices);\r\n    super(matrix, rowIndices.length, matrix.columns);\r\n    this.rowIndices = rowIndices;\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(this.rowIndices[rowIndex], columnIndex);\r\n  }\r\n}\r\n","import { checkIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSelectionView extends BaseView {\n  constructor(matrix, rowIndices, columnIndices) {\n    let indices = checkIndices(matrix, rowIndices, columnIndices);\n    super(matrix, indices.row.length, indices.column.length);\n    this.rowIndices = indices.row;\n    this.columnIndices = indices.column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n    );\n  }\n}\n","import { checkRange } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSubView extends BaseView {\n  constructor(matrix, startRow, endRow, startColumn, endColumn) {\n    checkRange(matrix, startRow, endRow, startColumn, endColumn);\n    super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);\n    this.startRow = startRow;\n    this.startColumn = startColumn;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n    );\n  }\n}\n","import BaseView from './base';\r\n\r\nexport default class MatrixTransposeView extends BaseView {\r\n  constructor(matrix) {\r\n    super(matrix, matrix.columns, matrix.rows);\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(columnIndex, rowIndex, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(columnIndex, rowIndex);\r\n  }\r\n}\r\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix1D extends AbstractMatrix {\n  constructor(data, options = {}) {\n    const { rows = 1 } = options;\n\n    if (data.length % rows !== 0) {\n      throw new Error('the data length is not divisible by the number of rows');\n    }\n    super();\n    this.rows = rows;\n    this.columns = data.length / rows;\n    this.data = data;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    this.data[index] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    return this.data[index];\n  }\n\n  _calculateIndex(row, column) {\n    return row * this.columns + column;\n  }\n}\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix2D extends AbstractMatrix {\n  constructor(data) {\n    super();\n    this.data = data;\n    this.rows = data.length;\n    this.columns = data[0].length;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n}\n","import WrapperMatrix1D from './WrapperMatrix1D';\nimport WrapperMatrix2D from './WrapperMatrix2D';\n\nexport function wrap(array, options) {\n  if (Array.isArray(array)) {\n    if (array[0] && Array.isArray(array[0])) {\n      return new WrapperMatrix2D(array);\n    } else {\n      return new WrapperMatrix1D(array, options);\n    }\n  } else {\n    throw new Error('the argument is not an array');\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class LuDecomposition {\n  constructor(matrix) {\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n\n    let lu = matrix.clone();\n    let rows = lu.rows;\n    let columns = lu.columns;\n    let pivotVector = new Float64Array(rows);\n    let pivotSign = 1;\n    let i, j, k, p, s, t, v;\n    let LUcolj, kmax;\n\n    for (i = 0; i < rows; i++) {\n      pivotVector[i] = i;\n    }\n\n    LUcolj = new Float64Array(rows);\n\n    for (j = 0; j < columns; j++) {\n      for (i = 0; i < rows; i++) {\n        LUcolj[i] = lu.get(i, j);\n      }\n\n      for (i = 0; i < rows; i++) {\n        kmax = Math.min(i, j);\n        s = 0;\n        for (k = 0; k < kmax; k++) {\n          s += lu.get(i, k) * LUcolj[k];\n        }\n        LUcolj[i] -= s;\n        lu.set(i, j, LUcolj[i]);\n      }\n\n      p = j;\n      for (i = j + 1; i < rows; i++) {\n        if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {\n          p = i;\n        }\n      }\n\n      if (p !== j) {\n        for (k = 0; k < columns; k++) {\n          t = lu.get(p, k);\n          lu.set(p, k, lu.get(j, k));\n          lu.set(j, k, t);\n        }\n\n        v = pivotVector[p];\n        pivotVector[p] = pivotVector[j];\n        pivotVector[j] = v;\n\n        pivotSign = -pivotSign;\n      }\n\n      if (j < rows && lu.get(j, j) !== 0) {\n        for (i = j + 1; i < rows; i++) {\n          lu.set(i, j, lu.get(i, j) / lu.get(j, j));\n        }\n      }\n    }\n\n    this.LU = lu;\n    this.pivotVector = pivotVector;\n    this.pivotSign = pivotSign;\n  }\n\n  isSingular() {\n    let data = this.LU;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      if (data.get(j, j) === 0) {\n        return true;\n      }\n    }\n    return false;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let lu = this.LU;\n    let rows = lu.rows;\n\n    if (rows !== value.rows) {\n      throw new Error('Invalid matrix dimensions');\n    }\n    if (this.isSingular()) {\n      throw new Error('LU matrix is singular');\n    }\n\n    let count = value.columns;\n    let X = value.subMatrixRow(this.pivotVector, 0, count - 1);\n    let columns = lu.columns;\n    let i, j, k;\n\n    for (k = 0; k < columns; k++) {\n      for (i = k + 1; i < columns; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    for (k = columns - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / lu.get(k, k));\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    return X;\n  }\n\n  get determinant() {\n    let data = this.LU;\n    if (!data.isSquare()) {\n      throw new Error('Matrix must be square');\n    }\n    let determinant = this.pivotSign;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      determinant *= data.get(j, j);\n    }\n    return determinant;\n  }\n\n  get lowerTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i > j) {\n          X.set(i, j, data.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, 1);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get upperTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i <= j) {\n          X.set(i, j, data.get(i, j));\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get pivotPermutationVector() {\n    return Array.from(this.pivotVector);\n  }\n}\n","export function hypotenuse(a, b) {\n  let r = 0;\n  if (Math.abs(a) > Math.abs(b)) {\n    r = b / a;\n    return Math.abs(a) * Math.sqrt(1 + r * r);\n  }\n  if (b !== 0) {\n    r = a / b;\n    return Math.abs(b) * Math.sqrt(1 + r * r);\n  }\n  return 0;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class QrDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let qr = value.clone();\n    let m = value.rows;\n    let n = value.columns;\n    let rdiag = new Float64Array(n);\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      let nrm = 0;\n      for (i = k; i < m; i++) {\n        nrm = hypotenuse(nrm, qr.get(i, k));\n      }\n      if (nrm !== 0) {\n        if (qr.get(k, k) < 0) {\n          nrm = -nrm;\n        }\n        for (i = k; i < m; i++) {\n          qr.set(i, k, qr.get(i, k) / nrm);\n        }\n        qr.set(k, k, qr.get(k, k) + 1);\n        for (j = k + 1; j < n; j++) {\n          s = 0;\n          for (i = k; i < m; i++) {\n            s += qr.get(i, k) * qr.get(i, j);\n          }\n          s = -s / qr.get(k, k);\n          for (i = k; i < m; i++) {\n            qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n      rdiag[k] = -nrm;\n    }\n\n    this.QR = qr;\n    this.Rdiag = rdiag;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let qr = this.QR;\n    let m = qr.rows;\n\n    if (value.rows !== m) {\n      throw new Error('Matrix row dimensions must agree');\n    }\n    if (!this.isFullRank()) {\n      throw new Error('Matrix is rank deficient');\n    }\n\n    let count = value.columns;\n    let X = value.clone();\n    let n = qr.columns;\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      for (j = 0; j < count; j++) {\n        s = 0;\n        for (i = k; i < m; i++) {\n          s += qr.get(i, k) * X.get(i, j);\n        }\n        s = -s / qr.get(k, k);\n        for (i = k; i < m; i++) {\n          X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n        }\n      }\n    }\n    for (k = n - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / this.Rdiag[k]);\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));\n        }\n      }\n    }\n\n    return X.subMatrix(0, n - 1, 0, count - 1);\n  }\n\n  isFullRank() {\n    let columns = this.QR.columns;\n    for (let i = 0; i < columns; i++) {\n      if (this.Rdiag[i] === 0) {\n        return false;\n      }\n    }\n    return true;\n  }\n\n  get upperTriangularMatrix() {\n    let qr = this.QR;\n    let n = qr.columns;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        if (i < j) {\n          X.set(i, j, qr.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, this.Rdiag[i]);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get orthogonalMatrix() {\n    let qr = this.QR;\n    let rows = qr.rows;\n    let columns = qr.columns;\n    let X = new Matrix(rows, columns);\n    let i, j, k, s;\n\n    for (k = columns - 1; k >= 0; k--) {\n      for (i = 0; i < rows; i++) {\n        X.set(i, k, 0);\n      }\n      X.set(k, k, 1);\n      for (j = k; j < columns; j++) {\n        if (qr.get(k, k) !== 0) {\n          s = 0;\n          for (i = k; i < rows; i++) {\n            s += qr.get(i, k) * X.get(i, j);\n          }\n\n          s = -s / qr.get(k, k);\n\n          for (i = k; i < rows; i++) {\n            X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n    }\n    return X;\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class SingularValueDecomposition {\n  constructor(value, options = {}) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let m = value.rows;\n    let n = value.columns;\n\n    const {\n      computeLeftSingularVectors = true,\n      computeRightSingularVectors = true,\n      autoTranspose = false,\n    } = options;\n\n    let wantu = Boolean(computeLeftSingularVectors);\n    let wantv = Boolean(computeRightSingularVectors);\n\n    let swapped = false;\n    let a;\n    if (m < n) {\n      if (!autoTranspose) {\n        a = value.clone();\n        // eslint-disable-next-line no-console\n        console.warn(\n          'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose',\n        );\n      } else {\n        a = value.transpose();\n        m = a.rows;\n        n = a.columns;\n        swapped = true;\n        let aux = wantu;\n        wantu = wantv;\n        wantv = aux;\n      }\n    } else {\n      a = value.clone();\n    }\n\n    let nu = Math.min(m, n);\n    let ni = Math.min(m + 1, n);\n    let s = new Float64Array(ni);\n    let U = new Matrix(m, nu);\n    let V = new Matrix(n, n);\n\n    let e = new Float64Array(n);\n    let work = new Float64Array(m);\n\n    let si = new Float64Array(ni);\n    for (let i = 0; i < ni; i++) si[i] = i;\n\n    let nct = Math.min(m - 1, n);\n    let nrt = Math.max(0, Math.min(n - 2, m));\n    let mrc = Math.max(nct, nrt);\n\n    for (let k = 0; k < mrc; k++) {\n      if (k < nct) {\n        s[k] = 0;\n        for (let i = k; i < m; i++) {\n          s[k] = hypotenuse(s[k], a.get(i, k));\n        }\n        if (s[k] !== 0) {\n          if (a.get(k, k) < 0) {\n            s[k] = -s[k];\n          }\n          for (let i = k; i < m; i++) {\n            a.set(i, k, a.get(i, k) / s[k]);\n          }\n          a.set(k, k, a.get(k, k) + 1);\n        }\n        s[k] = -s[k];\n      }\n\n      for (let j = k + 1; j < n; j++) {\n        if (k < nct && s[k] !== 0) {\n          let t = 0;\n          for (let i = k; i < m; i++) {\n            t += a.get(i, k) * a.get(i, j);\n          }\n          t = -t / a.get(k, k);\n          for (let i = k; i < m; i++) {\n            a.set(i, j, a.get(i, j) + t * a.get(i, k));\n          }\n        }\n        e[j] = a.get(k, j);\n      }\n\n      if (wantu && k < nct) {\n        for (let i = k; i < m; i++) {\n          U.set(i, k, a.get(i, k));\n        }\n      }\n\n      if (k < nrt) {\n        e[k] = 0;\n        for (let i = k + 1; i < n; i++) {\n          e[k] = hypotenuse(e[k], e[i]);\n        }\n        if (e[k] !== 0) {\n          if (e[k + 1] < 0) {\n            e[k] = 0 - e[k];\n          }\n          for (let i = k + 1; i < n; i++) {\n            e[i] /= e[k];\n          }\n          e[k + 1] += 1;\n        }\n        e[k] = -e[k];\n        if (k + 1 < m && e[k] !== 0) {\n          for (let i = k + 1; i < m; i++) {\n            work[i] = 0;\n          }\n          for (let i = k + 1; i < m; i++) {\n            for (let j = k + 1; j < n; j++) {\n              work[i] += e[j] * a.get(i, j);\n            }\n          }\n          for (let j = k + 1; j < n; j++) {\n            let t = -e[j] / e[k + 1];\n            for (let i = k + 1; i < m; i++) {\n              a.set(i, j, a.get(i, j) + t * work[i]);\n            }\n          }\n        }\n        if (wantv) {\n          for (let i = k + 1; i < n; i++) {\n            V.set(i, k, e[i]);\n          }\n        }\n      }\n    }\n\n    let p = Math.min(n, m + 1);\n    if (nct < n) {\n      s[nct] = a.get(nct, nct);\n    }\n    if (m < p) {\n      s[p - 1] = 0;\n    }\n    if (nrt + 1 < p) {\n      e[nrt] = a.get(nrt, p - 1);\n    }\n    e[p - 1] = 0;\n\n    if (wantu) {\n      for (let j = nct; j < nu; j++) {\n        for (let i = 0; i < m; i++) {\n          U.set(i, j, 0);\n        }\n        U.set(j, j, 1);\n      }\n      for (let k = nct - 1; k >= 0; k--) {\n        if (s[k] !== 0) {\n          for (let j = k + 1; j < nu; j++) {\n            let t = 0;\n            for (let i = k; i < m; i++) {\n              t += U.get(i, k) * U.get(i, j);\n            }\n            t = -t / U.get(k, k);\n            for (let i = k; i < m; i++) {\n              U.set(i, j, U.get(i, j) + t * U.get(i, k));\n            }\n          }\n          for (let i = k; i < m; i++) {\n            U.set(i, k, -U.get(i, k));\n          }\n          U.set(k, k, 1 + U.get(k, k));\n          for (let i = 0; i < k - 1; i++) {\n            U.set(i, k, 0);\n          }\n        } else {\n          for (let i = 0; i < m; i++) {\n            U.set(i, k, 0);\n          }\n          U.set(k, k, 1);\n        }\n      }\n    }\n\n    if (wantv) {\n      for (let k = n - 1; k >= 0; k--) {\n        if (k < nrt && e[k] !== 0) {\n          for (let j = k + 1; j < n; j++) {\n            let t = 0;\n            for (let i = k + 1; i < n; i++) {\n              t += V.get(i, k) * V.get(i, j);\n            }\n            t = -t / V.get(k + 1, k);\n            for (let i = k + 1; i < n; i++) {\n              V.set(i, j, V.get(i, j) + t * V.get(i, k));\n            }\n          }\n        }\n        for (let i = 0; i < n; i++) {\n          V.set(i, k, 0);\n        }\n        V.set(k, k, 1);\n      }\n    }\n\n    let pp = p - 1;\n    let iter = 0;\n    let eps = Number.EPSILON;\n    while (p > 0) {\n      let k, kase;\n      for (k = p - 2; k >= -1; k--) {\n        if (k === -1) {\n          break;\n        }\n        const alpha =\n          Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));\n        if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {\n          e[k] = 0;\n          break;\n        }\n      }\n      if (k === p - 2) {\n        kase = 4;\n      } else {\n        let ks;\n        for (ks = p - 1; ks >= k; ks--) {\n          if (ks === k) {\n            break;\n          }\n          let t =\n            (ks !== p ? Math.abs(e[ks]) : 0) +\n            (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);\n          if (Math.abs(s[ks]) <= eps * t) {\n            s[ks] = 0;\n            break;\n          }\n        }\n        if (ks === k) {\n          kase = 3;\n        } else if (ks === p - 1) {\n          kase = 1;\n        } else {\n          kase = 2;\n          k = ks;\n        }\n      }\n\n      k++;\n\n      switch (kase) {\n        case 1: {\n          let f = e[p - 2];\n          e[p - 2] = 0;\n          for (let j = p - 2; j >= k; j--) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            if (j !== k) {\n              f = -sn * e[j - 1];\n              e[j - 1] = cs * e[j - 1];\n            }\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, p - 1);\n                V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));\n                V.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 2: {\n          let f = e[k - 1];\n          e[k - 1] = 0;\n          for (let j = k; j < p; j++) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            f = -sn * e[j];\n            e[j] = cs * e[j];\n            if (wantu) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, k - 1);\n                U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 3: {\n          const scale = Math.max(\n            Math.abs(s[p - 1]),\n            Math.abs(s[p - 2]),\n            Math.abs(e[p - 2]),\n            Math.abs(s[k]),\n            Math.abs(e[k]),\n          );\n          const sp = s[p - 1] / scale;\n          const spm1 = s[p - 2] / scale;\n          const epm1 = e[p - 2] / scale;\n          const sk = s[k] / scale;\n          const ek = e[k] / scale;\n          const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;\n          const c = sp * epm1 * (sp * epm1);\n          let shift = 0;\n          if (b !== 0 || c !== 0) {\n            if (b < 0) {\n              shift = 0 - Math.sqrt(b * b + c);\n            } else {\n              shift = Math.sqrt(b * b + c);\n            }\n            shift = c / (b + shift);\n          }\n          let f = (sk + sp) * (sk - sp) + shift;\n          let g = sk * ek;\n          for (let j = k; j < p - 1; j++) {\n            let t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            let cs = f / t;\n            let sn = g / t;\n            if (j !== k) {\n              e[j - 1] = t;\n            }\n            f = cs * s[j] + sn * e[j];\n            e[j] = cs * e[j] - sn * s[j];\n            g = sn * s[j + 1];\n            s[j + 1] = cs * s[j + 1];\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, j + 1);\n                V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));\n                V.set(i, j, t);\n              }\n            }\n            t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            cs = f / t;\n            sn = g / t;\n            s[j] = t;\n            f = cs * e[j] + sn * s[j + 1];\n            s[j + 1] = -sn * e[j] + cs * s[j + 1];\n            g = sn * e[j + 1];\n            e[j + 1] = cs * e[j + 1];\n            if (wantu && j < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, j + 1);\n                U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          e[p - 2] = f;\n          iter = iter + 1;\n          break;\n        }\n        case 4: {\n          if (s[k] <= 0) {\n            s[k] = s[k] < 0 ? -s[k] : 0;\n            if (wantv) {\n              for (let i = 0; i <= pp; i++) {\n                V.set(i, k, -V.get(i, k));\n              }\n            }\n          }\n          while (k < pp) {\n            if (s[k] >= s[k + 1]) {\n              break;\n            }\n            let t = s[k];\n            s[k] = s[k + 1];\n            s[k + 1] = t;\n            if (wantv && k < n - 1) {\n              for (let i = 0; i < n; i++) {\n                t = V.get(i, k + 1);\n                V.set(i, k + 1, V.get(i, k));\n                V.set(i, k, t);\n              }\n            }\n            if (wantu && k < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = U.get(i, k + 1);\n                U.set(i, k + 1, U.get(i, k));\n                U.set(i, k, t);\n              }\n            }\n            k++;\n          }\n          iter = 0;\n          p--;\n          break;\n        }\n        // no default\n      }\n    }\n\n    if (swapped) {\n      let tmp = V;\n      V = U;\n      U = tmp;\n    }\n\n    this.m = m;\n    this.n = n;\n    this.s = s;\n    this.U = U;\n    this.V = V;\n  }\n\n  solve(value) {\n    let Y = value;\n    let e = this.threshold;\n    let scols = this.s.length;\n    let Ls = Matrix.zeros(scols, scols);\n\n    for (let i = 0; i < scols; i++) {\n      if (Math.abs(this.s[i]) <= e) {\n        Ls.set(i, i, 0);\n      } else {\n        Ls.set(i, i, 1 / this.s[i]);\n      }\n    }\n\n    let U = this.U;\n    let V = this.rightSingularVectors;\n\n    let VL = V.mmul(Ls);\n    let vrows = V.rows;\n    let urows = U.rows;\n    let VLU = Matrix.zeros(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < scols; k++) {\n          sum += VL.get(i, k) * U.get(j, k);\n        }\n        VLU.set(i, j, sum);\n      }\n    }\n\n    return VLU.mmul(Y);\n  }\n\n  solveForDiagonal(value) {\n    return this.solve(Matrix.diag(value));\n  }\n\n  inverse() {\n    let V = this.V;\n    let e = this.threshold;\n    let vrows = V.rows;\n    let vcols = V.columns;\n    let X = new Matrix(vrows, this.s.length);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < vcols; j++) {\n        if (Math.abs(this.s[j]) > e) {\n          X.set(i, j, V.get(i, j) / this.s[j]);\n        }\n      }\n    }\n\n    let U = this.U;\n\n    let urows = U.rows;\n    let ucols = U.columns;\n    let Y = new Matrix(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < ucols; k++) {\n          sum += X.get(i, k) * U.get(j, k);\n        }\n        Y.set(i, j, sum);\n      }\n    }\n\n    return Y;\n  }\n\n  get condition() {\n    return this.s[0] / this.s[Math.min(this.m, this.n) - 1];\n  }\n\n  get norm2() {\n    return this.s[0];\n  }\n\n  get rank() {\n    let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;\n    let r = 0;\n    let s = this.s;\n    for (let i = 0, ii = s.length; i < ii; i++) {\n      if (s[i] > tol) {\n        r++;\n      }\n    }\n    return r;\n  }\n\n  get diagonal() {\n    return Array.from(this.s);\n  }\n\n  get threshold() {\n    return (Number.EPSILON / 2) * Math.max(this.m, this.n) * this.s[0];\n  }\n\n  get leftSingularVectors() {\n    return this.U;\n  }\n\n  get rightSingularVectors() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    return Matrix.diag(this.s);\n  }\n}\n","import LuDecomposition from './dc/lu';\nimport QrDecomposition from './dc/qr';\nimport SingularValueDecomposition from './dc/svd';\nimport Matrix from './matrix';\nimport WrapperMatrix2D from './wrap/WrapperMatrix2D';\n\nexport function inverse(matrix, useSVD = false) {\n  matrix = WrapperMatrix2D.checkMatrix(matrix);\n  if (useSVD) {\n    return new SingularValueDecomposition(matrix).inverse();\n  } else {\n    return solve(matrix, Matrix.eye(matrix.rows));\n  }\n}\n\nexport function solve(leftHandSide, rightHandSide, useSVD = false) {\n  leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);\n  rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);\n  if (useSVD) {\n    return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);\n  } else {\n    return leftHandSide.isSquare()\n      ? new LuDecomposition(leftHandSide).solve(rightHandSide)\n      : new QrDecomposition(leftHandSide).solve(rightHandSide);\n  }\n}\n","import Matrix from './matrix';\nimport LuDecomposition from './dc/lu';\nimport MatrixSelectionView from './views/selection';\n\nexport function determinant(matrix) {\n  matrix = Matrix.checkMatrix(matrix);\n  if (matrix.isSquare()) {\n    let a, b, c, d;\n    if (matrix.columns === 2) {\n      // 2 x 2 matrix\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(1, 0);\n      d = matrix.get(1, 1);\n\n      return a * d - b * c;\n    } else if (matrix.columns === 3) {\n      // 3 x 3 matrix\n      let subMatrix0, subMatrix1, subMatrix2;\n      subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);\n      subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);\n      subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(0, 2);\n\n      return (\n        a * determinant(subMatrix0) -\n        b * determinant(subMatrix1) +\n        c * determinant(subMatrix2)\n      );\n    } else {\n      // general purpose determinant using the LU decomposition\n      return new LuDecomposition(matrix).determinant;\n    }\n  } else {\n    throw Error('determinant can only be calculated for a square matrix');\n  }\n}\n","import Matrix from './matrix';\nimport SingularValueDecomposition from './dc/svd';\n\nfunction xrange(n, exception) {\n  let range = [];\n  for (let i = 0; i < n; i++) {\n    if (i !== exception) {\n      range.push(i);\n    }\n  }\n  return range;\n}\n\nfunction dependenciesOneRow(\n  error,\n  matrix,\n  index,\n  thresholdValue = 10e-10,\n  thresholdError = 10e-10,\n) {\n  if (error > thresholdError) {\n    return new Array(matrix.rows + 1).fill(0);\n  } else {\n    let returnArray = matrix.addRow(index, [0]);\n    for (let i = 0; i < returnArray.rows; i++) {\n      if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {\n        returnArray.set(i, 0, 0);\n      }\n    }\n    return returnArray.to1DArray();\n  }\n}\n\nexport function linearDependencies(matrix, options = {}) {\n  const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;\n  matrix = Matrix.checkMatrix(matrix);\n\n  let n = matrix.rows;\n  let results = new Matrix(n, n);\n\n  for (let i = 0; i < n; i++) {\n    let b = Matrix.columnVector(matrix.getRow(i));\n    let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();\n    let svd = new SingularValueDecomposition(Abis);\n    let x = svd.solve(b);\n    let error = Matrix.sub(b, Abis.mmul(x))\n      .abs()\n      .max();\n    results.setRow(\n      i,\n      dependenciesOneRow(error, x, i, thresholdValue, thresholdError),\n    );\n  }\n  return results;\n}\n","import SVD from './dc/svd';\nimport Matrix from './matrix';\n\nexport function pseudoInverse(matrix, threshold = Number.EPSILON) {\n  matrix = Matrix.checkMatrix(matrix);\n  let svdSolution = new SVD(matrix, { autoTranspose: true });\n\n  let U = svdSolution.leftSingularVectors;\n  let V = svdSolution.rightSingularVectors;\n  let s = svdSolution.diagonal;\n\n  for (let i = 0; i < s.length; i++) {\n    if (Math.abs(s[i]) > threshold) {\n      s[i] = 1.0 / s[i];\n    } else {\n      s[i] = 0.0;\n    }\n  }\n\n  return V.mmul(Matrix.diag(s).mmul(U.transpose()));\n}\n","import Matrix from './matrix';\n\nexport function covariance(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = Matrix.checkMatrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = Matrix.checkMatrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n  const { center = true } = options;\n  if (center) {\n    xMatrix = xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix = yMatrix.center('column');\n    }\n  }\n  const cov = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < cov.rows; i++) {\n    for (let j = 0; j < cov.columns; j++) {\n      cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));\n    }\n  }\n  return cov;\n}\n","import Matrix from './matrix';\n\nexport function correlation(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = Matrix.checkMatrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = Matrix.checkMatrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n\n  const { center = true, scale = true } = options;\n  if (center) {\n    xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix.center('column');\n    }\n  }\n  if (scale) {\n    xMatrix.scale('column');\n    if (!yIsSame) {\n      yMatrix.scale('column');\n    }\n  }\n\n  const sdx = xMatrix.standardDeviation('column', { unbiased: true });\n  const sdy = yIsSame\n    ? sdx\n    : yMatrix.standardDeviation('column', { unbiased: true });\n\n  const corr = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < corr.rows; i++) {\n    for (let j = 0; j < corr.columns; j++) {\n      corr.set(\n        i,\n        j,\n        corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)),\n      );\n    }\n  }\n  return corr;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class EigenvalueDecomposition {\n  constructor(matrix, options = {}) {\n    const { assumeSymmetric = false } = options;\n\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n    if (!matrix.isSquare()) {\n      throw new Error('Matrix is not a square matrix');\n    }\n\n    let n = matrix.columns;\n    let V = new Matrix(n, n);\n    let d = new Float64Array(n);\n    let e = new Float64Array(n);\n    let value = matrix;\n    let i, j;\n\n    let isSymmetric = false;\n    if (assumeSymmetric) {\n      isSymmetric = true;\n    } else {\n      isSymmetric = matrix.isSymmetric();\n    }\n\n    if (isSymmetric) {\n      for (i = 0; i < n; i++) {\n        for (j = 0; j < n; j++) {\n          V.set(i, j, value.get(i, j));\n        }\n      }\n      tred2(n, e, d, V);\n      tql2(n, e, d, V);\n    } else {\n      let H = new Matrix(n, n);\n      let ort = new Float64Array(n);\n      for (j = 0; j < n; j++) {\n        for (i = 0; i < n; i++) {\n          H.set(i, j, value.get(i, j));\n        }\n      }\n      orthes(n, H, ort, V);\n      hqr2(n, e, d, V, H);\n    }\n\n    this.n = n;\n    this.e = e;\n    this.d = d;\n    this.V = V;\n  }\n\n  get realEigenvalues() {\n    return Array.from(this.d);\n  }\n\n  get imaginaryEigenvalues() {\n    return Array.from(this.e);\n  }\n\n  get eigenvectorMatrix() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    let n = this.n;\n    let e = this.e;\n    let d = this.d;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        X.set(i, j, 0);\n      }\n      X.set(i, i, d[i]);\n      if (e[i] > 0) {\n        X.set(i, i + 1, e[i]);\n      } else if (e[i] < 0) {\n        X.set(i, i - 1, e[i]);\n      }\n    }\n    return X;\n  }\n}\n\nfunction tred2(n, e, d, V) {\n  let f, g, h, i, j, k, hh, scale;\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n  }\n\n  for (i = n - 1; i > 0; i--) {\n    scale = 0;\n    h = 0;\n    for (k = 0; k < i; k++) {\n      scale = scale + Math.abs(d[k]);\n    }\n\n    if (scale === 0) {\n      e[i] = d[i - 1];\n      for (j = 0; j < i; j++) {\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n        V.set(j, i, 0);\n      }\n    } else {\n      for (k = 0; k < i; k++) {\n        d[k] /= scale;\n        h += d[k] * d[k];\n      }\n\n      f = d[i - 1];\n      g = Math.sqrt(h);\n      if (f > 0) {\n        g = -g;\n      }\n\n      e[i] = scale * g;\n      h = h - f * g;\n      d[i - 1] = f - g;\n      for (j = 0; j < i; j++) {\n        e[j] = 0;\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        V.set(j, i, f);\n        g = e[j] + V.get(j, j) * f;\n        for (k = j + 1; k <= i - 1; k++) {\n          g += V.get(k, j) * d[k];\n          e[k] += V.get(k, j) * f;\n        }\n        e[j] = g;\n      }\n\n      f = 0;\n      for (j = 0; j < i; j++) {\n        e[j] /= h;\n        f += e[j] * d[j];\n      }\n\n      hh = f / (h + h);\n      for (j = 0; j < i; j++) {\n        e[j] -= hh * d[j];\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        g = e[j];\n        for (k = j; k <= i - 1; k++) {\n          V.set(k, j, V.get(k, j) - (f * e[k] + g * d[k]));\n        }\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n      }\n    }\n    d[i] = h;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    V.set(n - 1, i, V.get(i, i));\n    V.set(i, i, 1);\n    h = d[i + 1];\n    if (h !== 0) {\n      for (k = 0; k <= i; k++) {\n        d[k] = V.get(k, i + 1) / h;\n      }\n\n      for (j = 0; j <= i; j++) {\n        g = 0;\n        for (k = 0; k <= i; k++) {\n          g += V.get(k, i + 1) * V.get(k, j);\n        }\n        for (k = 0; k <= i; k++) {\n          V.set(k, j, V.get(k, j) - g * d[k]);\n        }\n      }\n    }\n\n    for (k = 0; k <= i; k++) {\n      V.set(k, i + 1, 0);\n    }\n  }\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n    V.set(n - 1, j, 0);\n  }\n\n  V.set(n - 1, n - 1, 1);\n  e[0] = 0;\n}\n\nfunction tql2(n, e, d, V) {\n  let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2, iter;\n\n  for (i = 1; i < n; i++) {\n    e[i - 1] = e[i];\n  }\n\n  e[n - 1] = 0;\n\n  let f = 0;\n  let tst1 = 0;\n  let eps = Number.EPSILON;\n\n  for (l = 0; l < n; l++) {\n    tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));\n    m = l;\n    while (m < n) {\n      if (Math.abs(e[m]) <= eps * tst1) {\n        break;\n      }\n      m++;\n    }\n\n    if (m > l) {\n      iter = 0;\n      do {\n        iter = iter + 1;\n\n        g = d[l];\n        p = (d[l + 1] - g) / (2 * e[l]);\n        r = hypotenuse(p, 1);\n        if (p < 0) {\n          r = -r;\n        }\n\n        d[l] = e[l] / (p + r);\n        d[l + 1] = e[l] * (p + r);\n        dl1 = d[l + 1];\n        h = g - d[l];\n        for (i = l + 2; i < n; i++) {\n          d[i] -= h;\n        }\n\n        f = f + h;\n\n        p = d[m];\n        c = 1;\n        c2 = c;\n        c3 = c;\n        el1 = e[l + 1];\n        s = 0;\n        s2 = 0;\n        for (i = m - 1; i >= l; i--) {\n          c3 = c2;\n          c2 = c;\n          s2 = s;\n          g = c * e[i];\n          h = c * p;\n          r = hypotenuse(p, e[i]);\n          e[i + 1] = s * r;\n          s = e[i] / r;\n          c = p / r;\n          p = c * d[i] - s * g;\n          d[i + 1] = h + s * (c * g + s * d[i]);\n\n          for (k = 0; k < n; k++) {\n            h = V.get(k, i + 1);\n            V.set(k, i + 1, s * V.get(k, i) + c * h);\n            V.set(k, i, c * V.get(k, i) - s * h);\n          }\n        }\n\n        p = (-s * s2 * c3 * el1 * e[l]) / dl1;\n        e[l] = s * p;\n        d[l] = c * p;\n      } while (Math.abs(e[l]) > eps * tst1);\n    }\n    d[l] = d[l] + f;\n    e[l] = 0;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    k = i;\n    p = d[i];\n    for (j = i + 1; j < n; j++) {\n      if (d[j] < p) {\n        k = j;\n        p = d[j];\n      }\n    }\n\n    if (k !== i) {\n      d[k] = d[i];\n      d[i] = p;\n      for (j = 0; j < n; j++) {\n        p = V.get(j, i);\n        V.set(j, i, V.get(j, k));\n        V.set(j, k, p);\n      }\n    }\n  }\n}\n\nfunction orthes(n, H, ort, V) {\n  let low = 0;\n  let high = n - 1;\n  let f, g, h, i, j, m;\n  let scale;\n\n  for (m = low + 1; m <= high - 1; m++) {\n    scale = 0;\n    for (i = m; i <= high; i++) {\n      scale = scale + Math.abs(H.get(i, m - 1));\n    }\n\n    if (scale !== 0) {\n      h = 0;\n      for (i = high; i >= m; i--) {\n        ort[i] = H.get(i, m - 1) / scale;\n        h += ort[i] * ort[i];\n      }\n\n      g = Math.sqrt(h);\n      if (ort[m] > 0) {\n        g = -g;\n      }\n\n      h = h - ort[m] * g;\n      ort[m] = ort[m] - g;\n\n      for (j = m; j < n; j++) {\n        f = 0;\n        for (i = high; i >= m; i--) {\n          f += ort[i] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (i = m; i <= high; i++) {\n          H.set(i, j, H.get(i, j) - f * ort[i]);\n        }\n      }\n\n      for (i = 0; i <= high; i++) {\n        f = 0;\n        for (j = high; j >= m; j--) {\n          f += ort[j] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (j = m; j <= high; j++) {\n          H.set(i, j, H.get(i, j) - f * ort[j]);\n        }\n      }\n\n      ort[m] = scale * ort[m];\n      H.set(m, m - 1, scale * g);\n    }\n  }\n\n  for (i = 0; i < n; i++) {\n    for (j = 0; j < n; j++) {\n      V.set(i, j, i === j ? 1 : 0);\n    }\n  }\n\n  for (m = high - 1; m >= low + 1; m--) {\n    if (H.get(m, m - 1) !== 0) {\n      for (i = m + 1; i <= high; i++) {\n        ort[i] = H.get(i, m - 1);\n      }\n\n      for (j = m; j <= high; j++) {\n        g = 0;\n        for (i = m; i <= high; i++) {\n          g += ort[i] * V.get(i, j);\n        }\n\n        g = g / ort[m] / H.get(m, m - 1);\n        for (i = m; i <= high; i++) {\n          V.set(i, j, V.get(i, j) + g * ort[i]);\n        }\n      }\n    }\n  }\n}\n\nfunction hqr2(nn, e, d, V, H) {\n  let n = nn - 1;\n  let low = 0;\n  let high = nn - 1;\n  let eps = Number.EPSILON;\n  let exshift = 0;\n  let norm = 0;\n  let p = 0;\n  let q = 0;\n  let r = 0;\n  let s = 0;\n  let z = 0;\n  let iter = 0;\n  let i, j, k, l, m, t, w, x, y;\n  let ra, sa, vr, vi;\n  let notlast, cdivres;\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      d[i] = H.get(i, i);\n      e[i] = 0;\n    }\n\n    for (j = Math.max(i - 1, 0); j < nn; j++) {\n      norm = norm + Math.abs(H.get(i, j));\n    }\n  }\n\n  while (n >= low) {\n    l = n;\n    while (l > low) {\n      s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));\n      if (s === 0) {\n        s = norm;\n      }\n      if (Math.abs(H.get(l, l - 1)) < eps * s) {\n        break;\n      }\n      l--;\n    }\n\n    if (l === n) {\n      H.set(n, n, H.get(n, n) + exshift);\n      d[n] = H.get(n, n);\n      e[n] = 0;\n      n--;\n      iter = 0;\n    } else if (l === n - 1) {\n      w = H.get(n, n - 1) * H.get(n - 1, n);\n      p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;\n      q = p * p + w;\n      z = Math.sqrt(Math.abs(q));\n      H.set(n, n, H.get(n, n) + exshift);\n      H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);\n      x = H.get(n, n);\n\n      if (q >= 0) {\n        z = p >= 0 ? p + z : p - z;\n        d[n - 1] = x + z;\n        d[n] = d[n - 1];\n        if (z !== 0) {\n          d[n] = x - w / z;\n        }\n        e[n - 1] = 0;\n        e[n] = 0;\n        x = H.get(n, n - 1);\n        s = Math.abs(x) + Math.abs(z);\n        p = x / s;\n        q = z / s;\n        r = Math.sqrt(p * p + q * q);\n        p = p / r;\n        q = q / r;\n\n        for (j = n - 1; j < nn; j++) {\n          z = H.get(n - 1, j);\n          H.set(n - 1, j, q * z + p * H.get(n, j));\n          H.set(n, j, q * H.get(n, j) - p * z);\n        }\n\n        for (i = 0; i <= n; i++) {\n          z = H.get(i, n - 1);\n          H.set(i, n - 1, q * z + p * H.get(i, n));\n          H.set(i, n, q * H.get(i, n) - p * z);\n        }\n\n        for (i = low; i <= high; i++) {\n          z = V.get(i, n - 1);\n          V.set(i, n - 1, q * z + p * V.get(i, n));\n          V.set(i, n, q * V.get(i, n) - p * z);\n        }\n      } else {\n        d[n - 1] = x + p;\n        d[n] = x + p;\n        e[n - 1] = z;\n        e[n] = -z;\n      }\n\n      n = n - 2;\n      iter = 0;\n    } else {\n      x = H.get(n, n);\n      y = 0;\n      w = 0;\n      if (l < n) {\n        y = H.get(n - 1, n - 1);\n        w = H.get(n, n - 1) * H.get(n - 1, n);\n      }\n\n      if (iter === 10) {\n        exshift += x;\n        for (i = low; i <= n; i++) {\n          H.set(i, i, H.get(i, i) - x);\n        }\n        s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));\n        x = y = 0.75 * s;\n        w = -0.4375 * s * s;\n      }\n\n      if (iter === 30) {\n        s = (y - x) / 2;\n        s = s * s + w;\n        if (s > 0) {\n          s = Math.sqrt(s);\n          if (y < x) {\n            s = -s;\n          }\n          s = x - w / ((y - x) / 2 + s);\n          for (i = low; i <= n; i++) {\n            H.set(i, i, H.get(i, i) - s);\n          }\n          exshift += s;\n          x = y = w = 0.964;\n        }\n      }\n\n      iter = iter + 1;\n\n      m = n - 2;\n      while (m >= l) {\n        z = H.get(m, m);\n        r = x - z;\n        s = y - z;\n        p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);\n        q = H.get(m + 1, m + 1) - z - r - s;\n        r = H.get(m + 2, m + 1);\n        s = Math.abs(p) + Math.abs(q) + Math.abs(r);\n        p = p / s;\n        q = q / s;\n        r = r / s;\n        if (m === l) {\n          break;\n        }\n        if (\n          Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) <\n          eps *\n            (Math.abs(p) *\n              (Math.abs(H.get(m - 1, m - 1)) +\n                Math.abs(z) +\n                Math.abs(H.get(m + 1, m + 1))))\n        ) {\n          break;\n        }\n        m--;\n      }\n\n      for (i = m + 2; i <= n; i++) {\n        H.set(i, i - 2, 0);\n        if (i > m + 2) {\n          H.set(i, i - 3, 0);\n        }\n      }\n\n      for (k = m; k <= n - 1; k++) {\n        notlast = k !== n - 1;\n        if (k !== m) {\n          p = H.get(k, k - 1);\n          q = H.get(k + 1, k - 1);\n          r = notlast ? H.get(k + 2, k - 1) : 0;\n          x = Math.abs(p) + Math.abs(q) + Math.abs(r);\n          if (x !== 0) {\n            p = p / x;\n            q = q / x;\n            r = r / x;\n          }\n        }\n\n        if (x === 0) {\n          break;\n        }\n\n        s = Math.sqrt(p * p + q * q + r * r);\n        if (p < 0) {\n          s = -s;\n        }\n\n        if (s !== 0) {\n          if (k !== m) {\n            H.set(k, k - 1, -s * x);\n          } else if (l !== m) {\n            H.set(k, k - 1, -H.get(k, k - 1));\n          }\n\n          p = p + s;\n          x = p / s;\n          y = q / s;\n          z = r / s;\n          q = q / p;\n          r = r / p;\n\n          for (j = k; j < nn; j++) {\n            p = H.get(k, j) + q * H.get(k + 1, j);\n            if (notlast) {\n              p = p + r * H.get(k + 2, j);\n              H.set(k + 2, j, H.get(k + 2, j) - p * z);\n            }\n\n            H.set(k, j, H.get(k, j) - p * x);\n            H.set(k + 1, j, H.get(k + 1, j) - p * y);\n          }\n\n          for (i = 0; i <= Math.min(n, k + 3); i++) {\n            p = x * H.get(i, k) + y * H.get(i, k + 1);\n            if (notlast) {\n              p = p + z * H.get(i, k + 2);\n              H.set(i, k + 2, H.get(i, k + 2) - p * r);\n            }\n\n            H.set(i, k, H.get(i, k) - p);\n            H.set(i, k + 1, H.get(i, k + 1) - p * q);\n          }\n\n          for (i = low; i <= high; i++) {\n            p = x * V.get(i, k) + y * V.get(i, k + 1);\n            if (notlast) {\n              p = p + z * V.get(i, k + 2);\n              V.set(i, k + 2, V.get(i, k + 2) - p * r);\n            }\n\n            V.set(i, k, V.get(i, k) - p);\n            V.set(i, k + 1, V.get(i, k + 1) - p * q);\n          }\n        }\n      }\n    }\n  }\n\n  if (norm === 0) {\n    return;\n  }\n\n  for (n = nn - 1; n >= 0; n--) {\n    p = d[n];\n    q = e[n];\n\n    if (q === 0) {\n      l = n;\n      H.set(n, n, 1);\n      for (i = n - 1; i >= 0; i--) {\n        w = H.get(i, i) - p;\n        r = 0;\n        for (j = l; j <= n; j++) {\n          r = r + H.get(i, j) * H.get(j, n);\n        }\n\n        if (e[i] < 0) {\n          z = w;\n          s = r;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];\n            t = (x * s - z * r) / q;\n            H.set(i, n, t);\n            H.set(\n              i + 1,\n              n,\n              Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z,\n            );\n          }\n\n          t = Math.abs(H.get(i, n));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    } else if (q < 0) {\n      l = n - 1;\n\n      if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {\n        H.set(n - 1, n - 1, q / H.get(n, n - 1));\n        H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));\n      } else {\n        cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);\n        H.set(n - 1, n - 1, cdivres[0]);\n        H.set(n - 1, n, cdivres[1]);\n      }\n\n      H.set(n, n - 1, 0);\n      H.set(n, n, 1);\n      for (i = n - 2; i >= 0; i--) {\n        ra = 0;\n        sa = 0;\n        for (j = l; j <= n; j++) {\n          ra = ra + H.get(i, j) * H.get(j, n - 1);\n          sa = sa + H.get(i, j) * H.get(j, n);\n        }\n\n        w = H.get(i, i) - p;\n\n        if (e[i] < 0) {\n          z = w;\n          r = ra;\n          s = sa;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            cdivres = cdiv(-ra, -sa, w, q);\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;\n            vi = (d[i] - p) * 2 * q;\n            if (vr === 0 && vi === 0) {\n              vr =\n                eps *\n                norm *\n                (Math.abs(w) +\n                  Math.abs(q) +\n                  Math.abs(x) +\n                  Math.abs(y) +\n                  Math.abs(z));\n            }\n            cdivres = cdiv(\n              x * r - z * ra + q * sa,\n              x * s - z * sa - q * ra,\n              vr,\n              vi,\n            );\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n            if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {\n              H.set(\n                i + 1,\n                n - 1,\n                (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x,\n              );\n              H.set(\n                i + 1,\n                n,\n                (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x,\n              );\n            } else {\n              cdivres = cdiv(\n                -r - y * H.get(i, n - 1),\n                -s - y * H.get(i, n),\n                z,\n                q,\n              );\n              H.set(i + 1, n - 1, cdivres[0]);\n              H.set(i + 1, n, cdivres[1]);\n            }\n          }\n\n          t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n - 1, H.get(j, n - 1) / t);\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    }\n  }\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      for (j = i; j < nn; j++) {\n        V.set(i, j, H.get(i, j));\n      }\n    }\n  }\n\n  for (j = nn - 1; j >= low; j--) {\n    for (i = low; i <= high; i++) {\n      z = 0;\n      for (k = low; k <= Math.min(j, high); k++) {\n        z = z + V.get(i, k) * H.get(k, j);\n      }\n      V.set(i, j, z);\n    }\n  }\n}\n\nfunction cdiv(xr, xi, yr, yi) {\n  let r, d;\n  if (Math.abs(yr) > Math.abs(yi)) {\n    r = yi / yr;\n    d = yr + r * yi;\n    return [(xr + r * xi) / d, (xi - r * xr) / d];\n  } else {\n    r = yr / yi;\n    d = yi + r * yr;\n    return [(r * xr + xi) / d, (r * xi - xr) / d];\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class CholeskyDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n    if (!value.isSymmetric()) {\n      throw new Error('Matrix is not symmetric');\n    }\n\n    let a = value;\n    let dimension = a.rows;\n    let l = new Matrix(dimension, dimension);\n    let positiveDefinite = true;\n    let i, j, k;\n\n    for (j = 0; j < dimension; j++) {\n      let d = 0;\n      for (k = 0; k < j; k++) {\n        let s = 0;\n        for (i = 0; i < k; i++) {\n          s += l.get(k, i) * l.get(j, i);\n        }\n        s = (a.get(j, k) - s) / l.get(k, k);\n        l.set(j, k, s);\n        d = d + s * s;\n      }\n\n      d = a.get(j, j) - d;\n\n      positiveDefinite &= d > 0;\n      l.set(j, j, Math.sqrt(Math.max(d, 0)));\n      for (k = j + 1; k < dimension; k++) {\n        l.set(j, k, 0);\n      }\n    }\n\n    this.L = l;\n    this.positiveDefinite = Boolean(positiveDefinite);\n  }\n\n  isPositiveDefinite() {\n    return this.positiveDefinite;\n  }\n\n  solve(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let l = this.L;\n    let dimension = l.rows;\n\n    if (value.rows !== dimension) {\n      throw new Error('Matrix dimensions do not match');\n    }\n    if (this.isPositiveDefinite() === false) {\n      throw new Error('Matrix is not positive definite');\n    }\n\n    let count = value.columns;\n    let B = value.clone();\n    let i, j, k;\n\n    for (k = 0; k < dimension; k++) {\n      for (j = 0; j < count; j++) {\n        for (i = 0; i < k; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    for (k = dimension - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        for (i = k + 1; i < dimension; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    return B;\n  }\n\n  get lowerTriangularMatrix() {\n    return this.L;\n  }\n}\n","import WrapperMatrix2D from '../wrap/WrapperMatrix2D';\nimport Matrix from '../matrix';\n\nexport default class nipals {\n  constructor(X, options = {}) {\n    X = WrapperMatrix2D.checkMatrix(X);\n    let { Y } = options;\n    const {\n      scaleScores = false,\n      maxIterations = 1000,\n      terminationCriteria = 1e-10,\n    } = options;\n\n    let u;\n    if (Y) {\n      if (Array.isArray(Y) && typeof Y[0] === 'number') {\n        Y = Matrix.columnVector(Y);\n      } else {\n        Y = WrapperMatrix2D.checkMatrix(Y);\n      }\n      if (!Y.isColumnVector() || Y.rows !== X.rows) {\n        throw new Error('Y must be a column vector of length X.rows');\n      }\n      u = Y;\n    } else {\n      u = X.getColumnVector(0);\n    }\n\n    let diff = 1;\n    let t, q, w, tOld;\n\n    for (\n      let counter = 0;\n      counter < maxIterations && diff > terminationCriteria;\n      counter++\n    ) {\n      w = X.transpose()\n        .mmul(u)\n        .div(\n          u\n            .transpose()\n            .mmul(u)\n            .get(0, 0),\n        );\n      w = w.div(w.norm());\n\n      t = X.mmul(w).div(\n        w\n          .transpose()\n          .mmul(w)\n          .get(0, 0),\n      );\n\n      if (counter > 0) {\n        diff = t\n          .clone()\n          .sub(tOld)\n          .pow(2)\n          .sum();\n      }\n      tOld = t.clone();\n\n      if (Y) {\n        q = Y.transpose()\n          .mmul(t)\n          .div(\n            t\n              .transpose()\n              .mmul(t)\n              .get(0, 0),\n          );\n        q = q.div(q.norm());\n\n        u = Y.mmul(q).div(\n          q\n            .transpose()\n            .mmul(q)\n            .get(0, 0),\n        );\n      } else {\n        u = t;\n      }\n    }\n\n    if (Y) {\n      let p = X.transpose()\n        .mmul(t)\n        .div(\n          t\n            .transpose()\n            .mmul(t)\n            .get(0, 0),\n        );\n      p = p.div(p.norm());\n      let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));\n      let residual = u\n        .transpose()\n        .mmul(t)\n        .div(\n          t\n            .transpose()\n            .mmul(t)\n            .get(0, 0),\n        );\n      let yResidual = Y.clone().sub(\n        t\n          .clone()\n          .mulS(residual.get(0, 0))\n          .mmul(q.transpose()),\n      );\n\n      this.t = t;\n      this.p = p.transpose();\n      this.w = w.transpose();\n      this.q = q;\n      this.u = u;\n      this.s = t.transpose().mmul(t);\n      this.xResidual = xResidual;\n      this.yResidual = yResidual;\n      this.betas = residual;\n    } else {\n      this.w = w.transpose();\n      this.s = t\n        .transpose()\n        .mmul(t)\n        .sqrt();\n      if (scaleScores) {\n        this.t = t.clone().div(this.s.get(0, 0));\n      } else {\n        this.t = t;\n      }\n      this.xResidual = X.sub(t.mmul(w.transpose()));\n    }\n  }\n}\n","import isArray from 'is-any-array';\n\n/**\n * Computes the mean of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction sum(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += input[i];\n  }\n\n  return sumValue;\n}\n\nexport default sum;\n","import sum from 'ml-array-sum';\n\n/**\n * Computes the mean of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction mean(input) {\n  return sum(input) / input.length;\n}\n\nexport default mean;\n","import Matrix from 'ml-matrix';\nimport meanArray from 'ml-array-mean';\n\n/**\n * @private\n * return an array of probabilities of each class\n * @param {Array} array - contains the classes\n * @param {number} numberOfClasses\n * @return {Matrix} - rowVector of probabilities.\n */\nexport function toDiscreteDistribution(array, numberOfClasses) {\n  let counts = new Array(numberOfClasses).fill(0);\n  for (let i = 0; i < array.length; ++i) {\n    counts[array[i]] += 1 / array.length;\n  }\n\n  return Matrix.rowVector(counts);\n}\n\n/**\n * @private\n * Retrieves the impurity of array of predictions\n * @param {Array} array - predictions.\n * @return {number} Gini impurity\n */\nexport function giniImpurity(array) {\n  if (array.length === 0) {\n    return 0;\n  }\n\n  let probabilities = toDiscreteDistribution(\n    array,\n    getNumberOfClasses(array),\n  ).getRow(0);\n\n  let sum = 0.0;\n  for (let i = 0; i < probabilities.length; ++i) {\n    sum += probabilities[i] * probabilities[i];\n  }\n\n  return 1 - sum;\n}\n\n/**\n * @private\n * Return the number of classes given the array of predictions.\n * @param {Array} array - predictions.\n * @return {number} Number of classes.\n */\nexport function getNumberOfClasses(array) {\n  return array\n    .filter(function(val, i, arr) {\n      return arr.indexOf(val) === i;\n    })\n    .map((val) => val + 1)\n    .reduce((a, b) => Math.max(a, b));\n}\n\n/**\n * @private\n * Calculates the Gini Gain of an array of predictions and those predictions splitted by a feature.\n * @param {Array} array - Predictions\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - Gini Gain.\n */\n\nexport function giniGain(array, splitted) {\n  let splitsImpurity = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    splitsImpurity +=\n      (giniImpurity(currentSplit) * currentSplit.length) / array.length;\n  }\n\n  return giniImpurity(array) - splitsImpurity;\n}\n\n/**\n * @private\n * Calculates the squared error of a predictions values.\n * @param {Array} array - predictions values\n * @return {number} squared error.\n */\nexport function squaredError(array) {\n  let l = array.length;\n\n  let m = meanArray(array);\n  let error = 0.0;\n\n  for (let i = 0; i < l; ++i) {\n    let currentElement = array[i];\n    error += (currentElement - m) * (currentElement - m);\n  }\n\n  return error;\n}\n\n/**\n * @private\n * Calculates the sum of squared error of the two arrays that contains the splitted values.\n * @param {Array} array - this argument is no necessary but is used to fit with the main interface.\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - sum of squared errors.\n */\nexport function regressionError(array, splitted) {\n  let error = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    error += squaredError(currentSplit);\n  }\n  return error;\n}\n\n/**\n * @private\n * Split the training set and values from a given column of the training set if is less than a value\n * @param {Matrix} X - Training set.\n * @param {Array} y - Training values.\n * @param {number} column - Column to split.\n * @param {number} value - value to split the Training set and values.\n * @return {object} - Object that contains the splitted values.\n */\nexport function matrixSplitter(X, y, column, value) {\n  let lesserX = [];\n  let greaterX = [];\n  let lesserY = [];\n  let greaterY = [];\n\n  for (let i = 0; i < X.rows; ++i) {\n    if (X.get(i, column) < value) {\n      lesserX.push(X.getRow(i));\n      lesserY.push(y[i]);\n    } else {\n      greaterX.push(X.getRow(i));\n      greaterY.push(y[i]);\n    }\n  }\n\n  return {\n    greaterX: greaterX,\n    greaterY: greaterY,\n    lesserX: lesserX,\n    lesserY: lesserY,\n  };\n}\n\n/**\n * @private\n * Calculates the mean between two values\n * @param {number} a\n * @param {number} b\n * @return {number}\n */\nexport function mean(a, b) {\n  return (a + b) / 2;\n}\n\n/**\n * @private\n * Returns a list of tuples that contains the i-th element of each array.\n * @param {Array} a\n * @param {Array} b\n * @return {Array} list of tuples.\n */\nexport function zip(a, b) {\n  if (a.length !== b.length) {\n    throw new TypeError(\n      `Error on zip: the size of a: ${a.length} is different from b: ${b.length}`,\n    );\n  }\n\n  let ret = new Array(a.length);\n  for (let i = 0; i < a.length; ++i) {\n    ret[i] = [a[i], b[i]];\n  }\n\n  return ret;\n}\n","import Matrix from 'ml-matrix';\nimport mean from 'ml-array-mean';\n\nimport * as Utils from './utils';\n\nconst gainFunctions = {\n  gini: Utils.giniGain,\n  regression: Utils.regressionError,\n};\n\nconst splitFunctions = {\n  mean: Utils.mean,\n};\n\nexport default class TreeNode {\n  /**\n   * @private\n   * Constructor for a tree node given the options received on the main classes (DecisionTreeClassifier, DecisionTreeRegression)\n   * @param {object|TreeNode} options for loading\n   * @constructor\n   */\n  constructor(options) {\n    // options parameters\n    this.kind = options.kind;\n    this.gainFunction = options.gainFunction;\n    this.splitFunction = options.splitFunction;\n    this.minNumSamples = options.minNumSamples;\n    this.maxDepth = options.maxDepth;\n  }\n\n  /**\n   * @private\n   * Function that retrieve the best feature to make the split.\n   * @param {Matrix} XTranspose - Training set transposed\n   * @param {Array} y - labels or values (depending of the decision tree)\n   * @return {object} - return tree values, the best gain, column and the split value.\n   */\n  bestSplit(XTranspose, y) {\n    // Depending in the node tree class, we set the variables to check information gain (to classify)\n    // or error (for regression)\n\n    let bestGain = this.kind === 'classifier' ? -Infinity : Infinity;\n    let check = this.kind === 'classifier' ? (a, b) => a > b : (a, b) => a < b;\n\n    let maxColumn;\n    let maxValue;\n\n    for (let i = 0; i < XTranspose.rows; ++i) {\n      let currentFeature = XTranspose.getRow(i);\n      let splitValues = this.featureSplit(currentFeature, y);\n      for (let j = 0; j < splitValues.length; ++j) {\n        let currentSplitVal = splitValues[j];\n        let splitted = this.split(currentFeature, y, currentSplitVal);\n\n        let gain = gainFunctions[this.gainFunction](y, splitted);\n        if (check(gain, bestGain)) {\n          maxColumn = i;\n          maxValue = currentSplitVal;\n          bestGain = gain;\n        }\n      }\n    }\n\n    return {\n      maxGain: bestGain,\n      maxColumn: maxColumn,\n      maxValue: maxValue,\n    };\n  }\n\n  /**\n   * @private\n   * Makes the split of the training labels or values from the training set feature given a split value.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @param {number} splitValue\n   * @return {object}\n   */\n  split(x, y, splitValue) {\n    let lesser = [];\n    let greater = [];\n\n    for (let i = 0; i < x.length; ++i) {\n      if (x[i] < splitValue) {\n        lesser.push(y[i]);\n      } else {\n        greater.push(y[i]);\n      }\n    }\n\n    return {\n      greater: greater,\n      lesser: lesser,\n    };\n  }\n\n  /**\n   * @private\n   * Calculates the possible points to split over the tree given a training set feature and corresponding labels or values.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @return {Array} possible split values.\n   */\n  featureSplit(x, y) {\n    let splitValues = [];\n    let arr = Utils.zip(x, y);\n    arr.sort(function(a, b) {\n      return a[0] - b[0];\n    });\n\n    for (let i = 1; i < arr.length; ++i) {\n      if (arr[i - 1][1] !== arr[i][1]) {\n        splitValues.push(\n          splitFunctions[this.splitFunction](arr[i - 1][0], arr[i][0]),\n        );\n      }\n    }\n\n    return splitValues;\n  }\n\n  /**\n   * @private\n   * Calculate the predictions of a leaf tree node given the training labels or values\n   * @param {Array} y\n   */\n  calculatePrediction(y) {\n    if (this.kind === 'classifier') {\n      this.distribution = Utils.toDiscreteDistribution(\n        y,\n        Utils.getNumberOfClasses(y),\n      );\n      if (this.distribution.columns === 0) {\n        throw new TypeError('Error on calculate the prediction');\n      }\n    } else {\n      this.distribution = mean(y);\n    }\n  }\n\n  /**\n   * @private\n   * Train a node given the training set and labels, because it trains recursively, it also receive\n   * the current depth of the node, parent gain to avoid infinite recursion and boolean value to check if\n   * the training set is transposed.\n   * @param {Matrix} X - Training set (could be transposed or not given transposed).\n   * @param {Array} y - Training labels or values.\n   * @param {number} currentDepth - Current depth of the node.\n   * @param {number} parentGain - parent node gain or error.\n   */\n  train(X, y, currentDepth, parentGain) {\n    if (X.rows <= this.minNumSamples) {\n      this.calculatePrediction(y);\n      return;\n    }\n    if (parentGain === undefined) parentGain = 0.0;\n\n    let XTranspose = X.transpose();\n    let split = this.bestSplit(XTranspose, y);\n\n    this.splitValue = split.maxValue;\n    this.splitColumn = split.maxColumn;\n    this.gain = split.maxGain;\n\n    let splittedMatrix = Utils.matrixSplitter(\n      X,\n      y,\n      this.splitColumn,\n      this.splitValue,\n    );\n\n    if (\n      currentDepth < this.maxDepth &&\n      (this.gain > 0.01 && this.gain !== parentGain) &&\n      (splittedMatrix.lesserX.length > 0 && splittedMatrix.greaterX.length > 0)\n    ) {\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      let lesserX = new Matrix(splittedMatrix.lesserX);\n      let greaterX = new Matrix(splittedMatrix.greaterX);\n\n      this.left.train(\n        lesserX,\n        splittedMatrix.lesserY,\n        currentDepth + 1,\n        this.gain,\n      );\n      this.right.train(\n        greaterX,\n        splittedMatrix.greaterY,\n        currentDepth + 1,\n        this.gain,\n      );\n    } else {\n      this.calculatePrediction(y);\n    }\n  }\n\n  /**\n   * @private\n   * Calculates the prediction of a given element.\n   * @param {Array} row\n   * @return {number|Array} prediction\n   *          * if a node is a classifier returns an array of probabilities of each class.\n   *          * if a node is for regression returns a number with the prediction.\n   */\n  classify(row) {\n    if (this.right && this.left) {\n      if (row[this.splitColumn] < this.splitValue) {\n        return this.left.classify(row);\n      } else {\n        return this.right.classify(row);\n      }\n    }\n\n    return this.distribution;\n  }\n\n  /**\n   * @private\n   * Set the parameter of the current node and their children.\n   * @param {object} node - parameters of the current node and the children.\n   */\n  setNodeParameters(node) {\n    if (node.distribution !== undefined) {\n      this.distribution =\n        node.distribution.constructor === Array\n          ? new Matrix(node.distribution)\n          : node.distribution;\n    } else {\n      this.distribution = undefined;\n      this.splitValue = node.splitValue;\n      this.splitColumn = node.splitColumn;\n      this.gain = node.gain;\n\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      if (node.left !== {}) {\n        this.left.setNodeParameters(node.left);\n      }\n      if (node.right !== {}) {\n        this.right.setNodeParameters(node.right);\n      }\n    }\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'gini',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeClassifier {\n  /**\n   * Create new Decision Tree Classifier with CART implementation with the given options\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"gini\"] - gain function to get the best split, \"gini\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   * @constructor\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'classifier';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    this.root = new Tree(this.options);\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    this.root.train(trainingSet, trainingLabels, 0, null);\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    toPredict = Matrix.checkMatrix(toPredict);\n    let predictions = new Array(toPredict.rows);\n\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root\n        .classify(toPredict.getRow(i))\n        .maxRowIndex(0)[1];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {DecisionTreeClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'DTClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new DecisionTreeClassifier(true, model);\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'regression',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeRegression {\n  /**\n   * Create new Decision Tree Regression with CART implementation with the given options.\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"regression\"] - gain function to get the best split, \"regression\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'regression';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and values.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    this.root = new Tree(this.options);\n\n    if (\n      typeof trainingSet[0] !== 'undefined' &&\n      trainingSet[0].length === undefined\n    ) {\n      trainingSet = Matrix.columnVector(trainingSet);\n    } else {\n      trainingSet = Matrix.checkMatrix(trainingSet);\n    }\n    this.root.train(trainingSet, trainingValues, 0);\n  }\n\n  /**\n   * Predicts the values given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    if (\n      typeof toPredict[0] !== 'undefined' &&\n      toPredict[0].length === undefined\n    ) {\n      toPredict = Matrix.columnVector(toPredict);\n    }\n    toPredict = Matrix.checkMatrix(toPredict);\n\n    let predictions = new Array(toPredict.rows);\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root.classify(toPredict.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree regression with the given model.\n   * @param {object} model\n   * @return {DecisionTreeRegression}\n   */\n  static load(model) {\n    if (model.name !== 'DTRegression') {\n      throw new RangeError(`Invalid model:${model.name}`);\n    }\n\n    return new DecisionTreeRegression(true, model);\n  }\n}\n","const SMALLEST_UNSAFE_INTEGER = 0x20000000000000;\r\nconst LARGEST_SAFE_INTEGER = SMALLEST_UNSAFE_INTEGER - 1;\r\nconst UINT32_MAX = -1 >>> 0;\r\nconst UINT32_SIZE = UINT32_MAX + 1;\r\nconst INT32_SIZE = UINT32_SIZE / 2;\r\nconst INT32_MAX = INT32_SIZE - 1;\r\nconst UINT21_SIZE = 1 << 21;\r\nconst UINT21_MAX = UINT21_SIZE - 1;\n\n/**\r\n * Returns a value within [-0x80000000, 0x7fffffff]\r\n */\r\nfunction int32(engine) {\r\n    return engine.next() | 0;\r\n}\n\nfunction add(distribution, addend) {\r\n    if (addend === 0) {\r\n        return distribution;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) + addend;\r\n    }\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n */\r\nfunction int53(engine) {\r\n    const high = engine.next() | 0;\r\n    const low = engine.next() >>> 0;\r\n    return ((high & UINT21_MAX) * UINT32_SIZE +\r\n        low +\r\n        (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n */\r\nfunction int53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & 0x400000) {\r\n            if ((high & 0x7fffff) === 0x400000 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return ((high & UINT21_MAX) * UINT32_SIZE +\r\n                low +\r\n                (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n        }\r\n    }\r\n}\n\n/**\r\n * Returns a value within [0, 0xffffffff]\r\n */\r\nfunction uint32(engine) {\r\n    return engine.next() >>> 0;\r\n}\n\n/**\r\n * Returns a value within [0, 0x1fffffffffffff]\r\n */\r\nfunction uint53(engine) {\r\n    const high = engine.next() & UINT21_MAX;\r\n    const low = engine.next() >>> 0;\r\n    return high * UINT32_SIZE + low;\r\n}\n\n/**\r\n * Returns a value within [0, 0x20000000000000]\r\n */\r\nfunction uint53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & UINT21_SIZE) {\r\n            if ((high & UINT21_MAX) === 0 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return (high & UINT21_MAX) * UINT32_SIZE + low;\r\n        }\r\n    }\r\n}\n\nfunction isPowerOfTwoMinusOne(value) {\r\n    return ((value + 1) & value) === 0;\r\n}\r\nfunction bitmask(masking) {\r\n    return (engine) => engine.next() & masking;\r\n}\r\nfunction downscaleToLoopCheckedRange(range) {\r\n    const extendedRange = range + 1;\r\n    const maximum = extendedRange * Math.floor(UINT32_SIZE / extendedRange);\r\n    return engine => {\r\n        let value = 0;\r\n        do {\r\n            value = engine.next() >>> 0;\r\n        } while (value >= maximum);\r\n        return value % extendedRange;\r\n    };\r\n}\r\nfunction downscaleToRange(range) {\r\n    if (isPowerOfTwoMinusOne(range)) {\r\n        return bitmask(range);\r\n    }\r\n    else {\r\n        return downscaleToLoopCheckedRange(range);\r\n    }\r\n}\r\nfunction isEvenlyDivisibleByMaxInt32(value) {\r\n    return (value | 0) === 0;\r\n}\r\nfunction upscaleWithHighMasking(masking) {\r\n    return engine => {\r\n        const high = engine.next() & masking;\r\n        const low = engine.next() >>> 0;\r\n        return high * UINT32_SIZE + low;\r\n    };\r\n}\r\nfunction upscaleToLoopCheckedRange(extendedRange) {\r\n    const maximum = extendedRange * Math.floor(SMALLEST_UNSAFE_INTEGER / extendedRange);\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() & UINT21_MAX;\r\n            const low = engine.next() >>> 0;\r\n            ret = high * UINT32_SIZE + low;\r\n        } while (ret >= maximum);\r\n        return ret % extendedRange;\r\n    };\r\n}\r\nfunction upscaleWithinU53(range) {\r\n    const extendedRange = range + 1;\r\n    if (isEvenlyDivisibleByMaxInt32(extendedRange)) {\r\n        const highRange = ((extendedRange / UINT32_SIZE) | 0) - 1;\r\n        if (isPowerOfTwoMinusOne(highRange)) {\r\n            return upscaleWithHighMasking(highRange);\r\n        }\r\n    }\r\n    return upscaleToLoopCheckedRange(extendedRange);\r\n}\r\nfunction upscaleWithinI53AndLoopCheck(min, max) {\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() | 0;\r\n            const low = engine.next() >>> 0;\r\n            ret =\r\n                (high & UINT21_MAX) * UINT32_SIZE +\r\n                    low +\r\n                    (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);\r\n        } while (ret < min || ret > max);\r\n        return ret;\r\n    };\r\n}\r\n/**\r\n * Returns a Distribution to return a value within [min, max]\r\n * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n */\r\nfunction integer(min, max) {\r\n    min = Math.floor(min);\r\n    max = Math.floor(max);\r\n    if (min < -SMALLEST_UNSAFE_INTEGER || !isFinite(min)) {\r\n        throw new RangeError(`Expected min to be at least ${-SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    else if (max > SMALLEST_UNSAFE_INTEGER || !isFinite(max)) {\r\n        throw new RangeError(`Expected max to be at most ${SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    const range = max - min;\r\n    if (range <= 0 || !isFinite(range)) {\r\n        return () => min;\r\n    }\r\n    else if (range === UINT32_MAX) {\r\n        if (min === 0) {\r\n            return uint32;\r\n        }\r\n        else {\r\n            return add(int32, min + INT32_SIZE);\r\n        }\r\n    }\r\n    else if (range < UINT32_MAX) {\r\n        return add(downscaleToRange(range), min);\r\n    }\r\n    else if (range === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53, min);\r\n    }\r\n    else if (range < LARGEST_SAFE_INTEGER) {\r\n        return add(upscaleWithinU53(range), min);\r\n    }\r\n    else if (max - 1 - min === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53Full, min);\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER &&\r\n        max === SMALLEST_UNSAFE_INTEGER) {\r\n        return int53Full;\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER && max === LARGEST_SAFE_INTEGER) {\r\n        return int53;\r\n    }\r\n    else if (min === -LARGEST_SAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(int53, 1);\r\n    }\r\n    else if (max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(upscaleWithinI53AndLoopCheck(min - 1, max - 1), 1);\r\n    }\r\n    else {\r\n        return upscaleWithinI53AndLoopCheck(min, max);\r\n    }\r\n}\n\nfunction isLeastBitTrue(engine) {\r\n    return (engine.next() & 1) === 1;\r\n}\r\nfunction lessThan(distribution, value) {\r\n    return engine => distribution(engine) < value;\r\n}\r\nfunction probability(percentage) {\r\n    if (percentage <= 0) {\r\n        return () => false;\r\n    }\r\n    else if (percentage >= 1) {\r\n        return () => true;\r\n    }\r\n    else {\r\n        const scaled = percentage * UINT32_SIZE;\r\n        if (scaled % 1 === 0) {\r\n            return lessThan(int32, (scaled - INT32_SIZE) | 0);\r\n        }\r\n        else {\r\n            return lessThan(uint53, Math.round(percentage * SMALLEST_UNSAFE_INTEGER));\r\n        }\r\n    }\r\n}\r\nfunction bool(numerator, denominator) {\r\n    if (denominator == null) {\r\n        if (numerator == null) {\r\n            return isLeastBitTrue;\r\n        }\r\n        return probability(numerator);\r\n    }\r\n    else {\r\n        if (numerator <= 0) {\r\n            return () => false;\r\n        }\r\n        else if (numerator >= denominator) {\r\n            return () => true;\r\n        }\r\n        return lessThan(integer(0, denominator - 1), numerator);\r\n    }\r\n}\n\n/**\r\n * Returns a Distribution that returns a random `Date` within the inclusive\r\n * range of [`start`, `end`].\r\n * @param start The minimum `Date`\r\n * @param end The maximum `Date`\r\n */\r\nfunction date(start, end) {\r\n    const distribution = integer(+start, +end);\r\n    return engine => new Date(distribution(engine));\r\n}\n\n/**\r\n * Returns a Distribution to return a value within [1, sideCount]\r\n * @param sideCount The number of sides of the die\r\n */\r\nfunction die(sideCount) {\r\n    return integer(1, sideCount);\r\n}\n\n/**\r\n * Returns a distribution that returns an array of length `dieCount` of values\r\n * within [1, `sideCount`]\r\n * @param sideCount The number of sides of each die\r\n * @param dieCount The number of dice\r\n */\r\nfunction dice(sideCount, dieCount) {\r\n    const distribution = die(sideCount);\r\n    return engine => {\r\n        const result = [];\r\n        for (let i = 0; i < dieCount; ++i) {\r\n            result.push(distribution(engine));\r\n        }\r\n        return result;\r\n    };\r\n}\n\n// tslint:disable:unified-signatures\r\n// has 2**x chars, for faster uniform distribution\r\nconst DEFAULT_STRING_POOL = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-\";\r\nfunction string(pool = DEFAULT_STRING_POOL) {\r\n    const poolLength = pool.length;\r\n    if (!poolLength) {\r\n        throw new Error(\"Expected pool not to be an empty string\");\r\n    }\r\n    const distribution = integer(0, poolLength - 1);\r\n    return (engine, length) => {\r\n        let result = \"\";\r\n        for (let i = 0; i < length; ++i) {\r\n            const j = distribution(engine);\r\n            result += pool.charAt(j);\r\n        }\r\n        return result;\r\n    };\r\n}\n\nconst LOWER_HEX_POOL = \"0123456789abcdef\";\r\nconst lowerHex = string(LOWER_HEX_POOL);\r\nconst upperHex = string(LOWER_HEX_POOL.toUpperCase());\r\n/**\r\n * Returns a Distribution that returns a random string comprised of numbers\r\n * or the characters `abcdef` (or `ABCDEF`) of length `length`.\r\n * @param length Length of the result string\r\n * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n */\r\nfunction hex(uppercase) {\r\n    if (uppercase) {\r\n        return upperHex;\r\n    }\r\n    else {\r\n        return lowerHex;\r\n    }\r\n}\n\nfunction convertSliceArgument(value, length) {\r\n    if (value < 0) {\r\n        return Math.max(value + length, 0);\r\n    }\r\n    else {\r\n        return Math.min(value, length);\r\n    }\r\n}\n\nfunction toInteger(value) {\r\n    const num = +value;\r\n    if (num < 0) {\r\n        return Math.ceil(num);\r\n    }\r\n    else {\r\n        return Math.floor(num);\r\n    }\r\n}\n\n/**\r\n * Returns a random value within the provided `source` within the sliced\r\n * bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction pick(engine, source, begin, end) {\r\n    const length = source.length;\r\n    if (length === 0) {\r\n        throw new RangeError(\"Cannot pick from an empty array\");\r\n    }\r\n    const start = begin == null ? 0 : convertSliceArgument(toInteger(begin), length);\r\n    const finish = end === void 0 ? length : convertSliceArgument(toInteger(end), length);\r\n    if (start >= finish) {\r\n        throw new RangeError(`Cannot pick between bounds ${start} and ${finish}`);\r\n    }\r\n    const distribution = integer(start, finish - 1);\r\n    return source[distribution(engine)];\r\n}\n\nfunction multiply(distribution, multiplier) {\r\n    if (multiplier === 1) {\r\n        return distribution;\r\n    }\r\n    else if (multiplier === 0) {\r\n        return () => 0;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) * multiplier;\r\n    }\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0)\r\n */\r\nfunction realZeroToOneExclusive(engine) {\r\n    return uint53(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0]\r\n */\r\nfunction realZeroToOneInclusive(engine) {\r\n    return uint53Full(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [min, max) or [min, max]\r\n * @param min The minimum floating-point value, inclusive.\r\n * @param max The maximum floating-point value.\r\n * @param inclusive If true, `max` will be inclusive.\r\n */\r\nfunction real(min, max, inclusive = false) {\r\n    if (!isFinite(min)) {\r\n        throw new RangeError(\"Expected min to be a finite number\");\r\n    }\r\n    else if (!isFinite(max)) {\r\n        throw new RangeError(\"Expected max to be a finite number\");\r\n    }\r\n    return add(multiply(inclusive ? realZeroToOneInclusive : realZeroToOneExclusive, max - min), min);\r\n}\n\nconst sliceArray = Array.prototype.slice;\n\n/**\r\n * Shuffles an array in-place\r\n * @param engine The Engine to use when choosing random values\r\n * @param array The array to shuffle\r\n * @param downTo minimum index to shuffle. Only used internally.\r\n */\r\nfunction shuffle(engine, array, downTo = 0) {\r\n    const length = array.length;\r\n    if (length) {\r\n        for (let i = (length - 1) >>> 0; i > downTo; --i) {\r\n            const distribution = integer(0, i);\r\n            const j = distribution(engine);\r\n            if (i !== j) {\r\n                const tmp = array[i];\r\n                array[i] = array[j];\r\n                array[j] = tmp;\r\n            }\r\n        }\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * From the population array, produce an array with sampleSize elements that\r\n * are randomly chosen without repeats.\r\n * @param engine The Engine to use when choosing random values\r\n * @param population An array that has items to choose a sample from\r\n * @param sampleSize The size of the result array\r\n */\r\nfunction sample(engine, population, sampleSize) {\r\n    if (sampleSize < 0 ||\r\n        sampleSize > population.length ||\r\n        !isFinite(sampleSize)) {\r\n        throw new RangeError(\"Expected sampleSize to be within 0 and the length of the population\");\r\n    }\r\n    if (sampleSize === 0) {\r\n        return [];\r\n    }\r\n    const clone = sliceArray.call(population);\r\n    const length = clone.length;\r\n    if (length === sampleSize) {\r\n        return shuffle(engine, clone, 0);\r\n    }\r\n    const tailLength = length - sampleSize;\r\n    return shuffle(engine, clone, tailLength - 1).slice(tailLength);\r\n}\n\nconst stringRepeat = (() => {\r\n    try {\r\n        if (\"x\".repeat(3) === \"xxx\") {\r\n            return (pattern, count) => pattern.repeat(count);\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return (pattern, count) => {\r\n        let result = \"\";\r\n        while (count > 0) {\r\n            if (count & 1) {\r\n                result += pattern;\r\n            }\r\n            count >>= 1;\r\n            pattern += pattern;\r\n        }\r\n        return result;\r\n    };\r\n})();\n\nfunction zeroPad(text, zeroCount) {\r\n    return stringRepeat(\"0\", zeroCount - text.length) + text;\r\n}\r\n/**\r\n * Returns a Universally Unique Identifier Version 4.\r\n *\r\n * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n */\r\nfunction uuid4(engine) {\r\n    const a = engine.next() >>> 0;\r\n    const b = engine.next() | 0;\r\n    const c = engine.next() | 0;\r\n    const d = engine.next() >>> 0;\r\n    return (zeroPad(a.toString(16), 8) +\r\n        \"-\" +\r\n        zeroPad((b & 0xffff).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad((((b >> 4) & 0x0fff) | 0x4000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c & 0x3fff) | 0x8000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c >> 4) & 0xffff).toString(16), 4) +\r\n        zeroPad(d.toString(16), 8));\r\n}\n\n/**\r\n * An int32-producing Engine that uses `Math.random()`\r\n */\r\nconst nativeMath = {\r\n    next() {\r\n        return (Math.random() * UINT32_SIZE) | 0;\r\n    }\r\n};\n\n// tslint:disable:unified-signatures\r\n/**\r\n * A wrapper around an Engine that provides easy-to-use methods for\r\n * producing values based on known distributions\r\n */\r\nclass Random {\r\n    /**\r\n     * Creates a new Random wrapper\r\n     * @param engine The engine to use (defaults to a `Math.random`-based implementation)\r\n     */\r\n    constructor(engine = nativeMath) {\r\n        this.engine = engine;\r\n    }\r\n    /**\r\n     * Returns a value within [-0x80000000, 0x7fffffff]\r\n     */\r\n    int32() {\r\n        return int32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0xffffffff]\r\n     */\r\n    uint32() {\r\n        return uint32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x1fffffffffffff]\r\n     */\r\n    uint53() {\r\n        return uint53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x20000000000000]\r\n     */\r\n    uint53Full() {\r\n        return uint53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n     */\r\n    int53() {\r\n        return int53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n     */\r\n    int53Full() {\r\n        return int53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [min, max]\r\n     * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n     * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n     */\r\n    integer(min, max) {\r\n        return integer(min, max)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0]\r\n     */\r\n    realZeroToOneInclusive() {\r\n        return realZeroToOneInclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0)\r\n     */\r\n    realZeroToOneExclusive() {\r\n        return realZeroToOneExclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [min, max) or [min, max]\r\n     * @param min The minimum floating-point value, inclusive.\r\n     * @param max The maximum floating-point value.\r\n     * @param inclusive If true, `max` will be inclusive.\r\n     */\r\n    real(min, max, inclusive = false) {\r\n        return real(min, max, inclusive)(this.engine);\r\n    }\r\n    bool(numerator, denominator) {\r\n        return bool(numerator, denominator)(this.engine);\r\n    }\r\n    /**\r\n     * Return a random value within the provided `source` within the sliced\r\n     * bounds of `begin` and `end`.\r\n     * @param source an array of items to pick from\r\n     * @param begin the beginning slice index (defaults to `0`)\r\n     * @param end the ending slice index (defaults to `source.length`)\r\n     */\r\n    pick(source, begin, end) {\r\n        return pick(this.engine, source, begin, end);\r\n    }\r\n    /**\r\n     * Shuffles an array in-place\r\n     * @param array The array to shuffle\r\n     */\r\n    shuffle(array) {\r\n        return shuffle(this.engine, array);\r\n    }\r\n    /**\r\n     * From the population array, returns an array with sampleSize elements that\r\n     * are randomly chosen without repeats.\r\n     * @param population An array that has items to choose a sample from\r\n     * @param sampleSize The size of the result array\r\n     */\r\n    sample(population, sampleSize) {\r\n        return sample(this.engine, population, sampleSize);\r\n    }\r\n    /**\r\n     * Returns a value within [1, sideCount]\r\n     * @param sideCount The number of sides of the die\r\n     */\r\n    die(sideCount) {\r\n        return die(sideCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns an array of length `dieCount` of values within [1, sideCount]\r\n     * @param sideCount The number of sides of each die\r\n     * @param dieCount The number of dice\r\n     */\r\n    dice(sideCount, dieCount) {\r\n        return dice(sideCount, dieCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a Universally Unique Identifier Version 4.\r\n     *\r\n     * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n     */\r\n    uuid4() {\r\n        return uuid4(this.engine);\r\n    }\r\n    string(length, pool) {\r\n        return string(pool)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random string comprised of numbers or the characters `abcdef`\r\n     * (or `ABCDEF`) of length `length`.\r\n     * @param length Length of the result string\r\n     * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n     */\r\n    hex(length, uppercase) {\r\n        return hex(uppercase)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random `Date` within the inclusive range of [`start`, `end`].\r\n     * @param start The minimum `Date`\r\n     * @param end The maximum `Date`\r\n     */\r\n    date(start, end) {\r\n        return date(start, end)(this.engine);\r\n    }\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array\r\n */\r\nconst I32Array = (() => {\r\n    try {\r\n        const buffer = new ArrayBuffer(4);\r\n        const view = new Int32Array(buffer);\r\n        view[0] = INT32_SIZE;\r\n        if (view[0] === -INT32_SIZE) {\r\n            return Int32Array;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return Array;\r\n})();\n\nlet data = null;\r\nconst COUNT = 128;\r\nlet index = COUNT;\r\n/**\r\n * An Engine that relies on the globally-available `crypto.getRandomValues`,\r\n * which is typically available in modern browsers.\r\n *\r\n * See https://developer.mozilla.org/en-US/docs/Web/API/Crypto/getRandomValues\r\n *\r\n * If unavailable or otherwise non-functioning, then `browserCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst browserCrypto = {\r\n    next() {\r\n        if (index >= COUNT) {\r\n            if (data === null) {\r\n                data = new I32Array(COUNT);\r\n            }\r\n            crypto.getRandomValues(data);\r\n            index = 0;\r\n        }\r\n        return data[index++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns an array of random int32 values, based on current time\r\n * and a random number engine\r\n *\r\n * @param engine an Engine to pull random values from, default `nativeMath`\r\n * @param length the length of the Array, minimum 1, default 16\r\n */\r\nfunction createEntropy(engine = nativeMath, length = 16) {\r\n    const array = [];\r\n    array.push(new Date().getTime() | 0);\r\n    for (let i = 1; i < length; ++i) {\r\n        array[i] = engine.next() | 0;\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/imul\r\n */\r\nconst imul = (() => {\r\n    try {\r\n        if (Math.imul(UINT32_MAX, 5) === -5) {\r\n            return Math.imul;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    const UINT16_MAX = 0xffff;\r\n    return (a, b) => {\r\n        const ah = (a >>> 16) & UINT16_MAX;\r\n        const al = a & UINT16_MAX;\r\n        const bh = (b >>> 16) & UINT16_MAX;\r\n        const bl = b & UINT16_MAX;\r\n        // the shift by 0 fixes the sign on the high part\r\n        // the final |0 converts the unsigned value into a signed value\r\n        return (al * bl + (((ah * bl + al * bh) << 16) >>> 0)) | 0;\r\n    };\r\n})();\n\nconst ARRAY_SIZE = 624;\r\nconst ARRAY_MAX = ARRAY_SIZE - 1;\r\nconst M = 397;\r\nconst ARRAY_SIZE_MINUS_M = ARRAY_SIZE - M;\r\nconst A = 0x9908b0df;\r\n/**\r\n * An Engine that is a pseudorandom number generator using the Mersenne\r\n * Twister algorithm based on the prime 2**19937 − 1\r\n *\r\n * See http://en.wikipedia.org/wiki/Mersenne_twister\r\n */\r\nclass MersenneTwister19937 {\r\n    /**\r\n     * MersenneTwister19937 should not be instantiated directly.\r\n     * Instead, use the static methods `seed`, `seedWithArray`, or `autoSeed`.\r\n     */\r\n    constructor() {\r\n        this.data = new I32Array(ARRAY_SIZE);\r\n        this.index = 0; // integer within [0, 624]\r\n        this.uses = 0;\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with an initial int32 value\r\n     * @param initial the initial seed value\r\n     */\r\n    static seed(initial) {\r\n        return new MersenneTwister19937().seed(initial);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with zero or more int32 values\r\n     * @param source A series of int32 values\r\n     */\r\n    static seedWithArray(source) {\r\n        return new MersenneTwister19937().seedWithArray(source);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with the current time and\r\n     * a series of natively-generated random values\r\n     */\r\n    static autoSeed() {\r\n        return MersenneTwister19937.seedWithArray(createEntropy());\r\n    }\r\n    /**\r\n     * Returns the next int32 value of the sequence\r\n     */\r\n    next() {\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        const value = this.data[this.index];\r\n        this.index = (this.index + 1) | 0;\r\n        this.uses += 1;\r\n        return temper(value) | 0;\r\n    }\r\n    /**\r\n     * Returns the number of times that the Engine has been used.\r\n     *\r\n     * This can be provided to an unused MersenneTwister19937 with the same\r\n     * seed, bringing it to the exact point that was left off.\r\n     */\r\n    getUseCount() {\r\n        return this.uses;\r\n    }\r\n    /**\r\n     * Discards one or more items from the engine\r\n     * @param count The count of items to discard\r\n     */\r\n    discard(count) {\r\n        if (count <= 0) {\r\n            return this;\r\n        }\r\n        this.uses += count;\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        while (count + this.index > ARRAY_SIZE) {\r\n            count -= ARRAY_SIZE - this.index;\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        this.index = (this.index + count) | 0;\r\n        return this;\r\n    }\r\n    seed(initial) {\r\n        let previous = 0;\r\n        this.data[0] = previous = initial | 0;\r\n        for (let i = 1; i < ARRAY_SIZE; i = (i + 1) | 0) {\r\n            this.data[i] = previous =\r\n                (imul(previous ^ (previous >>> 30), 0x6c078965) + i) | 0;\r\n        }\r\n        this.index = ARRAY_SIZE;\r\n        this.uses = 0;\r\n        return this;\r\n    }\r\n    seedWithArray(source) {\r\n        this.seed(0x012bd6aa);\r\n        seedWithArray(this.data, source);\r\n        return this;\r\n    }\r\n}\r\nfunction refreshData(data) {\r\n    let k = 0;\r\n    let tmp = 0;\r\n    for (; (k | 0) < ARRAY_SIZE_MINUS_M; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] = data[(k + M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    for (; (k | 0) < ARRAY_MAX; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] =\r\n            data[(k - ARRAY_SIZE_MINUS_M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    tmp = (data[ARRAY_MAX] & INT32_SIZE) | (data[0] & INT32_MAX);\r\n    data[ARRAY_MAX] = data[M - 1] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n}\r\nfunction temper(value) {\r\n    value ^= value >>> 11;\r\n    value ^= (value << 7) & 0x9d2c5680;\r\n    value ^= (value << 15) & 0xefc60000;\r\n    return value ^ (value >>> 18);\r\n}\r\nfunction seedWithArray(data, source) {\r\n    let i = 1;\r\n    let j = 0;\r\n    const sourceLength = source.length;\r\n    let k = Math.max(sourceLength, ARRAY_SIZE) | 0;\r\n    let previous = data[0] | 0;\r\n    for (; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x0019660d)) +\r\n                (source[j] | 0) +\r\n                (j | 0)) |\r\n                0;\r\n        i = (i + 1) | 0;\r\n        ++j;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n        if (j >= sourceLength) {\r\n            j = 0;\r\n        }\r\n    }\r\n    for (k = ARRAY_MAX; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x5d588b65)) - i) | 0;\r\n        i = (i + 1) | 0;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n    }\r\n    data[0] = INT32_SIZE;\r\n}\n\nlet data$1 = null;\r\nconst COUNT$1 = 128;\r\nlet index$1 = COUNT$1;\r\n/**\r\n * An Engine that relies on the node-available\r\n * `require('crypto').randomBytes`, which has been available since 0.58.\r\n *\r\n * See https://nodejs.org/api/crypto.html#crypto_crypto_randombytes_size_callback\r\n *\r\n * If unavailable or otherwise non-functioning, then `nodeCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst nodeCrypto = {\r\n    next() {\r\n        if (index$1 >= COUNT$1) {\r\n            data$1 = new Int32Array(new Int8Array(require(\"crypto\").randomBytes(4 * COUNT$1)).buffer);\r\n            index$1 = 0;\r\n        }\r\n        return data$1[index$1++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns a Distribution to random value within the provided `source`\r\n * within the sliced bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction picker(source, begin, end) {\r\n    const clone = sliceArray.call(source, begin, end);\r\n    if (clone.length === 0) {\r\n        throw new RangeError(`Cannot pick from a source with no items`);\r\n    }\r\n    const distribution = integer(0, clone.length - 1);\r\n    return engine => clone[distribution(engine)];\r\n}\n\nexport { Random, browserCrypto, nativeMath, MersenneTwister19937, nodeCrypto, bool, date, dice, die, hex, int32, int53, int53Full, integer, pick, picker, real, realZeroToOneExclusive, realZeroToOneInclusive, sample, shuffle, string, uint32, uint53, uint53Full, uuid4, createEntropy };\n//# sourceMappingURL=random-js.esm.js.map\n","import * as Random from 'random-js';\nimport Matrix from 'ml-matrix';\n\nexport function checkFloat(n) {\n  return n > 0.0 && n <= 1.0;\n}\n\n/**\n * Select n with replacement elements on the training set and values, where n is the size of the training set.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {Array} trainingValue\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object} with new X and y.\n */\nexport function examplesBaggingWithReplacement(\n  trainingSet,\n  trainingValue,\n  seed,\n) {\n  let engine;\n  let distribution = Random.integer(0, trainingSet.rows - 1);\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let Xr = new Array(trainingSet.rows);\n  let yr = new Array(trainingSet.rows);\n\n  for (let i = 0; i < trainingSet.rows; ++i) {\n    let index = distribution(engine);\n    Xr[i] = trainingSet.getRow(index);\n    yr[i] = trainingValue[index];\n  }\n\n  return {\n    X: new Matrix(Xr),\n    y: yr,\n  };\n}\n\n/**\n * selects n features from the training set with or without replacement, returns the new training set and the indexes used.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {number} n - features.\n * @param {boolean} replacement\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object}\n */\nexport function featureBagging(trainingSet, n, replacement, seed) {\n  if (trainingSet.columns < n) {\n    throw new RangeError(\n      'N should be less or equal to the number of columns of X',\n    );\n  }\n\n  let distribution = Random.integer(0, trainingSet.columns - 1);\n  let engine;\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let toRet = new Matrix(trainingSet.rows, n);\n\n  let usedIndex;\n  let index;\n  if (replacement) {\n    usedIndex = new Array(n);\n    for (let i = 0; i < n; ++i) {\n      index = distribution(engine);\n      usedIndex[i] = index;\n      toRet.setColumn(i, trainingSet.getColumn(index));\n    }\n  } else {\n    usedIndex = new Set();\n    index = distribution(engine);\n    for (let i = 0; i < n; ++i) {\n      while (usedIndex.has(index)) {\n        index = distribution(engine);\n      }\n      toRet.setColumn(i, trainingSet.getColumn(index));\n      usedIndex.add(index);\n    }\n    usedIndex = Array.from(usedIndex);\n  }\n\n  return {\n    X: toRet,\n    usedIndex: usedIndex,\n  };\n}\n","import {\n  DecisionTreeClassifier as DTClassifier,\n  DecisionTreeRegression as DTRegression,\n} from 'ml-cart';\nimport {\n  Matrix,\n  WrapperMatrix2D,\n  MatrixTransposeView,\n  MatrixColumnSelectionView,\n} from 'ml-matrix';\n\nimport * as Utils from './utils';\n\n/**\n * @class RandomForestBase\n */\nexport class RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number|String} [options.maxFeatures] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement] - use replacement over the sample features.\n   * @param {number} [options.seed] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators] - number of estimator to use.\n   * @param {object} [options.treeOptions] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.isClassifier] - boolean to check if is a classifier or regression model (used by subclasses).\n   * @param {boolean} [options.useSampleBagging] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.replacement = model.replacement;\n      this.maxFeatures = model.maxFeatures;\n      this.nEstimators = model.nEstimators;\n      this.treeOptions = model.treeOptions;\n      this.isClassifier = model.isClassifier;\n      this.seed = model.seed;\n      this.n = model.n;\n      this.indexes = model.indexes;\n      this.useSampleBagging = model.useSampleBagging;\n\n      let Estimator = this.isClassifier ? DTClassifier : DTRegression;\n      this.estimators = model.estimators.map((est) => Estimator.load(est));\n    } else {\n      this.replacement = options.replacement;\n      this.maxFeatures = options.maxFeatures;\n      this.nEstimators = options.nEstimators;\n      this.treeOptions = options.treeOptions;\n      this.isClassifier = options.isClassifier;\n      this.seed = options.seed;\n      this.useSampleBagging = options.useSampleBagging;\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    this.maxFeatures = this.maxFeatures || trainingSet.columns;\n\n    if (Utils.checkFloat(this.maxFeatures)) {\n      this.n = Math.floor(trainingSet.columns * this.maxFeatures);\n    } else if (Number.isInteger(this.maxFeatures)) {\n      if (this.maxFeatures > trainingSet.columns) {\n        throw new RangeError(\n          `The maxFeatures parameter should be less than ${trainingSet.columns}`,\n        );\n      } else {\n        this.n = this.maxFeatures;\n      }\n    } else {\n      throw new RangeError(\n        `Cannot process the maxFeatures parameter ${this.maxFeatures}`,\n      );\n    }\n\n    let Estimator;\n    if (this.isClassifier) {\n      Estimator = DTClassifier;\n    } else {\n      Estimator = DTRegression;\n    }\n\n    this.estimators = new Array(this.nEstimators);\n    this.indexes = new Array(this.nEstimators);\n\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let res = this.useSampleBagging\n        ? Utils.examplesBaggingWithReplacement(\n            trainingSet,\n            trainingValues,\n            this.seed,\n          )\n        : { X: trainingSet, y: trainingValues };\n      let X = res.X;\n      let y = res.y;\n\n      res = Utils.featureBagging(X, this.n, this.replacement, this.seed);\n      X = res.X;\n\n      this.indexes[i] = res.usedIndex;\n      this.estimators[i] = new Estimator(this.treeOptions);\n      this.estimators[i].train(X, y);\n    }\n  }\n\n  /**\n   * Method that returns the way the algorithm generates the predictions, for example, in classification\n   * you can return the mode of all predictions retrieved by the trees, or in case of regression you can\n   * use the mean or the median.\n   * @abstract\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction.\n   */\n  // eslint-disable-next-line no-unused-vars\n  selection(values) {\n    throw new Error(\"Abstract method 'selection' not implemented!\");\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    let predictionValues = new Array(this.nEstimators);\n    toPredict = Matrix.checkMatrix(toPredict);\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let X = new MatrixColumnSelectionView(toPredict, this.indexes[i]); // get features for estimator\n      predictionValues[i] = this.estimators[i].predict(X);\n    }\n\n    predictionValues = new MatrixTransposeView(\n      new WrapperMatrix2D(predictionValues),\n    );\n    let predictions = new Array(predictionValues.rows);\n    for (let i = 0; i < predictionValues.rows; ++i) {\n      predictions[i] = this.selection(predictionValues.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      indexes: this.indexes,\n      n: this.n,\n      replacement: this.replacement,\n      maxFeatures: this.maxFeatures,\n      nEstimators: this.nEstimators,\n      treeOptions: this.treeOptions,\n      isClassifier: this.isClassifier,\n      seed: this.seed,\n      estimators: this.estimators.map((est) => est.toJSON()),\n      useSampleBagging: this.useSampleBagging,\n    };\n  }\n}\n","import { RandomForestBase } from './RandomForestBase';\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: true,\n  nEstimators: 10,\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestClassifier\n * @augments RandomForestBase\n */\nexport class RandomForestClassifier extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n      options.isClassifier = true;\n      super(options);\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return mode(values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      name: 'RFClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'RFClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestClassifier(true, model);\n  }\n}\n\n/**\n * Return the most repeated element on the array.\n * @param {Array} arr\n * @return {number} mode\n */\nfunction mode(arr) {\n  return arr\n    .sort(\n      (a, b) =>\n        arr.filter((v) => v === a).length - arr.filter((v) => v === b).length,\n    )\n    .pop();\n}\n","(function(){function a(d){for(var e=0,f=d.length-1,g=void 0,h=void 0,i=void 0,j=c(e,f);!0;){if(f<=e)return d[j];if(f==e+1)return d[e]>d[f]&&b(d,e,f),d[j];for(g=c(e,f),d[g]>d[f]&&b(d,g,f),d[e]>d[f]&&b(d,e,f),d[g]>d[e]&&b(d,g,e),b(d,g,e+1),h=e+1,i=f;!0;){do h++;while(d[e]>d[h]);do i--;while(d[i]>d[e]);if(i<h)break;b(d,h,i)}b(d,e,i),i<=j&&(e=h),i>=j&&(f=i-1)}}var b=function b(d,e,f){var _ref;return _ref=[d[f],d[e]],d[e]=_ref[0],d[f]=_ref[1],_ref},c=function c(d,e){return~~((d+e)/2)};'undefined'!=typeof module&&module.exports?module.exports=a:window.median=a})();\n","import quickSelectMedian from 'median-quickselect';\nimport isArray from 'is-any-array';\n\n/**\n * Computes the median of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction median(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  return quickSelectMedian(input.slice());\n}\n\nexport default median;\n","import arrayMean from 'ml-array-mean';\nimport arrayMedian from 'ml-array-median';\n\nimport { RandomForestBase } from './RandomForestBase';\n\nconst selectionMethods = {\n  mean: arrayMean,\n  median: arrayMedian,\n};\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: false,\n  nEstimators: 10,\n  treeOptions: {},\n  selectionMethod: 'mean',\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestRegression\n * @augments RandomForestBase\n */\nexport class RandomForestRegression extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {string} [options.selectionMethod=\"mean\"] - the way to calculate the prediction from estimators, \"mean\" and \"median\" are supported.\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n      this.selectionMethod = model.selectionMethod;\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n\n      if (\n        !(\n          options.selectionMethod === 'mean' ||\n          options.selectionMethod === 'median'\n        )\n      ) {\n        throw new RangeError(\n          `Unsupported selection method ${options.selectionMethod}`,\n        );\n      }\n\n      options.isClassifier = false;\n\n      super(options);\n      this.selectionMethod = options.selectionMethod;\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return selectionMethods[this.selectionMethod](values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      selectionMethod: this.selectionMethod,\n      name: 'RFRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestRegression}\n   */\n  static load(model) {\n    if (model.name !== 'RFRegression') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestRegression(true, model);\n  }\n}\n","import { Matrix, MatrixTransposeView, EVD, SVD, NIPALS } from 'ml-matrix';\n\n/**\n * Creates new PCA (Principal Component Analysis) from the dataset\n * @param {Matrix} dataset - dataset or covariance matrix.\n * @param {Object} [options]\n * @param {boolean} [options.isCovarianceMatrix=false] - true if the dataset is a covariance matrix.\n * @param {string} [options.method='SVD'] - select which method to use: SVD (default), covarianceMatrirx or NIPALS.\n * @param {number} [options.nCompNIPALS=2] - number of components to be computed with NIPALS.\n * @param {boolean} [options.center=true] - should the data be centered (subtract the mean).\n * @param {boolean} [options.scale=false] - should the data be scaled (divide by the standard deviation).\n * @param {boolean} [options.ignoreZeroVariance=false] - ignore columns with zero variance if `scale` is `true`.\n * */\nexport class PCA {\n  constructor(dataset, options = {}) {\n    if (dataset === true) {\n      const model = options;\n      this.center = model.center;\n      this.scale = model.scale;\n      this.means = model.means;\n      this.stdevs = model.stdevs;\n      this.U = Matrix.checkMatrix(model.U);\n      this.S = model.S;\n      this.R = model.R;\n      this.excludedFeatures = model.excludedFeatures || [];\n      return;\n    }\n\n    dataset = new Matrix(dataset);\n\n    const {\n      isCovarianceMatrix = false,\n      method = 'SVD',\n      nCompNIPALS = 2,\n      center = true,\n      scale = false,\n      ignoreZeroVariance = false,\n    } = options;\n\n    this.center = center;\n    this.scale = scale;\n    this.means = null;\n    this.stdevs = null;\n    this.excludedFeatures = [];\n\n    if (isCovarianceMatrix) {\n      // User provided a covariance matrix instead of dataset.\n      this._computeFromCovarianceMatrix(dataset);\n      return;\n    }\n\n    this._adjust(dataset, ignoreZeroVariance);\n    switch (method) {\n      case 'covarianceMatrix': {\n        // User provided a dataset but wants us to compute and use the covariance matrix.\n        const covarianceMatrix = new MatrixTransposeView(dataset)\n          .mmul(dataset)\n          .div(dataset.rows - 1);\n        this._computeFromCovarianceMatrix(covarianceMatrix);\n        break;\n      }\n      case 'NIPALS': {\n        this._computeWithNIPALS(dataset, nCompNIPALS);\n        break;\n      }\n      case 'SVD': {\n        const svd = new SVD(dataset, {\n          computeLeftSingularVectors: false,\n          computeRightSingularVectors: true,\n          autoTranspose: true,\n        });\n\n        this.U = svd.rightSingularVectors;\n\n        const singularValues = svd.diagonal;\n        const eigenvalues = [];\n        for (const singularValue of singularValues) {\n          eigenvalues.push((singularValue * singularValue) / (dataset.rows - 1));\n        }\n        this.S = eigenvalues;\n        break;\n      }\n      default: {\n        throw new Error(`unknown method: ${method}`);\n      }\n    }\n  }\n\n  /**\n   * Load a PCA model from JSON\n   * @param {Object} model\n   * @return {PCA}\n   */\n  static load(model) {\n    if (typeof model.name !== 'string') {\n      throw new TypeError('model must have a name property');\n    }\n    if (model.name !== 'PCA') {\n      throw new RangeError(`invalid model: ${model.name}`);\n    }\n    return new PCA(true, model);\n  }\n\n  /**\n   * Project the dataset into the PCA space\n   * @param {Matrix} dataset\n   * @param {Object} options\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  predict(dataset, options = {}) {\n    const { nComponents = this.U.columns } = options;\n    dataset = new Matrix(dataset);\n    if (this.center) {\n      dataset.subRowVector(this.means);\n      if (this.scale) {\n        for (let i of this.excludedFeatures) {\n          dataset.removeColumn(i);\n        }\n        dataset.divRowVector(this.stdevs);\n      }\n    }\n    var predictions = dataset.mmul(this.U);\n    return predictions.subMatrix(0, predictions.rows - 1, 0, nComponents - 1);\n  }\n\n  /**\n   * Calculates the inverse PCA transform\n   * @param {Matrix} dataset\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  invert(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n\n    var inverse = dataset.mmul(this.U.transpose());\n\n    if (this.center) {\n      if (this.scale) {\n        inverse.mulRowVector(this.stdevs);\n      }\n      inverse.addRowVector(this.means);\n    }\n\n    return inverse;\n  }\n\n\n  /**\n   * Returns the proportion of variance for each component\n   * @return {[number]}\n   */\n  getExplainedVariance() {\n    var sum = 0;\n    for (const s of this.S) {\n      sum += s;\n    }\n    return this.S.map((value) => value / sum);\n  }\n\n  /**\n   * Returns the cumulative proportion of variance\n   * @return {[number]}\n   */\n  getCumulativeVariance() {\n    var explained = this.getExplainedVariance();\n    for (var i = 1; i < explained.length; i++) {\n      explained[i] += explained[i - 1];\n    }\n    return explained;\n  }\n\n  /**\n   * Returns the Eigenvectors of the covariance matrix\n   * @returns {Matrix}\n   */\n  getEigenvectors() {\n    return this.U;\n  }\n\n  /**\n   * Returns the Eigenvalues (on the diagonal)\n   * @returns {[number]}\n   */\n  getEigenvalues() {\n    return this.S;\n  }\n\n  /**\n   * Returns the standard deviations of the principal components\n   * @returns {[number]}\n   */\n  getStandardDeviations() {\n    return this.S.map((x) => Math.sqrt(x));\n  }\n\n  /**\n   * Returns the loadings matrix\n   * @return {Matrix}\n   */\n  getLoadings() {\n    return this.U.transpose();\n  }\n\n  /**\n   * Export the current model to a JSON object\n   * @return {Object} model\n   */\n  toJSON() {\n    return {\n      name: 'PCA',\n      center: this.center,\n      scale: this.scale,\n      means: this.means,\n      stdevs: this.stdevs,\n      U: this.U,\n      S: this.S,\n      excludedFeatures: this.excludedFeatures,\n    };\n  }\n\n  _adjust(dataset, ignoreZeroVariance) {\n    if (this.center) {\n      const mean = dataset.mean('column');\n      const stdevs = this.scale\n        ? dataset.standardDeviation('column', { mean })\n        : null;\n      this.means = mean;\n      dataset.subRowVector(mean);\n      if (this.scale) {\n        for (let i = 0; i < stdevs.length; i++) {\n          if (stdevs[i] === 0) {\n            if (ignoreZeroVariance) {\n              dataset.removeColumn(i);\n              stdevs.splice(i, 1);\n              this.excludedFeatures.push(i);\n              i--;\n            } else {\n              throw new RangeError(\n                `Cannot scale the dataset (standard deviation is zero at index ${i}`,\n              );\n            }\n          }\n        }\n        this.stdevs = stdevs;\n        dataset.divRowVector(stdevs);\n      }\n    }\n  }\n\n  _computeFromCovarianceMatrix(dataset) {\n    const evd = new EVD(dataset, { assumeSymmetric: true });\n    this.U = evd.eigenvectorMatrix;\n    this.U.flipRows();\n    this.S = evd.realEigenvalues;\n    this.S.reverse();\n  }\n\n  _computeWithNIPALS(dataset, nCompNIPALS) {\n    this.U = new Matrix(nCompNIPALS, dataset.columns);\n    this.S = [];\n\n    let x = dataset;\n    for (let i = 0; i < nCompNIPALS; i++) {\n      let dc = new NIPALS(x);\n\n      this.U.setRow(i, dc.w.transpose());\n      this.S.push(Math.pow(dc.s.get(0, 0), 2));\n\n      x = dc.xResidual;\n    }\n    this.U = this.U.transpose(); // to be compatible with API\n  }\n}\n","export function squaredEuclidean(p, q) {\r\n    let d = 0;\r\n    for (let i = 0; i < p.length; i++) {\r\n        d += (p[i] - q[i]) * (p[i] - q[i]);\r\n    }\r\n    return d;\r\n}\r\nexport function euclidean(p, q) {\r\n    return Math.sqrt(squaredEuclidean(p, q));\r\n}\r\n","/**\n * Computes a distance/similarity matrix given an array of data and a distance/similarity function.\n * @param {Array} data An array of data\n * @param {function} distanceFn  A function that accepts two arguments and computes a distance/similarity between them\n * @return {Array<Array>} The distance/similarity matrix. The matrix is square and has a size equal to the length of\n * the data array\n */\nexport default function distanceMatrix(data, distanceFn) {\n  const result = getMatrix(data.length);\n\n  // Compute upper distance matrix\n  for (let i = 0; i < data.length; i++) {\n    for (let j = 0; j <= i; j++) {\n      result[i][j] = distanceFn(data[i], data[j]);\n      result[j][i] = result[i][j];\n    }\n  }\n\n  return result;\n}\n\nfunction getMatrix(size) {\n  const matrix = [];\n  for (let i = 0; i < size; i++) {\n    const row = [];\n    matrix.push(row);\n    for (let j = 0; j < size; j++) {\n      row.push(0);\n    }\n  }\n  return matrix;\n}\n","// Generated by CoffeeScript 1.8.0\n(function() {\n  var Heap, defaultCmp, floor, heapify, heappop, heappush, heappushpop, heapreplace, insort, min, nlargest, nsmallest, updateItem, _siftdown, _siftup;\n\n  floor = Math.floor, min = Math.min;\n\n\n  /*\n  Default comparison function to be used\n   */\n\n  defaultCmp = function(x, y) {\n    if (x < y) {\n      return -1;\n    }\n    if (x > y) {\n      return 1;\n    }\n    return 0;\n  };\n\n\n  /*\n  Insert item x in list a, and keep it sorted assuming a is sorted.\n  \n  If x is already in a, insert it to the right of the rightmost x.\n  \n  Optional args lo (default 0) and hi (default a.length) bound the slice\n  of a to be searched.\n   */\n\n  insort = function(a, x, lo, hi, cmp) {\n    var mid;\n    if (lo == null) {\n      lo = 0;\n    }\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (lo < 0) {\n      throw new Error('lo must be non-negative');\n    }\n    if (hi == null) {\n      hi = a.length;\n    }\n    while (lo < hi) {\n      mid = floor((lo + hi) / 2);\n      if (cmp(x, a[mid]) < 0) {\n        hi = mid;\n      } else {\n        lo = mid + 1;\n      }\n    }\n    return ([].splice.apply(a, [lo, lo - lo].concat(x)), x);\n  };\n\n\n  /*\n  Push item onto heap, maintaining the heap invariant.\n   */\n\n  heappush = function(array, item, cmp) {\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    array.push(item);\n    return _siftdown(array, 0, array.length - 1, cmp);\n  };\n\n\n  /*\n  Pop the smallest item off the heap, maintaining the heap invariant.\n   */\n\n  heappop = function(array, cmp) {\n    var lastelt, returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    lastelt = array.pop();\n    if (array.length) {\n      returnitem = array[0];\n      array[0] = lastelt;\n      _siftup(array, 0, cmp);\n    } else {\n      returnitem = lastelt;\n    }\n    return returnitem;\n  };\n\n\n  /*\n  Pop and return the current smallest value, and add the new item.\n  \n  This is more efficient than heappop() followed by heappush(), and can be\n  more appropriate when using a fixed size heap. Note that the value\n  returned may be larger than item! That constrains reasonable use of\n  this routine unless written as part of a conditional replacement:\n      if item > array[0]\n        item = heapreplace(array, item)\n   */\n\n  heapreplace = function(array, item, cmp) {\n    var returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    returnitem = array[0];\n    array[0] = item;\n    _siftup(array, 0, cmp);\n    return returnitem;\n  };\n\n\n  /*\n  Fast version of a heappush followed by a heappop.\n   */\n\n  heappushpop = function(array, item, cmp) {\n    var _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (array.length && cmp(array[0], item) < 0) {\n      _ref = [array[0], item], item = _ref[0], array[0] = _ref[1];\n      _siftup(array, 0, cmp);\n    }\n    return item;\n  };\n\n\n  /*\n  Transform list into a heap, in-place, in O(array.length) time.\n   */\n\n  heapify = function(array, cmp) {\n    var i, _i, _j, _len, _ref, _ref1, _results, _results1;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    _ref1 = (function() {\n      _results1 = [];\n      for (var _j = 0, _ref = floor(array.length / 2); 0 <= _ref ? _j < _ref : _j > _ref; 0 <= _ref ? _j++ : _j--){ _results1.push(_j); }\n      return _results1;\n    }).apply(this).reverse();\n    _results = [];\n    for (_i = 0, _len = _ref1.length; _i < _len; _i++) {\n      i = _ref1[_i];\n      _results.push(_siftup(array, i, cmp));\n    }\n    return _results;\n  };\n\n\n  /*\n  Update the position of the given item in the heap.\n  This function should be called every time the item is being modified.\n   */\n\n  updateItem = function(array, item, cmp) {\n    var pos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    pos = array.indexOf(item);\n    if (pos === -1) {\n      return;\n    }\n    _siftdown(array, 0, pos, cmp);\n    return _siftup(array, pos, cmp);\n  };\n\n\n  /*\n  Find the n largest elements in a dataset.\n   */\n\n  nlargest = function(array, n, cmp) {\n    var elem, result, _i, _len, _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    result = array.slice(0, n);\n    if (!result.length) {\n      return result;\n    }\n    heapify(result, cmp);\n    _ref = array.slice(n);\n    for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n      elem = _ref[_i];\n      heappushpop(result, elem, cmp);\n    }\n    return result.sort(cmp).reverse();\n  };\n\n\n  /*\n  Find the n smallest elements in a dataset.\n   */\n\n  nsmallest = function(array, n, cmp) {\n    var elem, i, los, result, _i, _j, _len, _ref, _ref1, _results;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (n * 10 <= array.length) {\n      result = array.slice(0, n).sort(cmp);\n      if (!result.length) {\n        return result;\n      }\n      los = result[result.length - 1];\n      _ref = array.slice(n);\n      for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n        elem = _ref[_i];\n        if (cmp(elem, los) < 0) {\n          insort(result, elem, 0, null, cmp);\n          result.pop();\n          los = result[result.length - 1];\n        }\n      }\n      return result;\n    }\n    heapify(array, cmp);\n    _results = [];\n    for (i = _j = 0, _ref1 = min(n, array.length); 0 <= _ref1 ? _j < _ref1 : _j > _ref1; i = 0 <= _ref1 ? ++_j : --_j) {\n      _results.push(heappop(array, cmp));\n    }\n    return _results;\n  };\n\n  _siftdown = function(array, startpos, pos, cmp) {\n    var newitem, parent, parentpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    newitem = array[pos];\n    while (pos > startpos) {\n      parentpos = (pos - 1) >> 1;\n      parent = array[parentpos];\n      if (cmp(newitem, parent) < 0) {\n        array[pos] = parent;\n        pos = parentpos;\n        continue;\n      }\n      break;\n    }\n    return array[pos] = newitem;\n  };\n\n  _siftup = function(array, pos, cmp) {\n    var childpos, endpos, newitem, rightpos, startpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    endpos = array.length;\n    startpos = pos;\n    newitem = array[pos];\n    childpos = 2 * pos + 1;\n    while (childpos < endpos) {\n      rightpos = childpos + 1;\n      if (rightpos < endpos && !(cmp(array[childpos], array[rightpos]) < 0)) {\n        childpos = rightpos;\n      }\n      array[pos] = array[childpos];\n      pos = childpos;\n      childpos = 2 * pos + 1;\n    }\n    array[pos] = newitem;\n    return _siftdown(array, startpos, pos, cmp);\n  };\n\n  Heap = (function() {\n    Heap.push = heappush;\n\n    Heap.pop = heappop;\n\n    Heap.replace = heapreplace;\n\n    Heap.pushpop = heappushpop;\n\n    Heap.heapify = heapify;\n\n    Heap.updateItem = updateItem;\n\n    Heap.nlargest = nlargest;\n\n    Heap.nsmallest = nsmallest;\n\n    function Heap(cmp) {\n      this.cmp = cmp != null ? cmp : defaultCmp;\n      this.nodes = [];\n    }\n\n    Heap.prototype.push = function(x) {\n      return heappush(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pop = function() {\n      return heappop(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.peek = function() {\n      return this.nodes[0];\n    };\n\n    Heap.prototype.contains = function(x) {\n      return this.nodes.indexOf(x) !== -1;\n    };\n\n    Heap.prototype.replace = function(x) {\n      return heapreplace(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pushpop = function(x) {\n      return heappushpop(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.heapify = function() {\n      return heapify(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.updateItem = function(x) {\n      return updateItem(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.clear = function() {\n      return this.nodes = [];\n    };\n\n    Heap.prototype.empty = function() {\n      return this.nodes.length === 0;\n    };\n\n    Heap.prototype.size = function() {\n      return this.nodes.length;\n    };\n\n    Heap.prototype.clone = function() {\n      var heap;\n      heap = new Heap();\n      heap.nodes = this.nodes.slice(0);\n      return heap;\n    };\n\n    Heap.prototype.toArray = function() {\n      return this.nodes.slice(0);\n    };\n\n    Heap.prototype.insert = Heap.prototype.push;\n\n    Heap.prototype.top = Heap.prototype.peek;\n\n    Heap.prototype.front = Heap.prototype.peek;\n\n    Heap.prototype.has = Heap.prototype.contains;\n\n    Heap.prototype.copy = Heap.prototype.clone;\n\n    return Heap;\n\n  })();\n\n  (function(root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      return define([], factory);\n    } else if (typeof exports === 'object') {\n      return module.exports = factory();\n    } else {\n      return root.Heap = factory();\n    }\n  })(this, function() {\n    return Heap;\n  });\n\n}).call(this);\n","module.exports = require('./lib/heap');\n","import Heap from 'heap';\n\nexport default class Cluster {\n  constructor() {\n    this.children = [];\n    this.height = 0;\n    this.size = 1;\n    this.index = -1;\n    this.isLeaf = false;\n  }\n\n  /**\n   * Creates an array of clusters where the maximum height is smaller than the threshold\n   * @param {number} threshold\n   * @return {Array<Cluster>}\n   */\n  cut(threshold) {\n    if (typeof threshold !== 'number') {\n      throw new TypeError('threshold must be a number');\n    }\n    if (threshold < 0) {\n      throw new RangeError('threshold must be a positive number');\n    }\n    let list = [this];\n    const ans = [];\n    while (list.length > 0) {\n      const aux = list.shift();\n      if (threshold >= aux.height) {\n        ans.push(aux);\n      } else {\n        list = list.concat(aux.children);\n      }\n    }\n    return ans;\n  }\n\n  /**\n   * Merge the leaves in the minimum way to have `groups` number of clusters.\n   * @param {number} groups - Them number of children the first level of the tree should have.\n   * @return {Cluster}\n   */\n  group(groups) {\n    if (!Number.isInteger(groups) || groups < 1) {\n      throw new RangeError('groups must be a positive integer');\n    }\n\n    const heap = new Heap((a, b) => {\n      return b.height - a.height;\n    });\n\n    heap.push(this);\n\n    while (heap.size() < groups) {\n      var first = heap.pop();\n      if (first.children.length === 0) {\n        break;\n      }\n      first.children.forEach((child) => heap.push(child));\n    }\n\n    var root = new Cluster();\n    root.children = heap.toArray();\n    root.height = this.height;\n\n    return root;\n  }\n\n  /**\n   * Traverses the tree depth-first and calls the provided callback with each individual node\n   * @param {function} cb - The callback to be called on each node encounter\n   */\n  traverse(cb) {\n    function visit(root, callback) {\n      callback(root);\n      if (root.children) {\n        for (const child of root.children) {\n          visit(child, callback);\n        }\n      }\n    }\n    visit(this, cb);\n  }\n\n  /**\n   * Returns a list of indices for all the leaves of this cluster.\n   * The list is ordered in such a way that a dendrogram could be drawn without crossing branches.\n   * @returns {Array<number>}\n   */\n  indices() {\n    const result = [];\n    this.traverse((cluster) => {\n      if (cluster.isLeaf) {\n        result.push(cluster.index);\n      }\n    });\n    return result;\n  }\n}\n","import { euclidean } from 'ml-distance-euclidean';\nimport getDistanceMatrix from 'ml-distance-matrix';\nimport { Matrix } from 'ml-matrix';\n\nimport Cluster from './Cluster';\n\nfunction singleLink(dKI, dKJ) {\n  return Math.min(dKI, dKJ);\n}\n\nfunction completeLink(dKI, dKJ) {\n  return Math.max(dKI, dKJ);\n}\n\nfunction averageLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  return ai * dKI + aj * dKJ;\n}\n\nfunction weightedAverageLink(dKI, dKJ) {\n  return (dKI + dKJ) / 2;\n}\n\nfunction centroidLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  const b = -(ni * nj) / (ni + nj) ** 2;\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction medianLink(dKI, dKJ, dIJ) {\n  return dKI / 2 + dKJ / 2 - dIJ / 4;\n}\n\nfunction wardLink(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction wardLink2(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return Math.sqrt(ai * dKI * dKI + aj * dKJ * dKJ + b * dIJ * dIJ);\n}\n\n/**\n * Continuously merge nodes that have the least dissimilarity\n * @param {Array<Array<number>>} data - Array of points to be clustered\n * @param {object} [options]\n * @param {Function} [options.distanceFunction]\n * @param {string} [options.method] - Default: `'complete'`\n * @param {boolean} [options.isDistanceMatrix] - Is the input already a distance matrix?\n * @constructor\n */\nexport function agnes(data, options = {}) {\n  const {\n    distanceFunction = euclidean,\n    method = 'complete',\n    isDistanceMatrix = false,\n  } = options;\n\n  let updateFunc;\n  if (!isDistanceMatrix) {\n    data = getDistanceMatrix(data, distanceFunction);\n  }\n  let distanceMatrix = new Matrix(data);\n  const numLeaves = distanceMatrix.rows;\n\n  // allows to use a string or a given function\n  if (typeof method === 'string') {\n    switch (method.toLowerCase()) {\n      case 'single':\n        updateFunc = singleLink;\n        break;\n      case 'complete':\n        updateFunc = completeLink;\n        break;\n      case 'average':\n      case 'upgma':\n        updateFunc = averageLink;\n        break;\n      case 'wpgma':\n        updateFunc = weightedAverageLink;\n        break;\n      case 'centroid':\n      case 'upgmc':\n        updateFunc = centroidLink;\n        break;\n      case 'median':\n      case 'wpgmc':\n        updateFunc = medianLink;\n        break;\n      case 'ward':\n        updateFunc = wardLink;\n        break;\n      case 'ward2':\n        updateFunc = wardLink2;\n        break;\n      default:\n        throw new RangeError(`unknown clustering method: ${method}`);\n    }\n  } else if (typeof method !== 'function') {\n    throw new TypeError('method must be a string or function');\n  }\n\n  let clusters = [];\n  for (let i = 0; i < numLeaves; i++) {\n    const cluster = new Cluster();\n    cluster.isLeaf = true;\n    cluster.index = i;\n    clusters.push(cluster);\n  }\n\n  for (let n = 0; n < numLeaves - 1; n++) {\n    const [row, column, distance] = getSmallestDistance(distanceMatrix);\n    const cluster1 = clusters[row];\n    const cluster2 = clusters[column];\n    const newCluster = new Cluster();\n    newCluster.size = cluster1.size + cluster2.size;\n    newCluster.children.push(cluster1, cluster2);\n    newCluster.height = distance;\n\n    const newClusters = [newCluster];\n    const newDistanceMatrix = new Matrix(\n      distanceMatrix.rows - 1,\n      distanceMatrix.rows - 1,\n    );\n    const previous = (newIndex) =>\n      getPreviousIndex(newIndex, Math.min(row, column), Math.max(row, column));\n\n    for (let i = 1; i < newDistanceMatrix.rows; i++) {\n      const prevI = previous(i);\n      const prevICluster = clusters[prevI];\n      newClusters.push(prevICluster);\n      for (let j = 0; j < i; j++) {\n        if (j === 0) {\n          const dKI = distanceMatrix.get(row, prevI);\n          const dKJ = distanceMatrix.get(prevI, column);\n          const val = updateFunc(\n            dKI,\n            dKJ,\n            distance,\n            cluster1.size,\n            cluster2.size,\n            prevICluster.size,\n          );\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        } else {\n          // Just copy distance from previous matrix\n          const val = distanceMatrix.get(prevI, previous(j));\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        }\n      }\n    }\n\n    clusters = newClusters;\n    distanceMatrix = newDistanceMatrix;\n  }\n\n  return clusters[0];\n}\n\nfunction getSmallestDistance(distance) {\n  let smallest = Infinity;\n  let smallestI = 0;\n  let smallestJ = 0;\n  for (let i = 1; i < distance.rows; i++) {\n    for (let j = 0; j < i; j++) {\n      if (distance.get(i, j) < smallest) {\n        smallest = distance.get(i, j);\n        smallestI = i;\n        smallestJ = j;\n      }\n    }\n  }\n  return [smallestI, smallestJ, smallest];\n}\n\nfunction getPreviousIndex(newIndex, prev1, prev2) {\n  newIndex -= 1;\n  if (newIndex >= prev1) newIndex++;\n  if (newIndex >= prev2) newIndex++;\n  return newIndex;\n}\n","export * from './agnes';\n// export * from './diana';\n// export * from './birch';\n// export * './cure';\n// export * from './chameleon';\n","'use strict';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nconst defaultOptions = {\n    distanceFunction: squaredEuclidean\n};\nexport default function nearestVector(listVectors, vector, options = defaultOptions) {\n    const distanceFunction = options.distanceFunction || defaultOptions.distanceFunction;\n    const similarityFunction = options.similarityFunction || defaultOptions.similarityFunction;\n    let vectorIndex = -1;\n    if (typeof similarityFunction === 'function') {\n        // maximum similarity\n        let maxSim = Number.MIN_VALUE;\n        for (let j = 0; j < listVectors.length; j++) {\n            const sim = similarityFunction(vector, listVectors[j]);\n            if (sim > maxSim) {\n                maxSim = sim;\n                vectorIndex = j;\n            }\n        }\n    }\n    else if (typeof distanceFunction === 'function') {\n        // minimum distance\n        let minDist = Number.MAX_VALUE;\n        for (let i = 0; i < listVectors.length; i++) {\n            const dist = distanceFunction(vector, listVectors[i]);\n            if (dist < minDist) {\n                minDist = dist;\n                vectorIndex = i;\n            }\n        }\n    }\n    else {\n        throw new Error(\"A similarity or distance function it's required\");\n    }\n    return vectorIndex;\n}\nexport function findNearestVector(vectorList, vector, options = defaultOptions) {\n    const index = nearestVector(vectorList, vector, options);\n    return vectorList[index];\n}\n","import nearestVector from 'ml-nearest-vector';\n\n/**\n * Calculates the distance matrix for a given array of points\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {function} distance - Distance function to use between the points\n * @return {Array<Array<number>>} - matrix with the distance values\n */\nexport function calculateDistanceMatrix(data, distance) {\n  var distanceMatrix = new Array(data.length);\n  for (var i = 0; i < data.length; ++i) {\n    for (var j = i; j < data.length; ++j) {\n      if (!distanceMatrix[i]) {\n        distanceMatrix[i] = new Array(data.length);\n      }\n      if (!distanceMatrix[j]) {\n        distanceMatrix[j] = new Array(data.length);\n      }\n      const dist = distance(data[i], data[j]);\n      distanceMatrix[i][j] = dist;\n      distanceMatrix[j][i] = dist;\n    }\n  }\n  return distanceMatrix;\n}\n\n/**\n * Updates the cluster identifier based in the new data\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {function} distance - Distance function to use between the points\n * @return {Array} the cluster identifier for each data dot\n */\nexport function updateClusterID(data, centers, clusterID, distance) {\n  for (var i = 0; i < data.length; i++) {\n    clusterID[i] = nearestVector(centers, data[i], {\n      distanceFunction: distance\n    });\n  }\n  return clusterID;\n}\n\n/**\n * Update the center values based in the new configurations of the clusters\n * @ignore\n * @param {Array<Array<number>>} prevCenters - Centroids from the previous iteration\n * @param {Array <Array <number>>} data - the [x,y,z,...] points to cluster\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @return {Array} he K centers in format [x,y,z,...]\n */\nexport function updateCenters(prevCenters, data, clusterID, K) {\n  const nDim = data[0].length;\n\n  // copy previous centers\n  var centers = new Array(K);\n  var centersLen = new Array(K);\n  for (var i = 0; i < K; i++) {\n    centers[i] = new Array(nDim);\n    centersLen[i] = 0;\n    for (var j = 0; j < nDim; j++) {\n      centers[i][j] = 0;\n    }\n  }\n\n  // add the value for all dimensions of the point\n  for (var l = 0; l < data.length; l++) {\n    centersLen[clusterID[l]]++;\n    for (var dim = 0; dim < nDim; dim++) {\n      centers[clusterID[l]][dim] += data[l][dim];\n    }\n  }\n\n  // divides by length\n  for (var id = 0; id < K; id++) {\n    for (var d = 0; d < nDim; d++) {\n      if (centersLen[id]) {\n        centers[id][d] /= centersLen[id];\n      } else {\n        centers[id][d] = prevCenters[id][d];\n      }\n    }\n  }\n  return centers;\n}\n\n/**\n * The centers have moved more than the tolerance value?\n * @ignore\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} oldCenters - the K old centers in format [x,y,z,...]\n * @param {function} distanceFunction - Distance function to use between the points\n * @param {number} tolerance - Allowed distance for the centroids to move\n * @return {boolean}\n */\nexport function hasConverged(centers, oldCenters, distanceFunction, tolerance) {\n  for (var i = 0; i < centers.length; i++) {\n    if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {\n      return false;\n    }\n  }\n  return true;\n}\n","const LOOP = 8;\nconst FLOAT_MUL = 1 / 16777216;\nconst sh1 = 15;\nconst sh2 = 18;\nconst sh3 = 11;\nfunction multiply_uint32(n, m) {\n    n >>>= 0;\n    m >>>= 0;\n    const nlo = n & 0xffff;\n    const nhi = n - nlo;\n    return (((nhi * m) >>> 0) + nlo * m) >>> 0;\n}\nexport default class XSadd {\n    constructor(seed = Date.now()) {\n        this.state = new Uint32Array(4);\n        this.init(seed);\n        this.random = this.getFloat.bind(this);\n    }\n    /**\n     * Returns a 32-bit integer r (0 <= r < 2^32)\n     */\n    getUint32() {\n        this.nextState();\n        return (this.state[3] + this.state[2]) >>> 0;\n    }\n    /**\n     * Returns a floating point number r (0.0 <= r < 1.0)\n     */\n    getFloat() {\n        return (this.getUint32() >>> 8) * FLOAT_MUL;\n    }\n    init(seed) {\n        if (!Number.isInteger(seed)) {\n            throw new TypeError('seed must be an integer');\n        }\n        this.state[0] = seed;\n        this.state[1] = 0;\n        this.state[2] = 0;\n        this.state[3] = 0;\n        for (let i = 1; i < LOOP; i++) {\n            this.state[i & 3] ^=\n                (i +\n                    multiply_uint32(1812433253, this.state[(i - 1) & 3] ^ ((this.state[(i - 1) & 3] >>> 30) >>> 0))) >>>\n                    0;\n        }\n        this.periodCertification();\n        for (let i = 0; i < LOOP; i++) {\n            this.nextState();\n        }\n    }\n    periodCertification() {\n        if (this.state[0] === 0 &&\n            this.state[1] === 0 &&\n            this.state[2] === 0 &&\n            this.state[3] === 0) {\n            this.state[0] = 88; // X\n            this.state[1] = 83; // S\n            this.state[2] = 65; // A\n            this.state[3] = 68; // D\n        }\n    }\n    nextState() {\n        let t = this.state[0];\n        t ^= t << sh1;\n        t ^= t >>> sh2;\n        t ^= this.state[3] << sh3;\n        this.state[0] = this.state[1];\n        this.state[1] = this.state[2];\n        this.state[2] = this.state[3];\n        this.state[3] = t;\n    }\n}\n","const PROB_TOLERANCE = 0.00000001;\nfunction randomChoice(values, options = {}, random = Math.random) {\n    const { size = 1, replace = false, probabilities } = options;\n    let valuesArr;\n    let cumSum;\n    if (typeof values === 'number') {\n        valuesArr = getArray(values);\n    }\n    else {\n        valuesArr = values.slice();\n    }\n    if (probabilities) {\n        if (!replace) {\n            throw new Error('choice with probabilities and no replacement is not implemented');\n        }\n        // check input is sane\n        if (probabilities.length !== valuesArr.length) {\n            throw new Error('the length of probabilities option should be equal to the number of choices');\n        }\n        cumSum = [probabilities[0]];\n        for (let i = 1; i < probabilities.length; i++) {\n            cumSum[i] = cumSum[i - 1] + probabilities[i];\n        }\n        if (Math.abs(1 - cumSum[cumSum.length - 1]) > PROB_TOLERANCE) {\n            throw new Error(`probabilities should sum to 1, but instead sums to ${cumSum[cumSum.length - 1]}`);\n        }\n    }\n    if (replace === false && size > valuesArr.length) {\n        throw new Error('size option is too large');\n    }\n    const result = [];\n    for (let i = 0; i < size; i++) {\n        const index = randomIndex(valuesArr.length, random, cumSum);\n        result.push(valuesArr[index]);\n        if (!replace) {\n            valuesArr.splice(index, 1);\n        }\n    }\n    return result;\n}\nfunction getArray(n) {\n    const arr = [];\n    for (let i = 0; i < n; i++) {\n        arr.push(i);\n    }\n    return arr;\n}\nfunction randomIndex(n, random, cumSum) {\n    const rand = random();\n    if (!cumSum) {\n        return Math.floor(rand * n);\n    }\n    else {\n        let idx = 0;\n        while (rand > cumSum[idx]) {\n            idx++;\n        }\n        return idx;\n    }\n}\nexport default randomChoice;\n","// tslint:disable-next-line\nimport XSAdd from 'ml-xsadd';\nimport choice from './choice';\n/**\n * @classdesc Random class\n */\nexport default class Random {\n    /**\n     * @param [seedOrRandom=Math.random] - Control the random number generator used by the Random class instance. Pass a random number generator function with a uniform distribution over the half-open interval [0, 1[. If seed will pass it to ml-xsadd to create a seeded random number generator. If undefined will use Math.random.\n     */\n    constructor(seedOrRandom = Math.random) {\n        if (typeof seedOrRandom === 'number') {\n            const xsadd = new XSAdd(seedOrRandom);\n            this.randomGenerator = xsadd.random;\n        }\n        else {\n            this.randomGenerator = seedOrRandom;\n        }\n    }\n    choice(values, options) {\n        if (typeof values === 'number') {\n            return choice(values, options, this.randomGenerator);\n        }\n        return choice(values, options, this.randomGenerator);\n    }\n    /**\n     * Draw a random number from a uniform distribution on [0,1)\n     * @return The random number\n     */\n    random() {\n        return this.randomGenerator();\n    }\n    /**\n     * Draw a random integer from a uniform distribution on [low, high). If only low is specified, the number is drawn on [0, low)\n     * @param low - The lower bound of the uniform distribution interval.\n     * @param high - The higher bound of the uniform distribution interval.\n     */\n    randInt(low, high) {\n        if (high === undefined) {\n            high = low;\n            low = 0;\n        }\n        return low + Math.floor(this.randomGenerator() * (high - low));\n    }\n    /**\n     * Draw several random number from a uniform distribution on [0, 1)\n     * @param size - The number of number to draw\n     * @return - The list of drawn numbers.\n     */\n    randomSample(size) {\n        const result = [];\n        for (let i = 0; i < size; i++) {\n            result.push(this.random());\n        }\n        return result;\n    }\n}\n","import Random from 'ml-random';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nimport { Matrix } from 'ml-matrix';\n\n/**\n * Choose K different random points from the original data\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function random(data, K, seed) {\n  const random = new Random(seed);\n  return random.choice(data, { size: K });\n}\n\n/**\n * Chooses the most distant points to a first random pick\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {Array<Array<number>>} distanceMatrix - matrix with the distance values\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function mostDistant(data, K, distanceMatrix, seed) {\n  const random = new Random(seed);\n  var ans = new Array(K);\n  // chooses a random point as initial cluster\n  ans[0] = Math.floor(random.random() * data.length);\n\n  if (K > 1) {\n    // chooses the more distant point\n    var maxDist = { dist: -1, index: -1 };\n    for (var l = 0; l < data.length; ++l) {\n      if (distanceMatrix[ans[0]][l] > maxDist.dist) {\n        maxDist.dist = distanceMatrix[ans[0]][l];\n        maxDist.index = l;\n      }\n    }\n    ans[1] = maxDist.index;\n\n    if (K > 2) {\n      // chooses the set of points that maximises the min distance\n      for (var k = 2; k < K; ++k) {\n        var center = { dist: -1, index: -1 };\n        for (var m = 0; m < data.length; ++m) {\n          // minimum distance to centers\n          var minDistCent = { dist: Number.MAX_VALUE, index: -1 };\n          for (var n = 0; n < k; ++n) {\n            if (\n              distanceMatrix[n][m] < minDistCent.dist &&\n              ans.indexOf(m) === -1\n            ) {\n              minDistCent = {\n                dist: distanceMatrix[n][m],\n                index: m\n              };\n            }\n          }\n\n          if (\n            minDistCent.dist !== Number.MAX_VALUE &&\n            minDistCent.dist > center.dist\n          ) {\n            center = Object.assign({}, minDistCent);\n          }\n        }\n\n        ans[k] = center.index;\n      }\n    }\n  }\n\n  return ans.map((index) => data[index]);\n}\n\n// Implementation inspired from scikit\nexport function kmeanspp(X, K, options = {}) {\n  X = new Matrix(X);\n  const nSamples = X.rows;\n  const random = new Random(options.seed);\n  // Set the number of trials\n  const centers = [];\n  const localTrials = options.localTrials || 2 + Math.floor(Math.log(K));\n\n  // Pick the first center at random from the dataset\n  const firstCenterIdx = random.randInt(nSamples);\n  centers.push(X.getRow(firstCenterIdx));\n\n  // Init closest distances\n  let closestDistSquared = new Matrix(1, X.rows);\n  for (let i = 0; i < X.rows; i++) {\n    closestDistSquared.set(0, i, squaredEuclidean(X.getRow(i), centers[0]));\n  }\n  let cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n  const factor = 1 / cumSumClosestDistSquared[0][nSamples - 1];\n  let probabilities = Matrix.mul(closestDistSquared, factor);\n\n  // Iterate over the remaining centers\n  for (let i = 1; i < K; i++) {\n    const candidateIdx = random.choice(nSamples, {\n      replace: true,\n      size: localTrials,\n      probabilities: probabilities[0]\n    });\n\n    const candidates = X.selection(candidateIdx, range(X.columns));\n    const distanceToCandidates = euclideanDistances(candidates, X);\n\n    let bestCandidate;\n    let bestPot;\n    let bestDistSquared;\n\n    for (let j = 0; j < localTrials; j++) {\n      const newDistSquared = Matrix.min(closestDistSquared, [distanceToCandidates.getRow(j)]);\n      const newPot = newDistSquared.sum();\n      if (bestCandidate === undefined || newPot < bestPot) {\n        bestCandidate = candidateIdx[j];\n        bestPot = newPot;\n        bestDistSquared = newDistSquared;\n      }\n    }\n    centers[i] = X.getRow(bestCandidate);\n    closestDistSquared = bestDistSquared;\n    cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n    probabilities = Matrix.mul(\n      closestDistSquared,\n      1 / cumSumClosestDistSquared[0][nSamples - 1]\n    );\n  }\n  return centers;\n}\n\nfunction euclideanDistances(A, B) {\n  const result = new Matrix(A.rows, B.rows);\n  for (let i = 0; i < A.rows; i++) {\n    for (let j = 0; j < B.rows; j++) {\n      result.set(i, j, squaredEuclidean(A.getRow(i), B.getRow(j)));\n    }\n  }\n  return result;\n}\n\nfunction range(l) {\n  let r = [];\n  for (let i = 0; i < l; i++) {\n    r.push(i);\n  }\n  return r;\n}\n\nfunction cumSum(arr) {\n  let cumSum = [arr[0]];\n  for (let i = 1; i < arr.length; i++) {\n    cumSum[i] = cumSum[i - 1] + arr[i];\n  }\n  return cumSum;\n}\n","import { updateClusterID } from './utils';\n\nconst distanceSymbol = Symbol('distance');\n\nexport default class KMeansResult {\n  /**\n   * Result of the kmeans algorithm\n   * @param {Array<number>} clusters - the cluster identifier for each data dot\n   * @param {Array<Array<object>>} centroids - the K centers in format [x,y,z,...], the error and size of the cluster\n   * @param {boolean} converged - Converge criteria satisfied\n   * @param {number} iterations - Current number of iterations\n   * @param {function} distance - (*Private*) Distance function to use between the points\n   * @constructor\n   */\n  constructor(clusters, centroids, converged, iterations, distance) {\n    this.clusters = clusters;\n    this.centroids = centroids;\n    this.converged = converged;\n    this.iterations = iterations;\n    this[distanceSymbol] = distance;\n  }\n\n  /**\n   * Allows to compute for a new array of points their cluster id\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {Array<number>} - cluster id for each point\n   */\n  nearest(data) {\n    const clusterID = new Array(data.length);\n    const centroids = this.centroids.map(function (centroid) {\n      return centroid.centroid;\n    });\n    return updateClusterID(data, centroids, clusterID, this[distanceSymbol]);\n  }\n\n  /**\n   * Returns a KMeansResult with the error and size of the cluster\n   * @ignore\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {KMeansResult}\n   */\n  computeInformation(data) {\n    var enrichedCentroids = this.centroids.map(function (centroid) {\n      return {\n        centroid: centroid,\n        error: 0,\n        size: 0\n      };\n    });\n\n    for (var i = 0; i < data.length; i++) {\n      enrichedCentroids[this.clusters[i]].error += this[distanceSymbol](\n        data[i],\n        this.centroids[this.clusters[i]]\n      );\n      enrichedCentroids[this.clusters[i]].size++;\n    }\n\n    for (var j = 0; j < this.centroids.length; j++) {\n      if (enrichedCentroids[j].size) {\n        enrichedCentroids[j].error /= enrichedCentroids[j].size;\n      } else {\n        enrichedCentroids[j].error = null;\n      }\n    }\n\n    return new KMeansResult(\n      this.clusters,\n      enrichedCentroids,\n      this.converged,\n      this.iterations,\n      this[distanceSymbol]\n    );\n  }\n}\n","import { squaredEuclidean } from 'ml-distance-euclidean';\n\nimport {\n  updateClusterID,\n  updateCenters,\n  hasConverged,\n  calculateDistanceMatrix\n} from './utils';\nimport { mostDistant, random, kmeanspp } from './initialization';\nimport KMeansResult from './KMeansResult';\n\nconst defaultOptions = {\n  maxIterations: 100,\n  tolerance: 1e-6,\n  withIterations: false,\n  initialization: 'kmeans++',\n  distanceFunction: squaredEuclidean\n};\n\n/**\n * Each step operation for kmeans\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} iterations - Current number of iterations\n * @return {KMeansResult}\n */\nfunction step(centers, data, clusterID, K, options, iterations) {\n  clusterID = updateClusterID(\n    data,\n    centers,\n    clusterID,\n    options.distanceFunction\n  );\n  var newCenters = updateCenters(centers, data, clusterID, K);\n  var converged = hasConverged(\n    newCenters,\n    centers,\n    options.distanceFunction,\n    options.tolerance\n  );\n  return new KMeansResult(\n    clusterID,\n    newCenters,\n    converged,\n    iterations,\n    options.distanceFunction\n  );\n}\n\n/**\n * Generator version for the algorithm\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n */\nfunction* kmeansGenerator(centers, data, clusterID, K, options) {\n  var converged = false;\n  var stepNumber = 0;\n  var stepResult;\n  while (!converged && stepNumber < options.maxIterations) {\n    stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n    yield stepResult.computeInformation(data);\n    converged = stepResult.converged;\n    centers = stepResult.centroids;\n  }\n}\n\n/**\n * K-means algorithm\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} [options.maxIterations = 100] - Maximum of iterations allowed\n * @param {number} [options.tolerance = 1e-6] - Error tolerance\n * @param {boolean} [options.withIterations = false] - Store clusters and centroids for each iteration\n * @param {function} [options.distanceFunction = squaredDistance] - Distance function to use between the points\n * @param {number} [options.seed] - Seed for random initialization.\n * @param {string|Array<Array<number>>} [options.initialization = 'kmeans++'] - K centers in format [x,y,z,...] or a method for initialize the data:\n *  * You can either specify your custom start centroids, or select one of the following initialization method:\n *  * `'kmeans++'` will use the kmeans++ method as described by http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf\n *  * `'random'` will choose K random different values.\n *  * `'mostDistant'` will choose the more distant points to a first random pick\n * @return {KMeansResult} - Cluster identifier for each data dot and centroids with the following fields:\n *  * `'clusters'`: Array of indexes for the clusters.\n *  * `'centroids'`: Array with the resulting centroids.\n *  * `'iterations'`: Number of iterations that took to converge\n */\nexport default function kmeans(data, K, options) {\n  options = Object.assign({}, defaultOptions, options);\n\n  if (K <= 0 || K > data.length || !Number.isInteger(K)) {\n    throw new Error(\n      'K should be a positive integer smaller than the number of points'\n    );\n  }\n\n  var centers;\n  if (Array.isArray(options.initialization)) {\n    if (options.initialization.length !== K) {\n      throw new Error('The initial centers should have the same length as K');\n    } else {\n      centers = options.initialization;\n    }\n  } else {\n    switch (options.initialization) {\n      case 'kmeans++':\n        centers = kmeanspp(data, K, options);\n        break;\n      case 'random':\n        centers = random(data, K, options.seed);\n        break;\n      case 'mostDistant':\n        centers = mostDistant(\n          data,\n          K,\n          calculateDistanceMatrix(data, options.distanceFunction),\n          options.seed\n        );\n        break;\n      default:\n        throw new Error(\n          `Unknown initialization method: \"${options.initialization}\"`\n        );\n    }\n  }\n\n  // infinite loop until convergence\n  if (options.maxIterations === 0) {\n    options.maxIterations = Number.MAX_VALUE;\n  }\n\n  var clusterID = new Array(data.length);\n  if (options.withIterations) {\n    return kmeansGenerator(centers, data, clusterID, K, options);\n  } else {\n    var converged = false;\n    var stepNumber = 0;\n    var stepResult;\n    while (!converged && stepNumber < options.maxIterations) {\n      stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n      converged = stepResult.converged;\n      centers = stepResult.centroids;\n    }\n    return stepResult.computeInformation(data);\n  }\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that retuns an array of matrices of the cases that belong to each class.\n * @param {Matrix} X - dataset\n * @param {Array} y - predictions\n * @return {Array}\n */\nexport function separateClasses(X, y) {\n  var features = X.columns;\n\n  var classes = 0;\n  var totalPerClasses = new Array(10000); // max upperbound of classes\n  for (var i = 0; i < y.length; i++) {\n    if (totalPerClasses[y[i]] === undefined) {\n      totalPerClasses[y[i]] = 0;\n      classes++;\n    }\n    totalPerClasses[y[i]]++;\n  }\n  var separatedClasses = new Array(classes);\n  var currentIndex = new Array(classes);\n  for (i = 0; i < classes; ++i) {\n    separatedClasses[i] = new Matrix(totalPerClasses[i], features);\n    currentIndex[i] = 0;\n  }\n  for (i = 0; i < X.rows; ++i) {\n    separatedClasses[y[i]].setRow(currentIndex[y[i]], X.getRow(i));\n    currentIndex[y[i]]++;\n  }\n  return separatedClasses;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class GaussianNB {\n  /**\n   * Constructor for the Gaussian Naive Bayes classifier, the parameters here is just for loading purposes.\n   * @constructor\n   * @param {boolean} reload\n   * @param {object} model\n   */\n  constructor(reload, model) {\n    if (reload) {\n      this.means = model.means;\n      this.calculateProbabilities = model.calculateProbabilities;\n    }\n  }\n\n  /**\n   * Function that trains the classifier with a matrix that represents the training set and an array that\n   * represents the label of each row in the training set. the labels must be numbers between 0 to n-1 where\n   * n represents the number of classes.\n   *\n   * WARNING: in the case that one class, all the cases in one or more features have the same value, the\n   * Naive Bayes classifier will not work well.\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    var C1 = Math.sqrt(2 * Math.PI); // constant to precalculate the squared root\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separatedClasses = separateClasses(trainingSet, trainingLabels);\n    var calculateProbabilities = new Array(separatedClasses.length);\n    this.means = new Array(separatedClasses.length);\n    for (var i = 0; i < separatedClasses.length; ++i) {\n      var means = separatedClasses[i].mean('column');\n      var std = separatedClasses[i].standardDeviation('column', {\n        mean: means\n      });\n\n      var logPriorProbability = Math.log(\n        separatedClasses[i].rows / trainingSet.rows\n      );\n      calculateProbabilities[i] = new Array(means.length + 1);\n\n      calculateProbabilities[i][0] = logPriorProbability;\n      for (var j = 1; j < means.length + 1; ++j) {\n        var currentStd = std[j - 1];\n        calculateProbabilities[i][j] = [\n          1 / (C1 * currentStd),\n          -2 * currentStd * currentStd\n        ];\n      }\n\n      this.means[i] = means;\n    }\n\n    this.calculateProbabilities = calculateProbabilities;\n  }\n\n  /**\n   * function that predicts each row of the dataset (must be a matrix).\n   *\n   * @param {Matrix|Array} dataset\n   * @return {Array}\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    if (dataset.rows === this.calculateProbabilities[0].length) {\n      throw new RangeError(\n        'the dataset must have the same features as the training set'\n      );\n    }\n\n    var predictions = new Array(dataset.rows);\n\n    for (var i = 0; i < predictions.length; ++i) {\n      predictions[i] = getCurrentClass(\n        dataset.getRow(i),\n        this.means,\n        this.calculateProbabilities\n      );\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that export the NaiveBayes model.\n   * @return {object}\n   */\n  toJSON() {\n    return {\n      modelName: 'NaiveBayes',\n      means: this.means,\n      calculateProbabilities: this.calculateProbabilities\n    };\n  }\n\n  /**\n   * Function that create a GaussianNB classifier with the given model.\n   * @param {object} model\n   * @return {GaussianNB}\n   */\n  static load(model) {\n    if (model.modelName !== 'NaiveBayes') {\n      throw new RangeError(\n        'The current model is not a Multinomial Naive Bayes, current model:',\n        model.name\n      );\n    }\n\n    return new GaussianNB(true, model);\n  }\n}\n\n/**\n * @private\n * Function the retrieves a prediction with one case.\n *\n * @param {Array} currentCase\n * @param {Array} mean - Precalculated means of each class trained\n * @param {Array} classes - Precalculated value of each class (Prior probability and probability function of each feature)\n * @return {number}\n */\nfunction getCurrentClass(currentCase, mean, classes) {\n  var maxProbability = 0;\n  var predictedClass = -1;\n\n  // going through all precalculated values for the classes\n  for (var i = 0; i < classes.length; ++i) {\n    var currentProbability = classes[i][0]; // initialize with the prior probability\n    for (var j = 1; j < classes[0][1].length + 1; ++j) {\n      currentProbability += calculateLogProbability(\n        currentCase[j - 1],\n        mean[i][j - 1],\n        classes[i][j][0],\n        classes[i][j][1]\n      );\n    }\n\n    currentProbability = Math.exp(currentProbability);\n    if (currentProbability > maxProbability) {\n      maxProbability = currentProbability;\n      predictedClass = i;\n    }\n  }\n\n  return predictedClass;\n}\n\n/**\n * @private\n * function that retrieves the probability of the feature given the class.\n * @param {number} value - value of the feature.\n * @param {number} mean - mean of the feature for the given class.\n * @param {number} C1 - precalculated value of (1 / (sqrt(2*pi) * std)).\n * @param {number} C2 - precalculated value of (2 * std^2) for the denominator of the exponential.\n * @return {number}\n */\nfunction calculateLogProbability(value, mean, C1, C2) {\n  value = value - mean;\n  return Math.log(C1 * Math.exp((value * value) / C2));\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class MultinomialNB {\n  /**\n   * Constructor for Multinomial Naive Bayes, the model parameter is for load purposes.\n   * @constructor\n   * @param {object} model - for load purposes.\n   */\n  constructor(model) {\n    if (model) {\n      this.conditionalProbability = Matrix.checkMatrix(\n        model.conditionalProbability\n      );\n      this.priorProbability = Matrix.checkMatrix(model.priorProbability);\n    }\n  }\n\n  /**\n   * Train the classifier with the current training set and labels, the labels must be numbers between 0 and n.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separateClass = separateClasses(trainingSet, trainingLabels);\n\n    this.priorProbability = new Matrix(separateClass.length, 1);\n\n    for (var i = 0; i < separateClass.length; ++i) {\n      this.priorProbability.set(i, 0, Math.log(\n        separateClass[i].rows / trainingSet.rows\n      ));\n    }\n\n    var features = trainingSet.columns;\n    this.conditionalProbability = new Matrix(separateClass.length, features);\n    for (i = 0; i < separateClass.length; ++i) {\n      var classValues = Matrix.checkMatrix(separateClass[i]);\n      var total = classValues.sum();\n      var divisor = total + features;\n      this.conditionalProbability.setRow(\n        i,\n        Matrix.rowVector(classValues\n          .sum('column'))\n          .add(1)\n          .div(divisor)\n          .apply(matrixLog)\n      );\n    }\n  }\n\n  /**\n   * Retrieves the predictions for the dataset with the current model.\n   * @param {Matrix|Array} dataset\n   * @return {Array} - predictions from the dataset.\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    var predictions = new Array(dataset.rows);\n    for (var i = 0; i < dataset.rows; ++i) {\n      var currentElement = dataset.getRowVector(i);\n      const v = Matrix.columnVector(this.conditionalProbability\n        .clone()\n        .mulRowVector(currentElement)\n        .sum('row'));\n      predictions[i] = v\n        .add(this.priorProbability)\n        .maxIndex()[0];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that saves the current model.\n   * @return {object} - model in JSON format.\n   */\n  toJSON() {\n    return {\n      name: 'MultinomialNB',\n      priorProbability: this.priorProbability,\n      conditionalProbability: this.conditionalProbability\n    };\n  }\n\n  /**\n   * Creates a new MultinomialNB from the given model\n   * @param {object} model\n   * @return {MultinomialNB}\n   */\n  static load(model) {\n    if (model.name !== 'MultinomialNB') {\n      throw new RangeError(`${model.name} is not a Multinomial Naive Bayes`);\n    }\n\n    return new MultinomialNB(model);\n  }\n}\n\nfunction matrixLog(i, j) {\n  this.set(i, j, Math.log(this.get(i, j)));\n}\n","/*\n * Original code from:\n *\n * k-d Tree JavaScript - V 1.01\n *\n * https://github.com/ubilabs/kd-tree-javascript\n *\n * @author Mircea Pricop <pricop@ubilabs.net>, 2012\n * @author Martin Kleppe <kleppe@ubilabs.net>, 2012\n * @author Ubilabs http://ubilabs.net, 2012\n * @license MIT License <http://www.opensource.org/licenses/mit-license.php>\n */\n\nfunction Node(obj, dimension, parent) {\n  this.obj = obj;\n  this.left = null;\n  this.right = null;\n  this.parent = parent;\n  this.dimension = dimension;\n}\n\nexport default class KDTree {\n  constructor(points, metric) {\n    // If points is not an array, assume we're loading a pre-built tree\n    if (!Array.isArray(points)) {\n      this.dimensions = points.dimensions;\n      this.root = points;\n      restoreParent(this.root);\n    } else {\n      this.dimensions = new Array(points[0].length);\n      for (var i = 0; i < this.dimensions.length; i++) {\n        this.dimensions[i] = i;\n      }\n      this.root = buildTree(points, 0, null, this.dimensions);\n    }\n    this.metric = metric;\n  }\n\n  // Convert to a JSON serializable structure; this just requires removing\n  // the `parent` property\n  toJSON() {\n    const result = toJSONImpl(this.root, true);\n    result.dimensions = this.dimensions;\n    return result;\n  }\n\n  nearest(point, maxNodes, maxDistance) {\n    const metric = this.metric;\n    const dimensions = this.dimensions;\n    var i;\n\n    const bestNodes = new BinaryHeap(function (e) {\n      return -e[1];\n    });\n\n    function nearestSearch(node) {\n      const dimension = dimensions[node.dimension];\n      const ownDistance = metric(point, node.obj);\n      const linearPoint = {};\n      var bestChild, linearDistance, otherChild, i;\n\n      function saveNode(node, distance) {\n        bestNodes.push([node, distance]);\n        if (bestNodes.size() > maxNodes) {\n          bestNodes.pop();\n        }\n      }\n\n      for (i = 0; i < dimensions.length; i += 1) {\n        if (i === node.dimension) {\n          linearPoint[dimensions[i]] = point[dimensions[i]];\n        } else {\n          linearPoint[dimensions[i]] = node.obj[dimensions[i]];\n        }\n      }\n\n      linearDistance = metric(linearPoint, node.obj);\n\n      if (node.right === null && node.left === null) {\n        if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n          saveNode(node, ownDistance);\n        }\n        return;\n      }\n\n      if (node.right === null) {\n        bestChild = node.left;\n      } else if (node.left === null) {\n        bestChild = node.right;\n      } else {\n        if (point[dimension] < node.obj[dimension]) {\n          bestChild = node.left;\n        } else {\n          bestChild = node.right;\n        }\n      }\n\n      nearestSearch(bestChild);\n\n      if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n        saveNode(node, ownDistance);\n      }\n\n      if (\n        bestNodes.size() < maxNodes ||\n        Math.abs(linearDistance) < bestNodes.peek()[1]\n      ) {\n        if (bestChild === node.left) {\n          otherChild = node.right;\n        } else {\n          otherChild = node.left;\n        }\n        if (otherChild !== null) {\n          nearestSearch(otherChild);\n        }\n      }\n    }\n\n    if (maxDistance) {\n      for (i = 0; i < maxNodes; i += 1) {\n        bestNodes.push([null, maxDistance]);\n      }\n    }\n\n    if (this.root) {\n      nearestSearch(this.root);\n    }\n\n    const result = [];\n    for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {\n      if (bestNodes.content[i][0]) {\n        result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);\n      }\n    }\n    return result;\n  }\n}\n\nfunction toJSONImpl(src) {\n  const dest = new Node(src.obj, src.dimension, null);\n  if (src.left) dest.left = toJSONImpl(src.left);\n  if (src.right) dest.right = toJSONImpl(src.right);\n  return dest;\n}\n\nfunction buildTree(points, depth, parent, dimensions) {\n  const dim = depth % dimensions.length;\n\n  if (points.length === 0) {\n    return null;\n  }\n  if (points.length === 1) {\n    return new Node(points[0], dim, parent);\n  }\n\n  points.sort((a, b) => a[dimensions[dim]] - b[dimensions[dim]]);\n\n  const median = Math.floor(points.length / 2);\n  const node = new Node(points[median], dim, parent);\n  node.left = buildTree(points.slice(0, median), depth + 1, node, dimensions);\n  node.right = buildTree(points.slice(median + 1), depth + 1, node, dimensions);\n\n  return node;\n}\n\nfunction restoreParent(root) {\n  if (root.left) {\n    root.left.parent = root;\n    restoreParent(root.left);\n  }\n\n  if (root.right) {\n    root.right.parent = root;\n    restoreParent(root.right);\n  }\n}\n\n// Binary heap implementation from:\n// http://eloquentjavascript.net/appendix2.html\nclass BinaryHeap {\n  constructor(scoreFunction) {\n    this.content = [];\n    this.scoreFunction = scoreFunction;\n  }\n\n  push(element) {\n    // Add the new element to the end of the array.\n    this.content.push(element);\n    // Allow it to bubble up.\n    this.bubbleUp(this.content.length - 1);\n  }\n\n  pop() {\n    // Store the first element so we can return it later.\n    var result = this.content[0];\n    // Get the element at the end of the array.\n    var end = this.content.pop();\n    // If there are any elements left, put the end element at the\n    // start, and let it sink down.\n    if (this.content.length > 0) {\n      this.content[0] = end;\n      this.sinkDown(0);\n    }\n    return result;\n  }\n\n  peek() {\n    return this.content[0];\n  }\n\n  size() {\n    return this.content.length;\n  }\n\n  bubbleUp(n) {\n    // Fetch the element that has to be moved.\n    var element = this.content[n];\n    // When at 0, an element can not go up any further.\n    while (n > 0) {\n      // Compute the parent element's index, and fetch it.\n      const parentN = Math.floor((n + 1) / 2) - 1;\n      const parent = this.content[parentN];\n      // Swap the elements if the parent is greater.\n      if (this.scoreFunction(element) < this.scoreFunction(parent)) {\n        this.content[parentN] = element;\n        this.content[n] = parent;\n        // Update 'n' to continue at the new position.\n        n = parentN;\n      } else {\n        // Found a parent that is less, no need to move it further.\n        break;\n      }\n    }\n  }\n\n  sinkDown(n) {\n    // Look up the target element and its score.\n    var length = this.content.length;\n    var element = this.content[n];\n    var elemScore = this.scoreFunction(element);\n\n    while (true) {\n      // Compute the indices of the child elements.\n      var child2N = (n + 1) * 2;\n      var child1N = child2N - 1;\n      // This is used to store the new position of the element,\n      // if any.\n      var swap = null;\n      // If the first child exists (is inside the array)...\n      if (child1N < length) {\n        // Look it up and compute its score.\n        var child1 = this.content[child1N];\n        var child1Score = this.scoreFunction(child1);\n        // If the score is less than our element's, we need to swap.\n        if (child1Score < elemScore) {\n          swap = child1N;\n        }\n      }\n      // Do the same checks for the other child.\n      if (child2N < length) {\n        var child2 = this.content[child2N];\n        var child2Score = this.scoreFunction(child2);\n        if (child2Score < (swap === null ? elemScore : child1Score)) {\n          swap = child2N;\n        }\n      }\n\n      // If the element needs to be moved, swap it, and continue.\n      if (swap !== null) {\n        this.content[n] = this.content[swap];\n        this.content[swap] = element;\n        n = swap;\n      } else {\n        // Otherwise, we are done.\n        break;\n      }\n    }\n  }\n}\n","import { euclidean as euclideanDistance } from 'ml-distance-euclidean';\n\nimport KDTree from './KDTree';\n\nexport default class KNN {\n  /**\n   * @param {Array} dataset\n   * @param {Array} labels\n   * @param {object} options\n   * @param {number} [options.k=numberOfClasses + 1] - Number of neighbors to classify.\n   * @param {function} [options.distance=euclideanDistance] - Distance function that takes two parameters.\n   */\n  constructor(dataset, labels, options = {}) {\n    if (dataset === true) {\n      const model = labels;\n      this.kdTree = new KDTree(model.kdTree, options);\n      this.k = model.k;\n      this.classes = new Set(model.classes);\n      this.isEuclidean = model.isEuclidean;\n      return;\n    }\n\n    const classes = new Set(labels);\n\n    const { distance = euclideanDistance, k = classes.size + 1 } = options;\n\n    const points = new Array(dataset.length);\n    for (var i = 0; i < points.length; ++i) {\n      points[i] = dataset[i].slice();\n    }\n\n    for (i = 0; i < labels.length; ++i) {\n      points[i].push(labels[i]);\n    }\n\n    this.kdTree = new KDTree(points, distance);\n    this.k = k;\n    this.classes = classes;\n    this.isEuclidean = distance === euclideanDistance;\n  }\n\n  /**\n   * Create a new KNN instance with the given model.\n   * @param {object} model\n   * @param {function} distance=euclideanDistance - distance function must be provided if the model wasn't trained with euclidean distance.\n   * @return {KNN}\n   */\n  static load(model, distance = euclideanDistance) {\n    if (model.name !== 'KNN') {\n      throw new Error(`invalid model: ${model.name}`);\n    }\n    if (!model.isEuclidean && distance === euclideanDistance) {\n      throw new Error(\n        'a custom distance function was used to create the model. Please provide it again'\n      );\n    }\n    if (model.isEuclidean && distance !== euclideanDistance) {\n      throw new Error(\n        'the model was created with the default distance function. Do not load it with another one'\n      );\n    }\n    return new KNN(true, model, distance);\n  }\n\n  /**\n   * Return a JSON containing the kd-tree model.\n   * @return {object} JSON KNN model.\n   */\n  toJSON() {\n    return {\n      name: 'KNN',\n      kdTree: this.kdTree,\n      k: this.k,\n      classes: Array.from(this.classes),\n      isEuclidean: this.isEuclidean\n    };\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Array} dataset\n   * @return {Array} predictions\n   */\n  predict(dataset) {\n    if (Array.isArray(dataset)) {\n      if (typeof dataset[0] === 'number') {\n        return getSinglePrediction(this, dataset);\n      } else if (\n        Array.isArray(dataset[0]) &&\n        typeof dataset[0][0] === 'number'\n      ) {\n        const predictions = new Array(dataset.length);\n        for (var i = 0; i < dataset.length; i++) {\n          predictions[i] = getSinglePrediction(this, dataset[i]);\n        }\n        return predictions;\n      }\n    }\n    throw new TypeError('dataset to predict must be an array or a matrix');\n  }\n}\n\nfunction getSinglePrediction(knn, currentCase) {\n  var nearestPoints = knn.kdTree.nearest(currentCase, knn.k);\n  var pointsPerClass = {};\n  var predictedClass = -1;\n  var maxPoints = -1;\n  var lastElement = nearestPoints[0][0].length - 1;\n\n  for (var element of knn.classes) {\n    pointsPerClass[element] = 0;\n  }\n\n  for (var i = 0; i < nearestPoints.length; ++i) {\n    var currentClass = nearestPoints[i][0][lastElement];\n    var currentPoints = ++pointsPerClass[currentClass];\n    if (currentPoints > maxPoints) {\n      predictedClass = currentClass;\n      maxPoints = currentPoints;\n    }\n  }\n\n  return predictedClass;\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that given vector, returns its norm\n * @param {Vector} X\n * @return {number} Norm of the vector\n */\nexport function norm(X) {\n  return Math.sqrt(X.clone().apply(pow2array).sum());\n}\n\n/**\n * @private\n * Function that pow 2 each element of a Matrix or a Vector,\n * used in the apply method of the Matrix object\n * @param {number} i - index i.\n * @param {number} j - index j.\n * @return {Matrix} The Matrix object modified at the index i, j.\n * */\nexport function pow2array(i, j) {\n  this.set(i, j, this.get(i, j) ** 2);\n}\n\n/**\n * @private\n * Function that normalize the dataset and return the means and\n * standard deviation of each feature.\n * @param {Matrix} dataset\n * @return {object} dataset normalized, means and standard deviations\n */\nexport function featureNormalize(dataset) {\n  var means = dataset.mean('column');\n  var std = dataset.standardDeviation('column', { mean: means, unbiased: true });\n  var result = Matrix.checkMatrix(dataset).subRowVector(means);\n  return { result: result.divRowVector(std), means: means, std: std };\n}\n\n/**\n * @private\n * Function that initialize an array of matrices.\n * @param {Array} array\n * @param {boolean} isMatrix\n * @return {Array} array with the matrices initialized.\n */\nexport function initializeMatrices(array, isMatrix) {\n  if (isMatrix) {\n    for (var i = 0; i < array.length; ++i) {\n      for (var j = 0; j < array[i].length; ++j) {\n        var elem = array[i][j];\n        array[i][j] = elem !== null ? new Matrix(array[i][j]) : undefined;\n      }\n    }\n  } else {\n    for (i = 0; i < array.length; ++i) {\n      array[i] = new Matrix(array[i]);\n    }\n  }\n\n  return array;\n}\n","import Matrix from 'ml-matrix';\n\nimport * as Utils from './utils';\n\n/**\n * @class PLS\n */\nexport class PLS {\n  /**\n   * Constructor for Partial Least Squares (PLS)\n   * @param {object} options\n   * @param {number} [options.latentVectors] - Number of latent vector to get (if the algorithm doesn't find a good model below the tolerance)\n   * @param {number} [options.tolerance=1e-5]\n   * @param {boolean} [options.scale=true] - rescale dataset using mean.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.meanX = model.meanX;\n      this.stdDevX = model.stdDevX;\n      this.meanY = model.meanY;\n      this.stdDevY = model.stdDevY;\n      this.PBQ = Matrix.checkMatrix(model.PBQ);\n      this.R2X = model.R2X;\n      this.scale = model.scale;\n      this.scaleMethod = model.scaleMethod;\n      this.tolerance = model.tolerance;\n    } else {\n      var {\n        tolerance = 1e-5,\n        scale = true,\n      } = options;\n      this.tolerance = tolerance;\n      this.scale = scale;\n      this.latentVectors = options.latentVectors;\n    }\n  }\n\n  /**\n   * Fits the model with the given data and predictions, in this function is calculated the\n   * following outputs:\n   *\n   * T - Score matrix of X\n   * P - Loading matrix of X\n   * U - Score matrix of Y\n   * Q - Loading matrix of Y\n   * B - Matrix of regression coefficient\n   * W - Weight matrix of X\n   *\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    if (trainingSet.length !== trainingValues.length) {\n      throw new RangeError('The number of X rows must be equal to the number of Y rows');\n    }\n\n    this.meanX = trainingSet.mean('column');\n    this.stdDevX = trainingSet.standardDeviation('column', { mean: this.meanX, unbiased: true });\n    this.meanY = trainingValues.mean('column');\n    this.stdDevY = trainingValues.standardDeviation('column', { mean: this.meanY, unbiased: true });\n\n    if (this.scale) {\n      trainingSet = trainingSet.clone().subRowVector(this.meanX).divRowVector(this.stdDevX);\n      trainingValues = trainingValues.clone().subRowVector(this.meanY).divRowVector(this.stdDevY);\n    }\n\n    if (this.latentVectors === undefined) {\n      this.latentVectors = Math.min(trainingSet.rows - 1, trainingSet.columns);\n    }\n\n    var rx = trainingSet.rows;\n    var cx = trainingSet.columns;\n    var ry = trainingValues.rows;\n    var cy = trainingValues.columns;\n\n    var ssqXcal = trainingSet.clone().mul(trainingSet).sum(); // for the r²\n    var sumOfSquaresY = trainingValues.clone().mul(trainingValues).sum();\n\n    var tolerance = this.tolerance;\n    var n = this.latentVectors;\n    var T = Matrix.zeros(rx, n);\n    var P = Matrix.zeros(cx, n);\n    var U = Matrix.zeros(ry, n);\n    var Q = Matrix.zeros(cy, n);\n    var B = Matrix.zeros(n, n);\n    var W = P.clone();\n    var k = 0;\n\n    while (Utils.norm(trainingValues) > tolerance && k < n) {\n      var transposeX = trainingSet.transpose();\n      var transposeY = trainingValues.transpose();\n\n      var tIndex = maxSumColIndex(trainingSet.clone().mul(trainingSet));\n      var uIndex = maxSumColIndex(trainingValues.clone().mul(trainingValues));\n\n      var t1 = trainingSet.getColumnVector(tIndex);\n      var u = trainingValues.getColumnVector(uIndex);\n      var t = Matrix.zeros(rx, 1);\n\n      while (Utils.norm(t1.clone().sub(t)) > tolerance) {\n        var w = transposeX.mmul(u);\n        w.div(Utils.norm(w));\n        t = t1;\n        t1 = trainingSet.mmul(w);\n        var q = transposeY.mmul(t1);\n        q.div(Utils.norm(q));\n        u = trainingValues.mmul(q);\n      }\n\n      t = t1;\n      var num = transposeX.mmul(t);\n      var den = t.transpose().mmul(t).get(0, 0);\n      var p = num.div(den);\n      var pnorm = Utils.norm(p);\n      p.div(pnorm);\n      t.mul(pnorm);\n      w.mul(pnorm);\n\n      num = u.transpose().mmul(t);\n      den = t.transpose().mmul(t).get(0, 0);\n      var b = num.div(den).get(0, 0);\n      trainingSet.sub(t.mmul(p.transpose()));\n      trainingValues.sub(t.clone().mul(b).mmul(q.transpose()));\n\n      T.setColumn(k, t);\n      P.setColumn(k, p);\n      U.setColumn(k, u);\n      Q.setColumn(k, q);\n      W.setColumn(k, w);\n\n      B.set(k, k, b);\n      k++;\n    }\n\n    k--;\n    T = T.subMatrix(0, T.rows - 1, 0, k);\n    P = P.subMatrix(0, P.rows - 1, 0, k);\n    U = U.subMatrix(0, U.rows - 1, 0, k);\n    Q = Q.subMatrix(0, Q.rows - 1, 0, k);\n    W = W.subMatrix(0, W.rows - 1, 0, k);\n    B = B.subMatrix(0, k, 0, k);\n\n    // TODO: review of R2Y\n    // this.R2Y = t.transpose().mmul(t).mul(q[k][0]*q[k][0]).divS(ssqYcal)[0][0];\n    //\n    this.ssqYcal = sumOfSquaresY;\n    this.E = trainingSet;\n    this.F = trainingValues;\n    this.T = T;\n    this.P = P;\n    this.U = U;\n    this.Q = Q;\n    this.W = W;\n    this.B = B;\n    this.PBQ = P.mmul(B).mmul(Q.transpose());\n    this.R2X = t.transpose().mmul(t).mmul(p.transpose().mmul(p)).div(ssqXcal).get(0, 0);\n  }\n\n  /**\n   * Predicts the behavior of the given dataset.\n   * @param {Matrix|Array} dataset - data to be predicted.\n   * @return {Matrix} - predictions of each element of the dataset.\n   */\n  predict(dataset) {\n    var X = Matrix.checkMatrix(dataset);\n    if (this.scale) {\n      X = X.subRowVector(this.meanX).divRowVector(this.stdDevX);\n    }\n    var Y = X.mmul(this.PBQ);\n    Y = Y.mulRowVector(this.stdDevY).addRowVector(this.meanY);\n    return Y;\n  }\n\n  /**\n   * Returns the explained variance on training of the PLS model\n   * @return {number}\n   */\n  getExplainedVariance() {\n    return this.R2X;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      name: 'PLS',\n      R2X: this.R2X,\n      meanX: this.meanX,\n      stdDevX: this.stdDevX,\n      meanY: this.meanY,\n      stdDevY: this.stdDevY,\n      PBQ: this.PBQ,\n      tolerance: this.tolerance,\n      scale: this.scale,\n    };\n  }\n\n  /**\n   * Load a PLS model from a JSON Object\n   * @param {object} model\n   * @return {PLS} - PLS object from the given model\n   */\n  static load(model) {\n    if (model.name !== 'PLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n    return new PLS(true, model);\n  }\n}\n\n/**\n * @private\n * Function that returns the index where the sum of each\n * column vector is maximum.\n * @param {Matrix} data\n * @return {number} index of the maximum\n */\nfunction maxSumColIndex(data) {\n  return Matrix.rowVector(data.sum('column')).maxIndex()[0];\n}\n","import { Matrix, SingularValueDecomposition, inverse } from 'ml-matrix';\n\nimport { initializeMatrices } from './utils';\n\n/**\n * @class KOPLS\n */\nexport class KOPLS {\n  /**\n     * Constructor for Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)\n     * @param {object} options\n     * @param {number} [options.predictiveComponents] - Number of predictive components to use.\n     * @param {number} [options.orthogonalComponents] - Number of Y-Orthogonal components.\n     * @param {Kernel} [options.kernel] - Kernel object to apply, see [ml-kernel](https://github.com/mljs/kernel).\n     * @param {object} model - for load purposes.\n     */\n  constructor(options, model) {\n    if (options === true) {\n      this.trainingSet = new Matrix(model.trainingSet);\n      this.YLoadingMat = new Matrix(model.YLoadingMat);\n      this.SigmaPow = new Matrix(model.SigmaPow);\n      this.YScoreMat = new Matrix(model.YScoreMat);\n      this.predScoreMat = initializeMatrices(model.predScoreMat, false);\n      this.YOrthLoadingVec = initializeMatrices(model.YOrthLoadingVec, false);\n      this.YOrthEigen = model.YOrthEigen;\n      this.YOrthScoreMat = initializeMatrices(model.YOrthScoreMat, false);\n      this.toNorm = initializeMatrices(model.toNorm, false);\n      this.TURegressionCoeff = initializeMatrices(model.TURegressionCoeff, false);\n      this.kernelX = initializeMatrices(model.kernelX, true);\n      this.kernel = model.kernel;\n      this.orthogonalComp = model.orthogonalComp;\n      this.predictiveComp = model.predictiveComp;\n    } else {\n      if (options.predictiveComponents === undefined) {\n        throw new RangeError('no predictive components found!');\n      }\n      if (options.orthogonalComponents === undefined) {\n        throw new RangeError('no orthogonal components found!');\n      }\n      if (options.kernel === undefined) {\n        throw new RangeError('no kernel found!');\n      }\n\n      this.orthogonalComp = options.orthogonalComponents;\n      this.predictiveComp = options.predictiveComponents;\n      this.kernel = options.kernel;\n    }\n  }\n\n  /**\n     * Train the K-OPLS model with the given training set and labels.\n     * @param {Matrix|Array} trainingSet\n     * @param {Matrix|Array} trainingValues\n     */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    // to save and compute kernel with the prediction dataset.\n    this.trainingSet = trainingSet.clone();\n\n    var kernelX = this.kernel.compute(trainingSet);\n\n    var Identity = Matrix.eye(kernelX.rows, kernelX.rows, 1);\n    var temp = kernelX;\n    kernelX = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      kernelX[i] = new Array(this.orthogonalComp + 1);\n    }\n    kernelX[0][0] = temp;\n\n    var result = new SingularValueDecomposition(trainingValues.transpose().mmul(kernelX[0][0]).mmul(trainingValues), {\n      computeLeftSingularVectors: true,\n      computeRightSingularVectors: false\n    });\n    var YLoadingMat = result.leftSingularVectors;\n    var Sigma = result.diagonalMatrix;\n\n    YLoadingMat = YLoadingMat.subMatrix(0, YLoadingMat.rows - 1, 0, this.predictiveComp - 1);\n    Sigma = Sigma.subMatrix(0, this.predictiveComp - 1, 0, this.predictiveComp - 1);\n\n    var YScoreMat = trainingValues.mmul(YLoadingMat);\n\n    var predScoreMat = new Array(this.orthogonalComp + 1);\n    var TURegressionCoeff = new Array(this.orthogonalComp + 1);\n    var YOrthScoreMat = new Array(this.orthogonalComp);\n    var YOrthLoadingVec = new Array(this.orthogonalComp);\n    var YOrthEigen = new Array(this.orthogonalComp);\n    var YOrthScoreNorm = new Array(this.orthogonalComp);\n\n    var SigmaPow = Matrix.pow(Sigma, -0.5);\n    // to avoid errors, check infinity\n    SigmaPow.apply(function (i, j) {\n      if (this.get(i, j) === Infinity) {\n        this.set(i, j, 0);\n      }\n    });\n\n    for (var i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = kernelX[0][i].transpose().mmul(YScoreMat).mmul(SigmaPow);\n\n      var TpiPrime = predScoreMat[i].transpose();\n      TURegressionCoeff[i] = inverse(TpiPrime.mmul(predScoreMat[i])).mmul(TpiPrime).mmul(YScoreMat);\n\n      result = new SingularValueDecomposition(TpiPrime.mmul(Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime))).mmul(predScoreMat[i]), {\n        computeLeftSingularVectors: true,\n        computeRightSingularVectors: false\n      });\n      var CoTemp = result.leftSingularVectors;\n      var SoTemp = result.diagonalMatrix;\n\n      YOrthLoadingVec[i] = CoTemp.subMatrix(0, CoTemp.rows - 1, 0, 0);\n      YOrthEigen[i] = SoTemp.get(0, 0);\n\n      YOrthScoreMat[i] = Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime)).mmul(predScoreMat[i]).mmul(YOrthLoadingVec[i]).mul(Math.pow(YOrthEigen[i], -0.5));\n\n      var toiPrime = YOrthScoreMat[i].transpose();\n      YOrthScoreNorm[i] = Matrix.sqrt(toiPrime.mmul(YOrthScoreMat[i]));\n\n      YOrthScoreMat[i] = YOrthScoreMat[i].divRowVector(YOrthScoreNorm[i]);\n\n      var ITo = Matrix.sub(Identity, YOrthScoreMat[i].mmul(YOrthScoreMat[i].transpose()));\n\n      kernelX[0][i + 1] = kernelX[0][i].mmul(ITo);\n      kernelX[i + 1][i + 1] = ITo.mmul(kernelX[i][i]).mmul(ITo);\n    }\n\n    var lastScoreMat = predScoreMat[this.orthogonalComp] = kernelX[0][this.orthogonalComp].transpose().mmul(YScoreMat).mmul(SigmaPow);\n\n    var lastTpPrime = lastScoreMat.transpose();\n    TURegressionCoeff[this.orthogonalComp] = inverse(lastTpPrime.mmul(lastScoreMat)).mmul(lastTpPrime).mmul(YScoreMat);\n\n    this.YLoadingMat = YLoadingMat;\n    this.SigmaPow = SigmaPow;\n    this.YScoreMat = YScoreMat;\n    this.predScoreMat = predScoreMat;\n    this.YOrthLoadingVec = YOrthLoadingVec;\n    this.YOrthEigen = YOrthEigen;\n    this.YOrthScoreMat = YOrthScoreMat;\n    this.toNorm = YOrthScoreNorm;\n    this.TURegressionCoeff = TURegressionCoeff;\n    this.kernelX = kernelX;\n  }\n\n  /**\n     * Predicts the output given the matrix to predict.\n     * @param {Matrix|Array} toPredict\n     * @return {{y: Matrix, predScoreMat: Array<Matrix>, predYOrthVectors: Array<Matrix>}} predictions\n     */\n  predict(toPredict) {\n    var KTestTrain = this.kernel.compute(toPredict, this.trainingSet);\n\n    var temp = KTestTrain;\n    KTestTrain = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      KTestTrain[i] = new Array(this.orthogonalComp + 1);\n    }\n    KTestTrain[0][0] = temp;\n\n    var YOrthScoreVector = new Array(this.orthogonalComp);\n    var predScoreMat = new Array(this.orthogonalComp);\n\n    var i;\n    for (i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);\n\n      YOrthScoreVector[i] = Matrix.sub(KTestTrain[i][i], predScoreMat[i].mmul(this.predScoreMat[i].transpose())).mmul(this.predScoreMat[i]).mmul(this.YOrthLoadingVec[i]).mul(Math.pow(this.YOrthEigen[i], -0.5));\n\n      YOrthScoreVector[i] = YOrthScoreVector[i].divRowVector(this.toNorm[i]);\n\n      var scoreMatPrime = this.YOrthScoreMat[i].transpose();\n      KTestTrain[i + 1][0] = Matrix.sub(KTestTrain[i][0], YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[0][i].transpose()));\n\n      var p1 = Matrix.sub(KTestTrain[i][0], KTestTrain[i][i].mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime));\n      var p2 = YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[i][i]);\n      var p3 = p2.mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime);\n\n      KTestTrain[i + 1][i + 1] = p1.sub(p2).add(p3);\n    }\n\n    predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);\n    var prediction = predScoreMat[i].mmul(this.TURegressionCoeff[i]).mmul(this.YLoadingMat.transpose());\n\n    return {\n      prediction: prediction,\n      predScoreMat: predScoreMat,\n      predYOrthVectors: YOrthScoreVector\n    };\n  }\n\n  /**\n     * Export the current model to JSON.\n     * @return {object} - Current model.\n     */\n  toJSON() {\n    return {\n      name: 'K-OPLS',\n      YLoadingMat: this.YLoadingMat,\n      SigmaPow: this.SigmaPow,\n      YScoreMat: this.YScoreMat,\n      predScoreMat: this.predScoreMat,\n      YOrthLoadingVec: this.YOrthLoadingVec,\n      YOrthEigen: this.YOrthEigen,\n      YOrthScoreMat: this.YOrthScoreMat,\n      toNorm: this.toNorm,\n      TURegressionCoeff: this.TURegressionCoeff,\n      kernelX: this.kernelX,\n      trainingSet: this.trainingSet,\n      orthogonalComp: this.orthogonalComp,\n      predictiveComp: this.predictiveComp\n    };\n  }\n\n  /**\n     * Load a K-OPLS with the given model.\n     * @param {object} model\n     * @param {Kernel} kernel - kernel used on the model, see [ml-kernel](https://github.com/mljs/kernel).\n     * @return {KOPLS}\n     */\n  static load(model, kernel) {\n    if (model.name !== 'K-OPLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    if (!kernel) {\n      throw new RangeError('You must provide a kernel for the model!');\n    }\n\n    model.kernel = kernel;\n    return new KOPLS(true, model);\n  }\n}\n","/**\n *  Constructs a confusion matrix\n * @class ConfusionMatrix\n * @example\n * const CM = new ConfusionMatrix([[13, 2], [10, 5]], ['cat', 'dog'])\n * @param {Array<Array<number>>} matrix - The confusion matrix, a 2D Array. Rows represent the actual label and columns\n *     the predicted label.\n * @param {Array<any>} labels - Labels of the confusion matrix, a 1D Array\n */\nclass ConfusionMatrix {\n    constructor(matrix, labels) {\n        if (matrix.length !== matrix[0].length) {\n            throw new Error('Confusion matrix must be square');\n        }\n        if (labels.length !== matrix.length) {\n            throw new Error('Confusion matrix and labels should have the same length');\n        }\n        this.labels = labels;\n        this.matrix = matrix;\n    }\n\n\n    /**\n     * Construct confusion matrix from the predicted and actual labels (classes). Be sure to provide the arguments in\n     * the correct order!\n     * @param {Array<any>} actual  - The predicted labels of the classification\n     * @param {Array<any>} predicted     - The actual labels of the classification. Has to be of same length as\n     *     predicted.\n     * @param {object} [options] - Additional options\n     * @param {Array<any>} [options.labels] - The list of labels that should be used. If not provided the distinct set\n     *     of labels present in predicted and actual is used. Labels are compared using the strict equality operator\n     *     '==='\n     * @return {ConfusionMatrix} - Confusion matrix\n     */\n    static fromLabels(actual, predicted, options = {}) {\n        if (predicted.length !== actual.length) {\n            throw new Error('predicted and actual must have the same length');\n        }\n        let distinctLabels;\n        if (options.labels) {\n            distinctLabels = new Set(options.labels);\n        } else {\n            distinctLabels = new Set([...actual, ...predicted]);\n        }\n        distinctLabels = Array.from(distinctLabels);\n        if (options.sort) {\n            distinctLabels.sort(options.sort);\n        }\n\n        // Create confusion matrix and fill with 0's\n        const matrix = Array.from({length: distinctLabels.length});\n        for (let i = 0; i < matrix.length; i++) {\n            matrix[i] = new Array(matrix.length);\n            matrix[i].fill(0);\n        }\n\n        for (let i = 0; i < predicted.length; i++) {\n            const actualIdx = distinctLabels.indexOf(actual[i]);\n            const predictedIdx = distinctLabels.indexOf(predicted[i]);\n            if (actualIdx >= 0 && predictedIdx >= 0) {\n                matrix[actualIdx][predictedIdx]++;\n            }\n        }\n\n        return new ConfusionMatrix(matrix, distinctLabels);\n    }\n\n    /**\n     * Get the confusion matrix\n     * @return {Array<Array<number> >}\n     */\n    getMatrix() {\n        return this.matrix;\n    }\n\n    getLabels() {\n        return this.labels;\n    }\n\n    /**\n     * Get the total number of samples\n     * @return {number}\n     */\n    getTotalCount() {\n        let predicted = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            for (var j = 0; j < this.matrix.length; j++) {\n                predicted += this.matrix[i][j];\n            }\n        }\n        return predicted;\n    }\n\n    /**\n     * Get the total number of true predictions\n     * @return {number}\n     */\n    getTrueCount() {\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            count += this.matrix[i][i];\n        }\n        return count;\n    }\n\n    /**\n     * Get the total number of false predictions.\n     * @return {number}\n     */\n    getFalseCount() {\n        return this.getTotalCount() - this.getTrueCount();\n    }\n\n    /**\n     * Get the number of true positive predictions.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getTruePositiveCount(label) {\n        const index = this.getIndex(label);\n        return this.matrix[index][index];\n    }\n\n    /**\n     * Get the number of true negative predictions\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getTrueNegativeCount(label) {\n        const index = this.getIndex(label);\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            for (var j = 0; j < this.matrix.length; j++) {\n                if (i !== index && j !== index) {\n                    count += this.matrix[i][j];\n                }\n            }\n        }\n        return count;\n    }\n\n    /**\n     * Get the number of false positive predictions.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalsePositiveCount(label) {\n        const index = this.getIndex(label);\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            if (i !== index) {\n                count += this.matrix[i][index];\n            }\n        }\n        return count;\n    }\n\n    /**\n     * Get the number of false negative predictions.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseNegativeCount(label) {\n        const index = this.getIndex(label);\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            if (i !== index) {\n                count += this.matrix[index][i];\n            }\n        }\n        return count;\n    }\n\n    /**\n     * Get the number of real positive samples.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getPositiveCount(label) {\n        return this.getTruePositiveCount(label) + this.getFalseNegativeCount(label);\n    }\n\n    /**\n     * Get the number of real negative samples.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getNegativeCount(label) {\n        return this.getTrueNegativeCount(label) + this.getFalsePositiveCount(label);\n    }\n\n    /**\n     * Get the index in the confusion matrix that corresponds to the given label\n     * @param {any} label - The label to search for\n     * @throws if the label is not found\n     * @return {number}\n     */\n    getIndex(label) {\n        const index = this.labels.indexOf(label);\n        if (index === -1) throw new Error('The label does not exist');\n        return index;\n    }\n\n    /**\n     * Get the true positive rate a.k.a. sensitivity. Computes the ratio between the number of true positive predictions and the total number of positive samples.\n     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number} - The true positive rate [0-1]\n     */\n    getTruePositiveRate(label) {\n        return this.getTruePositiveCount(label) / this.getPositiveCount(label);\n    }\n\n    /**\n     * Get the true negative rate a.k.a. specificity. Computes the ration between the number of true negative predictions and the total number of negative samples.\n     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getTrueNegativeRate(label) {\n        return this.getTrueNegativeCount(label) / this.getNegativeCount(label);\n    }\n\n    /**\n     * Get the positive predictive value a.k.a. precision. Computes TP / (TP + FP)\n     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getPositivePredictiveValue(label) {\n        const TP = this.getTruePositiveCount(label);\n        return TP / (TP + this.getFalsePositiveCount(label));\n    }\n\n    /**\n     * Negative predictive value\n     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getNegativePredictiveValue(label) {\n        const TN = this.getTrueNegativeCount(label);\n        return TN / (TN + this.getFalseNegativeCount(label));\n    }\n\n    /**\n     * False negative rate a.k.a. miss rate.\n     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseNegativeRate(label) {\n        return 1 - this.getTruePositiveRate(label);\n    }\n\n    /**\n     * False positive rate a.k.a. fall-out rate.\n     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalsePositiveRate(label) {\n        return 1 - this.getTrueNegativeRate(label);\n    }\n\n    /**\n     * False discovery rate (FDR)\n     * {@link https://en.wikipedia.org/wiki/False_discovery_rate}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseDiscoveryRate(label) {\n        const FP = this.getFalsePositiveCount(label);\n        return FP / (FP + this.getTruePositiveCount(label));\n    }\n\n    /**\n     * False omission rate (FOR)\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseOmissionRate(label) {\n        const FN = this.getFalseNegativeCount(label);\n        return FN / (FN + this.getTruePositiveCount(label));\n    }\n\n    /**\n     * F1 score\n     * {@link https://en.wikipedia.org/wiki/F1_score}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getF1Score(label) {\n        const TP = this.getTruePositiveCount(label);\n        return 2 * TP / (2 * TP + this.getFalsePositiveCount(label) + this.getFalseNegativeCount(label));\n    }\n\n    /**\n     * Matthews correlation coefficient (MCC)\n     * {@link https://en.wikipedia.org/wiki/Matthews_correlation_coefficient}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getMatthewsCorrelationCoefficient(label) {\n        const TP = this.getTruePositiveCount(label);\n        const TN = this.getTrueNegativeCount(label);\n        const FP = this.getFalsePositiveCount(label);\n        const FN = this.getFalseNegativeCount(label);\n        return (TP * TN - FP * FN) / Math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN));\n    }\n\n    /**\n     * Informedness\n     * {@link https://en.wikipedia.org/wiki/Youden%27s_J_statistic}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getInformedness(label) {\n        return this.getTruePositiveRate(label) + this.getTrueNegativeRate(label) - 1;\n    }\n\n    /**\n     * Markedness\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getMarkedness(label) {\n        return this.getPositivePredictiveValue(label) + this.getNegativePredictiveValue(label) - 1;\n    }\n\n    /**\n     * Get the confusion table.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {Array<Array<number> >} - The 2x2 confusion table. [[TP, FN], [FP, TN]]\n     */\n    getConfusionTable(label) {\n        return [\n            [\n                this.getTruePositiveCount(label),\n                this.getFalseNegativeCount(label)\n            ],\n            [\n                this.getFalsePositiveCount(label),\n                this.getTrueNegativeCount(label)\n            ]\n        ];\n    }\n\n    /**\n     * Get total accuracy.\n     * @return {number} - The ratio between the number of true predictions and total number of classifications ([0-1])\n     */\n    getAccuracy() {\n        let correct = 0;\n        let incorrect = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            for (var j = 0; j < this.matrix.length; j++) {\n                if (i === j) correct += this.matrix[i][j];\n                else incorrect += this.matrix[i][j];\n            }\n        }\n        return correct / (correct + incorrect);\n    }\n\n\n    /**\n     * Returns the element in the confusion matrix that corresponds to the given actual and predicted labels.\n     * @param {any} actual - The true label\n     * @param {any} predicted - The predicted label\n     * @return {number} - The element in the confusion matrix\n     */\n    getCount(actual, predicted) {\n        const actualIndex = this.getIndex(actual);\n        const predictedIndex = this.getIndex(predicted);\n        return this.matrix[actualIndex][predictedIndex];\n    }\n\n    /**\n     * Compute the general prediction accuracy\n     * @deprecated Use getAccuracy\n     * @return {number} - The prediction accuracy ([0-1]\n     */\n    get accuracy() {\n        return this.getAccuracy();\n    }\n\n    /**\n     * Compute the number of predicted observations\n     * @deprecated Use getTotalCount\n     * @return {number}\n     */\n    get total() {\n        return this.getTotalCount();\n    }\n}\n\nmodule.exports = ConfusionMatrix;\n","'use strict';\nconst defaultOptions = {\n  mode: 'index'\n};\n\nmodule.exports = function *(M, N, options) {\n  options = Object.assign({}, defaultOptions, options);\n  var a = new Array(N);\n  var c = new Array(M);\n  var b = new Array(N);\n  var p = new Array(N + 2);\n  var x, y, z;\n\n  // init a and b\n  for (var i = 0; i < N; i++) {\n    a[i] = i;\n    if (i < N - M) b[i] = 0;\n    else b[i] = 1;\n  }\n\n  // init c\n  for (i = 0; i < M; i++) {\n    c[i] = N - M + i;\n  }\n\n  // init p\n  for (i = 0; i < p.length; i++) {\n    if (i === 0) p[i] = N + 1;\n    else if (i <= N - M) p[i] = 0;\n    else if (i <= N) p[i] = i - N + M;\n    else p[i] = -2;\n  }\n\n  function twiddle() {\n    var i, j, k;\n    j = 1;\n    while (p[j] <= 0) {\n      j++;\n    }\n    if (p[j - 1] === 0) {\n      for (i = j - 1; i !== 1; i--) {\n        p[i] = -1;\n      }\n      p[j] = 0;\n      x = z = 0;\n      p[1] = 1;\n      y = j - 1;\n    } else {\n      if (j > 1) {\n        p[j - 1] = 0;\n      }\n      do {\n        j++;\n      }\n      while (p[j] > 0);\n      k = j - 1;\n      i = j;\n      while (p[i] === 0) {\n        p[i++] = -1;\n      }\n      if (p[i] === -1) {\n        p[i] = p[k];\n        z = p[k] - 1;\n        x = i - 1;\n        y = k - 1;\n        p[k] = -1;\n      } else {\n        if (i === p[0]) {\n          return 0;\n        } else {\n          p[j] = p[i];\n          z = p[i] - 1;\n          p[i] = 0;\n          x = j - 1;\n          y = i - 1;\n        }\n      }\n    }\n    return 1;\n  }\n\n  if (options.mode === 'index') {\n    yield c.slice();\n    while (twiddle()) {\n      c[z] = a[x];\n      yield c.slice();\n    }\n  } else if (options.mode === 'mask') {\n    yield b.slice();\n    while (twiddle()) {\n      b[x] = 1;\n      b[y] = 0;\n      yield b.slice();\n    }\n  } else {\n    throw new Error('Invalid mode');\n  }\n};\n","'use strict';\n\nconst ConfusionMatrix = require('ml-confusion-matrix');\n\nconst CV = {};\nconst combinations = require('ml-combinations');\n\n/**\n * Performs a leave-one-out cross-validation (LOO-CV) of the given samples. In LOO-CV, 1 observation is used as the\n * validation set while the rest is used as the training set. This is repeated once for each observation. LOO-CV is a\n * special case of LPO-CV. @see leavePout\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nCV.leaveOneOut = function (Classifier, features, labels, classifierOptions) {\n    if (typeof labels === 'function') {\n        var callback = labels;\n        labels = features;\n        features = Classifier;\n        return CV.leavePOut(features, labels, 1, callback);\n    }\n    return CV.leavePOut(Classifier, features, labels, classifierOptions, 1);\n};\n\n\n/**\n * Performs a leave-p-out cross-validation (LPO-CV) of the given samples. In LPO-CV, p observations are used as the\n * validation set while the rest is used as the training set. This is repeated as many times as there are possible\n * ways to combine p observations from the set (unordered without replacement). Be aware that for relatively small\n * data-set size this can require a very large number of training and testing to do!\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} p - The size of the validation sub-samples' set\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nCV.leavePOut = function (Classifier, features, labels, classifierOptions, p) {\n    if (typeof classifierOptions === 'function') {\n        var callback = classifierOptions;\n        p = labels;\n        labels = features;\n        features = Classifier;\n    }\n    check(features, labels);\n    const distinct = getDistinct(labels);\n    const confusionMatrix = initMatrix(distinct.length, distinct.length);\n\n    var N = features.length;\n    var gen = combinations(p, N);\n    var allIdx = new Array(N);\n    for (let i = 0; i < N; i++) {\n        allIdx[i] = i;\n    }\n    for (const testIdx of gen) {\n        var trainIdx = allIdx.slice();\n\n        for (let i = testIdx.length - 1; i >= 0; i--) {\n            trainIdx.splice(testIdx[i], 1);\n        }\n\n        if (callback) {\n            validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);\n        } else {\n            validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);\n        }\n\n    }\n\n    return new ConfusionMatrix(confusionMatrix, distinct);\n};\n\n/**\n * Performs k-fold cross-validation (KF-CV). KF-CV separates the data-set into k random equally sized partitions, and\n * uses each as a validation set, with all other partitions used in the training set. Observations left over from if k\n * does not divide the number of observations are left out of the cross-validation process.\n * @param {function} Classifier - The classifier's to use for the cross validation. Expect ml-classifier api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} k - The number of partitions to create\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nCV.kFold = function (Classifier, features, labels, classifierOptions, k) {\n    if (typeof classifierOptions === 'function') {\n        var callback = classifierOptions;\n        k = labels;\n        labels = features;\n        features = Classifier;\n    }\n    check(features, labels);\n    const distinct = getDistinct(labels);\n    const confusionMatrix = initMatrix(distinct.length, distinct.length);\n    var N = features.length;\n    var allIdx = new Array(N);\n    for (var i = 0; i < N; i++) {\n        allIdx[i] = i;\n    }\n\n    var l = Math.floor(N / k);\n    // create random k-folds\n    var current = [];\n    var folds = [];\n    while (allIdx.length) {\n        var randi = Math.floor(Math.random() * allIdx.length);\n        current.push(allIdx[randi]);\n        allIdx.splice(randi, 1);\n        if (current.length === l) {\n            folds.push(current);\n            current = [];\n        }\n    }\n    if (current.length) folds.push(current);\n    folds = folds.slice(0, k);\n\n\n    for (i = 0; i < folds.length; i++) {\n        var testIdx = folds[i];\n        var trainIdx = [];\n        for (var j = 0; j < folds.length; j++) {\n            if (j !== i) trainIdx = trainIdx.concat(folds[j]);\n        }\n\n        if (callback) {\n            validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);\n        } else {\n            validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);\n        }\n    }\n\n    return new ConfusionMatrix(confusionMatrix, distinct);\n};\n\nfunction check(features, labels) {\n    if (features.length !== labels.length) {\n        throw new Error('features and labels should have the same length');\n    }\n}\n\nfunction initMatrix(rows, columns) {\n    return new Array(rows).fill(0).map(() => new Array(columns).fill(0));\n}\n\nfunction getDistinct(arr) {\n    var s = new Set();\n    for (let i = 0; i < arr.length; i++) {\n        s.add(arr[i]);\n    }\n    return Array.from(s);\n}\n\nfunction validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct) {\n    const {testFeatures, trainFeatures, testLabels, trainLabels} = getTrainTest(features, labels, testIdx, trainIdx);\n\n    var classifier;\n    if (Classifier.prototype.train) {\n        classifier = new Classifier(classifierOptions);\n        classifier.train(trainFeatures, trainLabels);\n    } else {\n        classifier = new Classifier(trainFeatures, trainLabels, classifierOptions);\n    }\n\n    var predictedLabels = classifier.predict(testFeatures);\n    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback) {\n    const {testFeatures, trainFeatures, testLabels, trainLabels} = getTrainTest(features, labels, testIdx, trainIdx);\n    const predictedLabels = callback(trainFeatures, trainLabels, testFeatures);\n    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct) {\n\n    for (var i = 0; i < predictedLabels.length; i++) {\n        const actualIdx = distinct.indexOf(testLabels[i]);\n        const predictedIdx = distinct.indexOf(predictedLabels[i]);\n        if (actualIdx < 0 || predictedIdx < 0) {\n            // eslint-disable-next-line no-console\n            console.warn(`ignore unknown predicted label ${predictedLabels[i]}`);\n        }\n        confusionMatrix[actualIdx][predictedIdx]++;\n    }\n}\n\n\nfunction getTrainTest(features, labels, testIdx, trainIdx) {\n    return {\n        testFeatures: testIdx.map(function (index) {\n            return features[index];\n        }),\n        trainFeatures: trainIdx.map(function (index) {\n            return features[index];\n        }),\n        testLabels: testIdx.map(function (index) {\n            return labels[index];\n        }),\n        trainLabels: trainIdx.map(function (index) {\n            return labels[index];\n        })\n    };\n}\n\nmodule.exports = CV;\n","'use strict';\n\nvar mlMatrix = require('ml-matrix');\n\nfunction logistic(val) {\n  return 1 / (1 + Math.exp(-val));\n}\n\nfunction expELU(val, param) {\n  return val < 0 ? param * (Math.exp(val) - 1) : val;\n}\n\nfunction softExponential(val, param) {\n  if (param < 0) {\n    return -Math.log(1 - param * (val + param)) / param;\n  }\n  if (param > 0) {\n    return ((Math.exp(param * val) - 1) / param) + param;\n  }\n  return val;\n}\n\nfunction softExponentialPrime(val, param) {\n  if (param < 0) {\n    return 1 / (1 - param * (param + val));\n  } else {\n    return Math.exp(param * val);\n  }\n}\n\nconst ACTIVATION_FUNCTIONS = {\n  tanh: {\n    activation: Math.tanh,\n    derivate: (val) => 1 - (val * val)\n  },\n  identity: {\n    activation: (val) => val,\n    derivate: () => 1\n  },\n  logistic: {\n    activation: logistic,\n    derivate: (val) => logistic(val) * (1 - logistic(val))\n  },\n  arctan: {\n    activation: Math.atan,\n    derivate: (val) => 1 / (val * val + 1)\n  },\n  softsign: {\n    activation: (val) => val / (1 + Math.abs(val)),\n    derivate: (val) => 1 / ((1 + Math.abs(val)) * (1 + Math.abs(val)))\n  },\n  relu: {\n    activation: (val) => (val < 0 ? 0 : val),\n    derivate: (val) => (val < 0 ? 0 : 1)\n  },\n  softplus: {\n    activation: (val) => Math.log(1 + Math.exp(val)),\n    derivate: (val) => 1 / (1 + Math.exp(-val))\n  },\n  bent: {\n    activation: (val) => ((Math.sqrt(val * val + 1) - 1) / 2) + val,\n    derivate: (val) => (val / (2 * Math.sqrt(val * val + 1))) + 1\n  },\n  sinusoid: {\n    activation: Math.sin,\n    derivate: Math.cos\n  },\n  sinc: {\n    activation: (val) => (val === 0 ? 1 : Math.sin(val) / val),\n    derivate: (val) => (val === 0 ? 0 : (Math.cos(val) / val) - (Math.sin(val) / (val * val)))\n  },\n  gaussian: {\n    activation: (val) => Math.exp(-(val * val)),\n    derivate: (val) => -2 * val * Math.exp(-(val * val))\n  },\n  'parametric-relu': {\n    activation: (val, param) => (val < 0 ? param * val : val),\n    derivate: (val, param) => (val < 0 ? param : 1)\n  },\n  'exponential-elu': {\n    activation: expELU,\n    derivate: (val, param) => (val < 0 ? expELU(val, param) + param : 1)\n  },\n  'soft-exponential': {\n    activation: softExponential,\n    derivate: softExponentialPrime\n  }\n};\n\nclass Layer {\n  /**\n     * @private\n     * Create a new layer with the given options\n     * @param {object} options\n     * @param {number} [options.inputSize] - Number of conections that enter the neurons.\n     * @param {number} [options.outputSize] - Number of conections that leave the neurons.\n     * @param {number} [options.regularization] - Regularization parameter.\n     * @param {number} [options.epsilon] - Learning rate parameter.\n     * @param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.\n     * @param {number} [options.activationParam] - Activation parameter if needed.\n     */\n  constructor(options) {\n    this.inputSize = options.inputSize;\n    this.outputSize = options.outputSize;\n    this.regularization = options.regularization;\n    this.epsilon = options.epsilon;\n    this.activation = options.activation;\n    this.activationParam = options.activationParam;\n\n    var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];\n    var params = selectedFunction.activation.length;\n\n    var actFunction = params > 1 ? (val) => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;\n    var derFunction = params > 1 ? (val) => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, actFunction(this.get(i, j)));\n    };\n    this.derivate = function (i, j) {\n      this.set(i, j, derFunction(this.get(i, j)));\n    };\n\n    if (options.model) {\n      // load model\n      this.W = mlMatrix.Matrix.checkMatrix(options.W);\n      this.b = mlMatrix.Matrix.checkMatrix(options.b);\n    } else {\n      // default constructor\n      this.W = mlMatrix.Matrix.rand(this.inputSize, this.outputSize);\n      this.b = mlMatrix.Matrix.zeros(1, this.outputSize);\n\n      this.W.apply(function (i, j) {\n        this.set(i, j, this.get(i, j) / Math.sqrt(options.inputSize));\n      });\n    }\n  }\n\n  /**\n     * @private\n     * propagate the given input through the current layer.\n     * @param {Matrix} X - input.\n     * @return {Matrix} output at the current layer.\n     */\n  forward(X) {\n    var z = X.mmul(this.W).addRowVector(this.b);\n    z.apply(this.activationFunction);\n    this.a = z.clone();\n    return z;\n  }\n\n  /**\n     * @private\n     * apply backpropagation algorithm at the current layer\n     * @param {Matrix} delta - delta values estimated at the following layer.\n     * @param {Matrix} a - 'a' values from the following layer.\n     * @return {Matrix} the new delta values for the next layer.\n     */\n  backpropagation(delta, a) {\n    this.dW = a.transpose().mmul(delta);\n    this.db = mlMatrix.Matrix.rowVector(delta.sum('column'));\n\n    var aCopy = a.clone();\n    return delta.mmul(this.W.transpose()).mul(aCopy.apply(this.derivate));\n  }\n\n  /**\n     * @private\n     * Function that updates the weights at the current layer with the derivatives.\n     */\n  update() {\n    this.dW.add(this.W.clone().mul(this.regularization));\n    this.W.add(this.dW.mul(-this.epsilon));\n    this.b.add(this.db.mul(-this.epsilon));\n  }\n\n  /**\n     * @private\n     * Export the current layer to JSON.\n     * @return {object} model\n     */\n  toJSON() {\n    return {\n      model: 'Layer',\n      inputSize: this.inputSize,\n      outputSize: this.outputSize,\n      regularization: this.regularization,\n      epsilon: this.epsilon,\n      activation: this.activation,\n      W: this.W,\n      b: this.b\n    };\n  }\n\n  /**\n     * @private\n     * Creates a new Layer with the given model.\n     * @param {object} model\n     * @return {Layer}\n     */\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n    return new Layer(model);\n  }\n}\n\nclass OutputLayer extends Layer {\n  constructor(options) {\n    super(options);\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, Math.exp(this.get(i, j)));\n    };\n  }\n\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n\n    return new OutputLayer(model);\n  }\n}\n\nclass FeedForwardNeuralNetworks {\n  /**\n   * Create a new Feedforward neural network model.\n   * @class FeedForwardNeuralNetworks\n   * @param {object} [options]\n   * @param {Array} [options.hiddenLayers=[10]] - Array that contains the sizes of the hidden layers.\n   * @param {number} [options.iterations=50] - Number of iterations at the training step.\n   * @param {number} [options.learningRate=0.01] - Learning rate of the neural net (also known as epsilon).\n   * @param {number} [options.regularization=0.01] - Regularization parameter af the neural net.\n   * @param {string} [options.activation='tanh'] - activation function to be used. (options: 'tanh'(default),\n   * 'identity', 'logistic', 'arctan', 'softsign', 'relu', 'softplus', 'bent', 'sinusoid', 'sinc', 'gaussian').\n   * (single-parametric options: 'parametric-relu', 'exponential-relu', 'soft-exponential').\n   * @param {number} [options.activationParam=1] - if the selected activation function needs a parameter.\n   */\n  constructor(options) {\n    options = options || {};\n    if (options.model) {\n      // load network\n      this.hiddenLayers = options.hiddenLayers;\n      this.iterations = options.iterations;\n      this.learningRate = options.learningRate;\n      this.regularization = options.regularization;\n      this.dicts = options.dicts;\n      this.activation = options.activation;\n      this.activationParam = options.activationParam;\n      this.model = new Array(options.layers.length);\n\n      for (var i = 0; i < this.model.length - 1; ++i) {\n        this.model[i] = Layer.load(options.layers[i]);\n      }\n      this.model[this.model.length - 1] = OutputLayer.load(options.layers[this.model.length - 1]);\n    } else {\n      // default constructor\n      this.hiddenLayers = options.hiddenLayers || [10];\n      this.iterations = options.iterations || 50;\n\n      this.learningRate = options.learningRate || 0.01;\n      this.regularization = options.regularization || 0.01;\n\n      this.activation = options.activation || 'tanh';\n      this.activationParam = options.activationParam || 1;\n      if (!(this.activation in Object.keys(ACTIVATION_FUNCTIONS))) {\n        this.activation = 'tanh';\n      }\n    }\n  }\n\n  /**\n   * @private\n   * Function that build and initialize the neural net.\n   * @param {number} inputSize - total of features to fit.\n   * @param {number} outputSize - total of labels of the prediction set.\n   */\n  buildNetwork(inputSize, outputSize) {\n    var size = 2 + (this.hiddenLayers.length - 1);\n    this.model = new Array(size);\n\n    // input layer\n    this.model[0] = new Layer({\n      inputSize: inputSize,\n      outputSize: this.hiddenLayers[0],\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n\n    // hidden layers\n    for (var i = 1; i < this.hiddenLayers.length; ++i) {\n      this.model[i] = new Layer({\n        inputSize: this.hiddenLayers[i - 1],\n        outputSize: this.hiddenLayers[i],\n        activation: this.activation,\n        activationParam: this.activationParam,\n        regularization: this.regularization,\n        epsilon: this.learningRate\n      });\n    }\n\n    // output layer\n    this.model[size - 1] = new OutputLayer({\n      inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],\n      outputSize: outputSize,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n  }\n\n  /**\n   * Train the neural net with the given features and labels.\n   * @param {Matrix|Array} features\n   * @param {Matrix|Array} labels\n   */\n  train(features, labels) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    this.dicts = dictOutputs(labels);\n\n    var inputSize = features.columns;\n    var outputSize = Object.keys(this.dicts.inputs).length;\n\n    if (!this.model) {\n      this.buildNetwork(inputSize, outputSize);\n    }\n\n    for (var i = 0; i < this.iterations; ++i) {\n      var probabilities = this.propagate(features);\n      this.backpropagation(features, labels, probabilities);\n    }\n  }\n\n  /**\n   * @private\n   * Propagate the input(training set) and retrives the probabilities of each class.\n   * @param {Matrix} X\n   * @return {Matrix} probabilities of each class.\n   */\n  propagate(X) {\n    var input = X;\n    for (var i = 0; i < this.model.length; ++i) {\n      input = this.model[i].forward(input);\n    }\n\n    // get probabilities\n    return input.divColumnVector(input.sum('row'));\n  }\n\n  /**\n   * @private\n   * Function that applies the backpropagation algorithm on each layer of the network\n   * in order to fit the features and labels.\n   * @param {Matrix} features\n   * @param {Array} labels\n   * @param {Matrix} probabilities - probabilities of each class of the feature set.\n   */\n  backpropagation(features, labels, probabilities) {\n    for (var i = 0; i < probabilities.rows; ++i) {\n      probabilities.set(i, this.dicts.inputs[labels[i]], probabilities.get(i, this.dicts.inputs[labels[i]]) - 1);\n    }\n\n    // remember, the last delta doesn't matter\n    var delta = probabilities;\n    for (i = this.model.length - 1; i >= 0; --i) {\n      var a = i > 0 ? this.model[i - 1].a : features;\n      delta = this.model[i].backpropagation(delta, a);\n    }\n\n    for (i = 0; i < this.model.length; ++i) {\n      this.model[i].update();\n    }\n  }\n\n  /**\n   * Predict the output given the feature set.\n   * @param {Array|Matrix} features\n   * @return {Array}\n   */\n  predict(features) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    var outputs = new Array(features.rows);\n    var probabilities = this.propagate(features);\n    for (var i = 0; i < features.rows; ++i) {\n      outputs[i] = this.dicts.outputs[probabilities.maxRowIndex(i)[1]];\n    }\n\n    return outputs;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} model\n   */\n  toJSON() {\n    var model = {\n      model: 'FNN',\n      hiddenLayers: this.hiddenLayers,\n      iterations: this.iterations,\n      learningRate: this.learningRate,\n      regularization: this.regularization,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      dicts: this.dicts,\n      layers: new Array(this.model.length)\n    };\n\n    for (var i = 0; i < this.model.length; ++i) {\n      model.layers[i] = this.model[i].toJSON();\n    }\n\n    return model;\n  }\n\n  /**\n   * Load a Feedforward Neural Network with the current model.\n   * @param {object} model\n   * @return {FeedForwardNeuralNetworks}\n   */\n  static load(model) {\n    if (model.model !== 'FNN') {\n      throw new RangeError('the current model is not a feed forward network');\n    }\n\n    return new FeedForwardNeuralNetworks(model);\n  }\n}\n\n/**\n * @private\n * Method that given an array of labels(predictions), returns two dictionaries, one to transform from labels to\n * numbers and other in the reverse way\n * @param {Array} array\n * @return {object}\n */\nfunction dictOutputs(array) {\n  var inputs = {};\n  var outputs = {};\n  var index = 0;\n  for (var i = 0; i < array.length; i += 1) {\n    if (inputs[array[i]] === undefined) {\n      inputs[array[i]] = index;\n      outputs[index] = array[i];\n      index++;\n    }\n  }\n\n  return {\n    inputs: inputs,\n    outputs: outputs\n  };\n}\n\nmodule.exports = FeedForwardNeuralNetworks;\n","function NodeSquare(x, y, weights, som) {\n    this.x = x;\n    this.y = y;\n    this.weights = weights;\n    this.som = som;\n    this.neighbors = {};\n}\n\nNodeSquare.prototype.adjustWeights = function adjustWeights(target, learningRate, influence) {\n    for (var i = 0, ii = this.weights.length; i < ii; i++) {\n        this.weights[i] += learningRate * influence * (target[i] - this.weights[i]);\n    }\n};\n\nNodeSquare.prototype.getDistance = function getDistance(otherNode) {\n    return Math.max(Math.abs(this.x - otherNode.x), Math.abs(this.y - otherNode.y));\n};\n\nNodeSquare.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.x - otherNode.x),\n        distY = Math.abs(this.y - otherNode.y);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY));\n};\n\nNodeSquare.prototype.getNeighbors = function getNeighbors(xy) {\n    if (!this.neighbors[xy]) {\n        this.neighbors[xy] = new Array(2);\n\n        // left or bottom neighbor\n        var v;\n        if (this[xy] > 0) {\n            v = this[xy] - 1;\n        } else if (this.som.torus) {\n            v = this.som.gridDim[xy] - 1\n        }\n        if (typeof v !== 'undefined') {\n            var x, y;\n            if (xy === 'x') {\n                x = v;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = v;\n            }\n            this.neighbors[xy][0] = this.som.nodes[x][y];\n        }\n\n        // top or right neighbor\n        var w;\n        if (this[xy] < (this.som.gridDim[xy] - 1)) {\n            w = this[xy] + 1;\n        } else if (this.som.torus) {\n            w = 0;\n        }\n        if (typeof w !== 'undefined') {\n            if (xy === 'x') {\n                x = w;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = w;\n            }\n            this.neighbors[xy][1] = this.som.nodes[x][y];\n        }\n    }\n    return this.neighbors[xy];\n};\n\nNodeSquare.prototype.getPos = function getPos(xy, element) {\n    var neighbors = this.getNeighbors(xy),\n        distance = this.som.distance,\n        bestNeighbor,\n        direction;\n    if(neighbors[0]) {\n        if (neighbors[1]) {\n            var dist1 = distance(element, neighbors[0].weights),\n                dist2 = distance(element, neighbors[1].weights);\n            if(dist1 < dist2) {\n                bestNeighbor = neighbors[0];\n                direction = -1;\n            } else {\n                bestNeighbor = neighbors[1];\n                direction = 1;\n            }\n        } else {\n            bestNeighbor = neighbors[0];\n            direction = -1;\n        }\n    } else {\n        bestNeighbor = neighbors[1];\n        direction = 1;\n    }\n    var simA = 1 - distance(element, this.weights),\n        simB = 1 - distance(element, bestNeighbor.weights);\n    var factor = ((simA - simB) / (2 - simA - simB));\n    return 0.5 + 0.5 * factor * direction;\n};\n\nNodeSquare.prototype.getPosition = function getPosition(element) {\n    return [\n        this.getPos('x', element),\n        this.getPos('y', element)\n    ];\n};\n\nmodule.exports = NodeSquare;","var NodeSquare = require('./node-square');\n\nfunction NodeHexagonal(x, y, weights, som) {\n\n    NodeSquare.call(this, x, y, weights, som);\n\n    this.hX = x - Math.floor(y / 2);\n    this.z = 0 - this.hX - y;\n\n}\n\nNodeHexagonal.prototype = new NodeSquare;\nNodeHexagonal.prototype.constructor = NodeHexagonal;\n\nNodeHexagonal.prototype.getDistance = function getDistanceHexagonal(otherNode) {\n    return Math.max(Math.abs(this.hX - otherNode.hX), Math.abs(this.y - otherNode.y), Math.abs(this.z - otherNode.z));\n};\n\nNodeHexagonal.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.hX - otherNode.hX),\n        distY = Math.abs(this.y - otherNode.y),\n        distZ = Math.abs(this.z - otherNode.z);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY), Math.min(distZ, this.som.gridDim.z - distZ));\n};\n\nNodeHexagonal.prototype.getPosition = function getPosition() {\n    throw new Error('Unimplemented : cannot get position of the points for hexagonal grid');\n};\n\nmodule.exports = NodeHexagonal;","'use strict';\n\nvar NodeSquare = require('./node-square'),\n    NodeHexagonal = require('./node-hexagonal');\n\nvar defaultOptions = {\n    fields: 3,\n    randomizer: Math.random,\n    distance: squareEuclidean,\n    iterations: 10,\n    learningRate: 0.1,\n    gridType: 'rect',\n    torus: true,\n    method: 'random'\n};\n\nfunction SOM(x, y, options, reload) {\n\n    this.x = x;\n    this.y = y;\n\n    options = options || {};\n    this.options = {};\n    for (var i in defaultOptions) {\n        if (options.hasOwnProperty(i)) {\n            this.options[i] = options[i];\n        } else {\n            this.options[i] = defaultOptions[i];\n        }\n    }\n\n    if (typeof this.options.fields === 'number') {\n        this.numWeights = this.options.fields;\n    } else if (Array.isArray(this.options.fields)) {\n        this.numWeights = this.options.fields.length;\n        var converters = getConverters(this.options.fields);\n        this.extractor = converters.extractor;\n        this.creator = converters.creator;\n    } else {\n        throw new Error('Invalid fields definition');\n    }\n\n    if (this.options.gridType === 'rect') {\n        this.nodeType = NodeSquare;\n        this.gridDim = {\n            x: x,\n            y: y\n        };\n    } else {\n        this.nodeType = NodeHexagonal;\n        var hx = this.x - Math.floor(this.y / 2);\n        this.gridDim = {\n            x: hx,\n            y: this.y,\n            z: -(0 - hx - this.y)\n        };\n    }\n\n    this.torus = this.options.torus;\n    this.distanceMethod = this.torus ? 'getDistanceTorus' : 'getDistance';\n\n    this.distance = this.options.distance;\n\n    this.maxDistance = getMaxDistance(this.distance, this.numWeights);\n\n    if (reload === true) { // For model loading\n        this.done = true;\n        return;\n    }\n    if (!(x > 0 && y > 0)) {\n        throw new Error('x and y must be positive');\n    }\n\n    this.times = {\n        findBMU: 0,\n        adjust: 0\n    };\n\n    this.randomizer = this.options.randomizer;\n\n    this.iterationCount = 0;\n    this.iterations = this.options.iterations;\n\n    this.startLearningRate = this.learningRate = this.options.learningRate;\n\n    this.mapRadius = Math.floor(Math.max(x, y) / 2);\n\n    this.algorithmMethod = this.options.method;\n\n    this._initNodes();\n\n    this.done = false;\n}\n\nSOM.load = function loadModel(model, distance) {\n    if (model.name === 'SOM') {\n        var x = model.data.length,\n            y = model.data[0].length;\n        if (distance) {\n            model.options.distance = distance;\n        } else if (model.options.distance) {\n            model.options.distance = eval('(' + model.options.distance + ')');\n        }\n        var som = new SOM(x, y, model.options, true);\n        som.nodes = new Array(x);\n        for (var i = 0; i < x; i++) {\n            som.nodes[i] = new Array(y);\n            for (var j = 0; j < y; j++) {\n                som.nodes[i][j] = new som.nodeType(i, j, model.data[i][j], som);\n            }\n        }\n        return som;\n    } else {\n        throw new Error('expecting a SOM model');\n    }\n};\n\nSOM.prototype.export = function exportModel(includeDistance) {\n    if (!this.done) {\n        throw new Error('model is not ready yet');\n    }\n    var model = {\n        name: 'SOM'\n    };\n    model.options = {\n        fields: this.options.fields,\n        gridType: this.options.gridType,\n        torus: this.options.torus\n    };\n    model.data = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        model.data[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            model.data[i][j] = this.nodes[i][j].weights;\n        }\n    }\n    if (includeDistance) {\n        model.options.distance = this.distance.toString();\n    }\n    return model;\n};\n\nSOM.prototype._initNodes = function initNodes() {\n    var now = Date.now(),\n        i, j, k;\n    this.nodes = new Array(this.x);\n    for (i = 0; i < this.x; i++) {\n        this.nodes[i] = new Array(this.y);\n        for (j = 0; j < this.y; j++) {\n            var weights = new Array(this.numWeights);\n            for (k = 0; k < this.numWeights; k++) {\n                weights[k] = this.randomizer();\n            }\n            this.nodes[i][j] = new this.nodeType(i, j, weights, this);\n        }\n    }\n    this.times.initNodes = Date.now() - now;\n};\n\nSOM.prototype.setTraining = function setTraining(trainingSet) {\n    if (this.trainingSet) {\n        throw new Error('training set has already been set');\n    }\n    var now = Date.now();\n    var convertedSet = trainingSet;\n    var i, l = trainingSet.length;\n    if (this.extractor) {\n        convertedSet = new Array(l);\n        for (i = 0; i < l; i++) {\n            convertedSet[i] = this.extractor(trainingSet[i]);\n        }\n    }\n    this.numIterations = this.iterations * l;\n\n    if (this.algorithmMethod === 'random') {\n        this.timeConstant = this.numIterations / Math.log(this.mapRadius);\n    } else {\n        this.timeConstant = l / Math.log(this.mapRadius);\n    }\n    this.trainingSet = convertedSet;\n    this.times.setTraining = Date.now() - now;\n};\n\nSOM.prototype.trainOne = function trainOne() {\n    if (this.done) {\n\n        return false;\n\n    } else if (this.numIterations-- > 0) {\n\n        var neighbourhoodRadius,\n            trainingValue,\n            trainingSetFactor;\n\n        if (this.algorithmMethod === 'random') { // Pick a random value of the training set at each step\n            neighbourhoodRadius = this.mapRadius * Math.exp(-this.iterationCount / this.timeConstant);\n            trainingValue = getRandomValue(this.trainingSet, this.randomizer);\n            this._adjust(trainingValue, neighbourhoodRadius);\n            this.learningRate = this.startLearningRate * Math.exp(-this.iterationCount / this.numIterations);\n        } else { // Get next input vector\n            trainingSetFactor = -Math.floor(this.iterationCount / this.trainingSet.length);\n            neighbourhoodRadius = this.mapRadius * Math.exp(trainingSetFactor / this.timeConstant);\n            trainingValue = this.trainingSet[this.iterationCount % this.trainingSet.length];\n            this._adjust(trainingValue, neighbourhoodRadius);\n            if (((this.iterationCount + 1) % this.trainingSet.length) === 0) {\n                this.learningRate = this.startLearningRate * Math.exp(trainingSetFactor / Math.floor(this.numIterations / this.trainingSet.length));\n            }\n        }\n\n        this.iterationCount++;\n\n        return true;\n\n    } else {\n\n        this.done = true;\n        return false;\n\n    }\n};\n\nSOM.prototype._adjust = function adjust(trainingValue, neighbourhoodRadius) {\n    var now = Date.now(),\n        x, y, dist, influence;\n\n    var bmu = this._findBestMatchingUnit(trainingValue);\n\n    var now2 = Date.now();\n    this.times.findBMU += now2 - now;\n\n    var radiusLimit = Math.floor(neighbourhoodRadius);\n    var xMin = bmu.x - radiusLimit,\n        xMax = bmu.x + radiusLimit,\n        yMin = bmu.y - radiusLimit,\n        yMax = bmu.y + radiusLimit;\n\n    for (x = xMin; x <= xMax; x++) {\n        var theX = x;\n        if (x < 0) {\n            theX += this.x;\n        } else if (x >= this.x) {\n            theX -= this.x;\n        }\n        for (y = yMin; y <= yMax; y++) {\n            var theY = y;\n            if (y < 0) {\n                theY += this.y;\n            } else if (y >= this.y) {\n                theY -= this.y;\n            }\n\n            dist = bmu[this.distanceMethod](this.nodes[theX][theY]);\n\n            if (dist < neighbourhoodRadius) {\n                influence = Math.exp(-dist / (2 * neighbourhoodRadius));\n                this.nodes[theX][theY].adjustWeights(trainingValue, this.learningRate, influence);\n            }\n\n        }\n    }\n\n    this.times.adjust += (Date.now() - now2);\n\n};\n\nSOM.prototype.train = function train(trainingSet) {\n    if (!this.done) {\n        this.setTraining(trainingSet);\n        while (this.trainOne()) {\n        }\n    }\n};\n\nSOM.prototype.getConvertedNodes = function getConvertedNodes() {\n    var result = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        result[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            var node = this.nodes[i][j];\n            result[i][j] = this.creator ? this.creator(node.weights) : node.weights;\n        }\n    }\n    return result;\n};\n\nSOM.prototype._findBestMatchingUnit = function findBestMatchingUnit(candidate) {\n\n    var bmu,\n        lowest = Infinity,\n        dist;\n\n    for (var i = 0; i < this.x; i++) {\n        for (var j = 0; j < this.y; j++) {\n            dist = this.distance(this.nodes[i][j].weights, candidate);\n            if (dist < lowest) {\n                lowest = dist;\n                bmu = this.nodes[i][j];\n            }\n        }\n    }\n\n    return bmu;\n\n};\n\nSOM.prototype.predict = function predict(data, computePosition) {\n    if (typeof data === 'boolean') {\n        computePosition = data;\n        data = null;\n    }\n    if (!data) {\n        data = this.trainingSet;\n    }\n    if (Array.isArray(data) && (Array.isArray(data[0]) || (typeof data[0] === 'object'))) { // predict a dataset\n        var self = this;\n        return data.map(function (element) {\n            return self._predict(element, computePosition);\n        });\n    } else { // predict a single element\n        return this._predict(data, computePosition);\n    }\n};\n\nSOM.prototype._predict = function _predict(element, computePosition) {\n    if (!Array.isArray(element)) {\n        element = this.extractor(element);\n    }\n    var bmu = this._findBestMatchingUnit(element);\n    var result = [bmu.x, bmu.y];\n    if (computePosition) {\n        result[2] = bmu.getPosition(element);\n    }\n    return result;\n};\n\n// As seen in http://www.scholarpedia.org/article/Kohonen_network\nSOM.prototype.getQuantizationError = function getQuantizationError() {\n    var fit = this.getFit(),\n        l = fit.length,\n        sum = 0;\n    for (var i = 0; i < l; i++) {\n        sum += fit[i];\n    }\n    return sum / l;\n};\n\nSOM.prototype.getFit = function getFit(dataset) {\n    if (!dataset) {\n        dataset = this.trainingSet;\n    }\n    var l = dataset.length,\n        bmu,\n        result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        bmu = this._findBestMatchingUnit(dataset[i]);\n        result[i] = Math.sqrt(this.distance(dataset[i], bmu.weights));\n    }\n    return result;\n};\n\nfunction getConverters(fields) {\n    var l = fields.length,\n        normalizers = new Array(l),\n        denormalizers = new Array(l);\n    for (var i = 0; i < l; i++) {\n        normalizers[i] = getNormalizer(fields[i].range);\n        denormalizers[i] = getDenormalizer(fields[i].range);\n    }\n    return {\n        extractor: function extractor(value) {\n            var result = new Array(l);\n            for (var i = 0; i < l; i++) {\n                result[i] = normalizers[i](value[fields[i].name]);\n            }\n            return result;\n        },\n        creator: function creator(value) {\n            var result = {};\n            for (var i = 0; i < l; i++) {\n                result[fields[i].name] = denormalizers[i](value[i]);\n            }\n            return result;\n        }\n    };\n}\n\nfunction getNormalizer(minMax) {\n    return function normalizer(value) {\n        return (value - minMax[0]) / (minMax[1] - minMax[0]);\n    };\n}\n\nfunction getDenormalizer(minMax) {\n    return function denormalizer(value) {\n        return (minMax[0] + value * (minMax[1] - minMax[0]));\n    };\n}\n\nfunction squareEuclidean(a, b) {\n    var d = 0;\n    for (var i = 0, ii = a.length; i < ii; i++) {\n        d += (a[i] - b[i]) * (a[i] - b[i]);\n    }\n    return d;\n}\n\nfunction getRandomValue(arr, randomizer) {\n    return arr[Math.floor(randomizer() * arr.length)];\n}\n\nfunction getMaxDistance(distance, numWeights) {\n    var zero = new Array(numWeights),\n        one = new Array(numWeights);\n    for (var i = 0; i < numWeights; i++) {\n        zero[i] = 0;\n        one[i] = 1;\n    }\n    return distance(zero, one);\n}\n\nmodule.exports = SOM;","export default function maybeToPrecision(value, digits) {\n  if (value < 0) {\n    value = 0 - value;\n    if (typeof digits === 'number') {\n      return `- ${value.toPrecision(digits)}`;\n    } else {\n      return `- ${value.toString()}`;\n    }\n  } else {\n    if (typeof digits === 'number') {\n      return value.toPrecision(digits);\n    } else {\n      return value.toString();\n    }\n  }\n}\n","export default function checkArraySize(x, y) {\n  if (!Array.isArray(x) || !Array.isArray(y)) {\n    throw new TypeError('x and y must be arrays');\n  }\n  if (x.length !== y.length) {\n    throw new RangeError('x and y arrays must have the same length');\n  }\n}\n","export { default as maybeToPrecision } from './maybeToPrecision';\nexport { default as checkArrayLength } from './checkArrayLength';\n\nexport default class BaseRegression {\n  constructor() {\n    if (new.target === BaseRegression) {\n      throw new Error('BaseRegression must be subclassed');\n    }\n  }\n\n  predict(x) {\n    if (typeof x === 'number') {\n      return this._predict(x);\n    } else if (Array.isArray(x)) {\n      const y = [];\n      for (let i = 0; i < x.length; i++) {\n        y.push(this._predict(x[i]));\n      }\n      return y;\n    } else {\n      throw new TypeError('x must be a number or array');\n    }\n  }\n\n  _predict() {\n    throw new Error('_predict must be implemented');\n  }\n\n  train() {\n    // Do nothing for this package\n  }\n\n  toString() {\n    return '';\n  }\n\n  toLaTeX() {\n    return '';\n  }\n\n  /**\n   * Return the correlation coefficient of determination (r) and chi-square.\n   * @param {Array<number>} x\n   * @param {Array<number>} y\n   * @return {object}\n   */\n  score(x, y) {\n    if (!Array.isArray(x) || !Array.isArray(y) || x.length !== y.length) {\n      throw new Error('x and y must be arrays of the same length');\n    }\n\n    const n = x.length;\n    const y2 = new Array(n);\n    for (let i = 0; i < n; i++) {\n      y2[i] = this._predict(x[i]);\n    }\n\n    let xSum = 0;\n    let ySum = 0;\n    let chi2 = 0;\n    let rmsd = 0;\n    let xSquared = 0;\n    let ySquared = 0;\n    let xY = 0;\n    for (let i = 0; i < n; i++) {\n      xSum += y2[i];\n      ySum += y[i];\n      xSquared += y2[i] * y2[i];\n      ySquared += y[i] * y[i];\n      xY += y2[i] * y[i];\n      if (y[i] !== 0) {\n        chi2 += ((y[i] - y2[i]) * (y[i] - y2[i])) / y[i];\n      }\n      rmsd += (y[i] - y2[i]) * (y[i] - y2[i]);\n    }\n\n    const r =\n      (n * xY - xSum * ySum) /\n      Math.sqrt((n * xSquared - xSum * xSum) * (n * ySquared - ySum * ySum));\n\n    return {\n      r: r,\n      r2: r * r,\n      chi2: chi2,\n      rmsd: Math.sqrt(rmsd / n)\n    };\n  }\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { Matrix, MatrixTransposeView, solve } from 'ml-matrix';\n\nexport default class PolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y, degree);\n    }\n  }\n\n  _predict(x) {\n    let y = 0;\n    for (let k = 0; k < this.powers.length; k++) {\n      y += this.coefficients[k] * Math.pow(x, this.powers[k]);\n    }\n    return y;\n  }\n\n  toJSON() {\n    return {\n      name: 'polynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) + times}x`;\n          } else {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) +\n              times\n              }x${\n                sup\n              }${this.powers[k]\n              }${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'polynomialRegression') {\n      throw new TypeError('not a polynomial regression model');\n    }\n    return new PolynomialRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y, degree) {\n  const n = x.length;\n  let powers;\n  if (Array.isArray(degree)) {\n    powers = degree;\n    degree = powers.length;\n  } else {\n    degree++;\n    powers = new Array(degree);\n    for (let k = 0; k < degree; k++) {\n      powers[k] = k;\n    }\n  }\n  const F = new Matrix(n, degree);\n  const Y = new Matrix([y]);\n  for (let k = 0; k < degree; k++) {\n    for (let i = 0; i < n; i++) {\n      if (powers[k] === 0) {\n        F.set(i, k, 1);\n      } else {\n        F.set(i, k, Math.pow(x[i], powers[k]));\n      }\n    }\n  }\n\n  const FT = new MatrixTransposeView(F);\n  const A = FT.mmul(F);\n  const B = FT.mmul(new MatrixTransposeView(Y));\n\n  pr.degree = degree - 1;\n  pr.powers = powers;\n  pr.coefficients = solve(A, B).to1DArray();\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\n\nexport default class SimpleLinearRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = [y.intercept, y.slope];\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'simpleLinearRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(x) {\n    return this.slope * x + this.intercept;\n  }\n\n  computeX(y) {\n    return (y - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    let result = 'f(x) = ';\n    if (this.slope !== 0) {\n      const xFactor = maybeToPrecision(this.slope, precision);\n      result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;\n      if (this.intercept !== 0) {\n        const absIntercept = Math.abs(this.intercept);\n        const operator = absIntercept === this.intercept ? '+' : '-';\n        result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'simpleLinearRegression') {\n      throw new TypeError('not a SLR model');\n    }\n    return new SimpleLinearRegression(true, json);\n  }\n}\n\nfunction regress(slr, x, y) {\n  const n = x.length;\n  let xSum = 0;\n  let ySum = 0;\n\n  let xSquared = 0;\n  let xY = 0;\n\n  for (let i = 0; i < n; i++) {\n    xSum += x[i];\n    ySum += y[i];\n    xSquared += x[i] * x[i];\n    xY += x[i] * y[i];\n  }\n\n  const numerator = n * xY - xSum * ySum;\n  slr.slope = numerator / (n * xSquared - xSum * xSum);\n  slr.intercept = (1 / n) * ySum - slr.slope * (1 / n) * xSum;\n  slr.coefficients = [slr.intercept, slr.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class ExponentialRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(input) {\n    return this.B * Math.exp(input * this.A);\n  }\n\n  toJSON() {\n    return {\n      name: 'exponentialRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return (\n      `f(x) = ${\n        maybeToPrecision(this.B, precision)\n      } * e^(${\n        maybeToPrecision(this.A, precision)\n      } * x)`\n    );\n  }\n\n  toLaTeX(precision) {\n    if (this.A >= 0) {\n      return (\n        `f(x) = ${\n          maybeToPrecision(this.B, precision)\n        }e^{${\n          maybeToPrecision(this.A, precision)\n        }x}`\n      );\n    } else {\n      return (\n        `f(x) = \\\\frac{${\n          maybeToPrecision(this.B, precision)\n        }}{e^{${\n          maybeToPrecision(-this.A, precision)\n        }x}}`\n      );\n    }\n  }\n\n  static load(json) {\n    if (json.name !== 'exponentialRegression') {\n      throw new TypeError('not a exponential regression model');\n    }\n    return new ExponentialRegression(true, json);\n  }\n}\n\nfunction regress(er, x, y) {\n  const n = x.length;\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(x, yl);\n  er.A = linear.slope;\n  er.B = Math.exp(linear.intercept);\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class PowerRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // reloading model\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(newInputs) {\n    return this.A * Math.pow(newInputs, this.B);\n  }\n\n  toJSON() {\n    return {\n      name: 'powerRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return `f(x) = ${maybeToPrecision(\n      this.A,\n      precision\n    )} * x^${maybeToPrecision(this.B, precision)}`;\n  }\n\n  toLaTeX(precision) {\n    let latex = '';\n    if (this.B >= 0) {\n      latex = `f(x) = ${maybeToPrecision(\n        this.A,\n        precision\n      )}x^{${maybeToPrecision(this.B, precision)}}`;\n    } else {\n      latex = `f(x) = \\\\frac{${maybeToPrecision(\n        this.A,\n        precision\n      )}}{x^{${maybeToPrecision(-this.B, precision)}}}`;\n    }\n    latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');\n    return latex;\n  }\n\n  static load(json) {\n    if (json.name !== 'powerRegression') {\n      throw new TypeError('not a power regression model');\n    }\n    return new PowerRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y) {\n  const n = x.length;\n  const xl = new Array(n);\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    xl[i] = Math.log(x[i]);\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(xl, yl);\n  pr.A = Math.exp(linear.intercept);\n  pr.B = linear.slope;\n}\n","import Matrix, { SVD, pseudoInverse } from 'ml-matrix';\n\nexport default class MultivariateLinearRegression {\n  constructor(x, y, options = {}) {\n    const { intercept = true, statistics = true } = options;\n    this.statistics = statistics;\n    if (x === true) {\n      this.weights = y.weights;\n      this.inputs = y.inputs;\n      this.outputs = y.outputs;\n      this.intercept = y.intercept;\n    } else {\n      x = new Matrix(x);\n      y = new Matrix(y);\n      if (intercept) {\n        x.addColumn(new Array(x.rows).fill(1));\n      }\n      let xt = x.transpose();\n      const xx = xt\n        .mmul(x);\n      const xy = xt\n        .mmul(y);\n      const invxx = new SVD(xx)\n        .inverse();\n      const beta = xy\n        .transpose()\n        .mmul(invxx)\n        .transpose();\n      this.weights = beta.to2DArray();\n      this.inputs = x.columns;\n      this.outputs = y.columns;\n      if (intercept) this.inputs--;\n      this.intercept = intercept;\n      if (statistics) {\n        /*\n         * Let's add some basic statistics about the beta's to be able to interpret them.\n         * source: http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat401/Notes/401-multreg.pdf\n         * validated against Excel Regression AddIn\n         * test: \"datamining statistics test\"\n         */\n        const fittedValues = x.mmul(beta);\n        const residuals = y.clone().addM(fittedValues.neg());\n        const variance =\n          residuals\n            .to2DArray()\n            .map((ri) => Math.pow(ri[0], 2))\n            .reduce((a, b) => a + b) /\n          (y.rows - x.columns);\n        this.stdError = Math.sqrt(variance);\n        this.stdErrorMatrix = pseudoInverse(xx).mul(variance);\n        this.stdErrors = this.stdErrorMatrix\n          .diagonal()\n          .map((d) => Math.sqrt(d));\n        this.tStats = this.weights.map((d, i) =>\n          (this.stdErrors[i] === 0 ? 0 : d[0] / this.stdErrors[i])\n        );\n      }\n    }\n  }\n\n  predict(x) {\n    if (Array.isArray(x)) {\n      if (typeof x[0] === 'number') {\n        return this._predict(x);\n      } else if (Array.isArray(x[0])) {\n        const y = new Array(x.length);\n        for (let i = 0; i < x.length; i++) {\n          y[i] = this._predict(x[i]);\n        }\n        return y;\n      }\n    } else if (Matrix.isMatrix(x)) {\n      const y = new Matrix(x.rows, this.outputs);\n      for (let i = 0; i < x.rows; i++) {\n        y.setRow(i, this._predict(x.getRow(i)));\n      }\n      return y;\n    }\n    throw new TypeError('x must be a matrix or array of numbers');\n  }\n\n  _predict(x) {\n    const result = new Array(this.outputs);\n    if (this.intercept) {\n      for (let i = 0; i < this.outputs; i++) {\n        result[i] = this.weights[this.inputs][i];\n      }\n    } else {\n      result.fill(0);\n    }\n    for (let i = 0; i < this.inputs; i++) {\n      for (let j = 0; j < this.outputs; j++) {\n        result[j] += this.weights[i][j] * x[i];\n      }\n    }\n    return result;\n  }\n\n  score() {\n    throw new Error('score method is not implemented yet');\n  }\n\n  toJSON() {\n    return {\n      name: 'multivariateLinearRegression',\n      weights: this.weights,\n      inputs: this.inputs,\n      outputs: this.outputs,\n      intercept: this.intercept,\n      summary: this.statistics\n        ? {\n          regressionStatistics: {\n            standardError: this.stdError,\n            observations: this.outputs\n          },\n          variables: this.weights.map((d, i) => {\n            return {\n              label:\n                  i === this.weights.length - 1\n                    ? 'Intercept'\n                    : `X Variable ${i + 1}`,\n              coefficients: d,\n              standardError: this.stdErrors[i],\n              tStat: this.tStats[i]\n            };\n          })\n        }\n        : undefined\n    };\n  }\n\n  static load(model) {\n    if (model.name !== 'multivariateLinearRegression') {\n      throw new Error('not a MLR model');\n    }\n    return new MultivariateLinearRegression(true, model);\n  }\n}\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass GaussianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = GaussianKernel;\n","'use strict';\n\nconst defaultOptions = {\n  degree: 1,\n  constant: 1,\n  scale: 1\n};\n\nclass PolynomialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n\n    this.degree = options.degree;\n    this.constant = options.constant;\n    this.scale = options.scale;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.pow(this.scale * sum + this.constant, this.degree);\n  }\n}\n\nmodule.exports = PolynomialKernel;\n","'use strict';\n\nconst defaultOptions = {\n  alpha: 0.01,\n  constant: -Math.E\n};\n\nclass SigmoidKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.alpha = options.alpha;\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.tanh(this.alpha * sum + this.constant);\n  }\n}\n\nmodule.exports = SigmoidKernel;\n","'use strict';\n\nconst defaultOptions = {\n  sigma: 1,\n  degree: 1\n};\n\nclass ANOVAKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.degree = options.degree;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    var len = Math.min(x.length, y.length);\n    for (var i = 1; i <= len; ++i) {\n      sum += Math.pow(\n        Math.exp(\n          -this.sigma *\n            Math.pow(Math.pow(x[i - 1], i) - Math.pow(y[i - 1], i), 2)\n        ),\n        this.degree\n      );\n    }\n    return sum;\n  }\n}\n\nmodule.exports = ANOVAKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass CauchyKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    return 1 / (1 + squaredEuclidean(x, y) / (this.sigma * this.sigma));\n  }\n}\n\nmodule.exports = CauchyKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass ExponentialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = ExponentialKernel;\n","'use strict';\n\nclass HistogramIntersectionKernel {\n  compute(x, y) {\n    var min = Math.min(x.length, y.length);\n    var sum = 0;\n    for (var i = 0; i < min; ++i) {\n      sum += Math.min(x[i], y[i]);\n    }\n\n    return sum;\n  }\n}\n\nmodule.exports = HistogramIntersectionKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass LaplacianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.sigma);\n  }\n}\n\nmodule.exports = LaplacianKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass MultiquadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    return Math.sqrt(squaredEuclidean(x, y) + this.constant * this.constant);\n  }\n}\n\nmodule.exports = MultiquadraticKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass RationalQuadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return 1 - distance / (distance + this.constant);\n  }\n}\n\nmodule.exports = RationalQuadraticKernel;\n","'use strict';\n\nconst { Matrix, MatrixTransposeView } = require('ml-matrix');\nconst GaussianKernel = require('ml-kernel-gaussian');\nconst PolynomialKernel = require('ml-kernel-polynomial');\nconst SigmoidKernel = require('ml-kernel-sigmoid');\n\nconst ANOVAKernel = require('./kernels/anova-kernel');\nconst CauchyKernel = require('./kernels/cauchy-kernel');\nconst ExponentialKernel = require('./kernels/exponential-kernel');\nconst HistogramKernel = require('./kernels/histogram-intersection-kernel');\nconst LaplacianKernel = require('./kernels/laplacian-kernel');\nconst MultiquadraticKernel = require('./kernels/multiquadratic-kernel');\nconst RationalKernel = require('./kernels/rational-quadratic-kernel');\n\nconst kernelType = {\n  gaussian: GaussianKernel,\n  rbf: GaussianKernel,\n  polynomial: PolynomialKernel,\n  poly: PolynomialKernel,\n  anova: ANOVAKernel,\n  cauchy: CauchyKernel,\n  exponential: ExponentialKernel,\n  histogram: HistogramKernel,\n  min: HistogramKernel,\n  laplacian: LaplacianKernel,\n  multiquadratic: MultiquadraticKernel,\n  rational: RationalKernel,\n  sigmoid: SigmoidKernel,\n  mlp: SigmoidKernel\n};\n\nclass Kernel {\n  constructor(type, options) {\n    this.kernelType = type;\n    if (type === 'linear') return;\n\n    if (typeof type === 'string') {\n      type = type.toLowerCase();\n\n      var KernelConstructor = kernelType[type];\n      if (KernelConstructor) {\n        this.kernelFunction = new KernelConstructor(options);\n      } else {\n        throw new Error(`unsupported kernel type: ${type}`);\n      }\n    } else if (typeof type === 'object' && typeof type.compute === 'function') {\n      this.kernelFunction = type;\n    } else {\n      throw new TypeError(\n        'first argument must be a valid kernel type or instance'\n      );\n    }\n  }\n\n  compute(inputs, landmarks) {\n    inputs = Matrix.checkMatrix(inputs);\n    if (landmarks === undefined) {\n      landmarks = inputs;\n    } else {\n      landmarks = Matrix.checkMatrix(landmarks);\n    }\n    if (this.kernelType === 'linear') {\n      return inputs.mmul(new MatrixTransposeView(landmarks));\n    }\n\n    const kernelMatrix = new Matrix(inputs.rows, landmarks.rows);\n    if (inputs === landmarks) {\n      // fast path, matrix is symmetric\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = i; j < inputs.rows; j++) {\n          const value = this.kernelFunction.compute(\n            inputs.getRow(i),\n            inputs.getRow(j)\n          );\n          kernelMatrix.set(i, j, value);\n          kernelMatrix.set(j, i, value);\n        }\n      }\n    } else {\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = 0; j < landmarks.rows; j++) {\n          kernelMatrix.set(\n            i,\n            j,\n            this.kernelFunction.compute(inputs.getRow(i), landmarks.getRow(j))\n          );\n        }\n      }\n    }\n    return kernelMatrix;\n  }\n}\n\nmodule.exports = Kernel;\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport median from 'ml-array-median';\n\nexport default class TheilSenRegression extends BaseRegression {\n  /**\n   * Theil–Sen estimator\n   * https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator\n   * @param {Array<number>|boolean} x\n   * @param {Array<number>|object} y\n   * @constructor\n   */\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // loads the model\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = y.coefficients;\n    } else {\n      // creates the model\n      checkArrayLength(x, y);\n      theilSen(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'TheilSenRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(input) {\n    return this.slope * input + this.intercept;\n  }\n\n  computeX(input) {\n    return (input - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    var result = 'f(x) = ';\n    if (this.slope) {\n      var xFactor = maybeToPrecision(this.slope, precision);\n      result += `${Math.abs(xFactor - 1) < 1e-5 ? '' : `${xFactor} * `}x`;\n      if (this.intercept) {\n        var absIntercept = Math.abs(this.intercept);\n        var operator = absIntercept === this.intercept ? '+' : '-';\n        result +=\n          ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'TheilSenRegression') {\n      throw new TypeError('not a Theil-Sen model');\n    }\n    return new TheilSenRegression(true, json);\n  }\n}\n\nfunction theilSen(regression, x, y) {\n  let len = x.length;\n  let slopes = new Array(len * len);\n  let count = 0;\n  for (let i = 0; i < len; ++i) {\n    for (let j = i + 1; j < len; ++j) {\n      if (x[i] !== x[j]) {\n        slopes[count++] = (y[j] - y[i]) / (x[j] - x[i]);\n      }\n    }\n  }\n  slopes.length = count;\n  let medianSlope = median(slopes);\n\n  let cuts = new Array(len);\n  for (let i = 0; i < len; ++i) {\n    cuts[i] = y[i] - medianSlope * x[i];\n  }\n\n  regression.slope = medianSlope;\n  regression.intercept = median(cuts);\n  regression.coefficients = [regression.intercept, regression.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { solve } from 'ml-matrix';\n\n/**\n * @class RobustPolynomialRegression\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree - polynomial degree\n */\nexport default class RobustPolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      robustPolynomial(this, x, y, degree);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'robustPolynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  _predict(x) {\n    return predict(x, this.powers, this.coefficients);\n  }\n\n  /**\n   * Display the formula\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  /**\n   * Display the formula in LaTeX format\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x`;\n          } else {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x${sup}${this.powers[k]}${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'robustPolynomialRegression') {\n      throw new TypeError('not a RobustPolynomialRegression model');\n    }\n    return new RobustPolynomialRegression(true, json);\n  }\n}\n\nfunction robustPolynomial(regression, x, y, degree) {\n  let powers = Array(degree)\n    .fill(0)\n    .map((_, index) => index);\n\n  const tuples = getRandomTuples(x, y, degree);\n\n  var min;\n  for (var i = 0; i < tuples.length; i++) {\n    var tuple = tuples[i];\n    var coefficients = calcCoefficients(tuple, powers);\n\n    var residuals = x.slice();\n    for (var j = 0; j < x.length; j++) {\n      residuals[j] = y[j] - predict(x[j], powers, coefficients);\n      residuals[j] = {\n        residual: residuals[j] * residuals[j],\n        coefficients\n      };\n    }\n\n    var median = residualsMedian(residuals);\n    if (!min || median.residual < min.residual) {\n      min = median;\n    }\n  }\n\n  regression.degree = degree;\n  regression.powers = powers;\n  regression.coefficients = min.coefficients;\n}\n\n/**\n * @ignore\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree\n * @return {Array<{x:number,y:number}>}\n */\nfunction getRandomTuples(x, y, degree) {\n  var len = Math.floor(x.length / degree);\n  var tuples = new Array(len);\n\n  for (var i = 0; i < x.length; i++) {\n    var pos = Math.floor(Math.random() * len);\n\n    var counter = 0;\n    while (counter < x.length) {\n      if (!tuples[pos]) {\n        tuples[pos] = [\n          {\n            x: x[i],\n            y: y[i]\n          }\n        ];\n        break;\n      } else if (tuples[pos].length < degree) {\n        tuples[pos].push({\n          x: x[i],\n          y: y[i]\n        });\n        break;\n      } else {\n        counter++;\n        pos = (pos + 1) % len;\n      }\n    }\n\n    if (counter === x.length) {\n      return tuples;\n    }\n  }\n  return tuples;\n}\n\n/**\n * @ignore\n * @param {{x:number,y:number}} tuple\n * @param {Array<number>} powers\n * @return {Array<number>}\n */\nfunction calcCoefficients(tuple, powers) {\n  var X = tuple.slice();\n  var Y = tuple.slice();\n  for (var i = 0; i < X.length; i++) {\n    Y[i] = [tuple[i].y];\n    X[i] = new Array(powers.length);\n    for (var j = 0; j < powers.length; j++) {\n      X[i][j] = Math.pow(tuple[i].x, powers[j]);\n    }\n  }\n\n  return solve(X, Y).to1DArray();\n}\n\nfunction predict(x, powers, coefficients) {\n  let y = 0;\n  for (let k = 0; k < powers.length; k++) {\n    y += coefficients[k] * Math.pow(x, powers[k]);\n  }\n  return y;\n}\n\nfunction residualsMedian(residuals) {\n  residuals.sort((a, b) => a.residual - b.residual);\n\n  var l = residuals.length;\n  var half = Math.floor(l / 2);\n  return l % 2 === 0 ? residuals[half - 1] : residuals[half];\n}\n","/**\n * Calculate current error\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} parameters - Array of current parameter values\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {number}\n */\nexport default function errorCalculation(\n  data,\n  parameters,\n  parameterizedFunction\n) {\n  var error = 0;\n  const func = parameterizedFunction(parameters);\n\n  for (var i = 0; i < data.x.length; i++) {\n    error += Math.abs(data.y[i] - func(data.x[i]));\n  }\n\n  return error;\n}\n","import { inverse, Matrix } from 'ml-matrix';\n\n/**\n * Difference of the matrix function over the parameters\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Matrix}\n */\nfunction gradientFunction(\n  data,\n  evaluatedData,\n  params,\n  gradientDifference,\n  paramFunction\n) {\n  const n = params.length;\n  const m = data.x.length;\n\n  var ans = new Array(n);\n\n  for (var param = 0; param < n; param++) {\n    ans[param] = new Array(m);\n    var auxParams = params.concat();\n    auxParams[param] += gradientDifference;\n    var funcParam = paramFunction(auxParams);\n\n    for (var point = 0; point < m; point++) {\n      ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);\n    }\n  }\n  return new Matrix(ans);\n}\n\n/**\n * Matrix function over the samples\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @return {Matrix}\n */\nfunction matrixFunction(data, evaluatedData) {\n  const m = data.x.length;\n\n  var ans = new Array(m);\n\n  for (var point = 0; point < m; point++) {\n    ans[point] = [data.y[point] - evaluatedData[point]];\n  }\n\n  return new Matrix(ans);\n}\n\n/**\n * Iteration for Levenberg-Marquardt\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} damping - Levenberg-Marquardt parameter\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Array<number>}\n */\nexport default function step(\n  data,\n  params,\n  damping,\n  gradientDifference,\n  parameterizedFunction\n) {\n  var value = damping * gradientDifference * gradientDifference;\n  var identity = Matrix.eye(params.length, params.length, value);\n\n  const func = parameterizedFunction(params);\n  var evaluatedData = data.x.map((e) => func(e));\n\n  var gradientFunc = gradientFunction(\n    data,\n    evaluatedData,\n    params,\n    gradientDifference,\n    parameterizedFunction\n  );\n  var matrixFunc = matrixFunction(data, evaluatedData);\n  var inverseMatrix = inverse(\n    identity.add(gradientFunc.mmul(gradientFunc.transpose()))\n  );\n\n  params = new Matrix([params]);\n  params = params.sub(\n    inverseMatrix\n      .mmul(gradientFunc)\n      .mmul(matrixFunc)\n      .mul(gradientDifference)\n      .transpose()\n  );\n\n  return params.to1DArray();\n}\n","import errorCalculation from './errorCalculation';\nimport step from './step';\n\n/**\n * Curve fitting algorithm\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @param {object} [options] - Options object\n * @param {number} [options.damping] - Levenberg-Marquardt parameter\n * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter\n * @param {Array<number>} [options.minValues] - Minimum allowed values for parameters\n * @param {Array<number>} [options.maxValues] - Maximum allowed values for parameters\n * @param {Array<number>} [options.initialValues] - Array of initial parameter values\n * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations\n * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point\n * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}\n */\nexport default function levenbergMarquardt(\n  data,\n  parameterizedFunction,\n  options = {}\n) {\n  let {\n    maxIterations = 100,\n    gradientDifference = 10e-2,\n    damping = 0,\n    errorTolerance = 10e-3,\n    minValues,\n    maxValues,\n    initialValues\n  } = options;\n\n  if (damping <= 0) {\n    throw new Error('The damping option must be a positive number');\n  } else if (!data.x || !data.y) {\n    throw new Error('The data parameter must have x and y elements');\n  } else if (\n    !Array.isArray(data.x) ||\n    data.x.length < 2 ||\n    !Array.isArray(data.y) ||\n    data.y.length < 2\n  ) {\n    throw new Error(\n      'The data parameter elements must be an array with more than 2 points'\n    );\n  } else if (data.x.length !== data.y.length) {\n    throw new Error('The data parameter elements must have the same size');\n  }\n\n  var parameters =\n    initialValues || new Array(parameterizedFunction.length).fill(1);\n  let parLen = parameters.length;\n  maxValues = maxValues || new Array(parLen).fill(Number.MAX_SAFE_INTEGER);\n  minValues = minValues || new Array(parLen).fill(Number.MIN_SAFE_INTEGER);\n\n  if (maxValues.length !== minValues.length) {\n    throw new Error('minValues and maxValues must be the same size');\n  }\n\n  if (!Array.isArray(parameters)) {\n    throw new Error('initialValues must be an array');\n  }\n\n  var error = errorCalculation(data, parameters, parameterizedFunction);\n\n  var converged = error <= errorTolerance;\n\n  for (\n    var iteration = 0;\n    iteration < maxIterations && !converged;\n    iteration++\n  ) {\n    parameters = step(\n      data,\n      parameters,\n      damping,\n      gradientDifference,\n      parameterizedFunction\n    );\n\n    for (let k = 0; k < parLen; k++) {\n      parameters[k] = Math.min(\n        Math.max(minValues[k], parameters[k]),\n        maxValues[k]\n      );\n    }\n\n    error = errorCalculation(data, parameters, parameterizedFunction);\n    if (isNaN(error)) break;\n    converged = error <= errorTolerance;\n  }\n\n  return {\n    parameterValues: parameters,\n    parameterError: error,\n    iterations: iteration\n  };\n}\n","/**\n * Returns a new array based on extraction of specific indices of an array\n * @private\n * @param {Array} vector\n * @param {Array} indices\n */\nexport default function selection(vector, indices) {\n  let u = []; //new Float64Array(indices.length);\n  for (let i = 0; i < indices.length; i++) {\n    u[i] = vector[indices[i]];\n  }\n  return u;\n}\n","/**\n *\n * @private\n * @param {Array of arrays} collection\n */\nexport default function sortCollectionSet(collection) {\n  let objectCollection = collection\n    .map((value, index) => {\n      let key = BigInt(0);\n      value.forEach((item) => (key |= BigInt(1) << BigInt(item)));\n      return { value, index, key };\n    })\n    .sort((a, b) => {\n      if (a.key - b.key < 0) return -1;\n      return 1;\n    });\n\n  let sorted = [];\n  let indices = [];\n\n  let key;\n  for (let set of objectCollection) {\n    if (set.key !== key) {\n      key = set.key;\n      indices.push([]);\n      sorted.push(set.value);\n    }\n    indices[indices.length - 1].push(set.index);\n  }\n\n  let result = {\n    values: sorted,\n    indices: indices,\n  };\n  return result;\n}\n","import {\n  Matrix,\n  LuDecomposition,\n  solve,\n  CholeskyDecomposition,\n} from 'ml-matrix';\n\nimport sortCollectionSet from './util/sortCollectionSet';\n\n/**\n * (Combinatorial Subspace Least Squares) - subfunction for the FC-NNLS\n * @private\n * @param {Matrix} XtX\n * @param {Matrix} XtY\n * @param {Array} Pset\n * @param {Numbers} l\n * @param {Numbers} p\n */\nexport default function cssls(XtX, XtY, Pset, l, p) {\n  // Solves the set of equation XtX*K = XtY for the variables in Pset\n  // if XtX (or XtX(vars,vars)) is singular, performs the svd and find pseudoinverse, otherwise (even if ill-conditioned) finds inverse with LU decomposition and solves the set of equation\n  // it is consistent with matlab results for ill-conditioned matrices (at least consistent with test 'ill-conditionned square X rank 2, Y 3x1' in cssls.test)\n\n  let K = Matrix.zeros(l, p);\n  if (Pset === null) {\n    let choXtX = new CholeskyDecomposition(XtX);\n    if (choXtX.isPositiveDefinite() === true) {\n      K = choXtX.solve(XtY);\n    } else {\n      let luXtX = new LuDecomposition(XtX);\n      if (luXtX.isSingular() === false) {\n        K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n      } else {\n        K = solve(XtX, XtY, { useSVD: true });\n      }\n    }\n  } else {\n    let sortedPset = sortCollectionSet(Pset).values;\n    let sortedEset = sortCollectionSet(Pset).indices;\n    if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === 0 &&\n      sortedEset[0].length === p\n    ) {\n      return K;\n    } else if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === l &&\n      sortedEset[0].length === p\n    ) {\n      let choXtX = new CholeskyDecomposition(XtX);\n      if (choXtX.isPositiveDefinite() === true) {\n        K = choXtX.solve(XtY);\n      } else {\n        let luXtX = new LuDecomposition(XtX);\n        if (luXtX.isSingular() === false) {\n          K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n        } else {\n          K = solve(XtX, XtY, { useSVD: true });\n        }\n      }\n    } else {\n      for (let k = 0; k < sortedPset.length; k++) {\n        let cols2Solve = sortedEset[k];\n        let vars = sortedPset[k];\n        let L;\n        let choXtX = new CholeskyDecomposition(XtX.selection(vars, vars));\n        if (choXtX.isPositiveDefinite() === true) {\n          L = choXtX.solve(XtY.selection(vars, cols2Solve));\n        } else {\n          let luXtX = new LuDecomposition(XtX.selection(vars, vars));\n          if (luXtX.isSingular() === false) {\n            L = luXtX\n              .solve(Matrix.eye(vars.length))\n              .mmul(XtY.selection(vars, cols2Solve));\n          } else {\n            L = solve(\n              XtX.selection(vars, vars),\n              XtY.selection(vars, cols2Solve),\n              { useSVD: true },\n            );\n          }\n        }\n        for (let i = 0; i < L.rows; i++) {\n          for (let j = 0; j < L.columns; j++) {\n            K.set(vars[i], cols2Solve[j], L.get(i, j));\n          }\n        }\n      }\n    }\n  }\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport cssls from './cssls';\n\nexport default function initialisation(X, Y) {\n  let n = X.rows;\n  let l = X.columns;\n  let p = Y.columns;\n  let iter = 0;\n\n  if (Y.rows !== n) throw new Error('ERROR: matrix size not compatible');\n\n  let W = Matrix.zeros(l, p);\n\n  // precomputes part of pseudoinverse\n  let XtX = X.transpose().mmul(X);\n  let XtY = X.transpose().mmul(Y);\n\n  let K = cssls(XtX, XtY, null, l, p); // K is lxp\n  let Pset = [];\n  for (let j = 0; j < p; j++) {\n    Pset[j] = [];\n    for (let i = 0; i < l; i++) {\n      if (K.get(i, j) > 0) {\n        Pset[j].push(i);\n      } else {\n        K.set(i, j, 0);\n      } //This is our initial solution, it's the solution found by overwriting the unconstrained least square solution\n    }\n  }\n  let Fset = [];\n  for (let j = 0; j < p; j++) {\n    if (Pset[j].length !== l) {\n      Fset.push(j);\n    }\n  }\n\n  let D = K.clone();\n\n  return { n, l, p, iter, W, XtX, XtY, K, Pset, Fset, D };\n}\n","/**\n * Computes the set difference A\\B\n * @private\n * @param {A} set A as an array\n * @param {B} set B as an array\n */\nexport default function setDifference(A, B) {\n  let C = [];\n  for (let i of A) {\n    if (!B.includes(i)) C.push(i);\n  }\n  return C;\n}\n","import setDifference from './util/setDifference';\n\n// Makes sure the solution has converged\nexport default function optimality(\n  iter,\n  maxIter,\n  XtX,\n  XtY,\n  Fset,\n  Pset,\n  W,\n  K,\n  l,\n  p,\n  D,\n) {\n  if (iter === maxIter) {\n    throw new Error('Maximum number of iterations exceeded');\n  }\n\n  // Check solution for optimality\n  let V = XtY.subMatrixColumn(Fset).subtract(XtX.mmul(K.subMatrixColumn(Fset)));\n  for (let j = 0; j < Fset.length; j++) {\n    W.setColumn(Fset[j], V.subMatrixColumn([j]));\n  }\n  let Jset = [];\n  let fullSet = [];\n  for (let i = 0; i < l; i++) {\n    fullSet.push(i);\n  }\n  for (let j = 0; j < Fset.length; j++) {\n    let notPset = setDifference(fullSet, Pset[Fset[j]]);\n    if (notPset.length === 0) {\n      Jset.push(Fset[j]);\n    } else if (W.selection(notPset, [Fset[j]]).max() <= 0) {\n      Jset.push(Fset[j]);\n    }\n  }\n  Fset = setDifference(Fset, Jset);\n\n  // For non-optimal solutions, add the appropriate variables to Pset\n  if (Fset.length !== 0) {\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (Pset[Fset[j]].includes(i)) W.set(i, Fset[j], -Infinity);\n      }\n      Pset[Fset[j]].push(W.subMatrixColumn(Fset).maxColumnIndex(j)[0]);\n    }\n    for (let j = 0; j < Fset.length; j++) {\n      D.setColumn(Fset[j], K.getColumn(Fset[j]));\n    }\n  }\n  for (let j = 0; j < p; j++) {\n    Pset[j].sort((a, b) => a - b);\n  }\n  return { Pset, Fset, W };\n}\n","import { Matrix } from 'ml-matrix';\n\nimport selection from './util/selection';\nimport cssls from './cssls';\nimport initialisation from './initialisation';\nimport optimality from './optimality';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with multiple Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {Matrix|number[][]} Y\n * @param {object} [options={}]\n * @param {number} [options.maxIterations] if empty maxIterations is set at 3 times the number of columns of X\n * @returns {Matrix} K\n */\nexport default function fcnnls(X, Y, options = {}) {\n  X = Matrix.checkMatrix(X);\n  Y = Matrix.checkMatrix(Y);\n  let { l, p, iter, W, XtX, XtY, K, Pset, Fset, D } = initialisation(X, Y);\n  const { maxIterations = X.columns * 3 } = options;\n\n  // Active set algorithm for NNLS main loop\n  while (Fset.length > 0) {\n    // Solves for the passive variables (uses subroutine below)\n    let L = cssls(\n      XtX,\n      XtY.subMatrixColumn(Fset),\n      selection(Pset, Fset),\n      l,\n      Fset.length,\n    );\n    for (let i = 0; i < l; i++) {\n      for (let j = 0; j < Fset.length; j++) {\n        K.set(i, Fset[j], L.get(i, j));\n      }\n    }\n\n    // Finds any infeasible solutions\n    let infeasIndex = [];\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (L.get(i, j) < 0) {\n          infeasIndex.push(j);\n          break;\n        }\n      }\n    }\n    let Hset = selection(Fset, infeasIndex);\n\n    // Makes infeasible solutions feasible (standard NNLS inner loop)\n    if (Hset.length > 0) {\n      let m = Hset.length;\n      let alpha = Matrix.ones(l, m);\n\n      while (m > 0 && iter < maxIterations) {\n        iter++;\n\n        alpha.mul(Infinity);\n\n        // Finds indices of negative variables in passive set\n        let hRowColIdx = [[], []]; // Indexes work in pairs, each pair reprensents a single element, first array is row index, second array is column index\n        let negRowColIdx = [[], []]; // Same as before\n        for (let j = 0; j < m; j++) {\n          for (let i = 0; i < Pset[Hset[j]].length; i++) {\n            if (K.get(Pset[Hset[j]][i], Hset[j]) < 0) {\n              hRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              hRowColIdx[1].push(j);\n              negRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              negRowColIdx[1].push(Hset[j]);\n            } // Compared to matlab, here we keep the row/column indexing (we are not taking the linear indexing)\n          }\n        }\n\n        for (let k = 0; k < hRowColIdx[0].length; k++) {\n          // could be hRowColIdx[1].length as well\n          alpha.set(\n            hRowColIdx[0][k],\n            hRowColIdx[1][k],\n            D.get(negRowColIdx[0][k], negRowColIdx[1][k]) /\n              (D.get(negRowColIdx[0][k], negRowColIdx[1][k]) -\n                K.get(negRowColIdx[0][k], negRowColIdx[1][k])),\n          );\n        }\n\n        let alphaMin = [];\n        let minIdx = [];\n        for (let j = 0; j < m; j++) {\n          alphaMin[j] = alpha.minColumn(j);\n          minIdx[j] = alpha.minColumnIndex(j)[0];\n        }\n\n        alphaMin = Matrix.rowVector(alphaMin);\n        for (let i = 0; i < l; i++) {\n          alpha.setSubMatrix(alphaMin, i, 0);\n        }\n\n        let E = new Matrix(l, m);\n        E = D.subMatrixColumn(Hset).subtract(\n          alpha\n            .subMatrix(0, l - 1, 0, m - 1)\n            .mul(D.subMatrixColumn(Hset).subtract(K.subMatrixColumn(Hset))),\n        );\n        for (let j = 0; j < m; j++) {\n          D.setColumn(Hset[j], E.subMatrixColumn([j]));\n        }\n\n        let idx2zero = [minIdx, Hset];\n        for (let k = 0; k < m; k++) {\n          D.set(idx2zero[0][k], idx2zero[1][k], 0);\n        }\n\n        for (let j = 0; j < m; j++) {\n          Pset[Hset[j]].splice(\n            Pset[Hset[j]].findIndex((item) => item === minIdx[j]),\n            1,\n          );\n        }\n\n        L = cssls(XtX, XtY.subMatrixColumn(Hset), selection(Pset, Hset), l, m);\n        for (let j = 0; j < m; j++) {\n          K.setColumn(Hset[j], L.subMatrixColumn([j]));\n        }\n\n        Hset = [];\n        for (let j = 0; j < K.columns; j++) {\n          for (let i = 0; i < l; i++) {\n            if (K.get(i, j) < 0) {\n              Hset.push(j);\n\n              break;\n            }\n          }\n        }\n        m = Hset.length;\n      }\n    }\n\n    let newParam = optimality(\n      iter,\n      maxIterations,\n      XtX,\n      XtY,\n      Fset,\n      Pset,\n      W,\n      K,\n      l,\n      p,\n      D,\n    );\n    Pset = newParam.Pset;\n    Fset = newParam.Fset;\n    W = newParam.W;\n  }\n\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport fcnnls from './fcnnls';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with single Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {number[]} y\n * @param {object} [options={}]\n * @param {boolean} [maxIterations] if true or empty maxIterations is set at 3 times the number of columns of X\n * @returns {Array} k\n */\nexport default function fcnnlsVector(X, y, options = {}) {\n  if (Array.isArray(y) === false) {\n    throw new TypeError('y must be a 1D Array');\n  }\n  let Y = Matrix.columnVector(y);\n  let K = fcnnls(X, Y, options);\n  let k = K.to1DArray();\n  return k;\n}\n","module.exports = function(haystack, needle, comparator, low, high) {\n  var mid, cmp;\n\n  if(low === undefined)\n    low = 0;\n\n  else {\n    low = low|0;\n    if(low < 0 || low >= haystack.length)\n      throw new RangeError(\"invalid lower bound\");\n  }\n\n  if(high === undefined)\n    high = haystack.length - 1;\n\n  else {\n    high = high|0;\n    if(high < low || high >= haystack.length)\n      throw new RangeError(\"invalid upper bound\");\n  }\n\n  while(low <= high) {\n    // The naive `low + high >>> 1` could fail for array lengths > 2**31\n    // because `>>>` converts its operands to int32. `low + (high - low >>> 1)`\n    // works for array lengths <= 2**32-1 which is also Javascript's max array\n    // length.\n    mid = low + ((high - low) >>> 1);\n    cmp = +comparator(haystack[mid], needle, mid, haystack);\n\n    // Too low.\n    if(cmp < 0.0)\n      low  = mid + 1;\n\n    // Too high.\n    else if(cmp > 0.0)\n      high = mid - 1;\n\n    // Key found.\n    else\n      return mid;\n  }\n\n  // Key not found.\n  return ~low;\n}\n","'use strict';\n\nfunction assertNumber(number) {\n\tif (typeof number !== 'number' || Number.isNaN(number)) {\n\t\tthrow new TypeError('Expected a number');\n\t}\n}\n\nexports.ascending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\treturn left - right;\n};\n\nexports.descending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\treturn right - left;\n};\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\nexport const largestPrime = 0x7fffffff;\n\nconst primeNumbers = [\n  // chunk #0\n  largestPrime, // 2^31-1\n\n  // chunk #1\n  5,\n  11,\n  23,\n  47,\n  97,\n  197,\n  397,\n  797,\n  1597,\n  3203,\n  6421,\n  12853,\n  25717,\n  51437,\n  102877,\n  205759,\n  411527,\n  823117,\n  1646237,\n  3292489,\n  6584983,\n  13169977,\n  26339969,\n  52679969,\n  105359939,\n  210719881,\n  421439783,\n  842879579,\n  1685759167,\n\n  // chunk #2\n  433,\n  877,\n  1759,\n  3527,\n  7057,\n  14143,\n  28289,\n  56591,\n  113189,\n  226379,\n  452759,\n  905551,\n  1811107,\n  3622219,\n  7244441,\n  14488931,\n  28977863,\n  57955739,\n  115911563,\n  231823147,\n  463646329,\n  927292699,\n  1854585413,\n\n  // chunk #3\n  953,\n  1907,\n  3821,\n  7643,\n  15287,\n  30577,\n  61169,\n  122347,\n  244703,\n  489407,\n  978821,\n  1957651,\n  3915341,\n  7830701,\n  15661423,\n  31322867,\n  62645741,\n  125291483,\n  250582987,\n  501165979,\n  1002331963,\n  2004663929,\n\n  // chunk #4\n  1039,\n  2081,\n  4177,\n  8363,\n  16729,\n  33461,\n  66923,\n  133853,\n  267713,\n  535481,\n  1070981,\n  2141977,\n  4283963,\n  8567929,\n  17135863,\n  34271747,\n  68543509,\n  137087021,\n  274174111,\n  548348231,\n  1096696463,\n\n  // chunk #5\n  31,\n  67,\n  137,\n  277,\n  557,\n  1117,\n  2237,\n  4481,\n  8963,\n  17929,\n  35863,\n  71741,\n  143483,\n  286973,\n  573953,\n  1147921,\n  2295859,\n  4591721,\n  9183457,\n  18366923,\n  36733847,\n  73467739,\n  146935499,\n  293871013,\n  587742049,\n  1175484103,\n\n  // chunk #6\n  599,\n  1201,\n  2411,\n  4831,\n  9677,\n  19373,\n  38747,\n  77509,\n  155027,\n  310081,\n  620171,\n  1240361,\n  2480729,\n  4961459,\n  9922933,\n  19845871,\n  39691759,\n  79383533,\n  158767069,\n  317534141,\n  635068283,\n  1270136683,\n\n  // chunk #7\n  311,\n  631,\n  1277,\n  2557,\n  5119,\n  10243,\n  20507,\n  41017,\n  82037,\n  164089,\n  328213,\n  656429,\n  1312867,\n  2625761,\n  5251529,\n  10503061,\n  21006137,\n  42012281,\n  84024581,\n  168049163,\n  336098327,\n  672196673,\n  1344393353,\n\n  // chunk #8\n  3,\n  7,\n  17,\n  37,\n  79,\n  163,\n  331,\n  673,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #9\n  43,\n  89,\n  179,\n  359,\n  719,\n  1439,\n  2879,\n  5779,\n  11579,\n  23159,\n  46327,\n  92657,\n  185323,\n  370661,\n  741337,\n  1482707,\n  2965421,\n  5930887,\n  11861791,\n  23723597,\n  47447201,\n  94894427,\n  189788857,\n  379577741,\n  759155483,\n  1518310967,\n\n  // chunk #10\n  379,\n  761,\n  1523,\n  3049,\n  6101,\n  12203,\n  24407,\n  48817,\n  97649,\n  195311,\n  390647,\n  781301,\n  1562611,\n  3125257,\n  6250537,\n  12501169,\n  25002389,\n  50004791,\n  100009607,\n  200019221,\n  400038451,\n  800076929,\n  1600153859,\n\n  // chunk #11\n  13,\n  29,\n  59,\n  127,\n  257,\n  521,\n  1049,\n  2099,\n  4201,\n  8419,\n  16843,\n  33703,\n  67409,\n  134837,\n  269683,\n  539389,\n  1078787,\n  2157587,\n  4315183,\n  8630387,\n  17260781,\n  34521589,\n  69043189,\n  138086407,\n  276172823,\n  552345671,\n  1104691373,\n\n  // chunk #12\n  19,\n  41,\n  83,\n  167,\n  337,\n  677,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #13\n  53,\n  107,\n  223,\n  449,\n  907,\n  1823,\n  3659,\n  7321,\n  14653,\n  29311,\n  58631,\n  117269,\n  234539,\n  469099,\n  938207,\n  1876417,\n  3752839,\n  7505681,\n  15011389,\n  30022781,\n  60045577,\n  120091177,\n  240182359,\n  480364727,\n  960729461,\n  1921458943\n];\n\nprimeNumbers.sort(ascending);\n\nexport function nextPrime(value) {\n  let index = binarySearch(primeNumbers, value, ascending);\n  if (index < 0) {\n    index = ~index;\n  }\n  return primeNumbers[index];\n}\n","import { largestPrime, nextPrime } from './primeFinder';\n\nconst FREE = 0;\nconst FULL = 1;\nconst REMOVED = 2;\n\nconst defaultInitialCapacity = 150;\nconst defaultMinLoadFactor = 1 / 6;\nconst defaultMaxLoadFactor = 2 / 3;\n\nexport default class HashTable {\n  constructor(options = {}) {\n    if (options instanceof HashTable) {\n      this.table = options.table.slice();\n      this.values = options.values.slice();\n      this.state = options.state.slice();\n      this.minLoadFactor = options.minLoadFactor;\n      this.maxLoadFactor = options.maxLoadFactor;\n      this.distinct = options.distinct;\n      this.freeEntries = options.freeEntries;\n      this.lowWaterMark = options.lowWaterMark;\n      this.highWaterMark = options.maxLoadFactor;\n      return;\n    }\n\n    const initialCapacity =\n      options.initialCapacity === undefined\n        ? defaultInitialCapacity\n        : options.initialCapacity;\n    if (initialCapacity < 0) {\n      throw new RangeError(\n        `initial capacity must not be less than zero: ${initialCapacity}`\n      );\n    }\n\n    const minLoadFactor =\n      options.minLoadFactor === undefined\n        ? defaultMinLoadFactor\n        : options.minLoadFactor;\n    const maxLoadFactor =\n      options.maxLoadFactor === undefined\n        ? defaultMaxLoadFactor\n        : options.maxLoadFactor;\n    if (minLoadFactor < 0 || minLoadFactor >= 1) {\n      throw new RangeError(`invalid minLoadFactor: ${minLoadFactor}`);\n    }\n    if (maxLoadFactor <= 0 || maxLoadFactor >= 1) {\n      throw new RangeError(`invalid maxLoadFactor: ${maxLoadFactor}`);\n    }\n    if (minLoadFactor >= maxLoadFactor) {\n      throw new RangeError(\n        `minLoadFactor (${minLoadFactor}) must be smaller than maxLoadFactor (${maxLoadFactor})`\n      );\n    }\n\n    let capacity = initialCapacity;\n    // User wants to put at least capacity elements. We need to choose the size based on the maxLoadFactor to\n    // avoid the need to rehash before this capacity is reached.\n    // actualCapacity * maxLoadFactor >= capacity\n    capacity = (capacity / maxLoadFactor) | 0;\n    capacity = nextPrime(capacity);\n    if (capacity === 0) capacity = 1;\n\n    this.table = newArray(capacity);\n    this.values = newArray(capacity);\n    this.state = newArray(capacity);\n\n    this.minLoadFactor = minLoadFactor;\n    if (capacity === largestPrime) {\n      this.maxLoadFactor = 1;\n    } else {\n      this.maxLoadFactor = maxLoadFactor;\n    }\n\n    this.distinct = 0;\n    this.freeEntries = capacity;\n\n    this.lowWaterMark = 0;\n    this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);\n  }\n\n  clone() {\n    return new HashTable(this);\n  }\n\n  get size() {\n    return this.distinct;\n  }\n\n  get(key) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return 0;\n    return this.values[i];\n  }\n\n  set(key, value) {\n    let i = this.indexOfInsertion(key);\n    if (i < 0) {\n      i = -i - 1;\n      this.values[i] = value;\n      return false;\n    }\n\n    if (this.distinct > this.highWaterMark) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n      return this.set(key, value);\n    }\n\n    this.table[i] = key;\n    this.values[i] = value;\n    if (this.state[i] === FREE) this.freeEntries--;\n    this.state[i] = FULL;\n    this.distinct++;\n\n    if (this.freeEntries < 1) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n\n    return true;\n  }\n\n  remove(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = REMOVED;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  delete(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = FREE;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  maybeShrinkCapacity() {\n    if (this.distinct < this.lowWaterMark) {\n      const newCapacity = chooseShrinkCapacity(\n        this.distinct,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n  }\n\n  containsKey(key) {\n    return this.indexOfKey(key) >= 0;\n  }\n\n  indexOfKey(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = this.table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === FREE) return -1;\n    return i;\n  }\n\n  containsValue(value) {\n    return this.indexOfValue(value) >= 0;\n  }\n\n  indexOfValue(value) {\n    const values = this.values;\n    const state = this.state;\n\n    for (var i = 0; i < state.length; i++) {\n      if (state[i] === FULL && values[i] === value) {\n        return i;\n      }\n    }\n\n    return -1;\n  }\n\n  indexOfInsertion(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] === FULL && table[i] !== key) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === REMOVED) {\n      const j = i;\n      while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n        i -= decrement;\n        if (i < 0) i += length;\n      }\n      if (state[i] === FREE) i = j;\n    }\n\n    if (state[i] === FULL) {\n      return -i - 1;\n    }\n\n    return i;\n  }\n\n  ensureCapacity(minCapacity) {\n    if (this.table.length < minCapacity) {\n      const newCapacity = nextPrime(minCapacity);\n      this.rehash(newCapacity);\n    }\n  }\n\n  rehash(newCapacity) {\n    const oldCapacity = this.table.length;\n\n    if (newCapacity <= this.distinct) throw new Error('Unexpected');\n\n    const oldTable = this.table;\n    const oldValues = this.values;\n    const oldState = this.state;\n\n    const newTable = newArray(newCapacity);\n    const newValues = newArray(newCapacity);\n    const newState = newArray(newCapacity);\n\n    this.lowWaterMark = chooseLowWaterMark(newCapacity, this.minLoadFactor);\n    this.highWaterMark = chooseHighWaterMark(newCapacity, this.maxLoadFactor);\n\n    this.table = newTable;\n    this.values = newValues;\n    this.state = newState;\n    this.freeEntries = newCapacity - this.distinct;\n\n    for (var i = 0; i < oldCapacity; i++) {\n      if (oldState[i] === FULL) {\n        var element = oldTable[i];\n        var index = this.indexOfInsertion(element);\n        newTable[index] = element;\n        newValues[index] = oldValues[i];\n        newState[index] = FULL;\n      }\n    }\n  }\n\n  forEachKey(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachValue(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachPair(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i], this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n}\n\nfunction chooseLowWaterMark(capacity, minLoad) {\n  return (capacity * minLoad) | 0;\n}\n\nfunction chooseHighWaterMark(capacity, maxLoad) {\n  return Math.min(capacity - 2, (capacity * maxLoad) | 0);\n}\n\nfunction chooseGrowCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (3 * minLoad + maxLoad)) | 0)\n  );\n}\n\nfunction chooseShrinkCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (minLoad + 3 * maxLoad)) | 0)\n  );\n}\n\nfunction newArray(size) {\n  return Array(size).fill(0);\n}\n","import HashTable from 'ml-hash-table';\n\nexport class SparseMatrix {\n  constructor(rows, columns, options = {}) {\n    if (rows instanceof SparseMatrix) {\n      // clone\n      const other = rows;\n      this._init(\n        other.rows,\n        other.columns,\n        other.elements.clone(),\n        other.threshold\n      );\n      return;\n    }\n\n    if (Array.isArray(rows)) {\n      const matrix = rows;\n      rows = matrix.length;\n      options = columns || {};\n      columns = matrix[0].length;\n      this._init(rows, columns, new HashTable(options), options.threshold);\n      for (var i = 0; i < rows; i++) {\n        for (var j = 0; j < columns; j++) {\n          var value = matrix[i][j];\n          if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n          if (value !== 0) {\n            this.elements.set(i * columns + j, matrix[i][j]);\n          }\n        }\n      }\n    } else {\n      this._init(rows, columns, new HashTable(options), options.threshold);\n    }\n  }\n\n  _init(rows, columns, elements, threshold) {\n    this.rows = rows;\n    this.columns = columns;\n    this.elements = elements;\n    this.threshold = threshold || 0;\n  }\n\n  static eye(rows = 1, columns = rows) {\n    const min = Math.min(rows, columns);\n    const matrix = new SparseMatrix(rows, columns, { initialCapacity: min });\n    for (var i = 0; i < min; i++) {\n      matrix.set(i, i, 1);\n    }\n    return matrix;\n  }\n\n  clone() {\n    return new SparseMatrix(this);\n  }\n\n  to2DArray() {\n    const copy = new Array(this.rows);\n    for (var i = 0; i < this.rows; i++) {\n      copy[i] = new Array(this.columns);\n      for (var j = 0; j < this.columns; j++) {\n        copy[i][j] = this.get(i, j);\n      }\n    }\n    return copy;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (!this.isSquare()) return false;\n\n    var symmetric = true;\n    this.forEachNonZero((i, j, v) => {\n      if (this.get(j, i) !== v) {\n        symmetric = false;\n        return false;\n      }\n      return v;\n    });\n    return symmetric;\n  }\n\n  /**\n   * Search for the wither band in the main diagonals\n   * @return {number}\n   */\n  bandWidth() {\n    let min = this.columns;\n    let max = -1;\n    this.forEachNonZero((i, j, v) => {\n      let diff = i - j;\n      min = Math.min(min, diff);\n      max = Math.max(max, diff);\n      return v;\n    });\n    return max - min;\n  }\n\n  /**\n   * Test if a matrix is consider banded using a threshold\n   * @param {number} width\n   * @return {boolean}\n   */\n  isBanded(width) {\n    let bandWidth = this.bandWidth();\n    return bandWidth <= width;\n  }\n\n  get cardinality() {\n    return this.elements.size;\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  get(row, column) {\n    return this.elements.get(row * this.columns + column);\n  }\n\n  set(row, column, value) {\n    if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n    if (value === 0) {\n      this.elements.remove(row * this.columns + column);\n    } else {\n      this.elements.set(row * this.columns + column, value);\n    }\n    return this;\n  }\n\n  mmul(other) {\n    if (this.columns !== other.rows) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        'Number of columns of left matrix are not equal to number of rows of right matrix.'\n      );\n    }\n\n    const m = this.rows;\n    const p = other.columns;\n\n    const result = new SparseMatrix(m, p);\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        if (j === k) {\n          result.set(i, l, result.get(i, l) + v1 * v2);\n        }\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  kroneckerProduct(other) {\n    const m = this.rows;\n    const n = this.columns;\n    const p = other.rows;\n    const q = other.columns;\n\n    const result = new SparseMatrix(m * p, n * q, {\n      initialCapacity: this.cardinality * other.cardinality\n    });\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        result.set(p * i + k, q * j + l, v1 * v2);\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  forEachNonZero(callback) {\n    this.elements.forEachPair((key, value) => {\n      const i = (key / this.columns) | 0;\n      const j = key % this.columns;\n      let r = callback(i, j, value);\n      if (r === false) return false; // stop iteration\n      if (this.threshold && Math.abs(r) < this.threshold) r = 0;\n      if (r !== value) {\n        if (r === 0) {\n          this.elements.remove(key, true);\n        } else {\n          this.elements.set(key, r);\n        }\n      }\n      return true;\n    });\n    this.elements.maybeShrinkCapacity();\n    return this;\n  }\n\n  getNonZeros() {\n    const cardinality = this.cardinality;\n    const rows = new Array(cardinality);\n    const columns = new Array(cardinality);\n    const values = new Array(cardinality);\n    var idx = 0;\n    this.forEachNonZero((i, j, value) => {\n      rows[idx] = i;\n      columns[idx] = j;\n      values[idx] = value;\n      idx++;\n      return value;\n    });\n    return { rows, columns, values };\n  }\n\n  setThreshold(newThreshold) {\n    if (newThreshold !== 0 && newThreshold !== this.threshold) {\n      this.threshold = newThreshold;\n      this.forEachNonZero((i, j, v) => v);\n    }\n    return this;\n  }\n\n  /**\n   * @return {SparseMatrix} - New transposed sparse matrix\n   */\n  transpose() {\n    let trans = new SparseMatrix(this.columns, this.rows, {\n      initialCapacity: this.cardinality\n    });\n    this.forEachNonZero((i, j, value) => {\n      trans.set(j, i, value);\n      return value;\n    });\n    return trans;\n  }\n}\n\nSparseMatrix.prototype.klass = 'Matrix';\n\nSparseMatrix.identity = SparseMatrix.eye;\nSparseMatrix.prototype.tensorProduct = SparseMatrix.prototype.kroneckerProduct;\n\n/*\n Add dynamically instance and static methods for mathematical operations\n */\n\nvar inplaceOperator = `\n(function %name%(value) {\n    if (typeof value === 'number') return this.%name%S(value);\n    return this.%name%M(value);\n})\n`;\n\nvar inplaceOperatorScalar = `\n(function %name%S(value) {\n    this.forEachNonZero((i, j, v) => v %op% value);\n    return this;\n})\n`;\n\nvar inplaceOperatorMatrix = `\n(function %name%M(matrix) {\n    matrix.forEachNonZero((i, j, v) => {\n        this.set(i, j, this.get(i, j) %op% v);\n        return v;\n    });\n    return this;\n})\n`;\n\nvar staticOperator = `\n(function %name%(matrix, value) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%(value);\n})\n`;\n\nvar inplaceMethod = `\n(function %name%() {\n    this.forEachNonZero((i, j, v) => %method%(v));\n    return this;\n})\n`;\n\nvar staticMethod = `\n(function %name%(matrix) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%();\n})\n`;\n\nconst operators = [\n  // Arithmetic operators\n  ['+', 'add'],\n  ['-', 'sub', 'subtract'],\n  ['*', 'mul', 'multiply'],\n  ['/', 'div', 'divide'],\n  ['%', 'mod', 'modulus'],\n  // Bitwise operators\n  ['&', 'and'],\n  ['|', 'or'],\n  ['^', 'xor'],\n  ['<<', 'leftShift'],\n  ['>>', 'signPropagatingRightShift'],\n  ['>>>', 'rightShift', 'zeroFillRightShift']\n];\n\nfor (const operator of operators) {\n  for (let i = 1; i < operator.length; i++) {\n    SparseMatrix.prototype[operator[i]] = eval(\n      fillTemplateFunction(inplaceOperator, {\n        name: operator[i],\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}S`] = eval(\n      fillTemplateFunction(inplaceOperatorScalar, {\n        name: `${operator[i]}S`,\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}M`] = eval(\n      fillTemplateFunction(inplaceOperatorMatrix, {\n        name: `${operator[i]}M`,\n        op: operator[0]\n      })\n    );\n\n    SparseMatrix[operator[i]] = eval(\n      fillTemplateFunction(staticOperator, { name: operator[i] })\n    );\n  }\n}\n\nvar methods = [['~', 'not']];\n\n[\n  'abs',\n  'acos',\n  'acosh',\n  'asin',\n  'asinh',\n  'atan',\n  'atanh',\n  'cbrt',\n  'ceil',\n  'clz32',\n  'cos',\n  'cosh',\n  'exp',\n  'expm1',\n  'floor',\n  'fround',\n  'log',\n  'log1p',\n  'log10',\n  'log2',\n  'round',\n  'sign',\n  'sin',\n  'sinh',\n  'sqrt',\n  'tan',\n  'tanh',\n  'trunc'\n].forEach(function (mathMethod) {\n  methods.push([`Math.${mathMethod}`, mathMethod]);\n});\n\nfor (const method of methods) {\n  for (let i = 1; i < method.length; i++) {\n    SparseMatrix.prototype[method[i]] = eval(\n      fillTemplateFunction(inplaceMethod, {\n        name: method[i],\n        method: method[0]\n      })\n    );\n    SparseMatrix[method[i]] = eval(\n      fillTemplateFunction(staticMethod, { name: method[i] })\n    );\n  }\n}\n\nfunction fillTemplateFunction(template, values) {\n  for (const i in values) {\n    template = template.replace(new RegExp(`%${i}%`, 'g'), values[i]);\n  }\n  return template;\n}\n","export default function additiveSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i]) * (a[i] + b[i])) / (a[i] * b[i]);\n  }\n  return 2 * d;\n}\n","export default function avg(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var ans = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    ans += aux;\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return (max + ans) / 2;\n}\n","export default function bhattacharyya(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return -Math.log(ans);\n}\n","export default function canberra(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]) / (a[i] + b[i]);\n  }\n  return ans;\n}\n","export default function chebyshev(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return max;\n}\n","export default function clark(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.sqrt(\n      ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]))\n    );\n  }\n  return 2 * d;\n}\n","export default function czekanowskiSimilarity(a, b) {\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < a.length; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return (2 * up) / down;\n}\n","import czekanowskiSimilarity from '../similarities/czekanowski';\n\nexport default function czekanowskiDistance(a, b) {\n  return 1 - czekanowskiSimilarity(a, b);\n}\n","export default function dice(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p + q1);\n}\n","export default function divergence(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]));\n  }\n  return 2 * d;\n}\n","export default function fidelity(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return ans;\n}\n","export default function gower(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]);\n  }\n  return ans / ii;\n}\n","export default function harmonicMean(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] * b[i]) / (a[i] + b[i]);\n  }\n  return 2 * ans;\n}\n","export default function hellinger(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return 2 * Math.sqrt(1 - ans);\n}\n","export default function innerProduct(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * b[i];\n  }\n  return ans;\n}\n","export default function intersection(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.min(a[i], b[i]);\n  }\n  return 1 - ans;\n}\n","export default function jaccard(a, b) {\n  var ii = a.length;\n  var p1 = 0;\n  var p2 = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p1 += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p2 + q1 - p1);\n}\n","export default function jeffreys(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] - b[i]) * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function jensenDifference(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (a[i] * Math.log(a[i]) + b[i] * Math.log(b[i])) / 2 -\n      ((a[i] + b[i]) / 2) * Math.log((a[i] + b[i]) / 2);\n  }\n  return ans;\n}\n","export default function jensenShannon(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n    q += b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return (p + q) / 2;\n}\n","export default function kdivergence(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function kulczynski(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.min(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function kullbackLeibler(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function kumarHassebrook(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (p2 + q2 - p);\n}\n","export default function kumarJohnson(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      Math.pow(a[i] * a[i] - b[i] * b[i], 2) / (2 * Math.pow(a[i] * b[i], 1.5));\n  }\n  return ans;\n}\n","export default function lorentzian(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.log(Math.abs(a[i] - b[i]) + 1);\n  }\n  return ans;\n}\n","export default function manhattan(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.abs(a[i] - b[i]);\n  }\n  return d;\n}\n","export default function matusita(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return Math.sqrt(2 - 2 * ans);\n}\n","export default function minkowski(a, b, p) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.pow(Math.abs(a[i] - b[i]), p);\n  }\n  return Math.pow(d, 1 / p);\n}\n","export default function motyka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return 1 - up / down;\n}\n","export default function neyman(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / a[i];\n  }\n  return d;\n}\n","export default function pearson(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / b[i];\n  }\n  return d;\n}\n","export default function probabilisticSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return 2 * d;\n}\n","export default function ruzicka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function soergel(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function sorensen(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += a[i] + b[i];\n  }\n  return up / down;\n}\n","export default function squared(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return d;\n}\n","export default function squaredChord(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (Math.sqrt(a[i]) - Math.sqrt(b[i])) * (Math.sqrt(a[i]) - Math.sqrt(b[i]));\n  }\n  return ans;\n}\n","export default function taneja(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      ((a[i] + b[i]) / 2) *\n      Math.log((a[i] + b[i]) / (2 * Math.sqrt(a[i] * b[i])));\n  }\n  return ans;\n}\n","export default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    var inter = 0;\n    var union = 0;\n    for (var j = 0; j < a.length; j++) {\n      inter += a[j] && b[j];\n      union += a[j] || b[j];\n    }\n    if (union === 0) {\n      return 1;\n    }\n    return inter / union;\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return 1 - (p + q - 2 * m) / (p + q - m);\n  }\n}\n","import tanimotoS from '../similarities/tanimoto';\n\nexport default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    return 1 - tanimotoS(a, b, bitvector);\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return (p + q - 2 * m) / (p + q - m);\n  }\n}\n","export default function topsoe(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      a[i] * Math.log((2 * a[i]) / (a[i] + b[i])) +\n      b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function waveHedges(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += 1 - Math.min(a[i], b[i]) / Math.max(a[i], b[i]);\n  }\n  return ans;\n}\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\n/**\n * Function that creates the tree\n * @param {Array<Array<number>>} spectrum\n * @param {object} [options]\n * @return {Tree|null}\n * left and right have the same structure than the parent,\n * or are null if they are leaves\n */\nexport function createTree(spectrum, options = {}) {\n  var X = spectrum[0];\n  const {\n    minWindow = 0.16,\n    threshold = 0.01,\n    from = X[0],\n    to = X[X.length - 1]\n  } = options;\n\n  return mainCreateTree(\n    spectrum[0],\n    spectrum[1],\n    from,\n    to,\n    minWindow,\n    threshold\n  );\n}\n\nfunction mainCreateTree(X, Y, from, to, minWindow, threshold) {\n  if (to - from < minWindow) {\n    return null;\n  }\n\n  // search first point\n  var start = binarySearch(X, from, ascending);\n  if (start < 0) {\n    start = ~start;\n  }\n\n  // stop at last point\n  var sum = 0;\n  var center = 0;\n  for (var i = start; i < X.length; i++) {\n    if (X[i] >= to) {\n      break;\n    }\n    sum += Y[i];\n    center += X[i] * Y[i];\n  }\n\n  if (sum < threshold) {\n    return null;\n  }\n\n  center /= sum;\n  if (center - from < 1e-6 || to - center < 1e-6) {\n    return null;\n  }\n  if (center - from < minWindow / 4) {\n    return mainCreateTree(X, Y, center, to, minWindow, threshold);\n  } else {\n    if (to - center < minWindow / 4) {\n      return mainCreateTree(X, Y, from, center, minWindow, threshold);\n    } else {\n      return new Tree(\n        sum,\n        center,\n        mainCreateTree(X, Y, from, center, minWindow, threshold),\n        mainCreateTree(X, Y, center, to, minWindow, threshold)\n      );\n    }\n  }\n}\n\nclass Tree {\n  constructor(sum, center, left, right) {\n    this.sum = sum;\n    this.center = center;\n    this.left = left;\n    this.right = right;\n  }\n}\n","import { createTree } from './createTree';\n\n/**\n * Similarity between two nodes\n * @param {Tree|Array<Array<number>>} a - tree A node\n * @param {Tree|Array<Array<number>>} b - tree B node\n * @param {object} [options]\n * @return {number} similarity measure between tree nodes\n */\nexport function getSimilarity(a, b, options = {}) {\n  const { alpha = 0.1, beta = 0.33, gamma = 0.001 } = options;\n\n  if (a === null || b === null) {\n    return 0;\n  }\n  if (Array.isArray(a)) {\n    a = createTree(a);\n  }\n  if (Array.isArray(b)) {\n    b = createTree(b);\n  }\n\n  var C =\n    (alpha * Math.min(a.sum, b.sum)) / Math.max(a.sum, b.sum) +\n    (1 - alpha) * Math.exp(-gamma * Math.abs(a.center - b.center));\n\n  return (\n    beta * C +\n    ((1 - beta) *\n      (getSimilarity(a.left, b.left, options) +\n        getSimilarity(a.right, b.right, options))) /\n      2\n  );\n}\n","import { getSimilarity } from './getSimilarity';\n\nexport { createTree } from './createTree';\n\nexport function treeSimilarity(A, B, options = {}) {\n  return getSimilarity(A, B, options);\n}\n\nexport function getFunction(options = {}) {\n  return (A, B) => getSimilarity(A, B, options);\n}\n","export default function cosine(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (Math.sqrt(p2) * Math.sqrt(q2));\n}\n","import diceD from '../distances/dice';\n\nexport default function dice(a, b) {\n  return 1 - diceD(a, b);\n}\n","import intersectionD from '../distances/intersection';\n\nexport default function intersection(a, b) {\n  return 1 - intersectionD(a, b);\n}\n","import jaccardD from '../distances/jaccard';\n\nexport default function jaccard(a, b) {\n  return 1 - jaccardD(a, b);\n}\n","import kulczynskiD from '../distances/kulczynski';\n\nexport default function kulczynski(a, b) {\n  return 1 / kulczynskiD(a, b);\n}\n","import motykaD from '../distances/motyka';\n\nexport default function motyka(a, b) {\n  return 1 - motykaD(a, b);\n}\n","import mean from 'ml-array-mean';\n\nimport cosine from './cosine';\n\nexport default function pearson(a, b) {\n  var avgA = mean(a);\n  var avgB = mean(b);\n\n  var newA = new Array(a.length);\n  var newB = new Array(b.length);\n  for (var i = 0; i < newA.length; i++) {\n    newA[i] = a[i] - avgA;\n    newB[i] = b[i] - avgB;\n  }\n\n  return cosine(newA, newB);\n}\n","import squaredChordD from '../distances/squaredChord';\n\nexport default function squaredChord(a, b) {\n  return 1 - squaredChordD(a, b);\n}\n","'use strict';\n\n// Accuracy\nexports.acc = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.tn[i] + pred.tp[i]) / (l - 1);\n    }\n    return result;\n};\n\n// Error rate\nexports.err = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.fp[i] / (l - 1));\n    }\n    return result;\n};\n\n// False positive rate\nexports.fpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fp[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// True positive rate\nexports.tpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tp[i] / pred.nPos;\n    }\n    return result;\n};\n\n// False negative rate\nexports.fnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fn[i] / pred.nPos;\n    }\n    return result;\n};\n\n// True negative rate\nexports.tnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tn[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// Positive predictive value\nexports.ppv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 0;\n    }\n    return result;\n};\n\n// Negative predictive value\nexports.npv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 0;\n    }\n    return result;\n};\n\n// Prediction conditioned fallout\nexports.pcfall = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? 1 - (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 1;\n    }\n    return result;\n};\n\n// Prediction conditioned miss\nexports.pcmiss = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? 1 - (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 1;\n    }\n    return result;\n};\n\n// Lift value\nexports.lift = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.nPosPred[i] !== 0) ? ((pred.tp[i] / pred.nPos) / (pred.nPosPred[i] / pred.nSamples)) : 0;\n    }\n    return result;\n};\n\n// Rate of positive predictions\nexports.rpp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nPosPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Rate of negative predictions\nexports.rnp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nNegPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Threshold\nexports.threshold = pred => {\n    const clone = pred.cutoffs.slice();\n    clone[0] = clone[1]; // Remove the infinite value\n    return clone;\n};\n","'use strict';\n\nconst measures = require('./measures');\n\nclass Performance {\n    /**\n     *\n     * @param prediction - The prediction matrix\n     * @param target - The target matrix (values: truthy for same class, falsy for different class)\n     * @param options\n     *\n     * @option    all    True if the entire matrix must be used. False to ignore the diagonal and lower part (default is false, for similarity/distance matrices)\n     * @option    max    True if the max value corresponds to a perfect match (like in similarity matrices), false if it is the min value (default is false, like in distance matrices. All values will be multiplied by -1)\n     */\n    constructor(prediction, target, options) {\n        options = options || {};\n        if (prediction.length !== target.length || prediction[0].length !== target[0].length) {\n            throw new Error('dimensions of prediction and target do not match');\n        }\n        const rows = prediction.length;\n        const columns = prediction[0].length;\n        const isDistance = !options.max;\n\n        const predP = [];\n\n        if (options.all) {\n            for (var i = 0; i < rows; i++) {\n                for (var j = 0; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        } else {\n            if (rows < 3 || rows !== columns) {\n                throw new Error('When \"all\" option is false, the prediction matrix must be square and have at least 3 columns');\n            }\n            for (var i = 0; i < rows - 1; i++) {\n                for (var j = i + 1; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        }\n\n        if (isDistance) {\n            predP.sort((a, b) => a.pred - b.pred);\n        } else {\n            predP.sort((a, b) => b.pred - a.pred);\n        }\n        \n        const cutoffs = this.cutoffs = [isDistance ? Number.MIN_VALUE : Number.MAX_VALUE];\n        const fp = this.fp = [0];\n        const tp = this.tp = [0];\n\n        var nPos = 0;\n        var nNeg = 0;\n\n        var currentPred = predP[0].pred;\n        var nTp = 0;\n        var nFp = 0;\n        for (var i = 0; i < predP.length; i++) {\n            if (predP[i].pred !== currentPred) {\n                cutoffs.push(currentPred);\n                fp.push(nFp);\n                tp.push(nTp);\n                currentPred = predP[i].pred;\n            }\n            if (predP[i].targ) {\n                nPos++;\n                nTp++;\n            } else {\n                nNeg++;\n                nFp++;\n            }\n        }\n        cutoffs.push(currentPred);\n        fp.push(nFp);\n        tp.push(nTp);\n\n        const l = cutoffs.length;\n        const fn = this.fn = new Array(l);\n        const tn = this.tn = new Array(l);\n        const nPosPred = this.nPosPred = new Array(l);\n        const nNegPred = this.nNegPred = new Array(l);\n\n        for (var i = 0; i < l; i++) {\n            fn[i] = nPos - tp[i];\n            tn[i] = nNeg - fp[i];\n\n            nPosPred[i] = tp[i] + fp[i];\n            nNegPred[i] = tn[i] + fn[i];\n        }\n\n        this.nPos = nPos;\n        this.nNeg = nNeg;\n        this.nSamples = nPos + nNeg;\n    }\n\n    /**\n     * Computes a measure from the prediction object.\n     *\n     * Many measures are available and can be combined :\n     * To create a ROC curve, you need fpr and tpr\n     * To create a DET curve, you need fnr and fpr\n     * To create a Lift chart, you need rpp and lift\n     *\n     * Possible measures are : threshold (Threshold), acc (Accuracy), err (Error rate),\n     * fpr (False positive rate), tpr (True positive rate), fnr (False negative rate), tnr (True negative rate), ppv (Positive predictive value),\n     * npv (Negative predictive value), pcfall (Prediction-conditioned fallout), pcmiss (Prediction-conditioned miss), lift (Lift value), rpp (Rate of positive predictions), rnp (Rate of negative predictions)\n     *\n     * @param measure - The short name of the measure\n     *\n     * @return [number]\n     */\n    getMeasure(measure) {\n        if (typeof measure !== 'string') {\n            throw new Error('No measure specified');\n        }\n        if (!measures[measure]) {\n            throw new Error(`The specified measure (${measure}) does not exist`);\n        }\n        return measures[measure](this);\n    }\n\n    /**\n     * Returns the area under the ROC curve\n     */\n    getAURC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fp[i] / this.nNeg;\n            y[i] = this.tp[i] / this.nPos;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] - x[i - 1]) * (y[i] + y[i - 1]);\n        }\n        return auc;\n    }\n\n    /**\n     * Returns the area under the DET curve\n     */\n    getAUDC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fn[i] / this.nPos;\n            y[i] = this.fp[i] / this.nNeg;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] + x[i - 1]) * (y[i] - y[i - 1]);\n        }\n        return auc;\n    }\n\n    getDistribution(options) {\n        options = options || {};\n        var cutLength = this.cutoffs.length;\n        var cutLow = options.xMin || Math.floor(this.cutoffs[cutLength - 1] * 100) / 100;\n        var cutHigh = options.xMax || Math.ceil(this.cutoffs[1] * 100) / 100;\n        var interval = options.interval || Math.floor(((cutHigh - cutLow) / 20 * 10000000) - 1) / 10000000; // Trick to avoid the precision problem of float numbers\n\n        var xLabels = [];\n        var interValues = [];\n        var intraValues = [];\n        var interCumPercent = [];\n        var intraCumPercent = [];\n\n        var nTP = this.tp[cutLength - 1], currentTP = 0;\n        var nFP = this.fp[cutLength - 1], currentFP = 0;\n\n        for (var i = cutLow, j = (cutLength - 1); i <= cutHigh; i += interval) {\n            while (this.cutoffs[j] < i)\n                j--;\n\n            xLabels.push(i);\n\n            var thisTP = nTP - currentTP - this.tp[j];\n            var thisFP = nFP - currentFP - this.fp[j];\n\n            currentTP += thisTP;\n            currentFP += thisFP;\n\n            interValues.push(thisFP);\n            intraValues.push(thisTP);\n\n            interCumPercent.push(100 - (nFP - this.fp[j]) / nFP * 100);\n            intraCumPercent.push(100 - (nTP - this.tp[j]) / nTP * 100);\n        }\n\n        return {\n            xLabels: xLabels,\n            interValues: interValues,\n            intraValues: intraValues,\n            interCumPercent: interCumPercent,\n            intraCumPercent: intraCumPercent\n        };\n    }\n}\n\nPerformance.names = {\n    acc: 'Accuracy',\n    err: 'Error rate',\n    fpr: 'False positive rate',\n    tpr: 'True positive rate',\n    fnr: 'False negative rate',\n    tnr: 'True negative rate',\n    ppv: 'Positive predictive value',\n    npv: 'Negative predictive value',\n    pcfall: 'Prediction-conditioned fallout',\n    pcmiss: 'Prediction-conditioned miss',\n    lift: 'Lift value',\n    rpp: 'Rate of positive predictions',\n    rnp: 'Rate of negative predictions',\n    threshold: 'Threshold'\n};\n\nmodule.exports = Performance;\n","'use strict';\n\nvar defaultOptions = {\n  size: 1,\n  value: 0\n};\n\n/**\n * Case when the entry is an array\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction arrayCase(data, options) {\n  var len = data.length;\n  if (typeof options.size === 'number') {\n    options.size = [options.size, options.size];\n  }\n\n  var cond = len + options.size[0] + options.size[1];\n\n  var output;\n  if (options.output) {\n    if (options.output.length !== cond) {\n      throw new RangeError('Wrong output size');\n    }\n    output = options.output;\n  } else {\n    output = new Array(cond);\n  }\n\n  var i;\n  if (options.value === 'circular') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) {\n        output[i] = data[(len - (options.size[0] % len) + i) % len];\n      } else if (i < options.size[0] + len) {\n        output[i] = data[i - options.size[0]];\n      } else {\n        output[i] = data[(i - options.size[0]) % len];\n      }\n    }\n  } else if (options.value === 'replicate') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[0];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[len - 1];\n    }\n  } else if (options.value === 'symmetric') {\n    if (options.size[0] > len || options.size[1] > len) {\n      throw new RangeError(\n        'expanded value should not be bigger than the data length'\n      );\n    }\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[options.size[0] - 1 - i];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[2 * len + options.size[0] - i - 1];\n    }\n  } else {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = options.value;\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = options.value;\n    }\n  }\n\n  return output;\n}\n\n/**\n * Case when the entry is a matrix\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction matrixCase(data, options) {\n  // var row = data.length;\n  // var col = data[0].length;\n  if (options.size[0] === undefined) {\n    options.size = [options.size, options.size, options.size, options.size];\n  }\n  throw new Error('matrix not supported yet, sorry');\n}\n\n/**\n * Pads and array\n * @param {Array <number>} data\n * @param {object} options\n */\nfunction padArray(data, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (Array.isArray(data)) {\n    if (Array.isArray(data[0])) return matrixCase(data, options);\n    else return arrayCase(data, options);\n  } else {\n    throw new TypeError('data should be an array');\n  }\n}\n\nmodule.exports = padArray;\n","import { Matrix, MatrixTransposeView, inverse } from 'ml-matrix';\nimport padArray from 'ml-pad-array';\n\nconst defaultOptions = {\n  windowSize: 5,\n  derivative: 1,\n  polynomial: 2,\n  pad: 'none',\n  padValue: 'replicate',\n};\n\n/**\n * Savitzky-Golay filter\n * @param {Array <number>} data\n * @param {number} h\n * @param {Object} options\n * @returns {Array}\n */\nexport default function savitzkyGolay(data, h, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (\n    options.windowSize % 2 === 0 ||\n    options.windowSize < 5 ||\n    !Number.isInteger(options.windowSize)\n  ) {\n    throw new RangeError(\n      'Invalid window size (should be odd and at least 5 integer number)',\n    );\n  }\n  if (options.derivative < 0 || !Number.isInteger(options.derivative)) {\n    throw new RangeError('Derivative should be a positive integer');\n  }\n  if (options.polynomial < 1 || !Number.isInteger(options.polynomial)) {\n    throw new RangeError('Polynomial should be a positive integer');\n  }\n\n  let C, norm;\n  let step = Math.floor(options.windowSize / 2);\n\n  if (options.pad === 'pre') {\n    data = padArray(data, { size: step, value: options.padValue });\n  }\n\n  let ans = new Array(data.length - 2 * step);\n\n  if (\n    options.windowSize === 5 &&\n    options.polynomial === 2 &&\n    (options.derivative === 1 || options.derivative === 2)\n  ) {\n    if (options.derivative === 1) {\n      C = [-2, -1, 0, 1, 2];\n      norm = 10;\n    } else {\n      C = [2, -1, -2, -1, 2];\n      norm = 7;\n    }\n  } else {\n    let J = Matrix.ones(options.windowSize, options.polynomial + 1);\n    let inic = -(options.windowSize - 1) / 2;\n    for (let i = 0; i < J.rows; i++) {\n      for (let j = 0; j < J.columns; j++) {\n        if (inic + 1 !== 0 || j !== 0) J.set(i, j, Math.pow(inic + i, j));\n      }\n    }\n    let Jtranspose = new MatrixTransposeView(J);\n    let Jinv = inverse(Jtranspose.mmul(J));\n    C = Jinv.mmul(Jtranspose);\n    C = C.getRow(options.derivative);\n    norm = 1;\n  }\n  let det = norm * Math.pow(h, options.derivative);\n  for (let k = step; k < data.length - step; k++) {\n    let d = 0;\n    for (let l = 0; l < C.length; l++) d += (C[l] * data[l + k - step]) / det;\n    ans[k - step] = d;\n  }\n\n  if (options.pad === 'post') {\n    ans = padArray(ans, { size: step, value: options.padValue });\n  }\n\n  return ans;\n}\n","// auxiliary file to create the 256 look at table elements\n\nvar ans = new Array(256);\nfor (var i = 0; i < 256; i++) {\n    var num = i;\n    var c = 0;\n    while (num) {\n        num = num & (num - 1);\n        c++;\n    }\n    ans[i] = c;\n}\n\nmodule.exports = ans;","'use strict';\n\nvar eightBits = require('./creator');\n\n/**\n * Count the number of true values in an array\n * @param {Array} arr\n * @return {number}\n */\nfunction count(arr) {\n    var c = 0;\n    for (var i = 0; i < arr.length; i++) {\n        c += eightBits[arr[i] & 0xff] + eightBits[(arr[i] >> 8) & 0xff] + eightBits[(arr[i] >> 16) & 0xff] + eightBits[(arr[i] >> 24) & 0xff];\n    }\n    return c;\n}\n\n/**\n * Logical AND operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction and(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] & arr2[i];\n    return ans;\n}\n\n/**\n * Logical OR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction or(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] | arr2[i];\n    return ans;\n}\n\n/**\n * Logical XOR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction xor(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] ^ arr2[i];\n    return ans;\n}\n\n/**\n * Logical NOT operation\n * @param {Array} arr\n * @return {Array}\n */\nfunction not(arr) {\n    var ans = new Array(arr.length);\n    for (var i = 0; i < ans.length; i++)\n        ans[i] = ~arr[i];\n    return ans;\n}\n\n/**\n * Gets the n value of array arr\n * @param {Array} arr\n * @param {number} n\n * @return {boolean}\n */\nfunction getBit(arr, n) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    return Boolean(arr[index] & mask);\n}\n\n/**\n * Sets the n value of array arr to the value val\n * @param {Array} arr\n * @param {number} n\n * @param {boolean} val\n * @return {Array}\n */\nfunction setBit(arr, n, val) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    if (val)\n        arr[index] = mask | arr[index];\n    else\n        arr[index] = ~mask & arr[index];\n    return arr;\n}\n\n/**\n * Translates an array of numbers to a string of bits\n * @param {Array} arr\n * @returns {string}\n */\nfunction toBinaryString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(2);\n        str += '00000000000000000000000000000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a string of bits\n * @param {string} str\n * @returns {Array}\n */\nfunction parseBinaryString(str) {\n    var len = str.length / 32;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*32, 32), 2) | 0;\n    }\n    return ans;\n}\n\n/**\n * Translates an array of numbers to a hex string\n * @param {Array} arr\n * @returns {string}\n */\nfunction toHexString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(16);\n        str += '00000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a hex string\n * @param {string} str\n * @returns {Array}\n */\nfunction parseHexString(str) {\n    var len = str.length / 8;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*8, 8), 16) | 0;\n    }\n    return ans;\n}\n\n/**\n * Creates a human readable string of the array\n * @param {Array} arr\n * @returns {string}\n */\nfunction toDebug(arr) {\n    var binary = toBinaryString(arr);\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        str += '0000'.substr((i * 32).toString(16).length) + (i * 32).toString(16) + ':';\n        for (var j = 0; j < 32; j += 4) {\n            str += ' ' + binary.substr(i * 32 + j, 4);\n        }\n        if (i < arr.length - 1) str += '\\n';\n    }\n    return str\n}\n\nmodule.exports = {\n    count: count,\n    and: and,\n    or: or,\n    xor: xor,\n    not: not,\n    getBit: getBit,\n    setBit: setBit,\n    toBinaryString: toBinaryString,\n    parseBinaryString: parseBinaryString,\n    toHexString: toHexString,\n    parseHexString: parseHexString,\n    toDebug: toDebug\n};\n","import isArray from 'is-any-array';\n\n/**\n * Computes the mode of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction mode(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var maxValue = 0;\n  var maxCount = 0;\n  var count = 0;\n  var counts = {};\n\n  for (var i = 0; i < input.length; ++i) {\n    var element = input[i];\n    count = counts[element];\n\n    if (count) {\n      counts[element]++;\n      count++;\n    } else {\n      counts[element] = count = 1;\n    }\n\n    if (count > maxCount) {\n      maxCount = count;\n      maxValue = input[i];\n    }\n  }\n\n  return maxValue;\n}\n\nexport default mode;\n","import max from 'ml-array-max';\nimport sum from 'ml-array-sum';\n\n/**\n * Computes the norm of the given values\n * @param {Array<number>} input\n * @param {object} [options={}]\n * @param {string} [options.algorithm='absolute'] absolute, sum or max\n * @return {number}\n */\n\nfunction norm(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  var _options$algorithm = options.algorithm,\n      algorithm = _options$algorithm === void 0 ? 'absolute' : _options$algorithm;\n\n  if (!Array.isArray(input)) {\n    throw new Error('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new Error('input must not be empty');\n  }\n\n  switch (algorithm.toLowerCase()) {\n    case 'absolute':\n      {\n        var absoluteSumValue = absoluteSum(input);\n        if (absoluteSumValue === 0) return input.slice(0);\n        return input.map(function (element) {\n          return element / absoluteSumValue;\n        });\n      }\n\n    case 'max':\n      {\n        var maxValue = max(input);\n        if (maxValue === 0) return input.slice(0);\n        return input.map(function (element) {\n          return element / maxValue;\n        });\n      }\n\n    case 'sum':\n      {\n        var sumValue = sum(input);\n        if (sumValue === 0) return input.slice(0);\n        return input.map(function (element) {\n          return element / sumValue;\n        });\n      }\n\n    default:\n      throw new Error(\"norm: unknown algorithm: \".concat(algorithm));\n  }\n}\n\nfunction absoluteSum(input) {\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += Math.abs(input[i]);\n  }\n\n  return sumValue;\n}\n\nexport default norm;\n","import isArray from 'is-any-array';\n\nfunction _typeof(obj) {\n  if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") {\n    _typeof = function (obj) {\n      return typeof obj;\n    };\n  } else {\n    _typeof = function (obj) {\n      return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj;\n    };\n  }\n\n  return _typeof(obj);\n}\n\n/**\n * Fill an array with sequential numbers\n * @param {Array<number>} [input] - optional destination array (if not provided a new array will be created)\n * @param {object} [options={}]\n * @param {number} [options.from=0] - first value in the array\n * @param {number} [options.to=10] - last value in the array\n * @param {number} [options.size=input.length] - size of the array (if not provided calculated from step)\n * @param {number} [options.step] - if not provided calculated from size\n * @return {Array<number>}\n */\n\nfunction sequentialFill() {\n  var input = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (_typeof(input) === 'object' && !isArray(input)) {\n    options = input;\n    input = [];\n  }\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options = options,\n      _options$from = _options.from,\n      from = _options$from === void 0 ? 0 : _options$from,\n      _options$to = _options.to,\n      to = _options$to === void 0 ? 10 : _options$to,\n      _options$size = _options.size,\n      size = _options$size === void 0 ? input.length : _options$size,\n      step = _options.step;\n\n  if (size && step) {\n    throw new Error('step is defined by the array size');\n  }\n\n  if (!size) {\n    if (step) {\n      size = Math.floor((to - from) / step) + 1;\n    } else {\n      size = to - from + 1;\n    }\n  }\n\n  if (!step && size) {\n    step = (to - from) / (size - 1);\n  }\n\n  if (Array.isArray(input)) {\n    input.length = 0; // only works with normal array\n\n    for (var i = 0; i < size; i++) {\n      input.push(from);\n      from += step;\n    }\n  } else {\n    if (input.length !== size) {\n      throw new Error('sequentialFill typed array must have the correct length');\n    }\n\n    for (var _i = 0; _i < size; _i++) {\n      input[_i] = from;\n      from += step;\n    }\n  }\n\n  return input;\n}\n\nexport default sequentialFill;\n","import arrayMean from 'ml-array-mean';\nimport isArray from 'is-any-array';\n\n/**\n * Computes the variance of the given values\n * @param {Array} values\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @param {number} [options.mean = arrayMean] - precalculated mean, if any.\n * @return {number}\n */\n\nfunction variance(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(values)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options$unbiased = options.unbiased,\n      unbiased = _options$unbiased === void 0 ? true : _options$unbiased,\n      _options$mean = options.mean,\n      mean = _options$mean === void 0 ? arrayMean(values) : _options$mean;\n  var sqrError = 0;\n\n  for (var i = 0; i < values.length; i++) {\n    var x = values[i] - mean;\n    sqrError += x * x;\n  }\n\n  if (unbiased) {\n    return sqrError / (values.length - 1);\n  } else {\n    return sqrError / values.length;\n  }\n}\n\nexport default variance;\n","import variance from 'ml-array-variance';\n\n/**\n * Computes the standard deviation of the given values\n * @param {Array} values\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @param {number} [options.mean = arrayMean] - precalculated mean, if any.\n * @return {number}\n */\n\nfunction standardDeviation(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  return Math.sqrt(variance(values, options));\n}\n\nexport default standardDeviation;\n","/**\n * Merge abscissa values if the ordinate value is in a list of centroids\n * @param {object} originalPoints\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {Array<number>} centroids\n * @param {object} [options]\n * @param {number} [options.window = 0.01] - has to be a positive number\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function mergeByCentroids(\n  originalPoints,\n  centroids,\n  options = {}\n) {\n  const { window = 0.01 } = options;\n\n  var mergedPoints = {\n    x: centroids.slice(),\n    y: new Array(centroids.length).fill(0)\n  };\n\n  var originalIndex = 0;\n  var mergedIndex = 0;\n  while (\n    originalIndex < originalPoints.x.length &&\n    mergedIndex < centroids.length\n  ) {\n    var diff = originalPoints.x[originalIndex] - centroids[mergedIndex];\n    if (Math.abs(diff) < window) {\n      mergedPoints.y[mergedIndex] += originalPoints.y[originalIndex++];\n    } else if (diff < 0) {\n      originalIndex++;\n    } else {\n      mergedIndex++;\n    }\n  }\n\n  return mergedPoints;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {*} options\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function closestX(points, options) {\n  const { x, y } = points;\n  const { target = x[0], reverse = false } = options;\n\n  let index;\n  if (reverse) {\n    index = binarySearch(x, target, descending);\n  } else {\n    index = binarySearch(x, target, ascending);\n  }\n\n  if (index >= 0) {\n    return {\n      x: x[index],\n      y: y[index]\n    };\n  } else {\n    index = ~index;\n    if (\n      (index !== 0 && Math.abs(x[index] - target) > 0.5) ||\n      index === x.length\n    ) {\n      return {\n        x: x[index - 1],\n        y: y[index - 1]\n      };\n    } else {\n      return {\n        x: x[index],\n        y: y[index]\n      };\n    }\n  }\n}\n","import mean from 'ml-array-mean';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} points.x\n * @param {Array<number>} points.y\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @return {number}\n */\nexport default function covariance(points, options = {}) {\n  const { x, y } = points;\n  const { unbiased = true } = options;\n\n  const meanX = mean(x);\n  const meanY = mean(y);\n\n  var error = 0;\n\n  for (let i = 0; i < x.length; i++) {\n    error += (x[i] - meanX) * (y[i] - meanY);\n  }\n\n  if (unbiased) {\n    return error / (x.length - 1);\n  } else {\n    return error / x.length;\n  }\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function maxMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var maxAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      maxAbscissa.x.push(x[index]);\n      maxAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      if (y[index] > maxAbscissa.y[size - 1]) {\n        maxAbscissa.x[size - 1] = x[index];\n        maxAbscissa.y[size - 1] = y[index];\n      }\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  merged.x = maxAbscissa.x.slice();\n\n  return merged;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {object} [options.from = {index: 0}]\n * @param {object} [options.to = {index: x.length-1}]\n * @param {boolean} [options.reverse = false]\n * @return {{index: number, value: number}}\n */\nexport default function maxY(points, options = {}) {\n  const { x, y } = points;\n  let {\n    from = { index: 0 },\n    to = { index: x.length },\n    reverse = false\n  } = options;\n\n  if (from.value !== undefined && from.index === undefined) {\n    from.index = calculateIndex(from.value, x, reverse);\n  }\n\n  if (to.value !== undefined && to.index === undefined) {\n    to.index = calculateIndex(to.value, x, reverse);\n  }\n\n  var currentMax = Number.MIN_VALUE;\n  var currentIndex;\n  for (var i = from.index; i < to.index; i++) {\n    if (currentMax < y[i]) {\n      currentMax = y[i];\n      currentIndex = i;\n    }\n  }\n\n  return {\n    index: currentIndex,\n    value: currentMax\n  };\n}\n\n/**\n * @param {number} value\n * @param {Array<number>} x\n * @param {boolean} reverse\n * @return {number} index of the value in the array\n */\nfunction calculateIndex(value, x, reverse) {\n  let index;\n  if (reverse) {\n    index = binarySearch(x, value, descending);\n  } else {\n    index = binarySearch(x, value, ascending);\n  }\n\n  if (index < 0) {\n    throw new Error(`the value ${value} doesn't belongs to the abscissa value`);\n  }\n\n  return index;\n}\n","export default function sortX(points, options = {}) {\n  const { x, y } = points;\n  const { reverse = false } = options;\n\n  var sortFunc;\n  if (!reverse) {\n    sortFunc = (a, b) => a.x - b.x;\n  } else {\n    sortFunc = (a, b) => b.x - a.x;\n  }\n\n  var grouped = x\n    .map((val, index) => ({\n      x: val,\n      y: y[index]\n    }))\n    .sort(sortFunc);\n\n  var response = { x: x.slice(), y: y.slice() };\n  for (var i = 0; i < x.length; i++) {\n    response.x[i] = grouped[i].x;\n    response.y[i] = grouped[i].y;\n  }\n\n  return response;\n}\n","\n/**\n * In place modification of the 2 arrays to make X unique and sum the Y if X has the same value\n * @param {object} [points={}] : Object of points contains property x (an array) and y (an array)\n * @return points\n */\n\nexport default function uniqueX(points = {}) {\n  const { x, y } = points;\n  if (x.length < 2) return;\n  if (x.length !== y.length) {\n    throw new Error('The X and Y arrays mush have the same length');\n  }\n\n  let current = x[0];\n  let counter = 0;\n\n  for (let i = 1; i < x.length; i++) {\n    if (current !== x[i]) {\n      counter++;\n      current = x[i];\n      x[counter] = x[i];\n      if (i !== counter) {\n        y[counter] = 0;\n      }\n    }\n    if (i !== counter) {\n      y[counter] += y[i];\n    }\n  }\n\n  x.length = counter + 1;\n  y.length = counter + 1;\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function weightedMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var weightedAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      weightedAbscissa.x.push(x[index] * y[index]);\n      weightedAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      weightedAbscissa.x[size - 1] += x[index] * y[index];\n      weightedAbscissa.y[size - 1] += y[index];\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  for (var i = 0; i < merged.x.length; i++) {\n    merged.x[i] = weightedAbscissa.x[i] / weightedAbscissa.y[i];\n  }\n\n  return merged;\n}\n","/**\n * Function that calculates the integral of the line between two\n * x-coordinates, given the slope and intercept of the line.\n * @param {number} x0\n * @param {number} x1\n * @param {number} slope\n * @param {number} intercept\n * @return {number} integral value.\n */\nexport default function integral(x0, x1, slope, intercept) {\n  return (\n    0.5 * slope * x1 * x1 +\n    intercept * x1 -\n    (0.5 * slope * x0 * x0 + intercept * x0)\n  );\n}\n","import integral from './integral';\n\n/**\n * function that retrieves the getEquallySpacedData with the variant \"smooth\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"smooth\"\n */\nexport default function equallySpacedSmooth(x, y, from, to, numberOfPoints) {\n  var xLength = x.length;\n\n  var step = (to - from) / (numberOfPoints - 1);\n  var halfStep = step / 2;\n\n  var output = new Array(numberOfPoints);\n\n  var initialOriginalStep = x[1] - x[0];\n  var lastOriginalStep = x[xLength - 1] - x[xLength - 2];\n\n  // Init main variables\n  var min = from - halfStep;\n  var max = from + halfStep;\n\n  var previousX = Number.MIN_VALUE;\n  var previousY = 0;\n  var nextX = x[0] - initialOriginalStep;\n  var nextY = 0;\n\n  var currentValue = 0;\n  var slope = 0;\n  var intercept = 0;\n  var sumAtMin = 0;\n  var sumAtMax = 0;\n\n  var i = 0; // index of input\n  var j = 0; // index of output\n\n  function getSlope(x0, y0, x1, y1) {\n    return (y1 - y0) / (x1 - x0);\n  }\n\n  main: while (true) {\n    if (previousX <= min && min <= nextX) {\n      add = integral(0, min - previousX, slope, previousY);\n      sumAtMin = currentValue + add;\n    }\n\n    while (nextX - max >= 0) {\n      // no overlap with original point, just consume current value\n      var add = integral(0, max - previousX, slope, previousY);\n      sumAtMax = currentValue + add;\n\n      output[j++] = (sumAtMax - sumAtMin) / step;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      sumAtMin = sumAtMax;\n    }\n\n    currentValue += integral(previousX, nextX, slope, intercept);\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else if (i === xLength) {\n      nextX += lastOriginalStep;\n      nextY = 0;\n    }\n\n    slope = getSlope(previousX, previousY, nextX, nextY);\n    intercept = -slope * previousX + previousY;\n  }\n\n  return output;\n}\n","/**\n * function that retrieves the getEquallySpacedData with the variant \"slot\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"slot\"\n */\nexport default function equallySpacedSlot(x, y, from, to, numberOfPoints) {\n  var xLength = x.length;\n\n  var step = (to - from) / (numberOfPoints - 1);\n  var halfStep = step / 2;\n  var lastStep = x[x.length - 1] - x[x.length - 2];\n\n  var start = from - halfStep;\n  var output = new Array(numberOfPoints);\n\n  // Init main variables\n  var min = start;\n  var max = start + step;\n\n  var previousX = -Number.MAX_VALUE;\n  var previousY = 0;\n  var nextX = x[0];\n  var nextY = y[0];\n  var frontOutsideSpectra = 0;\n  var backOutsideSpectra = true;\n\n  var currentValue = 0;\n\n  // for slot algorithm\n  var currentPoints = 0;\n\n  var i = 1; // index of input\n  var j = 0; // index of output\n\n  main: while (true) {\n    if (previousX >= nextX) throw new Error('x must be an increasing serie');\n    while (previousX - max > 0) {\n      // no overlap with original point, just consume current value\n      if (backOutsideSpectra) {\n        currentPoints++;\n        backOutsideSpectra = false;\n      }\n\n      output[j] = currentPoints <= 0 ? 0 : currentValue / currentPoints;\n      j++;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      currentValue = 0;\n      currentPoints = 0;\n    }\n\n    if (previousX > min) {\n      currentValue += previousY;\n      currentPoints++;\n    }\n\n    if (previousX === -Number.MAX_VALUE || frontOutsideSpectra > 1) {\n      currentPoints--;\n    }\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else {\n      nextX += lastStep;\n      nextY = 0;\n      frontOutsideSpectra++;\n    }\n  }\n\n  return output;\n}\n","export default function getZones(from, to, numberOfPoints, exclusions = []) {\n  if (from > to) {\n    [from, to] = [to, from];\n  }\n\n  // in exclusions from and to have to be defined\n  exclusions = exclusions.filter(\n    (exclusion) => exclusion.from !== undefined && exclusion.to !== undefined\n  );\n\n  exclusions = JSON.parse(JSON.stringify(exclusions));\n  // we ensure that from before to\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from > exclusion.to) {\n      [exclusion.to, exclusion.from] = [exclusion.from, exclusion.to];\n    }\n  });\n\n  exclusions.sort((a, b) => a.from - b.from);\n\n  // we will rework the exclusions in order to remove overlap and outside range (from / to)\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from < from) exclusion.from = from;\n    if (exclusion.to > to) exclusion.to = to;\n  });\n  for (let i = 0; i < exclusions.length - 1; i++) {\n    if (exclusions[i].to > exclusions[i + 1].from) {\n      exclusions[i].to = exclusions[i + 1].from;\n    }\n  }\n  exclusions = exclusions.filter((exclusion) => exclusion.from < exclusion.to);\n\n  if (!exclusions || exclusions.length === 0) {\n    return [{ from, to, numberOfPoints }];\n  }\n\n  // need to deal with overlapping exclusions and out of bound exclusions\n\n  let toRemove = exclusions.reduce(\n    (previous, exclusion) => (previous += exclusion.to - exclusion.from),\n    0\n  );\n  let total = to - from;\n  let unitsPerPoint = (total - toRemove) / numberOfPoints;\n  let zones = [];\n  let currentFrom = from;\n  let totalPoints = 0;\n  for (let exclusion of exclusions) {\n    let currentNbPoints = Math.round(\n      (exclusion.from - currentFrom) / unitsPerPoint\n    );\n    totalPoints += currentNbPoints;\n    if (currentNbPoints > 0) {\n      zones.push({\n        from: currentFrom,\n        to: exclusion.from,\n        numberOfPoints: currentNbPoints\n      });\n    }\n\n    currentFrom = exclusion.to;\n  }\n  if (numberOfPoints - totalPoints > 0) {\n    zones.push({\n      from: currentFrom,\n      to: to,\n      numberOfPoints: numberOfPoints - totalPoints\n    });\n  }\n\n  return zones;\n}\n","import sequentialFill from 'ml-array-sequential-fill';\n\nimport equallySpacedSmooth from './equallySpacedSmooth';\nimport equallySpacedSlot from './equallySpacedSlot';\nimport getZones from './getZones';\n\n/**\n * Function that returns a Number array of equally spaced numberOfPoints\n * containing a representation of intensities of the spectra arguments x\n * and y.\n *\n * The options parameter contains an object in the following form:\n * from: starting point\n * to: last point\n * numberOfPoints: number of points between from and to\n * variant: \"slot\" or \"smooth\" - smooth is the default option\n *\n * The slot variant consist that each point in the new array is calculated\n * averaging the existing points between the slot that belongs to the current\n * value. The smooth variant is the same but takes the integral of the range\n * of the slot and divide by the step size between two points in the new array.\n *\n * @param {object} [arrayXY={}] - object containing 2 properties x and y (both an array)\n * @param {object} [options={}]\n * @param {number} [options.from=x[0]]\n * @param {number} [options.to=x[x.length-1]]\n * @param {string} [options.variant='smooth']\n * @param {number} [options.numberOfPoints=100]\n * @param {Array} [options.exclusions=[]] array of from / to that should be skipped for the generation of the points\n * @return {object<x: Array, y:Array>} new object with x / y array with the equally spaced data.\n */\n\nexport default function equallySpaced(arrayXY = {}, options = {}) {\n  let { x, y } = arrayXY;\n  let xLength = x.length;\n  let reverse = false;\n  if (x.length > 1 && x[0] > x[1]) {\n    x = x.slice().reverse();\n    y = y.slice().reverse();\n    reverse = true;\n  }\n\n  let {\n    from = x[0],\n    to = x[xLength - 1],\n    variant = 'smooth',\n    numberOfPoints = 100,\n    exclusions = []\n  } = options;\n\n  if (xLength !== y.length) {\n    throw new RangeError(\"the x and y vector doesn't have the same size.\");\n  }\n\n  if (typeof from !== 'number' || isNaN(from)) {\n    throw new RangeError(\"'from' option must be a number\");\n  }\n\n  if (typeof to !== 'number' || isNaN(to)) {\n    throw new RangeError(\"'to' option must be a number\");\n  }\n\n  if (typeof numberOfPoints !== 'number' || isNaN(numberOfPoints)) {\n    throw new RangeError(\"'numberOfPoints' option must be a number\");\n  }\n\n  if (numberOfPoints < 2) {\n    throw new RangeError(\"'numberOfPoints' option must be greater than 1\");\n  }\n\n  let zones = getZones(from, to, numberOfPoints, exclusions);\n\n  let xResult = [];\n  let yResult = [];\n  for (let zone of zones) {\n    let zoneResult = processZone(\n      x,\n      y,\n      zone.from,\n      zone.to,\n      zone.numberOfPoints,\n      variant,\n      reverse\n    );\n    xResult = xResult.concat(zoneResult.x);\n    yResult = yResult.concat(zoneResult.y);\n  }\n\n  if (reverse) {\n    if (from < to) {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    } else {\n      return { x: xResult, y: yResult };\n    }\n  } else {\n    if (from < to) {\n      return { x: xResult, y: yResult };\n    } else {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    }\n  }\n}\n\nfunction processZone(x, y, from, to, numberOfPoints, variant) {\n  if (numberOfPoints < 1) {\n    throw new RangeError('the number of points must be at least 1');\n  }\n\n  var output =\n    variant === 'slot'\n      ? equallySpacedSlot(x, y, from, to, numberOfPoints)\n      : equallySpacedSmooth(x, y, from, to, numberOfPoints);\n\n  return {\n    x: sequentialFill({\n      from,\n      to,\n      size: numberOfPoints\n    }),\n    y: output\n  };\n}\n","export default function getZones(from, to, exclusions = []) {\n  if (from > to) {\n    [from, to] = [to, from];\n  }\n\n  // in exclusions from and to have to be defined\n  exclusions = exclusions.filter(\n    (exclusion) => exclusion.from !== undefined && exclusion.to !== undefined\n  );\n\n  exclusions = JSON.parse(JSON.stringify(exclusions));\n  // we ensure that from before to\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from > exclusion.to) {\n      [exclusion.to, exclusion.from] = [exclusion.from, exclusion.to];\n    }\n  });\n\n  exclusions.sort((a, b) => a.from - b.from);\n\n  // we will rework the exclusions in order to remove overlap and outside range (from / to)\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from < from) exclusion.from = from;\n    if (exclusion.to > to) exclusion.to = to;\n  });\n  for (let i = 0; i < exclusions.length - 1; i++) {\n    if (exclusions[i].to > exclusions[i + 1].from) {\n      exclusions[i].to = exclusions[i + 1].from;\n    }\n  }\n  exclusions = exclusions.filter((exclusion) => exclusion.from < exclusion.to);\n\n  if (!exclusions || exclusions.length === 0) {\n    return [{ from, to }];\n  }\n\n  let zones = [];\n  let currentFrom = from;\n  for (let exclusion of exclusions) {\n    if (currentFrom < exclusion.from) {\n      zones.push({\n        from: currentFrom,\n        to: exclusion.from\n      });\n    }\n\n    currentFrom = exclusion.to;\n  }\n  if (currentFrom < to) {\n    zones.push({\n      from: currentFrom,\n      to: to\n    });\n  }\n\n  return zones;\n}\n","import getZones from './getZones';\n\n/**\n * Filter an array x/y based on various criteria\n * x points are expected to be sorted\n *\n * @param {object} points\n * @param {object} [options={}]\n * @param {array} [options.from]\n * @param {array} [options.to]\n * @param {array} [options.exclusions=[]]\n * @return {{x: Array<number>, y: Array<number>}}\n */\n\nexport default function filterX(points, options = {}) {\n  const { x, y } = points;\n  const { from = x[0], to = x[x.length - 1], exclusions = [] } = options;\n\n  let zones = getZones(from, to, exclusions);\n\n\n  let currentZoneIndex = 0;\n  let newX = [];\n  let newY = [];\n  let position = 0;\n  while (position < x.length) {\n    if (\n      x[position] <= zones[currentZoneIndex].to &&\n      x[position] >= zones[currentZoneIndex].from\n    ) {\n      newX.push(x[position]);\n      newY.push(y[position]);\n    } else {\n      if (x[position] > zones[currentZoneIndex].to) {\n        currentZoneIndex++;\n        if (!zones[currentZoneIndex]) break;\n      }\n    }\n    position++;\n  }\n\n  return {\n    x: newX,\n    y: newY\n  };\n}\n","import { DecisionTreeClassifier, DecisionTreeRegression } from 'ml-cart';\nimport {\n  RandomForestClassifier,\n  RandomForestRegression\n} from 'ml-random-forest';\n\n// Try to keep this list in the same structure as the README.\n\n// Unsupervised learning\nexport { PCA } from 'ml-pca';\nimport * as HClust from 'ml-hclust';\nexport { HClust };\nexport { default as KMeans } from 'ml-kmeans';\n\n// Supervised learning\nimport * as NaiveBayes from 'ml-naivebayes';\nexport { NaiveBayes };\nexport { default as KNN } from 'ml-knn';\nexport { PLS, KOPLS } from 'ml-pls';\nexport { default as CrossValidation } from 'ml-cross-validation';\nexport { default as ConfusionMatrix } from 'ml-confusion-matrix';\nexport { DecisionTreeClassifier };\nexport { RandomForestClassifier };\n\n// Artificial neural networks\nexport { default as FNN } from 'ml-fnn';\nexport { default as SOM } from 'ml-som';\n\n// Regression\nexport {\n  SimpleLinearRegression,\n  PolynomialRegression,\n  MultivariateLinearRegression,\n  PowerRegression,\n  ExponentialRegression,\n  TheilSenRegression,\n  RobustPolynomialRegression\n} from 'ml-regression';\nexport { DecisionTreeRegression };\nexport { RandomForestRegression };\n\n// Optimization\nexport { default as levenbergMarquardt } from 'ml-levenberg-marquardt';\nimport * as FCNNLS from 'ml-fcnnls';\nexport { FCNNLS };\n\n// Math\nimport * as MatrixLib from 'ml-matrix';\nconst {\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n} = MatrixLib;\nexport {\n  MatrixLib,\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n};\n\nexport { SparseMatrix } from 'ml-sparse-matrix';\nexport { default as Kernel } from 'ml-kernel';\nimport { distance, similarity } from 'ml-distance';\nexport { distance as Distance, similarity as Similarity };\nexport { default as distanceMatrix } from 'ml-distance-matrix';\nexport { default as XSadd } from 'ml-xsadd';\n\n// Statistics\nexport { default as Performance } from 'ml-performance';\n\n// Data preprocessing\nexport { default as savitzkyGolay } from 'ml-savitzky-golay';\n\n// Utility\nexport { default as BitArray } from 'ml-bit-array';\nexport { default as HashTable } from 'ml-hash-table';\nexport { default as padArray } from 'ml-pad-array';\nexport { default as binarySearch } from 'binary-search';\nimport * as numSort from 'num-sort';\nexport { numSort };\nexport { default as Random } from 'ml-random';\n\nimport min from 'ml-array-min';\nimport max from 'ml-array-max';\nimport median from 'ml-array-median';\nimport mean from 'ml-array-mean';\nimport mode from 'ml-array-mode';\nimport normed from 'ml-array-normed';\nimport rescale from 'ml-array-rescale';\nimport sequentialFill from 'ml-array-sequential-fill';\nimport sum from 'ml-array-sum';\nimport standardDeviation from 'ml-array-standard-deviation';\nimport variance from 'ml-array-variance';\nexport const Array = {\n  min,\n  max,\n  median,\n  mean,\n  mode,\n  normed,\n  rescale,\n  sequentialFill,\n  standardDeviation,\n  sum,\n  variance\n};\n\nimport centroidsMerge from 'ml-array-xy-centroids-merge';\nimport closestX from 'ml-arrayxy-closestx';\nimport covariance from 'ml-array-xy-covariance';\nimport maxMerge from 'ml-array-xy-max-merge';\nimport maxY from 'ml-array-xy-max-y';\nimport sortX from 'ml-array-xy-sort-x';\nimport uniqueX from 'ml-arrayxy-uniquex';\nimport weightedMerge from 'ml-array-xy-weighted-merge';\nimport equallySpaced from 'ml-array-xy-equally-spaced';\nimport filterX from 'ml-array-xy-filter-x';\nexport const ArrayXY = {\n  centroidsMerge,\n  closestX,\n  covariance,\n  maxMerge,\n  maxY,\n  sortX,\n  uniqueX,\n  weightedMerge,\n  equallySpaced,\n  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toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction max(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var maxValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\n\nfunction min(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var minValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] < minValue) minValue = input[i];\n  }\n\n  return minValue;\n}\n\nexport default min;\n","import isArray from 'is-any-array';\nimport max from 'ml-array-max';\nimport min from 'ml-array-min';\n\nfunction rescale(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  } else if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var output;\n\n  if (options.output !== undefined) {\n    if (!isArray(options.output)) {\n      throw new TypeError('output option must be an array if specified');\n    }\n\n    output = options.output;\n  } else {\n    output = new Array(input.length);\n  }\n\n  var currentMin = min(input);\n  var currentMax = max(input);\n\n  if (currentMin === currentMax) {\n    throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');\n  }\n\n  var _options$min = options.min,\n      minValue = _options$min === void 0 ? options.autoMinMax ? currentMin : 0 : _options$min,\n      _options$max = options.max,\n      maxValue = _options$max === void 0 ? options.autoMinMax ? currentMax : 1 : _options$max;\n\n  if (minValue >= maxValue) {\n    throw new RangeError('min option must be smaller than max option');\n  }\n\n  var factor = (maxValue - minValue) / (currentMax - currentMin);\n\n  for (var i = 0; i < input.length; i++) {\n    output[i] = (input[i] - currentMin) * factor + minValue;\n  }\n\n  return output;\n}\n\nexport default rescale;\n","const indent = ' '.repeat(2);\nconst indentData = ' '.repeat(4);\n\nexport function inspectMatrix() {\n  return inspectMatrixWithOptions(this);\n}\n\nexport function inspectMatrixWithOptions(matrix, options = {}) {\n  const { maxRows = 15, maxColumns = 10, maxNumSize = 8 } = options;\n  return `${matrix.constructor.name} {\n${indent}[\n${indentData}${inspectData(matrix, maxRows, maxColumns, maxNumSize)}\n${indent}]\n${indent}rows: ${matrix.rows}\n${indent}columns: ${matrix.columns}\n}`;\n}\n\nfunction inspectData(matrix, maxRows, maxColumns, maxNumSize) {\n  const { rows, columns } = matrix;\n  const maxI = Math.min(rows, maxRows);\n  const maxJ = Math.min(columns, maxColumns);\n  const result = [];\n  for (let i = 0; i < maxI; i++) {\n    let line = [];\n    for (let j = 0; j < maxJ; j++) {\n      line.push(formatNumber(matrix.get(i, j), maxNumSize));\n    }\n    result.push(`${line.join(' ')}`);\n  }\n  if (maxJ !== columns) {\n    result[result.length - 1] += ` ... ${columns - maxColumns} more columns`;\n  }\n  if (maxI !== rows) {\n    result.push(`... ${rows - maxRows} more rows`);\n  }\n  return result.join(`\\n${indentData}`);\n}\n\nfunction formatNumber(num, maxNumSize) {\n  const numStr = String(num);\n  if (numStr.length <= maxNumSize) {\n    return numStr.padEnd(maxNumSize, ' ');\n  }\n  const precise = num.toPrecision(maxNumSize - 2);\n  if (precise.length <= maxNumSize) {\n    return precise;\n  }\n  const exponential = num.toExponential(maxNumSize - 2);\n  const eIndex = exponential.indexOf('e');\n  const e = exponential.slice(eIndex);\n  return exponential.slice(0, maxNumSize - e.length) + e;\n}\n","export function installMathOperations(AbstractMatrix, Matrix) {\r\n  AbstractMatrix.prototype.add = function add(value) {\r\n    if (typeof value === 'number') return this.addS(value);\r\n    return this.addM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.addS = function addS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) + value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.addM = function addM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) + matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.add = function add(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.add(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.sub = function sub(value) {\r\n    if (typeof value === 'number') return this.subS(value);\r\n    return this.subM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.subS = function subS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) - value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.subM = function subM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) - matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sub = function sub(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sub(value);\r\n  };\r\n  AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;\r\n  AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;\r\n  AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;\r\n  AbstractMatrix.subtract = AbstractMatrix.sub;\r\n\r\n  AbstractMatrix.prototype.mul = function mul(value) {\r\n    if (typeof value === 'number') return this.mulS(value);\r\n    return this.mulM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.mulS = function mulS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) * value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.mulM = function mulM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) * matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.mul = function mul(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.mul(value);\r\n  };\r\n  AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;\r\n  AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;\r\n  AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;\r\n  AbstractMatrix.multiply = AbstractMatrix.mul;\r\n\r\n  AbstractMatrix.prototype.div = function div(value) {\r\n    if (typeof value === 'number') return this.divS(value);\r\n    return this.divM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.divS = function divS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) / value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.divM = function divM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) / matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.div = function div(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.div(value);\r\n  };\r\n  AbstractMatrix.prototype.divide = AbstractMatrix.prototype.div;\r\n  AbstractMatrix.prototype.divideS = AbstractMatrix.prototype.divS;\r\n  AbstractMatrix.prototype.divideM = AbstractMatrix.prototype.divM;\r\n  AbstractMatrix.divide = AbstractMatrix.div;\r\n\r\n  AbstractMatrix.prototype.mod = function mod(value) {\r\n    if (typeof value === 'number') return this.modS(value);\r\n    return this.modM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.modS = function modS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) % value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.modM = function modM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) % matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.mod = function mod(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.mod(value);\r\n  };\r\n  AbstractMatrix.prototype.modulus = AbstractMatrix.prototype.mod;\r\n  AbstractMatrix.prototype.modulusS = AbstractMatrix.prototype.modS;\r\n  AbstractMatrix.prototype.modulusM = AbstractMatrix.prototype.modM;\r\n  AbstractMatrix.modulus = AbstractMatrix.mod;\r\n\r\n  AbstractMatrix.prototype.and = function and(value) {\r\n    if (typeof value === 'number') return this.andS(value);\r\n    return this.andM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.andS = function andS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) & value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.andM = function andM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) & matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.and = function and(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.and(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.or = function or(value) {\r\n    if (typeof value === 'number') return this.orS(value);\r\n    return this.orM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.orS = function orS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) | value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.orM = function orM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) | matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.or = function or(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.or(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.xor = function xor(value) {\r\n    if (typeof value === 'number') return this.xorS(value);\r\n    return this.xorM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.xorS = function xorS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) ^ value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.xorM = function xorM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) ^ matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.xor = function xor(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.xor(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.leftShift = function leftShift(value) {\r\n    if (typeof value === 'number') return this.leftShiftS(value);\r\n    return this.leftShiftM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.leftShiftS = function leftShiftS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) << value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.leftShiftM = function leftShiftM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) << matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.leftShift = function leftShift(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.leftShift(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.signPropagatingRightShift = function signPropagatingRightShift(value) {\r\n    if (typeof value === 'number') return this.signPropagatingRightShiftS(value);\r\n    return this.signPropagatingRightShiftM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.signPropagatingRightShiftS = function signPropagatingRightShiftS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >> value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.signPropagatingRightShiftM = function signPropagatingRightShiftM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >> matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.signPropagatingRightShift = function signPropagatingRightShift(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.signPropagatingRightShift(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.rightShift = function rightShift(value) {\r\n    if (typeof value === 'number') return this.rightShiftS(value);\r\n    return this.rightShiftM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.rightShiftS = function rightShiftS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >>> value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.rightShiftM = function rightShiftM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >>> matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.rightShift = function rightShift(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.rightShift(value);\r\n  };\r\n  AbstractMatrix.prototype.zeroFillRightShift = AbstractMatrix.prototype.rightShift;\r\n  AbstractMatrix.prototype.zeroFillRightShiftS = AbstractMatrix.prototype.rightShiftS;\r\n  AbstractMatrix.prototype.zeroFillRightShiftM = AbstractMatrix.prototype.rightShiftM;\r\n  AbstractMatrix.zeroFillRightShift = AbstractMatrix.rightShift;\r\n\r\n  AbstractMatrix.prototype.not = function not() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, ~(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.not = function not(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.not();\r\n  };\r\n\r\n  AbstractMatrix.prototype.abs = function abs() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.abs(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.abs = function abs(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.abs();\r\n  };\r\n\r\n  AbstractMatrix.prototype.acos = function acos() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.acos(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.acos = function acos(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.acos();\r\n  };\r\n\r\n  AbstractMatrix.prototype.acosh = function acosh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.acosh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.acosh = function acosh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.acosh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.asin = function asin() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.asin(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.asin = function asin(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.asin();\r\n  };\r\n\r\n  AbstractMatrix.prototype.asinh = function asinh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.asinh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.asinh = function asinh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.asinh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.atan = function atan() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.atan(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.atan = function atan(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.atan();\r\n  };\r\n\r\n  AbstractMatrix.prototype.atanh = function atanh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.atanh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.atanh = function atanh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.atanh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.cbrt = function cbrt() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.cbrt(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.cbrt = function cbrt(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.cbrt();\r\n  };\r\n\r\n  AbstractMatrix.prototype.ceil = function ceil() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.ceil(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.ceil = function ceil(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.ceil();\r\n  };\r\n\r\n  AbstractMatrix.prototype.clz32 = function clz32() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.clz32(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.clz32 = function clz32(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.clz32();\r\n  };\r\n\r\n  AbstractMatrix.prototype.cos = function cos() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.cos(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.cos = function cos(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.cos();\r\n  };\r\n\r\n  AbstractMatrix.prototype.cosh = function cosh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.cosh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.cosh = function cosh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.cosh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.exp = function exp() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.exp(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.exp = function exp(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.exp();\r\n  };\r\n\r\n  AbstractMatrix.prototype.expm1 = function expm1() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.expm1(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.expm1 = function expm1(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.expm1();\r\n  };\r\n\r\n  AbstractMatrix.prototype.floor = function floor() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.floor(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.floor = function floor(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.floor();\r\n  };\r\n\r\n  AbstractMatrix.prototype.fround = function fround() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.fround(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.fround = function fround(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.fround();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log = function log() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log = function log(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log1p = function log1p() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log1p(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log1p = function log1p(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log1p();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log10 = function log10() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log10(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log10 = function log10(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log10();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log2 = function log2() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log2(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log2 = function log2(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log2();\r\n  };\r\n\r\n  AbstractMatrix.prototype.round = function round() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.round(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.round = function round(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.round();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sign = function sign() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sign(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sign = function sign(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sign();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sin = function sin() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sin(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sin = function sin(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sin();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sinh = function sinh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sinh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sinh = function sinh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sinh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sqrt = function sqrt() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sqrt(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sqrt = function sqrt(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sqrt();\r\n  };\r\n\r\n  AbstractMatrix.prototype.tan = function tan() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.tan(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.tan = function tan(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.tan();\r\n  };\r\n\r\n  AbstractMatrix.prototype.tanh = function tanh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.tanh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.tanh = function tanh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.tanh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.trunc = function trunc() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.trunc(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.trunc = function trunc(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.trunc();\r\n  };\r\n\r\n  AbstractMatrix.pow = function pow(matrix, arg0) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.pow(arg0);\r\n  };\r\n\r\n  AbstractMatrix.prototype.pow = function pow(value) {\r\n    if (typeof value === 'number') return this.powS(value);\r\n    return this.powM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.powS = function powS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.pow(this.get(i, j), value));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.powM = function powM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.pow(this.get(i, j), matrix.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n}\r\n","/**\n * @private\n * Check that a row index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkRowIndex(matrix, index, outer) {\n  let max = outer ? matrix.rows : matrix.rows - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Row index out of range');\n  }\n}\n\n/**\n * @private\n * Check that a column index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkColumnIndex(matrix, index, outer) {\n  let max = outer ? matrix.columns : matrix.columns - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Column index out of range');\n  }\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkRowVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.columns) {\n    throw new RangeError(\n      'vector size must be the same as the number of columns',\n    );\n  }\n  return vector;\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkColumnVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.rows) {\n    throw new RangeError('vector size must be the same as the number of rows');\n  }\n  return vector;\n}\n\nexport function checkIndices(matrix, rowIndices, columnIndices) {\n  return {\n    row: checkRowIndices(matrix, rowIndices),\n    column: checkColumnIndices(matrix, columnIndices),\n  };\n}\n\nexport function checkRowIndices(matrix, rowIndices) {\n  if (typeof rowIndices !== 'object') {\n    throw new TypeError('unexpected type for row indices');\n  }\n\n  let rowOut = rowIndices.some((r) => {\n    return r < 0 || r >= matrix.rows;\n  });\n\n  if (rowOut) {\n    throw new RangeError('row indices are out of range');\n  }\n\n  if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);\n\n  return rowIndices;\n}\n\nexport function checkColumnIndices(matrix, columnIndices) {\n  if (typeof columnIndices !== 'object') {\n    throw new TypeError('unexpected type for column indices');\n  }\n\n  let columnOut = columnIndices.some((c) => {\n    return c < 0 || c >= matrix.columns;\n  });\n\n  if (columnOut) {\n    throw new RangeError('column indices are out of range');\n  }\n  if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);\n\n  return columnIndices;\n}\n\nexport function checkRange(matrix, startRow, endRow, startColumn, endColumn) {\n  if (arguments.length !== 5) {\n    throw new RangeError('expected 4 arguments');\n  }\n  checkNumber('startRow', startRow);\n  checkNumber('endRow', endRow);\n  checkNumber('startColumn', startColumn);\n  checkNumber('endColumn', endColumn);\n  if (\n    startRow > endRow ||\n    startColumn > endColumn ||\n    startRow < 0 ||\n    startRow >= matrix.rows ||\n    endRow < 0 ||\n    endRow >= matrix.rows ||\n    startColumn < 0 ||\n    startColumn >= matrix.columns ||\n    endColumn < 0 ||\n    endColumn >= matrix.columns\n  ) {\n    throw new RangeError('Submatrix indices are out of range');\n  }\n}\n\nexport function newArray(length, value = 0) {\n  let array = [];\n  for (let i = 0; i < length; i++) {\n    array.push(value);\n  }\n  return array;\n}\n\nfunction checkNumber(name, value) {\n  if (typeof value !== 'number') {\n    throw new TypeError(`${name} must be a number`);\n  }\n}\n","import { newArray } from './util';\n\nexport function sumByRow(matrix) {\n  let sum = newArray(matrix.rows);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumByColumn(matrix) {\n  let sum = newArray(matrix.columns);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumAll(matrix) {\n  let v = 0;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v += matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function productByRow(matrix) {\n  let sum = newArray(matrix.rows, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productByColumn(matrix) {\n  let sum = newArray(matrix.columns, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productAll(matrix) {\n  let v = 1;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v *= matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function varianceByRow(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let i = 0; i < rows; i++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean[i];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / cols) / (cols - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / cols) / cols);\n    }\n  }\n  return variance;\n}\n\nexport function varianceByColumn(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let j = 0; j < cols; j++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let i = 0; i < rows; i++) {\n      x = matrix.get(i, j) - mean[j];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / rows) / (rows - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / rows) / rows);\n    }\n  }\n  return variance;\n}\n\nexport function varianceAll(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const size = rows * cols;\n\n  let sum1 = 0;\n  let sum2 = 0;\n  let x = 0;\n  for (let i = 0; i < rows; i++) {\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean;\n      sum1 += x;\n      sum2 += x * x;\n    }\n  }\n  if (unbiased) {\n    return (sum2 - (sum1 * sum1) / size) / (size - 1);\n  } else {\n    return (sum2 - (sum1 * sum1) / size) / size;\n  }\n}\n\nexport function centerByRow(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[i]);\n    }\n  }\n}\n\nexport function centerByColumn(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[j]);\n    }\n  }\n}\n\nexport function centerAll(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean);\n    }\n  }\n}\n\nexport function getScaleByRow(matrix) {\n  const scale = [];\n  for (let i = 0; i < matrix.rows; i++) {\n    let sum = 0;\n    for (let j = 0; j < matrix.columns; j++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.columns - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByRow(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[i]);\n    }\n  }\n}\n\nexport function getScaleByColumn(matrix) {\n  const scale = [];\n  for (let j = 0; j < matrix.columns; j++) {\n    let sum = 0;\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.rows - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByColumn(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[j]);\n    }\n  }\n}\n\nexport function getScaleAll(matrix) {\n  const divider = matrix.size - 1;\n  let sum = 0;\n  for (let j = 0; j < matrix.columns; j++) {\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / divider;\n    }\n  }\n  return Math.sqrt(sum);\n}\n\nexport function scaleAll(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale);\n    }\n  }\n}\n","import rescale from 'ml-array-rescale';\n\nimport { inspectMatrix, inspectMatrixWithOptions } from './inspect';\nimport { installMathOperations } from './mathOperations';\nimport {\n  sumByRow,\n  sumByColumn,\n  sumAll,\n  productByRow,\n  productByColumn,\n  productAll,\n  varianceByRow,\n  varianceByColumn,\n  varianceAll,\n  centerByRow,\n  centerByColumn,\n  centerAll,\n  scaleByRow,\n  scaleByColumn,\n  scaleAll,\n  getScaleByRow,\n  getScaleByColumn,\n  getScaleAll,\n} from './stat';\nimport {\n  checkRowVector,\n  checkRowIndex,\n  checkColumnIndex,\n  checkColumnVector,\n  checkRange,\n  checkIndices,\n} from './util';\n\nexport class AbstractMatrix {\n  static from1DArray(newRows, newColumns, newData) {\n    let length = newRows * newColumns;\n    if (length !== newData.length) {\n      throw new RangeError('data length does not match given dimensions');\n    }\n    let newMatrix = new Matrix(newRows, newColumns);\n    for (let row = 0; row < newRows; row++) {\n      for (let column = 0; column < newColumns; column++) {\n        newMatrix.set(row, column, newData[row * newColumns + column]);\n      }\n    }\n    return newMatrix;\n  }\n\n  static rowVector(newData) {\n    let vector = new Matrix(1, newData.length);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(0, i, newData[i]);\n    }\n    return vector;\n  }\n\n  static columnVector(newData) {\n    let vector = new Matrix(newData.length, 1);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(i, 0, newData[i]);\n    }\n    return vector;\n  }\n\n  static zeros(rows, columns) {\n    return new Matrix(rows, columns);\n  }\n\n  static ones(rows, columns) {\n    return new Matrix(rows, columns).fill(1);\n  }\n\n  static rand(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { random = Math.random } = options;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.set(i, j, random());\n      }\n    }\n    return matrix;\n  }\n\n  static randInt(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1000, random = Math.random } = options;\n    if (!Number.isInteger(min)) throw new TypeError('min must be an integer');\n    if (!Number.isInteger(max)) throw new TypeError('max must be an integer');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let interval = max - min;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        let value = min + Math.round(random() * interval);\n        matrix.set(i, j, value);\n      }\n    }\n    return matrix;\n  }\n\n  static eye(rows, columns, value) {\n    if (columns === undefined) columns = rows;\n    if (value === undefined) value = 1;\n    let min = Math.min(rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, value);\n    }\n    return matrix;\n  }\n\n  static diag(data, rows, columns) {\n    let l = data.length;\n    if (rows === undefined) rows = l;\n    if (columns === undefined) columns = rows;\n    let min = Math.min(l, rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, data[i]);\n    }\n    return matrix;\n  }\n\n  static min(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static max(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new this(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static checkMatrix(value) {\n    return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);\n  }\n\n  static isMatrix(value) {\n    return value != null && value.klass === 'Matrix';\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  apply(callback) {\n    if (typeof callback !== 'function') {\n      throw new TypeError('callback must be a function');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        callback.call(this, i, j);\n      }\n    }\n    return this;\n  }\n\n  to1DArray() {\n    let array = [];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        array.push(this.get(i, j));\n      }\n    }\n    return array;\n  }\n\n  to2DArray() {\n    let copy = [];\n    for (let i = 0; i < this.rows; i++) {\n      copy.push([]);\n      for (let j = 0; j < this.columns; j++) {\n        copy[i].push(this.get(i, j));\n      }\n    }\n    return copy;\n  }\n\n  toJSON() {\n    return this.to2DArray();\n  }\n\n  isRowVector() {\n    return this.rows === 1;\n  }\n\n  isColumnVector() {\n    return this.columns === 1;\n  }\n\n  isVector() {\n    return this.rows === 1 || this.columns === 1;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (this.isSquare()) {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j <= i; j++) {\n          if (this.get(i, j) !== this.get(j, i)) {\n            return false;\n          }\n        }\n      }\n      return true;\n    }\n    return false;\n  }\n\n  isEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isEchelonForm = false;\n          checked = true;\n        }\n      }\n      i++;\n    }\n    return isEchelonForm;\n  }\n\n  isReducedEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isReducedEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isReducedEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isReducedEchelonForm = false;\n          checked = true;\n        }\n      }\n      for (let k = j + 1; k < this.rows; k++) {\n        if (this.get(i, k) !== 0) {\n          isReducedEchelonForm = false;\n        }\n      }\n      i++;\n    }\n    return isReducedEchelonForm;\n  }\n\n  echelonForm() {\n    let result = this.clone();\n    let h = 0;\n    let k = 0;\n    while (h < result.rows && k < result.columns) {\n      let iMax = h;\n      for (let i = h; i < result.rows; i++) {\n        if (result.get(i, k) > result.get(iMax, k)) {\n          iMax = i;\n        }\n      }\n      if (result.get(iMax, k) === 0) {\n        k++;\n      } else {\n        result.swapRows(h, iMax);\n        let tmp = result.get(h, k);\n        for (let j = k; j < result.columns; j++) {\n          result.set(h, j, result.get(h, j) / tmp);\n        }\n        for (let i = h + 1; i < result.rows; i++) {\n          let factor = result.get(i, k) / result.get(h, k);\n          result.set(i, k, 0);\n          for (let j = k + 1; j < result.columns; j++) {\n            result.set(i, j, result.get(i, j) - result.get(h, j) * factor);\n          }\n        }\n        h++;\n        k++;\n      }\n    }\n    return result;\n  }\n\n  reducedEchelonForm() {\n    let result = this.echelonForm();\n    let m = result.columns;\n    let n = result.rows;\n    let h = n - 1;\n    while (h >= 0) {\n      if (result.maxRow(h) === 0) {\n        h--;\n      } else {\n        let p = 0;\n        let pivot = false;\n        while (p < n && pivot === false) {\n          if (result.get(h, p) === 1) {\n            pivot = true;\n          } else {\n            p++;\n          }\n        }\n        for (let i = 0; i < h; i++) {\n          let factor = result.get(i, p);\n          for (let j = p; j < m; j++) {\n            let tmp = result.get(i, j) - factor * result.get(h, j);\n            result.set(i, j, tmp);\n          }\n        }\n        h--;\n      }\n    }\n    return result;\n  }\n\n  set() {\n    throw new Error('set method is unimplemented');\n  }\n\n  get() {\n    throw new Error('get method is unimplemented');\n  }\n\n  repeat(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { rows = 1, columns = 1 } = options;\n    if (!Number.isInteger(rows) || rows <= 0) {\n      throw new TypeError('rows must be a positive integer');\n    }\n    if (!Number.isInteger(columns) || columns <= 0) {\n      throw new TypeError('columns must be a positive integer');\n    }\n    let matrix = new Matrix(this.rows * rows, this.columns * columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.setSubMatrix(this, this.rows * i, this.columns * j);\n      }\n    }\n    return matrix;\n  }\n\n  fill(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, value);\n      }\n    }\n    return this;\n  }\n\n  neg() {\n    return this.mulS(-1);\n  }\n\n  getRow(index) {\n    checkRowIndex(this, index);\n    let row = [];\n    for (let i = 0; i < this.columns; i++) {\n      row.push(this.get(index, i));\n    }\n    return row;\n  }\n\n  getRowVector(index) {\n    return Matrix.rowVector(this.getRow(index));\n  }\n\n  setRow(index, array) {\n    checkRowIndex(this, index);\n    array = checkRowVector(this, array);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, array[i]);\n    }\n    return this;\n  }\n\n  swapRows(row1, row2) {\n    checkRowIndex(this, row1);\n    checkRowIndex(this, row2);\n    for (let i = 0; i < this.columns; i++) {\n      let temp = this.get(row1, i);\n      this.set(row1, i, this.get(row2, i));\n      this.set(row2, i, temp);\n    }\n    return this;\n  }\n\n  getColumn(index) {\n    checkColumnIndex(this, index);\n    let column = [];\n    for (let i = 0; i < this.rows; i++) {\n      column.push(this.get(i, index));\n    }\n    return column;\n  }\n\n  getColumnVector(index) {\n    return Matrix.columnVector(this.getColumn(index));\n  }\n\n  setColumn(index, array) {\n    checkColumnIndex(this, index);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, array[i]);\n    }\n    return this;\n  }\n\n  swapColumns(column1, column2) {\n    checkColumnIndex(this, column1);\n    checkColumnIndex(this, column2);\n    for (let i = 0; i < this.rows; i++) {\n      let temp = this.get(i, column1);\n      this.set(i, column1, this.get(i, column2));\n      this.set(i, column2, temp);\n    }\n    return this;\n  }\n\n  addRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[j]);\n      }\n    }\n    return this;\n  }\n\n  subRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[j]);\n      }\n    }\n    return this;\n  }\n\n  mulRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[j]);\n      }\n    }\n    return this;\n  }\n\n  divRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[j]);\n      }\n    }\n    return this;\n  }\n\n  addColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[i]);\n      }\n    }\n    return this;\n  }\n\n  subColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[i]);\n      }\n    }\n    return this;\n  }\n\n  divColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulRow(index, value) {\n    checkRowIndex(this, index);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, this.get(index, i) * value);\n    }\n    return this;\n  }\n\n  mulColumn(index, value) {\n    checkColumnIndex(this, index);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, this.get(i, index) * value);\n    }\n    return this;\n  }\n\n  max() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  maxIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  min() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  minIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  maxRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  maxRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  minRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  minRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  maxColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  maxColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  minColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  minColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  diag() {\n    let min = Math.min(this.rows, this.columns);\n    let diag = [];\n    for (let i = 0; i < min; i++) {\n      diag.push(this.get(i, i));\n    }\n    return diag;\n  }\n\n  norm(type = 'frobenius') {\n    let result = 0;\n    if (type === 'max') {\n      return this.max();\n    } else if (type === 'frobenius') {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j < this.columns; j++) {\n          result = result + this.get(i, j) * this.get(i, j);\n        }\n      }\n      return Math.sqrt(result);\n    } else {\n      throw new RangeError(`unknown norm type: ${type}`);\n    }\n  }\n\n  cumulativeSum() {\n    let sum = 0;\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        sum += this.get(i, j);\n        this.set(i, j, sum);\n      }\n    }\n    return this;\n  }\n\n  dot(vector2) {\n    if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();\n    let vector1 = this.to1DArray();\n    if (vector1.length !== vector2.length) {\n      throw new RangeError('vectors do not have the same size');\n    }\n    let dot = 0;\n    for (let i = 0; i < vector1.length; i++) {\n      dot += vector1[i] * vector2[i];\n    }\n    return dot;\n  }\n\n  mmul(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.columns;\n\n    let result = new Matrix(m, p);\n\n    let Bcolj = new Float64Array(n);\n    for (let j = 0; j < p; j++) {\n      for (let k = 0; k < n; k++) {\n        Bcolj[k] = other.get(k, j);\n      }\n\n      for (let i = 0; i < m; i++) {\n        let s = 0;\n        for (let k = 0; k < n; k++) {\n          s += this.get(i, k) * Bcolj[k];\n        }\n\n        result.set(i, j, s);\n      }\n    }\n    return result;\n  }\n\n  strassen2x2(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(2, 2);\n    const a11 = this.get(0, 0);\n    const b11 = other.get(0, 0);\n    const a12 = this.get(0, 1);\n    const b12 = other.get(0, 1);\n    const a21 = this.get(1, 0);\n    const b21 = other.get(1, 0);\n    const a22 = this.get(1, 1);\n    const b22 = other.get(1, 1);\n\n    // Compute intermediate values.\n    const m1 = (a11 + a22) * (b11 + b22);\n    const m2 = (a21 + a22) * b11;\n    const m3 = a11 * (b12 - b22);\n    const m4 = a22 * (b21 - b11);\n    const m5 = (a11 + a12) * b22;\n    const m6 = (a21 - a11) * (b11 + b12);\n    const m7 = (a12 - a22) * (b21 + b22);\n\n    // Combine intermediate values into the output.\n    const c00 = m1 + m4 - m5 + m7;\n    const c01 = m3 + m5;\n    const c10 = m2 + m4;\n    const c11 = m1 - m2 + m3 + m6;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    return result;\n  }\n\n  strassen3x3(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(3, 3);\n\n    const a00 = this.get(0, 0);\n    const a01 = this.get(0, 1);\n    const a02 = this.get(0, 2);\n    const a10 = this.get(1, 0);\n    const a11 = this.get(1, 1);\n    const a12 = this.get(1, 2);\n    const a20 = this.get(2, 0);\n    const a21 = this.get(2, 1);\n    const a22 = this.get(2, 2);\n\n    const b00 = other.get(0, 0);\n    const b01 = other.get(0, 1);\n    const b02 = other.get(0, 2);\n    const b10 = other.get(1, 0);\n    const b11 = other.get(1, 1);\n    const b12 = other.get(1, 2);\n    const b20 = other.get(2, 0);\n    const b21 = other.get(2, 1);\n    const b22 = other.get(2, 2);\n\n    const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;\n    const m2 = (a00 - a10) * (-b01 + b11);\n    const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);\n    const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);\n    const m5 = (a10 + a11) * (-b00 + b01);\n    const m6 = a00 * b00;\n    const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);\n    const m8 = (-a00 + a20) * (b02 - b12);\n    const m9 = (a20 + a21) * (-b00 + b02);\n    const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;\n    const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);\n    const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);\n    const m13 = (a02 - a22) * (b11 - b21);\n    const m14 = a02 * b20;\n    const m15 = (a21 + a22) * (-b20 + b21);\n    const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);\n    const m17 = (a02 - a12) * (b12 - b22);\n    const m18 = (a11 + a12) * (-b20 + b22);\n    const m19 = a01 * b10;\n    const m20 = a12 * b21;\n    const m21 = a10 * b02;\n    const m22 = a20 * b01;\n    const m23 = a22 * b22;\n\n    const c00 = m6 + m14 + m19;\n    const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;\n    const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;\n    const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;\n    const c11 = m2 + m4 + m5 + m6 + m20;\n    const c12 = m14 + m16 + m17 + m18 + m21;\n    const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;\n    const c21 = m12 + m13 + m14 + m15 + m22;\n    const c22 = m6 + m7 + m8 + m9 + m23;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(0, 2, c02);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    result.set(1, 2, c12);\n    result.set(2, 0, c20);\n    result.set(2, 1, c21);\n    result.set(2, 2, c22);\n    return result;\n  }\n\n  mmulStrassen(y) {\n    y = Matrix.checkMatrix(y);\n    let x = this.clone();\n    let r1 = x.rows;\n    let c1 = x.columns;\n    let r2 = y.rows;\n    let c2 = y.columns;\n    if (c1 !== r2) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        `Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`,\n      );\n    }\n\n    // Put a matrix into the top left of a matrix of zeros.\n    // `rows` and `cols` are the dimensions of the output matrix.\n    function embed(mat, rows, cols) {\n      let r = mat.rows;\n      let c = mat.columns;\n      if (r === rows && c === cols) {\n        return mat;\n      } else {\n        let resultat = AbstractMatrix.zeros(rows, cols);\n        resultat = resultat.setSubMatrix(mat, 0, 0);\n        return resultat;\n      }\n    }\n\n    // Make sure both matrices are the same size.\n    // This is exclusively for simplicity:\n    // this algorithm can be implemented with matrices of different sizes.\n\n    let r = Math.max(r1, r2);\n    let c = Math.max(c1, c2);\n    x = embed(x, r, c);\n    y = embed(y, r, c);\n\n    // Our recursive multiplication function.\n    function blockMult(a, b, rows, cols) {\n      // For small matrices, resort to naive multiplication.\n      if (rows <= 512 || cols <= 512) {\n        return a.mmul(b); // a is equivalent to this\n      }\n\n      // Apply dynamic padding.\n      if (rows % 2 === 1 && cols % 2 === 1) {\n        a = embed(a, rows + 1, cols + 1);\n        b = embed(b, rows + 1, cols + 1);\n      } else if (rows % 2 === 1) {\n        a = embed(a, rows + 1, cols);\n        b = embed(b, rows + 1, cols);\n      } else if (cols % 2 === 1) {\n        a = embed(a, rows, cols + 1);\n        b = embed(b, rows, cols + 1);\n      }\n\n      let halfRows = parseInt(a.rows / 2, 10);\n      let halfCols = parseInt(a.columns / 2, 10);\n      // Subdivide input matrices.\n      let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n      let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n\n      let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);\n      let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);\n\n      let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);\n      let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);\n\n      let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);\n      let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);\n\n      // Compute intermediate values.\n      let m1 = blockMult(\n        AbstractMatrix.add(a11, a22),\n        AbstractMatrix.add(b11, b22),\n        halfRows,\n        halfCols,\n      );\n      let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);\n      let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);\n      let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);\n      let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);\n      let m6 = blockMult(\n        AbstractMatrix.sub(a21, a11),\n        AbstractMatrix.add(b11, b12),\n        halfRows,\n        halfCols,\n      );\n      let m7 = blockMult(\n        AbstractMatrix.sub(a12, a22),\n        AbstractMatrix.add(b21, b22),\n        halfRows,\n        halfCols,\n      );\n\n      // Combine intermediate values into the output.\n      let c11 = AbstractMatrix.add(m1, m4);\n      c11.sub(m5);\n      c11.add(m7);\n      let c12 = AbstractMatrix.add(m3, m5);\n      let c21 = AbstractMatrix.add(m2, m4);\n      let c22 = AbstractMatrix.sub(m1, m2);\n      c22.add(m3);\n      c22.add(m6);\n\n      // Crop output to the desired size (undo dynamic padding).\n      let resultat = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);\n      resultat = resultat.setSubMatrix(c11, 0, 0);\n      resultat = resultat.setSubMatrix(c12, c11.rows, 0);\n      resultat = resultat.setSubMatrix(c21, 0, c11.columns);\n      resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);\n      return resultat.subMatrix(0, rows - 1, 0, cols - 1);\n    }\n    return blockMult(x, y, r, c);\n  }\n\n  scaleRows(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.rows; i++) {\n      const row = this.getRow(i);\n      rescale(row, { min, max, output: row });\n      newMatrix.setRow(i, row);\n    }\n    return newMatrix;\n  }\n\n  scaleColumns(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.columns; i++) {\n      const column = this.getColumn(i);\n      rescale(column, {\n        min: min,\n        max: max,\n        output: column,\n      });\n      newMatrix.setColumn(i, column);\n    }\n    return newMatrix;\n  }\n\n  flipRows() {\n    const middle = Math.ceil(this.columns / 2);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < middle; j++) {\n        let first = this.get(i, j);\n        let last = this.get(i, this.columns - 1 - j);\n        this.set(i, j, last);\n        this.set(i, this.columns - 1 - j, first);\n      }\n    }\n    return this;\n  }\n\n  flipColumns() {\n    const middle = Math.ceil(this.rows / 2);\n    for (let j = 0; j < this.columns; j++) {\n      for (let i = 0; i < middle; i++) {\n        let first = this.get(i, j);\n        let last = this.get(this.rows - 1 - i, j);\n        this.set(i, j, last);\n        this.set(this.rows - 1 - i, j, first);\n      }\n    }\n    return this;\n  }\n\n  kroneckerProduct(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.rows;\n    let q = other.columns;\n\n    let result = new Matrix(m * p, n * q);\n    for (let i = 0; i < m; i++) {\n      for (let j = 0; j < n; j++) {\n        for (let k = 0; k < p; k++) {\n          for (let l = 0; l < q; l++) {\n            result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));\n          }\n        }\n      }\n    }\n    return result;\n  }\n\n  transpose() {\n    let result = new Matrix(this.columns, this.rows);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        result.set(j, i, this.get(i, j));\n      }\n    }\n    return result;\n  }\n\n  sortRows(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.rows; i++) {\n      this.setRow(i, this.getRow(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  sortColumns(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.columns; i++) {\n      this.setColumn(i, this.getColumn(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  subMatrix(startRow, endRow, startColumn, endColumn) {\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    let newMatrix = new Matrix(\n      endRow - startRow + 1,\n      endColumn - startColumn + 1,\n    );\n    for (let i = startRow; i <= endRow; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        newMatrix.set(i - startRow, j - startColumn, this.get(i, j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixRow(indices, startColumn, endColumn) {\n    if (startColumn === undefined) startColumn = 0;\n    if (endColumn === undefined) endColumn = this.columns - 1;\n    if (\n      startColumn > endColumn ||\n      startColumn < 0 ||\n      startColumn >= this.columns ||\n      endColumn < 0 ||\n      endColumn >= this.columns\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        if (indices[i] < 0 || indices[i] >= this.rows) {\n          throw new RangeError(`Row index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(i, j - startColumn, this.get(indices[i], j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixColumn(indices, startRow, endRow) {\n    if (startRow === undefined) startRow = 0;\n    if (endRow === undefined) endRow = this.rows - 1;\n    if (\n      startRow > endRow ||\n      startRow < 0 ||\n      startRow >= this.rows ||\n      endRow < 0 ||\n      endRow >= this.rows\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(endRow - startRow + 1, indices.length);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startRow; j <= endRow; j++) {\n        if (indices[i] < 0 || indices[i] >= this.columns) {\n          throw new RangeError(`Column index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(j - startRow, i, this.get(j, indices[i]));\n      }\n    }\n    return newMatrix;\n  }\n\n  setSubMatrix(matrix, startRow, startColumn) {\n    matrix = Matrix.checkMatrix(matrix);\n    let endRow = startRow + matrix.rows - 1;\n    let endColumn = startColumn + matrix.columns - 1;\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    for (let i = 0; i < matrix.rows; i++) {\n      for (let j = 0; j < matrix.columns; j++) {\n        this.set(startRow + i, startColumn + j, matrix.get(i, j));\n      }\n    }\n    return this;\n  }\n\n  selection(rowIndices, columnIndices) {\n    let indices = checkIndices(this, rowIndices, columnIndices);\n    let newMatrix = new Matrix(rowIndices.length, columnIndices.length);\n    for (let i = 0; i < indices.row.length; i++) {\n      let rowIndex = indices.row[i];\n      for (let j = 0; j < indices.column.length; j++) {\n        let columnIndex = indices.column[j];\n        newMatrix.set(i, j, this.get(rowIndex, columnIndex));\n      }\n    }\n    return newMatrix;\n  }\n\n  trace() {\n    let min = Math.min(this.rows, this.columns);\n    let trace = 0;\n    for (let i = 0; i < min; i++) {\n      trace += this.get(i, i);\n    }\n    return trace;\n  }\n\n  clone() {\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let row = 0; row < this.rows; row++) {\n      for (let column = 0; column < this.columns; column++) {\n        newMatrix.set(row, column, this.get(row, column));\n      }\n    }\n    return newMatrix;\n  }\n\n  sum(by) {\n    switch (by) {\n      case 'row':\n        return sumByRow(this);\n      case 'column':\n        return sumByColumn(this);\n      case undefined:\n        return sumAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  product(by) {\n    switch (by) {\n      case 'row':\n        return productByRow(this);\n      case 'column':\n        return productByColumn(this);\n      case undefined:\n        return productAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  mean(by) {\n    const sum = this.sum(by);\n    switch (by) {\n      case 'row': {\n        for (let i = 0; i < this.rows; i++) {\n          sum[i] /= this.columns;\n        }\n        return sum;\n      }\n      case 'column': {\n        for (let i = 0; i < this.columns; i++) {\n          sum[i] /= this.rows;\n        }\n        return sum;\n      }\n      case undefined:\n        return sum / this.size;\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  variance(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { unbiased = true, mean = this.mean(by) } = options;\n    if (typeof unbiased !== 'boolean') {\n      throw new TypeError('unbiased must be a boolean');\n    }\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByRow(this, unbiased, mean);\n      }\n      case 'column': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByColumn(this, unbiased, mean);\n      }\n      case undefined: {\n        if (typeof mean !== 'number') {\n          throw new TypeError('mean must be a number');\n        }\n        return varianceAll(this, unbiased, mean);\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  standardDeviation(by, options) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    const variance = this.variance(by, options);\n    if (by === undefined) {\n      return Math.sqrt(variance);\n    } else {\n      for (let i = 0; i < variance.length; i++) {\n        variance[i] = Math.sqrt(variance[i]);\n      }\n      return variance;\n    }\n  }\n\n  center(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { center = this.mean(by) } = options;\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByRow(this, center);\n        return this;\n      }\n      case 'column': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByColumn(this, center);\n        return this;\n      }\n      case undefined: {\n        if (typeof center !== 'number') {\n          throw new TypeError('center must be a number');\n        }\n        centerAll(this, center);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  scale(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    let scale = options.scale;\n    switch (by) {\n      case 'row': {\n        if (scale === undefined) {\n          scale = getScaleByRow(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByRow(this, scale);\n        return this;\n      }\n      case 'column': {\n        if (scale === undefined) {\n          scale = getScaleByColumn(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByColumn(this, scale);\n        return this;\n      }\n      case undefined: {\n        if (scale === undefined) {\n          scale = getScaleAll(this);\n        } else if (typeof scale !== 'number') {\n          throw new TypeError('scale must be a number');\n        }\n        scaleAll(this, scale);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  toString(options) {\n    return inspectMatrixWithOptions(this, options);\n  }\n}\n\nAbstractMatrix.prototype.klass = 'Matrix';\nif (typeof Symbol !== 'undefined') {\n  AbstractMatrix.prototype[\n    Symbol.for('nodejs.util.inspect.custom')\n  ] = inspectMatrix;\n}\n\nfunction compareNumbers(a, b) {\n  return a - b;\n}\n\n// Synonyms\nAbstractMatrix.random = AbstractMatrix.rand;\nAbstractMatrix.randomInt = AbstractMatrix.randInt;\nAbstractMatrix.diagonal = AbstractMatrix.diag;\nAbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;\nAbstractMatrix.identity = AbstractMatrix.eye;\nAbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;\nAbstractMatrix.prototype.tensorProduct =\n  AbstractMatrix.prototype.kroneckerProduct;\n\nexport default class Matrix extends AbstractMatrix {\n  constructor(nRows, nColumns) {\n    super();\n    if (Matrix.isMatrix(nRows)) {\n      return nRows.clone();\n    } else if (Number.isInteger(nRows) && nRows > 0) {\n      // Create an empty matrix\n      this.data = [];\n      if (Number.isInteger(nColumns) && nColumns > 0) {\n        for (let i = 0; i < nRows; i++) {\n          this.data.push(new Float64Array(nColumns));\n        }\n      } else {\n        throw new TypeError('nColumns must be a positive integer');\n      }\n    } else if (Array.isArray(nRows)) {\n      // Copy the values from the 2D array\n      const arrayData = nRows;\n      nRows = arrayData.length;\n      nColumns = arrayData[0].length;\n      if (typeof nColumns !== 'number' || nColumns === 0) {\n        throw new TypeError(\n          'Data must be a 2D array with at least one element',\n        );\n      }\n      this.data = [];\n      for (let i = 0; i < nRows; i++) {\n        if (arrayData[i].length !== nColumns) {\n          throw new RangeError('Inconsistent array dimensions');\n        }\n        this.data.push(Float64Array.from(arrayData[i]));\n      }\n    } else {\n      throw new TypeError(\n        'First argument must be a positive number or an array',\n      );\n    }\n    this.rows = nRows;\n    this.columns = nColumns;\n    return this;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n\n  removeRow(index) {\n    checkRowIndex(this, index);\n    if (this.rows === 1) {\n      throw new RangeError('A matrix cannot have less than one row');\n    }\n    this.data.splice(index, 1);\n    this.rows -= 1;\n    return this;\n  }\n\n  addRow(index, array) {\n    if (array === undefined) {\n      array = index;\n      index = this.rows;\n    }\n    checkRowIndex(this, index, true);\n    array = Float64Array.from(checkRowVector(this, array, true));\n    this.data.splice(index, 0, array);\n    this.rows += 1;\n    return this;\n  }\n\n  removeColumn(index) {\n    checkColumnIndex(this, index);\n    if (this.columns === 1) {\n      throw new RangeError('A matrix cannot have less than one column');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns - 1);\n      for (let j = 0; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      for (let j = index + 1; j < this.columns; j++) {\n        newRow[j - 1] = this.data[i][j];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns -= 1;\n    return this;\n  }\n\n  addColumn(index, array) {\n    if (typeof array === 'undefined') {\n      array = index;\n      index = this.columns;\n    }\n    checkColumnIndex(this, index, true);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns + 1);\n      let j = 0;\n      for (; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      newRow[j++] = array[i];\n      for (; j < this.columns + 1; j++) {\n        newRow[j] = this.data[i][j - 1];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns += 1;\n    return this;\n  }\n}\n\ninstallMathOperations(AbstractMatrix, Matrix);\n","import { AbstractMatrix } from '../matrix';\n\nexport default class BaseView extends AbstractMatrix {\n  constructor(matrix, rows, columns) {\n    super();\n    this.matrix = matrix;\n    this.rows = rows;\n    this.columns = columns;\n  }\n}\n","import { checkColumnIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixColumnView extends BaseView {\n  constructor(matrix, column) {\n    checkColumnIndex(matrix, column);\n    super(matrix, matrix.rows, 1);\n    this.column = column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.column, value);\n    return this;\n  }\n\n  get(rowIndex) {\n    return this.matrix.get(rowIndex, this.column);\n  }\n}\n","import { checkColumnIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixColumnSelectionView extends BaseView {\n  constructor(matrix, columnIndices) {\n    columnIndices = checkColumnIndices(matrix, columnIndices);\n    super(matrix, matrix.rows, columnIndices.length);\n    this.columnIndices = columnIndices;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);\n  }\n}\n","import BaseView from './base';\n\nexport default class MatrixFlipColumnView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.rows, matrix.columns);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(rowIndex, this.columns - columnIndex - 1);\n  }\n}\n","import BaseView from './base';\n\nexport default class MatrixFlipRowView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.rows, matrix.columns);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.rows - rowIndex - 1, columnIndex);\n  }\n}\n","import { checkRowIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixRowView extends BaseView {\n  constructor(matrix, row) {\n    checkRowIndex(matrix, row);\n    super(matrix, 1, matrix.columns);\n    this.row = row;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.row, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.row, columnIndex);\n  }\n}\n","import { checkRowIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixRowSelectionView extends BaseView {\n  constructor(matrix, rowIndices) {\n    rowIndices = checkRowIndices(matrix, rowIndices);\n    super(matrix, rowIndices.length, matrix.columns);\n    this.rowIndices = rowIndices;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.rowIndices[rowIndex], columnIndex);\n  }\n}\n","import { checkIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSelectionView extends BaseView {\n  constructor(matrix, rowIndices, columnIndices) {\n    let indices = checkIndices(matrix, rowIndices, columnIndices);\n    super(matrix, indices.row.length, indices.column.length);\n    this.rowIndices = indices.row;\n    this.columnIndices = indices.column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n    );\n  }\n}\n","import { checkRange } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSubView extends BaseView {\n  constructor(matrix, startRow, endRow, startColumn, endColumn) {\n    checkRange(matrix, startRow, endRow, startColumn, endColumn);\n    super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);\n    this.startRow = startRow;\n    this.startColumn = startColumn;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n    );\n  }\n}\n","import BaseView from './base';\n\nexport default class MatrixTransposeView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.columns, matrix.rows);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(columnIndex, rowIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(columnIndex, rowIndex);\n  }\n}\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix1D extends AbstractMatrix {\n  constructor(data, options = {}) {\n    const { rows = 1 } = options;\n\n    if (data.length % rows !== 0) {\n      throw new Error('the data length is not divisible by the number of rows');\n    }\n    super();\n    this.rows = rows;\n    this.columns = data.length / rows;\n    this.data = data;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    this.data[index] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    return this.data[index];\n  }\n\n  _calculateIndex(row, column) {\n    return row * this.columns + column;\n  }\n}\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix2D extends AbstractMatrix {\n  constructor(data) {\n    super();\n    this.data = data;\n    this.rows = data.length;\n    this.columns = data[0].length;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n}\n","import WrapperMatrix1D from './WrapperMatrix1D';\nimport WrapperMatrix2D from './WrapperMatrix2D';\n\nexport function wrap(array, options) {\n  if (Array.isArray(array)) {\n    if (array[0] && Array.isArray(array[0])) {\n      return new WrapperMatrix2D(array);\n    } else {\n      return new WrapperMatrix1D(array, options);\n    }\n  } else {\n    throw new Error('the argument is not an array');\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class LuDecomposition {\n  constructor(matrix) {\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n\n    let lu = matrix.clone();\n    let rows = lu.rows;\n    let columns = lu.columns;\n    let pivotVector = new Float64Array(rows);\n    let pivotSign = 1;\n    let i, j, k, p, s, t, v;\n    let LUcolj, kmax;\n\n    for (i = 0; i < rows; i++) {\n      pivotVector[i] = i;\n    }\n\n    LUcolj = new Float64Array(rows);\n\n    for (j = 0; j < columns; j++) {\n      for (i = 0; i < rows; i++) {\n        LUcolj[i] = lu.get(i, j);\n      }\n\n      for (i = 0; i < rows; i++) {\n        kmax = Math.min(i, j);\n        s = 0;\n        for (k = 0; k < kmax; k++) {\n          s += lu.get(i, k) * LUcolj[k];\n        }\n        LUcolj[i] -= s;\n        lu.set(i, j, LUcolj[i]);\n      }\n\n      p = j;\n      for (i = j + 1; i < rows; i++) {\n        if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {\n          p = i;\n        }\n      }\n\n      if (p !== j) {\n        for (k = 0; k < columns; k++) {\n          t = lu.get(p, k);\n          lu.set(p, k, lu.get(j, k));\n          lu.set(j, k, t);\n        }\n\n        v = pivotVector[p];\n        pivotVector[p] = pivotVector[j];\n        pivotVector[j] = v;\n\n        pivotSign = -pivotSign;\n      }\n\n      if (j < rows && lu.get(j, j) !== 0) {\n        for (i = j + 1; i < rows; i++) {\n          lu.set(i, j, lu.get(i, j) / lu.get(j, j));\n        }\n      }\n    }\n\n    this.LU = lu;\n    this.pivotVector = pivotVector;\n    this.pivotSign = pivotSign;\n  }\n\n  isSingular() {\n    let data = this.LU;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      if (data.get(j, j) === 0) {\n        return true;\n      }\n    }\n    return false;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let lu = this.LU;\n    let rows = lu.rows;\n\n    if (rows !== value.rows) {\n      throw new Error('Invalid matrix dimensions');\n    }\n    if (this.isSingular()) {\n      throw new Error('LU matrix is singular');\n    }\n\n    let count = value.columns;\n    let X = value.subMatrixRow(this.pivotVector, 0, count - 1);\n    let columns = lu.columns;\n    let i, j, k;\n\n    for (k = 0; k < columns; k++) {\n      for (i = k + 1; i < columns; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    for (k = columns - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / lu.get(k, k));\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    return X;\n  }\n\n  get determinant() {\n    let data = this.LU;\n    if (!data.isSquare()) {\n      throw new Error('Matrix must be square');\n    }\n    let determinant = this.pivotSign;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      determinant *= data.get(j, j);\n    }\n    return determinant;\n  }\n\n  get lowerTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i > j) {\n          X.set(i, j, data.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, 1);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get upperTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i <= j) {\n          X.set(i, j, data.get(i, j));\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get pivotPermutationVector() {\n    return Array.from(this.pivotVector);\n  }\n}\n","export function hypotenuse(a, b) {\n  let r = 0;\n  if (Math.abs(a) > Math.abs(b)) {\n    r = b / a;\n    return Math.abs(a) * Math.sqrt(1 + r * r);\n  }\n  if (b !== 0) {\n    r = a / b;\n    return Math.abs(b) * Math.sqrt(1 + r * r);\n  }\n  return 0;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class QrDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let qr = value.clone();\n    let m = value.rows;\n    let n = value.columns;\n    let rdiag = new Float64Array(n);\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      let nrm = 0;\n      for (i = k; i < m; i++) {\n        nrm = hypotenuse(nrm, qr.get(i, k));\n      }\n      if (nrm !== 0) {\n        if (qr.get(k, k) < 0) {\n          nrm = -nrm;\n        }\n        for (i = k; i < m; i++) {\n          qr.set(i, k, qr.get(i, k) / nrm);\n        }\n        qr.set(k, k, qr.get(k, k) + 1);\n        for (j = k + 1; j < n; j++) {\n          s = 0;\n          for (i = k; i < m; i++) {\n            s += qr.get(i, k) * qr.get(i, j);\n          }\n          s = -s / qr.get(k, k);\n          for (i = k; i < m; i++) {\n            qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n      rdiag[k] = -nrm;\n    }\n\n    this.QR = qr;\n    this.Rdiag = rdiag;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let qr = this.QR;\n    let m = qr.rows;\n\n    if (value.rows !== m) {\n      throw new Error('Matrix row dimensions must agree');\n    }\n    if (!this.isFullRank()) {\n      throw new Error('Matrix is rank deficient');\n    }\n\n    let count = value.columns;\n    let X = value.clone();\n    let n = qr.columns;\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      for (j = 0; j < count; j++) {\n        s = 0;\n        for (i = k; i < m; i++) {\n          s += qr.get(i, k) * X.get(i, j);\n        }\n        s = -s / qr.get(k, k);\n        for (i = k; i < m; i++) {\n          X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n        }\n      }\n    }\n    for (k = n - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / this.Rdiag[k]);\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));\n        }\n      }\n    }\n\n    return X.subMatrix(0, n - 1, 0, count - 1);\n  }\n\n  isFullRank() {\n    let columns = this.QR.columns;\n    for (let i = 0; i < columns; i++) {\n      if (this.Rdiag[i] === 0) {\n        return false;\n      }\n    }\n    return true;\n  }\n\n  get upperTriangularMatrix() {\n    let qr = this.QR;\n    let n = qr.columns;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        if (i < j) {\n          X.set(i, j, qr.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, this.Rdiag[i]);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get orthogonalMatrix() {\n    let qr = this.QR;\n    let rows = qr.rows;\n    let columns = qr.columns;\n    let X = new Matrix(rows, columns);\n    let i, j, k, s;\n\n    for (k = columns - 1; k >= 0; k--) {\n      for (i = 0; i < rows; i++) {\n        X.set(i, k, 0);\n      }\n      X.set(k, k, 1);\n      for (j = k; j < columns; j++) {\n        if (qr.get(k, k) !== 0) {\n          s = 0;\n          for (i = k; i < rows; i++) {\n            s += qr.get(i, k) * X.get(i, j);\n          }\n\n          s = -s / qr.get(k, k);\n\n          for (i = k; i < rows; i++) {\n            X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n    }\n    return X;\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class SingularValueDecomposition {\n  constructor(value, options = {}) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let m = value.rows;\n    let n = value.columns;\n\n    const {\n      computeLeftSingularVectors = true,\n      computeRightSingularVectors = true,\n      autoTranspose = false,\n    } = options;\n\n    let wantu = Boolean(computeLeftSingularVectors);\n    let wantv = Boolean(computeRightSingularVectors);\n\n    let swapped = false;\n    let a;\n    if (m < n) {\n      if (!autoTranspose) {\n        a = value.clone();\n        // eslint-disable-next-line no-console\n        console.warn(\n          'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose',\n        );\n      } else {\n        a = value.transpose();\n        m = a.rows;\n        n = a.columns;\n        swapped = true;\n        let aux = wantu;\n        wantu = wantv;\n        wantv = aux;\n      }\n    } else {\n      a = value.clone();\n    }\n\n    let nu = Math.min(m, n);\n    let ni = Math.min(m + 1, n);\n    let s = new Float64Array(ni);\n    let U = new Matrix(m, nu);\n    let V = new Matrix(n, n);\n\n    let e = new Float64Array(n);\n    let work = new Float64Array(m);\n\n    let si = new Float64Array(ni);\n    for (let i = 0; i < ni; i++) si[i] = i;\n\n    let nct = Math.min(m - 1, n);\n    let nrt = Math.max(0, Math.min(n - 2, m));\n    let mrc = Math.max(nct, nrt);\n\n    for (let k = 0; k < mrc; k++) {\n      if (k < nct) {\n        s[k] = 0;\n        for (let i = k; i < m; i++) {\n          s[k] = hypotenuse(s[k], a.get(i, k));\n        }\n        if (s[k] !== 0) {\n          if (a.get(k, k) < 0) {\n            s[k] = -s[k];\n          }\n          for (let i = k; i < m; i++) {\n            a.set(i, k, a.get(i, k) / s[k]);\n          }\n          a.set(k, k, a.get(k, k) + 1);\n        }\n        s[k] = -s[k];\n      }\n\n      for (let j = k + 1; j < n; j++) {\n        if (k < nct && s[k] !== 0) {\n          let t = 0;\n          for (let i = k; i < m; i++) {\n            t += a.get(i, k) * a.get(i, j);\n          }\n          t = -t / a.get(k, k);\n          for (let i = k; i < m; i++) {\n            a.set(i, j, a.get(i, j) + t * a.get(i, k));\n          }\n        }\n        e[j] = a.get(k, j);\n      }\n\n      if (wantu && k < nct) {\n        for (let i = k; i < m; i++) {\n          U.set(i, k, a.get(i, k));\n        }\n      }\n\n      if (k < nrt) {\n        e[k] = 0;\n        for (let i = k + 1; i < n; i++) {\n          e[k] = hypotenuse(e[k], e[i]);\n        }\n        if (e[k] !== 0) {\n          if (e[k + 1] < 0) {\n            e[k] = 0 - e[k];\n          }\n          for (let i = k + 1; i < n; i++) {\n            e[i] /= e[k];\n          }\n          e[k + 1] += 1;\n        }\n        e[k] = -e[k];\n        if (k + 1 < m && e[k] !== 0) {\n          for (let i = k + 1; i < m; i++) {\n            work[i] = 0;\n          }\n          for (let i = k + 1; i < m; i++) {\n            for (let j = k + 1; j < n; j++) {\n              work[i] += e[j] * a.get(i, j);\n            }\n          }\n          for (let j = k + 1; j < n; j++) {\n            let t = -e[j] / e[k + 1];\n            for (let i = k + 1; i < m; i++) {\n              a.set(i, j, a.get(i, j) + t * work[i]);\n            }\n          }\n        }\n        if (wantv) {\n          for (let i = k + 1; i < n; i++) {\n            V.set(i, k, e[i]);\n          }\n        }\n      }\n    }\n\n    let p = Math.min(n, m + 1);\n    if (nct < n) {\n      s[nct] = a.get(nct, nct);\n    }\n    if (m < p) {\n      s[p - 1] = 0;\n    }\n    if (nrt + 1 < p) {\n      e[nrt] = a.get(nrt, p - 1);\n    }\n    e[p - 1] = 0;\n\n    if (wantu) {\n      for (let j = nct; j < nu; j++) {\n        for (let i = 0; i < m; i++) {\n          U.set(i, j, 0);\n        }\n        U.set(j, j, 1);\n      }\n      for (let k = nct - 1; k >= 0; k--) {\n        if (s[k] !== 0) {\n          for (let j = k + 1; j < nu; j++) {\n            let t = 0;\n            for (let i = k; i < m; i++) {\n              t += U.get(i, k) * U.get(i, j);\n            }\n            t = -t / U.get(k, k);\n            for (let i = k; i < m; i++) {\n              U.set(i, j, U.get(i, j) + t * U.get(i, k));\n            }\n          }\n          for (let i = k; i < m; i++) {\n            U.set(i, k, -U.get(i, k));\n          }\n          U.set(k, k, 1 + U.get(k, k));\n          for (let i = 0; i < k - 1; i++) {\n            U.set(i, k, 0);\n          }\n        } else {\n          for (let i = 0; i < m; i++) {\n            U.set(i, k, 0);\n          }\n          U.set(k, k, 1);\n        }\n      }\n    }\n\n    if (wantv) {\n      for (let k = n - 1; k >= 0; k--) {\n        if (k < nrt && e[k] !== 0) {\n          for (let j = k + 1; j < n; j++) {\n            let t = 0;\n            for (let i = k + 1; i < n; i++) {\n              t += V.get(i, k) * V.get(i, j);\n            }\n            t = -t / V.get(k + 1, k);\n            for (let i = k + 1; i < n; i++) {\n              V.set(i, j, V.get(i, j) + t * V.get(i, k));\n            }\n          }\n        }\n        for (let i = 0; i < n; i++) {\n          V.set(i, k, 0);\n        }\n        V.set(k, k, 1);\n      }\n    }\n\n    let pp = p - 1;\n    let iter = 0;\n    let eps = Number.EPSILON;\n    while (p > 0) {\n      let k, kase;\n      for (k = p - 2; k >= -1; k--) {\n        if (k === -1) {\n          break;\n        }\n        const alpha =\n          Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));\n        if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {\n          e[k] = 0;\n          break;\n        }\n      }\n      if (k === p - 2) {\n        kase = 4;\n      } else {\n        let ks;\n        for (ks = p - 1; ks >= k; ks--) {\n          if (ks === k) {\n            break;\n          }\n          let t =\n            (ks !== p ? Math.abs(e[ks]) : 0) +\n            (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);\n          if (Math.abs(s[ks]) <= eps * t) {\n            s[ks] = 0;\n            break;\n          }\n        }\n        if (ks === k) {\n          kase = 3;\n        } else if (ks === p - 1) {\n          kase = 1;\n        } else {\n          kase = 2;\n          k = ks;\n        }\n      }\n\n      k++;\n\n      switch (kase) {\n        case 1: {\n          let f = e[p - 2];\n          e[p - 2] = 0;\n          for (let j = p - 2; j >= k; j--) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            if (j !== k) {\n              f = -sn * e[j - 1];\n              e[j - 1] = cs * e[j - 1];\n            }\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, p - 1);\n                V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));\n                V.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 2: {\n          let f = e[k - 1];\n          e[k - 1] = 0;\n          for (let j = k; j < p; j++) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            f = -sn * e[j];\n            e[j] = cs * e[j];\n            if (wantu) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, k - 1);\n                U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 3: {\n          const scale = Math.max(\n            Math.abs(s[p - 1]),\n            Math.abs(s[p - 2]),\n            Math.abs(e[p - 2]),\n            Math.abs(s[k]),\n            Math.abs(e[k]),\n          );\n          const sp = s[p - 1] / scale;\n          const spm1 = s[p - 2] / scale;\n          const epm1 = e[p - 2] / scale;\n          const sk = s[k] / scale;\n          const ek = e[k] / scale;\n          const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;\n          const c = sp * epm1 * (sp * epm1);\n          let shift = 0;\n          if (b !== 0 || c !== 0) {\n            if (b < 0) {\n              shift = 0 - Math.sqrt(b * b + c);\n            } else {\n              shift = Math.sqrt(b * b + c);\n            }\n            shift = c / (b + shift);\n          }\n          let f = (sk + sp) * (sk - sp) + shift;\n          let g = sk * ek;\n          for (let j = k; j < p - 1; j++) {\n            let t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            let cs = f / t;\n            let sn = g / t;\n            if (j !== k) {\n              e[j - 1] = t;\n            }\n            f = cs * s[j] + sn * e[j];\n            e[j] = cs * e[j] - sn * s[j];\n            g = sn * s[j + 1];\n            s[j + 1] = cs * s[j + 1];\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, j + 1);\n                V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));\n                V.set(i, j, t);\n              }\n            }\n            t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            cs = f / t;\n            sn = g / t;\n            s[j] = t;\n            f = cs * e[j] + sn * s[j + 1];\n            s[j + 1] = -sn * e[j] + cs * s[j + 1];\n            g = sn * e[j + 1];\n            e[j + 1] = cs * e[j + 1];\n            if (wantu && j < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, j + 1);\n                U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          e[p - 2] = f;\n          iter = iter + 1;\n          break;\n        }\n        case 4: {\n          if (s[k] <= 0) {\n            s[k] = s[k] < 0 ? -s[k] : 0;\n            if (wantv) {\n              for (let i = 0; i <= pp; i++) {\n                V.set(i, k, -V.get(i, k));\n              }\n            }\n          }\n          while (k < pp) {\n            if (s[k] >= s[k + 1]) {\n              break;\n            }\n            let t = s[k];\n            s[k] = s[k + 1];\n            s[k + 1] = t;\n            if (wantv && k < n - 1) {\n              for (let i = 0; i < n; i++) {\n                t = V.get(i, k + 1);\n                V.set(i, k + 1, V.get(i, k));\n                V.set(i, k, t);\n              }\n            }\n            if (wantu && k < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = U.get(i, k + 1);\n                U.set(i, k + 1, U.get(i, k));\n                U.set(i, k, t);\n              }\n            }\n            k++;\n          }\n          iter = 0;\n          p--;\n          break;\n        }\n        // no default\n      }\n    }\n\n    if (swapped) {\n      let tmp = V;\n      V = U;\n      U = tmp;\n    }\n\n    this.m = m;\n    this.n = n;\n    this.s = s;\n    this.U = U;\n    this.V = V;\n  }\n\n  solve(value) {\n    let Y = value;\n    let e = this.threshold;\n    let scols = this.s.length;\n    let Ls = Matrix.zeros(scols, scols);\n\n    for (let i = 0; i < scols; i++) {\n      if (Math.abs(this.s[i]) <= e) {\n        Ls.set(i, i, 0);\n      } else {\n        Ls.set(i, i, 1 / this.s[i]);\n      }\n    }\n\n    let U = this.U;\n    let V = this.rightSingularVectors;\n\n    let VL = V.mmul(Ls);\n    let vrows = V.rows;\n    let urows = U.rows;\n    let VLU = Matrix.zeros(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < scols; k++) {\n          sum += VL.get(i, k) * U.get(j, k);\n        }\n        VLU.set(i, j, sum);\n      }\n    }\n\n    return VLU.mmul(Y);\n  }\n\n  solveForDiagonal(value) {\n    return this.solve(Matrix.diag(value));\n  }\n\n  inverse() {\n    let V = this.V;\n    let e = this.threshold;\n    let vrows = V.rows;\n    let vcols = V.columns;\n    let X = new Matrix(vrows, this.s.length);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < vcols; j++) {\n        if (Math.abs(this.s[j]) > e) {\n          X.set(i, j, V.get(i, j) / this.s[j]);\n        }\n      }\n    }\n\n    let U = this.U;\n\n    let urows = U.rows;\n    let ucols = U.columns;\n    let Y = new Matrix(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < ucols; k++) {\n          sum += X.get(i, k) * U.get(j, k);\n        }\n        Y.set(i, j, sum);\n      }\n    }\n\n    return Y;\n  }\n\n  get condition() {\n    return this.s[0] / this.s[Math.min(this.m, this.n) - 1];\n  }\n\n  get norm2() {\n    return this.s[0];\n  }\n\n  get rank() {\n    let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;\n    let r = 0;\n    let s = this.s;\n    for (let i = 0, ii = s.length; i < ii; i++) {\n      if (s[i] > tol) {\n        r++;\n      }\n    }\n    return r;\n  }\n\n  get diagonal() {\n    return Array.from(this.s);\n  }\n\n  get threshold() {\n    return (Number.EPSILON / 2) * Math.max(this.m, this.n) * this.s[0];\n  }\n\n  get leftSingularVectors() {\n    return this.U;\n  }\n\n  get rightSingularVectors() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    return Matrix.diag(this.s);\n  }\n}\n","import LuDecomposition from './dc/lu';\nimport QrDecomposition from './dc/qr';\nimport SingularValueDecomposition from './dc/svd';\nimport Matrix from './matrix';\nimport WrapperMatrix2D from './wrap/WrapperMatrix2D';\n\nexport function inverse(matrix, useSVD = false) {\n  matrix = WrapperMatrix2D.checkMatrix(matrix);\n  if (useSVD) {\n    return new SingularValueDecomposition(matrix).inverse();\n  } else {\n    return solve(matrix, Matrix.eye(matrix.rows));\n  }\n}\n\nexport function solve(leftHandSide, rightHandSide, useSVD = false) {\n  leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);\n  rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);\n  if (useSVD) {\n    return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);\n  } else {\n    return leftHandSide.isSquare()\n      ? new LuDecomposition(leftHandSide).solve(rightHandSide)\n      : new QrDecomposition(leftHandSide).solve(rightHandSide);\n  }\n}\n","import LuDecomposition from './dc/lu';\nimport Matrix from './matrix';\nimport MatrixSelectionView from './views/selection';\n\nexport function determinant(matrix) {\n  matrix = Matrix.checkMatrix(matrix);\n  if (matrix.isSquare()) {\n    let a, b, c, d;\n    if (matrix.columns === 2) {\n      // 2 x 2 matrix\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(1, 0);\n      d = matrix.get(1, 1);\n\n      return a * d - b * c;\n    } else if (matrix.columns === 3) {\n      // 3 x 3 matrix\n      let subMatrix0, subMatrix1, subMatrix2;\n      subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);\n      subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);\n      subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(0, 2);\n\n      return (\n        a * determinant(subMatrix0) -\n        b * determinant(subMatrix1) +\n        c * determinant(subMatrix2)\n      );\n    } else {\n      // general purpose determinant using the LU decomposition\n      return new LuDecomposition(matrix).determinant;\n    }\n  } else {\n    throw Error('determinant can only be calculated for a square matrix');\n  }\n}\n","import SingularValueDecomposition from './dc/svd';\nimport Matrix from './matrix';\n\nfunction xrange(n, exception) {\n  let range = [];\n  for (let i = 0; i < n; i++) {\n    if (i !== exception) {\n      range.push(i);\n    }\n  }\n  return range;\n}\n\nfunction dependenciesOneRow(\n  error,\n  matrix,\n  index,\n  thresholdValue = 10e-10,\n  thresholdError = 10e-10,\n) {\n  if (error > thresholdError) {\n    return new Array(matrix.rows + 1).fill(0);\n  } else {\n    let returnArray = matrix.addRow(index, [0]);\n    for (let i = 0; i < returnArray.rows; i++) {\n      if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {\n        returnArray.set(i, 0, 0);\n      }\n    }\n    return returnArray.to1DArray();\n  }\n}\n\nexport function linearDependencies(matrix, options = {}) {\n  const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;\n  matrix = Matrix.checkMatrix(matrix);\n\n  let n = matrix.rows;\n  let results = new Matrix(n, n);\n\n  for (let i = 0; i < n; i++) {\n    let b = Matrix.columnVector(matrix.getRow(i));\n    let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();\n    let svd = new SingularValueDecomposition(Abis);\n    let x = svd.solve(b);\n    let error = Matrix.sub(b, Abis.mmul(x)).abs().max();\n    results.setRow(\n      i,\n      dependenciesOneRow(error, x, i, thresholdValue, thresholdError),\n    );\n  }\n  return results;\n}\n","import SVD from './dc/svd';\nimport Matrix from './matrix';\n\nexport function pseudoInverse(matrix, threshold = Number.EPSILON) {\n  matrix = Matrix.checkMatrix(matrix);\n  let svdSolution = new SVD(matrix, { autoTranspose: true });\n\n  let U = svdSolution.leftSingularVectors;\n  let V = svdSolution.rightSingularVectors;\n  let s = svdSolution.diagonal;\n\n  for (let i = 0; i < s.length; i++) {\n    if (Math.abs(s[i]) > threshold) {\n      s[i] = 1.0 / s[i];\n    } else {\n      s[i] = 0.0;\n    }\n  }\n\n  return V.mmul(Matrix.diag(s).mmul(U.transpose()));\n}\n","import Matrix from './matrix';\n\nexport function covariance(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = new Matrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = new Matrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n  const { center = true } = options;\n  if (center) {\n    xMatrix = xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix = yMatrix.center('column');\n    }\n  }\n  const cov = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < cov.rows; i++) {\n    for (let j = 0; j < cov.columns; j++) {\n      cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));\n    }\n  }\n  return cov;\n}\n","import Matrix from './matrix';\n\nexport function correlation(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = new Matrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = new Matrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n\n  const { center = true, scale = true } = options;\n  if (center) {\n    xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix.center('column');\n    }\n  }\n  if (scale) {\n    xMatrix.scale('column');\n    if (!yIsSame) {\n      yMatrix.scale('column');\n    }\n  }\n\n  const sdx = xMatrix.standardDeviation('column', { unbiased: true });\n  const sdy = yIsSame\n    ? sdx\n    : yMatrix.standardDeviation('column', { unbiased: true });\n\n  const corr = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < corr.rows; i++) {\n    for (let j = 0; j < corr.columns; j++) {\n      corr.set(\n        i,\n        j,\n        corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)),\n      );\n    }\n  }\n  return corr;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class EigenvalueDecomposition {\n  constructor(matrix, options = {}) {\n    const { assumeSymmetric = false } = options;\n\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n    if (!matrix.isSquare()) {\n      throw new Error('Matrix is not a square matrix');\n    }\n\n    let n = matrix.columns;\n    let V = new Matrix(n, n);\n    let d = new Float64Array(n);\n    let e = new Float64Array(n);\n    let value = matrix;\n    let i, j;\n\n    let isSymmetric = false;\n    if (assumeSymmetric) {\n      isSymmetric = true;\n    } else {\n      isSymmetric = matrix.isSymmetric();\n    }\n\n    if (isSymmetric) {\n      for (i = 0; i < n; i++) {\n        for (j = 0; j < n; j++) {\n          V.set(i, j, value.get(i, j));\n        }\n      }\n      tred2(n, e, d, V);\n      tql2(n, e, d, V);\n    } else {\n      let H = new Matrix(n, n);\n      let ort = new Float64Array(n);\n      for (j = 0; j < n; j++) {\n        for (i = 0; i < n; i++) {\n          H.set(i, j, value.get(i, j));\n        }\n      }\n      orthes(n, H, ort, V);\n      hqr2(n, e, d, V, H);\n    }\n\n    this.n = n;\n    this.e = e;\n    this.d = d;\n    this.V = V;\n  }\n\n  get realEigenvalues() {\n    return Array.from(this.d);\n  }\n\n  get imaginaryEigenvalues() {\n    return Array.from(this.e);\n  }\n\n  get eigenvectorMatrix() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    let n = this.n;\n    let e = this.e;\n    let d = this.d;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        X.set(i, j, 0);\n      }\n      X.set(i, i, d[i]);\n      if (e[i] > 0) {\n        X.set(i, i + 1, e[i]);\n      } else if (e[i] < 0) {\n        X.set(i, i - 1, e[i]);\n      }\n    }\n    return X;\n  }\n}\n\nfunction tred2(n, e, d, V) {\n  let f, g, h, i, j, k, hh, scale;\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n  }\n\n  for (i = n - 1; i > 0; i--) {\n    scale = 0;\n    h = 0;\n    for (k = 0; k < i; k++) {\n      scale = scale + Math.abs(d[k]);\n    }\n\n    if (scale === 0) {\n      e[i] = d[i - 1];\n      for (j = 0; j < i; j++) {\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n        V.set(j, i, 0);\n      }\n    } else {\n      for (k = 0; k < i; k++) {\n        d[k] /= scale;\n        h += d[k] * d[k];\n      }\n\n      f = d[i - 1];\n      g = Math.sqrt(h);\n      if (f > 0) {\n        g = -g;\n      }\n\n      e[i] = scale * g;\n      h = h - f * g;\n      d[i - 1] = f - g;\n      for (j = 0; j < i; j++) {\n        e[j] = 0;\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        V.set(j, i, f);\n        g = e[j] + V.get(j, j) * f;\n        for (k = j + 1; k <= i - 1; k++) {\n          g += V.get(k, j) * d[k];\n          e[k] += V.get(k, j) * f;\n        }\n        e[j] = g;\n      }\n\n      f = 0;\n      for (j = 0; j < i; j++) {\n        e[j] /= h;\n        f += e[j] * d[j];\n      }\n\n      hh = f / (h + h);\n      for (j = 0; j < i; j++) {\n        e[j] -= hh * d[j];\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        g = e[j];\n        for (k = j; k <= i - 1; k++) {\n          V.set(k, j, V.get(k, j) - (f * e[k] + g * d[k]));\n        }\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n      }\n    }\n    d[i] = h;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    V.set(n - 1, i, V.get(i, i));\n    V.set(i, i, 1);\n    h = d[i + 1];\n    if (h !== 0) {\n      for (k = 0; k <= i; k++) {\n        d[k] = V.get(k, i + 1) / h;\n      }\n\n      for (j = 0; j <= i; j++) {\n        g = 0;\n        for (k = 0; k <= i; k++) {\n          g += V.get(k, i + 1) * V.get(k, j);\n        }\n        for (k = 0; k <= i; k++) {\n          V.set(k, j, V.get(k, j) - g * d[k]);\n        }\n      }\n    }\n\n    for (k = 0; k <= i; k++) {\n      V.set(k, i + 1, 0);\n    }\n  }\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n    V.set(n - 1, j, 0);\n  }\n\n  V.set(n - 1, n - 1, 1);\n  e[0] = 0;\n}\n\nfunction tql2(n, e, d, V) {\n  let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2, iter;\n\n  for (i = 1; i < n; i++) {\n    e[i - 1] = e[i];\n  }\n\n  e[n - 1] = 0;\n\n  let f = 0;\n  let tst1 = 0;\n  let eps = Number.EPSILON;\n\n  for (l = 0; l < n; l++) {\n    tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));\n    m = l;\n    while (m < n) {\n      if (Math.abs(e[m]) <= eps * tst1) {\n        break;\n      }\n      m++;\n    }\n\n    if (m > l) {\n      iter = 0;\n      do {\n        iter = iter + 1;\n\n        g = d[l];\n        p = (d[l + 1] - g) / (2 * e[l]);\n        r = hypotenuse(p, 1);\n        if (p < 0) {\n          r = -r;\n        }\n\n        d[l] = e[l] / (p + r);\n        d[l + 1] = e[l] * (p + r);\n        dl1 = d[l + 1];\n        h = g - d[l];\n        for (i = l + 2; i < n; i++) {\n          d[i] -= h;\n        }\n\n        f = f + h;\n\n        p = d[m];\n        c = 1;\n        c2 = c;\n        c3 = c;\n        el1 = e[l + 1];\n        s = 0;\n        s2 = 0;\n        for (i = m - 1; i >= l; i--) {\n          c3 = c2;\n          c2 = c;\n          s2 = s;\n          g = c * e[i];\n          h = c * p;\n          r = hypotenuse(p, e[i]);\n          e[i + 1] = s * r;\n          s = e[i] / r;\n          c = p / r;\n          p = c * d[i] - s * g;\n          d[i + 1] = h + s * (c * g + s * d[i]);\n\n          for (k = 0; k < n; k++) {\n            h = V.get(k, i + 1);\n            V.set(k, i + 1, s * V.get(k, i) + c * h);\n            V.set(k, i, c * V.get(k, i) - s * h);\n          }\n        }\n\n        p = (-s * s2 * c3 * el1 * e[l]) / dl1;\n        e[l] = s * p;\n        d[l] = c * p;\n      } while (Math.abs(e[l]) > eps * tst1);\n    }\n    d[l] = d[l] + f;\n    e[l] = 0;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    k = i;\n    p = d[i];\n    for (j = i + 1; j < n; j++) {\n      if (d[j] < p) {\n        k = j;\n        p = d[j];\n      }\n    }\n\n    if (k !== i) {\n      d[k] = d[i];\n      d[i] = p;\n      for (j = 0; j < n; j++) {\n        p = V.get(j, i);\n        V.set(j, i, V.get(j, k));\n        V.set(j, k, p);\n      }\n    }\n  }\n}\n\nfunction orthes(n, H, ort, V) {\n  let low = 0;\n  let high = n - 1;\n  let f, g, h, i, j, m;\n  let scale;\n\n  for (m = low + 1; m <= high - 1; m++) {\n    scale = 0;\n    for (i = m; i <= high; i++) {\n      scale = scale + Math.abs(H.get(i, m - 1));\n    }\n\n    if (scale !== 0) {\n      h = 0;\n      for (i = high; i >= m; i--) {\n        ort[i] = H.get(i, m - 1) / scale;\n        h += ort[i] * ort[i];\n      }\n\n      g = Math.sqrt(h);\n      if (ort[m] > 0) {\n        g = -g;\n      }\n\n      h = h - ort[m] * g;\n      ort[m] = ort[m] - g;\n\n      for (j = m; j < n; j++) {\n        f = 0;\n        for (i = high; i >= m; i--) {\n          f += ort[i] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (i = m; i <= high; i++) {\n          H.set(i, j, H.get(i, j) - f * ort[i]);\n        }\n      }\n\n      for (i = 0; i <= high; i++) {\n        f = 0;\n        for (j = high; j >= m; j--) {\n          f += ort[j] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (j = m; j <= high; j++) {\n          H.set(i, j, H.get(i, j) - f * ort[j]);\n        }\n      }\n\n      ort[m] = scale * ort[m];\n      H.set(m, m - 1, scale * g);\n    }\n  }\n\n  for (i = 0; i < n; i++) {\n    for (j = 0; j < n; j++) {\n      V.set(i, j, i === j ? 1 : 0);\n    }\n  }\n\n  for (m = high - 1; m >= low + 1; m--) {\n    if (H.get(m, m - 1) !== 0) {\n      for (i = m + 1; i <= high; i++) {\n        ort[i] = H.get(i, m - 1);\n      }\n\n      for (j = m; j <= high; j++) {\n        g = 0;\n        for (i = m; i <= high; i++) {\n          g += ort[i] * V.get(i, j);\n        }\n\n        g = g / ort[m] / H.get(m, m - 1);\n        for (i = m; i <= high; i++) {\n          V.set(i, j, V.get(i, j) + g * ort[i]);\n        }\n      }\n    }\n  }\n}\n\nfunction hqr2(nn, e, d, V, H) {\n  let n = nn - 1;\n  let low = 0;\n  let high = nn - 1;\n  let eps = Number.EPSILON;\n  let exshift = 0;\n  let norm = 0;\n  let p = 0;\n  let q = 0;\n  let r = 0;\n  let s = 0;\n  let z = 0;\n  let iter = 0;\n  let i, j, k, l, m, t, w, x, y;\n  let ra, sa, vr, vi;\n  let notlast, cdivres;\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      d[i] = H.get(i, i);\n      e[i] = 0;\n    }\n\n    for (j = Math.max(i - 1, 0); j < nn; j++) {\n      norm = norm + Math.abs(H.get(i, j));\n    }\n  }\n\n  while (n >= low) {\n    l = n;\n    while (l > low) {\n      s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));\n      if (s === 0) {\n        s = norm;\n      }\n      if (Math.abs(H.get(l, l - 1)) < eps * s) {\n        break;\n      }\n      l--;\n    }\n\n    if (l === n) {\n      H.set(n, n, H.get(n, n) + exshift);\n      d[n] = H.get(n, n);\n      e[n] = 0;\n      n--;\n      iter = 0;\n    } else if (l === n - 1) {\n      w = H.get(n, n - 1) * H.get(n - 1, n);\n      p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;\n      q = p * p + w;\n      z = Math.sqrt(Math.abs(q));\n      H.set(n, n, H.get(n, n) + exshift);\n      H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);\n      x = H.get(n, n);\n\n      if (q >= 0) {\n        z = p >= 0 ? p + z : p - z;\n        d[n - 1] = x + z;\n        d[n] = d[n - 1];\n        if (z !== 0) {\n          d[n] = x - w / z;\n        }\n        e[n - 1] = 0;\n        e[n] = 0;\n        x = H.get(n, n - 1);\n        s = Math.abs(x) + Math.abs(z);\n        p = x / s;\n        q = z / s;\n        r = Math.sqrt(p * p + q * q);\n        p = p / r;\n        q = q / r;\n\n        for (j = n - 1; j < nn; j++) {\n          z = H.get(n - 1, j);\n          H.set(n - 1, j, q * z + p * H.get(n, j));\n          H.set(n, j, q * H.get(n, j) - p * z);\n        }\n\n        for (i = 0; i <= n; i++) {\n          z = H.get(i, n - 1);\n          H.set(i, n - 1, q * z + p * H.get(i, n));\n          H.set(i, n, q * H.get(i, n) - p * z);\n        }\n\n        for (i = low; i <= high; i++) {\n          z = V.get(i, n - 1);\n          V.set(i, n - 1, q * z + p * V.get(i, n));\n          V.set(i, n, q * V.get(i, n) - p * z);\n        }\n      } else {\n        d[n - 1] = x + p;\n        d[n] = x + p;\n        e[n - 1] = z;\n        e[n] = -z;\n      }\n\n      n = n - 2;\n      iter = 0;\n    } else {\n      x = H.get(n, n);\n      y = 0;\n      w = 0;\n      if (l < n) {\n        y = H.get(n - 1, n - 1);\n        w = H.get(n, n - 1) * H.get(n - 1, n);\n      }\n\n      if (iter === 10) {\n        exshift += x;\n        for (i = low; i <= n; i++) {\n          H.set(i, i, H.get(i, i) - x);\n        }\n        s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));\n        x = y = 0.75 * s;\n        w = -0.4375 * s * s;\n      }\n\n      if (iter === 30) {\n        s = (y - x) / 2;\n        s = s * s + w;\n        if (s > 0) {\n          s = Math.sqrt(s);\n          if (y < x) {\n            s = -s;\n          }\n          s = x - w / ((y - x) / 2 + s);\n          for (i = low; i <= n; i++) {\n            H.set(i, i, H.get(i, i) - s);\n          }\n          exshift += s;\n          x = y = w = 0.964;\n        }\n      }\n\n      iter = iter + 1;\n\n      m = n - 2;\n      while (m >= l) {\n        z = H.get(m, m);\n        r = x - z;\n        s = y - z;\n        p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);\n        q = H.get(m + 1, m + 1) - z - r - s;\n        r = H.get(m + 2, m + 1);\n        s = Math.abs(p) + Math.abs(q) + Math.abs(r);\n        p = p / s;\n        q = q / s;\n        r = r / s;\n        if (m === l) {\n          break;\n        }\n        if (\n          Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) <\n          eps *\n            (Math.abs(p) *\n              (Math.abs(H.get(m - 1, m - 1)) +\n                Math.abs(z) +\n                Math.abs(H.get(m + 1, m + 1))))\n        ) {\n          break;\n        }\n        m--;\n      }\n\n      for (i = m + 2; i <= n; i++) {\n        H.set(i, i - 2, 0);\n        if (i > m + 2) {\n          H.set(i, i - 3, 0);\n        }\n      }\n\n      for (k = m; k <= n - 1; k++) {\n        notlast = k !== n - 1;\n        if (k !== m) {\n          p = H.get(k, k - 1);\n          q = H.get(k + 1, k - 1);\n          r = notlast ? H.get(k + 2, k - 1) : 0;\n          x = Math.abs(p) + Math.abs(q) + Math.abs(r);\n          if (x !== 0) {\n            p = p / x;\n            q = q / x;\n            r = r / x;\n          }\n        }\n\n        if (x === 0) {\n          break;\n        }\n\n        s = Math.sqrt(p * p + q * q + r * r);\n        if (p < 0) {\n          s = -s;\n        }\n\n        if (s !== 0) {\n          if (k !== m) {\n            H.set(k, k - 1, -s * x);\n          } else if (l !== m) {\n            H.set(k, k - 1, -H.get(k, k - 1));\n          }\n\n          p = p + s;\n          x = p / s;\n          y = q / s;\n          z = r / s;\n          q = q / p;\n          r = r / p;\n\n          for (j = k; j < nn; j++) {\n            p = H.get(k, j) + q * H.get(k + 1, j);\n            if (notlast) {\n              p = p + r * H.get(k + 2, j);\n              H.set(k + 2, j, H.get(k + 2, j) - p * z);\n            }\n\n            H.set(k, j, H.get(k, j) - p * x);\n            H.set(k + 1, j, H.get(k + 1, j) - p * y);\n          }\n\n          for (i = 0; i <= Math.min(n, k + 3); i++) {\n            p = x * H.get(i, k) + y * H.get(i, k + 1);\n            if (notlast) {\n              p = p + z * H.get(i, k + 2);\n              H.set(i, k + 2, H.get(i, k + 2) - p * r);\n            }\n\n            H.set(i, k, H.get(i, k) - p);\n            H.set(i, k + 1, H.get(i, k + 1) - p * q);\n          }\n\n          for (i = low; i <= high; i++) {\n            p = x * V.get(i, k) + y * V.get(i, k + 1);\n            if (notlast) {\n              p = p + z * V.get(i, k + 2);\n              V.set(i, k + 2, V.get(i, k + 2) - p * r);\n            }\n\n            V.set(i, k, V.get(i, k) - p);\n            V.set(i, k + 1, V.get(i, k + 1) - p * q);\n          }\n        }\n      }\n    }\n  }\n\n  if (norm === 0) {\n    return;\n  }\n\n  for (n = nn - 1; n >= 0; n--) {\n    p = d[n];\n    q = e[n];\n\n    if (q === 0) {\n      l = n;\n      H.set(n, n, 1);\n      for (i = n - 1; i >= 0; i--) {\n        w = H.get(i, i) - p;\n        r = 0;\n        for (j = l; j <= n; j++) {\n          r = r + H.get(i, j) * H.get(j, n);\n        }\n\n        if (e[i] < 0) {\n          z = w;\n          s = r;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];\n            t = (x * s - z * r) / q;\n            H.set(i, n, t);\n            H.set(\n              i + 1,\n              n,\n              Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z,\n            );\n          }\n\n          t = Math.abs(H.get(i, n));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    } else if (q < 0) {\n      l = n - 1;\n\n      if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {\n        H.set(n - 1, n - 1, q / H.get(n, n - 1));\n        H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));\n      } else {\n        cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);\n        H.set(n - 1, n - 1, cdivres[0]);\n        H.set(n - 1, n, cdivres[1]);\n      }\n\n      H.set(n, n - 1, 0);\n      H.set(n, n, 1);\n      for (i = n - 2; i >= 0; i--) {\n        ra = 0;\n        sa = 0;\n        for (j = l; j <= n; j++) {\n          ra = ra + H.get(i, j) * H.get(j, n - 1);\n          sa = sa + H.get(i, j) * H.get(j, n);\n        }\n\n        w = H.get(i, i) - p;\n\n        if (e[i] < 0) {\n          z = w;\n          r = ra;\n          s = sa;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            cdivres = cdiv(-ra, -sa, w, q);\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;\n            vi = (d[i] - p) * 2 * q;\n            if (vr === 0 && vi === 0) {\n              vr =\n                eps *\n                norm *\n                (Math.abs(w) +\n                  Math.abs(q) +\n                  Math.abs(x) +\n                  Math.abs(y) +\n                  Math.abs(z));\n            }\n            cdivres = cdiv(\n              x * r - z * ra + q * sa,\n              x * s - z * sa - q * ra,\n              vr,\n              vi,\n            );\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n            if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {\n              H.set(\n                i + 1,\n                n - 1,\n                (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x,\n              );\n              H.set(\n                i + 1,\n                n,\n                (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x,\n              );\n            } else {\n              cdivres = cdiv(\n                -r - y * H.get(i, n - 1),\n                -s - y * H.get(i, n),\n                z,\n                q,\n              );\n              H.set(i + 1, n - 1, cdivres[0]);\n              H.set(i + 1, n, cdivres[1]);\n            }\n          }\n\n          t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n - 1, H.get(j, n - 1) / t);\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    }\n  }\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      for (j = i; j < nn; j++) {\n        V.set(i, j, H.get(i, j));\n      }\n    }\n  }\n\n  for (j = nn - 1; j >= low; j--) {\n    for (i = low; i <= high; i++) {\n      z = 0;\n      for (k = low; k <= Math.min(j, high); k++) {\n        z = z + V.get(i, k) * H.get(k, j);\n      }\n      V.set(i, j, z);\n    }\n  }\n}\n\nfunction cdiv(xr, xi, yr, yi) {\n  let r, d;\n  if (Math.abs(yr) > Math.abs(yi)) {\n    r = yi / yr;\n    d = yr + r * yi;\n    return [(xr + r * xi) / d, (xi - r * xr) / d];\n  } else {\n    r = yr / yi;\n    d = yi + r * yr;\n    return [(r * xr + xi) / d, (r * xi - xr) / d];\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class CholeskyDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n    if (!value.isSymmetric()) {\n      throw new Error('Matrix is not symmetric');\n    }\n\n    let a = value;\n    let dimension = a.rows;\n    let l = new Matrix(dimension, dimension);\n    let positiveDefinite = true;\n    let i, j, k;\n\n    for (j = 0; j < dimension; j++) {\n      let d = 0;\n      for (k = 0; k < j; k++) {\n        let s = 0;\n        for (i = 0; i < k; i++) {\n          s += l.get(k, i) * l.get(j, i);\n        }\n        s = (a.get(j, k) - s) / l.get(k, k);\n        l.set(j, k, s);\n        d = d + s * s;\n      }\n\n      d = a.get(j, j) - d;\n\n      positiveDefinite &= d > 0;\n      l.set(j, j, Math.sqrt(Math.max(d, 0)));\n      for (k = j + 1; k < dimension; k++) {\n        l.set(j, k, 0);\n      }\n    }\n\n    this.L = l;\n    this.positiveDefinite = Boolean(positiveDefinite);\n  }\n\n  isPositiveDefinite() {\n    return this.positiveDefinite;\n  }\n\n  solve(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let l = this.L;\n    let dimension = l.rows;\n\n    if (value.rows !== dimension) {\n      throw new Error('Matrix dimensions do not match');\n    }\n    if (this.isPositiveDefinite() === false) {\n      throw new Error('Matrix is not positive definite');\n    }\n\n    let count = value.columns;\n    let B = value.clone();\n    let i, j, k;\n\n    for (k = 0; k < dimension; k++) {\n      for (j = 0; j < count; j++) {\n        for (i = 0; i < k; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    for (k = dimension - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        for (i = k + 1; i < dimension; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    return B;\n  }\n\n  get lowerTriangularMatrix() {\n    return this.L;\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class nipals {\n  constructor(X, options = {}) {\n    X = WrapperMatrix2D.checkMatrix(X);\n    let { Y } = options;\n    const {\n      scaleScores = false,\n      maxIterations = 1000,\n      terminationCriteria = 1e-10,\n    } = options;\n\n    let u;\n    if (Y) {\n      if (Array.isArray(Y) && typeof Y[0] === 'number') {\n        Y = Matrix.columnVector(Y);\n      } else {\n        Y = WrapperMatrix2D.checkMatrix(Y);\n      }\n      if (!Y.isColumnVector() || Y.rows !== X.rows) {\n        throw new Error('Y must be a column vector of length X.rows');\n      }\n      u = Y;\n    } else {\n      u = X.getColumnVector(0);\n    }\n\n    let diff = 1;\n    let t, q, w, tOld;\n\n    for (\n      let counter = 0;\n      counter < maxIterations && diff > terminationCriteria;\n      counter++\n    ) {\n      w = X.transpose().mmul(u).div(u.transpose().mmul(u).get(0, 0));\n      w = w.div(w.norm());\n\n      t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0));\n\n      if (counter > 0) {\n        diff = t.clone().sub(tOld).pow(2).sum();\n      }\n      tOld = t.clone();\n\n      if (Y) {\n        q = Y.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n        q = q.div(q.norm());\n\n        u = Y.mmul(q).div(q.transpose().mmul(q).get(0, 0));\n      } else {\n        u = t;\n      }\n    }\n\n    if (Y) {\n      let p = X.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n      p = p.div(p.norm());\n      let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));\n      let residual = u.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n      let yResidual = Y.clone().sub(\n        t.clone().mulS(residual.get(0, 0)).mmul(q.transpose()),\n      );\n\n      this.t = t;\n      this.p = p.transpose();\n      this.w = w.transpose();\n      this.q = q;\n      this.u = u;\n      this.s = t.transpose().mmul(t);\n      this.xResidual = xResidual;\n      this.yResidual = yResidual;\n      this.betas = residual;\n    } else {\n      this.w = w.transpose();\n      this.s = t.transpose().mmul(t).sqrt();\n      if (scaleScores) {\n        this.t = t.clone().div(this.s.get(0, 0));\n      } else {\n        this.t = t;\n      }\n      this.xResidual = X.sub(t.mmul(w.transpose()));\n    }\n  }\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction sum(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += input[i];\n  }\n\n  return sumValue;\n}\n\nexport default sum;\n","import sum from 'ml-array-sum';\n\nfunction mean(input) {\n  return sum(input) / input.length;\n}\n\nexport default mean;\n","import Matrix from 'ml-matrix';\nimport meanArray from 'ml-array-mean';\n\n/**\n * @private\n * return an array of probabilities of each class\n * @param {Array} array - contains the classes\n * @param {number} numberOfClasses\n * @return {Matrix} - rowVector of probabilities.\n */\nexport function toDiscreteDistribution(array, numberOfClasses) {\n  let counts = new Array(numberOfClasses).fill(0);\n  for (let i = 0; i < array.length; ++i) {\n    counts[array[i]] += 1 / array.length;\n  }\n\n  return Matrix.rowVector(counts);\n}\n\n/**\n * @private\n * Retrieves the impurity of array of predictions\n * @param {Array} array - predictions.\n * @return {number} Gini impurity\n */\nexport function giniImpurity(array) {\n  if (array.length === 0) {\n    return 0;\n  }\n\n  let probabilities = toDiscreteDistribution(\n    array,\n    getNumberOfClasses(array),\n  ).getRow(0);\n\n  let sum = 0.0;\n  for (let i = 0; i < probabilities.length; ++i) {\n    sum += probabilities[i] * probabilities[i];\n  }\n\n  return 1 - sum;\n}\n\n/**\n * @private\n * Return the number of classes given the array of predictions.\n * @param {Array} array - predictions.\n * @return {number} Number of classes.\n */\nexport function getNumberOfClasses(array) {\n  return array\n    .filter(function(val, i, arr) {\n      return arr.indexOf(val) === i;\n    })\n    .map((val) => val + 1)\n    .reduce((a, b) => Math.max(a, b));\n}\n\n/**\n * @private\n * Calculates the Gini Gain of an array of predictions and those predictions splitted by a feature.\n * @param {Array} array - Predictions\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - Gini Gain.\n */\n\nexport function giniGain(array, splitted) {\n  let splitsImpurity = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    splitsImpurity +=\n      (giniImpurity(currentSplit) * currentSplit.length) / array.length;\n  }\n\n  return giniImpurity(array) - splitsImpurity;\n}\n\n/**\n * @private\n * Calculates the squared error of a predictions values.\n * @param {Array} array - predictions values\n * @return {number} squared error.\n */\nexport function squaredError(array) {\n  let l = array.length;\n\n  let m = meanArray(array);\n  let error = 0.0;\n\n  for (let i = 0; i < l; ++i) {\n    let currentElement = array[i];\n    error += (currentElement - m) * (currentElement - m);\n  }\n\n  return error;\n}\n\n/**\n * @private\n * Calculates the sum of squared error of the two arrays that contains the splitted values.\n * @param {Array} array - this argument is no necessary but is used to fit with the main interface.\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - sum of squared errors.\n */\nexport function regressionError(array, splitted) {\n  let error = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    error += squaredError(currentSplit);\n  }\n  return error;\n}\n\n/**\n * @private\n * Split the training set and values from a given column of the training set if is less than a value\n * @param {Matrix} X - Training set.\n * @param {Array} y - Training values.\n * @param {number} column - Column to split.\n * @param {number} value - value to split the Training set and values.\n * @return {object} - Object that contains the splitted values.\n */\nexport function matrixSplitter(X, y, column, value) {\n  let lesserX = [];\n  let greaterX = [];\n  let lesserY = [];\n  let greaterY = [];\n\n  for (let i = 0; i < X.rows; ++i) {\n    if (X.get(i, column) < value) {\n      lesserX.push(X.getRow(i));\n      lesserY.push(y[i]);\n    } else {\n      greaterX.push(X.getRow(i));\n      greaterY.push(y[i]);\n    }\n  }\n\n  return {\n    greaterX: greaterX,\n    greaterY: greaterY,\n    lesserX: lesserX,\n    lesserY: lesserY,\n  };\n}\n\n/**\n * @private\n * Calculates the mean between two values\n * @param {number} a\n * @param {number} b\n * @return {number}\n */\nexport function mean(a, b) {\n  return (a + b) / 2;\n}\n\n/**\n * @private\n * Returns a list of tuples that contains the i-th element of each array.\n * @param {Array} a\n * @param {Array} b\n * @return {Array} list of tuples.\n */\nexport function zip(a, b) {\n  if (a.length !== b.length) {\n    throw new TypeError(\n      `Error on zip: the size of a: ${a.length} is different from b: ${b.length}`,\n    );\n  }\n\n  let ret = new Array(a.length);\n  for (let i = 0; i < a.length; ++i) {\n    ret[i] = [a[i], b[i]];\n  }\n\n  return ret;\n}\n","import Matrix from 'ml-matrix';\nimport mean from 'ml-array-mean';\n\nimport * as Utils from './utils';\n\nconst gainFunctions = {\n  gini: Utils.giniGain,\n  regression: Utils.regressionError,\n};\n\nconst splitFunctions = {\n  mean: Utils.mean,\n};\n\nexport default class TreeNode {\n  /**\n   * @private\n   * Constructor for a tree node given the options received on the main classes (DecisionTreeClassifier, DecisionTreeRegression)\n   * @param {object|TreeNode} options for loading\n   * @constructor\n   */\n  constructor(options) {\n    // options parameters\n    this.kind = options.kind;\n    this.gainFunction = options.gainFunction;\n    this.splitFunction = options.splitFunction;\n    this.minNumSamples = options.minNumSamples;\n    this.maxDepth = options.maxDepth;\n  }\n\n  /**\n   * @private\n   * Function that retrieve the best feature to make the split.\n   * @param {Matrix} XTranspose - Training set transposed\n   * @param {Array} y - labels or values (depending of the decision tree)\n   * @return {object} - return tree values, the best gain, column and the split value.\n   */\n  bestSplit(XTranspose, y) {\n    // Depending in the node tree class, we set the variables to check information gain (to classify)\n    // or error (for regression)\n\n    let bestGain = this.kind === 'classifier' ? -Infinity : Infinity;\n    let check = this.kind === 'classifier' ? (a, b) => a > b : (a, b) => a < b;\n\n    let maxColumn;\n    let maxValue;\n\n    for (let i = 0; i < XTranspose.rows; ++i) {\n      let currentFeature = XTranspose.getRow(i);\n      let splitValues = this.featureSplit(currentFeature, y);\n      for (let j = 0; j < splitValues.length; ++j) {\n        let currentSplitVal = splitValues[j];\n        let splitted = this.split(currentFeature, y, currentSplitVal);\n\n        let gain = gainFunctions[this.gainFunction](y, splitted);\n        if (check(gain, bestGain)) {\n          maxColumn = i;\n          maxValue = currentSplitVal;\n          bestGain = gain;\n        }\n      }\n    }\n\n    return {\n      maxGain: bestGain,\n      maxColumn: maxColumn,\n      maxValue: maxValue,\n    };\n  }\n\n  /**\n   * @private\n   * Makes the split of the training labels or values from the training set feature given a split value.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @param {number} splitValue\n   * @return {object}\n   */\n  split(x, y, splitValue) {\n    let lesser = [];\n    let greater = [];\n\n    for (let i = 0; i < x.length; ++i) {\n      if (x[i] < splitValue) {\n        lesser.push(y[i]);\n      } else {\n        greater.push(y[i]);\n      }\n    }\n\n    return {\n      greater: greater,\n      lesser: lesser,\n    };\n  }\n\n  /**\n   * @private\n   * Calculates the possible points to split over the tree given a training set feature and corresponding labels or values.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @return {Array} possible split values.\n   */\n  featureSplit(x, y) {\n    let splitValues = [];\n    let arr = Utils.zip(x, y);\n    arr.sort(function(a, b) {\n      return a[0] - b[0];\n    });\n\n    for (let i = 1; i < arr.length; ++i) {\n      if (arr[i - 1][1] !== arr[i][1]) {\n        splitValues.push(\n          splitFunctions[this.splitFunction](arr[i - 1][0], arr[i][0]),\n        );\n      }\n    }\n\n    return splitValues;\n  }\n\n  /**\n   * @private\n   * Calculate the predictions of a leaf tree node given the training labels or values\n   * @param {Array} y\n   */\n  calculatePrediction(y) {\n    if (this.kind === 'classifier') {\n      this.distribution = Utils.toDiscreteDistribution(\n        y,\n        Utils.getNumberOfClasses(y),\n      );\n      if (this.distribution.columns === 0) {\n        throw new TypeError('Error on calculate the prediction');\n      }\n    } else {\n      this.distribution = mean(y);\n    }\n  }\n\n  /**\n   * @private\n   * Train a node given the training set and labels, because it trains recursively, it also receive\n   * the current depth of the node, parent gain to avoid infinite recursion and boolean value to check if\n   * the training set is transposed.\n   * @param {Matrix} X - Training set (could be transposed or not given transposed).\n   * @param {Array} y - Training labels or values.\n   * @param {number} currentDepth - Current depth of the node.\n   * @param {number} parentGain - parent node gain or error.\n   */\n  train(X, y, currentDepth, parentGain) {\n    if (X.rows <= this.minNumSamples) {\n      this.calculatePrediction(y);\n      return;\n    }\n    if (parentGain === undefined) parentGain = 0.0;\n\n    let XTranspose = X.transpose();\n    let split = this.bestSplit(XTranspose, y);\n\n    this.splitValue = split.maxValue;\n    this.splitColumn = split.maxColumn;\n    this.gain = split.maxGain;\n\n    let splittedMatrix = Utils.matrixSplitter(\n      X,\n      y,\n      this.splitColumn,\n      this.splitValue,\n    );\n\n    if (\n      currentDepth < this.maxDepth &&\n      (this.gain > 0.01 && this.gain !== parentGain) &&\n      (splittedMatrix.lesserX.length > 0 && splittedMatrix.greaterX.length > 0)\n    ) {\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      let lesserX = new Matrix(splittedMatrix.lesserX);\n      let greaterX = new Matrix(splittedMatrix.greaterX);\n\n      this.left.train(\n        lesserX,\n        splittedMatrix.lesserY,\n        currentDepth + 1,\n        this.gain,\n      );\n      this.right.train(\n        greaterX,\n        splittedMatrix.greaterY,\n        currentDepth + 1,\n        this.gain,\n      );\n    } else {\n      this.calculatePrediction(y);\n    }\n  }\n\n  /**\n   * @private\n   * Calculates the prediction of a given element.\n   * @param {Array} row\n   * @return {number|Array} prediction\n   *          * if a node is a classifier returns an array of probabilities of each class.\n   *          * if a node is for regression returns a number with the prediction.\n   */\n  classify(row) {\n    if (this.right && this.left) {\n      if (row[this.splitColumn] < this.splitValue) {\n        return this.left.classify(row);\n      } else {\n        return this.right.classify(row);\n      }\n    }\n\n    return this.distribution;\n  }\n\n  /**\n   * @private\n   * Set the parameter of the current node and their children.\n   * @param {object} node - parameters of the current node and the children.\n   */\n  setNodeParameters(node) {\n    if (node.distribution !== undefined) {\n      this.distribution =\n        node.distribution.constructor === Array\n          ? new Matrix(node.distribution)\n          : node.distribution;\n    } else {\n      this.distribution = undefined;\n      this.splitValue = node.splitValue;\n      this.splitColumn = node.splitColumn;\n      this.gain = node.gain;\n\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      if (node.left !== {}) {\n        this.left.setNodeParameters(node.left);\n      }\n      if (node.right !== {}) {\n        this.right.setNodeParameters(node.right);\n      }\n    }\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'gini',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeClassifier {\n  /**\n   * Create new Decision Tree Classifier with CART implementation with the given options\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"gini\"] - gain function to get the best split, \"gini\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   * @constructor\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'classifier';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    this.root = new Tree(this.options);\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    this.root.train(trainingSet, trainingLabels, 0, null);\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    toPredict = Matrix.checkMatrix(toPredict);\n    let predictions = new Array(toPredict.rows);\n\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root\n        .classify(toPredict.getRow(i))\n        .maxRowIndex(0)[1];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {DecisionTreeClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'DTClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new DecisionTreeClassifier(true, model);\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'regression',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeRegression {\n  /**\n   * Create new Decision Tree Regression with CART implementation with the given options.\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"regression\"] - gain function to get the best split, \"regression\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'regression';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and values.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    this.root = new Tree(this.options);\n\n    if (\n      typeof trainingSet[0] !== 'undefined' &&\n      trainingSet[0].length === undefined\n    ) {\n      trainingSet = Matrix.columnVector(trainingSet);\n    } else {\n      trainingSet = Matrix.checkMatrix(trainingSet);\n    }\n    this.root.train(trainingSet, trainingValues, 0);\n  }\n\n  /**\n   * Predicts the values given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    if (\n      typeof toPredict[0] !== 'undefined' &&\n      toPredict[0].length === undefined\n    ) {\n      toPredict = Matrix.columnVector(toPredict);\n    }\n    toPredict = Matrix.checkMatrix(toPredict);\n\n    let predictions = new Array(toPredict.rows);\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root.classify(toPredict.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree regression with the given model.\n   * @param {object} model\n   * @return {DecisionTreeRegression}\n   */\n  static load(model) {\n    if (model.name !== 'DTRegression') {\n      throw new RangeError(`Invalid model:${model.name}`);\n    }\n\n    return new DecisionTreeRegression(true, model);\n  }\n}\n","const SMALLEST_UNSAFE_INTEGER = 0x20000000000000;\r\nconst LARGEST_SAFE_INTEGER = SMALLEST_UNSAFE_INTEGER - 1;\r\nconst UINT32_MAX = -1 >>> 0;\r\nconst UINT32_SIZE = UINT32_MAX + 1;\r\nconst INT32_SIZE = UINT32_SIZE / 2;\r\nconst INT32_MAX = INT32_SIZE - 1;\r\nconst UINT21_SIZE = 1 << 21;\r\nconst UINT21_MAX = UINT21_SIZE - 1;\n\n/**\r\n * Returns a value within [-0x80000000, 0x7fffffff]\r\n */\r\nfunction int32(engine) {\r\n    return engine.next() | 0;\r\n}\n\nfunction add(distribution, addend) {\r\n    if (addend === 0) {\r\n        return distribution;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) + addend;\r\n    }\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n */\r\nfunction int53(engine) {\r\n    const high = engine.next() | 0;\r\n    const low = engine.next() >>> 0;\r\n    return ((high & UINT21_MAX) * UINT32_SIZE +\r\n        low +\r\n        (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n */\r\nfunction int53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & 0x400000) {\r\n            if ((high & 0x7fffff) === 0x400000 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return ((high & UINT21_MAX) * UINT32_SIZE +\r\n                low +\r\n                (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n        }\r\n    }\r\n}\n\n/**\r\n * Returns a value within [0, 0xffffffff]\r\n */\r\nfunction uint32(engine) {\r\n    return engine.next() >>> 0;\r\n}\n\n/**\r\n * Returns a value within [0, 0x1fffffffffffff]\r\n */\r\nfunction uint53(engine) {\r\n    const high = engine.next() & UINT21_MAX;\r\n    const low = engine.next() >>> 0;\r\n    return high * UINT32_SIZE + low;\r\n}\n\n/**\r\n * Returns a value within [0, 0x20000000000000]\r\n */\r\nfunction uint53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & UINT21_SIZE) {\r\n            if ((high & UINT21_MAX) === 0 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return (high & UINT21_MAX) * UINT32_SIZE + low;\r\n        }\r\n    }\r\n}\n\nfunction isPowerOfTwoMinusOne(value) {\r\n    return ((value + 1) & value) === 0;\r\n}\r\nfunction bitmask(masking) {\r\n    return (engine) => engine.next() & masking;\r\n}\r\nfunction downscaleToLoopCheckedRange(range) {\r\n    const extendedRange = range + 1;\r\n    const maximum = extendedRange * Math.floor(UINT32_SIZE / extendedRange);\r\n    return engine => {\r\n        let value = 0;\r\n        do {\r\n            value = engine.next() >>> 0;\r\n        } while (value >= maximum);\r\n        return value % extendedRange;\r\n    };\r\n}\r\nfunction downscaleToRange(range) {\r\n    if (isPowerOfTwoMinusOne(range)) {\r\n        return bitmask(range);\r\n    }\r\n    else {\r\n        return downscaleToLoopCheckedRange(range);\r\n    }\r\n}\r\nfunction isEvenlyDivisibleByMaxInt32(value) {\r\n    return (value | 0) === 0;\r\n}\r\nfunction upscaleWithHighMasking(masking) {\r\n    return engine => {\r\n        const high = engine.next() & masking;\r\n        const low = engine.next() >>> 0;\r\n        return high * UINT32_SIZE + low;\r\n    };\r\n}\r\nfunction upscaleToLoopCheckedRange(extendedRange) {\r\n    const maximum = extendedRange * Math.floor(SMALLEST_UNSAFE_INTEGER / extendedRange);\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() & UINT21_MAX;\r\n            const low = engine.next() >>> 0;\r\n            ret = high * UINT32_SIZE + low;\r\n        } while (ret >= maximum);\r\n        return ret % extendedRange;\r\n    };\r\n}\r\nfunction upscaleWithinU53(range) {\r\n    const extendedRange = range + 1;\r\n    if (isEvenlyDivisibleByMaxInt32(extendedRange)) {\r\n        const highRange = ((extendedRange / UINT32_SIZE) | 0) - 1;\r\n        if (isPowerOfTwoMinusOne(highRange)) {\r\n            return upscaleWithHighMasking(highRange);\r\n        }\r\n    }\r\n    return upscaleToLoopCheckedRange(extendedRange);\r\n}\r\nfunction upscaleWithinI53AndLoopCheck(min, max) {\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() | 0;\r\n            const low = engine.next() >>> 0;\r\n            ret =\r\n                (high & UINT21_MAX) * UINT32_SIZE +\r\n                    low +\r\n                    (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);\r\n        } while (ret < min || ret > max);\r\n        return ret;\r\n    };\r\n}\r\n/**\r\n * Returns a Distribution to return a value within [min, max]\r\n * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n */\r\nfunction integer(min, max) {\r\n    min = Math.floor(min);\r\n    max = Math.floor(max);\r\n    if (min < -SMALLEST_UNSAFE_INTEGER || !isFinite(min)) {\r\n        throw new RangeError(`Expected min to be at least ${-SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    else if (max > SMALLEST_UNSAFE_INTEGER || !isFinite(max)) {\r\n        throw new RangeError(`Expected max to be at most ${SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    const range = max - min;\r\n    if (range <= 0 || !isFinite(range)) {\r\n        return () => min;\r\n    }\r\n    else if (range === UINT32_MAX) {\r\n        if (min === 0) {\r\n            return uint32;\r\n        }\r\n        else {\r\n            return add(int32, min + INT32_SIZE);\r\n        }\r\n    }\r\n    else if (range < UINT32_MAX) {\r\n        return add(downscaleToRange(range), min);\r\n    }\r\n    else if (range === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53, min);\r\n    }\r\n    else if (range < LARGEST_SAFE_INTEGER) {\r\n        return add(upscaleWithinU53(range), min);\r\n    }\r\n    else if (max - 1 - min === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53Full, min);\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER &&\r\n        max === SMALLEST_UNSAFE_INTEGER) {\r\n        return int53Full;\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER && max === LARGEST_SAFE_INTEGER) {\r\n        return int53;\r\n    }\r\n    else if (min === -LARGEST_SAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(int53, 1);\r\n    }\r\n    else if (max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(upscaleWithinI53AndLoopCheck(min - 1, max - 1), 1);\r\n    }\r\n    else {\r\n        return upscaleWithinI53AndLoopCheck(min, max);\r\n    }\r\n}\n\nfunction isLeastBitTrue(engine) {\r\n    return (engine.next() & 1) === 1;\r\n}\r\nfunction lessThan(distribution, value) {\r\n    return engine => distribution(engine) < value;\r\n}\r\nfunction probability(percentage) {\r\n    if (percentage <= 0) {\r\n        return () => false;\r\n    }\r\n    else if (percentage >= 1) {\r\n        return () => true;\r\n    }\r\n    else {\r\n        const scaled = percentage * UINT32_SIZE;\r\n        if (scaled % 1 === 0) {\r\n            return lessThan(int32, (scaled - INT32_SIZE) | 0);\r\n        }\r\n        else {\r\n            return lessThan(uint53, Math.round(percentage * SMALLEST_UNSAFE_INTEGER));\r\n        }\r\n    }\r\n}\r\nfunction bool(numerator, denominator) {\r\n    if (denominator == null) {\r\n        if (numerator == null) {\r\n            return isLeastBitTrue;\r\n        }\r\n        return probability(numerator);\r\n    }\r\n    else {\r\n        if (numerator <= 0) {\r\n            return () => false;\r\n        }\r\n        else if (numerator >= denominator) {\r\n            return () => true;\r\n        }\r\n        return lessThan(integer(0, denominator - 1), numerator);\r\n    }\r\n}\n\n/**\r\n * Returns a Distribution that returns a random `Date` within the inclusive\r\n * range of [`start`, `end`].\r\n * @param start The minimum `Date`\r\n * @param end The maximum `Date`\r\n */\r\nfunction date(start, end) {\r\n    const distribution = integer(+start, +end);\r\n    return engine => new Date(distribution(engine));\r\n}\n\n/**\r\n * Returns a Distribution to return a value within [1, sideCount]\r\n * @param sideCount The number of sides of the die\r\n */\r\nfunction die(sideCount) {\r\n    return integer(1, sideCount);\r\n}\n\n/**\r\n * Returns a distribution that returns an array of length `dieCount` of values\r\n * within [1, `sideCount`]\r\n * @param sideCount The number of sides of each die\r\n * @param dieCount The number of dice\r\n */\r\nfunction dice(sideCount, dieCount) {\r\n    const distribution = die(sideCount);\r\n    return engine => {\r\n        const result = [];\r\n        for (let i = 0; i < dieCount; ++i) {\r\n            result.push(distribution(engine));\r\n        }\r\n        return result;\r\n    };\r\n}\n\n// tslint:disable:unified-signatures\r\n// has 2**x chars, for faster uniform distribution\r\nconst DEFAULT_STRING_POOL = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-\";\r\nfunction string(pool = DEFAULT_STRING_POOL) {\r\n    const poolLength = pool.length;\r\n    if (!poolLength) {\r\n        throw new Error(\"Expected pool not to be an empty string\");\r\n    }\r\n    const distribution = integer(0, poolLength - 1);\r\n    return (engine, length) => {\r\n        let result = \"\";\r\n        for (let i = 0; i < length; ++i) {\r\n            const j = distribution(engine);\r\n            result += pool.charAt(j);\r\n        }\r\n        return result;\r\n    };\r\n}\n\nconst LOWER_HEX_POOL = \"0123456789abcdef\";\r\nconst lowerHex = string(LOWER_HEX_POOL);\r\nconst upperHex = string(LOWER_HEX_POOL.toUpperCase());\r\n/**\r\n * Returns a Distribution that returns a random string comprised of numbers\r\n * or the characters `abcdef` (or `ABCDEF`) of length `length`.\r\n * @param length Length of the result string\r\n * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n */\r\nfunction hex(uppercase) {\r\n    if (uppercase) {\r\n        return upperHex;\r\n    }\r\n    else {\r\n        return lowerHex;\r\n    }\r\n}\n\nfunction convertSliceArgument(value, length) {\r\n    if (value < 0) {\r\n        return Math.max(value + length, 0);\r\n    }\r\n    else {\r\n        return Math.min(value, length);\r\n    }\r\n}\n\nfunction toInteger(value) {\r\n    const num = +value;\r\n    if (num < 0) {\r\n        return Math.ceil(num);\r\n    }\r\n    else {\r\n        return Math.floor(num);\r\n    }\r\n}\n\n/**\r\n * Returns a random value within the provided `source` within the sliced\r\n * bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction pick(engine, source, begin, end) {\r\n    const length = source.length;\r\n    if (length === 0) {\r\n        throw new RangeError(\"Cannot pick from an empty array\");\r\n    }\r\n    const start = begin == null ? 0 : convertSliceArgument(toInteger(begin), length);\r\n    const finish = end === void 0 ? length : convertSliceArgument(toInteger(end), length);\r\n    if (start >= finish) {\r\n        throw new RangeError(`Cannot pick between bounds ${start} and ${finish}`);\r\n    }\r\n    const distribution = integer(start, finish - 1);\r\n    return source[distribution(engine)];\r\n}\n\nfunction multiply(distribution, multiplier) {\r\n    if (multiplier === 1) {\r\n        return distribution;\r\n    }\r\n    else if (multiplier === 0) {\r\n        return () => 0;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) * multiplier;\r\n    }\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0)\r\n */\r\nfunction realZeroToOneExclusive(engine) {\r\n    return uint53(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0]\r\n */\r\nfunction realZeroToOneInclusive(engine) {\r\n    return uint53Full(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [min, max) or [min, max]\r\n * @param min The minimum floating-point value, inclusive.\r\n * @param max The maximum floating-point value.\r\n * @param inclusive If true, `max` will be inclusive.\r\n */\r\nfunction real(min, max, inclusive = false) {\r\n    if (!isFinite(min)) {\r\n        throw new RangeError(\"Expected min to be a finite number\");\r\n    }\r\n    else if (!isFinite(max)) {\r\n        throw new RangeError(\"Expected max to be a finite number\");\r\n    }\r\n    return add(multiply(inclusive ? realZeroToOneInclusive : realZeroToOneExclusive, max - min), min);\r\n}\n\nconst sliceArray = Array.prototype.slice;\n\n/**\r\n * Shuffles an array in-place\r\n * @param engine The Engine to use when choosing random values\r\n * @param array The array to shuffle\r\n * @param downTo minimum index to shuffle. Only used internally.\r\n */\r\nfunction shuffle(engine, array, downTo = 0) {\r\n    const length = array.length;\r\n    if (length) {\r\n        for (let i = (length - 1) >>> 0; i > downTo; --i) {\r\n            const distribution = integer(0, i);\r\n            const j = distribution(engine);\r\n            if (i !== j) {\r\n                const tmp = array[i];\r\n                array[i] = array[j];\r\n                array[j] = tmp;\r\n            }\r\n        }\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * From the population array, produce an array with sampleSize elements that\r\n * are randomly chosen without repeats.\r\n * @param engine The Engine to use when choosing random values\r\n * @param population An array that has items to choose a sample from\r\n * @param sampleSize The size of the result array\r\n */\r\nfunction sample(engine, population, sampleSize) {\r\n    if (sampleSize < 0 ||\r\n        sampleSize > population.length ||\r\n        !isFinite(sampleSize)) {\r\n        throw new RangeError(\"Expected sampleSize to be within 0 and the length of the population\");\r\n    }\r\n    if (sampleSize === 0) {\r\n        return [];\r\n    }\r\n    const clone = sliceArray.call(population);\r\n    const length = clone.length;\r\n    if (length === sampleSize) {\r\n        return shuffle(engine, clone, 0);\r\n    }\r\n    const tailLength = length - sampleSize;\r\n    return shuffle(engine, clone, tailLength - 1).slice(tailLength);\r\n}\n\nconst stringRepeat = (() => {\r\n    try {\r\n        if (\"x\".repeat(3) === \"xxx\") {\r\n            return (pattern, count) => pattern.repeat(count);\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return (pattern, count) => {\r\n        let result = \"\";\r\n        while (count > 0) {\r\n            if (count & 1) {\r\n                result += pattern;\r\n            }\r\n            count >>= 1;\r\n            pattern += pattern;\r\n        }\r\n        return result;\r\n    };\r\n})();\n\nfunction zeroPad(text, zeroCount) {\r\n    return stringRepeat(\"0\", zeroCount - text.length) + text;\r\n}\r\n/**\r\n * Returns a Universally Unique Identifier Version 4.\r\n *\r\n * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n */\r\nfunction uuid4(engine) {\r\n    const a = engine.next() >>> 0;\r\n    const b = engine.next() | 0;\r\n    const c = engine.next() | 0;\r\n    const d = engine.next() >>> 0;\r\n    return (zeroPad(a.toString(16), 8) +\r\n        \"-\" +\r\n        zeroPad((b & 0xffff).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad((((b >> 4) & 0x0fff) | 0x4000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c & 0x3fff) | 0x8000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c >> 4) & 0xffff).toString(16), 4) +\r\n        zeroPad(d.toString(16), 8));\r\n}\n\n/**\r\n * An int32-producing Engine that uses `Math.random()`\r\n */\r\nconst nativeMath = {\r\n    next() {\r\n        return (Math.random() * UINT32_SIZE) | 0;\r\n    }\r\n};\n\n// tslint:disable:unified-signatures\r\n/**\r\n * A wrapper around an Engine that provides easy-to-use methods for\r\n * producing values based on known distributions\r\n */\r\nclass Random {\r\n    /**\r\n     * Creates a new Random wrapper\r\n     * @param engine The engine to use (defaults to a `Math.random`-based implementation)\r\n     */\r\n    constructor(engine = nativeMath) {\r\n        this.engine = engine;\r\n    }\r\n    /**\r\n     * Returns a value within [-0x80000000, 0x7fffffff]\r\n     */\r\n    int32() {\r\n        return int32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0xffffffff]\r\n     */\r\n    uint32() {\r\n        return uint32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x1fffffffffffff]\r\n     */\r\n    uint53() {\r\n        return uint53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x20000000000000]\r\n     */\r\n    uint53Full() {\r\n        return uint53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n     */\r\n    int53() {\r\n        return int53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n     */\r\n    int53Full() {\r\n        return int53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [min, max]\r\n     * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n     * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n     */\r\n    integer(min, max) {\r\n        return integer(min, max)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0]\r\n     */\r\n    realZeroToOneInclusive() {\r\n        return realZeroToOneInclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0)\r\n     */\r\n    realZeroToOneExclusive() {\r\n        return realZeroToOneExclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [min, max) or [min, max]\r\n     * @param min The minimum floating-point value, inclusive.\r\n     * @param max The maximum floating-point value.\r\n     * @param inclusive If true, `max` will be inclusive.\r\n     */\r\n    real(min, max, inclusive = false) {\r\n        return real(min, max, inclusive)(this.engine);\r\n    }\r\n    bool(numerator, denominator) {\r\n        return bool(numerator, denominator)(this.engine);\r\n    }\r\n    /**\r\n     * Return a random value within the provided `source` within the sliced\r\n     * bounds of `begin` and `end`.\r\n     * @param source an array of items to pick from\r\n     * @param begin the beginning slice index (defaults to `0`)\r\n     * @param end the ending slice index (defaults to `source.length`)\r\n     */\r\n    pick(source, begin, end) {\r\n        return pick(this.engine, source, begin, end);\r\n    }\r\n    /**\r\n     * Shuffles an array in-place\r\n     * @param array The array to shuffle\r\n     */\r\n    shuffle(array) {\r\n        return shuffle(this.engine, array);\r\n    }\r\n    /**\r\n     * From the population array, returns an array with sampleSize elements that\r\n     * are randomly chosen without repeats.\r\n     * @param population An array that has items to choose a sample from\r\n     * @param sampleSize The size of the result array\r\n     */\r\n    sample(population, sampleSize) {\r\n        return sample(this.engine, population, sampleSize);\r\n    }\r\n    /**\r\n     * Returns a value within [1, sideCount]\r\n     * @param sideCount The number of sides of the die\r\n     */\r\n    die(sideCount) {\r\n        return die(sideCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns an array of length `dieCount` of values within [1, sideCount]\r\n     * @param sideCount The number of sides of each die\r\n     * @param dieCount The number of dice\r\n     */\r\n    dice(sideCount, dieCount) {\r\n        return dice(sideCount, dieCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a Universally Unique Identifier Version 4.\r\n     *\r\n     * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n     */\r\n    uuid4() {\r\n        return uuid4(this.engine);\r\n    }\r\n    string(length, pool) {\r\n        return string(pool)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random string comprised of numbers or the characters `abcdef`\r\n     * (or `ABCDEF`) of length `length`.\r\n     * @param length Length of the result string\r\n     * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n     */\r\n    hex(length, uppercase) {\r\n        return hex(uppercase)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random `Date` within the inclusive range of [`start`, `end`].\r\n     * @param start The minimum `Date`\r\n     * @param end The maximum `Date`\r\n     */\r\n    date(start, end) {\r\n        return date(start, end)(this.engine);\r\n    }\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array\r\n */\r\nconst I32Array = (() => {\r\n    try {\r\n        const buffer = new ArrayBuffer(4);\r\n        const view = new Int32Array(buffer);\r\n        view[0] = INT32_SIZE;\r\n        if (view[0] === -INT32_SIZE) {\r\n            return Int32Array;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return Array;\r\n})();\n\nlet data = null;\r\nconst COUNT = 128;\r\nlet index = COUNT;\r\n/**\r\n * An Engine that relies on the globally-available `crypto.getRandomValues`,\r\n * which is typically available in modern browsers.\r\n *\r\n * See https://developer.mozilla.org/en-US/docs/Web/API/Crypto/getRandomValues\r\n *\r\n * If unavailable or otherwise non-functioning, then `browserCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst browserCrypto = {\r\n    next() {\r\n        if (index >= COUNT) {\r\n            if (data === null) {\r\n                data = new I32Array(COUNT);\r\n            }\r\n            crypto.getRandomValues(data);\r\n            index = 0;\r\n        }\r\n        return data[index++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns an array of random int32 values, based on current time\r\n * and a random number engine\r\n *\r\n * @param engine an Engine to pull random values from, default `nativeMath`\r\n * @param length the length of the Array, minimum 1, default 16\r\n */\r\nfunction createEntropy(engine = nativeMath, length = 16) {\r\n    const array = [];\r\n    array.push(new Date().getTime() | 0);\r\n    for (let i = 1; i < length; ++i) {\r\n        array[i] = engine.next() | 0;\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/imul\r\n */\r\nconst imul = (() => {\r\n    try {\r\n        if (Math.imul(UINT32_MAX, 5) === -5) {\r\n            return Math.imul;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    const UINT16_MAX = 0xffff;\r\n    return (a, b) => {\r\n        const ah = (a >>> 16) & UINT16_MAX;\r\n        const al = a & UINT16_MAX;\r\n        const bh = (b >>> 16) & UINT16_MAX;\r\n        const bl = b & UINT16_MAX;\r\n        // the shift by 0 fixes the sign on the high part\r\n        // the final |0 converts the unsigned value into a signed value\r\n        return (al * bl + (((ah * bl + al * bh) << 16) >>> 0)) | 0;\r\n    };\r\n})();\n\nconst ARRAY_SIZE = 624;\r\nconst ARRAY_MAX = ARRAY_SIZE - 1;\r\nconst M = 397;\r\nconst ARRAY_SIZE_MINUS_M = ARRAY_SIZE - M;\r\nconst A = 0x9908b0df;\r\n/**\r\n * An Engine that is a pseudorandom number generator using the Mersenne\r\n * Twister algorithm based on the prime 2**19937 − 1\r\n *\r\n * See http://en.wikipedia.org/wiki/Mersenne_twister\r\n */\r\nclass MersenneTwister19937 {\r\n    /**\r\n     * MersenneTwister19937 should not be instantiated directly.\r\n     * Instead, use the static methods `seed`, `seedWithArray`, or `autoSeed`.\r\n     */\r\n    constructor() {\r\n        this.data = new I32Array(ARRAY_SIZE);\r\n        this.index = 0; // integer within [0, 624]\r\n        this.uses = 0;\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with an initial int32 value\r\n     * @param initial the initial seed value\r\n     */\r\n    static seed(initial) {\r\n        return new MersenneTwister19937().seed(initial);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with zero or more int32 values\r\n     * @param source A series of int32 values\r\n     */\r\n    static seedWithArray(source) {\r\n        return new MersenneTwister19937().seedWithArray(source);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with the current time and\r\n     * a series of natively-generated random values\r\n     */\r\n    static autoSeed() {\r\n        return MersenneTwister19937.seedWithArray(createEntropy());\r\n    }\r\n    /**\r\n     * Returns the next int32 value of the sequence\r\n     */\r\n    next() {\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        const value = this.data[this.index];\r\n        this.index = (this.index + 1) | 0;\r\n        this.uses += 1;\r\n        return temper(value) | 0;\r\n    }\r\n    /**\r\n     * Returns the number of times that the Engine has been used.\r\n     *\r\n     * This can be provided to an unused MersenneTwister19937 with the same\r\n     * seed, bringing it to the exact point that was left off.\r\n     */\r\n    getUseCount() {\r\n        return this.uses;\r\n    }\r\n    /**\r\n     * Discards one or more items from the engine\r\n     * @param count The count of items to discard\r\n     */\r\n    discard(count) {\r\n        if (count <= 0) {\r\n            return this;\r\n        }\r\n        this.uses += count;\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        while (count + this.index > ARRAY_SIZE) {\r\n            count -= ARRAY_SIZE - this.index;\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        this.index = (this.index + count) | 0;\r\n        return this;\r\n    }\r\n    seed(initial) {\r\n        let previous = 0;\r\n        this.data[0] = previous = initial | 0;\r\n        for (let i = 1; i < ARRAY_SIZE; i = (i + 1) | 0) {\r\n            this.data[i] = previous =\r\n                (imul(previous ^ (previous >>> 30), 0x6c078965) + i) | 0;\r\n        }\r\n        this.index = ARRAY_SIZE;\r\n        this.uses = 0;\r\n        return this;\r\n    }\r\n    seedWithArray(source) {\r\n        this.seed(0x012bd6aa);\r\n        seedWithArray(this.data, source);\r\n        return this;\r\n    }\r\n}\r\nfunction refreshData(data) {\r\n    let k = 0;\r\n    let tmp = 0;\r\n    for (; (k | 0) < ARRAY_SIZE_MINUS_M; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] = data[(k + M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    for (; (k | 0) < ARRAY_MAX; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] =\r\n            data[(k - ARRAY_SIZE_MINUS_M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    tmp = (data[ARRAY_MAX] & INT32_SIZE) | (data[0] & INT32_MAX);\r\n    data[ARRAY_MAX] = data[M - 1] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n}\r\nfunction temper(value) {\r\n    value ^= value >>> 11;\r\n    value ^= (value << 7) & 0x9d2c5680;\r\n    value ^= (value << 15) & 0xefc60000;\r\n    return value ^ (value >>> 18);\r\n}\r\nfunction seedWithArray(data, source) {\r\n    let i = 1;\r\n    let j = 0;\r\n    const sourceLength = source.length;\r\n    let k = Math.max(sourceLength, ARRAY_SIZE) | 0;\r\n    let previous = data[0] | 0;\r\n    for (; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x0019660d)) +\r\n                (source[j] | 0) +\r\n                (j | 0)) |\r\n                0;\r\n        i = (i + 1) | 0;\r\n        ++j;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n        if (j >= sourceLength) {\r\n            j = 0;\r\n        }\r\n    }\r\n    for (k = ARRAY_MAX; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x5d588b65)) - i) | 0;\r\n        i = (i + 1) | 0;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n    }\r\n    data[0] = INT32_SIZE;\r\n}\n\nlet data$1 = null;\r\nconst COUNT$1 = 128;\r\nlet index$1 = COUNT$1;\r\n/**\r\n * An Engine that relies on the node-available\r\n * `require('crypto').randomBytes`, which has been available since 0.58.\r\n *\r\n * See https://nodejs.org/api/crypto.html#crypto_crypto_randombytes_size_callback\r\n *\r\n * If unavailable or otherwise non-functioning, then `nodeCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst nodeCrypto = {\r\n    next() {\r\n        if (index$1 >= COUNT$1) {\r\n            data$1 = new Int32Array(new Int8Array(require(\"crypto\").randomBytes(4 * COUNT$1)).buffer);\r\n            index$1 = 0;\r\n        }\r\n        return data$1[index$1++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns a Distribution to random value within the provided `source`\r\n * within the sliced bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction picker(source, begin, end) {\r\n    const clone = sliceArray.call(source, begin, end);\r\n    if (clone.length === 0) {\r\n        throw new RangeError(`Cannot pick from a source with no items`);\r\n    }\r\n    const distribution = integer(0, clone.length - 1);\r\n    return engine => clone[distribution(engine)];\r\n}\n\nexport { Random, browserCrypto, nativeMath, MersenneTwister19937, nodeCrypto, bool, date, dice, die, hex, int32, int53, int53Full, integer, pick, picker, real, realZeroToOneExclusive, realZeroToOneInclusive, sample, shuffle, string, uint32, uint53, uint53Full, uuid4, createEntropy };\n//# sourceMappingURL=random-js.esm.js.map\n","import * as Random from 'random-js';\nimport Matrix from 'ml-matrix';\n\nexport function checkFloat(n) {\n  return n > 0.0 && n <= 1.0;\n}\n\n/**\n * Select n with replacement elements on the training set and values, where n is the size of the training set.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {Array} trainingValue\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object} with new X and y.\n */\nexport function examplesBaggingWithReplacement(\n  trainingSet,\n  trainingValue,\n  seed,\n) {\n  let engine;\n  let distribution = Random.integer(0, trainingSet.rows - 1);\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let Xr = new Array(trainingSet.rows);\n  let yr = new Array(trainingSet.rows);\n\n  for (let i = 0; i < trainingSet.rows; ++i) {\n    let index = distribution(engine);\n    Xr[i] = trainingSet.getRow(index);\n    yr[i] = trainingValue[index];\n  }\n\n  return {\n    X: new Matrix(Xr),\n    y: yr,\n  };\n}\n\n/**\n * selects n features from the training set with or without replacement, returns the new training set and the indexes used.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {number} n - features.\n * @param {boolean} replacement\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object}\n */\nexport function featureBagging(trainingSet, n, replacement, seed) {\n  if (trainingSet.columns < n) {\n    throw new RangeError(\n      'N should be less or equal to the number of columns of X',\n    );\n  }\n\n  let distribution = Random.integer(0, trainingSet.columns - 1);\n  let engine;\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let toRet = new Matrix(trainingSet.rows, n);\n\n  let usedIndex;\n  let index;\n  if (replacement) {\n    usedIndex = new Array(n);\n    for (let i = 0; i < n; ++i) {\n      index = distribution(engine);\n      usedIndex[i] = index;\n      toRet.setColumn(i, trainingSet.getColumn(index));\n    }\n  } else {\n    usedIndex = new Set();\n    index = distribution(engine);\n    for (let i = 0; i < n; ++i) {\n      while (usedIndex.has(index)) {\n        index = distribution(engine);\n      }\n      toRet.setColumn(i, trainingSet.getColumn(index));\n      usedIndex.add(index);\n    }\n    usedIndex = Array.from(usedIndex);\n  }\n\n  return {\n    X: toRet,\n    usedIndex: usedIndex,\n  };\n}\n","import {\n  DecisionTreeClassifier as DTClassifier,\n  DecisionTreeRegression as DTRegression,\n} from 'ml-cart';\nimport {\n  Matrix,\n  WrapperMatrix2D,\n  MatrixTransposeView,\n  MatrixColumnSelectionView,\n} from 'ml-matrix';\n\nimport * as Utils from './utils';\n\n/**\n * @class RandomForestBase\n */\nexport class RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number|String} [options.maxFeatures] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement] - use replacement over the sample features.\n   * @param {number} [options.seed] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators] - number of estimator to use.\n   * @param {object} [options.treeOptions] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.isClassifier] - boolean to check if is a classifier or regression model (used by subclasses).\n   * @param {boolean} [options.useSampleBagging] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.replacement = model.replacement;\n      this.maxFeatures = model.maxFeatures;\n      this.nEstimators = model.nEstimators;\n      this.treeOptions = model.treeOptions;\n      this.isClassifier = model.isClassifier;\n      this.seed = model.seed;\n      this.n = model.n;\n      this.indexes = model.indexes;\n      this.useSampleBagging = model.useSampleBagging;\n\n      let Estimator = this.isClassifier ? DTClassifier : DTRegression;\n      this.estimators = model.estimators.map((est) => Estimator.load(est));\n    } else {\n      this.replacement = options.replacement;\n      this.maxFeatures = options.maxFeatures;\n      this.nEstimators = options.nEstimators;\n      this.treeOptions = options.treeOptions;\n      this.isClassifier = options.isClassifier;\n      this.seed = options.seed;\n      this.useSampleBagging = options.useSampleBagging;\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    this.maxFeatures = this.maxFeatures || trainingSet.columns;\n\n    if (Utils.checkFloat(this.maxFeatures)) {\n      this.n = Math.floor(trainingSet.columns * this.maxFeatures);\n    } else if (Number.isInteger(this.maxFeatures)) {\n      if (this.maxFeatures > trainingSet.columns) {\n        throw new RangeError(\n          `The maxFeatures parameter should be less than ${trainingSet.columns}`,\n        );\n      } else {\n        this.n = this.maxFeatures;\n      }\n    } else {\n      throw new RangeError(\n        `Cannot process the maxFeatures parameter ${this.maxFeatures}`,\n      );\n    }\n\n    let Estimator;\n    if (this.isClassifier) {\n      Estimator = DTClassifier;\n    } else {\n      Estimator = DTRegression;\n    }\n\n    this.estimators = new Array(this.nEstimators);\n    this.indexes = new Array(this.nEstimators);\n\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let res = this.useSampleBagging\n        ? Utils.examplesBaggingWithReplacement(\n            trainingSet,\n            trainingValues,\n            this.seed,\n          )\n        : { X: trainingSet, y: trainingValues };\n      let X = res.X;\n      let y = res.y;\n\n      res = Utils.featureBagging(X, this.n, this.replacement, this.seed);\n      X = res.X;\n\n      this.indexes[i] = res.usedIndex;\n      this.estimators[i] = new Estimator(this.treeOptions);\n      this.estimators[i].train(X, y);\n    }\n  }\n\n  /**\n   * Method that returns the way the algorithm generates the predictions, for example, in classification\n   * you can return the mode of all predictions retrieved by the trees, or in case of regression you can\n   * use the mean or the median.\n   * @abstract\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction.\n   */\n  // eslint-disable-next-line no-unused-vars\n  selection(values) {\n    throw new Error(\"Abstract method 'selection' not implemented!\");\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    let predictionValues = new Array(this.nEstimators);\n    toPredict = Matrix.checkMatrix(toPredict);\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let X = new MatrixColumnSelectionView(toPredict, this.indexes[i]); // get features for estimator\n      predictionValues[i] = this.estimators[i].predict(X);\n    }\n\n    predictionValues = new MatrixTransposeView(\n      new WrapperMatrix2D(predictionValues),\n    );\n    let predictions = new Array(predictionValues.rows);\n    for (let i = 0; i < predictionValues.rows; ++i) {\n      predictions[i] = this.selection(predictionValues.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      indexes: this.indexes,\n      n: this.n,\n      replacement: this.replacement,\n      maxFeatures: this.maxFeatures,\n      nEstimators: this.nEstimators,\n      treeOptions: this.treeOptions,\n      isClassifier: this.isClassifier,\n      seed: this.seed,\n      estimators: this.estimators.map((est) => est.toJSON()),\n      useSampleBagging: this.useSampleBagging,\n    };\n  }\n}\n","import { RandomForestBase } from './RandomForestBase';\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: true,\n  nEstimators: 10,\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestClassifier\n * @augments RandomForestBase\n */\nexport class RandomForestClassifier extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n      options.isClassifier = true;\n      super(options);\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return mode(values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      name: 'RFClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'RFClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestClassifier(true, model);\n  }\n}\n\n/**\n * Return the most repeated element on the array.\n * @param {Array} arr\n * @return {number} mode\n */\nfunction mode(arr) {\n  return arr\n    .sort(\n      (a, b) =>\n        arr.filter((v) => v === a).length - arr.filter((v) => v === b).length,\n    )\n    .pop();\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","(function(){function a(d){for(var e=0,f=d.length-1,g=void 0,h=void 0,i=void 0,j=c(e,f);!0;){if(f<=e)return d[j];if(f==e+1)return d[e]>d[f]&&b(d,e,f),d[j];for(g=c(e,f),d[g]>d[f]&&b(d,g,f),d[e]>d[f]&&b(d,e,f),d[g]>d[e]&&b(d,g,e),b(d,g,e+1),h=e+1,i=f;!0;){do h++;while(d[e]>d[h]);do i--;while(d[i]>d[e]);if(i<h)break;b(d,h,i)}b(d,e,i),i<=j&&(e=h),i>=j&&(f=i-1)}}var b=function b(d,e,f){var _ref;return _ref=[d[f],d[e]],d[e]=_ref[0],d[f]=_ref[1],_ref},c=function c(d,e){return~~((d+e)/2)};'undefined'!=typeof module&&module.exports?module.exports=a:window.median=a})();\n","import isArray from 'is-any-array';\nimport quickSelectMedian from 'median-quickselect';\n\nfunction median(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  return quickSelectMedian(input.slice());\n}\n\nexport default median;\n","import arrayMean from 'ml-array-mean';\nimport arrayMedian from 'ml-array-median';\n\nimport { RandomForestBase } from './RandomForestBase';\n\nconst selectionMethods = {\n  mean: arrayMean,\n  median: arrayMedian,\n};\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: false,\n  nEstimators: 10,\n  treeOptions: {},\n  selectionMethod: 'mean',\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestRegression\n * @augments RandomForestBase\n */\nexport class RandomForestRegression extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {string} [options.selectionMethod=\"mean\"] - the way to calculate the prediction from estimators, \"mean\" and \"median\" are supported.\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n      this.selectionMethod = model.selectionMethod;\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n\n      if (\n        !(\n          options.selectionMethod === 'mean' ||\n          options.selectionMethod === 'median'\n        )\n      ) {\n        throw new RangeError(\n          `Unsupported selection method ${options.selectionMethod}`,\n        );\n      }\n\n      options.isClassifier = false;\n\n      super(options);\n      this.selectionMethod = options.selectionMethod;\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return selectionMethods[this.selectionMethod](values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      selectionMethod: this.selectionMethod,\n      name: 'RFRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestRegression}\n   */\n  static load(model) {\n    if (model.name !== 'RFRegression') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestRegression(true, model);\n  }\n}\n","import { Matrix, MatrixTransposeView, EVD, SVD, NIPALS } from 'ml-matrix';\n\n/**\n * Creates new PCA (Principal Component Analysis) from the dataset\n * @param {Matrix} dataset - dataset or covariance matrix.\n * @param {Object} [options]\n * @param {boolean} [options.isCovarianceMatrix=false] - true if the dataset is a covariance matrix.\n * @param {string} [options.method='SVD'] - select which method to use: SVD (default), covarianceMatrirx or NIPALS.\n * @param {number} [options.nCompNIPALS=2] - number of components to be computed with NIPALS.\n * @param {boolean} [options.center=true] - should the data be centered (subtract the mean).\n * @param {boolean} [options.scale=false] - should the data be scaled (divide by the standard deviation).\n * @param {boolean} [options.ignoreZeroVariance=false] - ignore columns with zero variance if `scale` is `true`.\n * */\nexport class PCA {\n  constructor(dataset, options = {}) {\n    if (dataset === true) {\n      const model = options;\n      this.center = model.center;\n      this.scale = model.scale;\n      this.means = model.means;\n      this.stdevs = model.stdevs;\n      this.U = Matrix.checkMatrix(model.U);\n      this.S = model.S;\n      this.R = model.R;\n      this.excludedFeatures = model.excludedFeatures || [];\n      return;\n    }\n\n    dataset = new Matrix(dataset);\n\n    const {\n      isCovarianceMatrix = false,\n      method = 'SVD',\n      nCompNIPALS = 2,\n      center = true,\n      scale = false,\n      ignoreZeroVariance = false,\n    } = options;\n\n    this.center = center;\n    this.scale = scale;\n    this.means = null;\n    this.stdevs = null;\n    this.excludedFeatures = [];\n\n    if (isCovarianceMatrix) {\n      // User provided a covariance matrix instead of dataset.\n      this._computeFromCovarianceMatrix(dataset);\n      return;\n    }\n\n    this._adjust(dataset, ignoreZeroVariance);\n    switch (method) {\n      case 'covarianceMatrix': {\n        // User provided a dataset but wants us to compute and use the covariance matrix.\n        const covarianceMatrix = new MatrixTransposeView(dataset)\n          .mmul(dataset)\n          .div(dataset.rows - 1);\n        this._computeFromCovarianceMatrix(covarianceMatrix);\n        break;\n      }\n      case 'NIPALS': {\n        this._computeWithNIPALS(dataset, nCompNIPALS);\n        break;\n      }\n      case 'SVD': {\n        const svd = new SVD(dataset, {\n          computeLeftSingularVectors: false,\n          computeRightSingularVectors: true,\n          autoTranspose: true,\n        });\n\n        this.U = svd.rightSingularVectors;\n\n        const singularValues = svd.diagonal;\n        const eigenvalues = [];\n        for (const singularValue of singularValues) {\n          eigenvalues.push((singularValue * singularValue) / (dataset.rows - 1));\n        }\n        this.S = eigenvalues;\n        break;\n      }\n      default: {\n        throw new Error(`unknown method: ${method}`);\n      }\n    }\n  }\n\n  /**\n   * Load a PCA model from JSON\n   * @param {Object} model\n   * @return {PCA}\n   */\n  static load(model) {\n    if (typeof model.name !== 'string') {\n      throw new TypeError('model must have a name property');\n    }\n    if (model.name !== 'PCA') {\n      throw new RangeError(`invalid model: ${model.name}`);\n    }\n    return new PCA(true, model);\n  }\n\n  /**\n   * Project the dataset into the PCA space\n   * @param {Matrix} dataset\n   * @param {Object} options\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  predict(dataset, options = {}) {\n    const { nComponents = this.U.columns } = options;\n    dataset = new Matrix(dataset);\n    if (this.center) {\n      dataset.subRowVector(this.means);\n      if (this.scale) {\n        for (let i of this.excludedFeatures) {\n          dataset.removeColumn(i);\n        }\n        dataset.divRowVector(this.stdevs);\n      }\n    }\n    var predictions = dataset.mmul(this.U);\n    return predictions.subMatrix(0, predictions.rows - 1, 0, nComponents - 1);\n  }\n\n  /**\n   * Calculates the inverse PCA transform\n   * @param {Matrix} dataset\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  invert(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n\n    var inverse = dataset.mmul(this.U.transpose());\n\n    if (this.center) {\n      if (this.scale) {\n        inverse.mulRowVector(this.stdevs);\n      }\n      inverse.addRowVector(this.means);\n    }\n\n    return inverse;\n  }\n\n\n  /**\n   * Returns the proportion of variance for each component\n   * @return {[number]}\n   */\n  getExplainedVariance() {\n    var sum = 0;\n    for (const s of this.S) {\n      sum += s;\n    }\n    return this.S.map((value) => value / sum);\n  }\n\n  /**\n   * Returns the cumulative proportion of variance\n   * @return {[number]}\n   */\n  getCumulativeVariance() {\n    var explained = this.getExplainedVariance();\n    for (var i = 1; i < explained.length; i++) {\n      explained[i] += explained[i - 1];\n    }\n    return explained;\n  }\n\n  /**\n   * Returns the Eigenvectors of the covariance matrix\n   * @returns {Matrix}\n   */\n  getEigenvectors() {\n    return this.U;\n  }\n\n  /**\n   * Returns the Eigenvalues (on the diagonal)\n   * @returns {[number]}\n   */\n  getEigenvalues() {\n    return this.S;\n  }\n\n  /**\n   * Returns the standard deviations of the principal components\n   * @returns {[number]}\n   */\n  getStandardDeviations() {\n    return this.S.map((x) => Math.sqrt(x));\n  }\n\n  /**\n   * Returns the loadings matrix\n   * @return {Matrix}\n   */\n  getLoadings() {\n    return this.U.transpose();\n  }\n\n  /**\n   * Export the current model to a JSON object\n   * @return {Object} model\n   */\n  toJSON() {\n    return {\n      name: 'PCA',\n      center: this.center,\n      scale: this.scale,\n      means: this.means,\n      stdevs: this.stdevs,\n      U: this.U,\n      S: this.S,\n      excludedFeatures: this.excludedFeatures,\n    };\n  }\n\n  _adjust(dataset, ignoreZeroVariance) {\n    if (this.center) {\n      const mean = dataset.mean('column');\n      const stdevs = this.scale\n        ? dataset.standardDeviation('column', { mean })\n        : null;\n      this.means = mean;\n      dataset.subRowVector(mean);\n      if (this.scale) {\n        for (let i = 0; i < stdevs.length; i++) {\n          if (stdevs[i] === 0) {\n            if (ignoreZeroVariance) {\n              dataset.removeColumn(i);\n              stdevs.splice(i, 1);\n              this.excludedFeatures.push(i);\n              i--;\n            } else {\n              throw new RangeError(\n                `Cannot scale the dataset (standard deviation is zero at index ${i}`,\n              );\n            }\n          }\n        }\n        this.stdevs = stdevs;\n        dataset.divRowVector(stdevs);\n      }\n    }\n  }\n\n  _computeFromCovarianceMatrix(dataset) {\n    const evd = new EVD(dataset, { assumeSymmetric: true });\n    this.U = evd.eigenvectorMatrix;\n    this.U.flipRows();\n    this.S = evd.realEigenvalues;\n    this.S.reverse();\n  }\n\n  _computeWithNIPALS(dataset, nCompNIPALS) {\n    this.U = new Matrix(nCompNIPALS, dataset.columns);\n    this.S = [];\n\n    let x = dataset;\n    for (let i = 0; i < nCompNIPALS; i++) {\n      let dc = new NIPALS(x);\n\n      this.U.setRow(i, dc.w.transpose());\n      this.S.push(Math.pow(dc.s.get(0, 0), 2));\n\n      x = dc.xResidual;\n    }\n    this.U = this.U.transpose(); // to be compatible with API\n  }\n}\n","export function squaredEuclidean(p, q) {\r\n    let d = 0;\r\n    for (let i = 0; i < p.length; i++) {\r\n        d += (p[i] - q[i]) * (p[i] - q[i]);\r\n    }\r\n    return d;\r\n}\r\nexport function euclidean(p, q) {\r\n    return Math.sqrt(squaredEuclidean(p, q));\r\n}\r\n","/**\n * Computes a distance/similarity matrix given an array of data and a distance/similarity function.\n * @param {Array} data An array of data\n * @param {function} distanceFn  A function that accepts two arguments and computes a distance/similarity between them\n * @return {Array<Array>} The distance/similarity matrix. The matrix is square and has a size equal to the length of\n * the data array\n */\nexport default function distanceMatrix(data, distanceFn) {\n  const result = getMatrix(data.length);\n\n  // Compute upper distance matrix\n  for (let i = 0; i < data.length; i++) {\n    for (let j = 0; j <= i; j++) {\n      result[i][j] = distanceFn(data[i], data[j]);\n      result[j][i] = result[i][j];\n    }\n  }\n\n  return result;\n}\n\nfunction getMatrix(size) {\n  const matrix = [];\n  for (let i = 0; i < size; i++) {\n    const row = [];\n    matrix.push(row);\n    for (let j = 0; j < size; j++) {\n      row.push(0);\n    }\n  }\n  return matrix;\n}\n","// Generated by CoffeeScript 1.8.0\n(function() {\n  var Heap, defaultCmp, floor, heapify, heappop, heappush, heappushpop, heapreplace, insort, min, nlargest, nsmallest, updateItem, _siftdown, _siftup;\n\n  floor = Math.floor, min = Math.min;\n\n\n  /*\n  Default comparison function to be used\n   */\n\n  defaultCmp = function(x, y) {\n    if (x < y) {\n      return -1;\n    }\n    if (x > y) {\n      return 1;\n    }\n    return 0;\n  };\n\n\n  /*\n  Insert item x in list a, and keep it sorted assuming a is sorted.\n  \n  If x is already in a, insert it to the right of the rightmost x.\n  \n  Optional args lo (default 0) and hi (default a.length) bound the slice\n  of a to be searched.\n   */\n\n  insort = function(a, x, lo, hi, cmp) {\n    var mid;\n    if (lo == null) {\n      lo = 0;\n    }\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (lo < 0) {\n      throw new Error('lo must be non-negative');\n    }\n    if (hi == null) {\n      hi = a.length;\n    }\n    while (lo < hi) {\n      mid = floor((lo + hi) / 2);\n      if (cmp(x, a[mid]) < 0) {\n        hi = mid;\n      } else {\n        lo = mid + 1;\n      }\n    }\n    return ([].splice.apply(a, [lo, lo - lo].concat(x)), x);\n  };\n\n\n  /*\n  Push item onto heap, maintaining the heap invariant.\n   */\n\n  heappush = function(array, item, cmp) {\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    array.push(item);\n    return _siftdown(array, 0, array.length - 1, cmp);\n  };\n\n\n  /*\n  Pop the smallest item off the heap, maintaining the heap invariant.\n   */\n\n  heappop = function(array, cmp) {\n    var lastelt, returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    lastelt = array.pop();\n    if (array.length) {\n      returnitem = array[0];\n      array[0] = lastelt;\n      _siftup(array, 0, cmp);\n    } else {\n      returnitem = lastelt;\n    }\n    return returnitem;\n  };\n\n\n  /*\n  Pop and return the current smallest value, and add the new item.\n  \n  This is more efficient than heappop() followed by heappush(), and can be\n  more appropriate when using a fixed size heap. Note that the value\n  returned may be larger than item! That constrains reasonable use of\n  this routine unless written as part of a conditional replacement:\n      if item > array[0]\n        item = heapreplace(array, item)\n   */\n\n  heapreplace = function(array, item, cmp) {\n    var returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    returnitem = array[0];\n    array[0] = item;\n    _siftup(array, 0, cmp);\n    return returnitem;\n  };\n\n\n  /*\n  Fast version of a heappush followed by a heappop.\n   */\n\n  heappushpop = function(array, item, cmp) {\n    var _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (array.length && cmp(array[0], item) < 0) {\n      _ref = [array[0], item], item = _ref[0], array[0] = _ref[1];\n      _siftup(array, 0, cmp);\n    }\n    return item;\n  };\n\n\n  /*\n  Transform list into a heap, in-place, in O(array.length) time.\n   */\n\n  heapify = function(array, cmp) {\n    var i, _i, _j, _len, _ref, _ref1, _results, _results1;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    _ref1 = (function() {\n      _results1 = [];\n      for (var _j = 0, _ref = floor(array.length / 2); 0 <= _ref ? _j < _ref : _j > _ref; 0 <= _ref ? _j++ : _j--){ _results1.push(_j); }\n      return _results1;\n    }).apply(this).reverse();\n    _results = [];\n    for (_i = 0, _len = _ref1.length; _i < _len; _i++) {\n      i = _ref1[_i];\n      _results.push(_siftup(array, i, cmp));\n    }\n    return _results;\n  };\n\n\n  /*\n  Update the position of the given item in the heap.\n  This function should be called every time the item is being modified.\n   */\n\n  updateItem = function(array, item, cmp) {\n    var pos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    pos = array.indexOf(item);\n    if (pos === -1) {\n      return;\n    }\n    _siftdown(array, 0, pos, cmp);\n    return _siftup(array, pos, cmp);\n  };\n\n\n  /*\n  Find the n largest elements in a dataset.\n   */\n\n  nlargest = function(array, n, cmp) {\n    var elem, result, _i, _len, _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    result = array.slice(0, n);\n    if (!result.length) {\n      return result;\n    }\n    heapify(result, cmp);\n    _ref = array.slice(n);\n    for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n      elem = _ref[_i];\n      heappushpop(result, elem, cmp);\n    }\n    return result.sort(cmp).reverse();\n  };\n\n\n  /*\n  Find the n smallest elements in a dataset.\n   */\n\n  nsmallest = function(array, n, cmp) {\n    var elem, i, los, result, _i, _j, _len, _ref, _ref1, _results;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (n * 10 <= array.length) {\n      result = array.slice(0, n).sort(cmp);\n      if (!result.length) {\n        return result;\n      }\n      los = result[result.length - 1];\n      _ref = array.slice(n);\n      for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n        elem = _ref[_i];\n        if (cmp(elem, los) < 0) {\n          insort(result, elem, 0, null, cmp);\n          result.pop();\n          los = result[result.length - 1];\n        }\n      }\n      return result;\n    }\n    heapify(array, cmp);\n    _results = [];\n    for (i = _j = 0, _ref1 = min(n, array.length); 0 <= _ref1 ? _j < _ref1 : _j > _ref1; i = 0 <= _ref1 ? ++_j : --_j) {\n      _results.push(heappop(array, cmp));\n    }\n    return _results;\n  };\n\n  _siftdown = function(array, startpos, pos, cmp) {\n    var newitem, parent, parentpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    newitem = array[pos];\n    while (pos > startpos) {\n      parentpos = (pos - 1) >> 1;\n      parent = array[parentpos];\n      if (cmp(newitem, parent) < 0) {\n        array[pos] = parent;\n        pos = parentpos;\n        continue;\n      }\n      break;\n    }\n    return array[pos] = newitem;\n  };\n\n  _siftup = function(array, pos, cmp) {\n    var childpos, endpos, newitem, rightpos, startpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    endpos = array.length;\n    startpos = pos;\n    newitem = array[pos];\n    childpos = 2 * pos + 1;\n    while (childpos < endpos) {\n      rightpos = childpos + 1;\n      if (rightpos < endpos && !(cmp(array[childpos], array[rightpos]) < 0)) {\n        childpos = rightpos;\n      }\n      array[pos] = array[childpos];\n      pos = childpos;\n      childpos = 2 * pos + 1;\n    }\n    array[pos] = newitem;\n    return _siftdown(array, startpos, pos, cmp);\n  };\n\n  Heap = (function() {\n    Heap.push = heappush;\n\n    Heap.pop = heappop;\n\n    Heap.replace = heapreplace;\n\n    Heap.pushpop = heappushpop;\n\n    Heap.heapify = heapify;\n\n    Heap.updateItem = updateItem;\n\n    Heap.nlargest = nlargest;\n\n    Heap.nsmallest = nsmallest;\n\n    function Heap(cmp) {\n      this.cmp = cmp != null ? cmp : defaultCmp;\n      this.nodes = [];\n    }\n\n    Heap.prototype.push = function(x) {\n      return heappush(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pop = function() {\n      return heappop(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.peek = function() {\n      return this.nodes[0];\n    };\n\n    Heap.prototype.contains = function(x) {\n      return this.nodes.indexOf(x) !== -1;\n    };\n\n    Heap.prototype.replace = function(x) {\n      return heapreplace(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pushpop = function(x) {\n      return heappushpop(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.heapify = function() {\n      return heapify(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.updateItem = function(x) {\n      return updateItem(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.clear = function() {\n      return this.nodes = [];\n    };\n\n    Heap.prototype.empty = function() {\n      return this.nodes.length === 0;\n    };\n\n    Heap.prototype.size = function() {\n      return this.nodes.length;\n    };\n\n    Heap.prototype.clone = function() {\n      var heap;\n      heap = new Heap();\n      heap.nodes = this.nodes.slice(0);\n      return heap;\n    };\n\n    Heap.prototype.toArray = function() {\n      return this.nodes.slice(0);\n    };\n\n    Heap.prototype.insert = Heap.prototype.push;\n\n    Heap.prototype.top = Heap.prototype.peek;\n\n    Heap.prototype.front = Heap.prototype.peek;\n\n    Heap.prototype.has = Heap.prototype.contains;\n\n    Heap.prototype.copy = Heap.prototype.clone;\n\n    return Heap;\n\n  })();\n\n  (function(root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      return define([], factory);\n    } else if (typeof exports === 'object') {\n      return module.exports = factory();\n    } else {\n      return root.Heap = factory();\n    }\n  })(this, function() {\n    return Heap;\n  });\n\n}).call(this);\n","module.exports = require('./lib/heap');\n","import Heap from 'heap';\n\nexport default class Cluster {\n  constructor() {\n    this.children = [];\n    this.height = 0;\n    this.size = 1;\n    this.index = -1;\n    this.isLeaf = false;\n  }\n\n  /**\n   * Creates an array of clusters where the maximum height is smaller than the threshold\n   * @param {number} threshold\n   * @return {Array<Cluster>}\n   */\n  cut(threshold) {\n    if (typeof threshold !== 'number') {\n      throw new TypeError('threshold must be a number');\n    }\n    if (threshold < 0) {\n      throw new RangeError('threshold must be a positive number');\n    }\n    let list = [this];\n    const ans = [];\n    while (list.length > 0) {\n      const aux = list.shift();\n      if (threshold >= aux.height) {\n        ans.push(aux);\n      } else {\n        list = list.concat(aux.children);\n      }\n    }\n    return ans;\n  }\n\n  /**\n   * Merge the leaves in the minimum way to have `groups` number of clusters.\n   * @param {number} groups - Them number of children the first level of the tree should have.\n   * @return {Cluster}\n   */\n  group(groups) {\n    if (!Number.isInteger(groups) || groups < 1) {\n      throw new RangeError('groups must be a positive integer');\n    }\n\n    const heap = new Heap((a, b) => {\n      return b.height - a.height;\n    });\n\n    heap.push(this);\n\n    while (heap.size() < groups) {\n      var first = heap.pop();\n      if (first.children.length === 0) {\n        break;\n      }\n      first.children.forEach((child) => heap.push(child));\n    }\n\n    var root = new Cluster();\n    root.children = heap.toArray();\n    root.height = this.height;\n\n    return root;\n  }\n\n  /**\n   * Traverses the tree depth-first and calls the provided callback with each individual node\n   * @param {function} cb - The callback to be called on each node encounter\n   */\n  traverse(cb) {\n    function visit(root, callback) {\n      callback(root);\n      if (root.children) {\n        for (const child of root.children) {\n          visit(child, callback);\n        }\n      }\n    }\n    visit(this, cb);\n  }\n\n  /**\n   * Returns a list of indices for all the leaves of this cluster.\n   * The list is ordered in such a way that a dendrogram could be drawn without crossing branches.\n   * @returns {Array<number>}\n   */\n  indices() {\n    const result = [];\n    this.traverse((cluster) => {\n      if (cluster.isLeaf) {\n        result.push(cluster.index);\n      }\n    });\n    return result;\n  }\n}\n","import { euclidean } from 'ml-distance-euclidean';\nimport getDistanceMatrix from 'ml-distance-matrix';\nimport { Matrix } from 'ml-matrix';\n\nimport Cluster from './Cluster';\n\nfunction singleLink(dKI, dKJ) {\n  return Math.min(dKI, dKJ);\n}\n\nfunction completeLink(dKI, dKJ) {\n  return Math.max(dKI, dKJ);\n}\n\nfunction averageLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  return ai * dKI + aj * dKJ;\n}\n\nfunction weightedAverageLink(dKI, dKJ) {\n  return (dKI + dKJ) / 2;\n}\n\nfunction centroidLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  const b = -(ni * nj) / (ni + nj) ** 2;\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction medianLink(dKI, dKJ, dIJ) {\n  return dKI / 2 + dKJ / 2 - dIJ / 4;\n}\n\nfunction wardLink(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction wardLink2(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return Math.sqrt(ai * dKI * dKI + aj * dKJ * dKJ + b * dIJ * dIJ);\n}\n\n/**\n * Continuously merge nodes that have the least dissimilarity\n * @param {Array<Array<number>>} data - Array of points to be clustered\n * @param {object} [options]\n * @param {Function} [options.distanceFunction]\n * @param {string} [options.method] - Default: `'complete'`\n * @param {boolean} [options.isDistanceMatrix] - Is the input already a distance matrix?\n * @constructor\n */\nexport function agnes(data, options = {}) {\n  const {\n    distanceFunction = euclidean,\n    method = 'complete',\n    isDistanceMatrix = false,\n  } = options;\n\n  let updateFunc;\n  if (!isDistanceMatrix) {\n    data = getDistanceMatrix(data, distanceFunction);\n  }\n  let distanceMatrix = new Matrix(data);\n  const numLeaves = distanceMatrix.rows;\n\n  // allows to use a string or a given function\n  if (typeof method === 'string') {\n    switch (method.toLowerCase()) {\n      case 'single':\n        updateFunc = singleLink;\n        break;\n      case 'complete':\n        updateFunc = completeLink;\n        break;\n      case 'average':\n      case 'upgma':\n        updateFunc = averageLink;\n        break;\n      case 'wpgma':\n        updateFunc = weightedAverageLink;\n        break;\n      case 'centroid':\n      case 'upgmc':\n        updateFunc = centroidLink;\n        break;\n      case 'median':\n      case 'wpgmc':\n        updateFunc = medianLink;\n        break;\n      case 'ward':\n        updateFunc = wardLink;\n        break;\n      case 'ward2':\n        updateFunc = wardLink2;\n        break;\n      default:\n        throw new RangeError(`unknown clustering method: ${method}`);\n    }\n  } else if (typeof method !== 'function') {\n    throw new TypeError('method must be a string or function');\n  }\n\n  let clusters = [];\n  for (let i = 0; i < numLeaves; i++) {\n    const cluster = new Cluster();\n    cluster.isLeaf = true;\n    cluster.index = i;\n    clusters.push(cluster);\n  }\n\n  for (let n = 0; n < numLeaves - 1; n++) {\n    const [row, column, distance] = getSmallestDistance(distanceMatrix);\n    const cluster1 = clusters[row];\n    const cluster2 = clusters[column];\n    const newCluster = new Cluster();\n    newCluster.size = cluster1.size + cluster2.size;\n    newCluster.children.push(cluster1, cluster2);\n    newCluster.height = distance;\n\n    const newClusters = [newCluster];\n    const newDistanceMatrix = new Matrix(\n      distanceMatrix.rows - 1,\n      distanceMatrix.rows - 1,\n    );\n    const previous = (newIndex) =>\n      getPreviousIndex(newIndex, Math.min(row, column), Math.max(row, column));\n\n    for (let i = 1; i < newDistanceMatrix.rows; i++) {\n      const prevI = previous(i);\n      const prevICluster = clusters[prevI];\n      newClusters.push(prevICluster);\n      for (let j = 0; j < i; j++) {\n        if (j === 0) {\n          const dKI = distanceMatrix.get(row, prevI);\n          const dKJ = distanceMatrix.get(prevI, column);\n          const val = updateFunc(\n            dKI,\n            dKJ,\n            distance,\n            cluster1.size,\n            cluster2.size,\n            prevICluster.size,\n          );\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        } else {\n          // Just copy distance from previous matrix\n          const val = distanceMatrix.get(prevI, previous(j));\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        }\n      }\n    }\n\n    clusters = newClusters;\n    distanceMatrix = newDistanceMatrix;\n  }\n\n  return clusters[0];\n}\n\nfunction getSmallestDistance(distance) {\n  let smallest = Infinity;\n  let smallestI = 0;\n  let smallestJ = 0;\n  for (let i = 1; i < distance.rows; i++) {\n    for (let j = 0; j < i; j++) {\n      if (distance.get(i, j) < smallest) {\n        smallest = distance.get(i, j);\n        smallestI = i;\n        smallestJ = j;\n      }\n    }\n  }\n  return [smallestI, smallestJ, smallest];\n}\n\nfunction getPreviousIndex(newIndex, prev1, prev2) {\n  newIndex -= 1;\n  if (newIndex >= prev1) newIndex++;\n  if (newIndex >= prev2) newIndex++;\n  return newIndex;\n}\n","export * from './agnes';\n// export * from './diana';\n// export * from './birch';\n// export * './cure';\n// export * from './chameleon';\n","'use strict';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nconst defaultOptions = {\n    distanceFunction: squaredEuclidean\n};\nexport default function nearestVector(listVectors, vector, options = defaultOptions) {\n    const distanceFunction = options.distanceFunction || defaultOptions.distanceFunction;\n    const similarityFunction = options.similarityFunction || defaultOptions.similarityFunction;\n    let vectorIndex = -1;\n    if (typeof similarityFunction === 'function') {\n        // maximum similarity\n        let maxSim = Number.MIN_VALUE;\n        for (let j = 0; j < listVectors.length; j++) {\n            const sim = similarityFunction(vector, listVectors[j]);\n            if (sim > maxSim) {\n                maxSim = sim;\n                vectorIndex = j;\n            }\n        }\n    }\n    else if (typeof distanceFunction === 'function') {\n        // minimum distance\n        let minDist = Number.MAX_VALUE;\n        for (let i = 0; i < listVectors.length; i++) {\n            const dist = distanceFunction(vector, listVectors[i]);\n            if (dist < minDist) {\n                minDist = dist;\n                vectorIndex = i;\n            }\n        }\n    }\n    else {\n        throw new Error(\"A similarity or distance function it's required\");\n    }\n    return vectorIndex;\n}\nexport function findNearestVector(vectorList, vector, options = defaultOptions) {\n    const index = nearestVector(vectorList, vector, options);\n    return vectorList[index];\n}\n","import nearestVector from 'ml-nearest-vector';\n\n/**\n * Calculates the distance matrix for a given array of points\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {function} distance - Distance function to use between the points\n * @return {Array<Array<number>>} - matrix with the distance values\n */\nexport function calculateDistanceMatrix(data, distance) {\n  var distanceMatrix = new Array(data.length);\n  for (var i = 0; i < data.length; ++i) {\n    for (var j = i; j < data.length; ++j) {\n      if (!distanceMatrix[i]) {\n        distanceMatrix[i] = new Array(data.length);\n      }\n      if (!distanceMatrix[j]) {\n        distanceMatrix[j] = new Array(data.length);\n      }\n      const dist = distance(data[i], data[j]);\n      distanceMatrix[i][j] = dist;\n      distanceMatrix[j][i] = dist;\n    }\n  }\n  return distanceMatrix;\n}\n\n/**\n * Updates the cluster identifier based in the new data\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {function} distance - Distance function to use between the points\n * @return {Array} the cluster identifier for each data dot\n */\nexport function updateClusterID(data, centers, clusterID, distance) {\n  for (var i = 0; i < data.length; i++) {\n    clusterID[i] = nearestVector(centers, data[i], {\n      distanceFunction: distance\n    });\n  }\n  return clusterID;\n}\n\n/**\n * Update the center values based in the new configurations of the clusters\n * @ignore\n * @param {Array<Array<number>>} prevCenters - Centroids from the previous iteration\n * @param {Array <Array <number>>} data - the [x,y,z,...] points to cluster\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @return {Array} he K centers in format [x,y,z,...]\n */\nexport function updateCenters(prevCenters, data, clusterID, K) {\n  const nDim = data[0].length;\n\n  // copy previous centers\n  var centers = new Array(K);\n  var centersLen = new Array(K);\n  for (var i = 0; i < K; i++) {\n    centers[i] = new Array(nDim);\n    centersLen[i] = 0;\n    for (var j = 0; j < nDim; j++) {\n      centers[i][j] = 0;\n    }\n  }\n\n  // add the value for all dimensions of the point\n  for (var l = 0; l < data.length; l++) {\n    centersLen[clusterID[l]]++;\n    for (var dim = 0; dim < nDim; dim++) {\n      centers[clusterID[l]][dim] += data[l][dim];\n    }\n  }\n\n  // divides by length\n  for (var id = 0; id < K; id++) {\n    for (var d = 0; d < nDim; d++) {\n      if (centersLen[id]) {\n        centers[id][d] /= centersLen[id];\n      } else {\n        centers[id][d] = prevCenters[id][d];\n      }\n    }\n  }\n  return centers;\n}\n\n/**\n * The centers have moved more than the tolerance value?\n * @ignore\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} oldCenters - the K old centers in format [x,y,z,...]\n * @param {function} distanceFunction - Distance function to use between the points\n * @param {number} tolerance - Allowed distance for the centroids to move\n * @return {boolean}\n */\nexport function hasConverged(centers, oldCenters, distanceFunction, tolerance) {\n  for (var i = 0; i < centers.length; i++) {\n    if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {\n      return false;\n    }\n  }\n  return true;\n}\n","const LOOP = 8;\nconst FLOAT_MUL = 1 / 16777216;\nconst sh1 = 15;\nconst sh2 = 18;\nconst sh3 = 11;\nfunction multiply_uint32(n, m) {\n    n >>>= 0;\n    m >>>= 0;\n    const nlo = n & 0xffff;\n    const nhi = n - nlo;\n    return (((nhi * m) >>> 0) + nlo * m) >>> 0;\n}\nexport default class XSadd {\n    constructor(seed = Date.now()) {\n        this.state = new Uint32Array(4);\n        this.init(seed);\n        this.random = this.getFloat.bind(this);\n    }\n    /**\n     * Returns a 32-bit integer r (0 <= r < 2^32)\n     */\n    getUint32() {\n        this.nextState();\n        return (this.state[3] + this.state[2]) >>> 0;\n    }\n    /**\n     * Returns a floating point number r (0.0 <= r < 1.0)\n     */\n    getFloat() {\n        return (this.getUint32() >>> 8) * FLOAT_MUL;\n    }\n    init(seed) {\n        if (!Number.isInteger(seed)) {\n            throw new TypeError('seed must be an integer');\n        }\n        this.state[0] = seed;\n        this.state[1] = 0;\n        this.state[2] = 0;\n        this.state[3] = 0;\n        for (let i = 1; i < LOOP; i++) {\n            this.state[i & 3] ^=\n                (i +\n                    multiply_uint32(1812433253, this.state[(i - 1) & 3] ^ ((this.state[(i - 1) & 3] >>> 30) >>> 0))) >>>\n                    0;\n        }\n        this.periodCertification();\n        for (let i = 0; i < LOOP; i++) {\n            this.nextState();\n        }\n    }\n    periodCertification() {\n        if (this.state[0] === 0 &&\n            this.state[1] === 0 &&\n            this.state[2] === 0 &&\n            this.state[3] === 0) {\n            this.state[0] = 88; // X\n            this.state[1] = 83; // S\n            this.state[2] = 65; // A\n            this.state[3] = 68; // D\n        }\n    }\n    nextState() {\n        let t = this.state[0];\n        t ^= t << sh1;\n        t ^= t >>> sh2;\n        t ^= this.state[3] << sh3;\n        this.state[0] = this.state[1];\n        this.state[1] = this.state[2];\n        this.state[2] = this.state[3];\n        this.state[3] = t;\n    }\n}\n","const PROB_TOLERANCE = 0.00000001;\nfunction randomChoice(values, options = {}, random = Math.random) {\n    const { size = 1, replace = false, probabilities } = options;\n    let valuesArr;\n    let cumSum;\n    if (typeof values === 'number') {\n        valuesArr = getArray(values);\n    }\n    else {\n        valuesArr = values.slice();\n    }\n    if (probabilities) {\n        if (!replace) {\n            throw new Error('choice with probabilities and no replacement is not implemented');\n        }\n        // check input is sane\n        if (probabilities.length !== valuesArr.length) {\n            throw new Error('the length of probabilities option should be equal to the number of choices');\n        }\n        cumSum = [probabilities[0]];\n        for (let i = 1; i < probabilities.length; i++) {\n            cumSum[i] = cumSum[i - 1] + probabilities[i];\n        }\n        if (Math.abs(1 - cumSum[cumSum.length - 1]) > PROB_TOLERANCE) {\n            throw new Error(`probabilities should sum to 1, but instead sums to ${cumSum[cumSum.length - 1]}`);\n        }\n    }\n    if (replace === false && size > valuesArr.length) {\n        throw new Error('size option is too large');\n    }\n    const result = [];\n    for (let i = 0; i < size; i++) {\n        const index = randomIndex(valuesArr.length, random, cumSum);\n        result.push(valuesArr[index]);\n        if (!replace) {\n            valuesArr.splice(index, 1);\n        }\n    }\n    return result;\n}\nfunction getArray(n) {\n    const arr = [];\n    for (let i = 0; i < n; i++) {\n        arr.push(i);\n    }\n    return arr;\n}\nfunction randomIndex(n, random, cumSum) {\n    const rand = random();\n    if (!cumSum) {\n        return Math.floor(rand * n);\n    }\n    else {\n        let idx = 0;\n        while (rand > cumSum[idx]) {\n            idx++;\n        }\n        return idx;\n    }\n}\nexport default randomChoice;\n","// tslint:disable-next-line\nimport XSAdd from 'ml-xsadd';\nimport choice from './choice';\n/**\n * @classdesc Random class\n */\nexport default class Random {\n    /**\n     * @param [seedOrRandom=Math.random] - Control the random number generator used by the Random class instance. Pass a random number generator function with a uniform distribution over the half-open interval [0, 1[. If seed will pass it to ml-xsadd to create a seeded random number generator. If undefined will use Math.random.\n     */\n    constructor(seedOrRandom = Math.random) {\n        if (typeof seedOrRandom === 'number') {\n            const xsadd = new XSAdd(seedOrRandom);\n            this.randomGenerator = xsadd.random;\n        }\n        else {\n            this.randomGenerator = seedOrRandom;\n        }\n    }\n    choice(values, options) {\n        if (typeof values === 'number') {\n            return choice(values, options, this.randomGenerator);\n        }\n        return choice(values, options, this.randomGenerator);\n    }\n    /**\n     * Draw a random number from a uniform distribution on [0,1)\n     * @return The random number\n     */\n    random() {\n        return this.randomGenerator();\n    }\n    /**\n     * Draw a random integer from a uniform distribution on [low, high). If only low is specified, the number is drawn on [0, low)\n     * @param low - The lower bound of the uniform distribution interval.\n     * @param high - The higher bound of the uniform distribution interval.\n     */\n    randInt(low, high) {\n        if (high === undefined) {\n            high = low;\n            low = 0;\n        }\n        return low + Math.floor(this.randomGenerator() * (high - low));\n    }\n    /**\n     * Draw several random number from a uniform distribution on [0, 1)\n     * @param size - The number of number to draw\n     * @return - The list of drawn numbers.\n     */\n    randomSample(size) {\n        const result = [];\n        for (let i = 0; i < size; i++) {\n            result.push(this.random());\n        }\n        return result;\n    }\n}\n","import Random from 'ml-random';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nimport { Matrix } from 'ml-matrix';\n\n/**\n * Choose K different random points from the original data\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function random(data, K, seed) {\n  const random = new Random(seed);\n  return random.choice(data, { size: K });\n}\n\n/**\n * Chooses the most distant points to a first random pick\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {Array<Array<number>>} distanceMatrix - matrix with the distance values\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function mostDistant(data, K, distanceMatrix, seed) {\n  const random = new Random(seed);\n  var ans = new Array(K);\n  // chooses a random point as initial cluster\n  ans[0] = Math.floor(random.random() * data.length);\n\n  if (K > 1) {\n    // chooses the more distant point\n    var maxDist = { dist: -1, index: -1 };\n    for (var l = 0; l < data.length; ++l) {\n      if (distanceMatrix[ans[0]][l] > maxDist.dist) {\n        maxDist.dist = distanceMatrix[ans[0]][l];\n        maxDist.index = l;\n      }\n    }\n    ans[1] = maxDist.index;\n\n    if (K > 2) {\n      // chooses the set of points that maximises the min distance\n      for (var k = 2; k < K; ++k) {\n        var center = { dist: -1, index: -1 };\n        for (var m = 0; m < data.length; ++m) {\n          // minimum distance to centers\n          var minDistCent = { dist: Number.MAX_VALUE, index: -1 };\n          for (var n = 0; n < k; ++n) {\n            if (\n              distanceMatrix[n][m] < minDistCent.dist &&\n              ans.indexOf(m) === -1\n            ) {\n              minDistCent = {\n                dist: distanceMatrix[n][m],\n                index: m\n              };\n            }\n          }\n\n          if (\n            minDistCent.dist !== Number.MAX_VALUE &&\n            minDistCent.dist > center.dist\n          ) {\n            center = Object.assign({}, minDistCent);\n          }\n        }\n\n        ans[k] = center.index;\n      }\n    }\n  }\n\n  return ans.map((index) => data[index]);\n}\n\n// Implementation inspired from scikit\nexport function kmeanspp(X, K, options = {}) {\n  X = new Matrix(X);\n  const nSamples = X.rows;\n  const random = new Random(options.seed);\n  // Set the number of trials\n  const centers = [];\n  const localTrials = options.localTrials || 2 + Math.floor(Math.log(K));\n\n  // Pick the first center at random from the dataset\n  const firstCenterIdx = random.randInt(nSamples);\n  centers.push(X.getRow(firstCenterIdx));\n\n  // Init closest distances\n  let closestDistSquared = new Matrix(1, X.rows);\n  for (let i = 0; i < X.rows; i++) {\n    closestDistSquared.set(0, i, squaredEuclidean(X.getRow(i), centers[0]));\n  }\n  let cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n  const factor = 1 / cumSumClosestDistSquared[0][nSamples - 1];\n  let probabilities = Matrix.mul(closestDistSquared, factor);\n\n  // Iterate over the remaining centers\n  for (let i = 1; i < K; i++) {\n    const candidateIdx = random.choice(nSamples, {\n      replace: true,\n      size: localTrials,\n      probabilities: probabilities[0]\n    });\n\n    const candidates = X.selection(candidateIdx, range(X.columns));\n    const distanceToCandidates = euclideanDistances(candidates, X);\n\n    let bestCandidate;\n    let bestPot;\n    let bestDistSquared;\n\n    for (let j = 0; j < localTrials; j++) {\n      const newDistSquared = Matrix.min(closestDistSquared, [distanceToCandidates.getRow(j)]);\n      const newPot = newDistSquared.sum();\n      if (bestCandidate === undefined || newPot < bestPot) {\n        bestCandidate = candidateIdx[j];\n        bestPot = newPot;\n        bestDistSquared = newDistSquared;\n      }\n    }\n    centers[i] = X.getRow(bestCandidate);\n    closestDistSquared = bestDistSquared;\n    cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n    probabilities = Matrix.mul(\n      closestDistSquared,\n      1 / cumSumClosestDistSquared[0][nSamples - 1]\n    );\n  }\n  return centers;\n}\n\nfunction euclideanDistances(A, B) {\n  const result = new Matrix(A.rows, B.rows);\n  for (let i = 0; i < A.rows; i++) {\n    for (let j = 0; j < B.rows; j++) {\n      result.set(i, j, squaredEuclidean(A.getRow(i), B.getRow(j)));\n    }\n  }\n  return result;\n}\n\nfunction range(l) {\n  let r = [];\n  for (let i = 0; i < l; i++) {\n    r.push(i);\n  }\n  return r;\n}\n\nfunction cumSum(arr) {\n  let cumSum = [arr[0]];\n  for (let i = 1; i < arr.length; i++) {\n    cumSum[i] = cumSum[i - 1] + arr[i];\n  }\n  return cumSum;\n}\n","import { updateClusterID } from './utils';\n\nconst distanceSymbol = Symbol('distance');\n\nexport default class KMeansResult {\n  /**\n   * Result of the kmeans algorithm\n   * @param {Array<number>} clusters - the cluster identifier for each data dot\n   * @param {Array<Array<object>>} centroids - the K centers in format [x,y,z,...], the error and size of the cluster\n   * @param {boolean} converged - Converge criteria satisfied\n   * @param {number} iterations - Current number of iterations\n   * @param {function} distance - (*Private*) Distance function to use between the points\n   * @constructor\n   */\n  constructor(clusters, centroids, converged, iterations, distance) {\n    this.clusters = clusters;\n    this.centroids = centroids;\n    this.converged = converged;\n    this.iterations = iterations;\n    this[distanceSymbol] = distance;\n  }\n\n  /**\n   * Allows to compute for a new array of points their cluster id\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {Array<number>} - cluster id for each point\n   */\n  nearest(data) {\n    const clusterID = new Array(data.length);\n    const centroids = this.centroids.map(function (centroid) {\n      return centroid.centroid;\n    });\n    return updateClusterID(data, centroids, clusterID, this[distanceSymbol]);\n  }\n\n  /**\n   * Returns a KMeansResult with the error and size of the cluster\n   * @ignore\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {KMeansResult}\n   */\n  computeInformation(data) {\n    var enrichedCentroids = this.centroids.map(function (centroid) {\n      return {\n        centroid: centroid,\n        error: 0,\n        size: 0\n      };\n    });\n\n    for (var i = 0; i < data.length; i++) {\n      enrichedCentroids[this.clusters[i]].error += this[distanceSymbol](\n        data[i],\n        this.centroids[this.clusters[i]]\n      );\n      enrichedCentroids[this.clusters[i]].size++;\n    }\n\n    for (var j = 0; j < this.centroids.length; j++) {\n      if (enrichedCentroids[j].size) {\n        enrichedCentroids[j].error /= enrichedCentroids[j].size;\n      } else {\n        enrichedCentroids[j].error = null;\n      }\n    }\n\n    return new KMeansResult(\n      this.clusters,\n      enrichedCentroids,\n      this.converged,\n      this.iterations,\n      this[distanceSymbol]\n    );\n  }\n}\n","import { squaredEuclidean } from 'ml-distance-euclidean';\n\nimport {\n  updateClusterID,\n  updateCenters,\n  hasConverged,\n  calculateDistanceMatrix\n} from './utils';\nimport { mostDistant, random, kmeanspp } from './initialization';\nimport KMeansResult from './KMeansResult';\n\nconst defaultOptions = {\n  maxIterations: 100,\n  tolerance: 1e-6,\n  withIterations: false,\n  initialization: 'kmeans++',\n  distanceFunction: squaredEuclidean\n};\n\n/**\n * Each step operation for kmeans\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} iterations - Current number of iterations\n * @return {KMeansResult}\n */\nfunction step(centers, data, clusterID, K, options, iterations) {\n  clusterID = updateClusterID(\n    data,\n    centers,\n    clusterID,\n    options.distanceFunction\n  );\n  var newCenters = updateCenters(centers, data, clusterID, K);\n  var converged = hasConverged(\n    newCenters,\n    centers,\n    options.distanceFunction,\n    options.tolerance\n  );\n  return new KMeansResult(\n    clusterID,\n    newCenters,\n    converged,\n    iterations,\n    options.distanceFunction\n  );\n}\n\n/**\n * Generator version for the algorithm\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n */\nfunction* kmeansGenerator(centers, data, clusterID, K, options) {\n  var converged = false;\n  var stepNumber = 0;\n  var stepResult;\n  while (!converged && stepNumber < options.maxIterations) {\n    stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n    yield stepResult.computeInformation(data);\n    converged = stepResult.converged;\n    centers = stepResult.centroids;\n  }\n}\n\n/**\n * K-means algorithm\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} [options.maxIterations = 100] - Maximum of iterations allowed\n * @param {number} [options.tolerance = 1e-6] - Error tolerance\n * @param {boolean} [options.withIterations = false] - Store clusters and centroids for each iteration\n * @param {function} [options.distanceFunction = squaredDistance] - Distance function to use between the points\n * @param {number} [options.seed] - Seed for random initialization.\n * @param {string|Array<Array<number>>} [options.initialization = 'kmeans++'] - K centers in format [x,y,z,...] or a method for initialize the data:\n *  * You can either specify your custom start centroids, or select one of the following initialization method:\n *  * `'kmeans++'` will use the kmeans++ method as described by http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf\n *  * `'random'` will choose K random different values.\n *  * `'mostDistant'` will choose the more distant points to a first random pick\n * @return {KMeansResult} - Cluster identifier for each data dot and centroids with the following fields:\n *  * `'clusters'`: Array of indexes for the clusters.\n *  * `'centroids'`: Array with the resulting centroids.\n *  * `'iterations'`: Number of iterations that took to converge\n */\nexport default function kmeans(data, K, options) {\n  options = Object.assign({}, defaultOptions, options);\n\n  if (K <= 0 || K > data.length || !Number.isInteger(K)) {\n    throw new Error(\n      'K should be a positive integer smaller than the number of points'\n    );\n  }\n\n  var centers;\n  if (Array.isArray(options.initialization)) {\n    if (options.initialization.length !== K) {\n      throw new Error('The initial centers should have the same length as K');\n    } else {\n      centers = options.initialization;\n    }\n  } else {\n    switch (options.initialization) {\n      case 'kmeans++':\n        centers = kmeanspp(data, K, options);\n        break;\n      case 'random':\n        centers = random(data, K, options.seed);\n        break;\n      case 'mostDistant':\n        centers = mostDistant(\n          data,\n          K,\n          calculateDistanceMatrix(data, options.distanceFunction),\n          options.seed\n        );\n        break;\n      default:\n        throw new Error(\n          `Unknown initialization method: \"${options.initialization}\"`\n        );\n    }\n  }\n\n  // infinite loop until convergence\n  if (options.maxIterations === 0) {\n    options.maxIterations = Number.MAX_VALUE;\n  }\n\n  var clusterID = new Array(data.length);\n  if (options.withIterations) {\n    return kmeansGenerator(centers, data, clusterID, K, options);\n  } else {\n    var converged = false;\n    var stepNumber = 0;\n    var stepResult;\n    while (!converged && stepNumber < options.maxIterations) {\n      stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n      converged = stepResult.converged;\n      centers = stepResult.centroids;\n    }\n    return stepResult.computeInformation(data);\n  }\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that retuns an array of matrices of the cases that belong to each class.\n * @param {Matrix} X - dataset\n * @param {Array} y - predictions\n * @return {Array}\n */\nexport function separateClasses(X, y) {\n  var features = X.columns;\n\n  var classes = 0;\n  var totalPerClasses = new Array(10000); // max upperbound of classes\n  for (var i = 0; i < y.length; i++) {\n    if (totalPerClasses[y[i]] === undefined) {\n      totalPerClasses[y[i]] = 0;\n      classes++;\n    }\n    totalPerClasses[y[i]]++;\n  }\n  var separatedClasses = new Array(classes);\n  var currentIndex = new Array(classes);\n  for (i = 0; i < classes; ++i) {\n    separatedClasses[i] = new Matrix(totalPerClasses[i], features);\n    currentIndex[i] = 0;\n  }\n  for (i = 0; i < X.rows; ++i) {\n    separatedClasses[y[i]].setRow(currentIndex[y[i]], X.getRow(i));\n    currentIndex[y[i]]++;\n  }\n  return separatedClasses;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class GaussianNB {\n  /**\n   * Constructor for the Gaussian Naive Bayes classifier, the parameters here is just for loading purposes.\n   * @constructor\n   * @param {boolean} reload\n   * @param {object} model\n   */\n  constructor(reload, model) {\n    if (reload) {\n      this.means = model.means;\n      this.calculateProbabilities = model.calculateProbabilities;\n    }\n  }\n\n  /**\n   * Function that trains the classifier with a matrix that represents the training set and an array that\n   * represents the label of each row in the training set. the labels must be numbers between 0 to n-1 where\n   * n represents the number of classes.\n   *\n   * WARNING: in the case that one class, all the cases in one or more features have the same value, the\n   * Naive Bayes classifier will not work well.\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    var C1 = Math.sqrt(2 * Math.PI); // constant to precalculate the squared root\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separatedClasses = separateClasses(trainingSet, trainingLabels);\n    var calculateProbabilities = new Array(separatedClasses.length);\n    this.means = new Array(separatedClasses.length);\n    for (var i = 0; i < separatedClasses.length; ++i) {\n      var means = separatedClasses[i].mean('column');\n      var std = separatedClasses[i].standardDeviation('column', {\n        mean: means\n      });\n\n      var logPriorProbability = Math.log(\n        separatedClasses[i].rows / trainingSet.rows\n      );\n      calculateProbabilities[i] = new Array(means.length + 1);\n\n      calculateProbabilities[i][0] = logPriorProbability;\n      for (var j = 1; j < means.length + 1; ++j) {\n        var currentStd = std[j - 1];\n        calculateProbabilities[i][j] = [\n          1 / (C1 * currentStd),\n          -2 * currentStd * currentStd\n        ];\n      }\n\n      this.means[i] = means;\n    }\n\n    this.calculateProbabilities = calculateProbabilities;\n  }\n\n  /**\n   * function that predicts each row of the dataset (must be a matrix).\n   *\n   * @param {Matrix|Array} dataset\n   * @return {Array}\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    if (dataset.rows === this.calculateProbabilities[0].length) {\n      throw new RangeError(\n        'the dataset must have the same features as the training set'\n      );\n    }\n\n    var predictions = new Array(dataset.rows);\n\n    for (var i = 0; i < predictions.length; ++i) {\n      predictions[i] = getCurrentClass(\n        dataset.getRow(i),\n        this.means,\n        this.calculateProbabilities\n      );\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that export the NaiveBayes model.\n   * @return {object}\n   */\n  toJSON() {\n    return {\n      modelName: 'NaiveBayes',\n      means: this.means,\n      calculateProbabilities: this.calculateProbabilities\n    };\n  }\n\n  /**\n   * Function that create a GaussianNB classifier with the given model.\n   * @param {object} model\n   * @return {GaussianNB}\n   */\n  static load(model) {\n    if (model.modelName !== 'NaiveBayes') {\n      throw new RangeError(\n        'The current model is not a Multinomial Naive Bayes, current model:',\n        model.name\n      );\n    }\n\n    return new GaussianNB(true, model);\n  }\n}\n\n/**\n * @private\n * Function the retrieves a prediction with one case.\n *\n * @param {Array} currentCase\n * @param {Array} mean - Precalculated means of each class trained\n * @param {Array} classes - Precalculated value of each class (Prior probability and probability function of each feature)\n * @return {number}\n */\nfunction getCurrentClass(currentCase, mean, classes) {\n  var maxProbability = 0;\n  var predictedClass = -1;\n\n  // going through all precalculated values for the classes\n  for (var i = 0; i < classes.length; ++i) {\n    var currentProbability = classes[i][0]; // initialize with the prior probability\n    for (var j = 1; j < classes[0][1].length + 1; ++j) {\n      currentProbability += calculateLogProbability(\n        currentCase[j - 1],\n        mean[i][j - 1],\n        classes[i][j][0],\n        classes[i][j][1]\n      );\n    }\n\n    currentProbability = Math.exp(currentProbability);\n    if (currentProbability > maxProbability) {\n      maxProbability = currentProbability;\n      predictedClass = i;\n    }\n  }\n\n  return predictedClass;\n}\n\n/**\n * @private\n * function that retrieves the probability of the feature given the class.\n * @param {number} value - value of the feature.\n * @param {number} mean - mean of the feature for the given class.\n * @param {number} C1 - precalculated value of (1 / (sqrt(2*pi) * std)).\n * @param {number} C2 - precalculated value of (2 * std^2) for the denominator of the exponential.\n * @return {number}\n */\nfunction calculateLogProbability(value, mean, C1, C2) {\n  value = value - mean;\n  return Math.log(C1 * Math.exp((value * value) / C2));\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class MultinomialNB {\n  /**\n   * Constructor for Multinomial Naive Bayes, the model parameter is for load purposes.\n   * @constructor\n   * @param {object} model - for load purposes.\n   */\n  constructor(model) {\n    if (model) {\n      this.conditionalProbability = Matrix.checkMatrix(\n        model.conditionalProbability\n      );\n      this.priorProbability = Matrix.checkMatrix(model.priorProbability);\n    }\n  }\n\n  /**\n   * Train the classifier with the current training set and labels, the labels must be numbers between 0 and n.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separateClass = separateClasses(trainingSet, trainingLabels);\n\n    this.priorProbability = new Matrix(separateClass.length, 1);\n\n    for (var i = 0; i < separateClass.length; ++i) {\n      this.priorProbability.set(i, 0, Math.log(\n        separateClass[i].rows / trainingSet.rows\n      ));\n    }\n\n    var features = trainingSet.columns;\n    this.conditionalProbability = new Matrix(separateClass.length, features);\n    for (i = 0; i < separateClass.length; ++i) {\n      var classValues = Matrix.checkMatrix(separateClass[i]);\n      var total = classValues.sum();\n      var divisor = total + features;\n      this.conditionalProbability.setRow(\n        i,\n        Matrix.rowVector(classValues\n          .sum('column'))\n          .add(1)\n          .div(divisor)\n          .apply(matrixLog)\n      );\n    }\n  }\n\n  /**\n   * Retrieves the predictions for the dataset with the current model.\n   * @param {Matrix|Array} dataset\n   * @return {Array} - predictions from the dataset.\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    var predictions = new Array(dataset.rows);\n    for (var i = 0; i < dataset.rows; ++i) {\n      var currentElement = dataset.getRowVector(i);\n      const v = Matrix.columnVector(this.conditionalProbability\n        .clone()\n        .mulRowVector(currentElement)\n        .sum('row'));\n      predictions[i] = v\n        .add(this.priorProbability)\n        .maxIndex()[0];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that saves the current model.\n   * @return {object} - model in JSON format.\n   */\n  toJSON() {\n    return {\n      name: 'MultinomialNB',\n      priorProbability: this.priorProbability,\n      conditionalProbability: this.conditionalProbability\n    };\n  }\n\n  /**\n   * Creates a new MultinomialNB from the given model\n   * @param {object} model\n   * @return {MultinomialNB}\n   */\n  static load(model) {\n    if (model.name !== 'MultinomialNB') {\n      throw new RangeError(`${model.name} is not a Multinomial Naive Bayes`);\n    }\n\n    return new MultinomialNB(model);\n  }\n}\n\nfunction matrixLog(i, j) {\n  this.set(i, j, Math.log(this.get(i, j)));\n}\n","/*\n * Original code from:\n *\n * k-d Tree JavaScript - V 1.01\n *\n * https://github.com/ubilabs/kd-tree-javascript\n *\n * @author Mircea Pricop <pricop@ubilabs.net>, 2012\n * @author Martin Kleppe <kleppe@ubilabs.net>, 2012\n * @author Ubilabs http://ubilabs.net, 2012\n * @license MIT License <http://www.opensource.org/licenses/mit-license.php>\n */\n\nfunction Node(obj, dimension, parent) {\n  this.obj = obj;\n  this.left = null;\n  this.right = null;\n  this.parent = parent;\n  this.dimension = dimension;\n}\n\nexport default class KDTree {\n  constructor(points, metric) {\n    // If points is not an array, assume we're loading a pre-built tree\n    if (!Array.isArray(points)) {\n      this.dimensions = points.dimensions;\n      this.root = points;\n      restoreParent(this.root);\n    } else {\n      this.dimensions = new Array(points[0].length);\n      for (var i = 0; i < this.dimensions.length; i++) {\n        this.dimensions[i] = i;\n      }\n      this.root = buildTree(points, 0, null, this.dimensions);\n    }\n    this.metric = metric;\n  }\n\n  // Convert to a JSON serializable structure; this just requires removing\n  // the `parent` property\n  toJSON() {\n    const result = toJSONImpl(this.root, true);\n    result.dimensions = this.dimensions;\n    return result;\n  }\n\n  nearest(point, maxNodes, maxDistance) {\n    const metric = this.metric;\n    const dimensions = this.dimensions;\n    var i;\n\n    const bestNodes = new BinaryHeap(function (e) {\n      return -e[1];\n    });\n\n    function nearestSearch(node) {\n      const dimension = dimensions[node.dimension];\n      const ownDistance = metric(point, node.obj);\n      const linearPoint = {};\n      var bestChild, linearDistance, otherChild, i;\n\n      function saveNode(node, distance) {\n        bestNodes.push([node, distance]);\n        if (bestNodes.size() > maxNodes) {\n          bestNodes.pop();\n        }\n      }\n\n      for (i = 0; i < dimensions.length; i += 1) {\n        if (i === node.dimension) {\n          linearPoint[dimensions[i]] = point[dimensions[i]];\n        } else {\n          linearPoint[dimensions[i]] = node.obj[dimensions[i]];\n        }\n      }\n\n      linearDistance = metric(linearPoint, node.obj);\n\n      if (node.right === null && node.left === null) {\n        if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n          saveNode(node, ownDistance);\n        }\n        return;\n      }\n\n      if (node.right === null) {\n        bestChild = node.left;\n      } else if (node.left === null) {\n        bestChild = node.right;\n      } else {\n        if (point[dimension] < node.obj[dimension]) {\n          bestChild = node.left;\n        } else {\n          bestChild = node.right;\n        }\n      }\n\n      nearestSearch(bestChild);\n\n      if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n        saveNode(node, ownDistance);\n      }\n\n      if (\n        bestNodes.size() < maxNodes ||\n        Math.abs(linearDistance) < bestNodes.peek()[1]\n      ) {\n        if (bestChild === node.left) {\n          otherChild = node.right;\n        } else {\n          otherChild = node.left;\n        }\n        if (otherChild !== null) {\n          nearestSearch(otherChild);\n        }\n      }\n    }\n\n    if (maxDistance) {\n      for (i = 0; i < maxNodes; i += 1) {\n        bestNodes.push([null, maxDistance]);\n      }\n    }\n\n    if (this.root) {\n      nearestSearch(this.root);\n    }\n\n    const result = [];\n    for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {\n      if (bestNodes.content[i][0]) {\n        result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);\n      }\n    }\n    return result;\n  }\n}\n\nfunction toJSONImpl(src) {\n  const dest = new Node(src.obj, src.dimension, null);\n  if (src.left) dest.left = toJSONImpl(src.left);\n  if (src.right) dest.right = toJSONImpl(src.right);\n  return dest;\n}\n\nfunction buildTree(points, depth, parent, dimensions) {\n  const dim = depth % dimensions.length;\n\n  if (points.length === 0) {\n    return null;\n  }\n  if (points.length === 1) {\n    return new Node(points[0], dim, parent);\n  }\n\n  points.sort((a, b) => a[dimensions[dim]] - b[dimensions[dim]]);\n\n  const median = Math.floor(points.length / 2);\n  const node = new Node(points[median], dim, parent);\n  node.left = buildTree(points.slice(0, median), depth + 1, node, dimensions);\n  node.right = buildTree(points.slice(median + 1), depth + 1, node, dimensions);\n\n  return node;\n}\n\nfunction restoreParent(root) {\n  if (root.left) {\n    root.left.parent = root;\n    restoreParent(root.left);\n  }\n\n  if (root.right) {\n    root.right.parent = root;\n    restoreParent(root.right);\n  }\n}\n\n// Binary heap implementation from:\n// http://eloquentjavascript.net/appendix2.html\nclass BinaryHeap {\n  constructor(scoreFunction) {\n    this.content = [];\n    this.scoreFunction = scoreFunction;\n  }\n\n  push(element) {\n    // Add the new element to the end of the array.\n    this.content.push(element);\n    // Allow it to bubble up.\n    this.bubbleUp(this.content.length - 1);\n  }\n\n  pop() {\n    // Store the first element so we can return it later.\n    var result = this.content[0];\n    // Get the element at the end of the array.\n    var end = this.content.pop();\n    // If there are any elements left, put the end element at the\n    // start, and let it sink down.\n    if (this.content.length > 0) {\n      this.content[0] = end;\n      this.sinkDown(0);\n    }\n    return result;\n  }\n\n  peek() {\n    return this.content[0];\n  }\n\n  size() {\n    return this.content.length;\n  }\n\n  bubbleUp(n) {\n    // Fetch the element that has to be moved.\n    var element = this.content[n];\n    // When at 0, an element can not go up any further.\n    while (n > 0) {\n      // Compute the parent element's index, and fetch it.\n      const parentN = Math.floor((n + 1) / 2) - 1;\n      const parent = this.content[parentN];\n      // Swap the elements if the parent is greater.\n      if (this.scoreFunction(element) < this.scoreFunction(parent)) {\n        this.content[parentN] = element;\n        this.content[n] = parent;\n        // Update 'n' to continue at the new position.\n        n = parentN;\n      } else {\n        // Found a parent that is less, no need to move it further.\n        break;\n      }\n    }\n  }\n\n  sinkDown(n) {\n    // Look up the target element and its score.\n    var length = this.content.length;\n    var element = this.content[n];\n    var elemScore = this.scoreFunction(element);\n\n    while (true) {\n      // Compute the indices of the child elements.\n      var child2N = (n + 1) * 2;\n      var child1N = child2N - 1;\n      // This is used to store the new position of the element,\n      // if any.\n      var swap = null;\n      // If the first child exists (is inside the array)...\n      if (child1N < length) {\n        // Look it up and compute its score.\n        var child1 = this.content[child1N];\n        var child1Score = this.scoreFunction(child1);\n        // If the score is less than our element's, we need to swap.\n        if (child1Score < elemScore) {\n          swap = child1N;\n        }\n      }\n      // Do the same checks for the other child.\n      if (child2N < length) {\n        var child2 = this.content[child2N];\n        var child2Score = this.scoreFunction(child2);\n        if (child2Score < (swap === null ? elemScore : child1Score)) {\n          swap = child2N;\n        }\n      }\n\n      // If the element needs to be moved, swap it, and continue.\n      if (swap !== null) {\n        this.content[n] = this.content[swap];\n        this.content[swap] = element;\n        n = swap;\n      } else {\n        // Otherwise, we are done.\n        break;\n      }\n    }\n  }\n}\n","import { euclidean as euclideanDistance } from 'ml-distance-euclidean';\n\nimport KDTree from './KDTree';\n\nexport default class KNN {\n  /**\n   * @param {Array} dataset\n   * @param {Array} labels\n   * @param {object} options\n   * @param {number} [options.k=numberOfClasses + 1] - Number of neighbors to classify.\n   * @param {function} [options.distance=euclideanDistance] - Distance function that takes two parameters.\n   */\n  constructor(dataset, labels, options = {}) {\n    if (dataset === true) {\n      const model = labels;\n      this.kdTree = new KDTree(model.kdTree, options);\n      this.k = model.k;\n      this.classes = new Set(model.classes);\n      this.isEuclidean = model.isEuclidean;\n      return;\n    }\n\n    const classes = new Set(labels);\n\n    const { distance = euclideanDistance, k = classes.size + 1 } = options;\n\n    const points = new Array(dataset.length);\n    for (var i = 0; i < points.length; ++i) {\n      points[i] = dataset[i].slice();\n    }\n\n    for (i = 0; i < labels.length; ++i) {\n      points[i].push(labels[i]);\n    }\n\n    this.kdTree = new KDTree(points, distance);\n    this.k = k;\n    this.classes = classes;\n    this.isEuclidean = distance === euclideanDistance;\n  }\n\n  /**\n   * Create a new KNN instance with the given model.\n   * @param {object} model\n   * @param {function} distance=euclideanDistance - distance function must be provided if the model wasn't trained with euclidean distance.\n   * @return {KNN}\n   */\n  static load(model, distance = euclideanDistance) {\n    if (model.name !== 'KNN') {\n      throw new Error(`invalid model: ${model.name}`);\n    }\n    if (!model.isEuclidean && distance === euclideanDistance) {\n      throw new Error(\n        'a custom distance function was used to create the model. Please provide it again'\n      );\n    }\n    if (model.isEuclidean && distance !== euclideanDistance) {\n      throw new Error(\n        'the model was created with the default distance function. Do not load it with another one'\n      );\n    }\n    return new KNN(true, model, distance);\n  }\n\n  /**\n   * Return a JSON containing the kd-tree model.\n   * @return {object} JSON KNN model.\n   */\n  toJSON() {\n    return {\n      name: 'KNN',\n      kdTree: this.kdTree,\n      k: this.k,\n      classes: Array.from(this.classes),\n      isEuclidean: this.isEuclidean\n    };\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Array} dataset\n   * @return {Array} predictions\n   */\n  predict(dataset) {\n    if (Array.isArray(dataset)) {\n      if (typeof dataset[0] === 'number') {\n        return getSinglePrediction(this, dataset);\n      } else if (\n        Array.isArray(dataset[0]) &&\n        typeof dataset[0][0] === 'number'\n      ) {\n        const predictions = new Array(dataset.length);\n        for (var i = 0; i < dataset.length; i++) {\n          predictions[i] = getSinglePrediction(this, dataset[i]);\n        }\n        return predictions;\n      }\n    }\n    throw new TypeError('dataset to predict must be an array or a matrix');\n  }\n}\n\nfunction getSinglePrediction(knn, currentCase) {\n  var nearestPoints = knn.kdTree.nearest(currentCase, knn.k);\n  var pointsPerClass = {};\n  var predictedClass = -1;\n  var maxPoints = -1;\n  var lastElement = nearestPoints[0][0].length - 1;\n\n  for (var element of knn.classes) {\n    pointsPerClass[element] = 0;\n  }\n\n  for (var i = 0; i < nearestPoints.length; ++i) {\n    var currentClass = nearestPoints[i][0][lastElement];\n    var currentPoints = ++pointsPerClass[currentClass];\n    if (currentPoints > maxPoints) {\n      predictedClass = currentClass;\n      maxPoints = currentPoints;\n    }\n  }\n\n  return predictedClass;\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that given vector, returns its norm\n * @param {Vector} X\n * @return {number} Norm of the vector\n */\nexport function norm(X) {\n  return Math.sqrt(\n    X.clone()\n      .apply(pow2array)\n      .sum(),\n  );\n}\n\n/**\n * @private\n * Function that pow 2 each element of a Matrix or a Vector,\n * used in the apply method of the Matrix object\n * @param {number} i - index i.\n * @param {number} j - index j.\n * @return {Matrix} The Matrix object modified at the index i, j.\n * */\nexport function pow2array(i, j) {\n  this.set(i, j, this.get(i, j) ** 2);\n}\n\n/**\n * @private\n * Function that normalize the dataset and return the means and\n * standard deviation of each feature.\n * @param {Matrix} dataset\n * @return {object} dataset normalized, means and standard deviations\n */\nexport function featureNormalize(dataset) {\n  let means = dataset.mean('column');\n  let std = dataset.standardDeviation('column', {\n    mean: means,\n    unbiased: true,\n  });\n  let result = Matrix.checkMatrix(dataset).subRowVector(means);\n  return { result: result.divRowVector(std), means: means, std: std };\n}\n\n/**\n * @private\n * Function that initialize an array of matrices.\n * @param {Array} array\n * @param {boolean} isMatrix\n * @return {Array} array with the matrices initialized.\n */\nexport function initializeMatrices(array, isMatrix) {\n  if (isMatrix) {\n    for (let i = 0; i < array.length; ++i) {\n      for (let j = 0; j < array[i].length; ++j) {\n        let elem = array[i][j];\n        array[i][j] = elem !== null ? new Matrix(array[i][j]) : undefined;\n      }\n    }\n  } else {\n    for (let i = 0; i < array.length; ++i) {\n      array[i] = new Matrix(array[i]);\n    }\n  }\n\n  return array;\n}\n","import Matrix from 'ml-matrix';\n\nimport * as Utils from './util/utils';\n\n/**\n * @class PLS\n */\nexport class PLS {\n  /**\n   * Constructor for Partial Least Squares (PLS)\n   * @param {object} options\n   * @param {number} [options.latentVectors] - Number of latent vector to get (if the algorithm doesn't find a good model below the tolerance)\n   * @param {number} [options.tolerance=1e-5]\n   * @param {boolean} [options.scale=true] - rescale dataset using mean.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.meanX = model.meanX;\n      this.stdDevX = model.stdDevX;\n      this.meanY = model.meanY;\n      this.stdDevY = model.stdDevY;\n      this.PBQ = Matrix.checkMatrix(model.PBQ);\n      this.R2X = model.R2X;\n      this.scale = model.scale;\n      this.scaleMethod = model.scaleMethod;\n      this.tolerance = model.tolerance;\n    } else {\n      let { tolerance = 1e-5, scale = true } = options;\n      this.tolerance = tolerance;\n      this.scale = scale;\n      this.latentVectors = options.latentVectors;\n    }\n  }\n\n  /**\n   * Fits the model with the given data and predictions, in this function is calculated the\n   * following outputs:\n   *\n   * T - Score matrix of X\n   * P - Loading matrix of X\n   * U - Score matrix of Y\n   * Q - Loading matrix of Y\n   * B - Matrix of regression coefficient\n   * W - Weight matrix of X\n   *\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    if (trainingSet.length !== trainingValues.length) {\n      throw new RangeError(\n        'The number of X rows must be equal to the number of Y rows',\n      );\n    }\n\n    this.meanX = trainingSet.mean('column');\n    this.stdDevX = trainingSet.standardDeviation('column', {\n      mean: this.meanX,\n      unbiased: true,\n    });\n    this.meanY = trainingValues.mean('column');\n    this.stdDevY = trainingValues.standardDeviation('column', {\n      mean: this.meanY,\n      unbiased: true,\n    });\n\n    if (this.scale) {\n      trainingSet = trainingSet\n        .clone()\n        .subRowVector(this.meanX)\n        .divRowVector(this.stdDevX);\n      trainingValues = trainingValues\n        .clone()\n        .subRowVector(this.meanY)\n        .divRowVector(this.stdDevY);\n    }\n\n    if (this.latentVectors === undefined) {\n      this.latentVectors = Math.min(trainingSet.rows - 1, trainingSet.columns);\n    }\n\n    let rx = trainingSet.rows;\n    let cx = trainingSet.columns;\n    let ry = trainingValues.rows;\n    let cy = trainingValues.columns;\n\n    let ssqXcal = trainingSet\n      .clone()\n      .mul(trainingSet)\n      .sum(); // for the r²\n    let sumOfSquaresY = trainingValues\n      .clone()\n      .mul(trainingValues)\n      .sum();\n\n    let tolerance = this.tolerance;\n    let n = this.latentVectors;\n    let T = Matrix.zeros(rx, n);\n    let P = Matrix.zeros(cx, n);\n    let U = Matrix.zeros(ry, n);\n    let Q = Matrix.zeros(cy, n);\n    let B = Matrix.zeros(n, n);\n    let W = P.clone();\n    let k = 0;\n    let t;\n    let w;\n    let q;\n    let p;\n\n    while (Utils.norm(trainingValues) > tolerance && k < n) {\n      let transposeX = trainingSet.transpose();\n      let transposeY = trainingValues.transpose();\n\n      let tIndex = maxSumColIndex(trainingSet.clone().mul(trainingSet));\n      let uIndex = maxSumColIndex(trainingValues.clone().mul(trainingValues));\n\n      let t1 = trainingSet.getColumnVector(tIndex);\n      let u = trainingValues.getColumnVector(uIndex);\n      t = Matrix.zeros(rx, 1);\n\n      while (Utils.norm(t1.clone().sub(t)) > tolerance) {\n        w = transposeX.mmul(u);\n        w.div(Utils.norm(w));\n        t = t1;\n        t1 = trainingSet.mmul(w);\n        q = transposeY.mmul(t1);\n        q.div(Utils.norm(q));\n        u = trainingValues.mmul(q);\n      }\n\n      t = t1;\n      let num = transposeX.mmul(t);\n      let den = t\n        .transpose()\n        .mmul(t)\n        .get(0, 0);\n      p = num.div(den);\n      let pnorm = Utils.norm(p);\n      p.div(pnorm);\n      t.mul(pnorm);\n      w.mul(pnorm);\n\n      num = u.transpose().mmul(t);\n      den = t\n        .transpose()\n        .mmul(t)\n        .get(0, 0);\n      let b = num.div(den).get(0, 0);\n      trainingSet.sub(t.mmul(p.transpose()));\n      trainingValues.sub(\n        t\n          .clone()\n          .mul(b)\n          .mmul(q.transpose()),\n      );\n\n      T.setColumn(k, t);\n      P.setColumn(k, p);\n      U.setColumn(k, u);\n      Q.setColumn(k, q);\n      W.setColumn(k, w);\n\n      B.set(k, k, b);\n      k++;\n    }\n\n    k--;\n    T = T.subMatrix(0, T.rows - 1, 0, k);\n    P = P.subMatrix(0, P.rows - 1, 0, k);\n    U = U.subMatrix(0, U.rows - 1, 0, k);\n    Q = Q.subMatrix(0, Q.rows - 1, 0, k);\n    W = W.subMatrix(0, W.rows - 1, 0, k);\n    B = B.subMatrix(0, k, 0, k);\n\n    this.ssqYcal = sumOfSquaresY;\n    this.E = trainingSet;\n    this.F = trainingValues;\n    this.T = T;\n    this.P = P;\n    this.U = U;\n    this.Q = Q;\n    this.W = W;\n    this.B = B;\n    this.PBQ = P.mmul(B).mmul(Q.transpose());\n    this.R2X = t\n      .transpose()\n      .mmul(t)\n      .mmul(p.transpose().mmul(p))\n      .div(ssqXcal)\n      .get(0, 0);\n  }\n\n  /**\n   * Predicts the behavior of the given dataset.\n   * @param {Matrix|Array} dataset - data to be predicted.\n   * @return {Matrix} - predictions of each element of the dataset.\n   */\n  predict(dataset) {\n    let X = Matrix.checkMatrix(dataset);\n    if (this.scale) {\n      X = X.subRowVector(this.meanX).divRowVector(this.stdDevX);\n    }\n    let Y = X.mmul(this.PBQ);\n    Y = Y.mulRowVector(this.stdDevY).addRowVector(this.meanY);\n    return Y;\n  }\n\n  /**\n   * Returns the explained variance on training of the PLS model\n   * @return {number}\n   */\n  getExplainedVariance() {\n    return this.R2X;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      name: 'PLS',\n      R2X: this.R2X,\n      meanX: this.meanX,\n      stdDevX: this.stdDevX,\n      meanY: this.meanY,\n      stdDevY: this.stdDevY,\n      PBQ: this.PBQ,\n      tolerance: this.tolerance,\n      scale: this.scale,\n    };\n  }\n\n  /**\n   * Load a PLS model from a JSON Object\n   * @param {object} model\n   * @return {PLS} - PLS object from the given model\n   */\n  static load(model) {\n    if (model.name !== 'PLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n    return new PLS(true, model);\n  }\n}\n\n/**\n * @private\n * Function that returns the index where the sum of each\n * column vector is maximum.\n * @param {Matrix} data\n * @return {number} index of the maximum\n */\nfunction maxSumColIndex(data) {\n  return Matrix.rowVector(data.sum('column')).maxIndex()[0];\n}\n","import { Matrix, SingularValueDecomposition, inverse } from 'ml-matrix';\n\nimport { initializeMatrices } from './util/utils';\n\n/**\n * @class KOPLS\n */\nexport class KOPLS {\n  /**\n   * Constructor for Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)\n   * @param {object} options\n   * @param {number} [options.predictiveComponents] - Number of predictive components to use.\n   * @param {number} [options.orthogonalComponents] - Number of Y-Orthogonal components.\n   * @param {Kernel} [options.kernel] - Kernel object to apply, see [ml-kernel](https://github.com/mljs/kernel).\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.trainingSet = new Matrix(model.trainingSet);\n      this.YLoadingMat = new Matrix(model.YLoadingMat);\n      this.SigmaPow = new Matrix(model.SigmaPow);\n      this.YScoreMat = new Matrix(model.YScoreMat);\n      this.predScoreMat = initializeMatrices(model.predScoreMat, false);\n      this.YOrthLoadingVec = initializeMatrices(model.YOrthLoadingVec, false);\n      this.YOrthEigen = model.YOrthEigen;\n      this.YOrthScoreMat = initializeMatrices(model.YOrthScoreMat, false);\n      this.toNorm = initializeMatrices(model.toNorm, false);\n      this.TURegressionCoeff = initializeMatrices(\n        model.TURegressionCoeff,\n        false,\n      );\n      this.kernelX = initializeMatrices(model.kernelX, true);\n      this.kernel = model.kernel;\n      this.orthogonalComp = model.orthogonalComp;\n      this.predictiveComp = model.predictiveComp;\n    } else {\n      if (options.predictiveComponents === undefined) {\n        throw new RangeError('no predictive components found!');\n      }\n      if (options.orthogonalComponents === undefined) {\n        throw new RangeError('no orthogonal components found!');\n      }\n      if (options.kernel === undefined) {\n        throw new RangeError('no kernel found!');\n      }\n\n      this.orthogonalComp = options.orthogonalComponents;\n      this.predictiveComp = options.predictiveComponents;\n      this.kernel = options.kernel;\n    }\n  }\n\n  /**\n   * Train the K-OPLS model with the given training set and labels.\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    // to save and compute kernel with the prediction dataset.\n    this.trainingSet = trainingSet.clone();\n\n    let kernelX = this.kernel.compute(trainingSet);\n\n    let Identity = Matrix.eye(kernelX.rows, kernelX.rows, 1);\n    let temp = kernelX;\n    kernelX = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      kernelX[i] = new Array(this.orthogonalComp + 1);\n    }\n    kernelX[0][0] = temp;\n\n    let result = new SingularValueDecomposition(\n      trainingValues\n        .transpose()\n        .mmul(kernelX[0][0])\n        .mmul(trainingValues),\n      {\n        computeLeftSingularVectors: true,\n        computeRightSingularVectors: false,\n      },\n    );\n    let YLoadingMat = result.leftSingularVectors;\n    let Sigma = result.diagonalMatrix;\n\n    YLoadingMat = YLoadingMat.subMatrix(\n      0,\n      YLoadingMat.rows - 1,\n      0,\n      this.predictiveComp - 1,\n    );\n    Sigma = Sigma.subMatrix(\n      0,\n      this.predictiveComp - 1,\n      0,\n      this.predictiveComp - 1,\n    );\n\n    let YScoreMat = trainingValues.mmul(YLoadingMat);\n\n    let predScoreMat = new Array(this.orthogonalComp + 1);\n    let TURegressionCoeff = new Array(this.orthogonalComp + 1);\n    let YOrthScoreMat = new Array(this.orthogonalComp);\n    let YOrthLoadingVec = new Array(this.orthogonalComp);\n    let YOrthEigen = new Array(this.orthogonalComp);\n    let YOrthScoreNorm = new Array(this.orthogonalComp);\n\n    let SigmaPow = Matrix.pow(Sigma, -0.5);\n    // to avoid errors, check infinity\n    SigmaPow.apply(function(i, j) {\n      if (this.get(i, j) === Infinity) {\n        this.set(i, j, 0);\n      }\n    });\n\n    for (let i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = kernelX[0][i]\n        .transpose()\n        .mmul(YScoreMat)\n        .mmul(SigmaPow);\n\n      let TpiPrime = predScoreMat[i].transpose();\n      TURegressionCoeff[i] = inverse(TpiPrime.mmul(predScoreMat[i]))\n        .mmul(TpiPrime)\n        .mmul(YScoreMat);\n\n      result = new SingularValueDecomposition(\n        TpiPrime.mmul(\n          Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime)),\n        ).mmul(predScoreMat[i]),\n        {\n          computeLeftSingularVectors: true,\n          computeRightSingularVectors: false,\n        },\n      );\n      let CoTemp = result.leftSingularVectors;\n      let SoTemp = result.diagonalMatrix;\n\n      YOrthLoadingVec[i] = CoTemp.subMatrix(0, CoTemp.rows - 1, 0, 0);\n      YOrthEigen[i] = SoTemp.get(0, 0);\n\n      YOrthScoreMat[i] = Matrix.sub(\n        kernelX[i][i],\n        predScoreMat[i].mmul(TpiPrime),\n      )\n        .mmul(predScoreMat[i])\n        .mmul(YOrthLoadingVec[i])\n        .mul(Math.pow(YOrthEigen[i], -0.5));\n\n      let toiPrime = YOrthScoreMat[i].transpose();\n      YOrthScoreNorm[i] = Matrix.sqrt(toiPrime.mmul(YOrthScoreMat[i]));\n\n      YOrthScoreMat[i] = YOrthScoreMat[i].divRowVector(YOrthScoreNorm[i]);\n\n      let ITo = Matrix.sub(\n        Identity,\n        YOrthScoreMat[i].mmul(YOrthScoreMat[i].transpose()),\n      );\n\n      kernelX[0][i + 1] = kernelX[0][i].mmul(ITo);\n      kernelX[i + 1][i + 1] = ITo.mmul(kernelX[i][i]).mmul(ITo);\n    }\n\n    let lastScoreMat = (predScoreMat[this.orthogonalComp] = kernelX[0][\n      this.orthogonalComp\n    ]\n      .transpose()\n      .mmul(YScoreMat)\n      .mmul(SigmaPow));\n\n    let lastTpPrime = lastScoreMat.transpose();\n    TURegressionCoeff[this.orthogonalComp] = inverse(\n      lastTpPrime.mmul(lastScoreMat),\n    )\n      .mmul(lastTpPrime)\n      .mmul(YScoreMat);\n\n    this.YLoadingMat = YLoadingMat;\n    this.SigmaPow = SigmaPow;\n    this.YScoreMat = YScoreMat;\n    this.predScoreMat = predScoreMat;\n    this.YOrthLoadingVec = YOrthLoadingVec;\n    this.YOrthEigen = YOrthEigen;\n    this.YOrthScoreMat = YOrthScoreMat;\n    this.toNorm = YOrthScoreNorm;\n    this.TURegressionCoeff = TURegressionCoeff;\n    this.kernelX = kernelX;\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|Array} toPredict\n   * @return {{y: Matrix, predScoreMat: Array<Matrix>, predYOrthVectors: Array<Matrix>}} predictions\n   */\n  predict(toPredict) {\n    let KTestTrain = this.kernel.compute(toPredict, this.trainingSet);\n\n    let temp = KTestTrain;\n    KTestTrain = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      KTestTrain[i] = new Array(this.orthogonalComp + 1);\n    }\n    KTestTrain[0][0] = temp;\n\n    let YOrthScoreVector = new Array(this.orthogonalComp);\n    let predScoreMat = new Array(this.orthogonalComp);\n\n    let i;\n    for (i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = KTestTrain[i][0]\n        .mmul(this.YScoreMat)\n        .mmul(this.SigmaPow);\n\n      YOrthScoreVector[i] = Matrix.sub(\n        KTestTrain[i][i],\n        predScoreMat[i].mmul(this.predScoreMat[i].transpose()),\n      )\n        .mmul(this.predScoreMat[i])\n        .mmul(this.YOrthLoadingVec[i])\n        .mul(Math.pow(this.YOrthEigen[i], -0.5));\n\n      YOrthScoreVector[i] = YOrthScoreVector[i].divRowVector(this.toNorm[i]);\n\n      let scoreMatPrime = this.YOrthScoreMat[i].transpose();\n      KTestTrain[i + 1][0] = Matrix.sub(\n        KTestTrain[i][0],\n        YOrthScoreVector[i]\n          .mmul(scoreMatPrime)\n          .mmul(this.kernelX[0][i].transpose()),\n      );\n\n      let p1 = Matrix.sub(\n        KTestTrain[i][0],\n        KTestTrain[i][i].mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime),\n      );\n      let p2 = YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[i][i]);\n      let p3 = p2.mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime);\n\n      KTestTrain[i + 1][i + 1] = p1.sub(p2).add(p3);\n    }\n\n    predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);\n    let prediction = predScoreMat[i]\n      .mmul(this.TURegressionCoeff[i])\n      .mmul(this.YLoadingMat.transpose());\n\n    return {\n      prediction: prediction,\n      predScoreMat: predScoreMat,\n      predYOrthVectors: YOrthScoreVector,\n    };\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      name: 'K-OPLS',\n      YLoadingMat: this.YLoadingMat,\n      SigmaPow: this.SigmaPow,\n      YScoreMat: this.YScoreMat,\n      predScoreMat: this.predScoreMat,\n      YOrthLoadingVec: this.YOrthLoadingVec,\n      YOrthEigen: this.YOrthEigen,\n      YOrthScoreMat: this.YOrthScoreMat,\n      toNorm: this.toNorm,\n      TURegressionCoeff: this.TURegressionCoeff,\n      kernelX: this.kernelX,\n      trainingSet: this.trainingSet,\n      orthogonalComp: this.orthogonalComp,\n      predictiveComp: this.predictiveComp,\n    };\n  }\n\n  /**\n   * Load a K-OPLS with the given model.\n   * @param {object} model\n   * @param {Kernel} kernel - kernel used on the model, see [ml-kernel](https://github.com/mljs/kernel).\n   * @return {KOPLS}\n   */\n  static load(model, kernel) {\n    if (model.name !== 'K-OPLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    if (!kernel) {\n      throw new RangeError('You must provide a kernel for the model!');\n    }\n\n    model.kernel = kernel;\n    return new KOPLS(true, model);\n  }\n}\n","/**\n *  Constructs a confusion matrix\n * @class ConfusionMatrix\n * @example\n * const CM = new ConfusionMatrix([[13, 2], [10, 5]], ['cat', 'dog'])\n * @param {Array<Array<number>>} matrix - The confusion matrix, a 2D Array. Rows represent the actual label and columns\n *     the predicted label.\n * @param {Array<any>} labels - Labels of the confusion matrix, a 1D Array\n */\nexport default class ConfusionMatrix {\n  constructor(matrix, labels) {\n    if (matrix.length !== matrix[0].length) {\n      throw new Error('Confusion matrix must be square');\n    }\n    if (labels.length !== matrix.length) {\n      throw new Error(\n        'Confusion matrix and labels should have the same length',\n      );\n    }\n    this.labels = labels;\n    this.matrix = matrix;\n  }\n\n  /**\n   * Construct confusion matrix from the predicted and actual labels (classes). Be sure to provide the arguments in\n   * the correct order!\n   * @param {Array<any>} actual  - The predicted labels of the classification\n   * @param {Array<any>} predicted     - The actual labels of the classification. Has to be of same length as\n   *     predicted.\n   * @param {object} [options] - Additional options\n   * @param {Array<any>} [options.labels] - The list of labels that should be used. If not provided the distinct set\n   *     of labels present in predicted and actual is used. Labels are compared using the strict equality operator\n   *     '==='\n   * @return {ConfusionMatrix} - Confusion matrix\n   */\n  static fromLabels(actual, predicted, options = {}) {\n    if (predicted.length !== actual.length) {\n      throw new Error('predicted and actual must have the same length');\n    }\n    let distinctLabels;\n    if (options.labels) {\n      distinctLabels = new Set(options.labels);\n    } else {\n      distinctLabels = new Set([...actual, ...predicted]);\n    }\n    distinctLabels = Array.from(distinctLabels);\n    if (options.sort) {\n      distinctLabels.sort(options.sort);\n    }\n\n    // Create confusion matrix and fill with 0's\n    const matrix = Array.from({ length: distinctLabels.length });\n    for (let i = 0; i < matrix.length; i++) {\n      matrix[i] = new Array(matrix.length);\n      matrix[i].fill(0);\n    }\n\n    for (let i = 0; i < predicted.length; i++) {\n      const actualIdx = distinctLabels.indexOf(actual[i]);\n      const predictedIdx = distinctLabels.indexOf(predicted[i]);\n      if (actualIdx >= 0 && predictedIdx >= 0) {\n        matrix[actualIdx][predictedIdx]++;\n      }\n    }\n\n    return new ConfusionMatrix(matrix, distinctLabels);\n  }\n\n  /**\n   * Get the confusion matrix\n   * @return {Array<Array<number> >}\n   */\n  getMatrix() {\n    return this.matrix;\n  }\n\n  getLabels() {\n    return this.labels;\n  }\n\n  /**\n   * Get the total number of samples\n   * @return {number}\n   */\n  getTotalCount() {\n    let predicted = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      for (let j = 0; j < this.matrix.length; j++) {\n        predicted += this.matrix[i][j];\n      }\n    }\n    return predicted;\n  }\n\n  /**\n   * Get the total number of true predictions\n   * @return {number}\n   */\n  getTrueCount() {\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      count += this.matrix[i][i];\n    }\n    return count;\n  }\n\n  /**\n   * Get the total number of false predictions.\n   * @return {number}\n   */\n  getFalseCount() {\n    return this.getTotalCount() - this.getTrueCount();\n  }\n\n  /**\n   * Get the number of true positive predictions.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getTruePositiveCount(label) {\n    const index = this.getIndex(label);\n    return this.matrix[index][index];\n  }\n\n  /**\n   * Get the number of true negative predictions\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getTrueNegativeCount(label) {\n    const index = this.getIndex(label);\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      for (let j = 0; j < this.matrix.length; j++) {\n        if (i !== index && j !== index) {\n          count += this.matrix[i][j];\n        }\n      }\n    }\n    return count;\n  }\n\n  /**\n   * Get the number of false positive predictions.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalsePositiveCount(label) {\n    const index = this.getIndex(label);\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      if (i !== index) {\n        count += this.matrix[i][index];\n      }\n    }\n    return count;\n  }\n\n  /**\n   * Get the number of false negative predictions.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseNegativeCount(label) {\n    const index = this.getIndex(label);\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      if (i !== index) {\n        count += this.matrix[index][i];\n      }\n    }\n    return count;\n  }\n\n  /**\n   * Get the number of real positive samples.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getPositiveCount(label) {\n    return this.getTruePositiveCount(label) + this.getFalseNegativeCount(label);\n  }\n\n  /**\n   * Get the number of real negative samples.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getNegativeCount(label) {\n    return this.getTrueNegativeCount(label) + this.getFalsePositiveCount(label);\n  }\n\n  /**\n   * Get the index in the confusion matrix that corresponds to the given label\n   * @param {any} label - The label to search for\n   * @throws if the label is not found\n   * @return {number}\n   */\n  getIndex(label) {\n    const index = this.labels.indexOf(label);\n    if (index === -1) throw new Error('The label does not exist');\n    return index;\n  }\n\n  /**\n   * Get the true positive rate a.k.a. sensitivity. Computes the ratio between the number of true positive predictions and the total number of positive samples.\n   * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number} - The true positive rate [0-1]\n   */\n  getTruePositiveRate(label) {\n    return this.getTruePositiveCount(label) / this.getPositiveCount(label);\n  }\n\n  /**\n   * Get the true negative rate a.k.a. specificity. Computes the ration between the number of true negative predictions and the total number of negative samples.\n   * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getTrueNegativeRate(label) {\n    return this.getTrueNegativeCount(label) / this.getNegativeCount(label);\n  }\n\n  /**\n   * Get the positive predictive value a.k.a. precision. Computes TP / (TP + FP)\n   * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getPositivePredictiveValue(label) {\n    const TP = this.getTruePositiveCount(label);\n    return TP / (TP + this.getFalsePositiveCount(label));\n  }\n\n  /**\n   * Negative predictive value\n   * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getNegativePredictiveValue(label) {\n    const TN = this.getTrueNegativeCount(label);\n    return TN / (TN + this.getFalseNegativeCount(label));\n  }\n\n  /**\n   * False negative rate a.k.a. miss rate.\n   * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseNegativeRate(label) {\n    return 1 - this.getTruePositiveRate(label);\n  }\n\n  /**\n   * False positive rate a.k.a. fall-out rate.\n   * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalsePositiveRate(label) {\n    return 1 - this.getTrueNegativeRate(label);\n  }\n\n  /**\n   * False discovery rate (FDR)\n   * {@link https://en.wikipedia.org/wiki/False_discovery_rate}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseDiscoveryRate(label) {\n    const FP = this.getFalsePositiveCount(label);\n    return FP / (FP + this.getTruePositiveCount(label));\n  }\n\n  /**\n   * False omission rate (FOR)\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseOmissionRate(label) {\n    const FN = this.getFalseNegativeCount(label);\n    return FN / (FN + this.getTruePositiveCount(label));\n  }\n\n  /**\n   * F1 score\n   * {@link https://en.wikipedia.org/wiki/F1_score}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getF1Score(label) {\n    const TP = this.getTruePositiveCount(label);\n    return (\n      (2 * TP) /\n      (2 * TP +\n        this.getFalsePositiveCount(label) +\n        this.getFalseNegativeCount(label))\n    );\n  }\n\n  /**\n   * Matthews correlation coefficient (MCC)\n   * {@link https://en.wikipedia.org/wiki/Matthews_correlation_coefficient}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getMatthewsCorrelationCoefficient(label) {\n    const TP = this.getTruePositiveCount(label);\n    const TN = this.getTrueNegativeCount(label);\n    const FP = this.getFalsePositiveCount(label);\n    const FN = this.getFalseNegativeCount(label);\n    return (\n      (TP * TN - FP * FN) /\n      Math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))\n    );\n  }\n\n  /**\n   * Informedness\n   * {@link https://en.wikipedia.org/wiki/Youden%27s_J_statistic}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getInformedness(label) {\n    return (\n      this.getTruePositiveRate(label) + this.getTrueNegativeRate(label) - 1\n    );\n  }\n\n  /**\n   * Markedness\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getMarkedness(label) {\n    return (\n      this.getPositivePredictiveValue(label) +\n      this.getNegativePredictiveValue(label) -\n      1\n    );\n  }\n\n  /**\n   * Get the confusion table.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {Array<Array<number> >} - The 2x2 confusion table. [[TP, FN], [FP, TN]]\n   */\n  getConfusionTable(label) {\n    return [\n      [this.getTruePositiveCount(label), this.getFalseNegativeCount(label)],\n      [this.getFalsePositiveCount(label), this.getTrueNegativeCount(label)],\n    ];\n  }\n\n  /**\n   * Get total accuracy.\n   * @return {number} - The ratio between the number of true predictions and total number of classifications ([0-1])\n   */\n  getAccuracy() {\n    let correct = 0;\n    let incorrect = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      for (let j = 0; j < this.matrix.length; j++) {\n        if (i === j) correct += this.matrix[i][j];\n        else incorrect += this.matrix[i][j];\n      }\n    }\n    return correct / (correct + incorrect);\n  }\n\n  /**\n   * Returns the element in the confusion matrix that corresponds to the given actual and predicted labels.\n   * @param {any} actual - The true label\n   * @param {any} predicted - The predicted label\n   * @return {number} - The element in the confusion matrix\n   */\n  getCount(actual, predicted) {\n    const actualIndex = this.getIndex(actual);\n    const predictedIndex = this.getIndex(predicted);\n    return this.matrix[actualIndex][predictedIndex];\n  }\n\n  /**\n   * Compute the general prediction accuracy\n   * @deprecated Use getAccuracy\n   * @return {number} - The prediction accuracy ([0-1]\n   */\n  get accuracy() {\n    return this.getAccuracy();\n  }\n\n  /**\n   * Compute the number of predicted observations\n   * @deprecated Use getTotalCount\n   * @return {number}\n   */\n  get total() {\n    return this.getTotalCount();\n  }\n}\n","(function (global, factory) {\n\ttypeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n\ttypeof define === 'function' && define.amd ? define(factory) :\n\t(factory());\n}(this, (function () { 'use strict';\n\n\tfunction createCommonjsModule(fn, module) {\n\t\treturn module = { exports: {} }, fn(module, module.exports), module.exports;\n\t}\n\n\tvar runtime = createCommonjsModule(function (module) {\n\t/**\n\t * Copyright (c) 2014-present, Facebook, Inc.\n\t *\n\t * This source code is licensed under the MIT license found in the\n\t * LICENSE file in the root directory of this source tree.\n\t */\n\n\t!(function(global) {\n\n\t  var Op = Object.prototype;\n\t  var hasOwn = Op.hasOwnProperty;\n\t  var undefined; // More compressible than void 0.\n\t  var $Symbol = typeof Symbol === \"function\" ? Symbol : {};\n\t  var iteratorSymbol = $Symbol.iterator || \"@@iterator\";\n\t  var asyncIteratorSymbol = $Symbol.asyncIterator || \"@@asyncIterator\";\n\t  var toStringTagSymbol = $Symbol.toStringTag || \"@@toStringTag\";\n\t  var runtime = global.regeneratorRuntime;\n\t  if (runtime) {\n\t    {\n\t      // If regeneratorRuntime is defined globally and we're in a module,\n\t      // make the exports object identical to regeneratorRuntime.\n\t      module.exports = runtime;\n\t    }\n\t    // Don't bother evaluating the rest of this file if the runtime was\n\t    // already defined globally.\n\t    return;\n\t  }\n\n\t  // Define the runtime globally (as expected by generated code) as either\n\t  // module.exports (if we're in a module) or a new, empty object.\n\t  runtime = global.regeneratorRuntime = module.exports;\n\n\t  function wrap(innerFn, outerFn, self, tryLocsList) {\n\t    // If outerFn provided and outerFn.prototype is a Generator, then outerFn.prototype instanceof Generator.\n\t    var protoGenerator = outerFn && outerFn.prototype instanceof Generator ? outerFn : Generator;\n\t    var generator = Object.create(protoGenerator.prototype);\n\t    var context = new Context(tryLocsList || []);\n\n\t    // The ._invoke method unifies the implementations of the .next,\n\t    // .throw, and .return methods.\n\t    generator._invoke = makeInvokeMethod(innerFn, self, context);\n\n\t    return generator;\n\t  }\n\t  runtime.wrap = wrap;\n\n\t  // Try/catch helper to minimize deoptimizations. Returns a completion\n\t  // record like context.tryEntries[i].completion. This interface could\n\t  // have been (and was previously) designed to take a closure to be\n\t  // invoked without arguments, but in all the cases we care about we\n\t  // already have an existing method we want to call, so there's no need\n\t  // to create a new function object. We can even get away with assuming\n\t  // the method takes exactly one argument, since that happens to be true\n\t  // in every case, so we don't have to touch the arguments object. The\n\t  // only additional allocation required is the completion record, which\n\t  // has a stable shape and so hopefully should be cheap to allocate.\n\t  function tryCatch(fn, obj, arg) {\n\t    try {\n\t      return { type: \"normal\", arg: fn.call(obj, arg) };\n\t    } catch (err) {\n\t      return { type: \"throw\", arg: err };\n\t    }\n\t  }\n\n\t  var GenStateSuspendedStart = \"suspendedStart\";\n\t  var GenStateSuspendedYield = \"suspendedYield\";\n\t  var GenStateExecuting = \"executing\";\n\t  var GenStateCompleted = \"completed\";\n\n\t  // Returning this object from the innerFn has the same effect as\n\t  // breaking out of the dispatch switch statement.\n\t  var ContinueSentinel = {};\n\n\t  // Dummy constructor functions that we use as the .constructor and\n\t  // .constructor.prototype properties for functions that return Generator\n\t  // objects. For full spec compliance, you may wish to configure your\n\t  // minifier not to mangle the names of these two functions.\n\t  function Generator() {}\n\t  function GeneratorFunction() {}\n\t  function GeneratorFunctionPrototype() {}\n\n\t  // This is a polyfill for %IteratorPrototype% for environments that\n\t  // don't natively support it.\n\t  var IteratorPrototype = {};\n\t  IteratorPrototype[iteratorSymbol] = function () {\n\t    return this;\n\t  };\n\n\t  var getProto = Object.getPrototypeOf;\n\t  var NativeIteratorPrototype = getProto && getProto(getProto(values([])));\n\t  if (NativeIteratorPrototype &&\n\t      NativeIteratorPrototype !== Op &&\n\t      hasOwn.call(NativeIteratorPrototype, iteratorSymbol)) {\n\t    // This environment has a native %IteratorPrototype%; use it instead\n\t    // of the polyfill.\n\t    IteratorPrototype = NativeIteratorPrototype;\n\t  }\n\n\t  var Gp = GeneratorFunctionPrototype.prototype =\n\t    Generator.prototype = Object.create(IteratorPrototype);\n\t  GeneratorFunction.prototype = Gp.constructor = GeneratorFunctionPrototype;\n\t  GeneratorFunctionPrototype.constructor = GeneratorFunction;\n\t  GeneratorFunctionPrototype[toStringTagSymbol] =\n\t    GeneratorFunction.displayName = \"GeneratorFunction\";\n\n\t  // Helper for defining the .next, .throw, and .return methods of the\n\t  // Iterator interface in terms of a single ._invoke method.\n\t  function defineIteratorMethods(prototype) {\n\t    [\"next\", \"throw\", \"return\"].forEach(function(method) {\n\t      prototype[method] = function(arg) {\n\t        return this._invoke(method, arg);\n\t      };\n\t    });\n\t  }\n\n\t  runtime.isGeneratorFunction = function(genFun) {\n\t    var ctor = typeof genFun === \"function\" && genFun.constructor;\n\t    return ctor\n\t      ? ctor === GeneratorFunction ||\n\t        // For the native GeneratorFunction constructor, the best we can\n\t        // do is to check its .name property.\n\t        (ctor.displayName || ctor.name) === \"GeneratorFunction\"\n\t      : false;\n\t  };\n\n\t  runtime.mark = function(genFun) {\n\t    if (Object.setPrototypeOf) {\n\t      Object.setPrototypeOf(genFun, GeneratorFunctionPrototype);\n\t    } else {\n\t      genFun.__proto__ = GeneratorFunctionPrototype;\n\t      if (!(toStringTagSymbol in genFun)) {\n\t        genFun[toStringTagSymbol] = \"GeneratorFunction\";\n\t      }\n\t    }\n\t    genFun.prototype = Object.create(Gp);\n\t    return genFun;\n\t  };\n\n\t  // Within the body of any async function, `await x` is transformed to\n\t  // `yield regeneratorRuntime.awrap(x)`, so that the runtime can test\n\t  // `hasOwn.call(value, \"__await\")` to determine if the yielded value is\n\t  // meant to be awaited.\n\t  runtime.awrap = function(arg) {\n\t    return { __await: arg };\n\t  };\n\n\t  function AsyncIterator(generator) {\n\t    function invoke(method, arg, resolve, reject) {\n\t      var record = tryCatch(generator[method], generator, arg);\n\t      if (record.type === \"throw\") {\n\t        reject(record.arg);\n\t      } else {\n\t        var result = record.arg;\n\t        var value = result.value;\n\t        if (value &&\n\t            typeof value === \"object\" &&\n\t            hasOwn.call(value, \"__await\")) {\n\t          return Promise.resolve(value.__await).then(function(value) {\n\t            invoke(\"next\", value, resolve, reject);\n\t          }, function(err) {\n\t            invoke(\"throw\", err, resolve, reject);\n\t          });\n\t        }\n\n\t        return Promise.resolve(value).then(function(unwrapped) {\n\t          // When a yielded Promise is resolved, its final value becomes\n\t          // the .value of the Promise<{value,done}> result for the\n\t          // current iteration. If the Promise is rejected, however, the\n\t          // result for this iteration will be rejected with the same\n\t          // reason. Note that rejections of yielded Promises are not\n\t          // thrown back into the generator function, as is the case\n\t          // when an awaited Promise is rejected. This difference in\n\t          // behavior between yield and await is important, because it\n\t          // allows the consumer to decide what to do with the yielded\n\t          // rejection (swallow it and continue, manually .throw it back\n\t          // into the generator, abandon iteration, whatever). With\n\t          // await, by contrast, there is no opportunity to examine the\n\t          // rejection reason outside the generator function, so the\n\t          // only option is to throw it from the await expression, and\n\t          // let the generator function handle the exception.\n\t          result.value = unwrapped;\n\t          resolve(result);\n\t        }, reject);\n\t      }\n\t    }\n\n\t    var previousPromise;\n\n\t    function enqueue(method, arg) {\n\t      function callInvokeWithMethodAndArg() {\n\t        return new Promise(function(resolve, reject) {\n\t          invoke(method, arg, resolve, reject);\n\t        });\n\t      }\n\n\t      return previousPromise =\n\t        // If enqueue has been called before, then we want to wait until\n\t        // all previous Promises have been resolved before calling invoke,\n\t        // so that results are always delivered in the correct order. If\n\t        // enqueue has not been called before, then it is important to\n\t        // call invoke immediately, without waiting on a callback to fire,\n\t        // so that the async generator function has the opportunity to do\n\t        // any necessary setup in a predictable way. This predictability\n\t        // is why the Promise constructor synchronously invokes its\n\t        // executor callback, and why async functions synchronously\n\t        // execute code before the first await. Since we implement simple\n\t        // async functions in terms of async generators, it is especially\n\t        // important to get this right, even though it requires care.\n\t        previousPromise ? previousPromise.then(\n\t          callInvokeWithMethodAndArg,\n\t          // Avoid propagating failures to Promises returned by later\n\t          // invocations of the iterator.\n\t          callInvokeWithMethodAndArg\n\t        ) : callInvokeWithMethodAndArg();\n\t    }\n\n\t    // Define the unified helper method that is used to implement .next,\n\t    // .throw, and .return (see defineIteratorMethods).\n\t    this._invoke = enqueue;\n\t  }\n\n\t  defineIteratorMethods(AsyncIterator.prototype);\n\t  AsyncIterator.prototype[asyncIteratorSymbol] = function () {\n\t    return this;\n\t  };\n\t  runtime.AsyncIterator = AsyncIterator;\n\n\t  // Note that simple async functions are implemented on top of\n\t  // AsyncIterator objects; they just return a Promise for the value of\n\t  // the final result produced by the iterator.\n\t  runtime.async = function(innerFn, outerFn, self, tryLocsList) {\n\t    var iter = new AsyncIterator(\n\t      wrap(innerFn, outerFn, self, tryLocsList)\n\t    );\n\n\t    return runtime.isGeneratorFunction(outerFn)\n\t      ? iter // If outerFn is a generator, return the full iterator.\n\t      : iter.next().then(function(result) {\n\t          return result.done ? result.value : iter.next();\n\t        });\n\t  };\n\n\t  function makeInvokeMethod(innerFn, self, context) {\n\t    var state = GenStateSuspendedStart;\n\n\t    return function invoke(method, arg) {\n\t      if (state === GenStateExecuting) {\n\t        throw new Error(\"Generator is already running\");\n\t      }\n\n\t      if (state === GenStateCompleted) {\n\t        if (method === \"throw\") {\n\t          throw arg;\n\t        }\n\n\t        // Be forgiving, per 25.3.3.3.3 of the spec:\n\t        // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-generatorresume\n\t        return doneResult();\n\t      }\n\n\t      context.method = method;\n\t      context.arg = arg;\n\n\t      while (true) {\n\t        var delegate = context.delegate;\n\t        if (delegate) {\n\t          var delegateResult = maybeInvokeDelegate(delegate, context);\n\t          if (delegateResult) {\n\t            if (delegateResult === ContinueSentinel) continue;\n\t            return delegateResult;\n\t          }\n\t        }\n\n\t        if (context.method === \"next\") {\n\t          // Setting context._sent for legacy support of Babel's\n\t          // function.sent implementation.\n\t          context.sent = context._sent = context.arg;\n\n\t        } else if (context.method === \"throw\") {\n\t          if (state === GenStateSuspendedStart) {\n\t            state = GenStateCompleted;\n\t            throw context.arg;\n\t          }\n\n\t          context.dispatchException(context.arg);\n\n\t        } else if (context.method === \"return\") {\n\t          context.abrupt(\"return\", context.arg);\n\t        }\n\n\t        state = GenStateExecuting;\n\n\t        var record = tryCatch(innerFn, self, context);\n\t        if (record.type === \"normal\") {\n\t          // If an exception is thrown from innerFn, we leave state ===\n\t          // GenStateExecuting and loop back for another invocation.\n\t          state = context.done\n\t            ? GenStateCompleted\n\t            : GenStateSuspendedYield;\n\n\t          if (record.arg === ContinueSentinel) {\n\t            continue;\n\t          }\n\n\t          return {\n\t            value: record.arg,\n\t            done: context.done\n\t          };\n\n\t        } else if (record.type === \"throw\") {\n\t          state = GenStateCompleted;\n\t          // Dispatch the exception by looping back around to the\n\t          // context.dispatchException(context.arg) call above.\n\t          context.method = \"throw\";\n\t          context.arg = record.arg;\n\t        }\n\t      }\n\t    };\n\t  }\n\n\t  // Call delegate.iterator[context.method](context.arg) and handle the\n\t  // result, either by returning a { value, done } result from the\n\t  // delegate iterator, or by modifying context.method and context.arg,\n\t  // setting context.delegate to null, and returning the ContinueSentinel.\n\t  function maybeInvokeDelegate(delegate, context) {\n\t    var method = delegate.iterator[context.method];\n\t    if (method === undefined) {\n\t      // A .throw or .return when the delegate iterator has no .throw\n\t      // method always terminates the yield* loop.\n\t      context.delegate = null;\n\n\t      if (context.method === \"throw\") {\n\t        if (delegate.iterator.return) {\n\t          // If the delegate iterator has a return method, give it a\n\t          // chance to clean up.\n\t          context.method = \"return\";\n\t          context.arg = undefined;\n\t          maybeInvokeDelegate(delegate, context);\n\n\t          if (context.method === \"throw\") {\n\t            // If maybeInvokeDelegate(context) changed context.method from\n\t            // \"return\" to \"throw\", let that override the TypeError below.\n\t            return ContinueSentinel;\n\t          }\n\t        }\n\n\t        context.method = \"throw\";\n\t        context.arg = new TypeError(\n\t          \"The iterator does not provide a 'throw' method\");\n\t      }\n\n\t      return ContinueSentinel;\n\t    }\n\n\t    var record = tryCatch(method, delegate.iterator, context.arg);\n\n\t    if (record.type === \"throw\") {\n\t      context.method = \"throw\";\n\t      context.arg = record.arg;\n\t      context.delegate = null;\n\t      return ContinueSentinel;\n\t    }\n\n\t    var info = record.arg;\n\n\t    if (! info) {\n\t      context.method = \"throw\";\n\t      context.arg = new TypeError(\"iterator result is not an object\");\n\t      context.delegate = null;\n\t      return ContinueSentinel;\n\t    }\n\n\t    if (info.done) {\n\t      // Assign the result of the finished delegate to the temporary\n\t      // variable specified by delegate.resultName (see delegateYield).\n\t      context[delegate.resultName] = info.value;\n\n\t      // Resume execution at the desired location (see delegateYield).\n\t      context.next = delegate.nextLoc;\n\n\t      // If context.method was \"throw\" but the delegate handled the\n\t      // exception, let the outer generator proceed normally. If\n\t      // context.method was \"next\", forget context.arg since it has been\n\t      // \"consumed\" by the delegate iterator. If context.method was\n\t      // \"return\", allow the original .return call to continue in the\n\t      // outer generator.\n\t      if (context.method !== \"return\") {\n\t        context.method = \"next\";\n\t        context.arg = undefined;\n\t      }\n\n\t    } else {\n\t      // Re-yield the result returned by the delegate method.\n\t      return info;\n\t    }\n\n\t    // The delegate iterator is finished, so forget it and continue with\n\t    // the outer generator.\n\t    context.delegate = null;\n\t    return ContinueSentinel;\n\t  }\n\n\t  // Define Generator.prototype.{next,throw,return} in terms of the\n\t  // unified ._invoke helper method.\n\t  defineIteratorMethods(Gp);\n\n\t  Gp[toStringTagSymbol] = \"Generator\";\n\n\t  // A Generator should always return itself as the iterator object when the\n\t  // @@iterator function is called on it. Some browsers' implementations of the\n\t  // iterator prototype chain incorrectly implement this, causing the Generator\n\t  // object to not be returned from this call. This ensures that doesn't happen.\n\t  // See https://github.com/facebook/regenerator/issues/274 for more details.\n\t  Gp[iteratorSymbol] = function() {\n\t    return this;\n\t  };\n\n\t  Gp.toString = function() {\n\t    return \"[object Generator]\";\n\t  };\n\n\t  function pushTryEntry(locs) {\n\t    var entry = { tryLoc: locs[0] };\n\n\t    if (1 in locs) {\n\t      entry.catchLoc = locs[1];\n\t    }\n\n\t    if (2 in locs) {\n\t      entry.finallyLoc = locs[2];\n\t      entry.afterLoc = locs[3];\n\t    }\n\n\t    this.tryEntries.push(entry);\n\t  }\n\n\t  function resetTryEntry(entry) {\n\t    var record = entry.completion || {};\n\t    record.type = \"normal\";\n\t    delete record.arg;\n\t    entry.completion = record;\n\t  }\n\n\t  function Context(tryLocsList) {\n\t    // The root entry object (effectively a try statement without a catch\n\t    // or a finally block) gives us a place to store values thrown from\n\t    // locations where there is no enclosing try statement.\n\t    this.tryEntries = [{ tryLoc: \"root\" }];\n\t    tryLocsList.forEach(pushTryEntry, this);\n\t    this.reset(true);\n\t  }\n\n\t  runtime.keys = function(object) {\n\t    var keys = [];\n\t    for (var key in object) {\n\t      keys.push(key);\n\t    }\n\t    keys.reverse();\n\n\t    // Rather than returning an object with a next method, we keep\n\t    // things simple and return the next function itself.\n\t    return function next() {\n\t      while (keys.length) {\n\t        var key = keys.pop();\n\t        if (key in object) {\n\t          next.value = key;\n\t          next.done = false;\n\t          return next;\n\t        }\n\t      }\n\n\t      // To avoid creating an additional object, we just hang the .value\n\t      // and .done properties off the next function object itself. This\n\t      // also ensures that the minifier will not anonymize the function.\n\t      next.done = true;\n\t      return next;\n\t    };\n\t  };\n\n\t  function values(iterable) {\n\t    if (iterable) {\n\t      var iteratorMethod = iterable[iteratorSymbol];\n\t      if (iteratorMethod) {\n\t        return iteratorMethod.call(iterable);\n\t      }\n\n\t      if (typeof iterable.next === \"function\") {\n\t        return iterable;\n\t      }\n\n\t      if (!isNaN(iterable.length)) {\n\t        var i = -1, next = function next() {\n\t          while (++i < iterable.length) {\n\t            if (hasOwn.call(iterable, i)) {\n\t              next.value = iterable[i];\n\t              next.done = false;\n\t              return next;\n\t            }\n\t          }\n\n\t          next.value = undefined;\n\t          next.done = true;\n\n\t          return next;\n\t        };\n\n\t        return next.next = next;\n\t      }\n\t    }\n\n\t    // Return an iterator with no values.\n\t    return { next: doneResult };\n\t  }\n\t  runtime.values = values;\n\n\t  function doneResult() {\n\t    return { value: undefined, done: true };\n\t  }\n\n\t  Context.prototype = {\n\t    constructor: Context,\n\n\t    reset: function(skipTempReset) {\n\t      this.prev = 0;\n\t      this.next = 0;\n\t      // Resetting context._sent for legacy support of Babel's\n\t      // function.sent implementation.\n\t      this.sent = this._sent = undefined;\n\t      this.done = false;\n\t      this.delegate = null;\n\n\t      this.method = \"next\";\n\t      this.arg = undefined;\n\n\t      this.tryEntries.forEach(resetTryEntry);\n\n\t      if (!skipTempReset) {\n\t        for (var name in this) {\n\t          // Not sure about the optimal order of these conditions:\n\t          if (name.charAt(0) === \"t\" &&\n\t              hasOwn.call(this, name) &&\n\t              !isNaN(+name.slice(1))) {\n\t            this[name] = undefined;\n\t          }\n\t        }\n\t      }\n\t    },\n\n\t    stop: function() {\n\t      this.done = true;\n\n\t      var rootEntry = this.tryEntries[0];\n\t      var rootRecord = rootEntry.completion;\n\t      if (rootRecord.type === \"throw\") {\n\t        throw rootRecord.arg;\n\t      }\n\n\t      return this.rval;\n\t    },\n\n\t    dispatchException: function(exception) {\n\t      if (this.done) {\n\t        throw exception;\n\t      }\n\n\t      var context = this;\n\t      function handle(loc, caught) {\n\t        record.type = \"throw\";\n\t        record.arg = exception;\n\t        context.next = loc;\n\n\t        if (caught) {\n\t          // If the dispatched exception was caught by a catch block,\n\t          // then let that catch block handle the exception normally.\n\t          context.method = \"next\";\n\t          context.arg = undefined;\n\t        }\n\n\t        return !! caught;\n\t      }\n\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        var record = entry.completion;\n\n\t        if (entry.tryLoc === \"root\") {\n\t          // Exception thrown outside of any try block that could handle\n\t          // it, so set the completion value of the entire function to\n\t          // throw the exception.\n\t          return handle(\"end\");\n\t        }\n\n\t        if (entry.tryLoc <= this.prev) {\n\t          var hasCatch = hasOwn.call(entry, \"catchLoc\");\n\t          var hasFinally = hasOwn.call(entry, \"finallyLoc\");\n\n\t          if (hasCatch && hasFinally) {\n\t            if (this.prev < entry.catchLoc) {\n\t              return handle(entry.catchLoc, true);\n\t            } else if (this.prev < entry.finallyLoc) {\n\t              return handle(entry.finallyLoc);\n\t            }\n\n\t          } else if (hasCatch) {\n\t            if (this.prev < entry.catchLoc) {\n\t              return handle(entry.catchLoc, true);\n\t            }\n\n\t          } else if (hasFinally) {\n\t            if (this.prev < entry.finallyLoc) {\n\t              return handle(entry.finallyLoc);\n\t            }\n\n\t          } else {\n\t            throw new Error(\"try statement without catch or finally\");\n\t          }\n\t        }\n\t      }\n\t    },\n\n\t    abrupt: function(type, arg) {\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        if (entry.tryLoc <= this.prev &&\n\t            hasOwn.call(entry, \"finallyLoc\") &&\n\t            this.prev < entry.finallyLoc) {\n\t          var finallyEntry = entry;\n\t          break;\n\t        }\n\t      }\n\n\t      if (finallyEntry &&\n\t          (type === \"break\" ||\n\t           type === \"continue\") &&\n\t          finallyEntry.tryLoc <= arg &&\n\t          arg <= finallyEntry.finallyLoc) {\n\t        // Ignore the finally entry if control is not jumping to a\n\t        // location outside the try/catch block.\n\t        finallyEntry = null;\n\t      }\n\n\t      var record = finallyEntry ? finallyEntry.completion : {};\n\t      record.type = type;\n\t      record.arg = arg;\n\n\t      if (finallyEntry) {\n\t        this.method = \"next\";\n\t        this.next = finallyEntry.finallyLoc;\n\t        return ContinueSentinel;\n\t      }\n\n\t      return this.complete(record);\n\t    },\n\n\t    complete: function(record, afterLoc) {\n\t      if (record.type === \"throw\") {\n\t        throw record.arg;\n\t      }\n\n\t      if (record.type === \"break\" ||\n\t          record.type === \"continue\") {\n\t        this.next = record.arg;\n\t      } else if (record.type === \"return\") {\n\t        this.rval = this.arg = record.arg;\n\t        this.method = \"return\";\n\t        this.next = \"end\";\n\t      } else if (record.type === \"normal\" && afterLoc) {\n\t        this.next = afterLoc;\n\t      }\n\n\t      return ContinueSentinel;\n\t    },\n\n\t    finish: function(finallyLoc) {\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        if (entry.finallyLoc === finallyLoc) {\n\t          this.complete(entry.completion, entry.afterLoc);\n\t          resetTryEntry(entry);\n\t          return ContinueSentinel;\n\t        }\n\t      }\n\t    },\n\n\t    \"catch\": function(tryLoc) {\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        if (entry.tryLoc === tryLoc) {\n\t          var record = entry.completion;\n\t          if (record.type === \"throw\") {\n\t            var thrown = record.arg;\n\t            resetTryEntry(entry);\n\t          }\n\t          return thrown;\n\t        }\n\t      }\n\n\t      // The context.catch method must only be called with a location\n\t      // argument that corresponds to a known catch block.\n\t      throw new Error(\"illegal catch attempt\");\n\t    },\n\n\t    delegateYield: function(iterable, resultName, nextLoc) {\n\t      this.delegate = {\n\t        iterator: values(iterable),\n\t        resultName: resultName,\n\t        nextLoc: nextLoc\n\t      };\n\n\t      if (this.method === \"next\") {\n\t        // Deliberately forget the last sent value so that we don't\n\t        // accidentally pass it on to the delegate.\n\t        this.arg = undefined;\n\t      }\n\n\t      return ContinueSentinel;\n\t    }\n\t  };\n\t})(\n\t  // In sloppy mode, unbound `this` refers to the global object, fallback to\n\t  // Function constructor if we're in global strict mode. That is sadly a form\n\t  // of indirect eval which violates Content Security Policy.\n\t  (function() { return this })() || Function(\"return this\")()\n\t);\n\t});\n\n\t/**\n\t * Copyright (c) 2014-present, Facebook, Inc.\n\t *\n\t * This source code is licensed under the MIT license found in the\n\t * LICENSE file in the root directory of this source tree.\n\t */\n\n\t// This method of obtaining a reference to the global object needs to be\n\t// kept identical to the way it is obtained in runtime.js\n\tvar g = (function() { return this })() || Function(\"return this\")();\n\n\t// Use `getOwnPropertyNames` because not all browsers support calling\n\t// `hasOwnProperty` on the global `self` object in a worker. 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i < N; i++) {\n    allIdx[i] = i;\n  }\n\n  let l = Math.floor(N / k);\n  // create random k-folds\n  let current = [];\n  let folds = [];\n  while (allIdx.length) {\n    let randi = Math.floor(Math.random() * allIdx.length);\n    current.push(allIdx[randi]);\n    allIdx.splice(randi, 1);\n    if (current.length === l) {\n      folds.push(current);\n      current = [];\n    }\n  }\n  // we push the remaining to the last fold so that the total length is\n  // preserved. Otherwise the Q2 will fail.\n  if (current.length) current.forEach((e) => folds[k - 1].push(e));\n  folds = folds.slice(0, k);\n\n  let foldsIndex = folds.map((x, idx) => ({\n    testIndex: x,\n    trainIndex: [].concat(...folds.filter((el, idx2) => idx2 !== idx)),\n  }));\n  return foldsIndex;\n}\n","/**\n * A function to sample a dataset maintaining classes equilibrated\n * @param {Array} classVector - an array containing class or group information\n * @param {Number} fraction - a fraction of the class to sample\n * @return {Object} - an object with indexes\n */\n\nexport function sampleAClass(classVector, fraction) {\n  // sort the vector\n  let classVectorSorted = JSON.parse(JSON.stringify(classVector));\n  let result = Array.from(Array(classVectorSorted.length).keys()).sort((a, b) =>\n    classVectorSorted[a] < classVectorSorted[b]\n      ? -1\n      : (classVectorSorted[b] < classVectorSorted[a]) | 0,\n  );\n  classVectorSorted.sort((a, b) => (a < b ? -1 : (b < a) | 0));\n\n  // counts the class elements\n  let counts = {};\n  classVectorSorted.forEach((x) => (counts[x] = (counts[x] || 0) + 1));\n\n  // pick a few per class\n  let indexOfSelected = [];\n\n  Object.keys(counts).forEach((e, i) => {\n    let shift = [];\n    Object.values(counts).reduce((a, c, item) => (shift[item] = a + c), 0);\n\n    let arr = [...Array(counts[e]).keys()];\n\n    let r = [];\n    for (let j = 0; j < Math.floor(counts[e] * fraction); j++) {\n      let n = arr[Math.floor(Math.random() * arr.length)];\n      r.push(n);\n      let ind = arr.indexOf(n);\n      arr.splice(ind, 1);\n    }\n\n    if (i === 0) {\n      indexOfSelected = indexOfSelected.concat(r);\n    } else {\n      indexOfSelected = indexOfSelected.concat(r.map((x) => x + shift[i - 1]));\n    }\n  });\n\n  // sort back the index\n  let trainIndex = [];\n  indexOfSelected.forEach((e) => trainIndex.push(result[e]));\n\n  let testIndex = [];\n  let mask = [];\n  classVector.forEach((el, idx) => {\n    if (trainIndex.includes(idx)) {\n      mask.push(true);\n    } else {\n      mask.push(false);\n      testIndex.push(idx);\n    }\n  });\n  return { trainIndex, testIndex, mask };\n}\n","import ConfusionMatrix from 'ml-confusion-matrix';\nimport combinations from 'ml-combinations';\n\nimport { getFolds } from './getFolds.js';\n\nexport { sampleAClass } from './sampleAClass.js';\nexport { getFolds } from './getFolds.js';\n\n/**\n * Performs a leave-one-out cross-validation (LOO-CV) of the given samples. In LOO-CV, 1 observation is used as the\n * validation set while the rest is used as the training set. This is repeated once for each observation. LOO-CV is a\n * special case of LPO-CV. @see leavePout\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\n\nexport function leaveOneOut(Classifier, features, labels, classifierOptions) {\n  if (typeof labels === 'function') {\n    let callback = labels;\n    labels = features;\n    features = Classifier;\n    return leavePOut(features, labels, 1, callback);\n  }\n  return leavePOut(Classifier, features, labels, classifierOptions, 1);\n}\n\n/**\n * Performs a leave-p-out cross-validation (LPO-CV) of the given samples. In LPO-CV, p observations are used as the\n * validation set while the rest is used as the training set. This is repeated as many times as there are possible\n * ways to combine p observations from the set (unordered without replacement). Be aware that for relatively small\n * data-set size this can require a very large number of training and testing to do!\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} p - The size of the validation sub-samples' set\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nexport function leavePOut(Classifier, features, labels, classifierOptions, p) {\n  let callback;\n  if (typeof classifierOptions === 'function') {\n    callback = classifierOptions;\n    p = labels;\n    labels = features;\n    features = Classifier;\n  }\n  check(features, labels);\n  const distinct = getDistinct(labels);\n  const confusionMatrix = initMatrix(distinct.length, distinct.length);\n\n  let N = features.length;\n  let gen = combinations(p, N);\n  let allIdx = new Array(N);\n  for (let i = 0; i < N; i++) {\n    allIdx[i] = i;\n  }\n  for (const testIdx of gen) {\n    let trainIdx = allIdx.slice();\n\n    for (let i = testIdx.length - 1; i >= 0; i--) {\n      trainIdx.splice(testIdx[i], 1);\n    }\n\n    if (callback) {\n      validateWithCallback(\n        features,\n        labels,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n        callback,\n      );\n    } else {\n      validate(\n        Classifier,\n        features,\n        labels,\n        classifierOptions,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n      );\n    }\n  }\n\n  return new ConfusionMatrix(confusionMatrix, distinct);\n}\n\n/**\n * Performs k-fold cross-validation (KF-CV). KF-CV separates the data-set into k random equally sized partitions, and\n * uses each as a validation set, with all other partitions used in the training set. Observations left over from if k\n * does not divide the number of observations are left out of the cross-validation process.\n * @param {function} Classifier - The classifier's to use for the cross validation. Expect ml-classifier api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} k - The number of partitions to create\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nexport function kFold(Classifier, features, labels, classifierOptions, k) {\n  let callback;\n  if (typeof classifierOptions === 'function') {\n    callback = classifierOptions;\n    k = labels;\n    labels = features;\n    features = Classifier;\n  }\n  check(features, labels);\n  const distinct = getDistinct(labels);\n  const confusionMatrix = initMatrix(distinct.length, distinct.length);\n\n  let folds = getFolds(features, k);\n\n  for (let i = 0; i < folds.length; i++) {\n    let testIdx = folds[i].testIndex;\n    let trainIdx = folds[i].trainIndex;\n\n    if (callback) {\n      validateWithCallback(\n        features,\n        labels,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n        callback,\n      );\n    } else {\n      validate(\n        Classifier,\n        features,\n        labels,\n        classifierOptions,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n      );\n    }\n  }\n\n  return new ConfusionMatrix(confusionMatrix, distinct);\n}\n\nfunction check(features, labels) {\n  if (features.length !== labels.length) {\n    throw new Error('features and labels should have the same length');\n  }\n}\n\nfunction initMatrix(rows, columns) {\n  return new Array(rows).fill(0).map(() => new Array(columns).fill(0));\n}\n\nfunction getDistinct(arr) {\n  let s = new Set();\n  for (let i = 0; i < arr.length; i++) {\n    s.add(arr[i]);\n  }\n  return Array.from(s);\n}\n\nfunction validate(\n  Classifier,\n  features,\n  labels,\n  classifierOptions,\n  testIdx,\n  trainIdx,\n  confusionMatrix,\n  distinct,\n) {\n  const { testFeatures, trainFeatures, testLabels, trainLabels } = getTrainTest(\n    features,\n    labels,\n    testIdx,\n    trainIdx,\n  );\n\n  let classifier;\n  if (Classifier.prototype.train) {\n    classifier = new Classifier(classifierOptions);\n    classifier.train(trainFeatures, trainLabels);\n  } else {\n    classifier = new Classifier(trainFeatures, trainLabels, classifierOptions);\n  }\n\n  let predictedLabels = classifier.predict(testFeatures);\n  updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction validateWithCallback(\n  features,\n  labels,\n  testIdx,\n  trainIdx,\n  confusionMatrix,\n  distinct,\n  callback,\n) {\n  const { testFeatures, trainFeatures, testLabels, trainLabels } = getTrainTest(\n    features,\n    labels,\n    testIdx,\n    trainIdx,\n  );\n  const predictedLabels = callback(trainFeatures, trainLabels, testFeatures);\n  updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction updateConfusionMatrix(\n  confusionMatrix,\n  testLabels,\n  predictedLabels,\n  distinct,\n) {\n  for (let i = 0; i < predictedLabels.length; i++) {\n    const actualIdx = distinct.indexOf(testLabels[i]);\n    const predictedIdx = distinct.indexOf(predictedLabels[i]);\n    if (actualIdx < 0 || predictedIdx < 0) {\n      // eslint-disable-next-line no-console\n      console.warn(`ignore unknown predicted label ${predictedLabels[i]}`);\n    }\n    confusionMatrix[actualIdx][predictedIdx]++;\n  }\n}\n\nexport function getTrainTest(features, labels, testIdx, trainIdx) {\n  return {\n    testFeatures: testIdx.map(function(index) {\n      return features[index];\n    }),\n    trainFeatures: trainIdx.map(function(index) {\n      return features[index];\n    }),\n    testLabels: testIdx.map(function(index) {\n      return labels[index];\n    }),\n    trainLabels: trainIdx.map(function(index) {\n      return labels[index];\n    }),\n  };\n}\n","import Matrix from 'ml-matrix';\n\nimport { norm } from './util/utils.js';\n\n/**\n * OPLS loop\n * @param {Array} x a matrix with features\n * @param {Array} y an array of labels (dependent variable)\n * @param {Object} options an object with options\n * @return {Object} an object with model (filteredX: err,\n    loadingsXOrtho: pOrtho,\n    scoresXOrtho: tOrtho,\n    weightsXOrtho: wOrtho,\n    weightsPred: w,\n    loadingsXpred: p,\n    scoresXpred: t,\n    loadingsY:)\n */\nexport function OPLSNipals(x, y, options = {}) {\n  const { numberOSC = 100 } = options;\n\n  let X = Matrix.checkMatrix(x);\n  let Y = Matrix.checkMatrix(y);\n\n  let u = Y.getColumnVector(0);\n\n  let diff = 1;\n  let t, c, w, uNew;\n  for (let i = 0; i < numberOSC && diff > 1e-10; i++) {\n    w = u\n      .transpose()\n      .mmul(X)\n      .div(\n        u\n          .transpose()\n          .mmul(u)\n          .get(0, 0),\n      );\n    w = w.transpose().div(norm(w));\n\n    t = X.mmul(w).div(\n      w\n        .transpose()\n        .mmul(w)\n        .get(0, 0),\n    ); // t_h paso 3\n\n    // calc loading\n    c = t\n      .transpose()\n      .mmul(Y)\n      .div(\n        t\n          .transpose()\n          .mmul(t)\n          .get(0, 0),\n      );\n\n    // calc new u and compare with one in previus iteration (stop criterion)\n    uNew = Y.mmul(c.transpose());\n    uNew = uNew.div(\n      c\n        .transpose()\n        .mmul(c)\n        .get(0, 0),\n    );\n\n    if (i > 0) {\n      diff =\n        uNew\n          .clone()\n          .sub(u)\n          .pow(2)\n          .sum() /\n        uNew\n          .clone()\n          .pow(2)\n          .sum();\n    }\n\n    u = uNew.clone();\n  }\n\n  // calc loadings\n  let p = t\n    .transpose()\n    .mmul(X)\n    .div(\n      t\n        .transpose()\n        .mmul(t)\n        .get(0, 0),\n    );\n\n  let wOrtho = p.clone().sub(\n    w\n      .transpose()\n      .mmul(p.transpose())\n      .div(\n        w\n          .transpose()\n          .mmul(w)\n          .get(0, 0),\n      )\n      .mmul(w.transpose()),\n  );\n  wOrtho.div(norm(wOrtho));\n\n  // orthogonal scores\n  let tOrtho = X.mmul(wOrtho.transpose()).div(\n    wOrtho.mmul(wOrtho.transpose()).get(0, 0),\n  );\n\n  // orthogonal loadings\n  let pOrtho = tOrtho\n    .transpose()\n    .mmul(X)\n    .div(\n      tOrtho\n        .transpose()\n        .mmul(tOrtho)\n        .get(0, 0),\n    );\n\n  // filtered data\n  let err = X.clone().sub(tOrtho.mmul(pOrtho));\n  return {\n    filteredX: err,\n    weightsXOrtho: wOrtho,\n    loadingsXOrtho: pOrtho,\n    scoresXOrtho: tOrtho,\n    weightsXPred: w,\n    loadingsXpred: p,\n    scoresXpred: t,\n    loadingsY: c,\n  };\n}\n","import { Matrix } from 'ml-matrix';\n\n/**\n * Get total sum of square\n * @param {Array} x an array\n * @return {Number} - the sum of the squares\n */\nexport function tss(x) {\n  return Matrix.mul(x, x).sum();\n}\n","import { Matrix, NIPALS } from 'ml-matrix';\nimport ConfusionMatrix from 'ml-confusion-matrix';\nimport { getFolds } from 'ml-cross-validation';\n\nimport { OPLSNipals } from './OPLSNipals.js';\nimport { tss } from './util/tss.js';\n\n/**\n * Creates new OPLS (orthogonal partial latent structures) from features and labels.\n * @param {Matrix} data - matrix containing data (X).\n * @param {Array} labels - 1D Array containing metadata (Y).\n * @param {Object} [options]\n * @param {number} [options.nComp = 3] - number of latent structures computed.\n * @param {boolean} [options.center = true] - should the data be centered (subtract the mean).\n * @param {boolean} [options.scale = false] - should the data be scaled (divide by the standard deviation).\n * @param {Array} [options.cvFolds = []] - allows to provide folds as 2D array for testing purpose.\n * */\n\nexport class OPLS {\n  constructor(data, labels, options = {}) {\n    if (data === true) {\n      const opls = options;\n      this.center = opls.center;\n      this.scale = opls.scale;\n      this.means = opls.means;\n      this.meansY = opls.meansY;\n      this.stdevs = opls.stdevs;\n      this.stdevs = opls.stdevsY;\n      this.model = opls.model;\n      this.tCV = opls.tCV;\n      this.tOrthCV = opls.tOrthCV;\n      this.yHatCV = opls.yHatCV;\n      this.mode = opls.mode;\n      return;\n    }\n\n    let features = data.clone();\n    // set default values\n    // cvFolds allows to define folds for testing purpose\n    const { nComp = 3, center = true, scale = true, cvFolds = [] } = options;\n\n    let group;\n    if (typeof labels[0] === 'number') {\n      // numeric labels: OPLS regression is used\n      this.mode = 'regression';\n      group = Matrix.from1DArray(labels.length, 1, labels);\n    } else if (typeof labels[0] === 'string') {\n      // non-numeric labels: OPLS-DA is used\n      this.mode = 'discriminantAnalysis';\n      group = labels;\n      throw new Error('discriminant analysis is not yet supported');\n    }\n\n    // check types of features and labels\n    if (features.constructor.name !== 'Matrix') {\n      throw new TypeError('features must be of class Matrix');\n    }\n    // getting center and scale the features (all)\n    this.center = center;\n    if (this.center) {\n      this.means = features.mean('column');\n      this.meansY = group.mean('column');\n    } else {\n      this.stdevs = null;\n    }\n    this.scale = scale;\n    if (this.scale) {\n      this.stdevs = features.standardDeviation('column');\n      this.stdevsY = group.standardDeviation('column');\n    } else {\n      this.means = null;\n    }\n\n    // check and remove for features with sd = 0 TODO here\n    // check opls.R line 70\n\n    let folds;\n    if (cvFolds.length > 0) {\n      folds = cvFolds;\n    } else {\n      folds = getFolds(labels, 5);\n    }\n\n    let Q2 = [];\n    this.model = [];\n\n    this.tCV = [];\n    this.tOrthCV = [];\n    this.yHatCV = [];\n    let oplsCV = [];\n\n    let modelNC = [];\n\n    // this code could be made more efficient by reverting the order of the loops\n    // this is a legacy loop to be consistent with R code from MetaboMate package\n    // this allows for having statistic (R2) from CV to decide wether to continue\n    // with more latent structures\n    let nc;\n    for (nc = 0; nc < nComp; nc++) {\n      let yHatk = new Matrix(group.rows, 1);\n      let tPredk = new Matrix(group.rows, 1);\n      let tOrthk = new Matrix(group.rows, 1);\n      let oplsk = [];\n\n      let f = 0;\n      for (let fold of folds) {\n        let trainTest = this._getTrainTest(features, group, fold);\n        let testXk = trainTest.testFeatures;\n        let Xk = trainTest.trainFeatures;\n        let Yk = trainTest.trainLabels;\n\n        // determine center and scale of training set\n        let dataCenter = Xk.mean('column');\n        let dataSD = Xk.standardDeviation('column');\n\n        // center and scale training set\n        if (center) {\n          Xk.center('column');\n          Yk.center('column');\n        }\n\n        if (scale) {\n          Xk.scale('column');\n          Yk.scale('column');\n        }\n\n        // perform opls\n        if (nc === 0) {\n          oplsk[f] = OPLSNipals(Xk, Yk);\n        } else {\n          oplsk[f] = OPLSNipals(oplsCV[nc - 1][f].filteredX, Yk);\n        }\n        // store model for next component\n        oplsCV[nc] = oplsk;\n\n        let plsCV = new NIPALS(oplsk[f].filteredX, { Y: Yk });\n\n        // scaling the test dataset with respect to the train\n        testXk.center('column', { center: dataCenter });\n        testXk.scale('column', { scale: dataSD });\n\n        let Eh = testXk;\n        // removing the orthogonal components from PLS\n        let scores;\n        for (let idx = 0; idx < nc + 1; idx++) {\n          scores = Eh.mmul(oplsCV[idx][f].weightsXOrtho.transpose()); // ok\n          Eh.sub(scores.mmul(oplsCV[idx][f].loadingsXOrtho));\n        }\n\n        // prediction\n        let tPred = Eh.mmul(plsCV.w.transpose());\n        // this should be summed over ncomp (pls_prediction.R line 23)\n        let yHat = tPred.mmul(plsCV.betas); // ok\n\n        // adding all prediction from all folds\n        for (let i = 0; i < fold.testIndex.length; i++) {\n          yHatk.setRow(fold.testIndex[i], [yHat.get(i, 0)]);\n          tPredk.setRow(fold.testIndex[i], [tPred.get(i, 0)]);\n          tOrthk.setRow(fold.testIndex[i], [scores.get(i, 0)]);\n        }\n        f++;\n      } // end of loop over folds\n\n      this.tCV.push(tPredk);\n      this.tOrthCV.push(tOrthk);\n      this.yHatCV.push(yHatk);\n\n      // calculate Q2y for all the prediction (all folds)\n      // ROC for DA is not implemented (check opls.R line 183) TODO\n      if (this.mode === 'regression') {\n        let tssy = tss(group.center('column').scale('column'));\n        let press = tss(group.clone().sub(yHatk));\n        let Q2y = 1 - press / tssy;\n        Q2.push(Q2y);\n      } else if (this.mode === 'discriminantAnalysis') {\n        throw new Error('discriminant analysis is not yet supported');\n      }\n\n      // calculate the R2y for the complete data\n      if (nc === 0) {\n        modelNC = this._predictAll(features, group);\n      } else {\n        modelNC = this._predictAll(\n          modelNC.xRes,\n          group,\n          (options = { scale: false, center: false }),\n        );\n      }\n\n      // adding the predictive statistics from CV\n      modelNC.Q2y = Q2;\n      // store the model for each component\n      this.model.push(modelNC);\n      // console.warn(`OPLS iteration over # of Components: ${nc + 1}`);\n    } // end of loop over nc\n\n    // store scores from CV\n    let tCV = this.tCV;\n    let tOrthCV = this.tOrthCV;\n\n    let m = this.model[nc - 1];\n    let XOrth = m.XOrth;\n    let FeaturesCS = features.center('column').scale('column');\n    let labelsCS = group.center('column').scale('column');\n    let Xres = FeaturesCS.clone().sub(XOrth);\n    let plsCall = new NIPALS(Xres, { Y: labelsCS });\n    let E = Xres.clone().sub(plsCall.t.mmul(plsCall.p));\n\n    let R2x = this.model.map((x) => x.R2x);\n    let R2y = this.model.map((x) => x.R2y);\n\n    this.output = {\n      Q2y: Q2,\n      R2x,\n      R2y,\n      tPred: m.plsC.t,\n      pPred: m.plsC.p,\n      wPred: m.plsC.w,\n      betasPred: m.plsC.betas,\n      Qpc: m.plsC.q,\n      tCV,\n      tOrthCV,\n      tOrth: m.tOrth,\n      pOrth: m.pOrth,\n      wOrth: m.wOrth,\n      XOrth,\n      yHat: m.totalPred,\n      Yres: m.plsC.yResidual,\n      E,\n    };\n  }\n\n  /**\n   * get access to all the computed elements\n   * Mainly for debug and testing\n   * @return {Object} output object\n   */\n  getLogs() {\n    return this.output;\n  }\n\n  getScores() {\n    let scoresX = this.tCV.map((x) => x.to1DArray());\n    let scoresY = this.tOrthCV.map((x) => x.to1DArray());\n    return { scoresX, scoresY };\n  }\n\n  /**\n   * Load an OPLS model from JSON\n   * @param {Object} model\n   * @return {OPLS}\n   */\n  static load(model) {\n    if (typeof model.name !== 'string') {\n      throw new TypeError('model must have a name property');\n    }\n    if (model.name !== 'OPLS') {\n      throw new RangeError(`invalid model: ${model.name}`);\n    }\n    return new OPLS(true, [], model);\n  }\n\n  /**\n   * Export the current model to a JSON object\n   * @return {Object} model\n   */\n  toJSON() {\n    return {\n      name: 'OPLS',\n      center: this.center,\n      scale: this.scale,\n      means: this.means,\n      stdevs: this.stdevs,\n      model: this.model,\n      tCV: this.tCV,\n      tOrthCV: this.tOrthCV,\n      yHatCV: this.yHatCV,\n    };\n  }\n\n  /**\n   * Predict scores for new data\n   * @param {Matrix} features - a matrix containing new data\n   * @param {Object} [options]\n   * @param {Array} [options.trueLabel] - an array with true values to compute confusion matrix\n   * @param {Number} [options.nc] - the number of components to be used\n   * @return {Object} - predictions\n   */\n  predict(newData, options = {}) {\n    let { trueLabels = [], nc = 1 } = options;\n    let labels = [];\n    if (trueLabels.length > 0) {\n      trueLabels = Matrix.from1DArray(trueLabels.length, 1, trueLabels);\n      labels = trueLabels.clone();\n    }\n\n    let features = newData.clone();\n\n    // scaling the test dataset with respect to the train\n    if (this.center) {\n      features.center('column', { center: this.means });\n      if (labels.rows > 0 && this.mode === 'regression') {\n        labels.center('column', { center: this.meansY });\n      }\n    }\n    if (this.scale) {\n      features.scale('column', { scale: this.stdevs });\n      if (labels.rows > 0 && this.mode === 'regression') {\n        labels.scale('column', { scale: this.stdevsY });\n      }\n    }\n\n    let Eh = features.clone();\n    // removing the orthogonal components from PLS\n    let tOrth;\n    let wOrth;\n    let pOrth;\n    let yHat;\n    let tPred;\n\n    for (let idx = 0; idx < nc; idx++) {\n      wOrth = this.model[idx].wOrth.transpose();\n      pOrth = this.model[idx].pOrth;\n      tOrth = Eh.mmul(wOrth);\n      Eh.sub(tOrth.mmul(pOrth));\n      // prediction\n      tPred = Eh.mmul(this.model[idx].plsC.w.transpose());\n      // this should be summed over ncomp (pls_prediction.R line 23)\n      yHat = tPred.mmul(this.model[idx].plsC.betas);\n    }\n\n    if (labels.rows > 0) {\n      if (this.mode === 'regression') {\n        let tssy = tss(labels);\n        let press = tss(labels.clone().sub(yHat));\n        let Q2y = 1 - press / tssy;\n\n        return { tPred, tOrth, yHat, Q2y };\n      } else if (this.mode === 'discriminantAnalysis') {\n        let confusionMatrix = [];\n        confusionMatrix = ConfusionMatrix.fromLabels(\n          trueLabels.to1DArray(),\n          yHat.to1DArray(),\n        );\n\n        return { tPred, tOrth, yHat, confusionMatrix };\n      }\n    } else {\n      return { tPred, tOrth, yHat };\n    }\n  }\n\n  _predictAll(features, labels, options = {}) {\n    // cannot use the global this.center here\n    // since it is used in the NC loop and\n    // centering and scaling should only be\n    // performed once\n    const { center = true, scale = true } = options;\n\n    if (center) {\n      features.center('column');\n      labels.center('column');\n    }\n\n    if (scale) {\n      features.scale('column');\n      labels.scale('column');\n      // reevaluate tssy and tssx after scaling\n      // must be global because re-used for next nc iteration\n      // tssx is only evaluate the first time\n      this.tssy = tss(labels);\n      this.tssx = tss(features);\n    }\n\n    let oplsC = OPLSNipals(features, labels);\n    let plsC = new NIPALS(oplsC.filteredX, { Y: labels });\n\n    let tPred = oplsC.filteredX.mmul(plsC.w.transpose());\n    let yHat = tPred.mmul(plsC.betas);\n\n    let rss = tss(labels.clone().sub(yHat));\n    let R2y = 1 - rss / this.tssy;\n\n    let xEx = plsC.t.mmul(plsC.p);\n    let rssx = tss(xEx);\n    let R2x = rssx / this.tssx;\n\n    return {\n      R2y,\n      R2x,\n      xRes: oplsC.filteredX,\n      tOrth: oplsC.scoresXOrtho,\n      pOrth: oplsC.loadingsXOrtho,\n      wOrth: oplsC.weightsXOrtho,\n      tPred: tPred,\n      totalPred: yHat,\n      XOrth: oplsC.scoresXOrtho.mmul(oplsC.loadingsXOrtho),\n      oplsC,\n      plsC,\n    };\n  }\n  /**\n   *\n   * @param {*} X - dataset matrix object\n   * @param {*} group - labels matrix object\n   * @param {*} index - train and test index (output from getFold())\n   */\n  _getTrainTest(X, group, index) {\n    let testFeatures = new Matrix(index.testIndex.length, X.columns);\n    let testLabels = new Matrix(index.testIndex.length, 1);\n    index.testIndex.forEach((el, idx) => {\n      testFeatures.setRow(idx, X.getRow(el));\n      testLabels.setRow(idx, group.getRow(el));\n    });\n\n    let trainFeatures = new Matrix(index.trainIndex.length, X.columns);\n    let trainLabels = new Matrix(index.trainIndex.length, 1);\n    index.trainIndex.forEach((el, idx) => {\n      trainFeatures.setRow(idx, X.getRow(el));\n      trainLabels.setRow(idx, group.getRow(el));\n    });\n\n    return {\n      trainFeatures,\n      testFeatures,\n      trainLabels,\n      testLabels,\n    };\n  }\n}\n","'use strict';\n\nvar mlMatrix = require('ml-matrix');\n\nfunction logistic(val) {\n  return 1 / (1 + Math.exp(-val));\n}\n\nfunction expELU(val, param) {\n  return val < 0 ? param * (Math.exp(val) - 1) : val;\n}\n\nfunction softExponential(val, param) {\n  if (param < 0) {\n    return -Math.log(1 - param * (val + param)) / param;\n  }\n  if (param > 0) {\n    return ((Math.exp(param * val) - 1) / param) + param;\n  }\n  return val;\n}\n\nfunction softExponentialPrime(val, param) {\n  if (param < 0) {\n    return 1 / (1 - param * (param + val));\n  } else {\n    return Math.exp(param * val);\n  }\n}\n\nconst ACTIVATION_FUNCTIONS = {\n  tanh: {\n    activation: Math.tanh,\n    derivate: (val) => 1 - (val * val)\n  },\n  identity: {\n    activation: (val) => val,\n    derivate: () => 1\n  },\n  logistic: {\n    activation: logistic,\n    derivate: (val) => logistic(val) * (1 - logistic(val))\n  },\n  arctan: {\n    activation: Math.atan,\n    derivate: (val) => 1 / (val * val + 1)\n  },\n  softsign: {\n    activation: (val) => val / (1 + Math.abs(val)),\n    derivate: (val) => 1 / ((1 + Math.abs(val)) * (1 + Math.abs(val)))\n  },\n  relu: {\n    activation: (val) => (val < 0 ? 0 : val),\n    derivate: (val) => (val < 0 ? 0 : 1)\n  },\n  softplus: {\n    activation: (val) => Math.log(1 + Math.exp(val)),\n    derivate: (val) => 1 / (1 + Math.exp(-val))\n  },\n  bent: {\n    activation: (val) => ((Math.sqrt(val * val + 1) - 1) / 2) + val,\n    derivate: (val) => (val / (2 * Math.sqrt(val * val + 1))) + 1\n  },\n  sinusoid: {\n    activation: Math.sin,\n    derivate: Math.cos\n  },\n  sinc: {\n    activation: (val) => (val === 0 ? 1 : Math.sin(val) / val),\n    derivate: (val) => (val === 0 ? 0 : (Math.cos(val) / val) - (Math.sin(val) / (val * val)))\n  },\n  gaussian: {\n    activation: (val) => Math.exp(-(val * val)),\n    derivate: (val) => -2 * val * Math.exp(-(val * val))\n  },\n  'parametric-relu': {\n    activation: (val, param) => (val < 0 ? param * val : val),\n    derivate: (val, param) => (val < 0 ? param : 1)\n  },\n  'exponential-elu': {\n    activation: expELU,\n    derivate: (val, param) => (val < 0 ? expELU(val, param) + param : 1)\n  },\n  'soft-exponential': {\n    activation: softExponential,\n    derivate: softExponentialPrime\n  }\n};\n\nclass Layer {\n  /**\n     * @private\n     * Create a new layer with the given options\n     * @param {object} options\n     * @param {number} [options.inputSize] - Number of conections that enter the neurons.\n     * @param {number} [options.outputSize] - Number of conections that leave the neurons.\n     * @param {number} [options.regularization] - Regularization parameter.\n     * @param {number} [options.epsilon] - Learning rate parameter.\n     * @param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.\n     * @param {number} [options.activationParam] - Activation parameter if needed.\n     */\n  constructor(options) {\n    this.inputSize = options.inputSize;\n    this.outputSize = options.outputSize;\n    this.regularization = options.regularization;\n    this.epsilon = options.epsilon;\n    this.activation = options.activation;\n    this.activationParam = options.activationParam;\n\n    var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];\n    var params = selectedFunction.activation.length;\n\n    var actFunction = params > 1 ? (val) => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;\n    var derFunction = params > 1 ? (val) => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, actFunction(this.get(i, j)));\n    };\n    this.derivate = function (i, j) {\n      this.set(i, j, derFunction(this.get(i, j)));\n    };\n\n    if (options.model) {\n      // load model\n      this.W = mlMatrix.Matrix.checkMatrix(options.W);\n      this.b = mlMatrix.Matrix.checkMatrix(options.b);\n    } else {\n      // default constructor\n      this.W = mlMatrix.Matrix.rand(this.inputSize, this.outputSize);\n      this.b = mlMatrix.Matrix.zeros(1, this.outputSize);\n\n      this.W.apply(function (i, j) {\n        this.set(i, j, this.get(i, j) / Math.sqrt(options.inputSize));\n      });\n    }\n  }\n\n  /**\n     * @private\n     * propagate the given input through the current layer.\n     * @param {Matrix} X - input.\n     * @return {Matrix} output at the current layer.\n     */\n  forward(X) {\n    var z = X.mmul(this.W).addRowVector(this.b);\n    z.apply(this.activationFunction);\n    this.a = z.clone();\n    return z;\n  }\n\n  /**\n     * @private\n     * apply backpropagation algorithm at the current layer\n     * @param {Matrix} delta - delta values estimated at the following layer.\n     * @param {Matrix} a - 'a' values from the following layer.\n     * @return {Matrix} the new delta values for the next layer.\n     */\n  backpropagation(delta, a) {\n    this.dW = a.transpose().mmul(delta);\n    this.db = mlMatrix.Matrix.rowVector(delta.sum('column'));\n\n    var aCopy = a.clone();\n    return delta.mmul(this.W.transpose()).mul(aCopy.apply(this.derivate));\n  }\n\n  /**\n     * @private\n     * Function that updates the weights at the current layer with the derivatives.\n     */\n  update() {\n    this.dW.add(this.W.clone().mul(this.regularization));\n    this.W.add(this.dW.mul(-this.epsilon));\n    this.b.add(this.db.mul(-this.epsilon));\n  }\n\n  /**\n     * @private\n     * Export the current layer to JSON.\n     * @return {object} model\n     */\n  toJSON() {\n    return {\n      model: 'Layer',\n      inputSize: this.inputSize,\n      outputSize: this.outputSize,\n      regularization: this.regularization,\n      epsilon: this.epsilon,\n      activation: this.activation,\n      W: this.W,\n      b: this.b\n    };\n  }\n\n  /**\n     * @private\n     * Creates a new Layer with the given model.\n     * @param {object} model\n     * @return {Layer}\n     */\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n    return new Layer(model);\n  }\n}\n\nclass OutputLayer extends Layer {\n  constructor(options) {\n    super(options);\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, Math.exp(this.get(i, j)));\n    };\n  }\n\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n\n    return new OutputLayer(model);\n  }\n}\n\nclass FeedForwardNeuralNetworks {\n  /**\n   * Create a new Feedforward neural network model.\n   * @class FeedForwardNeuralNetworks\n   * @param {object} [options]\n   * @param {Array} [options.hiddenLayers=[10]] - Array that contains the sizes of the hidden layers.\n   * @param {number} [options.iterations=50] - Number of iterations at the training step.\n   * @param {number} [options.learningRate=0.01] - Learning rate of the neural net (also known as epsilon).\n   * @param {number} [options.regularization=0.01] - Regularization parameter af the neural net.\n   * @param {string} [options.activation='tanh'] - activation function to be used. (options: 'tanh'(default),\n   * 'identity', 'logistic', 'arctan', 'softsign', 'relu', 'softplus', 'bent', 'sinusoid', 'sinc', 'gaussian').\n   * (single-parametric options: 'parametric-relu', 'exponential-relu', 'soft-exponential').\n   * @param {number} [options.activationParam=1] - if the selected activation function needs a parameter.\n   */\n  constructor(options) {\n    options = options || {};\n    if (options.model) {\n      // load network\n      this.hiddenLayers = options.hiddenLayers;\n      this.iterations = options.iterations;\n      this.learningRate = options.learningRate;\n      this.regularization = options.regularization;\n      this.dicts = options.dicts;\n      this.activation = options.activation;\n      this.activationParam = options.activationParam;\n      this.model = new Array(options.layers.length);\n\n      for (var i = 0; i < this.model.length - 1; ++i) {\n        this.model[i] = Layer.load(options.layers[i]);\n      }\n      this.model[this.model.length - 1] = OutputLayer.load(options.layers[this.model.length - 1]);\n    } else {\n      // default constructor\n      this.hiddenLayers = options.hiddenLayers || [10];\n      this.iterations = options.iterations || 50;\n\n      this.learningRate = options.learningRate || 0.01;\n      this.regularization = options.regularization || 0.01;\n\n      this.activation = options.activation || 'tanh';\n      this.activationParam = options.activationParam || 1;\n      if (!(this.activation in Object.keys(ACTIVATION_FUNCTIONS))) {\n        this.activation = 'tanh';\n      }\n    }\n  }\n\n  /**\n   * @private\n   * Function that build and initialize the neural net.\n   * @param {number} inputSize - total of features to fit.\n   * @param {number} outputSize - total of labels of the prediction set.\n   */\n  buildNetwork(inputSize, outputSize) {\n    var size = 2 + (this.hiddenLayers.length - 1);\n    this.model = new Array(size);\n\n    // input layer\n    this.model[0] = new Layer({\n      inputSize: inputSize,\n      outputSize: this.hiddenLayers[0],\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n\n    // hidden layers\n    for (var i = 1; i < this.hiddenLayers.length; ++i) {\n      this.model[i] = new Layer({\n        inputSize: this.hiddenLayers[i - 1],\n        outputSize: this.hiddenLayers[i],\n        activation: this.activation,\n        activationParam: this.activationParam,\n        regularization: this.regularization,\n        epsilon: this.learningRate\n      });\n    }\n\n    // output layer\n    this.model[size - 1] = new OutputLayer({\n      inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],\n      outputSize: outputSize,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n  }\n\n  /**\n   * Train the neural net with the given features and labels.\n   * @param {Matrix|Array} features\n   * @param {Matrix|Array} labels\n   */\n  train(features, labels) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    this.dicts = dictOutputs(labels);\n\n    var inputSize = features.columns;\n    var outputSize = Object.keys(this.dicts.inputs).length;\n\n    if (!this.model) {\n      this.buildNetwork(inputSize, outputSize);\n    }\n\n    for (var i = 0; i < this.iterations; ++i) {\n      var probabilities = this.propagate(features);\n      this.backpropagation(features, labels, probabilities);\n    }\n  }\n\n  /**\n   * @private\n   * Propagate the input(training set) and retrives the probabilities of each class.\n   * @param {Matrix} X\n   * @return {Matrix} probabilities of each class.\n   */\n  propagate(X) {\n    var input = X;\n    for (var i = 0; i < this.model.length; ++i) {\n      input = this.model[i].forward(input);\n    }\n\n    // get probabilities\n    return input.divColumnVector(input.sum('row'));\n  }\n\n  /**\n   * @private\n   * Function that applies the backpropagation algorithm on each layer of the network\n   * in order to fit the features and labels.\n   * @param {Matrix} features\n   * @param {Array} labels\n   * @param {Matrix} probabilities - probabilities of each class of the feature set.\n   */\n  backpropagation(features, labels, probabilities) {\n    for (var i = 0; i < probabilities.rows; ++i) {\n      probabilities.set(i, this.dicts.inputs[labels[i]], probabilities.get(i, this.dicts.inputs[labels[i]]) - 1);\n    }\n\n    // remember, the last delta doesn't matter\n    var delta = probabilities;\n    for (i = this.model.length - 1; i >= 0; --i) {\n      var a = i > 0 ? this.model[i - 1].a : features;\n      delta = this.model[i].backpropagation(delta, a);\n    }\n\n    for (i = 0; i < this.model.length; ++i) {\n      this.model[i].update();\n    }\n  }\n\n  /**\n   * Predict the output given the feature set.\n   * @param {Array|Matrix} features\n   * @return {Array}\n   */\n  predict(features) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    var outputs = new Array(features.rows);\n    var probabilities = this.propagate(features);\n    for (var i = 0; i < features.rows; ++i) {\n      outputs[i] = this.dicts.outputs[probabilities.maxRowIndex(i)[1]];\n    }\n\n    return outputs;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} model\n   */\n  toJSON() {\n    var model = {\n      model: 'FNN',\n      hiddenLayers: this.hiddenLayers,\n      iterations: this.iterations,\n      learningRate: this.learningRate,\n      regularization: this.regularization,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      dicts: this.dicts,\n      layers: new Array(this.model.length)\n    };\n\n    for (var i = 0; i < this.model.length; ++i) {\n      model.layers[i] = this.model[i].toJSON();\n    }\n\n    return model;\n  }\n\n  /**\n   * Load a Feedforward Neural Network with the current model.\n   * @param {object} model\n   * @return {FeedForwardNeuralNetworks}\n   */\n  static load(model) {\n    if (model.model !== 'FNN') {\n      throw new RangeError('the current model is not a feed forward network');\n    }\n\n    return new FeedForwardNeuralNetworks(model);\n  }\n}\n\n/**\n * @private\n * Method that given an array of labels(predictions), returns two dictionaries, one to transform from labels to\n * numbers and other in the reverse way\n * @param {Array} array\n * @return {object}\n */\nfunction dictOutputs(array) {\n  var inputs = {};\n  var outputs = {};\n  var index = 0;\n  for (var i = 0; i < array.length; i += 1) {\n    if (inputs[array[i]] === undefined) {\n      inputs[array[i]] = index;\n      outputs[index] = array[i];\n      index++;\n    }\n  }\n\n  return {\n    inputs: inputs,\n    outputs: outputs\n  };\n}\n\nmodule.exports = FeedForwardNeuralNetworks;\n","function NodeSquare(x, y, weights, som) {\n    this.x = x;\n    this.y = y;\n    this.weights = weights;\n    this.som = som;\n    this.neighbors = {};\n}\n\nNodeSquare.prototype.adjustWeights = function adjustWeights(target, learningRate, influence) {\n    for (var i = 0, ii = this.weights.length; i < ii; i++) {\n        this.weights[i] += learningRate * influence * (target[i] - this.weights[i]);\n    }\n};\n\nNodeSquare.prototype.getDistance = function getDistance(otherNode) {\n    return Math.max(Math.abs(this.x - otherNode.x), Math.abs(this.y - otherNode.y));\n};\n\nNodeSquare.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.x - otherNode.x),\n        distY = Math.abs(this.y - otherNode.y);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY));\n};\n\nNodeSquare.prototype.getNeighbors = function getNeighbors(xy) {\n    if (!this.neighbors[xy]) {\n        this.neighbors[xy] = new Array(2);\n\n        // left or bottom neighbor\n        var v;\n        if (this[xy] > 0) {\n            v = this[xy] - 1;\n        } else if (this.som.torus) {\n            v = this.som.gridDim[xy] - 1\n        }\n        if (typeof v !== 'undefined') {\n            var x, y;\n            if (xy === 'x') {\n                x = v;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = v;\n            }\n            this.neighbors[xy][0] = this.som.nodes[x][y];\n        }\n\n        // top or right neighbor\n        var w;\n        if (this[xy] < (this.som.gridDim[xy] - 1)) {\n            w = this[xy] + 1;\n        } else if (this.som.torus) {\n            w = 0;\n        }\n        if (typeof w !== 'undefined') {\n            if (xy === 'x') {\n                x = w;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = w;\n            }\n            this.neighbors[xy][1] = this.som.nodes[x][y];\n        }\n    }\n    return this.neighbors[xy];\n};\n\nNodeSquare.prototype.getPos = function getPos(xy, element) {\n    var neighbors = this.getNeighbors(xy),\n        distance = this.som.distance,\n        bestNeighbor,\n        direction;\n    if(neighbors[0]) {\n        if (neighbors[1]) {\n            var dist1 = distance(element, neighbors[0].weights),\n                dist2 = distance(element, neighbors[1].weights);\n            if(dist1 < dist2) {\n                bestNeighbor = neighbors[0];\n                direction = -1;\n            } else {\n                bestNeighbor = neighbors[1];\n                direction = 1;\n            }\n        } else {\n            bestNeighbor = neighbors[0];\n            direction = -1;\n        }\n    } else {\n        bestNeighbor = neighbors[1];\n        direction = 1;\n    }\n    var simA = 1 - distance(element, this.weights),\n        simB = 1 - distance(element, bestNeighbor.weights);\n    var factor = ((simA - simB) / (2 - simA - simB));\n    return 0.5 + 0.5 * factor * direction;\n};\n\nNodeSquare.prototype.getPosition = function getPosition(element) {\n    return [\n        this.getPos('x', element),\n        this.getPos('y', element)\n    ];\n};\n\nmodule.exports = NodeSquare;","var NodeSquare = require('./node-square');\n\nfunction NodeHexagonal(x, y, weights, som) {\n\n    NodeSquare.call(this, x, y, weights, som);\n\n    this.hX = x - Math.floor(y / 2);\n    this.z = 0 - this.hX - y;\n\n}\n\nNodeHexagonal.prototype = new NodeSquare;\nNodeHexagonal.prototype.constructor = NodeHexagonal;\n\nNodeHexagonal.prototype.getDistance = function getDistanceHexagonal(otherNode) {\n    return Math.max(Math.abs(this.hX - otherNode.hX), Math.abs(this.y - otherNode.y), Math.abs(this.z - otherNode.z));\n};\n\nNodeHexagonal.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.hX - otherNode.hX),\n        distY = Math.abs(this.y - otherNode.y),\n        distZ = Math.abs(this.z - otherNode.z);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY), Math.min(distZ, this.som.gridDim.z - distZ));\n};\n\nNodeHexagonal.prototype.getPosition = function getPosition() {\n    throw new Error('Unimplemented : cannot get position of the points for hexagonal grid');\n};\n\nmodule.exports = NodeHexagonal;","'use strict';\n\nvar NodeSquare = require('./node-square'),\n    NodeHexagonal = require('./node-hexagonal');\n\nvar defaultOptions = {\n    fields: 3,\n    randomizer: Math.random,\n    distance: squareEuclidean,\n    iterations: 10,\n    learningRate: 0.1,\n    gridType: 'rect',\n    torus: true,\n    method: 'random'\n};\n\nfunction SOM(x, y, options, reload) {\n\n    this.x = x;\n    this.y = y;\n\n    options = options || {};\n    this.options = {};\n    for (var i in defaultOptions) {\n        if (options.hasOwnProperty(i)) {\n            this.options[i] = options[i];\n        } else {\n            this.options[i] = defaultOptions[i];\n        }\n    }\n\n    if (typeof this.options.fields === 'number') {\n        this.numWeights = this.options.fields;\n    } else if (Array.isArray(this.options.fields)) {\n        this.numWeights = this.options.fields.length;\n        var converters = getConverters(this.options.fields);\n        this.extractor = converters.extractor;\n        this.creator = converters.creator;\n    } else {\n        throw new Error('Invalid fields definition');\n    }\n\n    if (this.options.gridType === 'rect') {\n        this.nodeType = NodeSquare;\n        this.gridDim = {\n            x: x,\n            y: y\n        };\n    } else {\n        this.nodeType = NodeHexagonal;\n        var hx = this.x - Math.floor(this.y / 2);\n        this.gridDim = {\n            x: hx,\n            y: this.y,\n            z: -(0 - hx - this.y)\n        };\n    }\n\n    this.torus = this.options.torus;\n    this.distanceMethod = this.torus ? 'getDistanceTorus' : 'getDistance';\n\n    this.distance = this.options.distance;\n\n    this.maxDistance = getMaxDistance(this.distance, this.numWeights);\n\n    if (reload === true) { // For model loading\n        this.done = true;\n        return;\n    }\n    if (!(x > 0 && y > 0)) {\n        throw new Error('x and y must be positive');\n    }\n\n    this.times = {\n        findBMU: 0,\n        adjust: 0\n    };\n\n    this.randomizer = this.options.randomizer;\n\n    this.iterationCount = 0;\n    this.iterations = this.options.iterations;\n\n    this.startLearningRate = this.learningRate = this.options.learningRate;\n\n    this.mapRadius = Math.floor(Math.max(x, y) / 2);\n\n    this.algorithmMethod = this.options.method;\n\n    this._initNodes();\n\n    this.done = false;\n}\n\nSOM.load = function loadModel(model, distance) {\n    if (model.name === 'SOM') {\n        var x = model.data.length,\n            y = model.data[0].length;\n        if (distance) {\n            model.options.distance = distance;\n        } else if (model.options.distance) {\n            model.options.distance = eval('(' + model.options.distance + ')');\n        }\n        var som = new SOM(x, y, model.options, true);\n        som.nodes = new Array(x);\n        for (var i = 0; i < x; i++) {\n            som.nodes[i] = new Array(y);\n            for (var j = 0; j < y; j++) {\n                som.nodes[i][j] = new som.nodeType(i, j, model.data[i][j], som);\n            }\n        }\n        return som;\n    } else {\n        throw new Error('expecting a SOM model');\n    }\n};\n\nSOM.prototype.export = function exportModel(includeDistance) {\n    if (!this.done) {\n        throw new Error('model is not ready yet');\n    }\n    var model = {\n        name: 'SOM'\n    };\n    model.options = {\n        fields: this.options.fields,\n        gridType: this.options.gridType,\n        torus: this.options.torus\n    };\n    model.data = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        model.data[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            model.data[i][j] = this.nodes[i][j].weights;\n        }\n    }\n    if (includeDistance) {\n        model.options.distance = this.distance.toString();\n    }\n    return model;\n};\n\nSOM.prototype._initNodes = function initNodes() {\n    var now = Date.now(),\n        i, j, k;\n    this.nodes = new Array(this.x);\n    for (i = 0; i < this.x; i++) {\n        this.nodes[i] = new Array(this.y);\n        for (j = 0; j < this.y; j++) {\n            var weights = new Array(this.numWeights);\n            for (k = 0; k < this.numWeights; k++) {\n                weights[k] = this.randomizer();\n            }\n            this.nodes[i][j] = new this.nodeType(i, j, weights, this);\n        }\n    }\n    this.times.initNodes = Date.now() - now;\n};\n\nSOM.prototype.setTraining = function setTraining(trainingSet) {\n    if (this.trainingSet) {\n        throw new Error('training set has already been set');\n    }\n    var now = Date.now();\n    var convertedSet = trainingSet;\n    var i, l = trainingSet.length;\n    if (this.extractor) {\n        convertedSet = new Array(l);\n        for (i = 0; i < l; i++) {\n            convertedSet[i] = this.extractor(trainingSet[i]);\n        }\n    }\n    this.numIterations = this.iterations * l;\n\n    if (this.algorithmMethod === 'random') {\n        this.timeConstant = this.numIterations / Math.log(this.mapRadius);\n    } else {\n        this.timeConstant = l / Math.log(this.mapRadius);\n    }\n    this.trainingSet = convertedSet;\n    this.times.setTraining = Date.now() - now;\n};\n\nSOM.prototype.trainOne = function trainOne() {\n    if (this.done) {\n\n        return false;\n\n    } else if (this.numIterations-- > 0) {\n\n        var neighbourhoodRadius,\n            trainingValue,\n            trainingSetFactor;\n\n        if (this.algorithmMethod === 'random') { // Pick a random value of the training set at each step\n            neighbourhoodRadius = this.mapRadius * Math.exp(-this.iterationCount / this.timeConstant);\n            trainingValue = getRandomValue(this.trainingSet, this.randomizer);\n            this._adjust(trainingValue, neighbourhoodRadius);\n            this.learningRate = this.startLearningRate * Math.exp(-this.iterationCount / this.numIterations);\n        } else { // Get next input vector\n            trainingSetFactor = -Math.floor(this.iterationCount / this.trainingSet.length);\n            neighbourhoodRadius = this.mapRadius * Math.exp(trainingSetFactor / this.timeConstant);\n            trainingValue = this.trainingSet[this.iterationCount % this.trainingSet.length];\n            this._adjust(trainingValue, neighbourhoodRadius);\n            if (((this.iterationCount + 1) % this.trainingSet.length) === 0) {\n                this.learningRate = this.startLearningRate * Math.exp(trainingSetFactor / Math.floor(this.numIterations / this.trainingSet.length));\n            }\n        }\n\n        this.iterationCount++;\n\n        return true;\n\n    } else {\n\n        this.done = true;\n        return false;\n\n    }\n};\n\nSOM.prototype._adjust = function adjust(trainingValue, neighbourhoodRadius) {\n    var now = Date.now(),\n        x, y, dist, influence;\n\n    var bmu = this._findBestMatchingUnit(trainingValue);\n\n    var now2 = Date.now();\n    this.times.findBMU += now2 - now;\n\n    var radiusLimit = Math.floor(neighbourhoodRadius);\n    var xMin = bmu.x - radiusLimit,\n        xMax = bmu.x + radiusLimit,\n        yMin = bmu.y - radiusLimit,\n        yMax = bmu.y + radiusLimit;\n\n    for (x = xMin; x <= xMax; x++) {\n        var theX = x;\n        if (x < 0) {\n            theX += this.x;\n        } else if (x >= this.x) {\n            theX -= this.x;\n        }\n        for (y = yMin; y <= yMax; y++) {\n            var theY = y;\n            if (y < 0) {\n                theY += this.y;\n            } else if (y >= this.y) {\n                theY -= this.y;\n            }\n\n            dist = bmu[this.distanceMethod](this.nodes[theX][theY]);\n\n            if (dist < neighbourhoodRadius) {\n                influence = Math.exp(-dist / (2 * neighbourhoodRadius));\n                this.nodes[theX][theY].adjustWeights(trainingValue, this.learningRate, influence);\n            }\n\n        }\n    }\n\n    this.times.adjust += (Date.now() - now2);\n\n};\n\nSOM.prototype.train = function train(trainingSet) {\n    if (!this.done) {\n        this.setTraining(trainingSet);\n        while (this.trainOne()) {\n        }\n    }\n};\n\nSOM.prototype.getConvertedNodes = function getConvertedNodes() {\n    var result = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        result[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            var node = this.nodes[i][j];\n            result[i][j] = this.creator ? this.creator(node.weights) : node.weights;\n        }\n    }\n    return result;\n};\n\nSOM.prototype._findBestMatchingUnit = function findBestMatchingUnit(candidate) {\n\n    var bmu,\n        lowest = Infinity,\n        dist;\n\n    for (var i = 0; i < this.x; i++) {\n        for (var j = 0; j < this.y; j++) {\n            dist = this.distance(this.nodes[i][j].weights, candidate);\n            if (dist < lowest) {\n                lowest = dist;\n                bmu = this.nodes[i][j];\n            }\n        }\n    }\n\n    return bmu;\n\n};\n\nSOM.prototype.predict = function predict(data, computePosition) {\n    if (typeof data === 'boolean') {\n        computePosition = data;\n        data = null;\n    }\n    if (!data) {\n        data = this.trainingSet;\n    }\n    if (Array.isArray(data) && (Array.isArray(data[0]) || (typeof data[0] === 'object'))) { // predict a dataset\n        var self = this;\n        return data.map(function (element) {\n            return self._predict(element, computePosition);\n        });\n    } else { // predict a single element\n        return this._predict(data, computePosition);\n    }\n};\n\nSOM.prototype._predict = function _predict(element, computePosition) {\n    if (!Array.isArray(element)) {\n        element = this.extractor(element);\n    }\n    var bmu = this._findBestMatchingUnit(element);\n    var result = [bmu.x, bmu.y];\n    if (computePosition) {\n        result[2] = bmu.getPosition(element);\n    }\n    return result;\n};\n\n// As seen in http://www.scholarpedia.org/article/Kohonen_network\nSOM.prototype.getQuantizationError = function getQuantizationError() {\n    var fit = this.getFit(),\n        l = fit.length,\n        sum = 0;\n    for (var i = 0; i < l; i++) {\n        sum += fit[i];\n    }\n    return sum / l;\n};\n\nSOM.prototype.getFit = function getFit(dataset) {\n    if (!dataset) {\n        dataset = this.trainingSet;\n    }\n    var l = dataset.length,\n        bmu,\n        result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        bmu = this._findBestMatchingUnit(dataset[i]);\n        result[i] = Math.sqrt(this.distance(dataset[i], bmu.weights));\n    }\n    return result;\n};\n\nfunction getConverters(fields) {\n    var l = fields.length,\n        normalizers = new Array(l),\n        denormalizers = new Array(l);\n    for (var i = 0; i < l; i++) {\n        normalizers[i] = getNormalizer(fields[i].range);\n        denormalizers[i] = getDenormalizer(fields[i].range);\n    }\n    return {\n        extractor: function extractor(value) {\n            var result = new Array(l);\n            for (var i = 0; i < l; i++) {\n                result[i] = normalizers[i](value[fields[i].name]);\n            }\n            return result;\n        },\n        creator: function creator(value) {\n            var result = {};\n            for (var i = 0; i < l; i++) {\n                result[fields[i].name] = denormalizers[i](value[i]);\n            }\n            return result;\n        }\n    };\n}\n\nfunction getNormalizer(minMax) {\n    return function normalizer(value) {\n        return (value - minMax[0]) / (minMax[1] - minMax[0]);\n    };\n}\n\nfunction getDenormalizer(minMax) {\n    return function denormalizer(value) {\n        return (minMax[0] + value * (minMax[1] - minMax[0]));\n    };\n}\n\nfunction squareEuclidean(a, b) {\n    var d = 0;\n    for (var i = 0, ii = a.length; i < ii; i++) {\n        d += (a[i] - b[i]) * (a[i] - b[i]);\n    }\n    return d;\n}\n\nfunction getRandomValue(arr, randomizer) {\n    return arr[Math.floor(randomizer() * arr.length)];\n}\n\nfunction getMaxDistance(distance, numWeights) {\n    var zero = new Array(numWeights),\n        one = new Array(numWeights);\n    for (var i = 0; i < numWeights; i++) {\n        zero[i] = 0;\n        one[i] = 1;\n    }\n    return distance(zero, one);\n}\n\nmodule.exports = SOM;","export default function maybeToPrecision(value, digits) {\n  if (value < 0) {\n    value = 0 - value;\n    if (typeof digits === 'number') {\n      return `- ${value.toPrecision(digits)}`;\n    } else {\n      return `- ${value.toString()}`;\n    }\n  } else {\n    if (typeof digits === 'number') {\n      return value.toPrecision(digits);\n    } else {\n      return value.toString();\n    }\n  }\n}\n","export default function checkArraySize(x, y) {\n  if (!Array.isArray(x) || !Array.isArray(y)) {\n    throw new TypeError('x and y must be arrays');\n  }\n  if (x.length !== y.length) {\n    throw new RangeError('x and y arrays must have the same length');\n  }\n}\n","export { default as maybeToPrecision } from './maybeToPrecision';\nexport { default as checkArrayLength } from './checkArrayLength';\n\nexport default class BaseRegression {\n  constructor() {\n    if (new.target === BaseRegression) {\n      throw new Error('BaseRegression must be subclassed');\n    }\n  }\n\n  predict(x) {\n    if (typeof x === 'number') {\n      return this._predict(x);\n    } else if (Array.isArray(x)) {\n      const y = [];\n      for (let i = 0; i < x.length; i++) {\n        y.push(this._predict(x[i]));\n      }\n      return y;\n    } else {\n      throw new TypeError('x must be a number or array');\n    }\n  }\n\n  _predict() {\n    throw new Error('_predict must be implemented');\n  }\n\n  train() {\n    // Do nothing for this package\n  }\n\n  toString() {\n    return '';\n  }\n\n  toLaTeX() {\n    return '';\n  }\n\n  /**\n   * Return the correlation coefficient of determination (r) and chi-square.\n   * @param {Array<number>} x\n   * @param {Array<number>} y\n   * @return {object}\n   */\n  score(x, y) {\n    if (!Array.isArray(x) || !Array.isArray(y) || x.length !== y.length) {\n      throw new Error('x and y must be arrays of the same length');\n    }\n\n    const n = x.length;\n    const y2 = new Array(n);\n    for (let i = 0; i < n; i++) {\n      y2[i] = this._predict(x[i]);\n    }\n\n    let xSum = 0;\n    let ySum = 0;\n    let chi2 = 0;\n    let rmsd = 0;\n    let xSquared = 0;\n    let ySquared = 0;\n    let xY = 0;\n    for (let i = 0; i < n; i++) {\n      xSum += y2[i];\n      ySum += y[i];\n      xSquared += y2[i] * y2[i];\n      ySquared += y[i] * y[i];\n      xY += y2[i] * y[i];\n      if (y[i] !== 0) {\n        chi2 += ((y[i] - y2[i]) * (y[i] - y2[i])) / y[i];\n      }\n      rmsd += (y[i] - y2[i]) * (y[i] - y2[i]);\n    }\n\n    const r =\n      (n * xY - xSum * ySum) /\n      Math.sqrt((n * xSquared - xSum * xSum) * (n * ySquared - ySum * ySum));\n\n    return {\n      r: r,\n      r2: r * r,\n      chi2: chi2,\n      rmsd: Math.sqrt(rmsd / n)\n    };\n  }\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { Matrix, MatrixTransposeView, solve } from 'ml-matrix';\n\nexport default class PolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y, degree);\n    }\n  }\n\n  _predict(x) {\n    let y = 0;\n    for (let k = 0; k < this.powers.length; k++) {\n      y += this.coefficients[k] * Math.pow(x, this.powers[k]);\n    }\n    return y;\n  }\n\n  toJSON() {\n    return {\n      name: 'polynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) + times}x`;\n          } else {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) +\n              times\n              }x${\n                sup\n              }${this.powers[k]\n              }${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'polynomialRegression') {\n      throw new TypeError('not a polynomial regression model');\n    }\n    return new PolynomialRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y, degree) {\n  const n = x.length;\n  let powers;\n  if (Array.isArray(degree)) {\n    powers = degree;\n    degree = powers.length;\n  } else {\n    degree++;\n    powers = new Array(degree);\n    for (let k = 0; k < degree; k++) {\n      powers[k] = k;\n    }\n  }\n  const F = new Matrix(n, degree);\n  const Y = new Matrix([y]);\n  for (let k = 0; k < degree; k++) {\n    for (let i = 0; i < n; i++) {\n      if (powers[k] === 0) {\n        F.set(i, k, 1);\n      } else {\n        F.set(i, k, Math.pow(x[i], powers[k]));\n      }\n    }\n  }\n\n  const FT = new MatrixTransposeView(F);\n  const A = FT.mmul(F);\n  const B = FT.mmul(new MatrixTransposeView(Y));\n\n  pr.degree = degree - 1;\n  pr.powers = powers;\n  pr.coefficients = solve(A, B).to1DArray();\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\n\nexport default class SimpleLinearRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = [y.intercept, y.slope];\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'simpleLinearRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(x) {\n    return this.slope * x + this.intercept;\n  }\n\n  computeX(y) {\n    return (y - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    let result = 'f(x) = ';\n    if (this.slope !== 0) {\n      const xFactor = maybeToPrecision(this.slope, precision);\n      result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;\n      if (this.intercept !== 0) {\n        const absIntercept = Math.abs(this.intercept);\n        const operator = absIntercept === this.intercept ? '+' : '-';\n        result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'simpleLinearRegression') {\n      throw new TypeError('not a SLR model');\n    }\n    return new SimpleLinearRegression(true, json);\n  }\n}\n\nfunction regress(slr, x, y) {\n  const n = x.length;\n  let xSum = 0;\n  let ySum = 0;\n\n  let xSquared = 0;\n  let xY = 0;\n\n  for (let i = 0; i < n; i++) {\n    xSum += x[i];\n    ySum += y[i];\n    xSquared += x[i] * x[i];\n    xY += x[i] * y[i];\n  }\n\n  const numerator = n * xY - xSum * ySum;\n  slr.slope = numerator / (n * xSquared - xSum * xSum);\n  slr.intercept = (1 / n) * ySum - slr.slope * (1 / n) * xSum;\n  slr.coefficients = [slr.intercept, slr.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class ExponentialRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(input) {\n    return this.B * Math.exp(input * this.A);\n  }\n\n  toJSON() {\n    return {\n      name: 'exponentialRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return (\n      `f(x) = ${\n        maybeToPrecision(this.B, precision)\n      } * e^(${\n        maybeToPrecision(this.A, precision)\n      } * x)`\n    );\n  }\n\n  toLaTeX(precision) {\n    if (this.A >= 0) {\n      return (\n        `f(x) = ${\n          maybeToPrecision(this.B, precision)\n        }e^{${\n          maybeToPrecision(this.A, precision)\n        }x}`\n      );\n    } else {\n      return (\n        `f(x) = \\\\frac{${\n          maybeToPrecision(this.B, precision)\n        }}{e^{${\n          maybeToPrecision(-this.A, precision)\n        }x}}`\n      );\n    }\n  }\n\n  static load(json) {\n    if (json.name !== 'exponentialRegression') {\n      throw new TypeError('not a exponential regression model');\n    }\n    return new ExponentialRegression(true, json);\n  }\n}\n\nfunction regress(er, x, y) {\n  const n = x.length;\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(x, yl);\n  er.A = linear.slope;\n  er.B = Math.exp(linear.intercept);\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class PowerRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // reloading model\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(newInputs) {\n    return this.A * Math.pow(newInputs, this.B);\n  }\n\n  toJSON() {\n    return {\n      name: 'powerRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return `f(x) = ${maybeToPrecision(\n      this.A,\n      precision\n    )} * x^${maybeToPrecision(this.B, precision)}`;\n  }\n\n  toLaTeX(precision) {\n    let latex = '';\n    if (this.B >= 0) {\n      latex = `f(x) = ${maybeToPrecision(\n        this.A,\n        precision\n      )}x^{${maybeToPrecision(this.B, precision)}}`;\n    } else {\n      latex = `f(x) = \\\\frac{${maybeToPrecision(\n        this.A,\n        precision\n      )}}{x^{${maybeToPrecision(-this.B, precision)}}}`;\n    }\n    latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');\n    return latex;\n  }\n\n  static load(json) {\n    if (json.name !== 'powerRegression') {\n      throw new TypeError('not a power regression model');\n    }\n    return new PowerRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y) {\n  const n = x.length;\n  const xl = new Array(n);\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    xl[i] = Math.log(x[i]);\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(xl, yl);\n  pr.A = Math.exp(linear.intercept);\n  pr.B = linear.slope;\n}\n","import Matrix, { SVD, pseudoInverse } from 'ml-matrix';\n\nexport default class MultivariateLinearRegression {\n  constructor(x, y, options = {}) {\n    const { intercept = true, statistics = true } = options;\n    this.statistics = statistics;\n    if (x === true) {\n      this.weights = y.weights;\n      this.inputs = y.inputs;\n      this.outputs = y.outputs;\n      this.intercept = y.intercept;\n    } else {\n      x = new Matrix(x);\n      y = new Matrix(y);\n      if (intercept) {\n        x.addColumn(new Array(x.rows).fill(1));\n      }\n      let xt = x.transpose();\n      const xx = xt\n        .mmul(x);\n      const xy = xt\n        .mmul(y);\n      const invxx = new SVD(xx)\n        .inverse();\n      const beta = xy\n        .transpose()\n        .mmul(invxx)\n        .transpose();\n      this.weights = beta.to2DArray();\n      this.inputs = x.columns;\n      this.outputs = y.columns;\n      if (intercept) this.inputs--;\n      this.intercept = intercept;\n      if (statistics) {\n        /*\n         * Let's add some basic statistics about the beta's to be able to interpret them.\n         * source: http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat401/Notes/401-multreg.pdf\n         * validated against Excel Regression AddIn\n         * test: \"datamining statistics test\"\n         */\n        const fittedValues = x.mmul(beta);\n        const residuals = y.clone().addM(fittedValues.neg());\n        const variance =\n          residuals\n            .to2DArray()\n            .map((ri) => Math.pow(ri[0], 2))\n            .reduce((a, b) => a + b) /\n          (y.rows - x.columns);\n        this.stdError = Math.sqrt(variance);\n        this.stdErrorMatrix = pseudoInverse(xx).mul(variance);\n        this.stdErrors = this.stdErrorMatrix\n          .diagonal()\n          .map((d) => Math.sqrt(d));\n        this.tStats = this.weights.map((d, i) =>\n          (this.stdErrors[i] === 0 ? 0 : d[0] / this.stdErrors[i])\n        );\n      }\n    }\n  }\n\n  predict(x) {\n    if (Array.isArray(x)) {\n      if (typeof x[0] === 'number') {\n        return this._predict(x);\n      } else if (Array.isArray(x[0])) {\n        const y = new Array(x.length);\n        for (let i = 0; i < x.length; i++) {\n          y[i] = this._predict(x[i]);\n        }\n        return y;\n      }\n    } else if (Matrix.isMatrix(x)) {\n      const y = new Matrix(x.rows, this.outputs);\n      for (let i = 0; i < x.rows; i++) {\n        y.setRow(i, this._predict(x.getRow(i)));\n      }\n      return y;\n    }\n    throw new TypeError('x must be a matrix or array of numbers');\n  }\n\n  _predict(x) {\n    const result = new Array(this.outputs);\n    if (this.intercept) {\n      for (let i = 0; i < this.outputs; i++) {\n        result[i] = this.weights[this.inputs][i];\n      }\n    } else {\n      result.fill(0);\n    }\n    for (let i = 0; i < this.inputs; i++) {\n      for (let j = 0; j < this.outputs; j++) {\n        result[j] += this.weights[i][j] * x[i];\n      }\n    }\n    return result;\n  }\n\n  score() {\n    throw new Error('score method is not implemented yet');\n  }\n\n  toJSON() {\n    return {\n      name: 'multivariateLinearRegression',\n      weights: this.weights,\n      inputs: this.inputs,\n      outputs: this.outputs,\n      intercept: this.intercept,\n      summary: this.statistics\n        ? {\n          regressionStatistics: {\n            standardError: this.stdError,\n            observations: this.outputs\n          },\n          variables: this.weights.map((d, i) => {\n            return {\n              label:\n                  i === this.weights.length - 1\n                    ? 'Intercept'\n                    : `X Variable ${i + 1}`,\n              coefficients: d,\n              standardError: this.stdErrors[i],\n              tStat: this.tStats[i]\n            };\n          })\n        }\n        : undefined\n    };\n  }\n\n  static load(model) {\n    if (model.name !== 'multivariateLinearRegression') {\n      throw new Error('not a MLR model');\n    }\n    return new MultivariateLinearRegression(true, model);\n  }\n}\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass GaussianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = GaussianKernel;\n","'use strict';\n\nconst defaultOptions = {\n  degree: 1,\n  constant: 1,\n  scale: 1\n};\n\nclass PolynomialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n\n    this.degree = options.degree;\n    this.constant = options.constant;\n    this.scale = options.scale;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.pow(this.scale * sum + this.constant, this.degree);\n  }\n}\n\nmodule.exports = PolynomialKernel;\n","'use strict';\n\nconst defaultOptions = {\n  alpha: 0.01,\n  constant: -Math.E\n};\n\nclass SigmoidKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.alpha = options.alpha;\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.tanh(this.alpha * sum + this.constant);\n  }\n}\n\nmodule.exports = SigmoidKernel;\n","'use strict';\n\nconst defaultOptions = {\n  sigma: 1,\n  degree: 1\n};\n\nclass ANOVAKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.degree = options.degree;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    var len = Math.min(x.length, y.length);\n    for (var i = 1; i <= len; ++i) {\n      sum += Math.pow(\n        Math.exp(\n          -this.sigma *\n            Math.pow(Math.pow(x[i - 1], i) - Math.pow(y[i - 1], i), 2)\n        ),\n        this.degree\n      );\n    }\n    return sum;\n  }\n}\n\nmodule.exports = ANOVAKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass CauchyKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    return 1 / (1 + squaredEuclidean(x, y) / (this.sigma * this.sigma));\n  }\n}\n\nmodule.exports = CauchyKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass ExponentialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = ExponentialKernel;\n","'use strict';\n\nclass HistogramIntersectionKernel {\n  compute(x, y) {\n    var min = Math.min(x.length, y.length);\n    var sum = 0;\n    for (var i = 0; i < min; ++i) {\n      sum += Math.min(x[i], y[i]);\n    }\n\n    return sum;\n  }\n}\n\nmodule.exports = HistogramIntersectionKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass LaplacianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.sigma);\n  }\n}\n\nmodule.exports = LaplacianKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass MultiquadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    return Math.sqrt(squaredEuclidean(x, y) + this.constant * this.constant);\n  }\n}\n\nmodule.exports = MultiquadraticKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass RationalQuadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return 1 - distance / (distance + this.constant);\n  }\n}\n\nmodule.exports = RationalQuadraticKernel;\n","'use strict';\n\nconst { Matrix, MatrixTransposeView } = require('ml-matrix');\nconst GaussianKernel = require('ml-kernel-gaussian');\nconst PolynomialKernel = require('ml-kernel-polynomial');\nconst SigmoidKernel = require('ml-kernel-sigmoid');\n\nconst ANOVAKernel = require('./kernels/anova-kernel');\nconst CauchyKernel = require('./kernels/cauchy-kernel');\nconst ExponentialKernel = require('./kernels/exponential-kernel');\nconst HistogramKernel = require('./kernels/histogram-intersection-kernel');\nconst LaplacianKernel = require('./kernels/laplacian-kernel');\nconst MultiquadraticKernel = require('./kernels/multiquadratic-kernel');\nconst RationalKernel = require('./kernels/rational-quadratic-kernel');\n\nconst kernelType = {\n  gaussian: GaussianKernel,\n  rbf: GaussianKernel,\n  polynomial: PolynomialKernel,\n  poly: PolynomialKernel,\n  anova: ANOVAKernel,\n  cauchy: CauchyKernel,\n  exponential: ExponentialKernel,\n  histogram: HistogramKernel,\n  min: HistogramKernel,\n  laplacian: LaplacianKernel,\n  multiquadratic: MultiquadraticKernel,\n  rational: RationalKernel,\n  sigmoid: SigmoidKernel,\n  mlp: SigmoidKernel\n};\n\nclass Kernel {\n  constructor(type, options) {\n    this.kernelType = type;\n    if (type === 'linear') return;\n\n    if (typeof type === 'string') {\n      type = type.toLowerCase();\n\n      var KernelConstructor = kernelType[type];\n      if (KernelConstructor) {\n        this.kernelFunction = new KernelConstructor(options);\n      } else {\n        throw new Error(`unsupported kernel type: ${type}`);\n      }\n    } else if (typeof type === 'object' && typeof type.compute === 'function') {\n      this.kernelFunction = type;\n    } else {\n      throw new TypeError(\n        'first argument must be a valid kernel type or instance'\n      );\n    }\n  }\n\n  compute(inputs, landmarks) {\n    inputs = Matrix.checkMatrix(inputs);\n    if (landmarks === undefined) {\n      landmarks = inputs;\n    } else {\n      landmarks = Matrix.checkMatrix(landmarks);\n    }\n    if (this.kernelType === 'linear') {\n      return inputs.mmul(new MatrixTransposeView(landmarks));\n    }\n\n    const kernelMatrix = new Matrix(inputs.rows, landmarks.rows);\n    if (inputs === landmarks) {\n      // fast path, matrix is symmetric\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = i; j < inputs.rows; j++) {\n          const value = this.kernelFunction.compute(\n            inputs.getRow(i),\n            inputs.getRow(j)\n          );\n          kernelMatrix.set(i, j, value);\n          kernelMatrix.set(j, i, value);\n        }\n      }\n    } else {\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = 0; j < landmarks.rows; j++) {\n          kernelMatrix.set(\n            i,\n            j,\n            this.kernelFunction.compute(inputs.getRow(i), landmarks.getRow(j))\n          );\n        }\n      }\n    }\n    return kernelMatrix;\n  }\n}\n\nmodule.exports = Kernel;\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport median from 'ml-array-median';\n\nexport default class TheilSenRegression extends BaseRegression {\n  /**\n   * Theil–Sen estimator\n   * https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator\n   * @param {Array<number>|boolean} x\n   * @param {Array<number>|object} y\n   * @constructor\n   */\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // loads the model\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = y.coefficients;\n    } else {\n      // creates the model\n      checkArrayLength(x, y);\n      theilSen(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'TheilSenRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(input) {\n    return this.slope * input + this.intercept;\n  }\n\n  computeX(input) {\n    return (input - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    var result = 'f(x) = ';\n    if (this.slope) {\n      var xFactor = maybeToPrecision(this.slope, precision);\n      result += `${Math.abs(xFactor - 1) < 1e-5 ? '' : `${xFactor} * `}x`;\n      if (this.intercept) {\n        var absIntercept = Math.abs(this.intercept);\n        var operator = absIntercept === this.intercept ? '+' : '-';\n        result +=\n          ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'TheilSenRegression') {\n      throw new TypeError('not a Theil-Sen model');\n    }\n    return new TheilSenRegression(true, json);\n  }\n}\n\nfunction theilSen(regression, x, y) {\n  let len = x.length;\n  let slopes = new Array(len * len);\n  let count = 0;\n  for (let i = 0; i < len; ++i) {\n    for (let j = i + 1; j < len; ++j) {\n      if (x[i] !== x[j]) {\n        slopes[count++] = (y[j] - y[i]) / (x[j] - x[i]);\n      }\n    }\n  }\n  slopes.length = count;\n  let medianSlope = median(slopes);\n\n  let cuts = new Array(len);\n  for (let i = 0; i < len; ++i) {\n    cuts[i] = y[i] - medianSlope * x[i];\n  }\n\n  regression.slope = medianSlope;\n  regression.intercept = median(cuts);\n  regression.coefficients = [regression.intercept, regression.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { solve } from 'ml-matrix';\n\n/**\n * @class RobustPolynomialRegression\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree - polynomial degree\n */\nexport default class RobustPolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      robustPolynomial(this, x, y, degree);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'robustPolynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  _predict(x) {\n    return predict(x, this.powers, this.coefficients);\n  }\n\n  /**\n   * Display the formula\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  /**\n   * Display the formula in LaTeX format\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x`;\n          } else {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x${sup}${this.powers[k]}${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'robustPolynomialRegression') {\n      throw new TypeError('not a RobustPolynomialRegression model');\n    }\n    return new RobustPolynomialRegression(true, json);\n  }\n}\n\nfunction robustPolynomial(regression, x, y, degree) {\n  let powers = Array(degree)\n    .fill(0)\n    .map((_, index) => index);\n\n  const tuples = getRandomTuples(x, y, degree);\n\n  var min;\n  for (var i = 0; i < tuples.length; i++) {\n    var tuple = tuples[i];\n    var coefficients = calcCoefficients(tuple, powers);\n\n    var residuals = x.slice();\n    for (var j = 0; j < x.length; j++) {\n      residuals[j] = y[j] - predict(x[j], powers, coefficients);\n      residuals[j] = {\n        residual: residuals[j] * residuals[j],\n        coefficients\n      };\n    }\n\n    var median = residualsMedian(residuals);\n    if (!min || median.residual < min.residual) {\n      min = median;\n    }\n  }\n\n  regression.degree = degree;\n  regression.powers = powers;\n  regression.coefficients = min.coefficients;\n}\n\n/**\n * @ignore\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree\n * @return {Array<{x:number,y:number}>}\n */\nfunction getRandomTuples(x, y, degree) {\n  var len = Math.floor(x.length / degree);\n  var tuples = new Array(len);\n\n  for (var i = 0; i < x.length; i++) {\n    var pos = Math.floor(Math.random() * len);\n\n    var counter = 0;\n    while (counter < x.length) {\n      if (!tuples[pos]) {\n        tuples[pos] = [\n          {\n            x: x[i],\n            y: y[i]\n          }\n        ];\n        break;\n      } else if (tuples[pos].length < degree) {\n        tuples[pos].push({\n          x: x[i],\n          y: y[i]\n        });\n        break;\n      } else {\n        counter++;\n        pos = (pos + 1) % len;\n      }\n    }\n\n    if (counter === x.length) {\n      return tuples;\n    }\n  }\n  return tuples;\n}\n\n/**\n * @ignore\n * @param {{x:number,y:number}} tuple\n * @param {Array<number>} powers\n * @return {Array<number>}\n */\nfunction calcCoefficients(tuple, powers) {\n  var X = tuple.slice();\n  var Y = tuple.slice();\n  for (var i = 0; i < X.length; i++) {\n    Y[i] = [tuple[i].y];\n    X[i] = new Array(powers.length);\n    for (var j = 0; j < powers.length; j++) {\n      X[i][j] = Math.pow(tuple[i].x, powers[j]);\n    }\n  }\n\n  return solve(X, Y).to1DArray();\n}\n\nfunction predict(x, powers, coefficients) {\n  let y = 0;\n  for (let k = 0; k < powers.length; k++) {\n    y += coefficients[k] * Math.pow(x, powers[k]);\n  }\n  return y;\n}\n\nfunction residualsMedian(residuals) {\n  residuals.sort((a, b) => a.residual - b.residual);\n\n  var l = residuals.length;\n  var half = Math.floor(l / 2);\n  return l % 2 === 0 ? residuals[half - 1] : residuals[half];\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","/**\n * Calculate current error\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} parameters - Array of current parameter values\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {number}\n */\nexport default function errorCalculation(\n  data,\n  parameters,\n  parameterizedFunction,\n) {\n  let error = 0;\n  const func = parameterizedFunction(parameters);\n\n  for (let i = 0; i < data.x.length; i++) {\n    error += Math.abs(data.y[i] - func(data.x[i]));\n  }\n\n  return error;\n}\n","import { inverse, Matrix } from 'ml-matrix';\n\n/**\n * Difference of the matrix function over the parameters\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Matrix}\n */\nfunction gradientFunction(\n  data,\n  evaluatedData,\n  params,\n  gradientDifference,\n  paramFunction,\n) {\n  const n = params.length;\n  const m = data.x.length;\n\n  let ans = new Array(n);\n\n  for (let param = 0; param < n; param++) {\n    ans[param] = new Array(m);\n    let auxParams = params.slice();\n    auxParams[param] += gradientDifference;\n    let funcParam = paramFunction(auxParams);\n\n    for (let point = 0; point < m; point++) {\n      ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);\n    }\n  }\n  return new Matrix(ans);\n}\n\n/**\n * Matrix function over the samples\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @return {Matrix}\n */\nfunction matrixFunction(data, evaluatedData) {\n  const m = data.x.length;\n\n  let ans = new Array(m);\n\n  for (let point = 0; point < m; point++) {\n    ans[point] = [data.y[point] - evaluatedData[point]];\n  }\n\n  return new Matrix(ans);\n}\n\n/**\n * Iteration for Levenberg-Marquardt\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} damping - Levenberg-Marquardt parameter\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Array<number>}\n */\nexport default function step(\n  data,\n  params,\n  damping,\n  gradientDifference,\n  parameterizedFunction,\n) {\n  let value = damping * gradientDifference * gradientDifference;\n  let identity = Matrix.eye(params.length, params.length, value);\n\n  const func = parameterizedFunction(params);\n\n  let evaluatedData = new Float64Array(data.x.length);\n  for (let i = 0; i < data.x.length; i++) {\n    evaluatedData[i] = func(data.x[i]);\n  }\n\n  let gradientFunc = gradientFunction(\n    data,\n    evaluatedData,\n    params,\n    gradientDifference,\n    parameterizedFunction,\n  );\n  let matrixFunc = matrixFunction(data, evaluatedData);\n  let inverseMatrix = inverse(\n    identity.add(gradientFunc.mmul(gradientFunc.transpose())),\n  );\n\n  params = new Matrix([params]);\n  params = params.sub(\n    inverseMatrix\n      .mmul(gradientFunc)\n      .mmul(matrixFunc)\n      .mul(gradientDifference)\n      .transpose(),\n  );\n\n  return params.to1DArray();\n}\n","import isArray from 'is-any-array';\n\nimport errorCalculation from './errorCalculation';\nimport step from './step';\n\n/**\n * Curve fitting algorithm\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @param {object} [options] - Options object\n * @param {number} [options.damping] - Levenberg-Marquardt parameter\n * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter\n * @param {Array<number>} [options.minValues] - Minimum allowed values for parameters\n * @param {Array<number>} [options.maxValues] - Maximum allowed values for parameters\n * @param {Array<number>} [options.initialValues] - Array of initial parameter values\n * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations\n * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point\n * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}\n */\nexport default function levenbergMarquardt(\n  data,\n  parameterizedFunction,\n  options = {},\n) {\n  let {\n    maxIterations = 100,\n    gradientDifference = 10e-2,\n    damping = 0,\n    errorTolerance = 10e-3,\n    minValues,\n    maxValues,\n    initialValues,\n  } = options;\n\n  if (damping <= 0) {\n    throw new Error('The damping option must be a positive number');\n  } else if (!data.x || !data.y) {\n    throw new Error('The data parameter must have x and y elements');\n  } else if (\n    !isArray(data.x) ||\n    data.x.length < 2 ||\n    !isArray(data.y) ||\n    data.y.length < 2\n  ) {\n    throw new Error(\n      'The data parameter elements must be an array with more than 2 points',\n    );\n  } else if (data.x.length !== data.y.length) {\n    throw new Error('The data parameter elements must have the same size');\n  }\n\n  let parameters =\n    initialValues || new Array(parameterizedFunction.length).fill(1);\n  let parLen = parameters.length;\n  maxValues = maxValues || new Array(parLen).fill(Number.MAX_SAFE_INTEGER);\n  minValues = minValues || new Array(parLen).fill(Number.MIN_SAFE_INTEGER);\n\n  if (maxValues.length !== minValues.length) {\n    throw new Error('minValues and maxValues must be the same size');\n  }\n\n  if (!isArray(parameters)) {\n    throw new Error('initialValues must be an array');\n  }\n\n  let error = errorCalculation(data, parameters, parameterizedFunction);\n\n  let converged = error <= errorTolerance;\n\n  let iteration;\n  for (iteration = 0; iteration < maxIterations && !converged; iteration++) {\n    parameters = step(\n      data,\n      parameters,\n      damping,\n      gradientDifference,\n      parameterizedFunction,\n    );\n\n    for (let k = 0; k < parLen; k++) {\n      parameters[k] = Math.min(\n        Math.max(minValues[k], parameters[k]),\n        maxValues[k],\n      );\n    }\n\n    error = errorCalculation(data, parameters, parameterizedFunction);\n    if (isNaN(error)) break;\n    converged = error <= errorTolerance;\n  }\n\n  return {\n    parameterValues: parameters,\n    parameterError: error,\n    iterations: iteration,\n  };\n}\n","/**\n * Returns a new array based on extraction of specific indices of an array\n * @private\n * @param {Array} vector\n * @param {Array} indices\n */\nexport default function selection(vector, indices) {\n  let u = []; //new Float64Array(indices.length);\n  for (let i = 0; i < indices.length; i++) {\n    u[i] = vector[indices[i]];\n  }\n  return u;\n}\n","/**\n *\n * @private\n * @param {Array of arrays} collection\n */\nexport default function sortCollectionSet(collection) {\n  let objectCollection = collection\n    .map((value, index) => {\n      let key = BigInt(0);\n      value.forEach((item) => (key |= BigInt(1) << BigInt(item)));\n      return { value, index, key };\n    })\n    .sort((a, b) => {\n      if (a.key - b.key < 0) return -1;\n      return 1;\n    });\n\n  let sorted = [];\n  let indices = [];\n\n  let key;\n  for (let set of objectCollection) {\n    if (set.key !== key) {\n      key = set.key;\n      indices.push([]);\n      sorted.push(set.value);\n    }\n    indices[indices.length - 1].push(set.index);\n  }\n\n  let result = {\n    values: sorted,\n    indices: indices,\n  };\n  return result;\n}\n","import {\n  Matrix,\n  LuDecomposition,\n  solve,\n  CholeskyDecomposition,\n} from 'ml-matrix';\n\nimport sortCollectionSet from './util/sortCollectionSet';\n\n/**\n * (Combinatorial Subspace Least Squares) - subfunction for the FC-NNLS\n * @private\n * @param {Matrix} XtX\n * @param {Matrix} XtY\n * @param {Array} Pset\n * @param {Numbers} l\n * @param {Numbers} p\n */\nexport default function cssls(XtX, XtY, Pset, l, p) {\n  // Solves the set of equation XtX*K = XtY for the variables in Pset\n  // if XtX (or XtX(vars,vars)) is singular, performs the svd and find pseudoinverse, otherwise (even if ill-conditioned) finds inverse with LU decomposition and solves the set of equation\n  // it is consistent with matlab results for ill-conditioned matrices (at least consistent with test 'ill-conditionned square X rank 2, Y 3x1' in cssls.test)\n\n  let K = Matrix.zeros(l, p);\n  if (Pset === null) {\n    let choXtX = new CholeskyDecomposition(XtX);\n    if (choXtX.isPositiveDefinite() === true) {\n      K = choXtX.solve(XtY);\n    } else {\n      let luXtX = new LuDecomposition(XtX);\n      if (luXtX.isSingular() === false) {\n        K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n      } else {\n        K = solve(XtX, XtY, { useSVD: true });\n      }\n    }\n  } else {\n    let sortedPset = sortCollectionSet(Pset).values;\n    let sortedEset = sortCollectionSet(Pset).indices;\n    if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === 0 &&\n      sortedEset[0].length === p\n    ) {\n      return K;\n    } else if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === l &&\n      sortedEset[0].length === p\n    ) {\n      let choXtX = new CholeskyDecomposition(XtX);\n      if (choXtX.isPositiveDefinite() === true) {\n        K = choXtX.solve(XtY);\n      } else {\n        let luXtX = new LuDecomposition(XtX);\n        if (luXtX.isSingular() === false) {\n          K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n        } else {\n          K = solve(XtX, XtY, { useSVD: true });\n        }\n      }\n    } else {\n      for (let k = 0; k < sortedPset.length; k++) {\n        let cols2Solve = sortedEset[k];\n        let vars = sortedPset[k];\n        let L;\n        let choXtX = new CholeskyDecomposition(XtX.selection(vars, vars));\n        if (choXtX.isPositiveDefinite() === true) {\n          L = choXtX.solve(XtY.selection(vars, cols2Solve));\n        } else {\n          let luXtX = new LuDecomposition(XtX.selection(vars, vars));\n          if (luXtX.isSingular() === false) {\n            L = luXtX\n              .solve(Matrix.eye(vars.length))\n              .mmul(XtY.selection(vars, cols2Solve));\n          } else {\n            L = solve(\n              XtX.selection(vars, vars),\n              XtY.selection(vars, cols2Solve),\n              { useSVD: true },\n            );\n          }\n        }\n        for (let i = 0; i < L.rows; i++) {\n          for (let j = 0; j < L.columns; j++) {\n            K.set(vars[i], cols2Solve[j], L.get(i, j));\n          }\n        }\n      }\n    }\n  }\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport cssls from './cssls';\n\nexport default function initialisation(X, Y) {\n  let n = X.rows;\n  let l = X.columns;\n  let p = Y.columns;\n  let iter = 0;\n\n  if (Y.rows !== n) throw new Error('ERROR: matrix size not compatible');\n\n  let W = Matrix.zeros(l, p);\n\n  // precomputes part of pseudoinverse\n  let XtX = X.transpose().mmul(X);\n  let XtY = X.transpose().mmul(Y);\n\n  let K = cssls(XtX, XtY, null, l, p); // K is lxp\n  let Pset = [];\n  for (let j = 0; j < p; j++) {\n    Pset[j] = [];\n    for (let i = 0; i < l; i++) {\n      if (K.get(i, j) > 0) {\n        Pset[j].push(i);\n      } else {\n        K.set(i, j, 0);\n      } //This is our initial solution, it's the solution found by overwriting the unconstrained least square solution\n    }\n  }\n  let Fset = [];\n  for (let j = 0; j < p; j++) {\n    if (Pset[j].length !== l) {\n      Fset.push(j);\n    }\n  }\n\n  let D = K.clone();\n\n  return { n, l, p, iter, W, XtX, XtY, K, Pset, Fset, D };\n}\n","/**\n * Computes the set difference A\\B\n * @private\n * @param {A} set A as an array\n * @param {B} set B as an array\n */\nexport default function setDifference(A, B) {\n  let C = [];\n  for (let i of A) {\n    if (!B.includes(i)) C.push(i);\n  }\n  return C;\n}\n","import setDifference from './util/setDifference';\n\n// Makes sure the solution has converged\nexport default function optimality(\n  iter,\n  maxIter,\n  XtX,\n  XtY,\n  Fset,\n  Pset,\n  W,\n  K,\n  l,\n  p,\n  D,\n) {\n  if (iter === maxIter) {\n    throw new Error('Maximum number of iterations exceeded');\n  }\n\n  // Check solution for optimality\n  let V = XtY.subMatrixColumn(Fset).subtract(XtX.mmul(K.subMatrixColumn(Fset)));\n  for (let j = 0; j < Fset.length; j++) {\n    W.setColumn(Fset[j], V.subMatrixColumn([j]));\n  }\n  let Jset = [];\n  let fullSet = [];\n  for (let i = 0; i < l; i++) {\n    fullSet.push(i);\n  }\n  for (let j = 0; j < Fset.length; j++) {\n    let notPset = setDifference(fullSet, Pset[Fset[j]]);\n    if (notPset.length === 0) {\n      Jset.push(Fset[j]);\n    } else if (W.selection(notPset, [Fset[j]]).max() <= 0) {\n      Jset.push(Fset[j]);\n    }\n  }\n  Fset = setDifference(Fset, Jset);\n\n  // For non-optimal solutions, add the appropriate variables to Pset\n  if (Fset.length !== 0) {\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (Pset[Fset[j]].includes(i)) W.set(i, Fset[j], -Infinity);\n      }\n      Pset[Fset[j]].push(W.subMatrixColumn(Fset).maxColumnIndex(j)[0]);\n    }\n    for (let j = 0; j < Fset.length; j++) {\n      D.setColumn(Fset[j], K.getColumn(Fset[j]));\n    }\n  }\n  for (let j = 0; j < p; j++) {\n    Pset[j].sort((a, b) => a - b);\n  }\n  return { Pset, Fset, W };\n}\n","import { Matrix } from 'ml-matrix';\n\nimport selection from './util/selection';\nimport cssls from './cssls';\nimport initialisation from './initialisation';\nimport optimality from './optimality';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with multiple Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {Matrix|number[][]} Y\n * @param {object} [options={}]\n * @param {number} [options.maxIterations] if empty maxIterations is set at 3 times the number of columns of X\n * @returns {Matrix} K\n */\nexport default function fcnnls(X, Y, options = {}) {\n  X = Matrix.checkMatrix(X);\n  Y = Matrix.checkMatrix(Y);\n  let { l, p, iter, W, XtX, XtY, K, Pset, Fset, D } = initialisation(X, Y);\n  const { maxIterations = X.columns * 3 } = options;\n\n  // Active set algorithm for NNLS main loop\n  while (Fset.length > 0) {\n    // Solves for the passive variables (uses subroutine below)\n    let L = cssls(\n      XtX,\n      XtY.subMatrixColumn(Fset),\n      selection(Pset, Fset),\n      l,\n      Fset.length,\n    );\n    for (let i = 0; i < l; i++) {\n      for (let j = 0; j < Fset.length; j++) {\n        K.set(i, Fset[j], L.get(i, j));\n      }\n    }\n\n    // Finds any infeasible solutions\n    let infeasIndex = [];\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (L.get(i, j) < 0) {\n          infeasIndex.push(j);\n          break;\n        }\n      }\n    }\n    let Hset = selection(Fset, infeasIndex);\n\n    // Makes infeasible solutions feasible (standard NNLS inner loop)\n    if (Hset.length > 0) {\n      let m = Hset.length;\n      let alpha = Matrix.ones(l, m);\n\n      while (m > 0 && iter < maxIterations) {\n        iter++;\n\n        alpha.mul(Infinity);\n\n        // Finds indices of negative variables in passive set\n        let hRowColIdx = [[], []]; // Indexes work in pairs, each pair reprensents a single element, first array is row index, second array is column index\n        let negRowColIdx = [[], []]; // Same as before\n        for (let j = 0; j < m; j++) {\n          for (let i = 0; i < Pset[Hset[j]].length; i++) {\n            if (K.get(Pset[Hset[j]][i], Hset[j]) < 0) {\n              hRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              hRowColIdx[1].push(j);\n              negRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              negRowColIdx[1].push(Hset[j]);\n            } // Compared to matlab, here we keep the row/column indexing (we are not taking the linear indexing)\n          }\n        }\n\n        for (let k = 0; k < hRowColIdx[0].length; k++) {\n          // could be hRowColIdx[1].length as well\n          alpha.set(\n            hRowColIdx[0][k],\n            hRowColIdx[1][k],\n            D.get(negRowColIdx[0][k], negRowColIdx[1][k]) /\n              (D.get(negRowColIdx[0][k], negRowColIdx[1][k]) -\n                K.get(negRowColIdx[0][k], negRowColIdx[1][k])),\n          );\n        }\n\n        let alphaMin = [];\n        let minIdx = [];\n        for (let j = 0; j < m; j++) {\n          alphaMin[j] = alpha.minColumn(j);\n          minIdx[j] = alpha.minColumnIndex(j)[0];\n        }\n\n        alphaMin = Matrix.rowVector(alphaMin);\n        for (let i = 0; i < l; i++) {\n          alpha.setSubMatrix(alphaMin, i, 0);\n        }\n\n        let E = new Matrix(l, m);\n        E = D.subMatrixColumn(Hset).subtract(\n          alpha\n            .subMatrix(0, l - 1, 0, m - 1)\n            .mul(D.subMatrixColumn(Hset).subtract(K.subMatrixColumn(Hset))),\n        );\n        for (let j = 0; j < m; j++) {\n          D.setColumn(Hset[j], E.subMatrixColumn([j]));\n        }\n\n        let idx2zero = [minIdx, Hset];\n        for (let k = 0; k < m; k++) {\n          D.set(idx2zero[0][k], idx2zero[1][k], 0);\n        }\n\n        for (let j = 0; j < m; j++) {\n          Pset[Hset[j]].splice(\n            Pset[Hset[j]].findIndex((item) => item === minIdx[j]),\n            1,\n          );\n        }\n\n        L = cssls(XtX, XtY.subMatrixColumn(Hset), selection(Pset, Hset), l, m);\n        for (let j = 0; j < m; j++) {\n          K.setColumn(Hset[j], L.subMatrixColumn([j]));\n        }\n\n        Hset = [];\n        for (let j = 0; j < K.columns; j++) {\n          for (let i = 0; i < l; i++) {\n            if (K.get(i, j) < 0) {\n              Hset.push(j);\n\n              break;\n            }\n          }\n        }\n        m = Hset.length;\n      }\n    }\n\n    let newParam = optimality(\n      iter,\n      maxIterations,\n      XtX,\n      XtY,\n      Fset,\n      Pset,\n      W,\n      K,\n      l,\n      p,\n      D,\n    );\n    Pset = newParam.Pset;\n    Fset = newParam.Fset;\n    W = newParam.W;\n  }\n\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport fcnnls from './fcnnls';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with single Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {number[]} y\n * @param {object} [options={}]\n * @param {boolean} [maxIterations] if true or empty maxIterations is set at 3 times the number of columns of X\n * @returns {Array} k\n */\nexport default function fcnnlsVector(X, y, options = {}) {\n  if (Array.isArray(y) === false) {\n    throw new TypeError('y must be a 1D Array');\n  }\n  let Y = Matrix.columnVector(y);\n  let K = fcnnls(X, Y, options);\n  let k = K.to1DArray();\n  return k;\n}\n","module.exports = function(haystack, needle, comparator, low, high) {\n  var mid, cmp;\n\n  if(low === undefined)\n    low = 0;\n\n  else {\n    low = low|0;\n    if(low < 0 || low >= haystack.length)\n      throw new RangeError(\"invalid lower bound\");\n  }\n\n  if(high === undefined)\n    high = haystack.length - 1;\n\n  else {\n    high = high|0;\n    if(high < low || high >= haystack.length)\n      throw new RangeError(\"invalid upper bound\");\n  }\n\n  while(low <= high) {\n    // The naive `low + high >>> 1` could fail for array lengths > 2**31\n    // because `>>>` converts its operands to int32. `low + (high - low >>> 1)`\n    // works for array lengths <= 2**32-1 which is also Javascript's max array\n    // length.\n    mid = low + ((high - low) >>> 1);\n    cmp = +comparator(haystack[mid], needle, mid, haystack);\n\n    // Too low.\n    if(cmp < 0.0)\n      low  = mid + 1;\n\n    // Too high.\n    else if(cmp > 0.0)\n      high = mid - 1;\n\n    // Key found.\n    else\n      return mid;\n  }\n\n  // Key not found.\n  return ~low;\n}\n","'use strict';\n\nfunction assertNumber(number) {\n\tif (typeof number !== 'number') {\n\t\tthrow new TypeError('Expected a number');\n\t}\n}\n\nexports.ascending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\n\tif (Number.isNaN(left)) {\n\t\treturn -1;\n\t}\n\n\tif (Number.isNaN(right)) {\n\t\treturn 1;\n\t}\n\n\treturn left - right;\n};\n\nexports.descending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\n\tif (Number.isNaN(left)) {\n\t\treturn 1;\n\t}\n\n\tif (Number.isNaN(right)) {\n\t\treturn -1;\n\t}\n\n\treturn right - left;\n};\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\nexport const largestPrime = 0x7fffffff;\n\nconst primeNumbers = [\n  // chunk #0\n  largestPrime, // 2^31-1\n\n  // chunk #1\n  5,\n  11,\n  23,\n  47,\n  97,\n  197,\n  397,\n  797,\n  1597,\n  3203,\n  6421,\n  12853,\n  25717,\n  51437,\n  102877,\n  205759,\n  411527,\n  823117,\n  1646237,\n  3292489,\n  6584983,\n  13169977,\n  26339969,\n  52679969,\n  105359939,\n  210719881,\n  421439783,\n  842879579,\n  1685759167,\n\n  // chunk #2\n  433,\n  877,\n  1759,\n  3527,\n  7057,\n  14143,\n  28289,\n  56591,\n  113189,\n  226379,\n  452759,\n  905551,\n  1811107,\n  3622219,\n  7244441,\n  14488931,\n  28977863,\n  57955739,\n  115911563,\n  231823147,\n  463646329,\n  927292699,\n  1854585413,\n\n  // chunk #3\n  953,\n  1907,\n  3821,\n  7643,\n  15287,\n  30577,\n  61169,\n  122347,\n  244703,\n  489407,\n  978821,\n  1957651,\n  3915341,\n  7830701,\n  15661423,\n  31322867,\n  62645741,\n  125291483,\n  250582987,\n  501165979,\n  1002331963,\n  2004663929,\n\n  // chunk #4\n  1039,\n  2081,\n  4177,\n  8363,\n  16729,\n  33461,\n  66923,\n  133853,\n  267713,\n  535481,\n  1070981,\n  2141977,\n  4283963,\n  8567929,\n  17135863,\n  34271747,\n  68543509,\n  137087021,\n  274174111,\n  548348231,\n  1096696463,\n\n  // chunk #5\n  31,\n  67,\n  137,\n  277,\n  557,\n  1117,\n  2237,\n  4481,\n  8963,\n  17929,\n  35863,\n  71741,\n  143483,\n  286973,\n  573953,\n  1147921,\n  2295859,\n  4591721,\n  9183457,\n  18366923,\n  36733847,\n  73467739,\n  146935499,\n  293871013,\n  587742049,\n  1175484103,\n\n  // chunk #6\n  599,\n  1201,\n  2411,\n  4831,\n  9677,\n  19373,\n  38747,\n  77509,\n  155027,\n  310081,\n  620171,\n  1240361,\n  2480729,\n  4961459,\n  9922933,\n  19845871,\n  39691759,\n  79383533,\n  158767069,\n  317534141,\n  635068283,\n  1270136683,\n\n  // chunk #7\n  311,\n  631,\n  1277,\n  2557,\n  5119,\n  10243,\n  20507,\n  41017,\n  82037,\n  164089,\n  328213,\n  656429,\n  1312867,\n  2625761,\n  5251529,\n  10503061,\n  21006137,\n  42012281,\n  84024581,\n  168049163,\n  336098327,\n  672196673,\n  1344393353,\n\n  // chunk #8\n  3,\n  7,\n  17,\n  37,\n  79,\n  163,\n  331,\n  673,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #9\n  43,\n  89,\n  179,\n  359,\n  719,\n  1439,\n  2879,\n  5779,\n  11579,\n  23159,\n  46327,\n  92657,\n  185323,\n  370661,\n  741337,\n  1482707,\n  2965421,\n  5930887,\n  11861791,\n  23723597,\n  47447201,\n  94894427,\n  189788857,\n  379577741,\n  759155483,\n  1518310967,\n\n  // chunk #10\n  379,\n  761,\n  1523,\n  3049,\n  6101,\n  12203,\n  24407,\n  48817,\n  97649,\n  195311,\n  390647,\n  781301,\n  1562611,\n  3125257,\n  6250537,\n  12501169,\n  25002389,\n  50004791,\n  100009607,\n  200019221,\n  400038451,\n  800076929,\n  1600153859,\n\n  // chunk #11\n  13,\n  29,\n  59,\n  127,\n  257,\n  521,\n  1049,\n  2099,\n  4201,\n  8419,\n  16843,\n  33703,\n  67409,\n  134837,\n  269683,\n  539389,\n  1078787,\n  2157587,\n  4315183,\n  8630387,\n  17260781,\n  34521589,\n  69043189,\n  138086407,\n  276172823,\n  552345671,\n  1104691373,\n\n  // chunk #12\n  19,\n  41,\n  83,\n  167,\n  337,\n  677,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #13\n  53,\n  107,\n  223,\n  449,\n  907,\n  1823,\n  3659,\n  7321,\n  14653,\n  29311,\n  58631,\n  117269,\n  234539,\n  469099,\n  938207,\n  1876417,\n  3752839,\n  7505681,\n  15011389,\n  30022781,\n  60045577,\n  120091177,\n  240182359,\n  480364727,\n  960729461,\n  1921458943\n];\n\nprimeNumbers.sort(ascending);\n\nexport function nextPrime(value) {\n  let index = binarySearch(primeNumbers, value, ascending);\n  if (index < 0) {\n    index = ~index;\n  }\n  return primeNumbers[index];\n}\n","import { largestPrime, nextPrime } from './primeFinder';\n\nconst FREE = 0;\nconst FULL = 1;\nconst REMOVED = 2;\n\nconst defaultInitialCapacity = 150;\nconst defaultMinLoadFactor = 1 / 6;\nconst defaultMaxLoadFactor = 2 / 3;\n\nexport default class HashTable {\n  constructor(options = {}) {\n    if (options instanceof HashTable) {\n      this.table = options.table.slice();\n      this.values = options.values.slice();\n      this.state = options.state.slice();\n      this.minLoadFactor = options.minLoadFactor;\n      this.maxLoadFactor = options.maxLoadFactor;\n      this.distinct = options.distinct;\n      this.freeEntries = options.freeEntries;\n      this.lowWaterMark = options.lowWaterMark;\n      this.highWaterMark = options.maxLoadFactor;\n      return;\n    }\n\n    const initialCapacity =\n      options.initialCapacity === undefined\n        ? defaultInitialCapacity\n        : options.initialCapacity;\n    if (initialCapacity < 0) {\n      throw new RangeError(\n        `initial capacity must not be less than zero: ${initialCapacity}`\n      );\n    }\n\n    const minLoadFactor =\n      options.minLoadFactor === undefined\n        ? defaultMinLoadFactor\n        : options.minLoadFactor;\n    const maxLoadFactor =\n      options.maxLoadFactor === undefined\n        ? defaultMaxLoadFactor\n        : options.maxLoadFactor;\n    if (minLoadFactor < 0 || minLoadFactor >= 1) {\n      throw new RangeError(`invalid minLoadFactor: ${minLoadFactor}`);\n    }\n    if (maxLoadFactor <= 0 || maxLoadFactor >= 1) {\n      throw new RangeError(`invalid maxLoadFactor: ${maxLoadFactor}`);\n    }\n    if (minLoadFactor >= maxLoadFactor) {\n      throw new RangeError(\n        `minLoadFactor (${minLoadFactor}) must be smaller than maxLoadFactor (${maxLoadFactor})`\n      );\n    }\n\n    let capacity = initialCapacity;\n    // User wants to put at least capacity elements. We need to choose the size based on the maxLoadFactor to\n    // avoid the need to rehash before this capacity is reached.\n    // actualCapacity * maxLoadFactor >= capacity\n    capacity = (capacity / maxLoadFactor) | 0;\n    capacity = nextPrime(capacity);\n    if (capacity === 0) capacity = 1;\n\n    this.table = newArray(capacity);\n    this.values = newArray(capacity);\n    this.state = newArray(capacity);\n\n    this.minLoadFactor = minLoadFactor;\n    if (capacity === largestPrime) {\n      this.maxLoadFactor = 1;\n    } else {\n      this.maxLoadFactor = maxLoadFactor;\n    }\n\n    this.distinct = 0;\n    this.freeEntries = capacity;\n\n    this.lowWaterMark = 0;\n    this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);\n  }\n\n  clone() {\n    return new HashTable(this);\n  }\n\n  get size() {\n    return this.distinct;\n  }\n\n  get(key) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return 0;\n    return this.values[i];\n  }\n\n  set(key, value) {\n    let i = this.indexOfInsertion(key);\n    if (i < 0) {\n      i = -i - 1;\n      this.values[i] = value;\n      return false;\n    }\n\n    if (this.distinct > this.highWaterMark) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n      return this.set(key, value);\n    }\n\n    this.table[i] = key;\n    this.values[i] = value;\n    if (this.state[i] === FREE) this.freeEntries--;\n    this.state[i] = FULL;\n    this.distinct++;\n\n    if (this.freeEntries < 1) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n\n    return true;\n  }\n\n  remove(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = REMOVED;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  delete(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = FREE;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  maybeShrinkCapacity() {\n    if (this.distinct < this.lowWaterMark) {\n      const newCapacity = chooseShrinkCapacity(\n        this.distinct,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n  }\n\n  containsKey(key) {\n    return this.indexOfKey(key) >= 0;\n  }\n\n  indexOfKey(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = this.table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === FREE) return -1;\n    return i;\n  }\n\n  containsValue(value) {\n    return this.indexOfValue(value) >= 0;\n  }\n\n  indexOfValue(value) {\n    const values = this.values;\n    const state = this.state;\n\n    for (var i = 0; i < state.length; i++) {\n      if (state[i] === FULL && values[i] === value) {\n        return i;\n      }\n    }\n\n    return -1;\n  }\n\n  indexOfInsertion(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] === FULL && table[i] !== key) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === REMOVED) {\n      const j = i;\n      while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n        i -= decrement;\n        if (i < 0) i += length;\n      }\n      if (state[i] === FREE) i = j;\n    }\n\n    if (state[i] === FULL) {\n      return -i - 1;\n    }\n\n    return i;\n  }\n\n  ensureCapacity(minCapacity) {\n    if (this.table.length < minCapacity) {\n      const newCapacity = nextPrime(minCapacity);\n      this.rehash(newCapacity);\n    }\n  }\n\n  rehash(newCapacity) {\n    const oldCapacity = this.table.length;\n\n    if (newCapacity <= this.distinct) throw new Error('Unexpected');\n\n    const oldTable = this.table;\n    const oldValues = this.values;\n    const oldState = this.state;\n\n    const newTable = newArray(newCapacity);\n    const newValues = newArray(newCapacity);\n    const newState = newArray(newCapacity);\n\n    this.lowWaterMark = chooseLowWaterMark(newCapacity, this.minLoadFactor);\n    this.highWaterMark = chooseHighWaterMark(newCapacity, this.maxLoadFactor);\n\n    this.table = newTable;\n    this.values = newValues;\n    this.state = newState;\n    this.freeEntries = newCapacity - this.distinct;\n\n    for (var i = 0; i < oldCapacity; i++) {\n      if (oldState[i] === FULL) {\n        var element = oldTable[i];\n        var index = this.indexOfInsertion(element);\n        newTable[index] = element;\n        newValues[index] = oldValues[i];\n        newState[index] = FULL;\n      }\n    }\n  }\n\n  forEachKey(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachValue(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachPair(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i], this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n}\n\nfunction chooseLowWaterMark(capacity, minLoad) {\n  return (capacity * minLoad) | 0;\n}\n\nfunction chooseHighWaterMark(capacity, maxLoad) {\n  return Math.min(capacity - 2, (capacity * maxLoad) | 0);\n}\n\nfunction chooseGrowCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (3 * minLoad + maxLoad)) | 0)\n  );\n}\n\nfunction chooseShrinkCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (minLoad + 3 * maxLoad)) | 0)\n  );\n}\n\nfunction newArray(size) {\n  return Array(size).fill(0);\n}\n","import HashTable from 'ml-hash-table';\n\nexport class SparseMatrix {\n  constructor(rows, columns, options = {}) {\n    if (rows instanceof SparseMatrix) {\n      // clone\n      const other = rows;\n      this._init(\n        other.rows,\n        other.columns,\n        other.elements.clone(),\n        other.threshold\n      );\n      return;\n    }\n\n    if (Array.isArray(rows)) {\n      const matrix = rows;\n      rows = matrix.length;\n      options = columns || {};\n      columns = matrix[0].length;\n      this._init(rows, columns, new HashTable(options), options.threshold);\n      for (var i = 0; i < rows; i++) {\n        for (var j = 0; j < columns; j++) {\n          var value = matrix[i][j];\n          if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n          if (value !== 0) {\n            this.elements.set(i * columns + j, matrix[i][j]);\n          }\n        }\n      }\n    } else {\n      this._init(rows, columns, new HashTable(options), options.threshold);\n    }\n  }\n\n  _init(rows, columns, elements, threshold) {\n    this.rows = rows;\n    this.columns = columns;\n    this.elements = elements;\n    this.threshold = threshold || 0;\n  }\n\n  static eye(rows = 1, columns = rows) {\n    const min = Math.min(rows, columns);\n    const matrix = new SparseMatrix(rows, columns, { initialCapacity: min });\n    for (var i = 0; i < min; i++) {\n      matrix.set(i, i, 1);\n    }\n    return matrix;\n  }\n\n  clone() {\n    return new SparseMatrix(this);\n  }\n\n  to2DArray() {\n    const copy = new Array(this.rows);\n    for (var i = 0; i < this.rows; i++) {\n      copy[i] = new Array(this.columns);\n      for (var j = 0; j < this.columns; j++) {\n        copy[i][j] = this.get(i, j);\n      }\n    }\n    return copy;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (!this.isSquare()) return false;\n\n    var symmetric = true;\n    this.forEachNonZero((i, j, v) => {\n      if (this.get(j, i) !== v) {\n        symmetric = false;\n        return false;\n      }\n      return v;\n    });\n    return symmetric;\n  }\n\n  /**\n   * Search for the wither band in the main diagonals\n   * @return {number}\n   */\n  bandWidth() {\n    let min = this.columns;\n    let max = -1;\n    this.forEachNonZero((i, j, v) => {\n      let diff = i - j;\n      min = Math.min(min, diff);\n      max = Math.max(max, diff);\n      return v;\n    });\n    return max - min;\n  }\n\n  /**\n   * Test if a matrix is consider banded using a threshold\n   * @param {number} width\n   * @return {boolean}\n   */\n  isBanded(width) {\n    let bandWidth = this.bandWidth();\n    return bandWidth <= width;\n  }\n\n  get cardinality() {\n    return this.elements.size;\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  get(row, column) {\n    return this.elements.get(row * this.columns + column);\n  }\n\n  set(row, column, value) {\n    if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n    if (value === 0) {\n      this.elements.remove(row * this.columns + column);\n    } else {\n      this.elements.set(row * this.columns + column, value);\n    }\n    return this;\n  }\n\n  mmul(other) {\n    if (this.columns !== other.rows) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        'Number of columns of left matrix are not equal to number of rows of right matrix.'\n      );\n    }\n\n    const m = this.rows;\n    const p = other.columns;\n\n    const result = new SparseMatrix(m, p);\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        if (j === k) {\n          result.set(i, l, result.get(i, l) + v1 * v2);\n        }\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  kroneckerProduct(other) {\n    const m = this.rows;\n    const n = this.columns;\n    const p = other.rows;\n    const q = other.columns;\n\n    const result = new SparseMatrix(m * p, n * q, {\n      initialCapacity: this.cardinality * other.cardinality\n    });\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        result.set(p * i + k, q * j + l, v1 * v2);\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  forEachNonZero(callback) {\n    this.elements.forEachPair((key, value) => {\n      const i = (key / this.columns) | 0;\n      const j = key % this.columns;\n      let r = callback(i, j, value);\n      if (r === false) return false; // stop iteration\n      if (this.threshold && Math.abs(r) < this.threshold) r = 0;\n      if (r !== value) {\n        if (r === 0) {\n          this.elements.remove(key, true);\n        } else {\n          this.elements.set(key, r);\n        }\n      }\n      return true;\n    });\n    this.elements.maybeShrinkCapacity();\n    return this;\n  }\n\n  getNonZeros() {\n    const cardinality = this.cardinality;\n    const rows = new Array(cardinality);\n    const columns = new Array(cardinality);\n    const values = new Array(cardinality);\n    var idx = 0;\n    this.forEachNonZero((i, j, value) => {\n      rows[idx] = i;\n      columns[idx] = j;\n      values[idx] = value;\n      idx++;\n      return value;\n    });\n    return { rows, columns, values };\n  }\n\n  setThreshold(newThreshold) {\n    if (newThreshold !== 0 && newThreshold !== this.threshold) {\n      this.threshold = newThreshold;\n      this.forEachNonZero((i, j, v) => v);\n    }\n    return this;\n  }\n\n  /**\n   * @return {SparseMatrix} - New transposed sparse matrix\n   */\n  transpose() {\n    let trans = new SparseMatrix(this.columns, this.rows, {\n      initialCapacity: this.cardinality\n    });\n    this.forEachNonZero((i, j, value) => {\n      trans.set(j, i, value);\n      return value;\n    });\n    return trans;\n  }\n}\n\nSparseMatrix.prototype.klass = 'Matrix';\n\nSparseMatrix.identity = SparseMatrix.eye;\nSparseMatrix.prototype.tensorProduct = SparseMatrix.prototype.kroneckerProduct;\n\n/*\n Add dynamically instance and static methods for mathematical operations\n */\n\nvar inplaceOperator = `\n(function %name%(value) {\n    if (typeof value === 'number') return this.%name%S(value);\n    return this.%name%M(value);\n})\n`;\n\nvar inplaceOperatorScalar = `\n(function %name%S(value) {\n    this.forEachNonZero((i, j, v) => v %op% value);\n    return this;\n})\n`;\n\nvar inplaceOperatorMatrix = `\n(function %name%M(matrix) {\n    matrix.forEachNonZero((i, j, v) => {\n        this.set(i, j, this.get(i, j) %op% v);\n        return v;\n    });\n    return this;\n})\n`;\n\nvar staticOperator = `\n(function %name%(matrix, value) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%(value);\n})\n`;\n\nvar inplaceMethod = `\n(function %name%() {\n    this.forEachNonZero((i, j, v) => %method%(v));\n    return this;\n})\n`;\n\nvar staticMethod = `\n(function %name%(matrix) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%();\n})\n`;\n\nconst operators = [\n  // Arithmetic operators\n  ['+', 'add'],\n  ['-', 'sub', 'subtract'],\n  ['*', 'mul', 'multiply'],\n  ['/', 'div', 'divide'],\n  ['%', 'mod', 'modulus'],\n  // Bitwise operators\n  ['&', 'and'],\n  ['|', 'or'],\n  ['^', 'xor'],\n  ['<<', 'leftShift'],\n  ['>>', 'signPropagatingRightShift'],\n  ['>>>', 'rightShift', 'zeroFillRightShift']\n];\n\nfor (const operator of operators) {\n  for (let i = 1; i < operator.length; i++) {\n    SparseMatrix.prototype[operator[i]] = eval(\n      fillTemplateFunction(inplaceOperator, {\n        name: operator[i],\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}S`] = eval(\n      fillTemplateFunction(inplaceOperatorScalar, {\n        name: `${operator[i]}S`,\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}M`] = eval(\n      fillTemplateFunction(inplaceOperatorMatrix, {\n        name: `${operator[i]}M`,\n        op: operator[0]\n      })\n    );\n\n    SparseMatrix[operator[i]] = eval(\n      fillTemplateFunction(staticOperator, { name: operator[i] })\n    );\n  }\n}\n\nvar methods = [['~', 'not']];\n\n[\n  'abs',\n  'acos',\n  'acosh',\n  'asin',\n  'asinh',\n  'atan',\n  'atanh',\n  'cbrt',\n  'ceil',\n  'clz32',\n  'cos',\n  'cosh',\n  'exp',\n  'expm1',\n  'floor',\n  'fround',\n  'log',\n  'log1p',\n  'log10',\n  'log2',\n  'round',\n  'sign',\n  'sin',\n  'sinh',\n  'sqrt',\n  'tan',\n  'tanh',\n  'trunc'\n].forEach(function (mathMethod) {\n  methods.push([`Math.${mathMethod}`, mathMethod]);\n});\n\nfor (const method of methods) {\n  for (let i = 1; i < method.length; i++) {\n    SparseMatrix.prototype[method[i]] = eval(\n      fillTemplateFunction(inplaceMethod, {\n        name: method[i],\n        method: method[0]\n      })\n    );\n    SparseMatrix[method[i]] = eval(\n      fillTemplateFunction(staticMethod, { name: method[i] })\n    );\n  }\n}\n\nfunction fillTemplateFunction(template, values) {\n  for (const i in values) {\n    template = template.replace(new RegExp(`%${i}%`, 'g'), values[i]);\n  }\n  return template;\n}\n","export default function additiveSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i]) * (a[i] + b[i])) / (a[i] * b[i]);\n  }\n  return 2 * d;\n}\n","export default function avg(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var ans = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    ans += aux;\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return (max + ans) / 2;\n}\n","export default function bhattacharyya(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return -Math.log(ans);\n}\n","export default function canberra(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]) / (a[i] + b[i]);\n  }\n  return ans;\n}\n","export default function chebyshev(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return max;\n}\n","export default function clark(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.sqrt(\n      ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]))\n    );\n  }\n  return 2 * d;\n}\n","export default function czekanowskiSimilarity(a, b) {\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < a.length; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return (2 * up) / down;\n}\n","import czekanowskiSimilarity from '../similarities/czekanowski';\n\nexport default function czekanowskiDistance(a, b) {\n  return 1 - czekanowskiSimilarity(a, b);\n}\n","export default function dice(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p + q1);\n}\n","export default function divergence(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]));\n  }\n  return 2 * d;\n}\n","export default function fidelity(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return ans;\n}\n","export default function gower(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]);\n  }\n  return ans / ii;\n}\n","export default function harmonicMean(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] * b[i]) / (a[i] + b[i]);\n  }\n  return 2 * ans;\n}\n","export default function hellinger(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return 2 * Math.sqrt(1 - ans);\n}\n","export default function innerProduct(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * b[i];\n  }\n  return ans;\n}\n","export default function intersection(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.min(a[i], b[i]);\n  }\n  return 1 - ans;\n}\n","export default function jaccard(a, b) {\n  var ii = a.length;\n  var p1 = 0;\n  var p2 = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p1 += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p2 + q1 - p1);\n}\n","export default function jeffreys(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] - b[i]) * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function jensenDifference(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (a[i] * Math.log(a[i]) + b[i] * Math.log(b[i])) / 2 -\n      ((a[i] + b[i]) / 2) * Math.log((a[i] + b[i]) / 2);\n  }\n  return ans;\n}\n","export default function jensenShannon(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n    q += b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return (p + q) / 2;\n}\n","export default function kdivergence(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function kulczynski(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.min(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function kullbackLeibler(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function kumarHassebrook(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (p2 + q2 - p);\n}\n","export default function kumarJohnson(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      Math.pow(a[i] * a[i] - b[i] * b[i], 2) / (2 * Math.pow(a[i] * b[i], 1.5));\n  }\n  return ans;\n}\n","export default function lorentzian(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.log(Math.abs(a[i] - b[i]) + 1);\n  }\n  return ans;\n}\n","export default function manhattan(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.abs(a[i] - b[i]);\n  }\n  return d;\n}\n","export default function matusita(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return Math.sqrt(2 - 2 * ans);\n}\n","export default function minkowski(a, b, p) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.pow(Math.abs(a[i] - b[i]), p);\n  }\n  return Math.pow(d, 1 / p);\n}\n","export default function motyka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return 1 - up / down;\n}\n","export default function neyman(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / a[i];\n  }\n  return d;\n}\n","export default function pearson(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / b[i];\n  }\n  return d;\n}\n","export default function probabilisticSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return 2 * d;\n}\n","export default function ruzicka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function soergel(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function sorensen(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += a[i] + b[i];\n  }\n  return up / down;\n}\n","export default function squared(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return d;\n}\n","export default function squaredChord(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (Math.sqrt(a[i]) - Math.sqrt(b[i])) * (Math.sqrt(a[i]) - Math.sqrt(b[i]));\n  }\n  return ans;\n}\n","export default function taneja(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      ((a[i] + b[i]) / 2) *\n      Math.log((a[i] + b[i]) / (2 * Math.sqrt(a[i] * b[i])));\n  }\n  return ans;\n}\n","export default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    var inter = 0;\n    var union = 0;\n    for (var j = 0; j < a.length; j++) {\n      inter += a[j] && b[j];\n      union += a[j] || b[j];\n    }\n    if (union === 0) {\n      return 1;\n    }\n    return inter / union;\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return 1 - (p + q - 2 * m) / (p + q - m);\n  }\n}\n","import tanimotoS from '../similarities/tanimoto';\n\nexport default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    return 1 - tanimotoS(a, b, bitvector);\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return (p + q - 2 * m) / (p + q - m);\n  }\n}\n","export default function topsoe(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      a[i] * Math.log((2 * a[i]) / (a[i] + b[i])) +\n      b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function waveHedges(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += 1 - Math.min(a[i], b[i]) / Math.max(a[i], b[i]);\n  }\n  return ans;\n}\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\n/**\n * Function that creates the tree\n * @param {Array<Array<number>>} spectrum\n * @param {object} [options]\n * @return {Tree|null}\n * left and right have the same structure than the parent,\n * or are null if they are leaves\n */\nexport function createTree(spectrum, options = {}) {\n  var X = spectrum[0];\n  const {\n    minWindow = 0.16,\n    threshold = 0.01,\n    from = X[0],\n    to = X[X.length - 1]\n  } = options;\n\n  return mainCreateTree(\n    spectrum[0],\n    spectrum[1],\n    from,\n    to,\n    minWindow,\n    threshold\n  );\n}\n\nfunction mainCreateTree(X, Y, from, to, minWindow, threshold) {\n  if (to - from < minWindow) {\n    return null;\n  }\n\n  // search first point\n  var start = binarySearch(X, from, ascending);\n  if (start < 0) {\n    start = ~start;\n  }\n\n  // stop at last point\n  var sum = 0;\n  var center = 0;\n  for (var i = start; i < X.length; i++) {\n    if (X[i] >= to) {\n      break;\n    }\n    sum += Y[i];\n    center += X[i] * Y[i];\n  }\n\n  if (sum < threshold) {\n    return null;\n  }\n\n  center /= sum;\n  if (center - from < 1e-6 || to - center < 1e-6) {\n    return null;\n  }\n  if (center - from < minWindow / 4) {\n    return mainCreateTree(X, Y, center, to, minWindow, threshold);\n  } else {\n    if (to - center < minWindow / 4) {\n      return mainCreateTree(X, Y, from, center, minWindow, threshold);\n    } else {\n      return new Tree(\n        sum,\n        center,\n        mainCreateTree(X, Y, from, center, minWindow, threshold),\n        mainCreateTree(X, Y, center, to, minWindow, threshold)\n      );\n    }\n  }\n}\n\nclass Tree {\n  constructor(sum, center, left, right) {\n    this.sum = sum;\n    this.center = center;\n    this.left = left;\n    this.right = right;\n  }\n}\n","import { createTree } from './createTree';\n\n/**\n * Similarity between two nodes\n * @param {Tree|Array<Array<number>>} a - tree A node\n * @param {Tree|Array<Array<number>>} b - tree B node\n * @param {object} [options]\n * @return {number} similarity measure between tree nodes\n */\nexport function getSimilarity(a, b, options = {}) {\n  const { alpha = 0.1, beta = 0.33, gamma = 0.001 } = options;\n\n  if (a === null || b === null) {\n    return 0;\n  }\n  if (Array.isArray(a)) {\n    a = createTree(a);\n  }\n  if (Array.isArray(b)) {\n    b = createTree(b);\n  }\n\n  var C =\n    (alpha * Math.min(a.sum, b.sum)) / Math.max(a.sum, b.sum) +\n    (1 - alpha) * Math.exp(-gamma * Math.abs(a.center - b.center));\n\n  return (\n    beta * C +\n    ((1 - beta) *\n      (getSimilarity(a.left, b.left, options) +\n        getSimilarity(a.right, b.right, options))) /\n      2\n  );\n}\n","import { getSimilarity } from './getSimilarity';\n\nexport { createTree } from './createTree';\n\nexport function treeSimilarity(A, B, options = {}) {\n  return getSimilarity(A, B, options);\n}\n\nexport function getFunction(options = {}) {\n  return (A, B) => getSimilarity(A, B, options);\n}\n","export default function cosine(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (Math.sqrt(p2) * Math.sqrt(q2));\n}\n","import diceD from '../distances/dice';\n\nexport default function dice(a, b) {\n  return 1 - diceD(a, b);\n}\n","import intersectionD from '../distances/intersection';\n\nexport default function intersection(a, b) {\n  return 1 - intersectionD(a, b);\n}\n","import jaccardD from '../distances/jaccard';\n\nexport default function jaccard(a, b) {\n  return 1 - jaccardD(a, b);\n}\n","import kulczynskiD from '../distances/kulczynski';\n\nexport default function kulczynski(a, b) {\n  return 1 / kulczynskiD(a, b);\n}\n","import motykaD from '../distances/motyka';\n\nexport default function motyka(a, b) {\n  return 1 - motykaD(a, b);\n}\n","import mean from 'ml-array-mean';\n\nimport cosine from './cosine';\n\nexport default function pearson(a, b) {\n  var avgA = mean(a);\n  var avgB = mean(b);\n\n  var newA = new Array(a.length);\n  var newB = new Array(b.length);\n  for (var i = 0; i < newA.length; i++) {\n    newA[i] = a[i] - avgA;\n    newB[i] = b[i] - avgB;\n  }\n\n  return cosine(newA, newB);\n}\n","import squaredChordD from '../distances/squaredChord';\n\nexport default function squaredChord(a, b) {\n  return 1 - squaredChordD(a, b);\n}\n","'use strict';\n\n// Accuracy\nexports.acc = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.tn[i] + pred.tp[i]) / (l - 1);\n    }\n    return result;\n};\n\n// Error rate\nexports.err = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.fp[i] / (l - 1));\n    }\n    return result;\n};\n\n// False positive rate\nexports.fpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fp[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// True positive rate\nexports.tpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tp[i] / pred.nPos;\n    }\n    return result;\n};\n\n// False negative rate\nexports.fnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fn[i] / pred.nPos;\n    }\n    return result;\n};\n\n// True negative rate\nexports.tnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tn[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// Positive predictive value\nexports.ppv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 0;\n    }\n    return result;\n};\n\n// Negative predictive value\nexports.npv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 0;\n    }\n    return result;\n};\n\n// Prediction conditioned fallout\nexports.pcfall = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? 1 - (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 1;\n    }\n    return result;\n};\n\n// Prediction conditioned miss\nexports.pcmiss = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? 1 - (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 1;\n    }\n    return result;\n};\n\n// Lift value\nexports.lift = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.nPosPred[i] !== 0) ? ((pred.tp[i] / pred.nPos) / (pred.nPosPred[i] / pred.nSamples)) : 0;\n    }\n    return result;\n};\n\n// Rate of positive predictions\nexports.rpp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nPosPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Rate of negative predictions\nexports.rnp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nNegPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Threshold\nexports.threshold = pred => {\n    const clone = pred.cutoffs.slice();\n    clone[0] = clone[1]; // Remove the infinite value\n    return clone;\n};\n","'use strict';\n\nconst measures = require('./measures');\n\nclass Performance {\n    /**\n     *\n     * @param prediction - The prediction matrix\n     * @param target - The target matrix (values: truthy for same class, falsy for different class)\n     * @param options\n     *\n     * @option    all    True if the entire matrix must be used. False to ignore the diagonal and lower part (default is false, for similarity/distance matrices)\n     * @option    max    True if the max value corresponds to a perfect match (like in similarity matrices), false if it is the min value (default is false, like in distance matrices. All values will be multiplied by -1)\n     */\n    constructor(prediction, target, options) {\n        options = options || {};\n        if (prediction.length !== target.length || prediction[0].length !== target[0].length) {\n            throw new Error('dimensions of prediction and target do not match');\n        }\n        const rows = prediction.length;\n        const columns = prediction[0].length;\n        const isDistance = !options.max;\n\n        const predP = [];\n\n        if (options.all) {\n            for (var i = 0; i < rows; i++) {\n                for (var j = 0; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        } else {\n            if (rows < 3 || rows !== columns) {\n                throw new Error('When \"all\" option is false, the prediction matrix must be square and have at least 3 columns');\n            }\n            for (var i = 0; i < rows - 1; i++) {\n                for (var j = i + 1; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        }\n\n        if (isDistance) {\n            predP.sort((a, b) => a.pred - b.pred);\n        } else {\n            predP.sort((a, b) => b.pred - a.pred);\n        }\n        \n        const cutoffs = this.cutoffs = [isDistance ? Number.MIN_VALUE : Number.MAX_VALUE];\n        const fp = this.fp = [0];\n        const tp = this.tp = [0];\n\n        var nPos = 0;\n        var nNeg = 0;\n\n        var currentPred = predP[0].pred;\n        var nTp = 0;\n        var nFp = 0;\n        for (var i = 0; i < predP.length; i++) {\n            if (predP[i].pred !== currentPred) {\n                cutoffs.push(currentPred);\n                fp.push(nFp);\n                tp.push(nTp);\n                currentPred = predP[i].pred;\n            }\n            if (predP[i].targ) {\n                nPos++;\n                nTp++;\n            } else {\n                nNeg++;\n                nFp++;\n            }\n        }\n        cutoffs.push(currentPred);\n        fp.push(nFp);\n        tp.push(nTp);\n\n        const l = cutoffs.length;\n        const fn = this.fn = new Array(l);\n        const tn = this.tn = new Array(l);\n        const nPosPred = this.nPosPred = new Array(l);\n        const nNegPred = this.nNegPred = new Array(l);\n\n        for (var i = 0; i < l; i++) {\n            fn[i] = nPos - tp[i];\n            tn[i] = nNeg - fp[i];\n\n            nPosPred[i] = tp[i] + fp[i];\n            nNegPred[i] = tn[i] + fn[i];\n        }\n\n        this.nPos = nPos;\n        this.nNeg = nNeg;\n        this.nSamples = nPos + nNeg;\n    }\n\n    /**\n     * Computes a measure from the prediction object.\n     *\n     * Many measures are available and can be combined :\n     * To create a ROC curve, you need fpr and tpr\n     * To create a DET curve, you need fnr and fpr\n     * To create a Lift chart, you need rpp and lift\n     *\n     * Possible measures are : threshold (Threshold), acc (Accuracy), err (Error rate),\n     * fpr (False positive rate), tpr (True positive rate), fnr (False negative rate), tnr (True negative rate), ppv (Positive predictive value),\n     * npv (Negative predictive value), pcfall (Prediction-conditioned fallout), pcmiss (Prediction-conditioned miss), lift (Lift value), rpp (Rate of positive predictions), rnp (Rate of negative predictions)\n     *\n     * @param measure - The short name of the measure\n     *\n     * @return [number]\n     */\n    getMeasure(measure) {\n        if (typeof measure !== 'string') {\n            throw new Error('No measure specified');\n        }\n        if (!measures[measure]) {\n            throw new Error(`The specified measure (${measure}) does not exist`);\n        }\n        return measures[measure](this);\n    }\n\n    /**\n     * Returns the area under the ROC curve\n     */\n    getAURC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fp[i] / this.nNeg;\n            y[i] = this.tp[i] / this.nPos;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] - x[i - 1]) * (y[i] + y[i - 1]);\n        }\n        return auc;\n    }\n\n    /**\n     * Returns the area under the DET curve\n     */\n    getAUDC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fn[i] / this.nPos;\n            y[i] = this.fp[i] / this.nNeg;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] + x[i - 1]) * (y[i] - y[i - 1]);\n        }\n        return auc;\n    }\n\n    getDistribution(options) {\n        options = options || {};\n        var cutLength = this.cutoffs.length;\n        var cutLow = options.xMin || Math.floor(this.cutoffs[cutLength - 1] * 100) / 100;\n        var cutHigh = options.xMax || Math.ceil(this.cutoffs[1] * 100) / 100;\n        var interval = options.interval || Math.floor(((cutHigh - cutLow) / 20 * 10000000) - 1) / 10000000; // Trick to avoid the precision problem of float numbers\n\n        var xLabels = [];\n        var interValues = [];\n        var intraValues = [];\n        var interCumPercent = [];\n        var intraCumPercent = [];\n\n        var nTP = this.tp[cutLength - 1], currentTP = 0;\n        var nFP = this.fp[cutLength - 1], currentFP = 0;\n\n        for (var i = cutLow, j = (cutLength - 1); i <= cutHigh; i += interval) {\n            while (this.cutoffs[j] < i)\n                j--;\n\n            xLabels.push(i);\n\n            var thisTP = nTP - currentTP - this.tp[j];\n            var thisFP = nFP - currentFP - this.fp[j];\n\n            currentTP += thisTP;\n            currentFP += thisFP;\n\n            interValues.push(thisFP);\n            intraValues.push(thisTP);\n\n            interCumPercent.push(100 - (nFP - this.fp[j]) / nFP * 100);\n            intraCumPercent.push(100 - (nTP - this.tp[j]) / nTP * 100);\n        }\n\n        return {\n            xLabels: xLabels,\n            interValues: interValues,\n            intraValues: intraValues,\n            interCumPercent: interCumPercent,\n            intraCumPercent: intraCumPercent\n        };\n    }\n}\n\nPerformance.names = {\n    acc: 'Accuracy',\n    err: 'Error rate',\n    fpr: 'False positive rate',\n    tpr: 'True positive rate',\n    fnr: 'False negative rate',\n    tnr: 'True negative rate',\n    ppv: 'Positive predictive value',\n    npv: 'Negative predictive value',\n    pcfall: 'Prediction-conditioned fallout',\n    pcmiss: 'Prediction-conditioned miss',\n    lift: 'Lift value',\n    rpp: 'Rate of positive predictions',\n    rnp: 'Rate of negative predictions',\n    threshold: 'Threshold'\n};\n\nmodule.exports = Performance;\n","'use strict';\n\nvar defaultOptions = {\n  size: 1,\n  value: 0\n};\n\n/**\n * Case when the entry is an array\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction arrayCase(data, options) {\n  var len = data.length;\n  if (typeof options.size === 'number') {\n    options.size = [options.size, options.size];\n  }\n\n  var cond = len + options.size[0] + options.size[1];\n\n  var output;\n  if (options.output) {\n    if (options.output.length !== cond) {\n      throw new RangeError('Wrong output size');\n    }\n    output = options.output;\n  } else {\n    output = new Array(cond);\n  }\n\n  var i;\n  if (options.value === 'circular') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) {\n        output[i] = data[(len - (options.size[0] % len) + i) % len];\n      } else if (i < options.size[0] + len) {\n        output[i] = data[i - options.size[0]];\n      } else {\n        output[i] = data[(i - options.size[0]) % len];\n      }\n    }\n  } else if (options.value === 'replicate') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[0];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[len - 1];\n    }\n  } else if (options.value === 'symmetric') {\n    if (options.size[0] > len || options.size[1] > len) {\n      throw new RangeError(\n        'expanded value should not be bigger than the data length'\n      );\n    }\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[options.size[0] - 1 - i];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[2 * len + options.size[0] - i - 1];\n    }\n  } else {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = options.value;\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = options.value;\n    }\n  }\n\n  return output;\n}\n\n/**\n * Case when the entry is a matrix\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction matrixCase(data, options) {\n  // var row = data.length;\n  // var col = data[0].length;\n  if (options.size[0] === undefined) {\n    options.size = [options.size, options.size, options.size, options.size];\n  }\n  throw new Error('matrix not supported yet, sorry');\n}\n\n/**\n * Pads and array\n * @param {Array <number>} data\n * @param {object} options\n */\nfunction padArray(data, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (Array.isArray(data)) {\n    if (Array.isArray(data[0])) return matrixCase(data, options);\n    else return arrayCase(data, options);\n  } else {\n    throw new TypeError('data should be an array');\n  }\n}\n\nmodule.exports = padArray;\n","import { Matrix, MatrixTransposeView, inverse } from 'ml-matrix';\nimport padArray from 'ml-pad-array';\n\n/**\n * Factorial of a number\n * @ignore\n * @param n\n * @return {number}\n */\nfunction factorial(n) {\n  let r = 1;\n  while (n > 0) r *= n--;\n  return r;\n}\n\nconst defaultOptions = {\n  windowSize: 5,\n  derivative: 1,\n  polynomial: 2,\n  pad: 'none',\n  padValue: 'replicate',\n};\n\n/**\n * Savitzky-Golay filter\n * @param {Array <number>} data\n * @param {number} h\n * @param {Object} options\n * @returns {Array}\n */\nexport default function savitzkyGolay(data, h, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (\n    options.windowSize % 2 === 0 ||\n    options.windowSize < 5 ||\n    !Number.isInteger(options.windowSize)\n  ) {\n    throw new RangeError(\n      'Invalid window size (should be odd and at least 5 integer number)',\n    );\n  }\n  if (options.derivative < 0 || !Number.isInteger(options.derivative)) {\n    throw new RangeError('Derivative should be a positive integer');\n  }\n  if (options.polynomial < 1 || !Number.isInteger(options.polynomial)) {\n    throw new RangeError('Polynomial should be a positive integer');\n  }\n\n  let C, norm;\n  let step = Math.floor(options.windowSize / 2);\n\n  if (options.pad === 'pre') {\n    data = padArray(data, { size: step, value: options.padValue });\n  }\n\n  let ans = new Array(data.length - 2 * step);\n\n  if (\n    options.windowSize === 5 &&\n    options.polynomial === 2 &&\n    (options.derivative === 1 || options.derivative === 2)\n  ) {\n    if (options.derivative === 1) {\n      C = [-2, -1, 0, 1, 2];\n      norm = 10;\n    } else {\n      C = [2, -1, -2, -1, 2];\n      norm = 7;\n    }\n  } else {\n    let J = Matrix.ones(options.windowSize, options.polynomial + 1);\n    let inic = -(options.windowSize - 1) / 2;\n    for (let i = 0; i < J.rows; i++) {\n      for (let j = 0; j < J.columns; j++) {\n        if (inic + 1 !== 0 || j !== 0) J.set(i, j, Math.pow(inic + i, j));\n      }\n    }\n    let Jtranspose = new MatrixTransposeView(J);\n    let Jinv = inverse(Jtranspose.mmul(J));\n    C = Jinv.mmul(Jtranspose);\n    C = C.getRow(options.derivative);\n    norm = 1 / factorial(options.derivative);\n  }\n  let det = norm * Math.pow(h, options.derivative);\n  for (let k = step; k < data.length - step; k++) {\n    let d = 0;\n    for (let l = 0; l < C.length; l++) d += (C[l] * data[l + k - step]) / det;\n    ans[k - step] = d;\n  }\n\n  if (options.pad === 'post') {\n    ans = padArray(ans, { size: step, value: options.padValue });\n  }\n\n  return ans;\n}\n","// auxiliary file to create the 256 look at table elements\n\nvar ans = new Array(256);\nfor (var i = 0; i < 256; i++) {\n    var num = i;\n    var c = 0;\n    while (num) {\n        num = num & (num - 1);\n        c++;\n    }\n    ans[i] = c;\n}\n\nmodule.exports = ans;","'use strict';\n\nvar eightBits = require('./creator');\n\n/**\n * Count the number of true values in an array\n * @param {Array} arr\n * @return {number}\n */\nfunction count(arr) {\n    var c = 0;\n    for (var i = 0; i < arr.length; i++) {\n        c += eightBits[arr[i] & 0xff] + eightBits[(arr[i] >> 8) & 0xff] + eightBits[(arr[i] >> 16) & 0xff] + eightBits[(arr[i] >> 24) & 0xff];\n    }\n    return c;\n}\n\n/**\n * Logical AND operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction and(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] & arr2[i];\n    return ans;\n}\n\n/**\n * Logical OR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction or(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] | arr2[i];\n    return ans;\n}\n\n/**\n * Logical XOR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction xor(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] ^ arr2[i];\n    return ans;\n}\n\n/**\n * Logical NOT operation\n * @param {Array} arr\n * @return {Array}\n */\nfunction not(arr) {\n    var ans = new Array(arr.length);\n    for (var i = 0; i < ans.length; i++)\n        ans[i] = ~arr[i];\n    return ans;\n}\n\n/**\n * Gets the n value of array arr\n * @param {Array} arr\n * @param {number} n\n * @return {boolean}\n */\nfunction getBit(arr, n) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    return Boolean(arr[index] & mask);\n}\n\n/**\n * Sets the n value of array arr to the value val\n * @param {Array} arr\n * @param {number} n\n * @param {boolean} val\n * @return {Array}\n */\nfunction setBit(arr, n, val) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    if (val)\n        arr[index] = mask | arr[index];\n    else\n        arr[index] = ~mask & arr[index];\n    return arr;\n}\n\n/**\n * Translates an array of numbers to a string of bits\n * @param {Array} arr\n * @returns {string}\n */\nfunction toBinaryString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(2);\n        str += '00000000000000000000000000000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a string of bits\n * @param {string} str\n * @returns {Array}\n */\nfunction parseBinaryString(str) {\n    var len = str.length / 32;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*32, 32), 2) | 0;\n    }\n    return ans;\n}\n\n/**\n * Translates an array of numbers to a hex string\n * @param {Array} arr\n * @returns {string}\n */\nfunction toHexString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(16);\n        str += '00000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a hex string\n * @param {string} str\n * @returns {Array}\n */\nfunction parseHexString(str) {\n    var len = str.length / 8;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*8, 8), 16) | 0;\n    }\n    return ans;\n}\n\n/**\n * Creates a human readable string of the array\n * @param {Array} arr\n * @returns {string}\n */\nfunction toDebug(arr) {\n    var binary = toBinaryString(arr);\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        str += '0000'.substr((i * 32).toString(16).length) + (i * 32).toString(16) + ':';\n        for (var j = 0; j < 32; j += 4) {\n            str += ' ' + binary.substr(i * 32 + j, 4);\n        }\n        if (i < arr.length - 1) str += '\\n';\n    }\n    return str\n}\n\nmodule.exports = {\n    count: count,\n    and: and,\n    or: or,\n    xor: xor,\n    not: not,\n    getBit: getBit,\n    setBit: setBit,\n    toBinaryString: toBinaryString,\n    parseBinaryString: parseBinaryString,\n    toHexString: toHexString,\n    parseHexString: parseHexString,\n    toDebug: toDebug\n};\n","export default function SavitzkyGolay(data, h, options = {}) {\n  let { windowSize = 9, derivative = 0, polynomial = 3 } = options;\n\n  if (windowSize % 2 === 0 || windowSize < 5 || !Number.isInteger(windowSize)) {\n    throw new RangeError(\n      'Invalid window size (should be odd and at least 5 integer number)',\n    );\n  }\n  if (windowSize > data.length) {\n    throw new RangeError(\n      `Window size is higher than the data length ${windowSize}>${data.length}`,\n    );\n  }\n  if (derivative < 0 || !Number.isInteger(derivative)) {\n    throw new RangeError('Derivative should be a positive integer');\n  }\n  if (polynomial < 1 || !Number.isInteger(polynomial)) {\n    throw new RangeError('Polynomial should be a positive integer');\n  }\n  if (polynomial >= 6) {\n    // eslint-disable-next-line no-console\n    console.warn(\n      'You should not use polynomial grade higher than 5 if you are' +\n        ' not sure that your data arises from such a model. Possible polynomial oscillation problems',\n    );\n  }\n\n  let half = Math.floor(windowSize / 2);\n  let np = data.length;\n  let ans = new Array(np);\n  let weights = fullWeights(windowSize, polynomial, derivative);\n  let hs = 0;\n  let constantH = true;\n  if (Array.isArray(h)) {\n    constantH = false;\n  } else {\n    hs = Math.pow(h, derivative);\n  }\n\n  //For the borders\n  for (let i = 0; i < half; i++) {\n    let wg1 = weights[half - i - 1];\n    let wg2 = weights[half + i + 1];\n    let d1 = 0;\n    let d2 = 0;\n    for (let l = 0; l < windowSize; l++) {\n      d1 += wg1[l] * data[l];\n      d2 += wg2[l] * data[np - windowSize + l];\n    }\n    if (constantH) {\n      ans[half - i - 1] = d1 / hs;\n      ans[np - half + i] = d2 / hs;\n    } else {\n      hs = getHs(h, half - i - 1, half, derivative);\n      ans[half - i - 1] = d1 / hs;\n      hs = getHs(h, np - half + i, half, derivative);\n      ans[np - half + i] = d2 / hs;\n    }\n  }\n\n  //For the internal points\n  let wg = weights[half];\n  for (let i = windowSize; i <= np; i++) {\n    let d = 0;\n    for (let l = 0; l < windowSize; l++) d += wg[l] * data[l + i - windowSize];\n    if (!constantH) hs = getHs(h, i - half - 1, half, derivative);\n    ans[i - half - 1] = d / hs;\n  }\n  return ans;\n}\n\nfunction getHs(h, center, half, derivative) {\n  let hs = 0;\n  let count = 0;\n  for (let i = center - half; i < center + half; i++) {\n    if (i >= 0 && i < h.length - 1) {\n      hs += h[i + 1] - h[i];\n      count++;\n    }\n  }\n  return Math.pow(hs / count, derivative);\n}\n\nfunction GramPoly(i, m, k, s) {\n  let Grampoly = 0;\n  if (k > 0) {\n    Grampoly =\n      ((4 * k - 2) / (k * (2 * m - k + 1))) *\n        (i * GramPoly(i, m, k - 1, s) + s * GramPoly(i, m, k - 1, s - 1)) -\n      (((k - 1) * (2 * m + k)) / (k * (2 * m - k + 1))) *\n        GramPoly(i, m, k - 2, s);\n  } else {\n    if (k === 0 && s === 0) {\n      Grampoly = 1;\n    } else {\n      Grampoly = 0;\n    }\n  }\n  return Grampoly;\n}\n\nfunction GenFact(a, b) {\n  let gf = 1;\n  if (a >= b) {\n    for (let j = a - b + 1; j <= a; j++) {\n      gf *= j;\n    }\n  }\n  return gf;\n}\n\nfunction Weight(i, t, m, n, s) {\n  let sum = 0;\n  for (let k = 0; k <= n; k++) {\n    //console.log(k);\n    sum +=\n      (2 * k + 1) *\n      (GenFact(2 * m, k) / GenFact(2 * m + k + 1, k + 1)) *\n      GramPoly(i, m, k, 0) *\n      GramPoly(t, m, k, s);\n  }\n  return sum;\n}\n\n/**\n *\n * @param m  Number of points\n * @param n  Polynomial grade\n * @param s  Derivative\n */\nfunction fullWeights(m, n, s) {\n  let weights = new Array(m);\n  let np = Math.floor(m / 2);\n  for (let t = -np; t <= np; t++) {\n    weights[t + np] = new Array(m);\n    for (let j = -np; j <= np; j++) {\n      weights[t + np][j + np] = Weight(j, t, np, n, s);\n    }\n  }\n  return weights;\n}\n\n/*function entropy(data,h,options){\n    var trend = SavitzkyGolay(data,h,trendOptions);\n    var copy = new Array(data.length);\n    var sum = 0;\n    var max = 0;\n    for(var i=0;i<data.length;i++){\n        copy[i] = data[i]-trend[i];\n    }\n\n    sum/=data.length;\n    console.log(sum+\" \"+max);\n    console.log(stat.array.standardDeviation(copy));\n    console.log(Math.abs(stat.array.mean(copy))/stat.array.standardDeviation(copy));\n    return sum;\n\n}\n\n\n\nfunction guessWindowSize(data, h){\n    console.log(\"entropy \"+entropy(data,h,trendOptions));\n    return 5;\n}\n*/\n","import SG from 'ml-savitzky-golay-generalized';\n\n/**\n * Global spectra deconvolution\n * @param {Array<number>} x - Independent variable\n * @param {Array<number>} yIn - Dependent variable\n * @param {object} [options] - Options object\n * @param {object} [options.sgOptions] - Options object for Savitzky-Golay filter. See https://github.com/mljs/savitzky-golay-generalized#options\n * @param {number} [options.sgOptions.windowSize = 9] - points to use in the approximations\n * @param {number} [options.sgOptions.polynomial = 3] - degree of the polynomial to use in the approximations\n * @param {number} [options.minMaxRatio = 0.00025] - Threshold to determine if a given peak should be considered as a noise\n * @param {number} [options.broadRatio = 0.00] - If `broadRatio` is higher than 0, then all the peaks which second derivative\n * smaller than `broadRatio * maxAbsSecondDerivative` will be marked with the soft mask equal to true.\n * @param {number} [options.noiseLevel = 0] - Noise threshold in spectrum units\n * @param {boolean} [options.maxCriteria = true] - Peaks are local maximum(true) or minimum(false)\n * @param {boolean} [options.smoothY = true] - Select the peak intensities from a smoothed version of the independent variables\n * @param {boolean} [options.realTopDetection = false] - Use a quadratic optimizations with the peak and its 3 closest neighbors\n * to determine the true x,y values of the peak?\n * @param {number} [options.heightFactor = 0] - Factor to multiply the calculated height (usually 2)\n * @param {number} [options.derivativeThreshold = -1] - Filters based on the amplitude of the first derivative\n * @return {Array<object>}\n */\nexport function gsd(x, yIn, options = {}) {\n  let {\n    noiseLevel,\n    sgOptions = {\n      windowSize: 9,\n      polynomial: 3,\n    },\n    smoothY = true,\n    heightFactor = 0,\n    broadRatio = 0.0,\n    maxCriteria = true,\n    minMaxRatio = 0.00025,\n    derivativeThreshold = -1,\n    realTopDetection = false,\n  } = options;\n\n  const y = yIn.slice();\n  let equalSpaced = isEqualSpaced(x);\n\n  if (noiseLevel === undefined) {\n    noiseLevel = equalSpaced ? getNoiseLevel(y) : 0;\n  }\n\n  const yCorrection = { m: 1, b: noiseLevel };\n\n  if (!maxCriteria) {\n    yCorrection.m = -1;\n    yCorrection.b *= -1;\n  }\n\n  for (let i = 0; i < y.length; i++) {\n    y[i] = yCorrection.m * y[i] - yCorrection.b;\n  }\n\n  for (let i = 0; i < y.length; i++) {\n    if (y[i] < 0) {\n      y[i] = 0;\n    }\n  }\n  // If the max difference between delta x is less than 5%, then,\n  // we can assume it to be equally spaced variable\n  let yData = y;\n  let dY, ddY;\n  const { windowSize, polynomial } = sgOptions;\n\n  if (equalSpaced) {\n    if (smoothY) {\n      yData = SG(y, x[1] - x[0], {\n        windowSize,\n        polynomial,\n        derivative: 0,\n      });\n    }\n    dY = SG(y, x[1] - x[0], {\n      windowSize,\n      polynomial,\n      derivative: 1,\n    });\n    ddY = SG(y, x[1] - x[0], {\n      windowSize,\n      polynomial,\n      derivative: 2,\n    });\n  } else {\n    if (smoothY) {\n      yData = SG(y, x, {\n        windowSize,\n        polynomial,\n        derivative: 0,\n      });\n    }\n    dY = SG(y, x, {\n      windowSize,\n      polynomial,\n      derivative: 1,\n    });\n    ddY = SG(y, x, {\n      windowSize,\n      polynomial,\n      derivative: 2,\n    });\n  }\n\n  const xData = x;\n  const dX = x[1] - x[0];\n  let maxDdy = 0;\n  let maxY = 0;\n  for (let i = 0; i < yData.length; i++) {\n    if (Math.abs(ddY[i]) > maxDdy) {\n      maxDdy = Math.abs(ddY[i]);\n    }\n    if (Math.abs(yData[i]) > maxY) {\n      maxY = Math.abs(yData[i]);\n    }\n  }\n\n  let lastMax = null;\n  let lastMin = null;\n  let minddY = new Array(yData.length - 2);\n  let intervalL = new Array(yData.length);\n  let intervalR = new Array(yData.length);\n  let broadMask = new Array(yData.length - 2);\n  let minddYLen = 0;\n  let intervalLLen = 0;\n  let intervalRLen = 0;\n  let broadMaskLen = 0;\n  // By the intermediate value theorem We cannot find 2 consecutive maximum or minimum\n  for (let i = 1; i < yData.length - 1; ++i) {\n    // filter based on derivativeThreshold\n    // console.log('pasa', y[i], dY[i], ddY[i]);\n    if (Math.abs(dY[i]) > derivativeThreshold) {\n      // Minimum in first derivative\n      if (\n        (dY[i] < dY[i - 1] && dY[i] <= dY[i + 1]) ||\n        (dY[i] <= dY[i - 1] && dY[i] < dY[i + 1])\n      ) {\n        lastMin = {\n          x: xData[i],\n          index: i,\n        };\n        if (dX > 0 && lastMax !== null) {\n          intervalL[intervalLLen++] = lastMax;\n          intervalR[intervalRLen++] = lastMin;\n        }\n      }\n\n      // Maximum in first derivative\n      if (\n        (dY[i] >= dY[i - 1] && dY[i] > dY[i + 1]) ||\n        (dY[i] > dY[i - 1] && dY[i] >= dY[i + 1])\n      ) {\n        lastMax = {\n          x: xData[i],\n          index: i,\n        };\n        if (dX < 0 && lastMin !== null) {\n          intervalL[intervalLLen++] = lastMax;\n          intervalR[intervalRLen++] = lastMin;\n        }\n      }\n    }\n\n    // Minimum in second derivative\n    if (ddY[i] < ddY[i - 1] && ddY[i] < ddY[i + 1]) {\n      // TODO should we change this to have 3 arrays ? Huge overhead creating arrays\n      minddY[minddYLen++] = i; // ( [xData[i], yData[i], i] );\n      broadMask[broadMaskLen++] = Math.abs(ddY[i]) <= broadRatio * maxDdy;\n    }\n  }\n  minddY.length = minddYLen;\n  intervalL.length = intervalLLen;\n  intervalR.length = intervalRLen;\n  broadMask.length = broadMaskLen;\n\n  let signals = new Array(minddY.length);\n  let signalsLen = 0;\n  let lastK = -1;\n  let possible, frequency, distanceJ, minDistance, gettingCloser;\n  for (let j = 0; j < minddY.length; ++j) {\n    frequency = xData[minddY[j]];\n    possible = -1;\n    let k = lastK + 1;\n    minDistance = Number.MAX_VALUE;\n    distanceJ = 0;\n    gettingCloser = true;\n    while (possible === -1 && k < intervalL.length && gettingCloser) {\n      distanceJ = Math.abs(frequency - (intervalL[k].x + intervalR[k].x) / 2);\n\n      // Still getting closer?\n      if (distanceJ < minDistance) {\n        minDistance = distanceJ;\n      } else {\n        gettingCloser = false;\n      }\n      if (distanceJ < Math.abs(intervalL[k].x - intervalR[k].x) / 2) {\n        possible = k;\n        lastK = k;\n      }\n      ++k;\n    }\n\n    if (possible !== -1) {\n      if (Math.abs(yData[minddY[j]]) > minMaxRatio * maxY) {\n        signals[signalsLen++] = {\n          index: minddY[j],\n          x: frequency,\n          y: (yData[minddY[j]] + yCorrection.b) / yCorrection.m,\n          width: Math.abs(intervalR[possible].x - intervalL[possible].x), // widthCorrection\n          soft: broadMask[j],\n        };\n\n        signals[signalsLen - 1].left = intervalL[possible];\n        signals[signalsLen - 1].right = intervalR[possible];\n\n        if (heightFactor) {\n          let yLeft = yData[intervalL[possible].index];\n          let yRight = yData[intervalR[possible].index];\n          signals[signalsLen - 1].height =\n            heightFactor * (signals[signalsLen - 1].y - (yLeft + yRight) / 2);\n        }\n      }\n    }\n  }\n  signals.length = signalsLen;\n\n  if (realTopDetection) {\n    determineRealTop(signals, xData, yData);\n  }\n\n  // Correct the values to fit the original spectra data\n  for (let j = 0; j < signals.length; j++) {\n    signals[j].base = noiseLevel;\n  }\n\n  signals.sort(function (a, b) {\n    return a.x - b.x;\n  });\n\n  return signals;\n}\n\nconst isEqualSpaced = (x) => {\n  let tmp;\n  let maxDx = 0;\n  let minDx = Number.MAX_SAFE_INTEGER;\n  for (let i = 0; i < x.length - 1; ++i) {\n    tmp = Math.abs(x[i + 1] - x[i]);\n    if (tmp < minDx) {\n      minDx = tmp;\n    }\n    if (tmp > maxDx) {\n      maxDx = tmp;\n    }\n  }\n  return (maxDx - minDx) / maxDx < 0.05;\n};\n\nconst getNoiseLevel = (y) => {\n  let mean = 0;\n\n  let stddev = 0;\n  let length = y.length;\n  for (let i = 0; i < length; ++i) {\n    mean += y[i];\n  }\n  mean /= length;\n  let averageDeviations = new Array(length);\n  for (let i = 0; i < length; ++i) {\n    averageDeviations[i] = Math.abs(y[i] - mean);\n  }\n  averageDeviations.sort((a, b) => a - b);\n  if (length % 2 === 1) {\n    stddev = averageDeviations[(length - 1) / 2] / 0.6745;\n  } else {\n    stddev =\n      (0.5 *\n        (averageDeviations[length / 2] + averageDeviations[length / 2 - 1])) /\n      0.6745;\n  }\n\n  return stddev;\n};\n\nconst determineRealTop = (peakList, x, y) => {\n  let alpha, beta, gamma, p, currentPoint;\n  for (let j = 0; j < peakList.length; j++) {\n    currentPoint = peakList[j].index; // peakList[j][2];\n    // The detected peak could be moved 1 or 2 units to left or right.\n    if (\n      y[currentPoint - 1] >= y[currentPoint - 2] &&\n      y[currentPoint - 1] >= y[currentPoint]\n    ) {\n      currentPoint--;\n    } else {\n      if (\n        y[currentPoint + 1] >= y[currentPoint] &&\n        y[currentPoint + 1] >= y[currentPoint + 2]\n      ) {\n        currentPoint++;\n      } else {\n        if (\n          y[currentPoint - 2] >= y[currentPoint - 3] &&\n          y[currentPoint - 2] >= y[currentPoint - 1]\n        ) {\n          currentPoint -= 2;\n        } else {\n          if (\n            y[currentPoint + 2] >= y[currentPoint + 1] &&\n            y[currentPoint + 2] >= y[currentPoint + 3]\n          ) {\n            currentPoint += 2;\n          }\n        }\n      }\n    }\n    // interpolation to a sin() function\n    if (\n      y[currentPoint - 1] > 0 &&\n      y[currentPoint + 1] > 0 &&\n      y[currentPoint] >= y[currentPoint - 1] &&\n      y[currentPoint] >= y[currentPoint + 1] &&\n      (y[currentPoint] !== y[currentPoint - 1] ||\n        y[currentPoint] !== y[currentPoint + 1])\n    ) {\n      alpha = 20 * Math.log10(y[currentPoint - 1]);\n      beta = 20 * Math.log10(y[currentPoint]);\n      gamma = 20 * Math.log10(y[currentPoint + 1]);\n      p = (0.5 * (alpha - gamma)) / (alpha - 2 * beta + gamma);\n      // console.log(alpha, beta, gamma, `p: ${p}`);\n      // console.log(x[currentPoint]+\" \"+tmp+\" \"+currentPoint);\n      peakList[j].x =\n        x[currentPoint] + (x[currentPoint] - x[currentPoint - 1]) * p;\n      peakList[j].y =\n        y[currentPoint] -\n        0.25 * (y[currentPoint - 1] - y[currentPoint + 1]) * p;\n    }\n  }\n};\n","/**\n * This function calculates the spectrum as a sum of gaussian functions. The Gaussian\n * parameters are divided in 3 batches. 1st: centers; 2nd: height; 3th: std's;\n * @param t Ordinate values\n * @param p Gaussian parameters\n * @param c Constant parameters(Not used)\n * @returns {*}\n */\nexport function sumOfGaussians(p) {\n  return function (t) {\n    let nL = p.length / 3;\n    let factor;\n    let rows = t.length;\n    let result = rows === undefined ? 0 : new Float64Array(rows).fill(0);\n    for (let i = 0; i < nL; i++) {\n      factor = Math.pow(p[i + nL * 2], 2) * 2;\n      if (rows === undefined) {\n        result += p[i + nL] * Math.exp(-Math.pow(t - p[i], 2) / factor);\n      } else {\n        for (let j = 0; j < rows; j++) {\n          result[j] += p[i + nL] * Math.exp(-Math.pow(t[j] - p[i], 2) / factor);\n        }\n      }\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { sumOfGaussians } from './sumOfGaussians';\n\n/**\n *\n * @param xy A two column matrix containing the x and y data to be fitted\n * @param group A set of initial lorentzian parameters to be optimized [center, heigth, half_width_at_half_height]\n * @returns {Array} A set of final lorentzian parameters [center, heigth, hwhh*2]\n */\nexport function optimizeGaussianSum(xy, group, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n  let nL = group.length;\n  let pInit = new Float64Array(nL * 3);\n  let pMin = new Float64Array(nL * 3);\n  let pMax = new Float64Array(nL * 3);\n  let dt = Math.abs(t[0] - t[1]);\n\n  for (let i = 0; i < nL; i++) {\n    pInit[i] = group[i].x;\n    pInit[i + nL] = group[i].y / maxY;\n    pInit[i + 2 * nL] = group[i].width;\n\n    pMin[i] = group[i].x - dt;\n    pMin[i + nL] = 0;\n    pMin[i + 2 * nL] = group[i].width / 4;\n\n    pMax[i] = group[i].x + dt;\n    pMax[i + nL] = (group[i].y * 1.2) / maxY;\n    pMax[i + 2 * nL] = group[i].width * 4;\n  }\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n  let result = new Array(nL);\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n\n  opts = Object.assign({}, lmOptions, opts);\n\n  let pFit = LM(data, sumOfGaussians, opts);\n  for (let i = 0; i < nL; i++) {\n    result[i] = {\n      parameters: [\n        pFit.parameterValues[i],\n        pFit.parameterValues[i + nL] * maxY,\n        pFit.parameterValues[i + nL * 2],\n      ],\n      error: pFit.parameterError,\n    };\n  }\n  return result;\n}\n","/**\n * Single 3 parameter gaussian function\n * @param t Ordinate values\n * @param p Gaussian parameters [mean, height, std]\n * @param c Constant parameters(Not used)\n * @returns {*}\n */\n\nexport function singleGaussian(p) {\n  return function (t) {\n    let factor2 = (p[2] * p[2]) / 2;\n    let rows = t.length;\n    if (!rows) return p[1] * Math.exp((-(t - p[0]) * (t - p[0])) / factor2);\n    let result = new Float64Array(t.length);\n    for (let i = 0; i < t.length; i++) {\n      result[i] = p[1] * Math.exp((-(t[i] - p[0]) * (t[i] - p[0])) / factor2);\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { singleGaussian } from './singleGaussian';\n\n/**\n * Fits a set of points to a gaussian bell. Returns the mean of the peak, the std and the height of the signal.\n * @param data,[y]\n * @returns {*[]}\n */\nexport function optimizeSingleGaussian(xy, peak, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n  let dt = Math.abs(t[0] - t[1]);\n  let pInit = new Float64Array([peak.x, 1, peak.width]);\n  let pMin = new Float64Array([peak.x - dt, 0, peak.width / 4]);\n  let pMax = new Float64Array([peak.x + dt, 1.25, peak.width * 4]);\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n\n  opts = Object.assign({}, lmOptions, opts);\n  let pFit = LM(data, singleGaussian, opts);\n  return {\n    parameters: [\n      pFit.parameterValues[0],\n      pFit.parameterValues[1] * maxY,\n      pFit.parameterValues[2],\n    ],\n    error: pFit.parameterError,\n  };\n}\n","/**\n * This function calculates the spectrum as a sum of lorentzian functions. The Lorentzian\n * parameters are divided in 3 batches. 1st: centers; 2nd: heights; 3th: widths;\n * @param t Ordinate values\n * @param p Lorentzian parameters\n * @returns {*}\n */\n\nexport function sumOfLorentzians(p) {\n  return function (t) {\n    let nL = p.length / 3;\n    let factor;\n    let p2;\n    let rows = t.length;\n    let result = rows === undefined ? 0 : new Float64Array(rows).fill(0);\n    for (let i = 0; i < nL; i++) {\n      p2 = Math.pow(p[i + nL * 2] / 2, 2);\n      factor = p[i + nL] * p2;\n      if (rows === undefined) {\n        result += factor / (Math.pow(t - p[i], 2) + p2);\n      } else {\n        for (let j = 0; j < rows; j++) {\n          result[j] += factor / (Math.pow(t[j] - p[i], 2) + p2);\n        }\n      }\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { sumOfLorentzians } from './sumOfLorentzians';\n\n/**\n *\n * @param xy A two column matrix containing the x and y data to be fitted\n * @param group A set of initial lorentzian parameters to be optimized [center, heigth, half_width_at_half_height]\n * @returns {Array} A set of final lorentzian parameters [center, heigth, hwhh*2]\n */\nexport function optimizeLorentzianSum(xy, group, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n\n  let nL = group.length;\n  let pInit = new Float64Array(nL * 3);\n  let pMin = new Float64Array(nL * 3);\n  let pMax = new Float64Array(nL * 3);\n  let dt = Math.abs(t[0] - t[1]);\n\n  for (let i = 0; i < nL; i++) {\n    pInit[i] = group[i].x;\n    pInit[i + nL] = 1;\n    pInit[i + 2 * nL] = group[i].width;\n\n    pMin[i] = group[i].x - dt;\n    pMin[i + nL] = 0;\n    pMin[i + 2 * nL] = group[i].width / 4;\n\n    pMax[i] = group[i].x + dt;\n    pMax[i + nL] = 1.5;\n    pMax[i + 2 * nL] = group[i].width * 4;\n  }\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n\n  let result = new Array(nL);\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n\n  opts = Object.assign({}, lmOptions, opts);\n\n  let pFit = LM(data, sumOfLorentzians, opts);\n  for (let i = 0; i < nL; i++) {\n    result[i] = {\n      parameters: [\n        pFit.parameterValues[i],\n        pFit.parameterValues[i + nL] * maxY,\n        pFit.parameterValues[i + nL * 2],\n      ],\n      error: pFit.parameterError,\n    };\n  }\n  return result;\n}\n","/**\n * Single 4 parameter lorentzian function\n * @param t Ordinate values\n * @param p Lorentzian parameters\n * @param c Constant parameters(Not used)\n * @returns {*}\n */\n\nexport function singleLorentzian(p) {\n  return function (t) {\n    let factor = p[1] * Math.pow(p[2] / 2, 2);\n    let rows = t.length;\n    if (!rows) return factor / (Math.pow(t - p[0], 2) + Math.pow(p[2] / 2, 2));\n    let result = new Float64Array(rows);\n    for (let i = 0; i < rows; i++) {\n      result[i] = factor / (Math.pow(t[i] - p[0], 2) + Math.pow(p[2] / 2, 2));\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { singleLorentzian } from './singleLorentzian';\n\n/**\n * * Fits a set of points to a Lorentzian function. Returns the center of the peak, the width at half height, and the height of the signal.\n * @param data,[y]\n * @returns {*[]}\n */\nexport function optimizeSingleLorentzian(xy, peak, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n  let dt = Math.abs(t[0] - t[1]);\n  let pInit = new Float64Array([peak.x, 1, peak.width]);\n  let pMin = new Float64Array([peak.x - dt, 0.75, peak.width / 4]);\n  let pMax = new Float64Array([peak.x + dt, 1.25, peak.width * 4]);\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n  opts = Object.assign({}, lmOptions, opts);\n  let pFit = LM(data, singleLorentzian, opts);\n  return {\n    parameters: [\n      pFit.parameterValues[0],\n      pFit.parameterValues[1] * maxY,\n      pFit.parameterValues[2],\n    ],\n    error: pFit.parameterError,\n  };\n}\n","import {\n  optimizeGaussianSum,\n  optimizeLorentzianSum,\n  optimizeSingleGaussian,\n  optimizeSingleLorentzian,\n} from 'ml-optimize-lorentzian';\n\nexport function optimizePeaks(peakList, x, y, options = {}) {\n  const {\n    functionName = 'gaussian',\n    factorWidth = 4,\n    optimizationOptions = {\n      damping: 1.5,\n      maxIterations: 100,\n      errorTolerance: 10e-5,\n    },\n  } = options;\n\n  let lastIndex = [0];\n  let groups = groupPeaks(peakList, factorWidth);\n  let result = [];\n  let factor = 1;\n  if (functionName === 'gaussian') {\n    factor = 1.17741;\n  } // From https://en.wikipedia.org/wiki/Gaussian_function#Properties\n  let sampling;\n  for (let i = 0; i < groups.length; i++) {\n    let peaks = groups[i].group;\n    if (peaks.length > 1) {\n      // Multiple peaks\n      sampling = sampleFunction(\n        groups[i].limits[0] - groups[i].limits[1],\n        groups[i].limits[0] + groups[i].limits[1],\n        x,\n        y,\n        lastIndex,\n      );\n      if (sampling[0].length > 5) {\n        let optPeaks = [];\n        if (functionName === 'gaussian') {\n          optPeaks = optimizeGaussianSum(sampling, peaks, optimizationOptions);\n        } else {\n          if (functionName === 'lorentzian') {\n            optPeaks = optimizeLorentzianSum(\n              sampling,\n              peaks,\n              optimizationOptions,\n            );\n          }\n        }\n\n        for (let j = 0; j < optPeaks.length; j++) {\n          let { parameters } = optPeaks[j];\n          result.push({\n            x: parameters[0],\n            y: parameters[1],\n            width: parameters[2] * factor,\n            index: peaks[j].index,\n          });\n        }\n      }\n    } else {\n      // Single peak\n      peaks = peaks[0];\n      sampling = sampleFunction(\n        peaks.x - factorWidth * peaks.width,\n        peaks.x + factorWidth * peaks.width,\n        x,\n        y,\n        lastIndex,\n      );\n\n      if (sampling[0].length > 5) {\n        let fitResult = [];\n        if (functionName === 'gaussian') {\n          fitResult = optimizeSingleGaussian(\n            [sampling[0], sampling[1]],\n            peaks,\n            optimizationOptions,\n          );\n        } else {\n          if (functionName === 'lorentzian') {\n            fitResult = optimizeSingleLorentzian(\n              [sampling[0], sampling[1]],\n              peaks,\n              optimizationOptions,\n            );\n          }\n        }\n\n        let { parameters } = fitResult;\n        result.push({\n          x: parameters[0],\n          y: parameters[1],\n          width: parameters[2] * factor,\n          index: peaks.index,\n        }); // From https://en.wikipedia.org/wiki/Gaussian_function#Properties}\n      }\n    }\n  }\n  return result;\n}\n\nfunction sampleFunction(from, to, x, y, lastIndex) {\n  let nbPoints = x.length;\n  let sampleX = [];\n  let sampleY = [];\n  let direction = Math.sign(x[1] - x[0]); // Direction of the derivative\n  if (direction === -1) {\n    lastIndex[0] = x.length - 1;\n  }\n\n  let delta = Math.abs(to - from) / 2;\n  let mid = (from + to) / 2;\n  let stop = false;\n  let index = lastIndex[0];\n  while (!stop && index < nbPoints && index >= 0) {\n    if (Math.abs(x[index] - mid) <= delta) {\n      sampleX.push(x[index]);\n      sampleY.push(y[index]);\n      index += direction;\n    } else {\n      // It is outside the range.\n      if (Math.sign(mid - x[index]) === 1) {\n        // We'll reach the mid going in the current direction\n        index += direction;\n      } else {\n        // There is not more peaks in the current range\n        stop = true;\n      }\n    }\n  }\n  lastIndex[0] = index;\n  return [sampleX, sampleY];\n}\n\nfunction groupPeaks(peakList, nL) {\n  let group = [];\n  let groups = [];\n  let limits = [peakList[0].x, nL * peakList[0].width];\n  let upperLimit, lowerLimit;\n  // Merge forward\n  for (let i = 0; i < peakList.length; i++) {\n    // If the 2 things overlaps\n    if (\n      Math.abs(peakList[i].x - limits[0]) <\n      nL * peakList[i].width + limits[1]\n    ) {\n      // Add the peak to the group\n      group.push(peakList[i]);\n      // Update the group limits\n      upperLimit = limits[0] + limits[1];\n      if (peakList[i].x + nL * peakList[i].width > upperLimit) {\n        upperLimit = peakList[i].x + nL * peakList[i].width;\n      }\n      lowerLimit = limits[0] - limits[1];\n      if (peakList[i].x - nL * peakList[i].width < lowerLimit) {\n        lowerLimit = peakList[i].x - nL * peakList[i].width;\n      }\n      limits = [\n        (upperLimit + lowerLimit) / 2,\n        Math.abs(upperLimit - lowerLimit) / 2,\n      ];\n    } else {\n      groups.push({ limits: limits, group: group });\n      // var optmimalPeak = fitSpectrum(group,limits,spectrum);\n      group = [peakList[i]];\n      limits = [peakList[i].x, nL * peakList[i].width];\n    }\n  }\n  groups.push({ limits: limits, group: group });\n  // Merge backward\n  for (let i = groups.length - 2; i >= 0; i--) {\n    // The groups overlaps\n    if (\n      Math.abs(groups[i].limits[0] - groups[i + 1].limits[0]) <\n      (groups[i].limits[1] + groups[i + 1].limits[1]) / 2\n    ) {\n      for (let j = 0; j < groups[i + 1].group.length; j++) {\n        groups[i].group.push(groups[i + 1].group[j]);\n      }\n      upperLimit = groups[i].limits[0] + groups[i].limits[1];\n      if (groups[i + 1].limits[0] + groups[i + 1].limits[1] > upperLimit) {\n        upperLimit = groups[i + 1].limits[0] + groups[i + 1].limits[1];\n      }\n      lowerLimit = groups[i].limits[0] - groups[i].limits[1];\n      if (groups[i + 1].limits[0] - groups[i + 1].limits[1] < lowerLimit) {\n        lowerLimit = groups[i + 1].limits[0] - groups[i + 1].limits[1];\n      }\n\n      groups[i].limits = [\n        (upperLimit + lowerLimit) / 2,\n        Math.abs(upperLimit - lowerLimit) / 2,\n      ];\n\n      groups.splice(i + 1, 1);\n    }\n  }\n  return groups;\n}\n","import { optimizeSingleLorentzian } from 'ml-optimize-lorentzian';\n\n/**\n * This function try to join the peaks that seems to belong to a broad signal in a single broad peak.\n * @param peakList\n * @param options\n */\nexport function joinBroadPeaks(peakList, options = {}) {\n  let width = options.width;\n  let broadLines = [];\n  // Optimize the possible broad lines\n  let max = 0;\n\n  let maxI = 0;\n\n  let count = 1;\n  for (let i = peakList.length - 1; i >= 0; i--) {\n    if (peakList[i].soft) {\n      broadLines.push(peakList.splice(i, 1)[0]);\n    }\n  }\n  // Push a feke peak\n  broadLines.push({ x: Number.MAX_VALUE });\n\n  let candidates = [[broadLines[0].x, broadLines[0].y]];\n  let indexes = [broadLines[0].index];\n\n  for (let i = 1; i < broadLines.length; i++) {\n    // console.log(broadLines[i-1].x+\" \"+broadLines[i].x);\n    if (Math.abs(broadLines[i - 1].x - broadLines[i].x) < width) {\n      candidates.push([broadLines[i].x, broadLines[i].y]);\n      if (broadLines[i].y > max) {\n        max = broadLines[i].y;\n        maxI = i;\n      }\n      indexes.push(broadLines[i].index);\n      count++;\n    } else {\n      if (count > 2) {\n        let fitted = optimizeSingleLorentzian(candidates, {\n          x: broadLines[maxI].x,\n          y: max,\n          width: Math.abs(\n            candidates[0][0] - candidates[candidates.length - 1][0],\n          ),\n        });\n        let { parameters } = fitted;\n        peakList.push({\n          x: parameters[0],\n          y: parameters[1],\n          width: parameters[2],\n          index: Math.floor(\n            indexes.reduce((a, b) => a + b, 0) / indexes.length,\n          ),\n          soft: false,\n        });\n      } else {\n        // Put back the candidates to the signals list\n        indexes.forEach((index) => {\n          peakList.push(broadLines[index]);\n        });\n      }\n      candidates = [[broadLines[i].x, broadLines[i].y]];\n      indexes = [i];\n      max = broadLines[i].y;\n      maxI = i;\n      count = 1;\n    }\n  }\n\n  peakList.sort(function (a, b) {\n    return a.x - b.x;\n  });\n\n  return peakList;\n}\n","/**\n * This method will allow to enlarge peaks and prevent overlap between peaks\n * Because peaks may not be symmetric after we add 2 properties, from and to.\n * @param {Array} peakList\n * @param {object} [options={}]\n * @param {number} [factor=2]\n * @param {boolean} [overlap=false] by default we don't allow overlap\n * @return {Array} peakList\n */\nexport function broadenPeaks(peakList, options = {}) {\n  const { factor = 2, overlap = false } = options;\n\n  for (let peak of peakList) {\n    if (!peak.right || !peak.left) {\n      peak.from = peak.x - (peak.width / 2) * factor;\n      peak.to = peak.x + (peak.width / 2) * factor;\n    } else {\n      peak.from = peak.x - (peak.x - peak.left.x) * factor;\n      peak.to = peak.x + (peak.right.x - peak.x) * factor;\n    }\n  }\n\n  if (!overlap) {\n    for (let i = 0; i < peakList.length - 1; i++) {\n      let peak = peakList[i];\n      let nextPeak = peakList[i + 1];\n      if (peak.to > nextPeak.from) {\n        peak.to = nextPeak.from = (peak.to + nextPeak.from) / 2;\n      }\n    }\n  }\n\n  for (let peak of peakList) {\n    peak.width = peak.to - peak.from;\n  }\n\n  return peakList;\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction min(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var minValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] < minValue) minValue = input[i];\n  }\n\n  return minValue;\n}\n\nexport default min;\n","import isArray from 'is-any-array';\n\nfunction max(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var maxValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\n\nfunction mode(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var maxValue = 0;\n  var maxCount = 0;\n  var count = 0;\n  var counts = {};\n\n  for (var i = 0; i < input.length; ++i) {\n    var element = input[i];\n    count = counts[element];\n\n    if (count) {\n      counts[element]++;\n      count++;\n    } else {\n      counts[element] = count = 1;\n    }\n\n    if (count > maxCount) {\n      maxCount = count;\n      maxValue = input[i];\n    }\n  }\n\n  return maxValue;\n}\n\nexport default mode;\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction max(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var maxValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\nimport max from 'ml-array-max';\nimport sum from 'ml-array-sum';\n\nfunction norm(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  var _options$algorithm = options.algorithm,\n      algorithm = _options$algorithm === void 0 ? 'absolute' : _options$algorithm,\n      _options$sumValue = options.sumValue,\n      sumValue = _options$sumValue === void 0 ? 1 : _options$sumValue,\n      _options$maxValue = options.maxValue,\n      maxValue = _options$maxValue === void 0 ? 1 : _options$maxValue;\n\n  if (!isArray(input)) {\n    throw new Error('input must be an array');\n  }\n\n  var output;\n\n  if (options.output !== undefined) {\n    if (!isArray(options.output)) {\n      throw new TypeError('output option must be an array if specified');\n    }\n\n    output = options.output;\n  } else {\n    output = new Array(input.length);\n  }\n\n  if (input.length === 0) {\n    throw new Error('input must not be empty');\n  }\n\n  switch (algorithm.toLowerCase()) {\n    case 'absolute':\n      {\n        var absoluteSumValue = absoluteSum(input) / sumValue;\n        if (absoluteSumValue === 0) return input.slice(0);\n\n        for (var i = 0; i < input.length; i++) {\n          output[i] = input[i] / absoluteSumValue;\n        }\n\n        return output;\n      }\n\n    case 'max':\n      {\n        var currentMaxValue = max(input);\n        if (currentMaxValue === 0) return input.slice(0);\n        var factor = maxValue / currentMaxValue;\n\n        for (var _i = 0; _i < input.length; _i++) {\n          output[_i] = input[_i] * factor;\n        }\n\n        return output;\n      }\n\n    case 'sum':\n      {\n        var sumFactor = sum(input) / sumValue;\n        if (sumFactor === 0) return input.slice(0);\n\n        for (var _i2 = 0; _i2 < input.length; _i2++) {\n          output[_i2] = input[_i2] / sumFactor;\n        }\n\n        return output;\n      }\n\n    default:\n      throw new Error(\"norm: unknown algorithm: \".concat(algorithm));\n  }\n}\n\nfunction absoluteSum(input) {\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += Math.abs(input[i]);\n  }\n\n  return sumValue;\n}\n\nexport default norm;\n","import isArray from 'is-any-array';\n\nfunction _typeof(obj) {\n  \"@babel/helpers - typeof\";\n\n  if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") {\n    _typeof = function (obj) {\n      return typeof obj;\n    };\n  } else {\n    _typeof = function (obj) {\n      return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj;\n    };\n  }\n\n  return _typeof(obj);\n}\n\n/**\r\n * Fill an array with sequential numbers\r\n * @param {Array<number>} [input] - optional destination array (if not provided a new array will be created)\r\n * @param {object} [options={}]\r\n * @param {number} [options.from=0] - first value in the array\r\n * @param {number} [options.to=10] - last value in the array\r\n * @param {number} [options.size=input.length] - size of the array (if not provided calculated from step)\r\n * @param {number} [options.step] - if not provided calculated from size\r\n * @return {Array<number>}\r\n */\n\nfunction sequentialFill() {\n  var input = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (_typeof(input) === 'object' && !isArray(input)) {\n    options = input;\n    input = [];\n  }\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options = options,\n      _options$from = _options.from,\n      from = _options$from === void 0 ? 0 : _options$from,\n      _options$to = _options.to,\n      to = _options$to === void 0 ? 10 : _options$to,\n      _options$size = _options.size,\n      size = _options$size === void 0 ? input.length : _options$size,\n      step = _options.step;\n\n  if (size !== 0 && step) {\n    throw new Error('step is defined by the array size');\n  }\n\n  if (!size) {\n    if (step) {\n      size = Math.floor((to - from) / step) + 1;\n    } else {\n      size = to - from + 1;\n    }\n  }\n\n  if (!step && size) {\n    step = (to - from) / (size - 1);\n  }\n\n  if (Array.isArray(input)) {\n    // only works with normal array\n    input.length = 0;\n\n    for (var i = 0; i < size; i++) {\n      input.push(from);\n      from += step;\n    }\n  } else {\n    if (input.length !== size) {\n      throw new Error('sequentialFill typed array must have the correct length');\n    }\n\n    for (var _i = 0; _i < size; _i++) {\n      input[_i] = from;\n      from += step;\n    }\n  }\n\n  return input;\n}\n\nexport default sequentialFill;\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\nimport arrayMean from 'ml-array-mean';\n\nfunction variance(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(values)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options$unbiased = options.unbiased,\n      unbiased = _options$unbiased === void 0 ? true : _options$unbiased,\n      _options$mean = options.mean,\n      mean = _options$mean === void 0 ? arrayMean(values) : _options$mean;\n  var sqrError = 0;\n\n  for (var i = 0; i < values.length; i++) {\n    var x = values[i] - mean;\n    sqrError += x * x;\n  }\n\n  if (unbiased) {\n    return sqrError / (values.length - 1);\n  } else {\n    return sqrError / values.length;\n  }\n}\n\nexport default variance;\n","import variance from 'ml-array-variance';\n\nfunction standardDeviation(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  return Math.sqrt(variance(values, options));\n}\n\nexport default standardDeviation;\n","/**\n * Merge abscissa values if the ordinate value is in a list of centroids\n * @param {object} originalPoints\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {Array<number>} centroids\n * @param {object} [options]\n * @param {number} [options.window = 0.01] - has to be a positive number\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function mergeByCentroids(\n  originalPoints,\n  centroids,\n  options = {}\n) {\n  const { window = 0.01 } = options;\n\n  var mergedPoints = {\n    x: centroids.slice(),\n    y: new Array(centroids.length).fill(0)\n  };\n\n  var originalIndex = 0;\n  var mergedIndex = 0;\n  while (\n    originalIndex < originalPoints.x.length &&\n    mergedIndex < centroids.length\n  ) {\n    var diff = originalPoints.x[originalIndex] - centroids[mergedIndex];\n    if (Math.abs(diff) < window) {\n      mergedPoints.y[mergedIndex] += originalPoints.y[originalIndex++];\n    } else if (diff < 0) {\n      originalIndex++;\n    } else {\n      mergedIndex++;\n    }\n  }\n\n  return mergedPoints;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {*} options\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function closestX(points, options) {\n  const { x, y } = points;\n  const { target = x[0], reverse = false } = options;\n\n  let index;\n  if (reverse) {\n    index = binarySearch(x, target, descending);\n  } else {\n    index = binarySearch(x, target, ascending);\n  }\n\n  if (index >= 0) {\n    return {\n      x: x[index],\n      y: y[index]\n    };\n  } else {\n    index = ~index;\n    if (\n      (index !== 0 && Math.abs(x[index] - target) > 0.5) ||\n      index === x.length\n    ) {\n      return {\n        x: x[index - 1],\n        y: y[index - 1]\n      };\n    } else {\n      return {\n        x: x[index],\n        y: y[index]\n      };\n    }\n  }\n}\n","import mean from 'ml-array-mean';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} points.x\n * @param {Array<number>} points.y\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @return {number}\n */\nexport default function covariance(points, options = {}) {\n  const { x, y } = points;\n  const { unbiased = true } = options;\n\n  const meanX = mean(x);\n  const meanY = mean(y);\n\n  var error = 0;\n\n  for (let i = 0; i < x.length; i++) {\n    error += (x[i] - meanX) * (y[i] - meanY);\n  }\n\n  if (unbiased) {\n    return error / (x.length - 1);\n  } else {\n    return error / x.length;\n  }\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function maxMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var maxAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      maxAbscissa.x.push(x[index]);\n      maxAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      if (y[index] > maxAbscissa.y[size - 1]) {\n        maxAbscissa.x[size - 1] = x[index];\n        maxAbscissa.y[size - 1] = y[index];\n      }\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  merged.x = maxAbscissa.x.slice();\n\n  return merged;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {object} [options.from = {index: 0}]\n * @param {object} [options.to = {index: x.length-1}]\n * @param {boolean} [options.reverse = false]\n * @return {{index: number, value: number}}\n */\nexport default function maxY(points, options = {}) {\n  const { x, y } = points;\n  let {\n    from = { index: 0 },\n    to = { index: x.length },\n    reverse = false\n  } = options;\n\n  if (from.value !== undefined && from.index === undefined) {\n    from.index = calculateIndex(from.value, x, reverse);\n  }\n\n  if (to.value !== undefined && to.index === undefined) {\n    to.index = calculateIndex(to.value, x, reverse);\n  }\n\n  var currentMax = Number.MIN_VALUE;\n  var currentIndex;\n  for (var i = from.index; i < to.index; i++) {\n    if (currentMax < y[i]) {\n      currentMax = y[i];\n      currentIndex = i;\n    }\n  }\n\n  return {\n    index: currentIndex,\n    value: currentMax\n  };\n}\n\n/**\n * @param {number} value\n * @param {Array<number>} x\n * @param {boolean} reverse\n * @return {number} index of the value in the array\n */\nfunction calculateIndex(value, x, reverse) {\n  let index;\n  if (reverse) {\n    index = binarySearch(x, value, descending);\n  } else {\n    index = binarySearch(x, value, ascending);\n  }\n\n  if (index < 0) {\n    throw new Error(`the value ${value} doesn't belongs to the abscissa value`);\n  }\n\n  return index;\n}\n","export default function sortX(points, options = {}) {\n  const { x, y } = points;\n  const { reverse = false } = options;\n\n  var sortFunc;\n  if (!reverse) {\n    sortFunc = (a, b) => a.x - b.x;\n  } else {\n    sortFunc = (a, b) => b.x - a.x;\n  }\n\n  var grouped = x\n    .map((val, index) => ({\n      x: val,\n      y: y[index]\n    }))\n    .sort(sortFunc);\n\n  var response = { x: x.slice(), y: y.slice() };\n  for (var i = 0; i < x.length; i++) {\n    response.x[i] = grouped[i].x;\n    response.y[i] = grouped[i].y;\n  }\n\n  return response;\n}\n","\n/**\n * In place modification of the 2 arrays to make X unique and sum the Y if X has the same value\n * @param {object} [points={}] : Object of points contains property x (an array) and y (an array)\n * @return points\n */\n\nexport default function uniqueX(points = {}) {\n  const { x, y } = points;\n  if (x.length < 2) return;\n  if (x.length !== y.length) {\n    throw new Error('The X and Y arrays mush have the same length');\n  }\n\n  let current = x[0];\n  let counter = 0;\n\n  for (let i = 1; i < x.length; i++) {\n    if (current !== x[i]) {\n      counter++;\n      current = x[i];\n      x[counter] = x[i];\n      if (i !== counter) {\n        y[counter] = 0;\n      }\n    }\n    if (i !== counter) {\n      y[counter] += y[i];\n    }\n  }\n\n  x.length = counter + 1;\n  y.length = counter + 1;\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function weightedMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var weightedAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      weightedAbscissa.x.push(x[index] * y[index]);\n      weightedAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      weightedAbscissa.x[size - 1] += x[index] * y[index];\n      weightedAbscissa.y[size - 1] += y[index];\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  for (var i = 0; i < merged.x.length; i++) {\n    merged.x[i] = weightedAbscissa.x[i] / weightedAbscissa.y[i];\n  }\n\n  return merged;\n}\n","/**\n * Normalize an array of zones:\n * - ensure than from < to\n * - merge overlapping zones\n *\n * The method will always check if from if lower than to and will swap if required.\n * @param {Array} [zones=[]]\n * @param {object} [options={}]\n * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of a zone\n * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of a zone\n */\n\nexport function normalize(zones = [], options = {}) {\n  if (zones.length === 0) return [];\n  let {\n    from = Number.NEGATIVE_INFINITY,\n    to = Number.POSITIVE_INFINITY,\n  } = options;\n  if (from > to) [from, to] = [to, from];\n\n  zones = JSON.parse(JSON.stringify(zones)).map((zone) =>\n    zone.from > zone.to ? { from: zone.to, to: zone.from } : zone,\n  );\n  zones = zones.sort((a, b) => {\n    if (a.from !== b.from) return a.from - b.from;\n    return a.to - b.to;\n  });\n\n  zones.forEach((zone) => {\n    if (from > zone.from) zone.from = from;\n    if (to < zone.to) zone.to = to;\n  });\n\n  zones = zones.filter((zone) => zone.from <= zone.to);\n  if (zones.length === 0) return [];\n\n  let currentZone = zones[0];\n  let result = [currentZone];\n  for (let i = 1; i < zones.length; i++) {\n    let zone = zones[i];\n    if (zone.from <= currentZone.to) {\n      currentZone.to = zone.to;\n    } else {\n      currentZone = zone;\n      result.push(currentZone);\n    }\n  }\n  return result;\n}\n","import { normalize } from './normalize';\n\n/**\n * Convert an array of exclusions and keep only from / to\n *\n * The method will always check if from if lower than to and will swap if required.\n * @param {Array} [exclusions=[]]\n * @param {object} [options={}]\n * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of zones (after inversion)\n * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of zones (after inversion)\n */\n\nexport function invert(exclusions = [], options = {}) {\n  let {\n    from = Number.NEGATIVE_INFINITY,\n    to = Number.POSITIVE_INFINITY,\n  } = options;\n  if (from > to) [from, to] = [to, from];\n\n  exclusions = normalize(exclusions, { from, to });\n  if (exclusions.length === 0) return [{ from, to }];\n\n  let zones = [];\n  for (let i = 0; i < exclusions.length; i++) {\n    let exclusion = exclusions[i];\n    let nextExclusion = exclusions[i + 1];\n    if (i === 0) {\n      if (exclusion.from > from) {\n        zones.push({ from, to: exclusion.from });\n      }\n    }\n    if (i === exclusions.length - 1) {\n      if (exclusion.to < to) {\n        zones.push({ from: exclusion.to, to });\n      }\n    } else {\n      zones.push({ from: exclusion.to, to: nextExclusion.from });\n    }\n  }\n\n  return zones;\n}\n","import { normalize } from './normalize';\n\n/**\n * Add the number of points per zone to reach a specified total\n * @param {Array} [zones=[]]\n * @param {number} [numberOfPoints] Total number of points to distribute between zones\n * @param {object} [options={}]\n * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of a zone\n * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of a zone\n */\n\nexport function zonesWithPoints(zones, numberOfPoints, options = {}) {\n  if (zones.length === 0) return zones;\n  zones = normalize(zones, options);\n\n  const totalSize = zones.reduce((previous, current) => {\n    return previous + (current.to - current.from);\n  }, 0);\n\n  let unitsPerPoint = totalSize / numberOfPoints;\n  let currentTotal = 0;\n  for (let i = 0; i < zones.length - 1; i++) {\n    let zone = zones[i];\n    zone.numberOfPoints = Math.min(\n      Math.round((zone.to - zone.from) / unitsPerPoint),\n      numberOfPoints - currentTotal,\n    );\n    currentTotal += zone.numberOfPoints;\n  }\n\n  zones[zones.length - 1].numberOfPoints = numberOfPoints - currentTotal;\n\n  return zones;\n}\n","/**\n * Function that calculates the integral of the line between two\n * x-coordinates, given the slope and intercept of the line.\n * @param {number} x0\n * @param {number} x1\n * @param {number} slope\n * @param {number} intercept\n * @return {number} integral value.\n */\nexport default function integral(x0, x1, slope, intercept) {\n  return (\n    0.5 * slope * x1 * x1 +\n    intercept * x1 -\n    (0.5 * slope * x0 * x0 + intercept * x0)\n  );\n}\n","import integral from './integral';\n\n/**\n * function that retrieves the getEquallySpacedData with the variant \"smooth\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"smooth\"\n */\nexport default function equallySpacedSmooth(x, y, from, to, numberOfPoints) {\n  let xLength = x.length;\n\n  let step = (to - from) / (numberOfPoints - 1);\n  let halfStep = step / 2;\n\n  let output = new Array(numberOfPoints);\n\n  let initialOriginalStep = x[1] - x[0];\n  let lastOriginalStep = x[xLength - 1] - x[xLength - 2];\n\n  // Init main variables\n  let min = from - halfStep;\n  let max = from + halfStep;\n\n  let previousX = Number.MIN_VALUE;\n  let previousY = 0;\n  let nextX = x[0] - initialOriginalStep;\n  let nextY = 0;\n\n  let currentValue = 0;\n  let slope = 0;\n  let intercept = 0;\n  let sumAtMin = 0;\n  let sumAtMax = 0;\n\n  let i = 0; // index of input\n  let j = 0; // index of output\n\n  function getSlope(x0, y0, x1, y1) {\n    return (y1 - y0) / (x1 - x0);\n  }\n\n  let add = 0;\n  main: while (true) {\n    if (previousX <= min && min <= nextX) {\n      add = integral(0, min - previousX, slope, previousY);\n      sumAtMin = currentValue + add;\n    }\n\n    while (nextX - max >= 0) {\n      // no overlap with original point, just consume current value\n      add = integral(0, max - previousX, slope, previousY);\n      sumAtMax = currentValue + add;\n\n      output[j++] = (sumAtMax - sumAtMin) / step;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      sumAtMin = sumAtMax;\n    }\n\n    currentValue += integral(previousX, nextX, slope, intercept);\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else if (i === xLength) {\n      nextX += lastOriginalStep;\n      nextY = 0;\n    }\n\n    slope = getSlope(previousX, previousY, nextX, nextY);\n    intercept = -slope * previousX + previousY;\n  }\n\n  return output;\n}\n","/**\n * function that retrieves the getEquallySpacedData with the variant \"slot\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"slot\"\n */\nexport default function equallySpacedSlot(x, y, from, to, numberOfPoints) {\n  let xLength = x.length;\n\n  let step = (to - from) / (numberOfPoints - 1);\n  let halfStep = step / 2;\n  let lastStep = x[x.length - 1] - x[x.length - 2];\n\n  let start = from - halfStep;\n  let output = new Array(numberOfPoints);\n\n  // Init main variables\n  let min = start;\n  let max = start + step;\n\n  let previousX = -Number.MAX_VALUE;\n  let previousY = 0;\n  let nextX = x[0];\n  let nextY = y[0];\n  let frontOutsideSpectra = 0;\n  let backOutsideSpectra = true;\n\n  let currentValue = 0;\n\n  // for slot algorithm\n  let currentPoints = 0;\n\n  let i = 1; // index of input\n  let j = 0; // index of output\n\n  main: while (true) {\n    if (previousX >= nextX) throw new Error('x must be an increasing serie');\n    while (previousX - max > 0) {\n      // no overlap with original point, just consume current value\n      if (backOutsideSpectra) {\n        currentPoints++;\n        backOutsideSpectra = false;\n      }\n\n      output[j] = currentPoints <= 0 ? 0 : currentValue / currentPoints;\n      j++;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      currentValue = 0;\n      currentPoints = 0;\n    }\n\n    if (previousX > min) {\n      currentValue += previousY;\n      currentPoints++;\n    }\n\n    if (previousX === -Number.MAX_VALUE || frontOutsideSpectra > 1) {\n      currentPoints--;\n    }\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else {\n      nextX += lastStep;\n      nextY = 0;\n      frontOutsideSpectra++;\n    }\n  }\n\n  return output;\n}\n","import sequentialFill from 'ml-array-sequential-fill';\nimport { zonesWithPoints, invert } from 'ml-zones';\n\nimport equallySpacedSmooth from './equallySpacedSmooth';\nimport equallySpacedSlot from './equallySpacedSlot';\n\n/**\n * Function that returns a Number array of equally spaced numberOfPoints\n * containing a representation of intensities of the spectra arguments x\n * and y.\n *\n * The options parameter contains an object in the following form:\n * from: starting point\n * to: last point\n * numberOfPoints: number of points between from and to\n * variant: \"slot\" or \"smooth\" - smooth is the default option\n *\n * The slot variant consist that each point in the new array is calculated\n * averaging the existing points between the slot that belongs to the current\n * value. The smooth variant is the same but takes the integral of the range\n * of the slot and divide by the step size between two points in the new array.\n *\n * If exclusions zone are present, zones are ignored !\n * @param {object} [arrayXY={}] - object containing 2 properties x and y (both an array)\n * @param {object} [options={}]\n * @param {number} [options.from=x[0]]\n * @param {number} [options.to=x[x.length-1]]\n * @param {string} [options.variant='smooth']\n * @param {number} [options.numberOfPoints=100]\n * @param {Array} [options.exclusions=[]] array of from / to that should be skipped for the generation of the points\n * @param {Array} [options.zones=[]] array of from / to that should be kept\n * @return {object<x: Array, y:Array>} new object with x / y array with the equally spaced data.\n */\n\nexport default function equallySpaced(arrayXY = {}, options = {}) {\n  let { x, y } = arrayXY;\n  let xLength = x.length;\n  let reverse = false;\n  if (x.length > 1 && x[0] > x[1]) {\n    x = x.slice().reverse();\n    y = y.slice().reverse();\n    reverse = true;\n  }\n\n  let {\n    from = x[0],\n    to = x[xLength - 1],\n    variant = 'smooth',\n    numberOfPoints = 100,\n    exclusions = [],\n    zones = [],\n  } = options;\n\n  if (xLength !== y.length) {\n    throw new RangeError(\"the x and y vector doesn't have the same size.\");\n  }\n\n  if (typeof from !== 'number' || isNaN(from)) {\n    throw new RangeError(\"'from' option must be a number\");\n  }\n\n  if (typeof to !== 'number' || isNaN(to)) {\n    throw new RangeError(\"'to' option must be a number\");\n  }\n\n  if (typeof numberOfPoints !== 'number' || isNaN(numberOfPoints)) {\n    throw new RangeError(\"'numberOfPoints' option must be a number\");\n  }\n\n  if (numberOfPoints < 2) {\n    throw new RangeError(\"'numberOfPoints' option must be greater than 1\");\n  }\n\n  if (zones.length === 0) {\n    zones = invert(exclusions, { from, to });\n  }\n\n  zones = zonesWithPoints(zones, numberOfPoints, { from, to });\n\n  let xResult = [];\n  let yResult = [];\n  for (let zone of zones) {\n    let zoneResult = processZone(\n      x,\n      y,\n      zone.from,\n      zone.to,\n      zone.numberOfPoints,\n      variant,\n      reverse,\n    );\n\n    xResult = xResult.concat(zoneResult.x);\n    yResult = yResult.concat(zoneResult.y);\n  }\n  if (reverse) {\n    if (from < to) {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    } else {\n      return { x: xResult, y: yResult };\n    }\n  } else {\n    if (from < to) {\n      return { x: xResult, y: yResult };\n    } else {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    }\n  }\n}\n\nfunction processZone(x, y, from, to, numberOfPoints, variant) {\n  if (numberOfPoints < 1) {\n    throw new RangeError('the number of points must be at least 1');\n  }\n\n  let output =\n    variant === 'slot'\n      ? equallySpacedSlot(x, y, from, to, numberOfPoints)\n      : equallySpacedSmooth(x, y, from, to, numberOfPoints);\n\n  return {\n    x: sequentialFill({\n      from,\n      to,\n      size: numberOfPoints,\n    }),\n    y: output,\n  };\n}\n","export default function getZones(from, to, exclusions = []) {\n  if (from > to) {\n    [from, to] = [to, from];\n  }\n\n  // in exclusions from and to have to be defined\n  exclusions = exclusions.filter(\n    (exclusion) => exclusion.from !== undefined && exclusion.to !== undefined\n  );\n\n  exclusions = JSON.parse(JSON.stringify(exclusions));\n  // we ensure that from before to\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from > exclusion.to) {\n      [exclusion.to, exclusion.from] = [exclusion.from, exclusion.to];\n    }\n  });\n\n  exclusions.sort((a, b) => a.from - b.from);\n\n  // we will rework the exclusions in order to remove overlap and outside range (from / to)\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from < from) exclusion.from = from;\n    if (exclusion.to > to) exclusion.to = to;\n  });\n  for (let i = 0; i < exclusions.length - 1; i++) {\n    if (exclusions[i].to > exclusions[i + 1].from) {\n      exclusions[i].to = exclusions[i + 1].from;\n    }\n  }\n  exclusions = exclusions.filter((exclusion) => exclusion.from < exclusion.to);\n\n  if (!exclusions || exclusions.length === 0) {\n    return [{ from, to }];\n  }\n\n  let zones = [];\n  let currentFrom = from;\n  for (let exclusion of exclusions) {\n    if (currentFrom < exclusion.from) {\n      zones.push({\n        from: currentFrom,\n        to: exclusion.from\n      });\n    }\n\n    currentFrom = exclusion.to;\n  }\n  if (currentFrom < to) {\n    zones.push({\n      from: currentFrom,\n      to: to\n    });\n  }\n\n  return zones;\n}\n","import getZones from './getZones';\n\n/**\n * Filter an array x/y based on various criteria\n * x points are expected to be sorted\n *\n * @param {object} points\n * @param {object} [options={}]\n * @param {array} [options.from]\n * @param {array} [options.to]\n * @param {array} [options.exclusions=[]]\n * @return {{x: Array<number>, y: Array<number>}}\n */\n\nexport default function filterX(points, options = {}) {\n  const { x, y } = points;\n  const { from = x[0], to = x[x.length - 1], exclusions = [] } = options;\n\n  let zones = getZones(from, to, exclusions);\n\n\n  let currentZoneIndex = 0;\n  let newX = [];\n  let newY = [];\n  let position = 0;\n  while (position < x.length) {\n    if (\n      x[position] <= zones[currentZoneIndex].to &&\n      x[position] >= zones[currentZoneIndex].from\n    ) {\n      newX.push(x[position]);\n      newY.push(y[position]);\n    } else {\n      if (x[position] > zones[currentZoneIndex].to) {\n        currentZoneIndex++;\n        if (!zones[currentZoneIndex]) break;\n      }\n    }\n    position++;\n  }\n\n  return {\n    x: newX,\n    y: newY\n  };\n}\n","import { DecisionTreeClassifier, DecisionTreeRegression } from \"ml-cart\";\nimport {\n  RandomForestClassifier,\n  RandomForestRegression\n} from \"ml-random-forest\";\n\n// Try to keep this list in the same structure as the README.\n\n// Unsupervised learning\nexport { PCA } from \"ml-pca\";\nimport * as HClust from \"ml-hclust\";\nexport { HClust };\nexport { default as KMeans } from \"ml-kmeans\";\n\n// Supervised learning\nimport * as NaiveBayes from \"ml-naivebayes\";\nexport { NaiveBayes };\nexport { default as KNN } from \"ml-knn\";\nexport { PLS, KOPLS, OPLS, OPLSNipals } from \"ml-pls\";\nimport * as CrossValidation from \"ml-cross-validation\";\nexport { CrossValidation };\nexport { default as ConfusionMatrix } from \"ml-confusion-matrix\";\nexport { DecisionTreeClassifier };\nexport { RandomForestClassifier };\n\n// Artificial neural networks\nexport { default as FNN } from \"ml-fnn\";\nexport { default as SOM } from \"ml-som\";\n\n// Regression\nexport {\n  SimpleLinearRegression,\n  PolynomialRegression,\n  MultivariateLinearRegression,\n  PowerRegression,\n  ExponentialRegression,\n  TheilSenRegression,\n  RobustPolynomialRegression\n} from \"ml-regression\";\nexport { DecisionTreeRegression };\nexport { RandomForestRegression };\n\n// Optimization\nexport { default as levenbergMarquardt } from \"ml-levenberg-marquardt\";\nimport * as FCNNLS from \"ml-fcnnls\";\nexport { FCNNLS };\n\n// Math\nimport * as MatrixLib from \"ml-matrix\";\nconst {\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n} = MatrixLib;\nexport {\n  MatrixLib,\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n};\n\nexport { SparseMatrix } from \"ml-sparse-matrix\";\nexport { default as Kernel } from \"ml-kernel\";\nimport { distance, similarity } from \"ml-distance\";\nexport { distance as Distance, similarity as Similarity };\nexport { default as distanceMatrix } from \"ml-distance-matrix\";\nexport { default as XSadd } from \"ml-xsadd\";\n\n// Statistics\nexport { default as Performance } from \"ml-performance\";\n\n// Data preprocessing\nexport { default as savitzkyGolay } from \"ml-savitzky-golay\";\n\n// Utility\nexport { default as BitArray } from \"ml-bit-array\";\nexport { default as HashTable } from \"ml-hash-table\";\nexport { default as padArray } from \"ml-pad-array\";\nexport { default as binarySearch } from \"binary-search\";\nimport * as numSort from \"num-sort\";\nexport { numSort };\nexport { default as Random } from \"ml-random\";\nimport * as GSD from 'ml-gsd';\nexport { GSD };\n\nimport min from \"ml-array-min\";\nimport max from \"ml-array-max\";\nimport median from \"ml-array-median\";\nimport mean from \"ml-array-mean\";\nimport mode from \"ml-array-mode\";\nimport normed from \"ml-array-normed\";\nimport rescale from \"ml-array-rescale\";\nimport sequentialFill from \"ml-array-sequential-fill\";\nimport sum from \"ml-array-sum\";\nimport standardDeviation from \"ml-array-standard-deviation\";\nimport variance from \"ml-array-variance\";\nexport const Array = {\n  min,\n  max,\n  median,\n  mean,\n  mode,\n  normed,\n  rescale,\n  sequentialFill,\n  standardDeviation,\n  sum,\n  variance\n};\n\nimport centroidsMerge from \"ml-array-xy-centroids-merge\";\nimport closestX from \"ml-arrayxy-closestx\";\nimport covariance from \"ml-array-xy-covariance\";\nimport maxMerge from \"ml-array-xy-max-merge\";\nimport maxY from \"ml-array-xy-max-y\";\nimport sortX from \"ml-array-xy-sort-x\";\nimport uniqueX from \"ml-arrayxy-uniquex\";\nimport weightedMerge from \"ml-array-xy-weighted-merge\";\nimport equallySpaced from \"ml-array-xy-equally-spaced\";\nimport filterX from \"ml-array-xy-filter-x\";\nexport const ArrayXY = {\n  centroidsMerge,\n  closestX,\n  covariance,\n  maxMerge,\n  maxY,\n  sortX,\n  uniqueX,\n  weightedMerge,\n  equallySpaced,\n  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diff --git a/dist/ml.min.js b/dist/ml.min.js
index 875774c..58ae0db 100644
--- a/dist/ml.min.js
+++ b/dist/ml.min.js
@@ -1,4 +1,4 @@
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e=0;e<this.columns;e++)this.set(t,e,Math.log2(this.get(t,e)));return this},t.log2=function(t){return new e(t).log2()},t.prototype.round=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.round(this.get(t,e)));return this},t.round=function(t){return new e(t).round()},t.prototype.sign=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sign(this.get(t,e)));return this},t.sign=function(t){return new e(t).sign()},t.prototype.sin=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sin(this.get(t,e)));return this},t.sin=function(t){return new e(t).sin()},t.prototype.sinh=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sinh(this.get(t,e)));return this},t.sinh=function(t){return new e(t).sinh()},t.prototype.sqrt=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sqrt(this.get(t,e)));return this},t.sqrt=function(t){return new e(t).sqrt()},t.prototype.tan=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tan(this.get(t,e)));return this},t.tan=function(t){return new e(t).tan()},t.prototype.tanh=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tanh(this.get(t,e)));return this},t.tanh=function(t){return new e(t).tanh()},t.prototype.trunc=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.trunc(this.get(t,e)));return this},t.trunc=function(t){return new e(t).trunc()},t.pow=function(t,r){return new e(t).pow(r)},t.prototype.pow=function(t){return"number"==typeof t?this.powS(t):this.powM(t)},t.prototype.powS=function(t){for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,Math.pow(this.get(e,r),t));return this},t.prototype.powM=function(t){if(t=e.checkMatrix(t),this.rows!==t.rows||this.columns!==t.columns)throw new RangeError("Matrices dimensions must be equal");for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,Math.pow(this.get(e,r),t.get(e,r)));return this}}class AbstractMatrix{static from1DArray(t,e,r){if(t*e!==r.length)throw new RangeError("data length does not match given dimensions");let i=new Matrix(t,e);for(let n=0;n<t;n++)for(let t=0;t<e;t++)i.set(n,t,r[n*e+t]);return i}static rowVector(t){let e=new Matrix(1,t.length);for(let r=0;r<t.length;r++)e.set(0,r,t[r]);return e}static columnVector(t){let e=new Matrix(t.length,1);for(let r=0;r<t.length;r++)e.set(r,0,t[r]);return e}static zeros(t,e){return new Matrix(t,e)}static ones(t,e){return new Matrix(t,e).fill(1)}static rand(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if("object"!=typeof r)throw new TypeError("options must be an object");const{random:i=Math.random}=r;let n=new Matrix(t,e);for(let r=0;r<t;r++)for(let t=0;t<e;t++)n.set(r,t,i());return n}static randInt(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if("object"!=typeof r)throw new TypeError("options must be an object");const{min:i=0,max:n=1e3,random:s=Math.random}=r;if(!Number.isInteger(i))throw new TypeError("min must be an integer");if(!Number.isInteger(n))throw new TypeError("max must be an integer");if(i>=n)throw new RangeError("min must be smaller than max");let o=n-i,a=new Matrix(t,e);for(let r=0;r<t;r++)for(let t=0;t<e;t++){let e=i+Math.round(s()*o);a.set(r,t,e)}return a}static eye(t,e,r){void 0===e&&(e=t),void 0===r&&(r=1);let i=Math.min(t,e),n=this.zeros(t,e);for(let t=0;t<i;t++)n.set(t,t,r);return n}static diag(t,e,r){let i=t.length;void 0===e&&(e=i),void 0===r&&(r=e);let n=Math.min(i,e,r),s=this.zeros(e,r);for(let e=0;e<n;e++)s.set(e,e,t[e]);return s}static min(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let r=t.rows,i=t.columns,n=new Matrix(r,i);for(let s=0;s<r;s++)for(let r=0;r<i;r++)n.set(s,r,Math.min(t.get(s,r),e.get(s,r)));return n}static max(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let r=t.rows,i=t.columns,n=new this(r,i);for(let s=0;s<r;s++)for(let r=0;r<i;r++)n.set(s,r,Math.max(t.get(s,r),e.get(s,r)));return n}static checkMatrix(t){return AbstractMatrix.isMatrix(t)?t:new Matrix(t)}static isMatrix(t){return null!=t&&"Matrix"===t.klass}get size(){return this.rows*this.columns}apply(t){if("function"!=typeof t)throw new TypeError("callback must be a function");for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.call(this,e,r);return this}to1DArray(){let t=[];for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.push(this.get(e,r));return t}to2DArray(){let t=[];for(let e=0;e<this.rows;e++){t.push([]);for(let r=0;r<this.columns;r++)t[e].push(this.get(e,r))}return t}toJSON(){return this.to2DArray()}isRowVector(){return 1===this.rows}isColumnVector(){return 1===this.columns}isVector(){return 1===this.rows||1===this.columns}isSquare(){return this.rows===this.columns}isSymmetric(){if(this.isSquare()){for(let t=0;t<this.rows;t++)for(let e=0;e<=t;e++)if(this.get(t,e)!==this.get(e,t))return!1;return!0}return!1}isEchelonForm(){let t=0,e=0,r=-1,i=!0,n=!1;for(;t<this.rows&&i;){for(e=0,n=!1;e<this.columns&&!1===n;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>r?(n=!0,r=e):(i=!1,n=!0);t++}return i}isReducedEchelonForm(){let t=0,e=0,r=-1,i=!0,n=!1;for(;t<this.rows&&i;){for(e=0,n=!1;e<this.columns&&!1===n;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>r?(n=!0,r=e):(i=!1,n=!0);for(let r=e+1;r<this.rows;r++)0!==this.get(t,r)&&(i=!1);t++}return i}echelonForm(){let t=this.clone(),e=0,r=0;for(;e<t.rows&&r<t.columns;){let i=e;for(let n=e;n<t.rows;n++)t.get(n,r)>t.get(i,r)&&(i=n);if(0===t.get(i,r))r++;else{t.swapRows(e,i);let n=t.get(e,r);for(let i=r;i<t.columns;i++)t.set(e,i,t.get(e,i)/n);for(let i=e+1;i<t.rows;i++){let n=t.get(i,r)/t.get(e,r);t.set(i,r,0);for(let s=r+1;s<t.columns;s++)t.set(i,s,t.get(i,s)-t.get(e,s)*n)}e++,r++}}return t}reducedEchelonForm(){let t=this.echelonForm(),e=t.columns,r=t.rows,i=r-1;for(;i>=0;)if(0===t.maxRow(i))i--;else{let n=0,s=!1;for(;n<r&&!1===s;)1===t.get(i,n)?s=!0:n++;for(let r=0;r<i;r++){let s=t.get(r,n);for(let o=n;o<e;o++){let e=t.get(r,o)-s*t.get(i,o);t.set(r,o,e)}}i--}return t}set(){throw new Error("set method is unimplemented")}get(){throw new Error("get method is unimplemented")}repeat(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if("object"!=typeof t)throw new TypeError("options must be an object");const{rows:e=1,columns:r=1}=t;if(!Number.isInteger(e)||e<=0)throw new TypeError("rows must be a positive integer");if(!Number.isInteger(r)||r<=0)throw new TypeError("columns must be a positive integer");let i=new Matrix(this.rows*e,this.columns*r);for(let t=0;t<e;t++)for(let e=0;e<r;e++)i.setSubMatrix(this,this.rows*t,this.columns*e);return i}fill(t){for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,t);return this}neg(){return this.mulS(-1)}getRow(t){checkRowIndex(this,t);let e=[];for(let r=0;r<this.columns;r++)e.push(this.get(t,r));return e}getRowVector(t){return Matrix.rowVector(this.getRow(t))}setRow(t,e){checkRowIndex(this,t),e=checkRowVector(this,e);for(let r=0;r<this.columns;r++)this.set(t,r,e[r]);return this}swapRows(t,e){checkRowIndex(this,t),checkRowIndex(this,e);for(let r=0;r<this.columns;r++){let i=this.get(t,r);this.set(t,r,this.get(e,r)),this.set(e,r,i)}return this}getColumn(t){checkColumnIndex(this,t);let e=[];for(let r=0;r<this.rows;r++)e.push(this.get(r,t));return e}getColumnVector(t){return Matrix.columnVector(this.getColumn(t))}setColumn(t,e){checkColumnIndex(this,t),e=checkColumnVector(this,e);for(let r=0;r<this.rows;r++)this.set(r,t,e[r]);return this}swapColumns(t,e){checkColumnIndex(this,t),checkColumnIndex(this,e);for(let r=0;r<this.rows;r++){let i=this.get(r,t);this.set(r,t,this.get(r,e)),this.set(r,e,i)}return this}addRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)+t[r]);return this}subRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)-t[r]);return this}mulRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)*t[r]);return this}divRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)/t[r]);return this}addColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)+t[e]);return this}subColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)-t[e]);return this}mulColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)*t[e]);return this}divColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)/t[e]);return this}mulRow(t,e){checkRowIndex(this,t);for(let r=0;r<this.columns;r++)this.set(t,r,this.get(t,r)*e);return this}mulColumn(t,e){checkColumnIndex(this,t);for(let r=0;r<this.rows;r++)this.set(r,t,this.get(r,t)*e);return this}max(){let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.get(e,r)>t&&(t=this.get(e,r));return t}maxIndex(){let t=this.get(0,0),e=[0,0];for(let r=0;r<this.rows;r++)for(let i=0;i<this.columns;i++)this.get(r,i)>t&&(t=this.get(r,i),e[0]=r,e[1]=i);return e}min(){let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.get(e,r)<t&&(t=this.get(e,r));return t}minIndex(){let t=this.get(0,0),e=[0,0];for(let r=0;r<this.rows;r++)for(let i=0;i<this.columns;i++)this.get(r,i)<t&&(t=this.get(r,i),e[0]=r,e[1]=i);return e}maxRow(t){checkRowIndex(this,t);let e=this.get(t,0);for(let r=1;r<this.columns;r++)this.get(t,r)>e&&(e=this.get(t,r));return e}maxRowIndex(t){checkRowIndex(this,t);let e=this.get(t,0),r=[t,0];for(let i=1;i<this.columns;i++)this.get(t,i)>e&&(e=this.get(t,i),r[1]=i);return r}minRow(t){checkRowIndex(this,t);let e=this.get(t,0);for(let r=1;r<this.columns;r++)this.get(t,r)<e&&(e=this.get(t,r));return e}minRowIndex(t){checkRowIndex(this,t);let e=this.get(t,0),r=[t,0];for(let i=1;i<this.columns;i++)this.get(t,i)<e&&(e=this.get(t,i),r[1]=i);return r}maxColumn(t){checkColumnIndex(this,t);let e=this.get(0,t);for(let r=1;r<this.rows;r++)this.get(r,t)>e&&(e=this.get(r,t));return e}maxColumnIndex(t){checkColumnIndex(this,t);let e=this.get(0,t),r=[0,t];for(let i=1;i<this.rows;i++)this.get(i,t)>e&&(e=this.get(i,t),r[0]=i);return r}minColumn(t){checkColumnIndex(this,t);let e=this.get(0,t);for(let r=1;r<this.rows;r++)this.get(r,t)<e&&(e=this.get(r,t));return e}minColumnIndex(t){checkColumnIndex(this,t);let e=this.get(0,t),r=[0,t];for(let i=1;i<this.rows;i++)this.get(i,t)<e&&(e=this.get(i,t),r[0]=i);return r}diag(){let t=Math.min(this.rows,this.columns),e=[];for(let r=0;r<t;r++)e.push(this.get(r,r));return e}norm(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"frobenius",e=0;if("max"===t)return this.max();if("frobenius"===t){for(let t=0;t<this.rows;t++)for(let r=0;r<this.columns;r++)e+=this.get(t,r)*this.get(t,r);return Math.sqrt(e)}throw new RangeError("unknown norm type: ".concat(t))}cumulativeSum(){let t=0;for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t+=this.get(e,r),this.set(e,r,t);return this}dot(t){AbstractMatrix.isMatrix(t)&&(t=t.to1DArray());let e=this.to1DArray();if(e.length!==t.length)throw new RangeError("vectors do not have the same size");let r=0;for(let i=0;i<e.length;i++)r+=e[i]*t[i];return r}mmul(t){t=Matrix.checkMatrix(t);let e=this.rows,r=this.columns,i=t.columns,n=new Matrix(e,i),s=new Float64Array(r);for(let o=0;o<i;o++){for(let e=0;e<r;e++)s[e]=t.get(e,o);for(let t=0;t<e;t++){let e=0;for(let i=0;i<r;i++)e+=this.get(t,i)*s[i];n.set(t,o,e)}}return n}strassen2x2(t){t=Matrix.checkMatrix(t);let e=new Matrix(2,2);const r=this.get(0,0),i=t.get(0,0),n=this.get(0,1),s=t.get(0,1),o=this.get(1,0),a=t.get(1,0),h=this.get(1,1),l=t.get(1,1),u=(r+h)*(i+l),c=(o+h)*i,f=r*(s-l),m=h*(a-i),g=(r+n)*l,d=u+m-g+(n-h)*(a+l),p=f+g,w=c+m,x=u-c+f+(o-r)*(i+s);return e.set(0,0,d),e.set(0,1,p),e.set(1,0,w),e.set(1,1,x),e}strassen3x3(t){t=Matrix.checkMatrix(t);let e=new Matrix(3,3);const r=this.get(0,0),i=this.get(0,1),n=this.get(0,2),s=this.get(1,0),o=this.get(1,1),a=this.get(1,2),h=this.get(2,0),l=this.get(2,1),u=this.get(2,2),c=t.get(0,0),f=t.get(0,1),m=t.get(0,2),g=t.get(1,0),d=t.get(1,1),p=t.get(1,2),w=t.get(2,0),x=t.get(2,1),M=t.get(2,2),y=(r-s)*(-f+d),v=(-r+s+o)*(c-f+d),b=(s+o)*(-c+f),S=r*c,A=(-r+h+l)*(c-m+p),E=(-r+h)*(m-p),R=(h+l)*(-c+m),k=(-n+l+u)*(d+w-x),C=(n-u)*(d-x),T=n*w,N=(l+u)*(-w+x),I=(-n+o+a)*(p+w-M),L=(n-a)*(p-M),F=(o+a)*(-w+M),P=S+T+i*g,O=(r+i+n-s-o-l-u)*d+v+b+S+k+T+N,D=S+A+R+(r+i+n-o-a-h-l)*p+T+I+F,V=y+o*(-c+f+g-d-p-w+M)+v+S+T+I+L,_=y+v+b+S+a*x,z=T+I+L+F+s*m,q=S+A+E+l*(-c+m+g-d-p-w+x)+k+C+T,$=k+C+T+N+h*f,U=S+A+E+R+u*M;return e.set(0,0,P),e.set(0,1,O),e.set(0,2,D),e.set(1,0,V),e.set(1,1,_),e.set(1,2,z),e.set(2,0,q),e.set(2,1,$),e.set(2,2,U),e}mmulStrassen(t){t=Matrix.checkMatrix(t);let e=this.clone(),r=e.rows,i=e.columns,n=t.rows,s=t.columns;function o(t,e,r){let i=t.rows,n=t.columns;if(i===e&&n===r)return t;{let i=AbstractMatrix.zeros(e,r);return i=i.setSubMatrix(t,0,0)}}i!==n&&console.warn("Multiplying ".concat(r," x ").concat(i," and ").concat(n," x ").concat(s," matrix: dimensions do not match."));let a=Math.max(r,n),h=Math.max(i,s);return function t(e,r,i,n){if(i<=512||n<=512)return e.mmul(r);i%2==1&&n%2==1?(e=o(e,i+1,n+1),r=o(r,i+1,n+1)):i%2==1?(e=o(e,i+1,n),r=o(r,i+1,n)):n%2==1&&(e=o(e,i,n+1),r=o(r,i,n+1));let s=parseInt(e.rows/2,10),a=parseInt(e.columns/2,10),h=e.subMatrix(0,s-1,0,a-1),l=r.subMatrix(0,s-1,0,a-1),u=e.subMatrix(0,s-1,a,e.columns-1),c=r.subMatrix(0,s-1,a,r.columns-1),f=e.subMatrix(s,e.rows-1,0,a-1),m=r.subMatrix(s,r.rows-1,0,a-1),g=e.subMatrix(s,e.rows-1,a,e.columns-1),d=r.subMatrix(s,r.rows-1,a,r.columns-1),p=t(AbstractMatrix.add(h,g),AbstractMatrix.add(l,d),s,a),w=t(AbstractMatrix.add(f,g),l,s,a),x=t(h,AbstractMatrix.sub(c,d),s,a),M=t(g,AbstractMatrix.sub(m,l),s,a),y=t(AbstractMatrix.add(h,u),d,s,a),v=t(AbstractMatrix.sub(f,h),AbstractMatrix.add(l,c),s,a),b=t(AbstractMatrix.sub(u,g),AbstractMatrix.add(m,d),s,a),S=AbstractMatrix.add(p,M);S.sub(y),S.add(b);let A=AbstractMatrix.add(x,y),E=AbstractMatrix.add(w,M),R=AbstractMatrix.sub(p,w);R.add(x),R.add(v);let k=AbstractMatrix.zeros(2*S.rows,2*S.columns);return(k=(k=(k=(k=k.setSubMatrix(S,0,0)).setSubMatrix(A,S.rows,0)).setSubMatrix(E,0,S.columns)).setSubMatrix(R,S.rows,S.columns)).subMatrix(0,i-1,0,n-1)}(e=o(e,a,h),t=o(t,a,h),a,h)}scaleRows(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:r=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(r))throw new TypeError("max must be a number");if(e>=r)throw new RangeError("min must be smaller than max");let i=new Matrix(this.rows,this.columns);for(let t=0;t<this.rows;t++){const n=this.getRow(t);rescale(n,{min:e,max:r,output:n}),i.setRow(t,n)}return i}scaleColumns(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:r=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(r))throw new TypeError("max must be a number");if(e>=r)throw new RangeError("min must be smaller than max");let i=new Matrix(this.rows,this.columns);for(let t=0;t<this.columns;t++){const n=this.getColumn(t);rescale(n,{min:e,max:r,output:n}),i.setColumn(t,n)}return i}flipRows(){const t=Math.ceil(this.columns/2);for(let e=0;e<this.rows;e++)for(let r=0;r<t;r++){let t=this.get(e,r),i=this.get(e,this.columns-1-r);this.set(e,r,i),this.set(e,this.columns-1-r,t)}return this}flipColumns(){const t=Math.ceil(this.rows/2);for(let e=0;e<this.columns;e++)for(let r=0;r<t;r++){let t=this.get(r,e),i=this.get(this.rows-1-r,e);this.set(r,e,i),this.set(this.rows-1-r,e,t)}return this}kroneckerProduct(t){t=Matrix.checkMatrix(t);let e=this.rows,r=this.columns,i=t.rows,n=t.columns,s=new Matrix(e*i,r*n);for(let o=0;o<e;o++)for(let e=0;e<r;e++)for(let r=0;r<i;r++)for(let a=0;a<n;a++)s.set(i*o+r,n*e+a,this.get(o,e)*t.get(r,a));return s}transpose(){let t=new Matrix(this.columns,this.rows);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.set(r,e,this.get(e,r));return t}sortRows(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:compareNumbers;for(let e=0;e<this.rows;e++)this.setRow(e,this.getRow(e).sort(t));return this}sortColumns(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:compareNumbers;for(let e=0;e<this.columns;e++)this.setColumn(e,this.getColumn(e).sort(t));return this}subMatrix(t,e,r,i){checkRange(this,t,e,r,i);let n=new Matrix(e-t+1,i-r+1);for(let s=t;s<=e;s++)for(let e=r;e<=i;e++)n.set(s-t,e-r,this.get(s,e));return n}subMatrixRow(t,e,r){if(void 0===e&&(e=0),void 0===r&&(r=this.columns-1),e>r||e<0||e>=this.columns||r<0||r>=this.columns)throw new RangeError("Argument out of range");let i=new Matrix(t.length,r-e+1);for(let n=0;n<t.length;n++)for(let s=e;s<=r;s++){if(t[n]<0||t[n]>=this.rows)throw new RangeError("Row index out of range: ".concat(t[n]));i.set(n,s-e,this.get(t[n],s))}return i}subMatrixColumn(t,e,r){if(void 0===e&&(e=0),void 0===r&&(r=this.rows-1),e>r||e<0||e>=this.rows||r<0||r>=this.rows)throw new RangeError("Argument out of range");let i=new Matrix(r-e+1,t.length);for(let n=0;n<t.length;n++)for(let s=e;s<=r;s++){if(t[n]<0||t[n]>=this.columns)throw new RangeError("Column index out of range: ".concat(t[n]));i.set(s-e,n,this.get(s,t[n]))}return i}setSubMatrix(t,e,r){checkRange(this,e,e+(t=Matrix.checkMatrix(t)).rows-1,r,r+t.columns-1);for(let i=0;i<t.rows;i++)for(let n=0;n<t.columns;n++)this.set(e+i,r+n,t.get(i,n));return this}selection(t,e){let r=checkIndices(this,t,e),i=new Matrix(t.length,e.length);for(let t=0;t<r.row.length;t++){let e=r.row[t];for(let n=0;n<r.column.length;n++){let s=r.column[n];i.set(t,n,this.get(e,s))}}return i}trace(){let t=Math.min(this.rows,this.columns),e=0;for(let r=0;r<t;r++)e+=this.get(r,r);return e}clone(){let t=new Matrix(this.rows,this.columns);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.set(e,r,this.get(e,r));return t}sum(t){switch(t){case"row":return sumByRow(this);case"column":return sumByColumn(this);case void 0:return sumAll(this);default:throw new Error("invalid option: ".concat(t))}}product(t){switch(t){case"row":return productByRow(this);case"column":return productByColumn(this);case void 0:return productAll(this);default:throw new Error("invalid option: ".concat(t))}}mean(t){const e=this.sum(t);switch(t){case"row":for(let t=0;t<this.rows;t++)e[t]/=this.columns;return e;case"column":for(let t=0;t<this.columns;t++)e[t]/=this.rows;return e;case void 0:return e/this.size;default:throw new Error("invalid option: ".concat(t))}}variance(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{unbiased:r=!0,mean:i=this.mean(t)}=e;if("boolean"!=typeof r)throw new TypeError("unbiased must be a boolean");switch(t){case"row":if(!Array.isArray(i))throw new TypeError("mean must be an array");return varianceByRow(this,r,i);case"column":if(!Array.isArray(i))throw new TypeError("mean must be an array");return varianceByColumn(this,r,i);case void 0:if("number"!=typeof i)throw new TypeError("mean must be a number");return varianceAll(this,r,i);default:throw new Error("invalid option: ".concat(t))}}standardDeviation(t,e){"object"==typeof t&&(e=t,t=void 0);const r=this.variance(t,e);if(void 0===t)return Math.sqrt(r);for(let t=0;t<r.length;t++)r[t]=Math.sqrt(r[t]);return r}center(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{center:r=this.mean(t)}=e;switch(t){case"row":if(!Array.isArray(r))throw new TypeError("center must be an array");return centerByRow(this,r),this;case"column":if(!Array.isArray(r))throw new TypeError("center must be an array");return centerByColumn(this,r),this;case void 0:if("number"!=typeof r)throw new TypeError("center must be a number");return centerAll(this,r),this;default:throw new Error("invalid option: ".concat(t))}}scale(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");let r=e.scale;switch(t){case"row":if(void 0===r)r=getScaleByRow(this);else if(!Array.isArray(r))throw new TypeError("scale must be an array");return scaleByRow(this,r),this;case"column":if(void 0===r)r=getScaleByColumn(this);else if(!Array.isArray(r))throw new TypeError("scale must be an array");return scaleByColumn(this,r),this;case void 0:if(void 0===r)r=getScaleAll(this);else if("number"!=typeof r)throw new TypeError("scale must be a number");return scaleAll(this,r),this;default:throw new Error("invalid option: ".concat(t))}}}function compareNumbers(t,e){return t-e}AbstractMatrix.prototype.klass="Matrix","undefined"!=typeof Symbol&&(AbstractMatrix.prototype[Symbol.for("nodejs.util.inspect.custom")]=inspectMatrix),AbstractMatrix.random=AbstractMatrix.rand,AbstractMatrix.randomInt=AbstractMatrix.randInt,AbstractMatrix.diagonal=AbstractMatrix.diag,AbstractMatrix.prototype.diagonal=AbstractMatrix.prototype.diag,AbstractMatrix.identity=AbstractMatrix.eye,AbstractMatrix.prototype.negate=AbstractMatrix.prototype.neg,AbstractMatrix.prototype.tensorProduct=AbstractMatrix.prototype.kroneckerProduct;class Matrix extends AbstractMatrix{constructor(t,e){if(super(),Matrix.isMatrix(t))return t.clone();if(Number.isInteger(t)&&t>0){if(this.data=[],!(Number.isInteger(e)&&e>0))throw new TypeError("nColumns must be a positive integer");for(let r=0;r<t;r++)this.data.push(new Float64Array(e))}else{if(!Array.isArray(t))throw new TypeError("First argument must be a positive number or an array");{const r=t;if(t=r.length,"number"!=typeof(e=r[0].length)||0===e)throw new TypeError("Data must be a 2D array with at least one element");this.data=[];for(let i=0;i<t;i++){if(r[i].length!==e)throw new RangeError("Inconsistent array dimensions");this.data.push(Float64Array.from(r[i]))}}}return this.rows=t,this.columns=e,this}set(t,e,r){return this.data[t][e]=r,this}get(t,e){return this.data[t][e]}removeRow(t){if(checkRowIndex(this,t),1===this.rows)throw new RangeError("A matrix cannot have less than one row");return this.data.splice(t,1),this.rows-=1,this}addRow(t,e){return void 0===e&&(e=t,t=this.rows),checkRowIndex(this,t,!0),e=Float64Array.from(checkRowVector(this,e)),this.data.splice(t,0,e),this.rows+=1,this}removeColumn(t){if(checkColumnIndex(this,t),1===this.columns)throw new RangeError("A matrix cannot have less than one column");for(let e=0;e<this.rows;e++){const r=new Float64Array(this.columns-1);for(let i=0;i<t;i++)r[i]=this.data[e][i];for(let i=t+1;i<this.columns;i++)r[i-1]=this.data[e][i];this.data[e]=r}return this.columns-=1,this}addColumn(t,e){void 0===e&&(e=t,t=this.columns),checkColumnIndex(this,t,!0),e=checkColumnVector(this,e);for(let r=0;r<this.rows;r++){const i=new Float64Array(this.columns+1);let n=0;for(;n<t;n++)i[n]=this.data[r][n];for(i[n++]=e[r];n<this.columns+1;n++)i[n]=this.data[r][n-1];this.data[r]=i}return this.columns+=1,this}}installMathOperations(AbstractMatrix,Matrix);class BaseView extends AbstractMatrix{constructor(t,e,r){super(),this.matrix=t,this.rows=e,this.columns=r}}class MatrixColumnView extends BaseView{constructor(t,e){checkColumnIndex(t,e),super(t,t.rows,1),this.column=e}set(t,e,r){return this.matrix.set(t,this.column,r),this}get(t){return this.matrix.get(t,this.column)}}class MatrixColumnSelectionView extends BaseView{constructor(t,e){e=checkColumnIndices(t,e),super(t,t.rows,e.length),this.columnIndices=e}set(t,e,r){return this.matrix.set(t,this.columnIndices[e],r),this}get(t,e){return this.matrix.get(t,this.columnIndices[e])}}class MatrixFlipColumnView extends BaseView{constructor(t){super(t,t.rows,t.columns)}set(t,e,r){return this.matrix.set(t,this.columns-e-1,r),this}get(t,e){return this.matrix.get(t,this.columns-e-1)}}class MatrixFlipRowView extends BaseView{constructor(t){super(t,t.rows,t.columns)}set(t,e,r){return this.matrix.set(this.rows-t-1,e,r),this}get(t,e){return this.matrix.get(this.rows-t-1,e)}}class MatrixRowView extends BaseView{constructor(t,e){checkRowIndex(t,e),super(t,1,t.columns),this.row=e}set(t,e,r){return this.matrix.set(this.row,e,r),this}get(t,e){return this.matrix.get(this.row,e)}}class MatrixRowSelectionView extends BaseView{constructor(t,e){super(t,(e=checkRowIndices(t,e)).length,t.columns),this.rowIndices=e}set(t,e,r){return this.matrix.set(this.rowIndices[t],e,r),this}get(t,e){return this.matrix.get(this.rowIndices[t],e)}}class MatrixSelectionView extends BaseView{constructor(t,e,r){let i=checkIndices(t,e,r);super(t,i.row.length,i.column.length),this.rowIndices=i.row,this.columnIndices=i.column}set(t,e,r){return this.matrix.set(this.rowIndices[t],this.columnIndices[e],r),this}get(t,e){return this.matrix.get(this.rowIndices[t],this.columnIndices[e])}}class MatrixSubView extends BaseView{constructor(t,e,r,i,n){checkRange(t,e,r,i,n),super(t,r-e+1,n-i+1),this.startRow=e,this.startColumn=i}set(t,e,r){return this.matrix.set(this.startRow+t,this.startColumn+e,r),this}get(t,e){return this.matrix.get(this.startRow+t,this.startColumn+e)}}class MatrixTransposeView extends BaseView{constructor(t){super(t,t.columns,t.rows)}set(t,e,r){return this.matrix.set(e,t,r),this}get(t,e){return this.matrix.get(e,t)}}class WrapperMatrix1D extends AbstractMatrix{constructor(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{rows:r=1}=e;if(t.length%r!=0)throw new Error("the data length is not divisible by the number of rows");super(),this.rows=r,this.columns=t.length/r,this.data=t}set(t,e,r){let i=this._calculateIndex(t,e);return this.data[i]=r,this}get(t,e){let r=this._calculateIndex(t,e);return this.data[r]}_calculateIndex(t,e){return t*this.columns+e}}class WrapperMatrix2D extends AbstractMatrix{constructor(t){super(),this.data=t,this.rows=t.length,this.columns=t[0].length}set(t,e,r){return this.data[t][e]=r,this}get(t,e){return this.data[t][e]}}function wrap(t,e){if(Array.isArray(t))return t[0]&&Array.isArray(t[0])?new WrapperMatrix2D(t):new WrapperMatrix1D(t,e);throw new Error("the argument is not an array")}class LuDecomposition{constructor(t){let e,r,i,n,s,o,a,h,l,u=(t=WrapperMatrix2D.checkMatrix(t)).clone(),c=u.rows,f=u.columns,m=new Float64Array(c),g=1;for(e=0;e<c;e++)m[e]=e;for(h=new Float64Array(c),r=0;r<f;r++){for(e=0;e<c;e++)h[e]=u.get(e,r);for(e=0;e<c;e++){for(l=Math.min(e,r),s=0,i=0;i<l;i++)s+=u.get(e,i)*h[i];h[e]-=s,u.set(e,r,h[e])}for(n=r,e=r+1;e<c;e++)Math.abs(h[e])>Math.abs(h[n])&&(n=e);if(n!==r){for(i=0;i<f;i++)o=u.get(n,i),u.set(n,i,u.get(r,i)),u.set(r,i,o);a=m[n],m[n]=m[r],m[r]=a,g=-g}if(r<c&&0!==u.get(r,r))for(e=r+1;e<c;e++)u.set(e,r,u.get(e,r)/u.get(r,r))}this.LU=u,this.pivotVector=m,this.pivotSign=g}isSingular(){let t=this.LU,e=t.columns;for(let r=0;r<e;r++)if(0===t.get(r,r))return!0;return!1}solve(t){t=Matrix.checkMatrix(t);let e=this.LU;if(e.rows!==t.rows)throw new Error("Invalid matrix dimensions");if(this.isSingular())throw new Error("LU matrix is singular");let r,i,n,s=t.columns,o=t.subMatrixRow(this.pivotVector,0,s-1),a=e.columns;for(n=0;n<a;n++)for(r=n+1;r<a;r++)for(i=0;i<s;i++)o.set(r,i,o.get(r,i)-o.get(n,i)*e.get(r,n));for(n=a-1;n>=0;n--){for(i=0;i<s;i++)o.set(n,i,o.get(n,i)/e.get(n,n));for(r=0;r<n;r++)for(i=0;i<s;i++)o.set(r,i,o.get(r,i)-o.get(n,i)*e.get(r,n))}return o}get determinant(){let t=this.LU;if(!t.isSquare())throw new Error("Matrix must be square");let e=this.pivotSign,r=t.columns;for(let i=0;i<r;i++)e*=t.get(i,i);return e}get lowerTriangularMatrix(){let t=this.LU,e=t.rows,r=t.columns,i=new Matrix(e,r);for(let n=0;n<e;n++)for(let e=0;e<r;e++)n>e?i.set(n,e,t.get(n,e)):n===e?i.set(n,e,1):i.set(n,e,0);return i}get upperTriangularMatrix(){let t=this.LU,e=t.rows,r=t.columns,i=new Matrix(e,r);for(let n=0;n<e;n++)for(let e=0;e<r;e++)n<=e?i.set(n,e,t.get(n,e)):i.set(n,e,0);return i}get pivotPermutationVector(){return Array.from(this.pivotVector)}}function hypotenuse(t,e){let r=0;return Math.abs(t)>Math.abs(e)?(r=e/t,Math.abs(t)*Math.sqrt(1+r*r)):0!==e?(r=t/e,Math.abs(e)*Math.sqrt(1+r*r)):0}class QrDecomposition{constructor(t){let e,r,i,n,s=(t=WrapperMatrix2D.checkMatrix(t)).clone(),o=t.rows,a=t.columns,h=new Float64Array(a);for(i=0;i<a;i++){let t=0;for(e=i;e<o;e++)t=hypotenuse(t,s.get(e,i));if(0!==t){for(s.get(i,i)<0&&(t=-t),e=i;e<o;e++)s.set(e,i,s.get(e,i)/t);for(s.set(i,i,s.get(i,i)+1),r=i+1;r<a;r++){for(n=0,e=i;e<o;e++)n+=s.get(e,i)*s.get(e,r);for(n=-n/s.get(i,i),e=i;e<o;e++)s.set(e,r,s.get(e,r)+n*s.get(e,i))}}h[i]=-t}this.QR=s,this.Rdiag=h}solve(t){t=Matrix.checkMatrix(t);let e=this.QR,r=e.rows;if(t.rows!==r)throw new Error("Matrix row dimensions must agree");if(!this.isFullRank())throw new Error("Matrix is rank deficient");let i,n,s,o,a=t.columns,h=t.clone(),l=e.columns;for(s=0;s<l;s++)for(n=0;n<a;n++){for(o=0,i=s;i<r;i++)o+=e.get(i,s)*h.get(i,n);for(o=-o/e.get(s,s),i=s;i<r;i++)h.set(i,n,h.get(i,n)+o*e.get(i,s))}for(s=l-1;s>=0;s--){for(n=0;n<a;n++)h.set(s,n,h.get(s,n)/this.Rdiag[s]);for(i=0;i<s;i++)for(n=0;n<a;n++)h.set(i,n,h.get(i,n)-h.get(s,n)*e.get(i,s))}return h.subMatrix(0,l-1,0,a-1)}isFullRank(){let t=this.QR.columns;for(let e=0;e<t;e++)if(0===this.Rdiag[e])return!1;return!0}get upperTriangularMatrix(){let t,e,r=this.QR,i=r.columns,n=new Matrix(i,i);for(t=0;t<i;t++)for(e=0;e<i;e++)t<e?n.set(t,e,r.get(t,e)):t===e?n.set(t,e,this.Rdiag[t]):n.set(t,e,0);return n}get orthogonalMatrix(){let t,e,r,i,n=this.QR,s=n.rows,o=n.columns,a=new Matrix(s,o);for(r=o-1;r>=0;r--){for(t=0;t<s;t++)a.set(t,r,0);for(a.set(r,r,1),e=r;e<o;e++)if(0!==n.get(r,r)){for(i=0,t=r;t<s;t++)i+=n.get(t,r)*a.get(t,e);for(i=-i/n.get(r,r),t=r;t<s;t++)a.set(t,e,a.get(t,e)+i*n.get(t,r))}}return a}}class SingularValueDecomposition{constructor(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},r=(t=WrapperMatrix2D.checkMatrix(t)).rows,i=t.columns;const{computeLeftSingularVectors:n=!0,computeRightSingularVectors:s=!0,autoTranspose:o=!1}=e;let a,h=Boolean(n),l=Boolean(s),u=!1;if(r<i)if(o){r=(a=t.transpose()).rows,i=a.columns,u=!0;let e=h;h=l,l=e}else a=t.clone(),console.warn("Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose");else a=t.clone();let c=Math.min(r,i),f=Math.min(r+1,i),m=new Float64Array(f),g=new Matrix(r,c),d=new Matrix(i,i),p=new Float64Array(i),w=new Float64Array(r),x=new Float64Array(f);for(let t=0;t<f;t++)x[t]=t;let M=Math.min(r-1,i),y=Math.max(0,Math.min(i-2,r)),v=Math.max(M,y);for(let t=0;t<v;t++){if(t<M){m[t]=0;for(let e=t;e<r;e++)m[t]=hypotenuse(m[t],a.get(e,t));if(0!==m[t]){a.get(t,t)<0&&(m[t]=-m[t]);for(let e=t;e<r;e++)a.set(e,t,a.get(e,t)/m[t]);a.set(t,t,a.get(t,t)+1)}m[t]=-m[t]}for(let e=t+1;e<i;e++){if(t<M&&0!==m[t]){let i=0;for(let n=t;n<r;n++)i+=a.get(n,t)*a.get(n,e);i=-i/a.get(t,t);for(let n=t;n<r;n++)a.set(n,e,a.get(n,e)+i*a.get(n,t))}p[e]=a.get(t,e)}if(h&&t<M)for(let e=t;e<r;e++)g.set(e,t,a.get(e,t));if(t<y){p[t]=0;for(let e=t+1;e<i;e++)p[t]=hypotenuse(p[t],p[e]);if(0!==p[t]){p[t+1]<0&&(p[t]=0-p[t]);for(let e=t+1;e<i;e++)p[e]/=p[t];p[t+1]+=1}if(p[t]=-p[t],t+1<r&&0!==p[t]){for(let e=t+1;e<r;e++)w[e]=0;for(let e=t+1;e<r;e++)for(let r=t+1;r<i;r++)w[e]+=p[r]*a.get(e,r);for(let e=t+1;e<i;e++){let i=-p[e]/p[t+1];for(let n=t+1;n<r;n++)a.set(n,e,a.get(n,e)+i*w[n])}}if(l)for(let e=t+1;e<i;e++)d.set(e,t,p[e])}}let b=Math.min(i,r+1);if(M<i&&(m[M]=a.get(M,M)),r<b&&(m[b-1]=0),y+1<b&&(p[y]=a.get(y,b-1)),p[b-1]=0,h){for(let t=M;t<c;t++){for(let e=0;e<r;e++)g.set(e,t,0);g.set(t,t,1)}for(let t=M-1;t>=0;t--)if(0!==m[t]){for(let e=t+1;e<c;e++){let i=0;for(let n=t;n<r;n++)i+=g.get(n,t)*g.get(n,e);i=-i/g.get(t,t);for(let n=t;n<r;n++)g.set(n,e,g.get(n,e)+i*g.get(n,t))}for(let e=t;e<r;e++)g.set(e,t,-g.get(e,t));g.set(t,t,1+g.get(t,t));for(let e=0;e<t-1;e++)g.set(e,t,0)}else{for(let e=0;e<r;e++)g.set(e,t,0);g.set(t,t,1)}}if(l)for(let t=i-1;t>=0;t--){if(t<y&&0!==p[t])for(let e=t+1;e<i;e++){let r=0;for(let n=t+1;n<i;n++)r+=d.get(n,t)*d.get(n,e);r=-r/d.get(t+1,t);for(let n=t+1;n<i;n++)d.set(n,e,d.get(n,e)+r*d.get(n,t))}for(let e=0;e<i;e++)d.set(e,t,0);d.set(t,t,1)}let S=b-1,A=Number.EPSILON;for(;b>0;){let t,e;for(t=b-2;t>=-1&&-1!==t;t--){const e=Number.MIN_VALUE+A*Math.abs(m[t]+Math.abs(m[t+1]));if(Math.abs(p[t])<=e||Number.isNaN(p[t])){p[t]=0;break}}if(t===b-2)e=4;else{let r;for(r=b-1;r>=t&&r!==t;r--){let e=(r!==b?Math.abs(p[r]):0)+(r!==t+1?Math.abs(p[r-1]):0);if(Math.abs(m[r])<=A*e){m[r]=0;break}}r===t?e=3:r===b-1?e=1:(e=2,t=r)}switch(t++,e){case 1:{let e=p[b-2];p[b-2]=0;for(let r=b-2;r>=t;r--){let n=hypotenuse(m[r],e),s=m[r]/n,o=e/n;if(m[r]=n,r!==t&&(e=-o*p[r-1],p[r-1]=s*p[r-1]),l)for(let t=0;t<i;t++)n=s*d.get(t,r)+o*d.get(t,b-1),d.set(t,b-1,-o*d.get(t,r)+s*d.get(t,b-1)),d.set(t,r,n)}break}case 2:{let e=p[t-1];p[t-1]=0;for(let i=t;i<b;i++){let n=hypotenuse(m[i],e),s=m[i]/n,o=e/n;if(m[i]=n,e=-o*p[i],p[i]=s*p[i],h)for(let e=0;e<r;e++)n=s*g.get(e,i)+o*g.get(e,t-1),g.set(e,t-1,-o*g.get(e,i)+s*g.get(e,t-1)),g.set(e,i,n)}break}case 3:{const e=Math.max(Math.abs(m[b-1]),Math.abs(m[b-2]),Math.abs(p[b-2]),Math.abs(m[t]),Math.abs(p[t])),n=m[b-1]/e,s=m[b-2]/e,o=p[b-2]/e,a=m[t]/e,u=p[t]/e,c=((s+n)*(s-n)+o*o)/2,f=n*o*(n*o);let w=0;0===c&&0===f||(w=f/(c+(w=c<0?0-Math.sqrt(c*c+f):Math.sqrt(c*c+f))));let x=(a+n)*(a-n)+w,M=a*u;for(let e=t;e<b-1;e++){let n=hypotenuse(x,M);0===n&&(n=Number.MIN_VALUE);let s=x/n,o=M/n;if(e!==t&&(p[e-1]=n),x=s*m[e]+o*p[e],p[e]=s*p[e]-o*m[e],M=o*m[e+1],m[e+1]=s*m[e+1],l)for(let t=0;t<i;t++)n=s*d.get(t,e)+o*d.get(t,e+1),d.set(t,e+1,-o*d.get(t,e)+s*d.get(t,e+1)),d.set(t,e,n);if(0===(n=hypotenuse(x,M))&&(n=Number.MIN_VALUE),s=x/n,o=M/n,m[e]=n,x=s*p[e]+o*m[e+1],m[e+1]=-o*p[e]+s*m[e+1],M=o*p[e+1],p[e+1]=s*p[e+1],h&&e<r-1)for(let t=0;t<r;t++)n=s*g.get(t,e)+o*g.get(t,e+1),g.set(t,e+1,-o*g.get(t,e)+s*g.get(t,e+1)),g.set(t,e,n)}p[b-2]=x;break}case 4:if(m[t]<=0&&(m[t]=m[t]<0?-m[t]:0,l))for(let e=0;e<=S;e++)d.set(e,t,-d.get(e,t));for(;t<S&&!(m[t]>=m[t+1]);){let e=m[t];if(m[t]=m[t+1],m[t+1]=e,l&&t<i-1)for(let r=0;r<i;r++)e=d.get(r,t+1),d.set(r,t+1,d.get(r,t)),d.set(r,t,e);if(h&&t<r-1)for(let i=0;i<r;i++)e=g.get(i,t+1),g.set(i,t+1,g.get(i,t)),g.set(i,t,e);t++}b--}}if(u){let t=d;d=g,g=t}this.m=r,this.n=i,this.s=m,this.U=g,this.V=d}solve(t){let e=t,r=this.threshold,i=this.s.length,n=Matrix.zeros(i,i);for(let t=0;t<i;t++)Math.abs(this.s[t])<=r?n.set(t,t,0):n.set(t,t,1/this.s[t]);let s=this.U,o=this.rightSingularVectors,a=o.mmul(n),h=o.rows,l=s.rows,u=Matrix.zeros(h,l);for(let t=0;t<h;t++)for(let e=0;e<l;e++){let r=0;for(let n=0;n<i;n++)r+=a.get(t,n)*s.get(e,n);u.set(t,e,r)}return u.mmul(e)}solveForDiagonal(t){return this.solve(Matrix.diag(t))}inverse(){let t=this.V,e=this.threshold,r=t.rows,i=t.columns,n=new Matrix(r,this.s.length);for(let s=0;s<r;s++)for(let r=0;r<i;r++)Math.abs(this.s[r])>e&&n.set(s,r,t.get(s,r)/this.s[r]);let s=this.U,o=s.rows,a=s.columns,h=new Matrix(r,o);for(let t=0;t<r;t++)for(let e=0;e<o;e++){let r=0;for(let i=0;i<a;i++)r+=n.get(t,i)*s.get(e,i);h.set(t,e,r)}return h}get condition(){return this.s[0]/this.s[Math.min(this.m,this.n)-1]}get norm2(){return this.s[0]}get rank(){let t=Math.max(this.m,this.n)*this.s[0]*Number.EPSILON,e=0,r=this.s;for(let i=0,n=r.length;i<n;i++)r[i]>t&&e++;return e}get diagonal(){return Array.from(this.s)}get threshold(){return Number.EPSILON/2*Math.max(this.m,this.n)*this.s[0]}get leftSingularVectors(){return this.U}get rightSingularVectors(){return this.V}get diagonalMatrix(){return Matrix.diag(this.s)}}function inverse(t){let e=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t=WrapperMatrix2D.checkMatrix(t),e?new SingularValueDecomposition(t).inverse():solve(t,Matrix.eye(t.rows))}function solve(t,e){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];return t=WrapperMatrix2D.checkMatrix(t),e=WrapperMatrix2D.checkMatrix(e),r?new SingularValueDecomposition(t).solve(e):t.isSquare()?new LuDecomposition(t).solve(e):new QrDecomposition(t).solve(e)}function determinant(t){if((t=Matrix.checkMatrix(t)).isSquare()){let e,r,i,n;if(2===t.columns)return e=t.get(0,0),r=t.get(0,1),i=t.get(1,0),e*(n=t.get(1,1))-r*i;if(3===t.columns){let n,s,o;return n=new MatrixSelectionView(t,[1,2],[1,2]),s=new MatrixSelectionView(t,[1,2],[0,2]),o=new MatrixSelectionView(t,[1,2],[0,1]),e=t.get(0,0),r=t.get(0,1),i=t.get(0,2),e*determinant(n)-r*determinant(s)+i*determinant(o)}return new LuDecomposition(t).determinant}throw Error("determinant can only be calculated for a square matrix")}function xrange(t,e){let r=[];for(let i=0;i<t;i++)i!==e&&r.push(i);return r}function dependenciesOneRow(t,e,r){let i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1e-9;if(t>(arguments.length>4&&void 0!==arguments[4]?arguments[4]:1e-9))return new Array(e.rows+1).fill(0);{let t=e.addRow(r,[0]);for(let e=0;e<t.rows;e++)Math.abs(t.get(e,0))<i&&t.set(e,0,0);return t.to1DArray()}}function linearDependencies(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{thresholdValue:r=1e-9,thresholdError:i=1e-9}=e;let n=(t=Matrix.checkMatrix(t)).rows,s=new Matrix(n,n);for(let e=0;e<n;e++){let o=Matrix.columnVector(t.getRow(e)),a=t.subMatrixRow(xrange(n,e)).transpose(),h=new SingularValueDecomposition(a).solve(o),l=Matrix.sub(o,a.mmul(h)).abs().max();s.setRow(e,dependenciesOneRow(l,h,e,r,i))}return s}function pseudoInverse(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Number.EPSILON;t=Matrix.checkMatrix(t);let r=new SingularValueDecomposition(t,{autoTranspose:!0}),i=r.leftSingularVectors,n=r.rightSingularVectors,s=r.diagonal;for(let t=0;t<s.length;t++)Math.abs(s[t])>e?s[t]=1/s[t]:s[t]=0;return n.mmul(Matrix.diag(s).mmul(i.transpose()))}function covariance(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};t=Matrix.checkMatrix(t);let i=!1;if("object"!=typeof e||Matrix.isMatrix(e)||Array.isArray(e)?e=Matrix.checkMatrix(e):(r=e,e=t,i=!0),t.rows!==e.rows)throw new TypeError("Both matrices must have the same number of rows");const{center:n=!0}=r;n&&(t=t.center("column"),i||(e=e.center("column")));const s=t.transpose().mmul(e);for(let e=0;e<s.rows;e++)for(let r=0;r<s.columns;r++)s.set(e,r,s.get(e,r)*(1/(t.rows-1)));return s}function correlation(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};t=Matrix.checkMatrix(t);let i=!1;if("object"!=typeof e||Matrix.isMatrix(e)||Array.isArray(e)?e=Matrix.checkMatrix(e):(r=e,e=t,i=!0),t.rows!==e.rows)throw new TypeError("Both matrices must have the same number of rows");const{center:n=!0,scale:s=!0}=r;n&&(t.center("column"),i||e.center("column")),s&&(t.scale("column"),i||e.scale("column"));const o=t.standardDeviation("column",{unbiased:!0}),a=i?o:e.standardDeviation("column",{unbiased:!0}),h=t.transpose().mmul(e);for(let e=0;e<h.rows;e++)for(let r=0;r<h.columns;r++)h.set(e,r,h.get(e,r)*(1/(o[e]*a[r]))*(1/(t.rows-1)));return h}class EigenvalueDecomposition{constructor(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{assumeSymmetric:r=!1}=e;if(!(t=WrapperMatrix2D.checkMatrix(t)).isSquare())throw new Error("Matrix is not a square matrix");let i,n,s=t.columns,o=new Matrix(s,s),a=new Float64Array(s),h=new Float64Array(s),l=t,u=!1;if(u=!!r||t.isSymmetric()){for(i=0;i<s;i++)for(n=0;n<s;n++)o.set(i,n,l.get(i,n));tred2(s,h,a,o),tql2(s,h,a,o)}else{let t=new Matrix(s,s),e=new Float64Array(s);for(n=0;n<s;n++)for(i=0;i<s;i++)t.set(i,n,l.get(i,n));orthes(s,t,e,o),hqr2(s,h,a,o,t)}this.n=s,this.e=h,this.d=a,this.V=o}get realEigenvalues(){return Array.from(this.d)}get imaginaryEigenvalues(){return Array.from(this.e)}get eigenvectorMatrix(){return this.V}get diagonalMatrix(){let t,e,r=this.n,i=this.e,n=this.d,s=new Matrix(r,r);for(t=0;t<r;t++){for(e=0;e<r;e++)s.set(t,e,0);s.set(t,t,n[t]),i[t]>0?s.set(t,t+1,i[t]):i[t]<0&&s.set(t,t-1,i[t])}return s}}function tred2(t,e,r,i){let n,s,o,a,h,l,u,c;for(h=0;h<t;h++)r[h]=i.get(t-1,h);for(a=t-1;a>0;a--){for(c=0,o=0,l=0;l<a;l++)c+=Math.abs(r[l]);if(0===c)for(e[a]=r[a-1],h=0;h<a;h++)r[h]=i.get(a-1,h),i.set(a,h,0),i.set(h,a,0);else{for(l=0;l<a;l++)r[l]/=c,o+=r[l]*r[l];for(n=r[a-1],s=Math.sqrt(o),n>0&&(s=-s),e[a]=c*s,o-=n*s,r[a-1]=n-s,h=0;h<a;h++)e[h]=0;for(h=0;h<a;h++){for(n=r[h],i.set(h,a,n),s=e[h]+i.get(h,h)*n,l=h+1;l<=a-1;l++)s+=i.get(l,h)*r[l],e[l]+=i.get(l,h)*n;e[h]=s}for(n=0,h=0;h<a;h++)e[h]/=o,n+=e[h]*r[h];for(u=n/(o+o),h=0;h<a;h++)e[h]-=u*r[h];for(h=0;h<a;h++){for(n=r[h],s=e[h],l=h;l<=a-1;l++)i.set(l,h,i.get(l,h)-(n*e[l]+s*r[l]));r[h]=i.get(a-1,h),i.set(a,h,0)}}r[a]=o}for(a=0;a<t-1;a++){if(i.set(t-1,a,i.get(a,a)),i.set(a,a,1),0!==(o=r[a+1])){for(l=0;l<=a;l++)r[l]=i.get(l,a+1)/o;for(h=0;h<=a;h++){for(s=0,l=0;l<=a;l++)s+=i.get(l,a+1)*i.get(l,h);for(l=0;l<=a;l++)i.set(l,h,i.get(l,h)-s*r[l])}}for(l=0;l<=a;l++)i.set(l,a+1,0)}for(h=0;h<t;h++)r[h]=i.get(t-1,h),i.set(t-1,h,0);i.set(t-1,t-1,1),e[0]=0}function tql2(t,e,r,i){let n,s,o,a,h,l,u,c,f,m,g,d,p,w,x,M;for(o=1;o<t;o++)e[o-1]=e[o];e[t-1]=0;let y=0,v=0,b=Number.EPSILON;for(l=0;l<t;l++){for(v=Math.max(v,Math.abs(r[l])+Math.abs(e[l])),u=l;u<t&&!(Math.abs(e[u])<=b*v);)u++;if(u>l)do{for(n=r[l],f=hypotenuse(c=(r[l+1]-n)/(2*e[l]),1),c<0&&(f=-f),r[l]=e[l]/(c+f),r[l+1]=e[l]*(c+f),m=r[l+1],s=n-r[l],o=l+2;o<t;o++)r[o]-=s;for(y+=s,c=r[u],d=g=1,p=g,w=e[l+1],x=0,M=0,o=u-1;o>=l;o--)for(p=d,d=g,M=x,n=g*e[o],s=g*c,f=hypotenuse(c,e[o]),e[o+1]=x*f,x=e[o]/f,c=(g=c/f)*r[o]-x*n,r[o+1]=s+x*(g*n+x*r[o]),h=0;h<t;h++)s=i.get(h,o+1),i.set(h,o+1,x*i.get(h,o)+g*s),i.set(h,o,g*i.get(h,o)-x*s);c=-x*M*p*w*e[l]/m,e[l]=x*c,r[l]=g*c}while(Math.abs(e[l])>b*v);r[l]=r[l]+y,e[l]=0}for(o=0;o<t-1;o++){for(h=o,c=r[o],a=o+1;a<t;a++)r[a]<c&&(h=a,c=r[a]);if(h!==o)for(r[h]=r[o],r[o]=c,a=0;a<t;a++)c=i.get(a,o),i.set(a,o,i.get(a,h)),i.set(a,h,c)}}function orthes(t,e,r,i){let n,s,o,a,h,l,u,c=t-1;for(l=1;l<=c-1;l++){for(u=0,a=l;a<=c;a++)u+=Math.abs(e.get(a,l-1));if(0!==u){for(o=0,a=c;a>=l;a--)r[a]=e.get(a,l-1)/u,o+=r[a]*r[a];for(s=Math.sqrt(o),r[l]>0&&(s=-s),o-=r[l]*s,r[l]=r[l]-s,h=l;h<t;h++){for(n=0,a=c;a>=l;a--)n+=r[a]*e.get(a,h);for(n/=o,a=l;a<=c;a++)e.set(a,h,e.get(a,h)-n*r[a])}for(a=0;a<=c;a++){for(n=0,h=c;h>=l;h--)n+=r[h]*e.get(a,h);for(n/=o,h=l;h<=c;h++)e.set(a,h,e.get(a,h)-n*r[h])}r[l]=u*r[l],e.set(l,l-1,u*s)}}for(a=0;a<t;a++)for(h=0;h<t;h++)i.set(a,h,a===h?1:0);for(l=c-1;l>=1;l--)if(0!==e.get(l,l-1)){for(a=l+1;a<=c;a++)r[a]=e.get(a,l-1);for(h=l;h<=c;h++){for(s=0,a=l;a<=c;a++)s+=r[a]*i.get(a,h);for(s=s/r[l]/e.get(l,l-1),a=l;a<=c;a++)i.set(a,h,i.get(a,h)+s*r[a])}}}function hqr2(t,e,r,i,n){let s,o,a,h,l,u,c,f,m,g,d,p,w,x,M,y=t-1,v=t-1,b=Number.EPSILON,S=0,A=0,E=0,R=0,k=0,C=0,T=0,N=0;for(s=0;s<t;s++)for((s<0||s>v)&&(r[s]=n.get(s,s),e[s]=0),o=Math.max(s-1,0);o<t;o++)A+=Math.abs(n.get(s,o));for(;y>=0;){for(h=y;h>0&&(0===(C=Math.abs(n.get(h-1,h-1))+Math.abs(n.get(h,h)))&&(C=A),!(Math.abs(n.get(h,h-1))<b*C));)h--;if(h===y)n.set(y,y,n.get(y,y)+S),r[y]=n.get(y,y),e[y]=0,y--,N=0;else if(h===y-1){if(c=n.get(y,y-1)*n.get(y-1,y),R=(E=(n.get(y-1,y-1)-n.get(y,y))/2)*E+c,T=Math.sqrt(Math.abs(R)),n.set(y,y,n.get(y,y)+S),n.set(y-1,y-1,n.get(y-1,y-1)+S),f=n.get(y,y),R>=0){for(T=E>=0?E+T:E-T,r[y-1]=f+T,r[y]=r[y-1],0!==T&&(r[y]=f-c/T),e[y-1]=0,e[y]=0,E=(f=n.get(y,y-1))/(C=Math.abs(f)+Math.abs(T)),R=T/C,E/=k=Math.sqrt(E*E+R*R),R/=k,o=y-1;o<t;o++)T=n.get(y-1,o),n.set(y-1,o,R*T+E*n.get(y,o)),n.set(y,o,R*n.get(y,o)-E*T);for(s=0;s<=y;s++)T=n.get(s,y-1),n.set(s,y-1,R*T+E*n.get(s,y)),n.set(s,y,R*n.get(s,y)-E*T);for(s=0;s<=v;s++)T=i.get(s,y-1),i.set(s,y-1,R*T+E*i.get(s,y)),i.set(s,y,R*i.get(s,y)-E*T)}else r[y-1]=f+E,r[y]=f+E,e[y-1]=T,e[y]=-T;y-=2,N=0}else{if(f=n.get(y,y),m=0,c=0,h<y&&(m=n.get(y-1,y-1),c=n.get(y,y-1)*n.get(y-1,y)),10===N){for(S+=f,s=0;s<=y;s++)n.set(s,s,n.get(s,s)-f);f=m=.75*(C=Math.abs(n.get(y,y-1))+Math.abs(n.get(y-1,y-2))),c=-.4375*C*C}if(30===N&&(C=(C=(m-f)/2)*C+c)>0){for(C=Math.sqrt(C),m<f&&(C=-C),C=f-c/((m-f)/2+C),s=0;s<=y;s++)n.set(s,s,n.get(s,s)-C);S+=C,f=m=c=.964}for(N+=1,l=y-2;l>=h&&(E=((k=f-(T=n.get(l,l)))*(C=m-T)-c)/n.get(l+1,l)+n.get(l,l+1),R=n.get(l+1,l+1)-T-k-C,k=n.get(l+2,l+1),E/=C=Math.abs(E)+Math.abs(R)+Math.abs(k),R/=C,k/=C,l!==h)&&!(Math.abs(n.get(l,l-1))*(Math.abs(R)+Math.abs(k))<b*(Math.abs(E)*(Math.abs(n.get(l-1,l-1))+Math.abs(T)+Math.abs(n.get(l+1,l+1)))));)l--;for(s=l+2;s<=y;s++)n.set(s,s-2,0),s>l+2&&n.set(s,s-3,0);for(a=l;a<=y-1&&(x=a!==y-1,a!==l&&(E=n.get(a,a-1),R=n.get(a+1,a-1),k=x?n.get(a+2,a-1):0,0!==(f=Math.abs(E)+Math.abs(R)+Math.abs(k))&&(E/=f,R/=f,k/=f)),0!==f);a++)if(C=Math.sqrt(E*E+R*R+k*k),E<0&&(C=-C),0!==C){for(a!==l?n.set(a,a-1,-C*f):h!==l&&n.set(a,a-1,-n.get(a,a-1)),f=(E+=C)/C,m=R/C,T=k/C,R/=E,k/=E,o=a;o<t;o++)E=n.get(a,o)+R*n.get(a+1,o),x&&(E+=k*n.get(a+2,o),n.set(a+2,o,n.get(a+2,o)-E*T)),n.set(a,o,n.get(a,o)-E*f),n.set(a+1,o,n.get(a+1,o)-E*m);for(s=0;s<=Math.min(y,a+3);s++)E=f*n.get(s,a)+m*n.get(s,a+1),x&&(E+=T*n.get(s,a+2),n.set(s,a+2,n.get(s,a+2)-E*k)),n.set(s,a,n.get(s,a)-E),n.set(s,a+1,n.get(s,a+1)-E*R);for(s=0;s<=v;s++)E=f*i.get(s,a)+m*i.get(s,a+1),x&&(E+=T*i.get(s,a+2),i.set(s,a+2,i.get(s,a+2)-E*k)),i.set(s,a,i.get(s,a)-E),i.set(s,a+1,i.get(s,a+1)-E*R)}}}if(0!==A){for(y=t-1;y>=0;y--)if(E=r[y],0===(R=e[y]))for(h=y,n.set(y,y,1),s=y-1;s>=0;s--){for(c=n.get(s,s)-E,k=0,o=h;o<=y;o++)k+=n.get(s,o)*n.get(o,y);if(e[s]<0)T=c,C=k;else if(h=s,0===e[s]?n.set(s,y,0!==c?-k/c:-k/(b*A)):(f=n.get(s,s+1),m=n.get(s+1,s),u=(f*C-T*k)/(R=(r[s]-E)*(r[s]-E)+e[s]*e[s]),n.set(s,y,u),n.set(s+1,y,Math.abs(f)>Math.abs(T)?(-k-c*u)/f:(-C-m*u)/T)),b*(u=Math.abs(n.get(s,y)))*u>1)for(o=s;o<=y;o++)n.set(o,y,n.get(o,y)/u)}else if(R<0)for(h=y-1,Math.abs(n.get(y,y-1))>Math.abs(n.get(y-1,y))?(n.set(y-1,y-1,R/n.get(y,y-1)),n.set(y-1,y,-(n.get(y,y)-E)/n.get(y,y-1))):(M=cdiv(0,-n.get(y-1,y),n.get(y-1,y-1)-E,R),n.set(y-1,y-1,M[0]),n.set(y-1,y,M[1])),n.set(y,y-1,0),n.set(y,y,1),s=y-2;s>=0;s--){for(g=0,d=0,o=h;o<=y;o++)g+=n.get(s,o)*n.get(o,y-1),d+=n.get(s,o)*n.get(o,y);if(c=n.get(s,s)-E,e[s]<0)T=c,k=g,C=d;else if(h=s,0===e[s]?(M=cdiv(-g,-d,c,R),n.set(s,y-1,M[0]),n.set(s,y,M[1])):(f=n.get(s,s+1),m=n.get(s+1,s),p=(r[s]-E)*(r[s]-E)+e[s]*e[s]-R*R,w=2*(r[s]-E)*R,0===p&&0===w&&(p=b*A*(Math.abs(c)+Math.abs(R)+Math.abs(f)+Math.abs(m)+Math.abs(T))),M=cdiv(f*k-T*g+R*d,f*C-T*d-R*g,p,w),n.set(s,y-1,M[0]),n.set(s,y,M[1]),Math.abs(f)>Math.abs(T)+Math.abs(R)?(n.set(s+1,y-1,(-g-c*n.get(s,y-1)+R*n.get(s,y))/f),n.set(s+1,y,(-d-c*n.get(s,y)-R*n.get(s,y-1))/f)):(M=cdiv(-k-m*n.get(s,y-1),-C-m*n.get(s,y),T,R),n.set(s+1,y-1,M[0]),n.set(s+1,y,M[1]))),b*(u=Math.max(Math.abs(n.get(s,y-1)),Math.abs(n.get(s,y))))*u>1)for(o=s;o<=y;o++)n.set(o,y-1,n.get(o,y-1)/u),n.set(o,y,n.get(o,y)/u)}for(s=0;s<t;s++)if(s<0||s>v)for(o=s;o<t;o++)i.set(s,o,n.get(s,o));for(o=t-1;o>=0;o--)for(s=0;s<=v;s++){for(T=0,a=0;a<=Math.min(o,v);a++)T+=i.get(s,a)*n.get(a,o);i.set(s,o,T)}}}function cdiv(t,e,r,i){let n,s;return Math.abs(r)>Math.abs(i)?[(t+(n=i/r)*e)/(s=r+n*i),(e-n*t)/s]:[((n=r/i)*t+e)/(s=i+n*r),(n*e-t)/s]}class CholeskyDecomposition{constructor(t){if(!(t=WrapperMatrix2D.checkMatrix(t)).isSymmetric())throw new Error("Matrix is not symmetric");let e,r,i,n=t,s=n.rows,o=new Matrix(s,s),a=!0;for(r=0;r<s;r++){let t=0;for(i=0;i<r;i++){let s=0;for(e=0;e<i;e++)s+=o.get(i,e)*o.get(r,e);s=(n.get(r,i)-s)/o.get(i,i),o.set(r,i,s),t+=s*s}for(a&=(t=n.get(r,r)-t)>0,o.set(r,r,Math.sqrt(Math.max(t,0))),i=r+1;i<s;i++)o.set(r,i,0)}this.L=o,this.positiveDefinite=Boolean(a)}isPositiveDefinite(){return this.positiveDefinite}solve(t){t=WrapperMatrix2D.checkMatrix(t);let e=this.L,r=e.rows;if(t.rows!==r)throw new Error("Matrix dimensions do not match");if(!1===this.isPositiveDefinite())throw new Error("Matrix is not positive definite");let i,n,s,o=t.columns,a=t.clone();for(s=0;s<r;s++)for(n=0;n<o;n++){for(i=0;i<s;i++)a.set(s,n,a.get(s,n)-a.get(i,n)*e.get(s,i));a.set(s,n,a.get(s,n)/e.get(s,s))}for(s=r-1;s>=0;s--)for(n=0;n<o;n++){for(i=s+1;i<r;i++)a.set(s,n,a.get(s,n)-a.get(i,n)*e.get(i,s));a.set(s,n,a.get(s,n)/e.get(s,s))}return a}get lowerTriangularMatrix(){return this.L}}class nipals{constructor(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t=WrapperMatrix2D.checkMatrix(t);let{Y:r}=e;const{scaleScores:i=!1,maxIterations:n=1e3,terminationCriteria:s=1e-10}=e;let o;if(r){if(!(r=Array.isArray(r)&&"number"==typeof r[0]?Matrix.columnVector(r):WrapperMatrix2D.checkMatrix(r)).isColumnVector()||r.rows!==t.rows)throw new Error("Y must be a column vector of length X.rows");o=r}else o=t.getColumnVector(0);let a,h,l,u,c=1;for(let e=0;e<n&&c>s;e++)l=(l=t.transpose().mmul(o).div(o.transpose().mmul(o).get(0,0))).div(l.norm()),a=t.mmul(l).div(l.transpose().mmul(l).get(0,0)),e>0&&(c=a.clone().sub(u).pow(2).sum()),u=a.clone(),r?(h=(h=r.transpose().mmul(a).div(a.transpose().mmul(a).get(0,0))).div(h.norm()),o=r.mmul(h).div(h.transpose().mmul(h).get(0,0))):o=a;if(r){let e=t.transpose().mmul(a).div(a.transpose().mmul(a).get(0,0));e=e.div(e.norm());let i=t.clone().sub(a.clone().mmul(e.transpose())),n=o.transpose().mmul(a).div(a.transpose().mmul(a).get(0,0)),s=r.clone().sub(a.clone().mulS(n.get(0,0)).mmul(h.transpose()));this.t=a,this.p=e.transpose(),this.w=l.transpose(),this.q=h,this.u=o,this.s=a.transpose().mmul(a),this.xResidual=i,this.yResidual=s,this.betas=n}else this.w=l.transpose(),this.s=a.transpose().mmul(a).sqrt(),this.t=i?a.clone().div(this.s.get(0,0)):a,this.xResidual=t.sub(a.mmul(l.transpose()))}}var MatrixLib=Object.freeze({__proto__:null,AbstractMatrix:AbstractMatrix,default:Matrix,Matrix:Matrix,wrap:wrap,WrapperMatrix1D:WrapperMatrix1D,WrapperMatrix2D:WrapperMatrix2D,solve:solve,inverse:inverse,determinant:determinant,linearDependencies:linearDependencies,pseudoInverse:pseudoInverse,covariance:covariance,correlation:correlation,SingularValueDecomposition:SingularValueDecomposition,SVD:SingularValueDecomposition,EigenvalueDecomposition:EigenvalueDecomposition,EVD:EigenvalueDecomposition,CholeskyDecomposition:CholeskyDecomposition,CHO:CholeskyDecomposition,LuDecomposition:LuDecomposition,LU:LuDecomposition,QrDecomposition:QrDecomposition,QR:QrDecomposition,Nipals:nipals,NIPALS:nipals,MatrixColumnView:MatrixColumnView,MatrixColumnSelectionView:MatrixColumnSelectionView,MatrixFlipColumnView:MatrixFlipColumnView,MatrixFlipRowView:MatrixFlipRowView,MatrixRowView:MatrixRowView,MatrixRowSelectionView:MatrixRowSelectionView,MatrixSelectionView:MatrixSelectionView,MatrixSubView:MatrixSubView,MatrixTransposeView:MatrixTransposeView});function sum(t){if(!src(t))throw new TypeError("input must be an array");if(0===t.length)throw new TypeError("input must not be empty");for(var e=0,r=0;r<t.length;r++)e+=t[r];return e}function mean(t){return sum(t)/t.length}function toDiscreteDistribution(t,e){let r=new Array(e).fill(0);for(let e=0;e<t.length;++e)r[t[e]]+=1/t.length;return Matrix.rowVector(r)}function giniImpurity(t){if(0===t.length)return 0;let e=toDiscreteDistribution(t,getNumberOfClasses(t)).getRow(0),r=0;for(let t=0;t<e.length;++t)r+=e[t]*e[t];return 1-r}function getNumberOfClasses(t){return t.filter((function(t,e,r){return r.indexOf(t)===e})).map(t=>t+1).reduce((t,e)=>Math.max(t,e))}function giniGain(t,e){let r=0,i=["greater","lesser"];for(let n=0;n<i.length;++n){let s=e[i[n]];r+=giniImpurity(s)*s.length/t.length}return giniImpurity(t)-r}function squaredError(t){let e=t.length,r=mean(t),i=0;for(let n=0;n<e;++n){let e=t[n];i+=(e-r)*(e-r)}return i}function regressionError(t,e){let r=0,i=["greater","lesser"];for(let t=0;t<i.length;++t){r+=squaredError(e[i[t]])}return r}function matrixSplitter(t,e,r,i){let n=[],s=[],o=[],a=[];for(let h=0;h<t.rows;++h)t.get(h,r)<i?(n.push(t.getRow(h)),o.push(e[h])):(s.push(t.getRow(h)),a.push(e[h]));return{greaterX:s,greaterY:a,lesserX:n,lesserY:o}}function mean$1(t,e){return(t+e)/2}function zip(t,e){if(t.length!==e.length)throw new TypeError("Error on zip: the size of a: ".concat(t.length," is different from b: ").concat(e.length));let r=new Array(t.length);for(let i=0;i<t.length;++i)r[i]=[t[i],e[i]];return r}const gainFunctions={gini:giniGain,regression:regressionError},splitFunctions={mean:mean$1};class TreeNode{constructor(t){this.kind=t.kind,this.gainFunction=t.gainFunction,this.splitFunction=t.splitFunction,this.minNumSamples=t.minNumSamples,this.maxDepth=t.maxDepth}bestSplit(t,e){let r,i,n="classifier"===this.kind?-1/0:1/0,s="classifier"===this.kind?(t,e)=>t>e:(t,e)=>t<e;for(let o=0;o<t.rows;++o){let a=t.getRow(o),h=this.featureSplit(a,e);for(let t=0;t<h.length;++t){let l=h[t],u=this.split(a,e,l),c=gainFunctions[this.gainFunction](e,u);s(c,n)&&(r=o,i=l,n=c)}}return{maxGain:n,maxColumn:r,maxValue:i}}split(t,e,r){let i=[],n=[];for(let s=0;s<t.length;++s)t[s]<r?i.push(e[s]):n.push(e[s]);return{greater:n,lesser:i}}featureSplit(t,e){let r=[],i=zip(t,e);i.sort((function(t,e){return t[0]-e[0]}));for(let t=1;t<i.length;++t)i[t-1][1]!==i[t][1]&&r.push(splitFunctions[this.splitFunction](i[t-1][0],i[t][0]));return r}calculatePrediction(t){if("classifier"===this.kind){if(this.distribution=toDiscreteDistribution(t,getNumberOfClasses(t)),0===this.distribution.columns)throw new TypeError("Error on calculate the prediction")}else this.distribution=mean(t)}train(t,e,r,i){if(t.rows<=this.minNumSamples)return void this.calculatePrediction(e);void 0===i&&(i=0);let n=t.transpose(),s=this.bestSplit(n,e);this.splitValue=s.maxValue,this.splitColumn=s.maxColumn,this.gain=s.maxGain;let o=matrixSplitter(t,e,this.splitColumn,this.splitValue);if(r<this.maxDepth&&this.gain>.01&&this.gain!==i&&o.lesserX.length>0&&o.greaterX.length>0){this.left=new TreeNode(this),this.right=new TreeNode(this);let t=new Matrix(o.lesserX),e=new Matrix(o.greaterX);this.left.train(t,o.lesserY,r+1,this.gain),this.right.train(e,o.greaterY,r+1,this.gain)}else this.calculatePrediction(e)}classify(t){return this.right&&this.left?t[this.splitColumn]<this.splitValue?this.left.classify(t):this.right.classify(t):this.distribution}setNodeParameters(t){void 0!==t.distribution?this.distribution=t.distribution.constructor===Array?new Matrix(t.distribution):t.distribution:(this.distribution=void 0,this.splitValue=t.splitValue,this.splitColumn=t.splitColumn,this.gain=t.gain,this.left=new TreeNode(this),this.right=new TreeNode(this),t.left!=={}&&this.left.setNodeParameters(t.left),t.right!=={}&&this.right.setNodeParameters(t.right))}}const defaultOptions={gainFunction:"gini",splitFunction:"mean",minNumSamples:3,maxDepth:1/0};class DecisionTreeClassifier{constructor(t,e){!0===t?(this.options=e.options,this.root=new TreeNode(e.options),this.root.setNodeParameters(e.root)):(this.options=Object.assign({},defaultOptions,t),this.options.kind="classifier")}train(t,e){this.root=new TreeNode(this.options),t=Matrix.checkMatrix(t),this.root.train(t,e,0,null)}predict(t){t=Matrix.checkMatrix(t);let e=new Array(t.rows);for(let r=0;r<t.rows;++r)e[r]=this.root.classify(t.getRow(r)).maxRowIndex(0)[1];return e}toJSON(){return{options:this.options,root:this.root,name:"DTClassifier"}}static load(t){if("DTClassifier"!==t.name)throw new RangeError("Invalid model: ".concat(t.name));return new DecisionTreeClassifier(!0,t)}}const defaultOptions$1={gainFunction:"regression",splitFunction:"mean",minNumSamples:3,maxDepth:1/0};class DecisionTreeRegression{constructor(t,e){!0===t?(this.options=e.options,this.root=new TreeNode(e.options),this.root.setNodeParameters(e.root)):(this.options=Object.assign({},defaultOptions$1,t),this.options.kind="regression")}train(t,e){this.root=new TreeNode(this.options),t=void 0!==t[0]&&void 0===t[0].length?Matrix.columnVector(t):Matrix.checkMatrix(t),this.root.train(t,e,0)}predict(t){void 0!==t[0]&&void 0===t[0].length&&(t=Matrix.columnVector(t)),t=Matrix.checkMatrix(t);let e=new Array(t.rows);for(let r=0;r<t.rows;++r)e[r]=this.root.classify(t.getRow(r));return e}toJSON(){return{options:this.options,root:this.root,name:"DTRegression"}}static load(t){if("DTRegression"!==t.name)throw new RangeError("Invalid model:".concat(t.name));return new DecisionTreeRegression(!0,t)}}const SMALLEST_UNSAFE_INTEGER=9007199254740992,LARGEST_SAFE_INTEGER=SMALLEST_UNSAFE_INTEGER-1,UINT32_MAX=-1>>>0,UINT32_SIZE=UINT32_MAX+1,INT32_SIZE=UINT32_SIZE/2,INT32_MAX=INT32_SIZE-1,UINT21_SIZE=1<<21,UINT21_MAX=UINT21_SIZE-1;function int32(t){return 0|t.next()}function add(t,e){return 0===e?t:r=>t(r)+e}function int53(t){const e=0|t.next(),r=t.next()>>>0;return(e&UINT21_MAX)*UINT32_SIZE+r+(e&UINT21_SIZE?-SMALLEST_UNSAFE_INTEGER:0)}function int53Full(t){for(;;){const e=0|t.next();if(!(4194304&e)){const r=t.next()>>>0;return(e&UINT21_MAX)*UINT32_SIZE+r+(e&UINT21_SIZE?-SMALLEST_UNSAFE_INTEGER:0)}if(4194304==(8388607&e)&&0==(0|t.next()))return SMALLEST_UNSAFE_INTEGER}}function uint32(t){return t.next()>>>0}function uint53(t){const e=t.next()&UINT21_MAX,r=t.next()>>>0;return e*UINT32_SIZE+r}function uint53Full(t){for(;;){const e=0|t.next();if(!(e&UINT21_SIZE)){const r=t.next()>>>0;return(e&UINT21_MAX)*UINT32_SIZE+r}if(0==(e&UINT21_MAX)&&0==(0|t.next()))return SMALLEST_UNSAFE_INTEGER}}function isPowerOfTwoMinusOne(t){return 0==(t+1&t)}function bitmask(t){return e=>e.next()&t}function downscaleToLoopCheckedRange(t){const e=t+1,r=e*Math.floor(UINT32_SIZE/e);return t=>{let i=0;do{i=t.next()>>>0}while(i>=r);return i%e}}function downscaleToRange(t){return isPowerOfTwoMinusOne(t)?bitmask(t):downscaleToLoopCheckedRange(t)}function isEvenlyDivisibleByMaxInt32(t){return 0==(0|t)}function upscaleWithHighMasking(t){return e=>{const r=e.next()&t,i=e.next()>>>0;return r*UINT32_SIZE+i}}function upscaleToLoopCheckedRange(t){const e=t*Math.floor(SMALLEST_UNSAFE_INTEGER/t);return r=>{let i=0;do{const t=r.next()&UINT21_MAX,e=r.next()>>>0;i=t*UINT32_SIZE+e}while(i>=e);return i%t}}function upscaleWithinU53(t){const e=t+1;if(isEvenlyDivisibleByMaxInt32(e)){const t=(e/UINT32_SIZE|0)-1;if(isPowerOfTwoMinusOne(t))return upscaleWithHighMasking(t)}return upscaleToLoopCheckedRange(e)}function upscaleWithinI53AndLoopCheck(t,e){return r=>{let i=0;do{const t=0|r.next(),e=r.next()>>>0;i=(t&UINT21_MAX)*UINT32_SIZE+e+(t&UINT21_SIZE?-SMALLEST_UNSAFE_INTEGER:0)}while(i<t||i>e);return i}}function integer(t,e){if(t=Math.floor(t),e=Math.floor(e),t<-SMALLEST_UNSAFE_INTEGER||!isFinite(t))throw new RangeError("Expected min to be at least ".concat(-SMALLEST_UNSAFE_INTEGER));if(e>SMALLEST_UNSAFE_INTEGER||!isFinite(e))throw new RangeError("Expected max to be at most ".concat(SMALLEST_UNSAFE_INTEGER));const r=e-t;return r<=0||!isFinite(r)?()=>t:r===UINT32_MAX?0===t?uint32:add(int32,t+INT32_SIZE):r<UINT32_MAX?add(downscaleToRange(r),t):r===LARGEST_SAFE_INTEGER?add(uint53,t):r<LARGEST_SAFE_INTEGER?add(upscaleWithinU53(r),t):e-1-t===LARGEST_SAFE_INTEGER?add(uint53Full,t):t===-SMALLEST_UNSAFE_INTEGER&&e===SMALLEST_UNSAFE_INTEGER?int53Full:t===-SMALLEST_UNSAFE_INTEGER&&e===LARGEST_SAFE_INTEGER?int53:t===-LARGEST_SAFE_INTEGER&&e===SMALLEST_UNSAFE_INTEGER?add(int53,1):e===SMALLEST_UNSAFE_INTEGER?add(upscaleWithinI53AndLoopCheck(t-1,e-1),1):upscaleWithinI53AndLoopCheck(t,e)}const DEFAULT_STRING_POOL="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-";function string(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:DEFAULT_STRING_POOL;const e=t.length;if(!e)throw new Error("Expected pool not to be an empty string");const r=integer(0,e-1);return(e,i)=>{let n="";for(let s=0;s<i;++s){const i=r(e);n+=t.charAt(i)}return n}}const LOWER_HEX_POOL="0123456789abcdef",lowerHex=string(LOWER_HEX_POOL),upperHex=string(LOWER_HEX_POOL.toUpperCase()),stringRepeat=(()=>{try{if("xxx"==="x".repeat(3))return(t,e)=>t.repeat(e)}catch(t){}return(t,e)=>{let r="";for(;e>0;)1&e&&(r+=t),e>>=1,t+=t;return r}})(),nativeMath={next:()=>Math.random()*UINT32_SIZE|0},I32Array=(()=>{try{const t=new ArrayBuffer(4),e=new Int32Array(t);if(e[0]=INT32_SIZE,e[0]===-INT32_SIZE)return Int32Array}catch(t){}return Array})();function createEntropy(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:nativeMath,e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:16;const r=[];r.push(0|(new Date).getTime());for(let i=1;i<e;++i)r[i]=0|t.next();return r}const imul=(()=>{try{if(-5===Math.imul(UINT32_MAX,5))return Math.imul}catch(t){}return(t,e)=>{const r=65535&t,i=65535&e;return r*i+((t>>>16&65535)*i+r*(e>>>16&65535)<<16>>>0)|0}})(),ARRAY_SIZE=624,ARRAY_MAX=ARRAY_SIZE-1,M=397,ARRAY_SIZE_MINUS_M=ARRAY_SIZE-M,A=2567483615;class MersenneTwister19937{constructor(){this.data=new I32Array(ARRAY_SIZE),this.index=0,this.uses=0}static seed(t){return(new MersenneTwister19937).seed(t)}static seedWithArray(t){return(new MersenneTwister19937).seedWithArray(t)}static autoSeed(){return MersenneTwister19937.seedWithArray(createEntropy())}next(){(0|this.index)>=ARRAY_SIZE&&(refreshData(this.data),this.index=0);const t=this.data[this.index];return this.index=this.index+1|0,this.uses+=1,0|temper(t)}getUseCount(){return this.uses}discard(t){if(t<=0)return this;for(this.uses+=t,(0|this.index)>=ARRAY_SIZE&&(refreshData(this.data),this.index=0);t+this.index>ARRAY_SIZE;)t-=ARRAY_SIZE-this.index,refreshData(this.data),this.index=0;return this.index=this.index+t|0,this}seed(t){let e=0;this.data[0]=e=0|t;for(let t=1;t<ARRAY_SIZE;t=t+1|0)this.data[t]=e=imul(e^e>>>30,1812433253)+t|0;return this.index=ARRAY_SIZE,this.uses=0,this}seedWithArray(t){return this.seed(19650218),seedWithArray(this.data,t),this}}function refreshData(t){let e=0,r=0;for(;(0|e)<ARRAY_SIZE_MINUS_M;e=e+1|0)r=t[e]&INT32_SIZE|t[e+1|0]&INT32_MAX,t[e]=t[e+M|0]^r>>>1^(1&r?A:0);for(;(0|e)<ARRAY_MAX;e=e+1|0)r=t[e]&INT32_SIZE|t[e+1|0]&INT32_MAX,t[e]=t[e-ARRAY_SIZE_MINUS_M|0]^r>>>1^(1&r?A:0);r=t[ARRAY_MAX]&INT32_SIZE|t[0]&INT32_MAX,t[ARRAY_MAX]=t[M-1]^r>>>1^(1&r?A:0)}function temper(t){return t^=t>>>11,t^=t<<7&2636928640,(t^=t<<15&4022730752)^t>>>18}function seedWithArray(t,e){let r=1,i=0;const n=e.length;let s=0|Math.max(n,ARRAY_SIZE),o=0|t[0];for(;(0|s)>0;--s)t[r]=o=(t[r]^imul(o^o>>>30,1664525))+(0|e[i])+(0|i)|0,++i,(0|(r=r+1|0))>ARRAY_MAX&&(t[0]=t[ARRAY_MAX],r=1),i>=n&&(i=0);for(s=ARRAY_MAX;(0|s)>0;--s)t[r]=o=(t[r]^imul(o^o>>>30,1566083941))-r|0,(0|(r=r+1|0))>ARRAY_MAX&&(t[0]=t[ARRAY_MAX],r=1);t[0]=INT32_SIZE}function checkFloat(t){return t>0&&t<=1}function examplesBaggingWithReplacement(t,e,r){let i,n=integer(0,t.rows-1);if(void 0===r)i=MersenneTwister19937.autoSeed();else{if(!Number.isInteger(r))throw new RangeError("Expected seed must be undefined or integer not ".concat(r));i=MersenneTwister19937.seed(r)}let s=new Array(t.rows),o=new Array(t.rows);for(let r=0;r<t.rows;++r){let a=n(i);s[r]=t.getRow(a),o[r]=e[a]}return{X:new Matrix(s),y:o}}function featureBagging(t,e,r,i){if(t.columns<e)throw new RangeError("N should be less or equal to the number of columns of X");let n,s=integer(0,t.columns-1);if(void 0===i)n=MersenneTwister19937.autoSeed();else{if(!Number.isInteger(i))throw new RangeError("Expected seed must be undefined or integer not ".concat(i));n=MersenneTwister19937.seed(i)}let o,a,h=new Matrix(t.rows,e);if(r){o=new Array(e);for(let r=0;r<e;++r)a=s(n),o[r]=a,h.setColumn(r,t.getColumn(a))}else{o=new Set,a=s(n);for(let r=0;r<e;++r){for(;o.has(a);)a=s(n);h.setColumn(r,t.getColumn(a)),o.add(a)}o=Array.from(o)}return{X:h,usedIndex:o}}class RandomForestBase{constructor(t,e){if(!0===t){this.replacement=e.replacement,this.maxFeatures=e.maxFeatures,this.nEstimators=e.nEstimators,this.treeOptions=e.treeOptions,this.isClassifier=e.isClassifier,this.seed=e.seed,this.n=e.n,this.indexes=e.indexes,this.useSampleBagging=e.useSampleBagging;let t=this.isClassifier?DecisionTreeClassifier:DecisionTreeRegression;this.estimators=e.estimators.map(e=>t.load(e))}else this.replacement=t.replacement,this.maxFeatures=t.maxFeatures,this.nEstimators=t.nEstimators,this.treeOptions=t.treeOptions,this.isClassifier=t.isClassifier,this.seed=t.seed,this.useSampleBagging=t.useSampleBagging}train(t,e){if(t=Matrix.checkMatrix(t),this.maxFeatures=this.maxFeatures||t.columns,checkFloat(this.maxFeatures))this.n=Math.floor(t.columns*this.maxFeatures);else{if(!Number.isInteger(this.maxFeatures))throw new RangeError("Cannot process the maxFeatures parameter ".concat(this.maxFeatures));if(this.maxFeatures>t.columns)throw new RangeError("The maxFeatures parameter should be less than ".concat(t.columns));this.n=this.maxFeatures}let r;r=this.isClassifier?DecisionTreeClassifier:DecisionTreeRegression,this.estimators=new Array(this.nEstimators),this.indexes=new Array(this.nEstimators);for(let i=0;i<this.nEstimators;++i){let n=this.useSampleBagging?examplesBaggingWithReplacement(t,e,this.seed):{X:t,y:e},s=n.X,o=n.y;s=(n=featureBagging(s,this.n,this.replacement,this.seed)).X,this.indexes[i]=n.usedIndex,this.estimators[i]=new r(this.treeOptions),this.estimators[i].train(s,o)}}selection(t){throw new Error("Abstract method 'selection' not implemented!")}predict(t){let e=new Array(this.nEstimators);t=Matrix.checkMatrix(t);for(let r=0;r<this.nEstimators;++r){let i=new MatrixColumnSelectionView(t,this.indexes[r]);e[r]=this.estimators[r].predict(i)}e=new MatrixTransposeView(new WrapperMatrix2D(e));let r=new Array(e.rows);for(let t=0;t<e.rows;++t)r[t]=this.selection(e.getRow(t));return r}toJSON(){return{indexes:this.indexes,n:this.n,replacement:this.replacement,maxFeatures:this.maxFeatures,nEstimators:this.nEstimators,treeOptions:this.treeOptions,isClassifier:this.isClassifier,seed:this.seed,estimators:this.estimators.map(t=>t.toJSON()),useSampleBagging:this.useSampleBagging}}}const defaultOptions$2={maxFeatures:1,replacement:!0,nEstimators:10,seed:42,useSampleBagging:!1};class RandomForestClassifier extends RandomForestBase{constructor(t,e){!0===t?super(!0,e.baseModel):((t=Object.assign({},defaultOptions$2,t)).isClassifier=!0,super(t))}selection(t){return mode(t)}toJSON(){return{baseModel:super.toJSON(),name:"RFClassifier"}}static load(t){if("RFClassifier"!==t.name)throw new RangeError("Invalid model: ".concat(t.name));return new RandomForestClassifier(!0,t)}}function mode(t){return t.sort((e,r)=>t.filter(t=>t===e).length-t.filter(t=>t===r).length).pop()}var commonjsGlobal="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function createCommonjsModule(t,e){return t(e={exports:{}},e.exports),e.exports}var medianQuickselect_min=createCommonjsModule((function(t){!function(){function e(t){for(var e=0,n=t.length-1,s=void 0,o=void 0,a=void 0,h=i(e,n);;){if(n<=e)return t[h];if(n==e+1)return t[e]>t[n]&&r(t,e,n),t[h];for(t[s=i(e,n)]>t[n]&&r(t,s,n),t[e]>t[n]&&r(t,e,n),t[s]>t[e]&&r(t,s,e),r(t,s,e+1),o=e+1,a=n;;){do{o++}while(t[e]>t[o]);do{a--}while(t[a]>t[e]);if(a<o)break;r(t,o,a)}r(t,e,a),a<=h&&(e=o),a>=h&&(n=a-1)}}var r=function(t,e,r){var i;return i=[t[r],t[e]],t[e]=i[0],t[r]=i[1],i},i=function(t,e){return~~((t+e)/2)};t.exports?t.exports=e:window.median=e}()}));function median(t){if(!src(t))throw new TypeError("input must be an array");if(0===t.length)throw new TypeError("input must not be empty");return medianQuickselect_min(t.slice())}const selectionMethods={mean:mean,median:median},defaultOptions$3={maxFeatures:1,replacement:!1,nEstimators:10,treeOptions:{},selectionMethod:"mean",seed:42,useSampleBagging:!1};class RandomForestRegression extends RandomForestBase{constructor(t,e){if(!0===t)super(!0,e.baseModel),this.selectionMethod=e.selectionMethod;else{if("mean"!==(t=Object.assign({},defaultOptions$3,t)).selectionMethod&&"median"!==t.selectionMethod)throw new RangeError("Unsupported selection method ".concat(t.selectionMethod));t.isClassifier=!1,super(t),this.selectionMethod=t.selectionMethod}}selection(t){return selectionMethods[this.selectionMethod](t)}toJSON(){return{baseModel:super.toJSON(),selectionMethod:this.selectionMethod,name:"RFRegression"}}static load(t){if("RFRegression"!==t.name)throw new RangeError("Invalid model: ".concat(t.name));return new RandomForestRegression(!0,t)}}class PCA{constructor(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!0===t){const t=e;return this.center=t.center,this.scale=t.scale,this.means=t.means,this.stdevs=t.stdevs,this.U=Matrix.checkMatrix(t.U),this.S=t.S,this.R=t.R,void(this.excludedFeatures=t.excludedFeatures||[])}t=new Matrix(t);const{isCovarianceMatrix:r=!1,method:i="SVD",nCompNIPALS:n=2,center:s=!0,scale:o=!1,ignoreZeroVariance:a=!1}=e;if(this.center=s,this.scale=o,this.means=null,this.stdevs=null,this.excludedFeatures=[],r)this._computeFromCovarianceMatrix(t);else switch(this._adjust(t,a),i){case"covarianceMatrix":{const e=new MatrixTransposeView(t).mmul(t).div(t.rows-1);this._computeFromCovarianceMatrix(e);break}case"NIPALS":this._computeWithNIPALS(t,n);break;case"SVD":{const e=new SingularValueDecomposition(t,{computeLeftSingularVectors:!1,computeRightSingularVectors:!0,autoTranspose:!0});this.U=e.rightSingularVectors;const r=e.diagonal,i=[];for(const e of r)i.push(e*e/(t.rows-1));this.S=i;break}default:throw new Error("unknown method: ".concat(i))}}static load(t){if("string"!=typeof t.name)throw new TypeError("model must have a name property");if("PCA"!==t.name)throw new RangeError("invalid model: ".concat(t.name));return new PCA(!0,t)}predict(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{nComponents:r=this.U.columns}=e;if(t=new Matrix(t),this.center&&(t.subRowVector(this.means),this.scale)){for(let e of this.excludedFeatures)t.removeColumn(e);t.divRowVector(this.stdevs)}var i=t.mmul(this.U);return i.subMatrix(0,i.rows-1,0,r-1)}invert(t){var e=(t=Matrix.checkMatrix(t)).mmul(this.U.transpose());return this.center&&(this.scale&&e.mulRowVector(this.stdevs),e.addRowVector(this.means)),e}getExplainedVariance(){var t=0;for(const e of this.S)t+=e;return this.S.map(e=>e/t)}getCumulativeVariance(){for(var t=this.getExplainedVariance(),e=1;e<t.length;e++)t[e]+=t[e-1];return t}getEigenvectors(){return this.U}getEigenvalues(){return this.S}getStandardDeviations(){return this.S.map(t=>Math.sqrt(t))}getLoadings(){return this.U.transpose()}toJSON(){return{name:"PCA",center:this.center,scale:this.scale,means:this.means,stdevs:this.stdevs,U:this.U,S:this.S,excludedFeatures:this.excludedFeatures}}_adjust(t,e){if(this.center){const r=t.mean("column"),i=this.scale?t.standardDeviation("column",{mean:r}):null;if(this.means=r,t.subRowVector(r),this.scale){for(let r=0;r<i.length;r++)if(0===i[r]){if(!e)throw new RangeError("Cannot scale the dataset (standard deviation is zero at index ".concat(r));t.removeColumn(r),i.splice(r,1),this.excludedFeatures.push(r),r--}this.stdevs=i,t.divRowVector(i)}}}_computeFromCovarianceMatrix(t){const e=new EigenvalueDecomposition(t,{assumeSymmetric:!0});this.U=e.eigenvectorMatrix,this.U.flipRows(),this.S=e.realEigenvalues,this.S.reverse()}_computeWithNIPALS(t,e){this.U=new Matrix(e,t.columns),this.S=[];let r=t;for(let t=0;t<e;t++){let e=new nipals(r);this.U.setRow(t,e.w.transpose()),this.S.push(Math.pow(e.s.get(0,0),2)),r=e.xResidual}this.U=this.U.transpose()}}function 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null==e&&(e=r),i=t.pop(),t.length?(n=t[0],t[0]=i,d(t,0,e)):n=i,n},h=function(t,e,i){var n;return null==i&&(i=r),n=t[0],t[0]=e,d(t,0,i),n},a=function(t,e,i){var n;return null==i&&(i=r),t.length&&i(t[0],e)<0&&(e=(n=[t[0],e])[0],t[0]=n[1],d(t,0,i)),e},n=function(t,e){var n,s,o,a,h,l;for(null==e&&(e=r),h=[],s=0,o=(a=function(){l=[];for(var e=0,r=i(t.length/2);0<=r?e<r:e>r;0<=r?e++:e--)l.push(e);return l}.apply(this).reverse()).length;s<o;s++)n=a[s],h.push(d(t,n,e));return h},m=function(t,e,i){var n;if(null==i&&(i=r),-1!==(n=t.indexOf(e)))return g(t,0,n,i),d(t,n,i)},c=function(t,e,i){var s,o,h,l,u;if(null==i&&(i=r),!(o=t.slice(0,e)).length)return o;for(n(o,i),h=0,l=(u=t.slice(e)).length;h<l;h++)s=u[h],a(o,s,i);return o.sort(i).reverse()},f=function(t,e,i){var o,a,h,c,f,m,g,d,p;if(null==i&&(i=r),10*e<=t.length){if(!(h=t.slice(0,e).sort(i)).length)return h;for(a=h[h.length-1],c=0,m=(g=t.slice(e)).length;c<m;c++)i(o=g[c],a)<0&&(l(h,o,0,null,i),h.pop(),a=h[h.length-1]);return 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n(this.nodes,this.cmp)},t.prototype.updateItem=function(t){return m(this.nodes,t,this.cmp)},t.prototype.clear=function(){return this.nodes=[]},t.prototype.empty=function(){return 0===this.nodes.length},t.prototype.size=function(){return this.nodes.length},t.prototype.clone=function(){var e;return(e=new t).nodes=this.nodes.slice(0),e},t.prototype.toArray=function(){return this.nodes.slice(0)},t.prototype.insert=t.prototype.push,t.prototype.top=t.prototype.peek,t.prototype.front=t.prototype.peek,t.prototype.has=t.prototype.contains,t.prototype.copy=t.prototype.clone,t}(),t.exports=e}).call(commonjsGlobal)})),heap$1=heap;class Cluster{constructor(){this.children=[],this.height=0,this.size=1,this.index=-1,this.isLeaf=!1}cut(t){if("number"!=typeof t)throw new TypeError("threshold must be a number");if(t<0)throw new RangeError("threshold must be a positive number");let e=[this];const r=[];for(;e.length>0;){const i=e.shift();t>=i.height?r.push(i):e=e.concat(i.children)}return r}group(t){if(!Number.isInteger(t)||t<1)throw new RangeError("groups must be a positive integer");const e=new heap$1((t,e)=>e.height-t.height);for(e.push(this);e.size()<t;){var r=e.pop();if(0===r.children.length)break;r.children.forEach(t=>e.push(t))}var i=new Cluster;return i.children=e.toArray(),i.height=this.height,i}traverse(t){!function t(e,r){if(r(e),e.children)for(const i of e.children)t(i,r)}(this,t)}indices(){const t=[];return this.traverse(e=>{e.isLeaf&&t.push(e.index)}),t}}function singleLink(t,e){return Math.min(t,e)}function completeLink(t,e){return Math.max(t,e)}function averageLink(t,e,r,i,n){return i/(i+n)*t+n/(i+n)*e}function weightedAverageLink(t,e){return(t+e)/2}function centroidLink(t,e,r,i,n){return i/(i+n)*t+n/(i+n)*e+-i*n/(i+n)**2*r}function medianLink(t,e,r){return t/2+e/2-r/4}function wardLink(t,e,r,i,n,s){return(i+s)/(i+n+s)*t+(n+s)/(i+n+s)*e+-s/(i+n+s)*r}function wardLink2(t,e,r,i,n,s){const o=(i+s)/(i+n+s),a=(n+s)/(i+n+s),h=-s/(i+n+s);return Math.sqrt(o*t*t+a*e*e+h*r*r)}function agnes(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{distanceFunction:r=euclidean,method:i="complete",isDistanceMatrix:n=!1}=e;let s;n||(t=distanceMatrix(t,r));let o=new Matrix(t);const a=o.rows;if("string"==typeof i)switch(i.toLowerCase()){case"single":s=singleLink;break;case"complete":s=completeLink;break;case"average":case"upgma":s=averageLink;break;case"wpgma":s=weightedAverageLink;break;case"centroid":case"upgmc":s=centroidLink;break;case"median":case"wpgmc":s=medianLink;break;case"ward":s=wardLink;break;case"ward2":s=wardLink2;break;default:throw new RangeError("unknown clustering method: ".concat(i))}else if("function"!=typeof i)throw new TypeError("method must be a string or function");let h=[];for(let t=0;t<a;t++){const e=new Cluster;e.isLeaf=!0,e.index=t,h.push(e)}for(let t=0;t<a-1;t++){const[t,e,r]=getSmallestDistance(o),i=h[t],n=h[e],a=new Cluster;a.size=i.size+n.size,a.children.push(i,n),a.height=r;const l=[a],u=new Matrix(o.rows-1,o.rows-1),c=r=>getPreviousIndex(r,Math.min(t,e),Math.max(t,e));for(let a=1;a<u.rows;a++){const f=c(a),m=h[f];l.push(m);for(let h=0;h<a;h++)if(0===h){const l=s(o.get(t,f),o.get(f,e),r,i.size,n.size,m.size);u.set(a,h,l),u.set(h,a,l)}else{const t=o.get(f,c(h));u.set(a,h,t),u.set(h,a,t)}}h=l,o=u}return h[0]}function getSmallestDistance(t){let e=1/0,r=0,i=0;for(let n=1;n<t.rows;n++)for(let s=0;s<n;s++)t.get(n,s)<e&&(e=t.get(n,s),r=n,i=s);return[r,i,e]}function getPreviousIndex(t,e,r){return(t-=1)>=e&&t++,t>=r&&t++,t}var index=Object.freeze({__proto__:null,agnes:agnes});const defaultOptions$4={distanceFunction:squaredEuclidean};function nearestVector(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:defaultOptions$4;const i=r.distanceFunction||defaultOptions$4.distanceFunction,n=r.similarityFunction||defaultOptions$4.similarityFunction;let s=-1;if("function"==typeof n){let r=Number.MIN_VALUE;for(let i=0;i<t.length;i++){const o=n(e,t[i]);o>r&&(r=o,s=i)}}else{if("function"!=typeof i)throw new Error("A similarity or distance function it's required");{let r=Number.MAX_VALUE;for(let n=0;n<t.length;n++){const o=i(e,t[n]);o<r&&(r=o,s=n)}}}return s}function calculateDistanceMatrix(t,e){for(var r=new Array(t.length),i=0;i<t.length;++i)for(var n=i;n<t.length;++n){r[i]||(r[i]=new Array(t.length)),r[n]||(r[n]=new Array(t.length));const s=e(t[i],t[n]);r[i][n]=s,r[n][i]=s}return r}function updateClusterID(t,e,r,i){for(var n=0;n<t.length;n++)r[n]=nearestVector(e,t[n],{distanceFunction:i});return r}function updateCenters(t,e,r,i){const n=e[0].length;for(var s=new Array(i),o=new Array(i),a=0;a<i;a++){s[a]=new Array(n),o[a]=0;for(var h=0;h<n;h++)s[a][h]=0}for(var l=0;l<e.length;l++){o[r[l]]++;for(var u=0;u<n;u++)s[r[l]][u]+=e[l][u]}for(var c=0;c<i;c++)for(var f=0;f<n;f++)o[c]?s[c][f]/=o[c]:s[c][f]=t[c][f];return s}function hasConverged(t,e,r,i){for(var n=0;n<t.length;n++)if(r(t[n],e[n])>i)return!1;return!0}const LOOP=8,FLOAT_MUL=1/16777216,sh1=15,sh2=18,sh3=11;function multiply_uint32(t,e){const r=65535&(t>>>=0);return((t-r)*(e>>>=0)>>>0)+r*e>>>0}class XSadd{constructor(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Date.now();this.state=new Uint32Array(4),this.init(t),this.random=this.getFloat.bind(this)}getUint32(){return this.nextState(),this.state[3]+this.state[2]>>>0}getFloat(){return(this.getUint32()>>>8)*FLOAT_MUL}init(t){if(!Number.isInteger(t))throw new TypeError("seed must be an integer");this.state[0]=t,this.state[1]=0,this.state[2]=0,this.state[3]=0;for(let t=1;t<LOOP;t++)this.state[3&t]^=t+multiply_uint32(1812433253,this.state[t-1&3]^this.state[t-1&3]>>>30>>>0)>>>0;this.periodCertification();for(let t=0;t<LOOP;t++)this.nextState()}periodCertification(){0===this.state[0]&&0===this.state[1]&&0===this.state[2]&&0===this.state[3]&&(this.state[0]=88,this.state[1]=83,this.state[2]=65,this.state[3]=68)}nextState(){let t=this.state[0];t^=t<<sh1,t^=t>>>sh2,t^=this.state[3]<<sh3,this.state[0]=this.state[1],this.state[1]=this.state[2],this.state[2]=this.state[3],this.state[3]=t}}const PROB_TOLERANCE=1e-8;function randomChoice(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:Math.random;const{size:i=1,replace:n=!1,probabilities:s}=e;let o,a;if(o="number"==typeof t?getArray(t):t.slice(),s){if(!n)throw new Error("choice with probabilities and no replacement is not implemented");if(s.length!==o.length)throw new Error("the length of probabilities option should be equal to the number of choices");a=[s[0]];for(let t=1;t<s.length;t++)a[t]=a[t-1]+s[t];if(Math.abs(1-a[a.length-1])>PROB_TOLERANCE)throw new Error("probabilities should sum to 1, but instead sums to ".concat(a[a.length-1]))}if(!1===n&&i>o.length)throw new Error("size option is too large");const h=[];for(let t=0;t<i;t++){const t=randomIndex(o.length,r,a);h.push(o[t]),n||o.splice(t,1)}return h}function getArray(t){const e=[];for(let r=0;r<t;r++)e.push(r);return e}function randomIndex(t,e,r){const i=e();if(r){let t=0;for(;i>r[t];)t++;return t}return Math.floor(i*t)}class Random{constructor(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:Math.random;if("number"==typeof t){const e=new XSadd(t);this.randomGenerator=e.random}else this.randomGenerator=t}choice(t,e){return randomChoice(t,e,this.randomGenerator)}random(){return this.randomGenerator()}randInt(t,e){return void 0===e&&(e=t,t=0),t+Math.floor(this.randomGenerator()*(e-t))}randomSample(t){const e=[];for(let r=0;r<t;r++)e.push(this.random());return e}}function random(t,e,r){return new Random(r).choice(t,{size:e})}function mostDistant(t,e,r,i){const n=new Random(i);var s=new Array(e);if(s[0]=Math.floor(n.random()*t.length),e>1){for(var o={dist:-1,index:-1},a=0;a<t.length;++a)r[s[0]][a]>o.dist&&(o.dist=r[s[0]][a],o.index=a);if(s[1]=o.index,e>2)for(var h=2;h<e;++h){for(var l={dist:-1,index:-1},u=0;u<t.length;++u){for(var c={dist:Number.MAX_VALUE,index:-1},f=0;f<h;++f)r[f][u]<c.dist&&-1===s.indexOf(u)&&(c={dist:r[f][u],index:u});c.dist!==Number.MAX_VALUE&&c.dist>l.dist&&(l=Object.assign({},c))}s[h]=l.index}}return s.map(e=>t[e])}function kmeanspp(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const i=(t=new Matrix(t)).rows,n=new Random(r.seed),s=[],o=r.localTrials||2+Math.floor(Math.log(e)),a=n.randInt(i);s.push(t.getRow(a));let h=new Matrix(1,t.rows);for(let e=0;e<t.rows;e++)h.set(0,e,squaredEuclidean(t.getRow(e),s[0]));let l=[cumSum(h.getRow(0))];const u=1/l[0][i-1];let c=Matrix.mul(h,u);for(let r=1;r<e;r++){const e=n.choice(i,{replace:!0,size:o,probabilities:c[0]}),a=euclideanDistances(t.selection(e,range(t.columns)),t);let u,f,m;for(let t=0;t<o;t++){const r=Matrix.min(h,[a.getRow(t)]),i=r.sum();(void 0===u||i<f)&&(u=e[t],f=i,m=r)}s[r]=t.getRow(u),l=[cumSum((h=m).getRow(0))],c=Matrix.mul(h,1/l[0][i-1])}return s}function euclideanDistances(t,e){const r=new Matrix(t.rows,e.rows);for(let i=0;i<t.rows;i++)for(let n=0;n<e.rows;n++)r.set(i,n,squaredEuclidean(t.getRow(i),e.getRow(n)));return r}function range(t){let e=[];for(let r=0;r<t;r++)e.push(r);return e}function cumSum(t){let e=[t[0]];for(let r=1;r<t.length;r++)e[r]=e[r-1]+t[r];return e}const distanceSymbol=Symbol("distance");class KMeansResult{constructor(t,e,r,i,n){this.clusters=t,this.centroids=e,this.converged=r,this.iterations=i,this[distanceSymbol]=n}nearest(t){const e=new Array(t.length);return updateClusterID(t,this.centroids.map((function(t){return t.centroid})),e,this[distanceSymbol])}computeInformation(t){for(var e=this.centroids.map((function(t){return{centroid:t,error:0,size:0}})),r=0;r<t.length;r++)e[this.clusters[r]].error+=this[distanceSymbol](t[r],this.centroids[this.clusters[r]]),e[this.clusters[r]].size++;for(var i=0;i<this.centroids.length;i++)e[i].size?e[i].error/=e[i].size:e[i].error=null;return new KMeansResult(this.clusters,e,this.converged,this.iterations,this[distanceSymbol])}}const defaultOptions$5={maxIterations:100,tolerance:1e-6,withIterations:!1,initialization:"kmeans++",distanceFunction:squaredEuclidean};function step(t,e,r,i,n,s){var o=updateCenters(t,e,r=updateClusterID(e,t,r,n.distanceFunction),i),a=hasConverged(o,t,n.distanceFunction,n.tolerance);return new KMeansResult(r,o,a,s,n.distanceFunction)}function*kmeansGenerator(t,e,r,i,n){for(var s,o=!1,a=0;!o&&a<n.maxIterations;)s=step(t,e,r,i,n,++a),yield s.computeInformation(e),o=s.converged,t=s.centroids}function kmeans(t,e,r){if(r=Object.assign({},defaultOptions$5,r),e<=0||e>t.length||!Number.isInteger(e))throw new Error("K should be a positive integer smaller than the number of points");var i;if(Array.isArray(r.initialization)){if(r.initialization.length!==e)throw new Error("The initial centers should have the same length as K");i=r.initialization}else switch(r.initialization){case"kmeans++":i=kmeanspp(t,e,r);break;case"random":i=random(t,e,r.seed);break;case"mostDistant":i=mostDistant(t,e,calculateDistanceMatrix(t,r.distanceFunction),r.seed);break;default:throw new Error('Unknown initialization method: "'.concat(r.initialization,'"'))}0===r.maxIterations&&(r.maxIterations=Number.MAX_VALUE);var n=new Array(t.length);if(r.withIterations)return kmeansGenerator(i,t,n,e,r);for(var s,o=!1,a=0;!o&&a<r.maxIterations;)o=(s=step(i,t,n,e,r,++a)).converged,i=s.centroids;return s.computeInformation(t)}function separateClasses(t,e){for(var r=t.columns,i=0,n=new Array(1e4),s=0;s<e.length;s++)void 0===n[e[s]]&&(n[e[s]]=0,i++),n[e[s]]++;var o=new Array(i),a=new Array(i);for(s=0;s<i;++s)o[s]=new Matrix(n[s],r),a[s]=0;for(s=0;s<t.rows;++s)o[e[s]].setRow(a[e[s]],t.getRow(s)),a[e[s]]++;return o}class GaussianNB{constructor(t,e){t&&(this.means=e.means,this.calculateProbabilities=e.calculateProbabilities)}train(t,e){var r=Math.sqrt(2*Math.PI);if((t=Matrix.checkMatrix(t)).rows!==e.length)throw new RangeError("the size of the training set and the training labels must be the same.");var i=separateClasses(t,e),n=new Array(i.length);this.means=new Array(i.length);for(var s=0;s<i.length;++s){var o=i[s].mean("column"),a=i[s].standardDeviation("column",{mean:o}),h=Math.log(i[s].rows/t.rows);n[s]=new Array(o.length+1),n[s][0]=h;for(var l=1;l<o.length+1;++l){var u=a[l-1];n[s][l]=[1/(r*u),-2*u*u]}this.means[s]=o}this.calculateProbabilities=n}predict(t){if((t=Matrix.checkMatrix(t)).rows===this.calculateProbabilities[0].length)throw new RangeError("the dataset must have the same features as the training set");for(var e=new Array(t.rows),r=0;r<e.length;++r)e[r]=getCurrentClass(t.getRow(r),this.means,this.calculateProbabilities);return e}toJSON(){return{modelName:"NaiveBayes",means:this.means,calculateProbabilities:this.calculateProbabilities}}static load(t){if("NaiveBayes"!==t.modelName)throw new RangeError("The current model is not a Multinomial Naive Bayes, current model:",t.name);return new GaussianNB(!0,t)}}function getCurrentClass(t,e,r){for(var i=0,n=-1,s=0;s<r.length;++s){for(var o=r[s][0],a=1;a<r[0][1].length+1;++a)o+=calculateLogProbability(t[a-1],e[s][a-1],r[s][a][0],r[s][a][1]);(o=Math.exp(o))>i&&(i=o,n=s)}return n}function calculateLogProbability(t,e,r,i){return t-=e,Math.log(r*Math.exp(t*t/i))}class MultinomialNB{constructor(t){t&&(this.conditionalProbability=Matrix.checkMatrix(t.conditionalProbability),this.priorProbability=Matrix.checkMatrix(t.priorProbability))}train(t,e){if((t=Matrix.checkMatrix(t)).rows!==e.length)throw new RangeError("the size of the training set and the training labels must be the same.");var r=separateClasses(t,e);this.priorProbability=new Matrix(r.length,1);for(var i=0;i<r.length;++i)this.priorProbability.set(i,0,Math.log(r[i].rows/t.rows));var n=t.columns;for(this.conditionalProbability=new Matrix(r.length,n),i=0;i<r.length;++i){var s=Matrix.checkMatrix(r[i]),o=s.sum()+n;this.conditionalProbability.setRow(i,Matrix.rowVector(s.sum("column")).add(1).div(o).apply(matrixLog))}}predict(t){t=Matrix.checkMatrix(t);for(var e=new Array(t.rows),r=0;r<t.rows;++r){var i=t.getRowVector(r);const n=Matrix.columnVector(this.conditionalProbability.clone().mulRowVector(i).sum("row"));e[r]=n.add(this.priorProbability).maxIndex()[0]}return e}toJSON(){return{name:"MultinomialNB",priorProbability:this.priorProbability,conditionalProbability:this.conditionalProbability}}static load(t){if("MultinomialNB"!==t.name)throw new RangeError("".concat(t.name," is not a Multinomial Naive Bayes"));return new MultinomialNB(t)}}function matrixLog(t,e){this.set(t,e,Math.log(this.get(t,e)))}var index$1=Object.freeze({__proto__:null,GaussianNB:GaussianNB,MultinomialNB:MultinomialNB});
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports):"function"==typeof define&&define.amd?define(["exports"],e):e((t="undefined"!=typeof globalThis?globalThis:t||self).ML={})}(this,(function(exports){"use strict";const toString=Object.prototype.toString;function isAnyArray(t){return toString.call(t).endsWith("Array]")}function max(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!isAnyArray(t))throw new TypeError("input must be an array");if(0===t.length)throw new TypeError("input must not be empty");var r=e.fromIndex,n=void 0===r?0:r,i=e.toIndex,o=void 0===i?t.length:i;if(n<0||n>=t.length||!Number.isInteger(n))throw new Error("fromIndex must be a positive integer smaller than length");if(o<=n||o>t.length||!Number.isInteger(o))throw new Error("toIndex must be an integer greater than fromIndex and at most equal to length");for(var s=t[n],a=n+1;a<o;a++)t[a]>s&&(s=t[a]);return s}function min(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!isAnyArray(t))throw new TypeError("input must be an array");if(0===t.length)throw new TypeError("input must not be empty");var r=e.fromIndex,n=void 0===r?0:r,i=e.toIndex,o=void 0===i?t.length:i;if(n<0||n>=t.length||!Number.isInteger(n))throw new Error("fromIndex must be a positive integer smaller than length");if(o<=n||o>t.length||!Number.isInteger(o))throw new Error("toIndex must be an integer greater than fromIndex and at most equal to length");for(var s=t[n],a=n+1;a<o;a++)t[a]<s&&(s=t[a]);return s}function rescale(t){var e,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!isAnyArray(t))throw new TypeError("input must be an array");if(0===t.length)throw new TypeError("input must not be empty");if(void 0!==r.output){if(!isAnyArray(r.output))throw new TypeError("output option must be an array if specified");e=r.output}else e=new Array(t.length);var n=min(t),i=max(t);if(n===i)throw new RangeError("minimum and maximum input values are equal. 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this},t.sqrt=function(t){return new e(t).sqrt()},t.prototype.tan=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tan(this.get(t,e)));return this},t.tan=function(t){return new e(t).tan()},t.prototype.tanh=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tanh(this.get(t,e)));return this},t.tanh=function(t){return new e(t).tanh()},t.prototype.trunc=function(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.trunc(this.get(t,e)));return this},t.trunc=function(t){return new e(t).trunc()},t.pow=function(t,r){return new e(t).pow(r)},t.prototype.pow=function(t){return"number"==typeof t?this.powS(t):this.powM(t)},t.prototype.powS=function(t){for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,Math.pow(this.get(e,r),t));return this},t.prototype.powM=function(t){if(t=e.checkMatrix(t),this.rows!==t.rows||this.columns!==t.columns)throw new RangeError("Matrices dimensions must be equal");for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,Math.pow(this.get(e,r),t.get(e,r)));return this}}function checkRowIndex(t,e,r){let n=r?t.rows:t.rows-1;if(e<0||e>n)throw new RangeError("Row index out of range")}function checkColumnIndex(t,e,r){let n=r?t.columns:t.columns-1;if(e<0||e>n)throw new RangeError("Column index out of range")}function checkRowVector(t,e){if(e.to1DArray&&(e=e.to1DArray()),e.length!==t.columns)throw new RangeError("vector size must be the same as the number of columns");return e}function checkColumnVector(t,e){if(e.to1DArray&&(e=e.to1DArray()),e.length!==t.rows)throw new RangeError("vector size must be the same as the number of rows");return e}function checkIndices(t,e,r){return{row:checkRowIndices(t,e),column:checkColumnIndices(t,r)}}function checkRowIndices(t,e){if("object"!=typeof e)throw new TypeError("unexpected type for row indices");if(e.some((e=>e<0||e>=t.rows)))throw new RangeError("row indices are out of range");return Array.isArray(e)||(e=Array.from(e)),e}function checkColumnIndices(t,e){if("object"!=typeof e)throw new TypeError("unexpected type for column indices");if(e.some((e=>e<0||e>=t.columns)))throw new RangeError("column indices are out of range");return Array.isArray(e)||(e=Array.from(e)),e}function checkRange(t,e,r,n,i){if(5!==arguments.length)throw new RangeError("expected 4 arguments");if(checkNumber("startRow",e),checkNumber("endRow",r),checkNumber("startColumn",n),checkNumber("endColumn",i),e>r||n>i||e<0||e>=t.rows||r<0||r>=t.rows||n<0||n>=t.columns||i<0||i>=t.columns)throw new RangeError("Submatrix indices are out of range")}function newArray(t,e=0){let r=[];for(let n=0;n<t;n++)r.push(e);return r}function checkNumber(t,e){if("number"!=typeof e)throw new TypeError(t+" must be a number")}function sumByRow(t){let e=newArray(t.rows);for(let r=0;r<t.rows;++r)for(let n=0;n<t.columns;++n)e[r]+=t.get(r,n);return e}function sumByColumn(t){let e=newArray(t.columns);for(let r=0;r<t.rows;++r)for(let n=0;n<t.columns;++n)e[n]+=t.get(r,n);return e}function sumAll(t){let e=0;for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)e+=t.get(r,n);return e}function productByRow(t){let e=newArray(t.rows,1);for(let r=0;r<t.rows;++r)for(let n=0;n<t.columns;++n)e[r]*=t.get(r,n);return e}function productByColumn(t){let e=newArray(t.columns,1);for(let r=0;r<t.rows;++r)for(let n=0;n<t.columns;++n)e[n]*=t.get(r,n);return e}function productAll(t){let e=1;for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)e*=t.get(r,n);return e}function varianceByRow(t,e,r){const n=t.rows,i=t.columns,o=[];for(let s=0;s<n;s++){let n=0,a=0,l=0;for(let e=0;e<i;e++)l=t.get(s,e)-r[s],n+=l,a+=l*l;e?o.push((a-n*n/i)/(i-1)):o.push((a-n*n/i)/i)}return o}function varianceByColumn(t,e,r){const n=t.rows,i=t.columns,o=[];for(let s=0;s<i;s++){let i=0,a=0,l=0;for(let e=0;e<n;e++)l=t.get(e,s)-r[s],i+=l,a+=l*l;e?o.push((a-i*i/n)/(n-1)):o.push((a-i*i/n)/n)}return o}function varianceAll(t,e,r){const n=t.rows,i=t.columns,o=n*i;let s=0,a=0,l=0;for(let e=0;e<n;e++)for(let n=0;n<i;n++)l=t.get(e,n)-r,s+=l,a+=l*l;return e?(a-s*s/o)/(o-1):(a-s*s/o)/o}function centerByRow(t,e){for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)t.set(r,n,t.get(r,n)-e[r])}function centerByColumn(t,e){for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)t.set(r,n,t.get(r,n)-e[n])}function centerAll(t,e){for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)t.set(r,n,t.get(r,n)-e)}function getScaleByRow(t){const e=[];for(let r=0;r<t.rows;r++){let n=0;for(let e=0;e<t.columns;e++)n+=Math.pow(t.get(r,e),2)/(t.columns-1);e.push(Math.sqrt(n))}return e}function scaleByRow(t,e){for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)t.set(r,n,t.get(r,n)/e[r])}function getScaleByColumn(t){const e=[];for(let r=0;r<t.columns;r++){let n=0;for(let e=0;e<t.rows;e++)n+=Math.pow(t.get(e,r),2)/(t.rows-1);e.push(Math.sqrt(n))}return e}function scaleByColumn(t,e){for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)t.set(r,n,t.get(r,n)/e[n])}function getScaleAll(t){const e=t.size-1;let r=0;for(let n=0;n<t.columns;n++)for(let i=0;i<t.rows;i++)r+=Math.pow(t.get(i,n),2)/e;return Math.sqrt(r)}function scaleAll(t,e){for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)t.set(r,n,t.get(r,n)/e)}class AbstractMatrix{static from1DArray(t,e,r){if(t*e!==r.length)throw new RangeError("data length does not match given dimensions");let n=new Matrix(t,e);for(let i=0;i<t;i++)for(let t=0;t<e;t++)n.set(i,t,r[i*e+t]);return n}static rowVector(t){let e=new Matrix(1,t.length);for(let r=0;r<t.length;r++)e.set(0,r,t[r]);return e}static columnVector(t){let e=new Matrix(t.length,1);for(let r=0;r<t.length;r++)e.set(r,0,t[r]);return e}static zeros(t,e){return new Matrix(t,e)}static ones(t,e){return new Matrix(t,e).fill(1)}static rand(t,e,r={}){if("object"!=typeof r)throw new TypeError("options must be an object");const{random:n=Math.random}=r;let i=new Matrix(t,e);for(let r=0;r<t;r++)for(let t=0;t<e;t++)i.set(r,t,n());return i}static randInt(t,e,r={}){if("object"!=typeof r)throw new TypeError("options must be an object");const{min:n=0,max:i=1e3,random:o=Math.random}=r;if(!Number.isInteger(n))throw new TypeError("min must be an integer");if(!Number.isInteger(i))throw new TypeError("max must be an integer");if(n>=i)throw new RangeError("min must be smaller than max");let s=i-n,a=new Matrix(t,e);for(let r=0;r<t;r++)for(let t=0;t<e;t++){let e=n+Math.round(o()*s);a.set(r,t,e)}return a}static eye(t,e,r){void 0===e&&(e=t),void 0===r&&(r=1);let n=Math.min(t,e),i=this.zeros(t,e);for(let t=0;t<n;t++)i.set(t,t,r);return i}static diag(t,e,r){let n=t.length;void 0===e&&(e=n),void 0===r&&(r=e);let i=Math.min(n,e,r),o=this.zeros(e,r);for(let e=0;e<i;e++)o.set(e,e,t[e]);return o}static min(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let r=t.rows,n=t.columns,i=new Matrix(r,n);for(let o=0;o<r;o++)for(let r=0;r<n;r++)i.set(o,r,Math.min(t.get(o,r),e.get(o,r)));return i}static max(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let r=t.rows,n=t.columns,i=new this(r,n);for(let o=0;o<r;o++)for(let r=0;r<n;r++)i.set(o,r,Math.max(t.get(o,r),e.get(o,r)));return i}static checkMatrix(t){return AbstractMatrix.isMatrix(t)?t:new Matrix(t)}static isMatrix(t){return null!=t&&"Matrix"===t.klass}get size(){return this.rows*this.columns}apply(t){if("function"!=typeof t)throw new TypeError("callback must be a function");for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.call(this,e,r);return this}to1DArray(){let t=[];for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.push(this.get(e,r));return t}to2DArray(){let t=[];for(let e=0;e<this.rows;e++){t.push([]);for(let r=0;r<this.columns;r++)t[e].push(this.get(e,r))}return t}toJSON(){return this.to2DArray()}isRowVector(){return 1===this.rows}isColumnVector(){return 1===this.columns}isVector(){return 1===this.rows||1===this.columns}isSquare(){return this.rows===this.columns}isSymmetric(){if(this.isSquare()){for(let t=0;t<this.rows;t++)for(let e=0;e<=t;e++)if(this.get(t,e)!==this.get(e,t))return!1;return!0}return!1}isEchelonForm(){let t=0,e=0,r=-1,n=!0,i=!1;for(;t<this.rows&&n;){for(e=0,i=!1;e<this.columns&&!1===i;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>r?(i=!0,r=e):(n=!1,i=!0);t++}return n}isReducedEchelonForm(){let t=0,e=0,r=-1,n=!0,i=!1;for(;t<this.rows&&n;){for(e=0,i=!1;e<this.columns&&!1===i;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>r?(i=!0,r=e):(n=!1,i=!0);for(let r=e+1;r<this.rows;r++)0!==this.get(t,r)&&(n=!1);t++}return n}echelonForm(){let t=this.clone(),e=0,r=0;for(;e<t.rows&&r<t.columns;){let n=e;for(let i=e;i<t.rows;i++)t.get(i,r)>t.get(n,r)&&(n=i);if(0===t.get(n,r))r++;else{t.swapRows(e,n);let i=t.get(e,r);for(let n=r;n<t.columns;n++)t.set(e,n,t.get(e,n)/i);for(let n=e+1;n<t.rows;n++){let i=t.get(n,r)/t.get(e,r);t.set(n,r,0);for(let o=r+1;o<t.columns;o++)t.set(n,o,t.get(n,o)-t.get(e,o)*i)}e++,r++}}return t}reducedEchelonForm(){let t=this.echelonForm(),e=t.columns,r=t.rows,n=r-1;for(;n>=0;)if(0===t.maxRow(n))n--;else{let i=0,o=!1;for(;i<r&&!1===o;)1===t.get(n,i)?o=!0:i++;for(let r=0;r<n;r++){let o=t.get(r,i);for(let s=i;s<e;s++){let e=t.get(r,s)-o*t.get(n,s);t.set(r,s,e)}}n--}return t}set(){throw new Error("set method is unimplemented")}get(){throw new Error("get method is unimplemented")}repeat(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{rows:e=1,columns:r=1}=t;if(!Number.isInteger(e)||e<=0)throw new TypeError("rows must be a positive integer");if(!Number.isInteger(r)||r<=0)throw new TypeError("columns must be a positive integer");let n=new Matrix(this.rows*e,this.columns*r);for(let t=0;t<e;t++)for(let e=0;e<r;e++)n.setSubMatrix(this,this.rows*t,this.columns*e);return n}fill(t){for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,t);return this}neg(){return this.mulS(-1)}getRow(t){checkRowIndex(this,t);let e=[];for(let r=0;r<this.columns;r++)e.push(this.get(t,r));return e}getRowVector(t){return Matrix.rowVector(this.getRow(t))}setRow(t,e){checkRowIndex(this,t),e=checkRowVector(this,e);for(let r=0;r<this.columns;r++)this.set(t,r,e[r]);return this}swapRows(t,e){checkRowIndex(this,t),checkRowIndex(this,e);for(let r=0;r<this.columns;r++){let n=this.get(t,r);this.set(t,r,this.get(e,r)),this.set(e,r,n)}return this}getColumn(t){checkColumnIndex(this,t);let e=[];for(let r=0;r<this.rows;r++)e.push(this.get(r,t));return e}getColumnVector(t){return Matrix.columnVector(this.getColumn(t))}setColumn(t,e){checkColumnIndex(this,t),e=checkColumnVector(this,e);for(let r=0;r<this.rows;r++)this.set(r,t,e[r]);return this}swapColumns(t,e){checkColumnIndex(this,t),checkColumnIndex(this,e);for(let r=0;r<this.rows;r++){let n=this.get(r,t);this.set(r,t,this.get(r,e)),this.set(r,e,n)}return this}addRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)+t[r]);return this}subRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)-t[r]);return this}mulRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)*t[r]);return this}divRowVector(t){t=checkRowVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)/t[r]);return this}addColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)+t[e]);return this}subColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)-t[e]);return this}mulColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)*t[e]);return this}divColumnVector(t){t=checkColumnVector(this,t);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.set(e,r,this.get(e,r)/t[e]);return this}mulRow(t,e){checkRowIndex(this,t);for(let r=0;r<this.columns;r++)this.set(t,r,this.get(t,r)*e);return this}mulColumn(t,e){checkColumnIndex(this,t);for(let r=0;r<this.rows;r++)this.set(r,t,this.get(r,t)*e);return this}max(){let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.get(e,r)>t&&(t=this.get(e,r));return t}maxIndex(){let t=this.get(0,0),e=[0,0];for(let r=0;r<this.rows;r++)for(let n=0;n<this.columns;n++)this.get(r,n)>t&&(t=this.get(r,n),e[0]=r,e[1]=n);return e}min(){let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)this.get(e,r)<t&&(t=this.get(e,r));return t}minIndex(){let t=this.get(0,0),e=[0,0];for(let r=0;r<this.rows;r++)for(let n=0;n<this.columns;n++)this.get(r,n)<t&&(t=this.get(r,n),e[0]=r,e[1]=n);return e}maxRow(t){checkRowIndex(this,t);let e=this.get(t,0);for(let r=1;r<this.columns;r++)this.get(t,r)>e&&(e=this.get(t,r));return e}maxRowIndex(t){checkRowIndex(this,t);let e=this.get(t,0),r=[t,0];for(let n=1;n<this.columns;n++)this.get(t,n)>e&&(e=this.get(t,n),r[1]=n);return r}minRow(t){checkRowIndex(this,t);let e=this.get(t,0);for(let r=1;r<this.columns;r++)this.get(t,r)<e&&(e=this.get(t,r));return e}minRowIndex(t){checkRowIndex(this,t);let e=this.get(t,0),r=[t,0];for(let n=1;n<this.columns;n++)this.get(t,n)<e&&(e=this.get(t,n),r[1]=n);return r}maxColumn(t){checkColumnIndex(this,t);let e=this.get(0,t);for(let r=1;r<this.rows;r++)this.get(r,t)>e&&(e=this.get(r,t));return e}maxColumnIndex(t){checkColumnIndex(this,t);let e=this.get(0,t),r=[0,t];for(let n=1;n<this.rows;n++)this.get(n,t)>e&&(e=this.get(n,t),r[0]=n);return r}minColumn(t){checkColumnIndex(this,t);let e=this.get(0,t);for(let r=1;r<this.rows;r++)this.get(r,t)<e&&(e=this.get(r,t));return e}minColumnIndex(t){checkColumnIndex(this,t);let e=this.get(0,t),r=[0,t];for(let n=1;n<this.rows;n++)this.get(n,t)<e&&(e=this.get(n,t),r[0]=n);return r}diag(){let t=Math.min(this.rows,this.columns),e=[];for(let r=0;r<t;r++)e.push(this.get(r,r));return e}norm(t="frobenius"){let e=0;if("max"===t)return this.max();if("frobenius"===t){for(let t=0;t<this.rows;t++)for(let r=0;r<this.columns;r++)e+=this.get(t,r)*this.get(t,r);return Math.sqrt(e)}throw new RangeError("unknown norm type: "+t)}cumulativeSum(){let t=0;for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t+=this.get(e,r),this.set(e,r,t);return this}dot(t){AbstractMatrix.isMatrix(t)&&(t=t.to1DArray());let e=this.to1DArray();if(e.length!==t.length)throw new RangeError("vectors do not have the same size");let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}mmul(t){t=Matrix.checkMatrix(t);let e=this.rows,r=this.columns,n=t.columns,i=new Matrix(e,n),o=new Float64Array(r);for(let s=0;s<n;s++){for(let e=0;e<r;e++)o[e]=t.get(e,s);for(let t=0;t<e;t++){let e=0;for(let n=0;n<r;n++)e+=this.get(t,n)*o[n];i.set(t,s,e)}}return i}strassen2x2(t){t=Matrix.checkMatrix(t);let e=new Matrix(2,2);const r=this.get(0,0),n=t.get(0,0),i=this.get(0,1),o=t.get(0,1),s=this.get(1,0),a=t.get(1,0),l=this.get(1,1),h=t.get(1,1),u=(r+l)*(n+h),c=(s+l)*n,f=r*(o-h),m=l*(a-n),g=(r+i)*h,p=u+m-g+(i-l)*(a+h),d=f+g,w=c+m,y=u-c+f+(s-r)*(n+o);return e.set(0,0,p),e.set(0,1,d),e.set(1,0,w),e.set(1,1,y),e}strassen3x3(t){t=Matrix.checkMatrix(t);let e=new Matrix(3,3);const r=this.get(0,0),n=this.get(0,1),i=this.get(0,2),o=this.get(1,0),s=this.get(1,1),a=this.get(1,2),l=this.get(2,0),h=this.get(2,1),u=this.get(2,2),c=t.get(0,0),f=t.get(0,1),m=t.get(0,2),g=t.get(1,0),p=t.get(1,1),d=t.get(1,2),w=t.get(2,0),y=t.get(2,1),x=t.get(2,2),M=(r-o)*(-f+p),v=(-r+o+s)*(c-f+p),b=(o+s)*(-c+f),S=r*c,A=(-r+l+h)*(c-m+d),E=(-r+l)*(m-d),R=(l+h)*(-c+m),k=(-i+h+u)*(p+w-y),N=(i-u)*(p-y),T=i*w,C=(h+u)*(-w+y),I=(-i+s+a)*(d+w-x),O=(i-a)*(d-x),L=(s+a)*(-w+x),F=S+T+n*g,P=(r+n+i-o-s-h-u)*p+v+b+S+k+T+C,V=S+A+R+(r+n+i-s-a-l-h)*d+T+I+L,_=M+s*(-c+f+g-p-d-w+x)+v+S+T+I+O,D=M+v+b+S+a*y,z=T+I+O+L+o*m,$=S+A+E+h*(-c+m+g-p-d-w+y)+k+N+T,q=k+N+T+C+l*f,j=S+A+E+R+u*x;return e.set(0,0,F),e.set(0,1,P),e.set(0,2,V),e.set(1,0,_),e.set(1,1,D),e.set(1,2,z),e.set(2,0,$),e.set(2,1,q),e.set(2,2,j),e}mmulStrassen(t){t=Matrix.checkMatrix(t);let e=this.clone(),r=e.rows,n=e.columns,i=t.rows,o=t.columns;function s(t,e,r){let n=t.rows,i=t.columns;if(n===e&&i===r)return t;{let n=AbstractMatrix.zeros(e,r);return n=n.setSubMatrix(t,0,0),n}}n!==i&&console.warn(`Multiplying ${r} x ${n} and ${i} x ${o} matrix: dimensions do not match.`);let a=Math.max(r,i),l=Math.max(n,o);return e=s(e,a,l),function t(e,r,n,i){if(n<=512||i<=512)return e.mmul(r);n%2==1&&i%2==1?(e=s(e,n+1,i+1),r=s(r,n+1,i+1)):n%2==1?(e=s(e,n+1,i),r=s(r,n+1,i)):i%2==1&&(e=s(e,n,i+1),r=s(r,n,i+1));let o=parseInt(e.rows/2,10),a=parseInt(e.columns/2,10),l=e.subMatrix(0,o-1,0,a-1),h=r.subMatrix(0,o-1,0,a-1),u=e.subMatrix(0,o-1,a,e.columns-1),c=r.subMatrix(0,o-1,a,r.columns-1),f=e.subMatrix(o,e.rows-1,0,a-1),m=r.subMatrix(o,r.rows-1,0,a-1),g=e.subMatrix(o,e.rows-1,a,e.columns-1),p=r.subMatrix(o,r.rows-1,a,r.columns-1),d=t(AbstractMatrix.add(l,g),AbstractMatrix.add(h,p),o,a),w=t(AbstractMatrix.add(f,g),h,o,a),y=t(l,AbstractMatrix.sub(c,p),o,a),x=t(g,AbstractMatrix.sub(m,h),o,a),M=t(AbstractMatrix.add(l,u),p,o,a),v=t(AbstractMatrix.sub(f,l),AbstractMatrix.add(h,c),o,a),b=t(AbstractMatrix.sub(u,g),AbstractMatrix.add(m,p),o,a),S=AbstractMatrix.add(d,x);S.sub(M),S.add(b);let A=AbstractMatrix.add(y,M),E=AbstractMatrix.add(w,x),R=AbstractMatrix.sub(d,w);R.add(y),R.add(v);let k=AbstractMatrix.zeros(2*S.rows,2*S.columns);return k=k.setSubMatrix(S,0,0),k=k.setSubMatrix(A,S.rows,0),k=k.setSubMatrix(E,0,S.columns),k=k.setSubMatrix(R,S.rows,S.columns),k.subMatrix(0,n-1,0,i-1)}(e,t=s(t,a,l),a,l)}scaleRows(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:r=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(r))throw new TypeError("max must be a number");if(e>=r)throw new RangeError("min must be smaller than max");let n=new Matrix(this.rows,this.columns);for(let t=0;t<this.rows;t++){const i=this.getRow(t);rescale(i,{min:e,max:r,output:i}),n.setRow(t,i)}return n}scaleColumns(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:r=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(r))throw new TypeError("max must be a number");if(e>=r)throw new RangeError("min must be smaller than max");let n=new Matrix(this.rows,this.columns);for(let t=0;t<this.columns;t++){const i=this.getColumn(t);rescale(i,{min:e,max:r,output:i}),n.setColumn(t,i)}return n}flipRows(){const t=Math.ceil(this.columns/2);for(let e=0;e<this.rows;e++)for(let r=0;r<t;r++){let t=this.get(e,r),n=this.get(e,this.columns-1-r);this.set(e,r,n),this.set(e,this.columns-1-r,t)}return this}flipColumns(){const t=Math.ceil(this.rows/2);for(let e=0;e<this.columns;e++)for(let r=0;r<t;r++){let t=this.get(r,e),n=this.get(this.rows-1-r,e);this.set(r,e,n),this.set(this.rows-1-r,e,t)}return this}kroneckerProduct(t){t=Matrix.checkMatrix(t);let e=this.rows,r=this.columns,n=t.rows,i=t.columns,o=new Matrix(e*n,r*i);for(let s=0;s<e;s++)for(let e=0;e<r;e++)for(let r=0;r<n;r++)for(let a=0;a<i;a++)o.set(n*s+r,i*e+a,this.get(s,e)*t.get(r,a));return o}transpose(){let t=new Matrix(this.columns,this.rows);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.set(r,e,this.get(e,r));return t}sortRows(t=compareNumbers){for(let e=0;e<this.rows;e++)this.setRow(e,this.getRow(e).sort(t));return this}sortColumns(t=compareNumbers){for(let e=0;e<this.columns;e++)this.setColumn(e,this.getColumn(e).sort(t));return this}subMatrix(t,e,r,n){checkRange(this,t,e,r,n);let i=new Matrix(e-t+1,n-r+1);for(let o=t;o<=e;o++)for(let e=r;e<=n;e++)i.set(o-t,e-r,this.get(o,e));return i}subMatrixRow(t,e,r){if(void 0===e&&(e=0),void 0===r&&(r=this.columns-1),e>r||e<0||e>=this.columns||r<0||r>=this.columns)throw new RangeError("Argument out of range");let n=new Matrix(t.length,r-e+1);for(let i=0;i<t.length;i++)for(let o=e;o<=r;o++){if(t[i]<0||t[i]>=this.rows)throw new RangeError("Row index out of range: "+t[i]);n.set(i,o-e,this.get(t[i],o))}return n}subMatrixColumn(t,e,r){if(void 0===e&&(e=0),void 0===r&&(r=this.rows-1),e>r||e<0||e>=this.rows||r<0||r>=this.rows)throw new RangeError("Argument out of range");let n=new Matrix(r-e+1,t.length);for(let i=0;i<t.length;i++)for(let o=e;o<=r;o++){if(t[i]<0||t[i]>=this.columns)throw new RangeError("Column index out of range: "+t[i]);n.set(o-e,i,this.get(o,t[i]))}return n}setSubMatrix(t,e,r){checkRange(this,e,e+(t=Matrix.checkMatrix(t)).rows-1,r,r+t.columns-1);for(let n=0;n<t.rows;n++)for(let i=0;i<t.columns;i++)this.set(e+n,r+i,t.get(n,i));return this}selection(t,e){let r=checkIndices(this,t,e),n=new Matrix(t.length,e.length);for(let t=0;t<r.row.length;t++){let e=r.row[t];for(let i=0;i<r.column.length;i++){let o=r.column[i];n.set(t,i,this.get(e,o))}}return n}trace(){let t=Math.min(this.rows,this.columns),e=0;for(let r=0;r<t;r++)e+=this.get(r,r);return e}clone(){let t=new Matrix(this.rows,this.columns);for(let e=0;e<this.rows;e++)for(let r=0;r<this.columns;r++)t.set(e,r,this.get(e,r));return t}sum(t){switch(t){case"row":return sumByRow(this);case"column":return sumByColumn(this);case void 0:return sumAll(this);default:throw new Error("invalid option: "+t)}}product(t){switch(t){case"row":return productByRow(this);case"column":return productByColumn(this);case void 0:return productAll(this);default:throw new Error("invalid option: "+t)}}mean(t){const e=this.sum(t);switch(t){case"row":for(let t=0;t<this.rows;t++)e[t]/=this.columns;return e;case"column":for(let t=0;t<this.columns;t++)e[t]/=this.rows;return e;case void 0:return e/this.size;default:throw new Error("invalid option: "+t)}}variance(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{unbiased:r=!0,mean:n=this.mean(t)}=e;if("boolean"!=typeof r)throw new TypeError("unbiased must be a boolean");switch(t){case"row":if(!Array.isArray(n))throw new TypeError("mean must be an array");return varianceByRow(this,r,n);case"column":if(!Array.isArray(n))throw new TypeError("mean must be an array");return varianceByColumn(this,r,n);case void 0:if("number"!=typeof n)throw new TypeError("mean must be a number");return varianceAll(this,r,n);default:throw new Error("invalid option: "+t)}}standardDeviation(t,e){"object"==typeof t&&(e=t,t=void 0);const r=this.variance(t,e);if(void 0===t)return Math.sqrt(r);for(let t=0;t<r.length;t++)r[t]=Math.sqrt(r[t]);return r}center(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{center:r=this.mean(t)}=e;switch(t){case"row":if(!Array.isArray(r))throw new TypeError("center must be an array");return centerByRow(this,r),this;case"column":if(!Array.isArray(r))throw new TypeError("center must be an array");return centerByColumn(this,r),this;case void 0:if("number"!=typeof r)throw new TypeError("center must be a number");return centerAll(this,r),this;default:throw new Error("invalid option: "+t)}}scale(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");let r=e.scale;switch(t){case"row":if(void 0===r)r=getScaleByRow(this);else if(!Array.isArray(r))throw new TypeError("scale must be an array");return scaleByRow(this,r),this;case"column":if(void 0===r)r=getScaleByColumn(this);else if(!Array.isArray(r))throw new TypeError("scale must be an array");return scaleByColumn(this,r),this;case void 0:if(void 0===r)r=getScaleAll(this);else if("number"!=typeof r)throw new TypeError("scale must be a number");return scaleAll(this,r),this;default:throw new Error("invalid option: "+t)}}toString(t){return inspectMatrixWithOptions(this,t)}}function compareNumbers(t,e){return t-e}AbstractMatrix.prototype.klass="Matrix","undefined"!=typeof Symbol&&(AbstractMatrix.prototype[Symbol.for("nodejs.util.inspect.custom")]=inspectMatrix),AbstractMatrix.random=AbstractMatrix.rand,AbstractMatrix.randomInt=AbstractMatrix.randInt,AbstractMatrix.diagonal=AbstractMatrix.diag,AbstractMatrix.prototype.diagonal=AbstractMatrix.prototype.diag,AbstractMatrix.identity=AbstractMatrix.eye,AbstractMatrix.prototype.negate=AbstractMatrix.prototype.neg,AbstractMatrix.prototype.tensorProduct=AbstractMatrix.prototype.kroneckerProduct;class Matrix extends AbstractMatrix{constructor(t,e){if(super(),Matrix.isMatrix(t))return t.clone();if(Number.isInteger(t)&&t>0){if(this.data=[],!(Number.isInteger(e)&&e>0))throw new TypeError("nColumns must be a positive integer");for(let r=0;r<t;r++)this.data.push(new Float64Array(e))}else{if(!Array.isArray(t))throw new TypeError("First argument must be a positive number or an array");{const r=t;if(t=r.length,"number"!=typeof(e=r[0].length)||0===e)throw new TypeError("Data must be a 2D array with at least one element");this.data=[];for(let n=0;n<t;n++){if(r[n].length!==e)throw new RangeError("Inconsistent array dimensions");this.data.push(Float64Array.from(r[n]))}}}return this.rows=t,this.columns=e,this}set(t,e,r){return this.data[t][e]=r,this}get(t,e){return this.data[t][e]}removeRow(t){if(checkRowIndex(this,t),1===this.rows)throw new RangeError("A matrix cannot have less than one row");return this.data.splice(t,1),this.rows-=1,this}addRow(t,e){return void 0===e&&(e=t,t=this.rows),checkRowIndex(this,t,!0),e=Float64Array.from(checkRowVector(this,e)),this.data.splice(t,0,e),this.rows+=1,this}removeColumn(t){if(checkColumnIndex(this,t),1===this.columns)throw new RangeError("A matrix cannot have less than one column");for(let e=0;e<this.rows;e++){const r=new Float64Array(this.columns-1);for(let n=0;n<t;n++)r[n]=this.data[e][n];for(let n=t+1;n<this.columns;n++)r[n-1]=this.data[e][n];this.data[e]=r}return this.columns-=1,this}addColumn(t,e){void 0===e&&(e=t,t=this.columns),checkColumnIndex(this,t,!0),e=checkColumnVector(this,e);for(let r=0;r<this.rows;r++){const n=new Float64Array(this.columns+1);let i=0;for(;i<t;i++)n[i]=this.data[r][i];for(n[i++]=e[r];i<this.columns+1;i++)n[i]=this.data[r][i-1];this.data[r]=n}return this.columns+=1,this}}installMathOperations(AbstractMatrix,Matrix);class BaseView extends AbstractMatrix{constructor(t,e,r){super(),this.matrix=t,this.rows=e,this.columns=r}}class MatrixColumnView extends BaseView{constructor(t,e){checkColumnIndex(t,e),super(t,t.rows,1),this.column=e}set(t,e,r){return this.matrix.set(t,this.column,r),this}get(t){return this.matrix.get(t,this.column)}}class MatrixColumnSelectionView extends BaseView{constructor(t,e){e=checkColumnIndices(t,e),super(t,t.rows,e.length),this.columnIndices=e}set(t,e,r){return this.matrix.set(t,this.columnIndices[e],r),this}get(t,e){return this.matrix.get(t,this.columnIndices[e])}}class MatrixFlipColumnView extends BaseView{constructor(t){super(t,t.rows,t.columns)}set(t,e,r){return this.matrix.set(t,this.columns-e-1,r),this}get(t,e){return this.matrix.get(t,this.columns-e-1)}}class MatrixFlipRowView extends BaseView{constructor(t){super(t,t.rows,t.columns)}set(t,e,r){return this.matrix.set(this.rows-t-1,e,r),this}get(t,e){return this.matrix.get(this.rows-t-1,e)}}class MatrixRowView extends BaseView{constructor(t,e){checkRowIndex(t,e),super(t,1,t.columns),this.row=e}set(t,e,r){return this.matrix.set(this.row,e,r),this}get(t,e){return this.matrix.get(this.row,e)}}class MatrixRowSelectionView extends BaseView{constructor(t,e){super(t,(e=checkRowIndices(t,e)).length,t.columns),this.rowIndices=e}set(t,e,r){return this.matrix.set(this.rowIndices[t],e,r),this}get(t,e){return this.matrix.get(this.rowIndices[t],e)}}class MatrixSelectionView extends BaseView{constructor(t,e,r){let n=checkIndices(t,e,r);super(t,n.row.length,n.column.length),this.rowIndices=n.row,this.columnIndices=n.column}set(t,e,r){return this.matrix.set(this.rowIndices[t],this.columnIndices[e],r),this}get(t,e){return this.matrix.get(this.rowIndices[t],this.columnIndices[e])}}class MatrixSubView extends BaseView{constructor(t,e,r,n,i){checkRange(t,e,r,n,i),super(t,r-e+1,i-n+1),this.startRow=e,this.startColumn=n}set(t,e,r){return this.matrix.set(this.startRow+t,this.startColumn+e,r),this}get(t,e){return this.matrix.get(this.startRow+t,this.startColumn+e)}}class MatrixTransposeView extends BaseView{constructor(t){super(t,t.columns,t.rows)}set(t,e,r){return this.matrix.set(e,t,r),this}get(t,e){return this.matrix.get(e,t)}}class WrapperMatrix1D extends AbstractMatrix{constructor(t,e={}){const{rows:r=1}=e;if(t.length%r!=0)throw new Error("the data length is not divisible by the number of rows");super(),this.rows=r,this.columns=t.length/r,this.data=t}set(t,e,r){let n=this._calculateIndex(t,e);return this.data[n]=r,this}get(t,e){let r=this._calculateIndex(t,e);return this.data[r]}_calculateIndex(t,e){return t*this.columns+e}}class WrapperMatrix2D extends AbstractMatrix{constructor(t){super(),this.data=t,this.rows=t.length,this.columns=t[0].length}set(t,e,r){return this.data[t][e]=r,this}get(t,e){return this.data[t][e]}}function wrap(t,e){if(Array.isArray(t))return t[0]&&Array.isArray(t[0])?new WrapperMatrix2D(t):new WrapperMatrix1D(t,e);throw new Error("the argument is not an array")}class LuDecomposition{constructor(t){let e,r,n,i,o,s,a,l,h,u=(t=WrapperMatrix2D.checkMatrix(t)).clone(),c=u.rows,f=u.columns,m=new Float64Array(c),g=1;for(e=0;e<c;e++)m[e]=e;for(l=new Float64Array(c),r=0;r<f;r++){for(e=0;e<c;e++)l[e]=u.get(e,r);for(e=0;e<c;e++){for(h=Math.min(e,r),o=0,n=0;n<h;n++)o+=u.get(e,n)*l[n];l[e]-=o,u.set(e,r,l[e])}for(i=r,e=r+1;e<c;e++)Math.abs(l[e])>Math.abs(l[i])&&(i=e);if(i!==r){for(n=0;n<f;n++)s=u.get(i,n),u.set(i,n,u.get(r,n)),u.set(r,n,s);a=m[i],m[i]=m[r],m[r]=a,g=-g}if(r<c&&0!==u.get(r,r))for(e=r+1;e<c;e++)u.set(e,r,u.get(e,r)/u.get(r,r))}this.LU=u,this.pivotVector=m,this.pivotSign=g}isSingular(){let t=this.LU,e=t.columns;for(let r=0;r<e;r++)if(0===t.get(r,r))return!0;return!1}solve(t){t=Matrix.checkMatrix(t);let e=this.LU;if(e.rows!==t.rows)throw new Error("Invalid matrix dimensions");if(this.isSingular())throw new Error("LU matrix is singular");let r,n,i,o=t.columns,s=t.subMatrixRow(this.pivotVector,0,o-1),a=e.columns;for(i=0;i<a;i++)for(r=i+1;r<a;r++)for(n=0;n<o;n++)s.set(r,n,s.get(r,n)-s.get(i,n)*e.get(r,i));for(i=a-1;i>=0;i--){for(n=0;n<o;n++)s.set(i,n,s.get(i,n)/e.get(i,i));for(r=0;r<i;r++)for(n=0;n<o;n++)s.set(r,n,s.get(r,n)-s.get(i,n)*e.get(r,i))}return s}get determinant(){let t=this.LU;if(!t.isSquare())throw new Error("Matrix must be square");let e=this.pivotSign,r=t.columns;for(let n=0;n<r;n++)e*=t.get(n,n);return e}get lowerTriangularMatrix(){let t=this.LU,e=t.rows,r=t.columns,n=new Matrix(e,r);for(let i=0;i<e;i++)for(let e=0;e<r;e++)i>e?n.set(i,e,t.get(i,e)):i===e?n.set(i,e,1):n.set(i,e,0);return n}get upperTriangularMatrix(){let t=this.LU,e=t.rows,r=t.columns,n=new Matrix(e,r);for(let i=0;i<e;i++)for(let e=0;e<r;e++)i<=e?n.set(i,e,t.get(i,e)):n.set(i,e,0);return n}get pivotPermutationVector(){return Array.from(this.pivotVector)}}function hypotenuse(t,e){let r=0;return Math.abs(t)>Math.abs(e)?(r=e/t,Math.abs(t)*Math.sqrt(1+r*r)):0!==e?(r=t/e,Math.abs(e)*Math.sqrt(1+r*r)):0}class QrDecomposition{constructor(t){let e,r,n,i,o=(t=WrapperMatrix2D.checkMatrix(t)).clone(),s=t.rows,a=t.columns,l=new Float64Array(a);for(n=0;n<a;n++){let t=0;for(e=n;e<s;e++)t=hypotenuse(t,o.get(e,n));if(0!==t){for(o.get(n,n)<0&&(t=-t),e=n;e<s;e++)o.set(e,n,o.get(e,n)/t);for(o.set(n,n,o.get(n,n)+1),r=n+1;r<a;r++){for(i=0,e=n;e<s;e++)i+=o.get(e,n)*o.get(e,r);for(i=-i/o.get(n,n),e=n;e<s;e++)o.set(e,r,o.get(e,r)+i*o.get(e,n))}}l[n]=-t}this.QR=o,this.Rdiag=l}solve(t){t=Matrix.checkMatrix(t);let e=this.QR,r=e.rows;if(t.rows!==r)throw new Error("Matrix row dimensions must agree");if(!this.isFullRank())throw new Error("Matrix is rank deficient");let n,i,o,s,a=t.columns,l=t.clone(),h=e.columns;for(o=0;o<h;o++)for(i=0;i<a;i++){for(s=0,n=o;n<r;n++)s+=e.get(n,o)*l.get(n,i);for(s=-s/e.get(o,o),n=o;n<r;n++)l.set(n,i,l.get(n,i)+s*e.get(n,o))}for(o=h-1;o>=0;o--){for(i=0;i<a;i++)l.set(o,i,l.get(o,i)/this.Rdiag[o]);for(n=0;n<o;n++)for(i=0;i<a;i++)l.set(n,i,l.get(n,i)-l.get(o,i)*e.get(n,o))}return l.subMatrix(0,h-1,0,a-1)}isFullRank(){let t=this.QR.columns;for(let e=0;e<t;e++)if(0===this.Rdiag[e])return!1;return!0}get upperTriangularMatrix(){let t,e,r=this.QR,n=r.columns,i=new Matrix(n,n);for(t=0;t<n;t++)for(e=0;e<n;e++)t<e?i.set(t,e,r.get(t,e)):t===e?i.set(t,e,this.Rdiag[t]):i.set(t,e,0);return i}get orthogonalMatrix(){let t,e,r,n,i=this.QR,o=i.rows,s=i.columns,a=new Matrix(o,s);for(r=s-1;r>=0;r--){for(t=0;t<o;t++)a.set(t,r,0);for(a.set(r,r,1),e=r;e<s;e++)if(0!==i.get(r,r)){for(n=0,t=r;t<o;t++)n+=i.get(t,r)*a.get(t,e);for(n=-n/i.get(r,r),t=r;t<o;t++)a.set(t,e,a.get(t,e)+n*i.get(t,r))}}return a}}class SingularValueDecomposition{constructor(t,e={}){let r=(t=WrapperMatrix2D.checkMatrix(t)).rows,n=t.columns;const{computeLeftSingularVectors:i=!0,computeRightSingularVectors:o=!0,autoTranspose:s=!1}=e;let a,l=Boolean(i),h=Boolean(o),u=!1;if(r<n)if(s){a=t.transpose(),r=a.rows,n=a.columns,u=!0;let e=l;l=h,h=e}else a=t.clone(),console.warn("Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose");else a=t.clone();let c=Math.min(r,n),f=Math.min(r+1,n),m=new Float64Array(f),g=new Matrix(r,c),p=new Matrix(n,n),d=new Float64Array(n),w=new Float64Array(r),y=new Float64Array(f);for(let t=0;t<f;t++)y[t]=t;let x=Math.min(r-1,n),M=Math.max(0,Math.min(n-2,r)),v=Math.max(x,M);for(let t=0;t<v;t++){if(t<x){m[t]=0;for(let e=t;e<r;e++)m[t]=hypotenuse(m[t],a.get(e,t));if(0!==m[t]){a.get(t,t)<0&&(m[t]=-m[t]);for(let e=t;e<r;e++)a.set(e,t,a.get(e,t)/m[t]);a.set(t,t,a.get(t,t)+1)}m[t]=-m[t]}for(let e=t+1;e<n;e++){if(t<x&&0!==m[t]){let n=0;for(let i=t;i<r;i++)n+=a.get(i,t)*a.get(i,e);n=-n/a.get(t,t);for(let i=t;i<r;i++)a.set(i,e,a.get(i,e)+n*a.get(i,t))}d[e]=a.get(t,e)}if(l&&t<x)for(let e=t;e<r;e++)g.set(e,t,a.get(e,t));if(t<M){d[t]=0;for(let e=t+1;e<n;e++)d[t]=hypotenuse(d[t],d[e]);if(0!==d[t]){d[t+1]<0&&(d[t]=0-d[t]);for(let e=t+1;e<n;e++)d[e]/=d[t];d[t+1]+=1}if(d[t]=-d[t],t+1<r&&0!==d[t]){for(let e=t+1;e<r;e++)w[e]=0;for(let e=t+1;e<r;e++)for(let r=t+1;r<n;r++)w[e]+=d[r]*a.get(e,r);for(let e=t+1;e<n;e++){let n=-d[e]/d[t+1];for(let i=t+1;i<r;i++)a.set(i,e,a.get(i,e)+n*w[i])}}if(h)for(let e=t+1;e<n;e++)p.set(e,t,d[e])}}let b=Math.min(n,r+1);if(x<n&&(m[x]=a.get(x,x)),r<b&&(m[b-1]=0),M+1<b&&(d[M]=a.get(M,b-1)),d[b-1]=0,l){for(let t=x;t<c;t++){for(let e=0;e<r;e++)g.set(e,t,0);g.set(t,t,1)}for(let t=x-1;t>=0;t--)if(0!==m[t]){for(let e=t+1;e<c;e++){let n=0;for(let i=t;i<r;i++)n+=g.get(i,t)*g.get(i,e);n=-n/g.get(t,t);for(let i=t;i<r;i++)g.set(i,e,g.get(i,e)+n*g.get(i,t))}for(let e=t;e<r;e++)g.set(e,t,-g.get(e,t));g.set(t,t,1+g.get(t,t));for(let e=0;e<t-1;e++)g.set(e,t,0)}else{for(let e=0;e<r;e++)g.set(e,t,0);g.set(t,t,1)}}if(h)for(let t=n-1;t>=0;t--){if(t<M&&0!==d[t])for(let e=t+1;e<n;e++){let r=0;for(let i=t+1;i<n;i++)r+=p.get(i,t)*p.get(i,e);r=-r/p.get(t+1,t);for(let i=t+1;i<n;i++)p.set(i,e,p.get(i,e)+r*p.get(i,t))}for(let e=0;e<n;e++)p.set(e,t,0);p.set(t,t,1)}let S=b-1,A=Number.EPSILON;for(;b>0;){let t,e;for(t=b-2;t>=-1&&-1!==t;t--){const e=Number.MIN_VALUE+A*Math.abs(m[t]+Math.abs(m[t+1]));if(Math.abs(d[t])<=e||Number.isNaN(d[t])){d[t]=0;break}}if(t===b-2)e=4;else{let r;for(r=b-1;r>=t&&r!==t;r--){let e=(r!==b?Math.abs(d[r]):0)+(r!==t+1?Math.abs(d[r-1]):0);if(Math.abs(m[r])<=A*e){m[r]=0;break}}r===t?e=3:r===b-1?e=1:(e=2,t=r)}switch(t++,e){case 1:{let e=d[b-2];d[b-2]=0;for(let r=b-2;r>=t;r--){let i=hypotenuse(m[r],e),o=m[r]/i,s=e/i;if(m[r]=i,r!==t&&(e=-s*d[r-1],d[r-1]=o*d[r-1]),h)for(let t=0;t<n;t++)i=o*p.get(t,r)+s*p.get(t,b-1),p.set(t,b-1,-s*p.get(t,r)+o*p.get(t,b-1)),p.set(t,r,i)}break}case 2:{let e=d[t-1];d[t-1]=0;for(let n=t;n<b;n++){let i=hypotenuse(m[n],e),o=m[n]/i,s=e/i;if(m[n]=i,e=-s*d[n],d[n]=o*d[n],l)for(let e=0;e<r;e++)i=o*g.get(e,n)+s*g.get(e,t-1),g.set(e,t-1,-s*g.get(e,n)+o*g.get(e,t-1)),g.set(e,n,i)}break}case 3:{const e=Math.max(Math.abs(m[b-1]),Math.abs(m[b-2]),Math.abs(d[b-2]),Math.abs(m[t]),Math.abs(d[t])),i=m[b-1]/e,o=m[b-2]/e,s=d[b-2]/e,a=m[t]/e,u=d[t]/e,c=((o+i)*(o-i)+s*s)/2,f=i*s*(i*s);let w=0;0===c&&0===f||(w=c<0?0-Math.sqrt(c*c+f):Math.sqrt(c*c+f),w=f/(c+w));let y=(a+i)*(a-i)+w,x=a*u;for(let e=t;e<b-1;e++){let i=hypotenuse(y,x);0===i&&(i=Number.MIN_VALUE);let o=y/i,s=x/i;if(e!==t&&(d[e-1]=i),y=o*m[e]+s*d[e],d[e]=o*d[e]-s*m[e],x=s*m[e+1],m[e+1]=o*m[e+1],h)for(let t=0;t<n;t++)i=o*p.get(t,e)+s*p.get(t,e+1),p.set(t,e+1,-s*p.get(t,e)+o*p.get(t,e+1)),p.set(t,e,i);if(i=hypotenuse(y,x),0===i&&(i=Number.MIN_VALUE),o=y/i,s=x/i,m[e]=i,y=o*d[e]+s*m[e+1],m[e+1]=-s*d[e]+o*m[e+1],x=s*d[e+1],d[e+1]=o*d[e+1],l&&e<r-1)for(let t=0;t<r;t++)i=o*g.get(t,e)+s*g.get(t,e+1),g.set(t,e+1,-s*g.get(t,e)+o*g.get(t,e+1)),g.set(t,e,i)}d[b-2]=y;break}case 4:if(m[t]<=0&&(m[t]=m[t]<0?-m[t]:0,h))for(let e=0;e<=S;e++)p.set(e,t,-p.get(e,t));for(;t<S&&!(m[t]>=m[t+1]);){let e=m[t];if(m[t]=m[t+1],m[t+1]=e,h&&t<n-1)for(let r=0;r<n;r++)e=p.get(r,t+1),p.set(r,t+1,p.get(r,t)),p.set(r,t,e);if(l&&t<r-1)for(let n=0;n<r;n++)e=g.get(n,t+1),g.set(n,t+1,g.get(n,t)),g.set(n,t,e);t++}b--}}if(u){let t=p;p=g,g=t}this.m=r,this.n=n,this.s=m,this.U=g,this.V=p}solve(t){let e=t,r=this.threshold,n=this.s.length,i=Matrix.zeros(n,n);for(let t=0;t<n;t++)Math.abs(this.s[t])<=r?i.set(t,t,0):i.set(t,t,1/this.s[t]);let o=this.U,s=this.rightSingularVectors,a=s.mmul(i),l=s.rows,h=o.rows,u=Matrix.zeros(l,h);for(let t=0;t<l;t++)for(let e=0;e<h;e++){let r=0;for(let i=0;i<n;i++)r+=a.get(t,i)*o.get(e,i);u.set(t,e,r)}return u.mmul(e)}solveForDiagonal(t){return this.solve(Matrix.diag(t))}inverse(){let t=this.V,e=this.threshold,r=t.rows,n=t.columns,i=new Matrix(r,this.s.length);for(let o=0;o<r;o++)for(let r=0;r<n;r++)Math.abs(this.s[r])>e&&i.set(o,r,t.get(o,r)/this.s[r]);let o=this.U,s=o.rows,a=o.columns,l=new Matrix(r,s);for(let t=0;t<r;t++)for(let e=0;e<s;e++){let r=0;for(let n=0;n<a;n++)r+=i.get(t,n)*o.get(e,n);l.set(t,e,r)}return l}get condition(){return this.s[0]/this.s[Math.min(this.m,this.n)-1]}get norm2(){return this.s[0]}get rank(){let t=Math.max(this.m,this.n)*this.s[0]*Number.EPSILON,e=0,r=this.s;for(let n=0,i=r.length;n<i;n++)r[n]>t&&e++;return e}get diagonal(){return Array.from(this.s)}get threshold(){return Number.EPSILON/2*Math.max(this.m,this.n)*this.s[0]}get leftSingularVectors(){return this.U}get rightSingularVectors(){return this.V}get diagonalMatrix(){return Matrix.diag(this.s)}}function inverse(t,e=!1){return t=WrapperMatrix2D.checkMatrix(t),e?new SingularValueDecomposition(t).inverse():solve(t,Matrix.eye(t.rows))}function solve(t,e,r=!1){return t=WrapperMatrix2D.checkMatrix(t),e=WrapperMatrix2D.checkMatrix(e),r?new SingularValueDecomposition(t).solve(e):t.isSquare()?new LuDecomposition(t).solve(e):new QrDecomposition(t).solve(e)}function determinant(t){if((t=Matrix.checkMatrix(t)).isSquare()){let e,r,n,i;if(2===t.columns)return e=t.get(0,0),r=t.get(0,1),n=t.get(1,0),i=t.get(1,1),e*i-r*n;if(3===t.columns){let i,o,s;return i=new MatrixSelectionView(t,[1,2],[1,2]),o=new MatrixSelectionView(t,[1,2],[0,2]),s=new MatrixSelectionView(t,[1,2],[0,1]),e=t.get(0,0),r=t.get(0,1),n=t.get(0,2),e*determinant(i)-r*determinant(o)+n*determinant(s)}return new LuDecomposition(t).determinant}throw Error("determinant can only be calculated for a square matrix")}function xrange(t,e){let r=[];for(let n=0;n<t;n++)n!==e&&r.push(n);return r}function dependenciesOneRow(t,e,r,n=1e-9,i=1e-9){if(t>i)return new Array(e.rows+1).fill(0);{let t=e.addRow(r,[0]);for(let e=0;e<t.rows;e++)Math.abs(t.get(e,0))<n&&t.set(e,0,0);return t.to1DArray()}}function linearDependencies(t,e={}){const{thresholdValue:r=1e-9,thresholdError:n=1e-9}=e;let i=(t=Matrix.checkMatrix(t)).rows,o=new Matrix(i,i);for(let e=0;e<i;e++){let s=Matrix.columnVector(t.getRow(e)),a=t.subMatrixRow(xrange(i,e)).transpose(),l=new SingularValueDecomposition(a).solve(s),h=Matrix.sub(s,a.mmul(l)).abs().max();o.setRow(e,dependenciesOneRow(h,l,e,r,n))}return o}function pseudoInverse(t,e=Number.EPSILON){t=Matrix.checkMatrix(t);let r=new SingularValueDecomposition(t,{autoTranspose:!0}),n=r.leftSingularVectors,i=r.rightSingularVectors,o=r.diagonal;for(let t=0;t<o.length;t++)Math.abs(o[t])>e?o[t]=1/o[t]:o[t]=0;return i.mmul(Matrix.diag(o).mmul(n.transpose()))}function covariance(t,e=t,r={}){t=new Matrix(t);let n=!1;if("object"!=typeof e||Matrix.isMatrix(e)||Array.isArray(e)?e=new Matrix(e):(r=e,e=t,n=!0),t.rows!==e.rows)throw new TypeError("Both matrices must have the same number of rows");const{center:i=!0}=r;i&&(t=t.center("column"),n||(e=e.center("column")));const o=t.transpose().mmul(e);for(let e=0;e<o.rows;e++)for(let r=0;r<o.columns;r++)o.set(e,r,o.get(e,r)*(1/(t.rows-1)));return o}function correlation(t,e=t,r={}){t=new Matrix(t);let n=!1;if("object"!=typeof e||Matrix.isMatrix(e)||Array.isArray(e)?e=new Matrix(e):(r=e,e=t,n=!0),t.rows!==e.rows)throw new TypeError("Both matrices must have the same number of rows");const{center:i=!0,scale:o=!0}=r;i&&(t.center("column"),n||e.center("column")),o&&(t.scale("column"),n||e.scale("column"));const s=t.standardDeviation("column",{unbiased:!0}),a=n?s:e.standardDeviation("column",{unbiased:!0}),l=t.transpose().mmul(e);for(let e=0;e<l.rows;e++)for(let r=0;r<l.columns;r++)l.set(e,r,l.get(e,r)*(1/(s[e]*a[r]))*(1/(t.rows-1)));return l}class EigenvalueDecomposition{constructor(t,e={}){const{assumeSymmetric:r=!1}=e;if(!(t=WrapperMatrix2D.checkMatrix(t)).isSquare())throw new Error("Matrix is not a square matrix");let n,i,o=t.columns,s=new Matrix(o,o),a=new Float64Array(o),l=new Float64Array(o),h=t,u=!1;if(u=!!r||t.isSymmetric(),u){for(n=0;n<o;n++)for(i=0;i<o;i++)s.set(n,i,h.get(n,i));tred2(o,l,a,s),tql2(o,l,a,s)}else{let t=new Matrix(o,o),e=new Float64Array(o);for(i=0;i<o;i++)for(n=0;n<o;n++)t.set(n,i,h.get(n,i));orthes(o,t,e,s),hqr2(o,l,a,s,t)}this.n=o,this.e=l,this.d=a,this.V=s}get realEigenvalues(){return Array.from(this.d)}get imaginaryEigenvalues(){return Array.from(this.e)}get eigenvectorMatrix(){return this.V}get diagonalMatrix(){let t,e,r=this.n,n=this.e,i=this.d,o=new Matrix(r,r);for(t=0;t<r;t++){for(e=0;e<r;e++)o.set(t,e,0);o.set(t,t,i[t]),n[t]>0?o.set(t,t+1,n[t]):n[t]<0&&o.set(t,t-1,n[t])}return o}}function tred2(t,e,r,n){let i,o,s,a,l,h,u,c;for(l=0;l<t;l++)r[l]=n.get(t-1,l);for(a=t-1;a>0;a--){for(c=0,s=0,h=0;h<a;h++)c+=Math.abs(r[h]);if(0===c)for(e[a]=r[a-1],l=0;l<a;l++)r[l]=n.get(a-1,l),n.set(a,l,0),n.set(l,a,0);else{for(h=0;h<a;h++)r[h]/=c,s+=r[h]*r[h];for(i=r[a-1],o=Math.sqrt(s),i>0&&(o=-o),e[a]=c*o,s-=i*o,r[a-1]=i-o,l=0;l<a;l++)e[l]=0;for(l=0;l<a;l++){for(i=r[l],n.set(l,a,i),o=e[l]+n.get(l,l)*i,h=l+1;h<=a-1;h++)o+=n.get(h,l)*r[h],e[h]+=n.get(h,l)*i;e[l]=o}for(i=0,l=0;l<a;l++)e[l]/=s,i+=e[l]*r[l];for(u=i/(s+s),l=0;l<a;l++)e[l]-=u*r[l];for(l=0;l<a;l++){for(i=r[l],o=e[l],h=l;h<=a-1;h++)n.set(h,l,n.get(h,l)-(i*e[h]+o*r[h]));r[l]=n.get(a-1,l),n.set(a,l,0)}}r[a]=s}for(a=0;a<t-1;a++){if(n.set(t-1,a,n.get(a,a)),n.set(a,a,1),s=r[a+1],0!==s){for(h=0;h<=a;h++)r[h]=n.get(h,a+1)/s;for(l=0;l<=a;l++){for(o=0,h=0;h<=a;h++)o+=n.get(h,a+1)*n.get(h,l);for(h=0;h<=a;h++)n.set(h,l,n.get(h,l)-o*r[h])}}for(h=0;h<=a;h++)n.set(h,a+1,0)}for(l=0;l<t;l++)r[l]=n.get(t-1,l),n.set(t-1,l,0);n.set(t-1,t-1,1),e[0]=0}function tql2(t,e,r,n){let i,o,s,a,l,h,u,c,f,m,g,p,d,w,y,x;for(s=1;s<t;s++)e[s-1]=e[s];e[t-1]=0;let M=0,v=0,b=Number.EPSILON;for(h=0;h<t;h++){for(v=Math.max(v,Math.abs(r[h])+Math.abs(e[h])),u=h;u<t&&!(Math.abs(e[u])<=b*v);)u++;if(u>h)do{for(i=r[h],c=(r[h+1]-i)/(2*e[h]),f=hypotenuse(c,1),c<0&&(f=-f),r[h]=e[h]/(c+f),r[h+1]=e[h]*(c+f),m=r[h+1],o=i-r[h],s=h+2;s<t;s++)r[s]-=o;for(M+=o,c=r[u],g=1,p=g,d=g,w=e[h+1],y=0,x=0,s=u-1;s>=h;s--)for(d=p,p=g,x=y,i=g*e[s],o=g*c,f=hypotenuse(c,e[s]),e[s+1]=y*f,y=e[s]/f,g=c/f,c=g*r[s]-y*i,r[s+1]=o+y*(g*i+y*r[s]),l=0;l<t;l++)o=n.get(l,s+1),n.set(l,s+1,y*n.get(l,s)+g*o),n.set(l,s,g*n.get(l,s)-y*o);c=-y*x*d*w*e[h]/m,e[h]=y*c,r[h]=g*c}while(Math.abs(e[h])>b*v);r[h]=r[h]+M,e[h]=0}for(s=0;s<t-1;s++){for(l=s,c=r[s],a=s+1;a<t;a++)r[a]<c&&(l=a,c=r[a]);if(l!==s)for(r[l]=r[s],r[s]=c,a=0;a<t;a++)c=n.get(a,s),n.set(a,s,n.get(a,l)),n.set(a,l,c)}}function orthes(t,e,r,n){let i,o,s,a,l,h,u,c=t-1;for(h=1;h<=c-1;h++){for(u=0,a=h;a<=c;a++)u+=Math.abs(e.get(a,h-1));if(0!==u){for(s=0,a=c;a>=h;a--)r[a]=e.get(a,h-1)/u,s+=r[a]*r[a];for(o=Math.sqrt(s),r[h]>0&&(o=-o),s-=r[h]*o,r[h]=r[h]-o,l=h;l<t;l++){for(i=0,a=c;a>=h;a--)i+=r[a]*e.get(a,l);for(i/=s,a=h;a<=c;a++)e.set(a,l,e.get(a,l)-i*r[a])}for(a=0;a<=c;a++){for(i=0,l=c;l>=h;l--)i+=r[l]*e.get(a,l);for(i/=s,l=h;l<=c;l++)e.set(a,l,e.get(a,l)-i*r[l])}r[h]=u*r[h],e.set(h,h-1,u*o)}}for(a=0;a<t;a++)for(l=0;l<t;l++)n.set(a,l,a===l?1:0);for(h=c-1;h>=1;h--)if(0!==e.get(h,h-1)){for(a=h+1;a<=c;a++)r[a]=e.get(a,h-1);for(l=h;l<=c;l++){for(o=0,a=h;a<=c;a++)o+=r[a]*n.get(a,l);for(o=o/r[h]/e.get(h,h-1),a=h;a<=c;a++)n.set(a,l,n.get(a,l)+o*r[a])}}}function hqr2(t,e,r,n,i){let o,s,a,l,h,u,c,f,m,g,p,d,w,y,x,M=t-1,v=t-1,b=Number.EPSILON,S=0,A=0,E=0,R=0,k=0,N=0,T=0,C=0;for(o=0;o<t;o++)for((o<0||o>v)&&(r[o]=i.get(o,o),e[o]=0),s=Math.max(o-1,0);s<t;s++)A+=Math.abs(i.get(o,s));for(;M>=0;){for(l=M;l>0&&(N=Math.abs(i.get(l-1,l-1))+Math.abs(i.get(l,l)),0===N&&(N=A),!(Math.abs(i.get(l,l-1))<b*N));)l--;if(l===M)i.set(M,M,i.get(M,M)+S),r[M]=i.get(M,M),e[M]=0,M--,C=0;else if(l===M-1){if(c=i.get(M,M-1)*i.get(M-1,M),E=(i.get(M-1,M-1)-i.get(M,M))/2,R=E*E+c,T=Math.sqrt(Math.abs(R)),i.set(M,M,i.get(M,M)+S),i.set(M-1,M-1,i.get(M-1,M-1)+S),f=i.get(M,M),R>=0){for(T=E>=0?E+T:E-T,r[M-1]=f+T,r[M]=r[M-1],0!==T&&(r[M]=f-c/T),e[M-1]=0,e[M]=0,f=i.get(M,M-1),N=Math.abs(f)+Math.abs(T),E=f/N,R=T/N,k=Math.sqrt(E*E+R*R),E/=k,R/=k,s=M-1;s<t;s++)T=i.get(M-1,s),i.set(M-1,s,R*T+E*i.get(M,s)),i.set(M,s,R*i.get(M,s)-E*T);for(o=0;o<=M;o++)T=i.get(o,M-1),i.set(o,M-1,R*T+E*i.get(o,M)),i.set(o,M,R*i.get(o,M)-E*T);for(o=0;o<=v;o++)T=n.get(o,M-1),n.set(o,M-1,R*T+E*n.get(o,M)),n.set(o,M,R*n.get(o,M)-E*T)}else r[M-1]=f+E,r[M]=f+E,e[M-1]=T,e[M]=-T;M-=2,C=0}else{if(f=i.get(M,M),m=0,c=0,l<M&&(m=i.get(M-1,M-1),c=i.get(M,M-1)*i.get(M-1,M)),10===C){for(S+=f,o=0;o<=M;o++)i.set(o,o,i.get(o,o)-f);N=Math.abs(i.get(M,M-1))+Math.abs(i.get(M-1,M-2)),f=m=.75*N,c=-.4375*N*N}if(30===C&&(N=(m-f)/2,N=N*N+c,N>0)){for(N=Math.sqrt(N),m<f&&(N=-N),N=f-c/((m-f)/2+N),o=0;o<=M;o++)i.set(o,o,i.get(o,o)-N);S+=N,f=m=c=.964}for(C+=1,h=M-2;h>=l&&(T=i.get(h,h),k=f-T,N=m-T,E=(k*N-c)/i.get(h+1,h)+i.get(h,h+1),R=i.get(h+1,h+1)-T-k-N,k=i.get(h+2,h+1),N=Math.abs(E)+Math.abs(R)+Math.abs(k),E/=N,R/=N,k/=N,h!==l)&&!(Math.abs(i.get(h,h-1))*(Math.abs(R)+Math.abs(k))<b*(Math.abs(E)*(Math.abs(i.get(h-1,h-1))+Math.abs(T)+Math.abs(i.get(h+1,h+1)))));)h--;for(o=h+2;o<=M;o++)i.set(o,o-2,0),o>h+2&&i.set(o,o-3,0);for(a=h;a<=M-1&&(y=a!==M-1,a!==h&&(E=i.get(a,a-1),R=i.get(a+1,a-1),k=y?i.get(a+2,a-1):0,f=Math.abs(E)+Math.abs(R)+Math.abs(k),0!==f&&(E/=f,R/=f,k/=f)),0!==f);a++)if(N=Math.sqrt(E*E+R*R+k*k),E<0&&(N=-N),0!==N){for(a!==h?i.set(a,a-1,-N*f):l!==h&&i.set(a,a-1,-i.get(a,a-1)),E+=N,f=E/N,m=R/N,T=k/N,R/=E,k/=E,s=a;s<t;s++)E=i.get(a,s)+R*i.get(a+1,s),y&&(E+=k*i.get(a+2,s),i.set(a+2,s,i.get(a+2,s)-E*T)),i.set(a,s,i.get(a,s)-E*f),i.set(a+1,s,i.get(a+1,s)-E*m);for(o=0;o<=Math.min(M,a+3);o++)E=f*i.get(o,a)+m*i.get(o,a+1),y&&(E+=T*i.get(o,a+2),i.set(o,a+2,i.get(o,a+2)-E*k)),i.set(o,a,i.get(o,a)-E),i.set(o,a+1,i.get(o,a+1)-E*R);for(o=0;o<=v;o++)E=f*n.get(o,a)+m*n.get(o,a+1),y&&(E+=T*n.get(o,a+2),n.set(o,a+2,n.get(o,a+2)-E*k)),n.set(o,a,n.get(o,a)-E),n.set(o,a+1,n.get(o,a+1)-E*R)}}}if(0!==A){for(M=t-1;M>=0;M--)if(E=r[M],R=e[M],0===R)for(l=M,i.set(M,M,1),o=M-1;o>=0;o--){for(c=i.get(o,o)-E,k=0,s=l;s<=M;s++)k+=i.get(o,s)*i.get(s,M);if(e[o]<0)T=c,N=k;else if(l=o,0===e[o]?i.set(o,M,0!==c?-k/c:-k/(b*A)):(f=i.get(o,o+1),m=i.get(o+1,o),R=(r[o]-E)*(r[o]-E)+e[o]*e[o],u=(f*N-T*k)/R,i.set(o,M,u),i.set(o+1,M,Math.abs(f)>Math.abs(T)?(-k-c*u)/f:(-N-m*u)/T)),u=Math.abs(i.get(o,M)),b*u*u>1)for(s=o;s<=M;s++)i.set(s,M,i.get(s,M)/u)}else if(R<0)for(l=M-1,Math.abs(i.get(M,M-1))>Math.abs(i.get(M-1,M))?(i.set(M-1,M-1,R/i.get(M,M-1)),i.set(M-1,M,-(i.get(M,M)-E)/i.get(M,M-1))):(x=cdiv(0,-i.get(M-1,M),i.get(M-1,M-1)-E,R),i.set(M-1,M-1,x[0]),i.set(M-1,M,x[1])),i.set(M,M-1,0),i.set(M,M,1),o=M-2;o>=0;o--){for(g=0,p=0,s=l;s<=M;s++)g+=i.get(o,s)*i.get(s,M-1),p+=i.get(o,s)*i.get(s,M);if(c=i.get(o,o)-E,e[o]<0)T=c,k=g,N=p;else if(l=o,0===e[o]?(x=cdiv(-g,-p,c,R),i.set(o,M-1,x[0]),i.set(o,M,x[1])):(f=i.get(o,o+1),m=i.get(o+1,o),d=(r[o]-E)*(r[o]-E)+e[o]*e[o]-R*R,w=2*(r[o]-E)*R,0===d&&0===w&&(d=b*A*(Math.abs(c)+Math.abs(R)+Math.abs(f)+Math.abs(m)+Math.abs(T))),x=cdiv(f*k-T*g+R*p,f*N-T*p-R*g,d,w),i.set(o,M-1,x[0]),i.set(o,M,x[1]),Math.abs(f)>Math.abs(T)+Math.abs(R)?(i.set(o+1,M-1,(-g-c*i.get(o,M-1)+R*i.get(o,M))/f),i.set(o+1,M,(-p-c*i.get(o,M)-R*i.get(o,M-1))/f)):(x=cdiv(-k-m*i.get(o,M-1),-N-m*i.get(o,M),T,R),i.set(o+1,M-1,x[0]),i.set(o+1,M,x[1]))),u=Math.max(Math.abs(i.get(o,M-1)),Math.abs(i.get(o,M))),b*u*u>1)for(s=o;s<=M;s++)i.set(s,M-1,i.get(s,M-1)/u),i.set(s,M,i.get(s,M)/u)}for(o=0;o<t;o++)if(o<0||o>v)for(s=o;s<t;s++)n.set(o,s,i.get(o,s));for(s=t-1;s>=0;s--)for(o=0;o<=v;o++){for(T=0,a=0;a<=Math.min(s,v);a++)T+=n.get(o,a)*i.get(a,s);n.set(o,s,T)}}}function cdiv(t,e,r,n){let i,o;return Math.abs(r)>Math.abs(n)?(i=n/r,o=r+i*n,[(t+i*e)/o,(e-i*t)/o]):(i=r/n,o=n+i*r,[(i*t+e)/o,(i*e-t)/o])}class CholeskyDecomposition{constructor(t){if(!(t=WrapperMatrix2D.checkMatrix(t)).isSymmetric())throw new Error("Matrix is not symmetric");let e,r,n,i=t,o=i.rows,s=new Matrix(o,o),a=!0;for(r=0;r<o;r++){let t=0;for(n=0;n<r;n++){let o=0;for(e=0;e<n;e++)o+=s.get(n,e)*s.get(r,e);o=(i.get(r,n)-o)/s.get(n,n),s.set(r,n,o),t+=o*o}for(t=i.get(r,r)-t,a&=t>0,s.set(r,r,Math.sqrt(Math.max(t,0))),n=r+1;n<o;n++)s.set(r,n,0)}this.L=s,this.positiveDefinite=Boolean(a)}isPositiveDefinite(){return this.positiveDefinite}solve(t){t=WrapperMatrix2D.checkMatrix(t);let e=this.L,r=e.rows;if(t.rows!==r)throw new Error("Matrix dimensions do not match");if(!1===this.isPositiveDefinite())throw new Error("Matrix is not positive definite");let n,i,o,s=t.columns,a=t.clone();for(o=0;o<r;o++)for(i=0;i<s;i++){for(n=0;n<o;n++)a.set(o,i,a.get(o,i)-a.get(n,i)*e.get(o,n));a.set(o,i,a.get(o,i)/e.get(o,o))}for(o=r-1;o>=0;o--)for(i=0;i<s;i++){for(n=o+1;n<r;n++)a.set(o,i,a.get(o,i)-a.get(n,i)*e.get(n,o));a.set(o,i,a.get(o,i)/e.get(o,o))}return a}get lowerTriangularMatrix(){return this.L}}class nipals{constructor(t,e={}){t=WrapperMatrix2D.checkMatrix(t);let{Y:r}=e;const{scaleScores:n=!1,maxIterations:i=1e3,terminationCriteria:o=1e-10}=e;let s;if(r){if(r=Array.isArray(r)&&"number"==typeof r[0]?Matrix.columnVector(r):WrapperMatrix2D.checkMatrix(r),!r.isColumnVector()||r.rows!==t.rows)throw new Error("Y must be a column vector of length X.rows");s=r}else s=t.getColumnVector(0);let a,l,h,u,c=1;for(let e=0;e<i&&c>o;e++)h=t.transpose().mmul(s).div(s.transpose().mmul(s).get(0,0)),h=h.div(h.norm()),a=t.mmul(h).div(h.transpose().mmul(h).get(0,0)),e>0&&(c=a.clone().sub(u).pow(2).sum()),u=a.clone(),r?(l=r.transpose().mmul(a).div(a.transpose().mmul(a).get(0,0)),l=l.div(l.norm()),s=r.mmul(l).div(l.transpose().mmul(l).get(0,0))):s=a;if(r){let e=t.transpose().mmul(a).div(a.transpose().mmul(a).get(0,0));e=e.div(e.norm());let n=t.clone().sub(a.clone().mmul(e.transpose())),i=s.transpose().mmul(a).div(a.transpose().mmul(a).get(0,0)),o=r.clone().sub(a.clone().mulS(i.get(0,0)).mmul(l.transpose()));this.t=a,this.p=e.transpose(),this.w=h.transpose(),this.q=l,this.u=s,this.s=a.transpose().mmul(a),this.xResidual=n,this.yResidual=o,this.betas=i}else this.w=h.transpose(),this.s=a.transpose().mmul(a).sqrt(),this.t=n?a.clone().div(this.s.get(0,0)):a,this.xResidual=t.sub(a.mmul(h.transpose()))}}var MatrixLib=Object.freeze({__proto__:null,AbstractMatrix:AbstractMatrix,default:Matrix,Matrix:Matrix,wrap:wrap,WrapperMatrix1D:WrapperMatrix1D,WrapperMatrix2D:WrapperMatrix2D,solve:solve,inverse:inverse,determinant:determinant,linearDependencies:linearDependencies,pseudoInverse:pseudoInverse,covariance:covariance,correlation:correlation,SingularValueDecomposition:SingularValueDecomposition,SVD:SingularValueDecomposition,EigenvalueDecomposition:EigenvalueDecomposition,EVD:EigenvalueDecomposition,CholeskyDecomposition:CholeskyDecomposition,CHO:CholeskyDecomposition,LuDecomposition:LuDecomposition,LU:LuDecomposition,QrDecomposition:QrDecomposition,QR:QrDecomposition,Nipals:nipals,NIPALS:nipals,MatrixColumnView:MatrixColumnView,MatrixColumnSelectionView:MatrixColumnSelectionView,MatrixFlipColumnView:MatrixFlipColumnView,MatrixFlipRowView:MatrixFlipRowView,MatrixRowView:MatrixRowView,MatrixRowSelectionView:MatrixRowSelectionView,MatrixSelectionView:MatrixSelectionView,MatrixSubView:MatrixSubView,MatrixTransposeView:MatrixTransposeView});const toString$1=Object.prototype.toString;function isAnyArray$1(t){return toString$1.call(t).endsWith("Array]")}function sum(t){if(!isAnyArray$1(t))throw new TypeError("input must be an array");if(0===t.length)throw new TypeError("input must not be empty");for(var e=0,r=0;r<t.length;r++)e+=t[r];return e}function mean(t){return sum(t)/t.length}function toDiscreteDistribution(t,e){let r=new Array(e).fill(0);for(let e=0;e<t.length;++e)r[t[e]]+=1/t.length;return Matrix.rowVector(r)}function giniImpurity(t){if(0===t.length)return 0;let e=toDiscreteDistribution(t,getNumberOfClasses(t)).getRow(0),r=0;for(let t=0;t<e.length;++t)r+=e[t]*e[t];return 1-r}function getNumberOfClasses(t){return t.filter((function(t,e,r){return r.indexOf(t)===e})).map((t=>t+1)).reduce(((t,e)=>Math.max(t,e)))}function giniGain(t,e){let r=0,n=["greater","lesser"];for(let i=0;i<n.length;++i){let o=e[n[i]];r+=giniImpurity(o)*o.length/t.length}return giniImpurity(t)-r}function squaredError(t){let e=t.length,r=mean(t),n=0;for(let i=0;i<e;++i){let e=t[i];n+=(e-r)*(e-r)}return n}function regressionError(t,e){let r=0,n=["greater","lesser"];for(let t=0;t<n.length;++t){r+=squaredError(e[n[t]])}return r}function matrixSplitter(t,e,r,n){let i=[],o=[],s=[],a=[];for(let l=0;l<t.rows;++l)t.get(l,r)<n?(i.push(t.getRow(l)),s.push(e[l])):(o.push(t.getRow(l)),a.push(e[l]));return{greaterX:o,greaterY:a,lesserX:i,lesserY:s}}function mean$1(t,e){return(t+e)/2}function zip(t,e){if(t.length!==e.length)throw new TypeError(`Error on zip: the size of a: ${t.length} is different from b: ${e.length}`);let r=new Array(t.length);for(let n=0;n<t.length;++n)r[n]=[t[n],e[n]];return r}const gainFunctions={gini:giniGain,regression:regressionError},splitFunctions={mean:mean$1};class TreeNode{constructor(t){this.kind=t.kind,this.gainFunction=t.gainFunction,this.splitFunction=t.splitFunction,this.minNumSamples=t.minNumSamples,this.maxDepth=t.maxDepth}bestSplit(t,e){let r,n,i="classifier"===this.kind?-1/0:1/0,o="classifier"===this.kind?(t,e)=>t>e:(t,e)=>t<e;for(let s=0;s<t.rows;++s){let a=t.getRow(s),l=this.featureSplit(a,e);for(let t=0;t<l.length;++t){let h=l[t],u=this.split(a,e,h),c=gainFunctions[this.gainFunction](e,u);o(c,i)&&(r=s,n=h,i=c)}}return{maxGain:i,maxColumn:r,maxValue:n}}split(t,e,r){let n=[],i=[];for(let o=0;o<t.length;++o)t[o]<r?n.push(e[o]):i.push(e[o]);return{greater:i,lesser:n}}featureSplit(t,e){let r=[],n=zip(t,e);n.sort((function(t,e){return t[0]-e[0]}));for(let t=1;t<n.length;++t)n[t-1][1]!==n[t][1]&&r.push(splitFunctions[this.splitFunction](n[t-1][0],n[t][0]));return r}calculatePrediction(t){if("classifier"===this.kind){if(this.distribution=toDiscreteDistribution(t,getNumberOfClasses(t)),0===this.distribution.columns)throw new TypeError("Error on calculate the prediction")}else this.distribution=mean(t)}train(t,e,r,n){if(t.rows<=this.minNumSamples)return void this.calculatePrediction(e);void 0===n&&(n=0);let i=t.transpose(),o=this.bestSplit(i,e);this.splitValue=o.maxValue,this.splitColumn=o.maxColumn,this.gain=o.maxGain;let s=matrixSplitter(t,e,this.splitColumn,this.splitValue);if(r<this.maxDepth&&this.gain>.01&&this.gain!==n&&s.lesserX.length>0&&s.greaterX.length>0){this.left=new TreeNode(this),this.right=new TreeNode(this);let t=new Matrix(s.lesserX),e=new Matrix(s.greaterX);this.left.train(t,s.lesserY,r+1,this.gain),this.right.train(e,s.greaterY,r+1,this.gain)}else this.calculatePrediction(e)}classify(t){return this.right&&this.left?t[this.splitColumn]<this.splitValue?this.left.classify(t):this.right.classify(t):this.distribution}setNodeParameters(t){void 0!==t.distribution?this.distribution=t.distribution.constructor===Array?new Matrix(t.distribution):t.distribution:(this.distribution=void 0,this.splitValue=t.splitValue,this.splitColumn=t.splitColumn,this.gain=t.gain,this.left=new TreeNode(this),this.right=new TreeNode(this),t.left!=={}&&this.left.setNodeParameters(t.left),t.right!=={}&&this.right.setNodeParameters(t.right))}}const defaultOptions={gainFunction:"gini",splitFunction:"mean",minNumSamples:3,maxDepth:1/0};class DecisionTreeClassifier{constructor(t,e){!0===t?(this.options=e.options,this.root=new TreeNode(e.options),this.root.setNodeParameters(e.root)):(this.options=Object.assign({},defaultOptions,t),this.options.kind="classifier")}train(t,e){this.root=new TreeNode(this.options),t=Matrix.checkMatrix(t),this.root.train(t,e,0,null)}predict(t){t=Matrix.checkMatrix(t);let e=new Array(t.rows);for(let r=0;r<t.rows;++r)e[r]=this.root.classify(t.getRow(r)).maxRowIndex(0)[1];return e}toJSON(){return{options:this.options,root:this.root,name:"DTClassifier"}}static load(t){if("DTClassifier"!==t.name)throw new RangeError("Invalid model: "+t.name);return new DecisionTreeClassifier(!0,t)}}const defaultOptions$1={gainFunction:"regression",splitFunction:"mean",minNumSamples:3,maxDepth:1/0};class DecisionTreeRegression{constructor(t,e){!0===t?(this.options=e.options,this.root=new TreeNode(e.options),this.root.setNodeParameters(e.root)):(this.options=Object.assign({},defaultOptions$1,t),this.options.kind="regression")}train(t,e){this.root=new TreeNode(this.options),t=void 0!==t[0]&&void 0===t[0].length?Matrix.columnVector(t):Matrix.checkMatrix(t),this.root.train(t,e,0)}predict(t){void 0!==t[0]&&void 0===t[0].length&&(t=Matrix.columnVector(t)),t=Matrix.checkMatrix(t);let e=new Array(t.rows);for(let r=0;r<t.rows;++r)e[r]=this.root.classify(t.getRow(r));return e}toJSON(){return{options:this.options,root:this.root,name:"DTRegression"}}static load(t){if("DTRegression"!==t.name)throw new RangeError("Invalid model:"+t.name);return new DecisionTreeRegression(!0,t)}}const SMALLEST_UNSAFE_INTEGER=9007199254740992,LARGEST_SAFE_INTEGER=SMALLEST_UNSAFE_INTEGER-1,UINT32_MAX=-1>>>0,UINT32_SIZE=UINT32_MAX+1,INT32_SIZE=UINT32_SIZE/2,INT32_MAX=INT32_SIZE-1,UINT21_SIZE=1<<21,UINT21_MAX=UINT21_SIZE-1;function int32(t){return 0|t.next()}function add(t,e){return 0===e?t:r=>t(r)+e}function int53(t){const e=0|t.next(),r=t.next()>>>0;return(e&UINT21_MAX)*UINT32_SIZE+r+(e&UINT21_SIZE?-SMALLEST_UNSAFE_INTEGER:0)}function int53Full(t){for(;;){const e=0|t.next();if(!(4194304&e)){const r=t.next()>>>0;return(e&UINT21_MAX)*UINT32_SIZE+r+(e&UINT21_SIZE?-SMALLEST_UNSAFE_INTEGER:0)}if(4194304==(8388607&e)&&0==(0|t.next()))return SMALLEST_UNSAFE_INTEGER}}function uint32(t){return t.next()>>>0}function uint53(t){const e=t.next()&UINT21_MAX,r=t.next()>>>0;return e*UINT32_SIZE+r}function uint53Full(t){for(;;){const e=0|t.next();if(!(e&UINT21_SIZE)){const r=t.next()>>>0;return(e&UINT21_MAX)*UINT32_SIZE+r}if(0==(e&UINT21_MAX)&&0==(0|t.next()))return SMALLEST_UNSAFE_INTEGER}}function isPowerOfTwoMinusOne(t){return 0==(t+1&t)}function bitmask(t){return e=>e.next()&t}function downscaleToLoopCheckedRange(t){const e=t+1,r=e*Math.floor(UINT32_SIZE/e);return t=>{let n=0;do{n=t.next()>>>0}while(n>=r);return n%e}}function downscaleToRange(t){return isPowerOfTwoMinusOne(t)?bitmask(t):downscaleToLoopCheckedRange(t)}function isEvenlyDivisibleByMaxInt32(t){return 0==(0|t)}function upscaleWithHighMasking(t){return e=>{const r=e.next()&t,n=e.next()>>>0;return r*UINT32_SIZE+n}}function upscaleToLoopCheckedRange(t){const e=t*Math.floor(SMALLEST_UNSAFE_INTEGER/t);return r=>{let n=0;do{const t=r.next()&UINT21_MAX,e=r.next()>>>0;n=t*UINT32_SIZE+e}while(n>=e);return n%t}}function upscaleWithinU53(t){const e=t+1;if(isEvenlyDivisibleByMaxInt32(e)){const t=(e/UINT32_SIZE|0)-1;if(isPowerOfTwoMinusOne(t))return upscaleWithHighMasking(t)}return upscaleToLoopCheckedRange(e)}function upscaleWithinI53AndLoopCheck(t,e){return r=>{let n=0;do{const t=0|r.next(),e=r.next()>>>0;n=(t&UINT21_MAX)*UINT32_SIZE+e+(t&UINT21_SIZE?-SMALLEST_UNSAFE_INTEGER:0)}while(n<t||n>e);return n}}function integer(t,e){if(t=Math.floor(t),e=Math.floor(e),t<-SMALLEST_UNSAFE_INTEGER||!isFinite(t))throw new RangeError("Expected min to be at least "+-SMALLEST_UNSAFE_INTEGER);if(e>SMALLEST_UNSAFE_INTEGER||!isFinite(e))throw new RangeError("Expected max to be at most "+SMALLEST_UNSAFE_INTEGER);const r=e-t;return r<=0||!isFinite(r)?()=>t:r===UINT32_MAX?0===t?uint32:add(int32,t+INT32_SIZE):r<UINT32_MAX?add(downscaleToRange(r),t):r===LARGEST_SAFE_INTEGER?add(uint53,t):r<LARGEST_SAFE_INTEGER?add(upscaleWithinU53(r),t):e-1-t===LARGEST_SAFE_INTEGER?add(uint53Full,t):t===-SMALLEST_UNSAFE_INTEGER&&e===SMALLEST_UNSAFE_INTEGER?int53Full:t===-SMALLEST_UNSAFE_INTEGER&&e===LARGEST_SAFE_INTEGER?int53:t===-LARGEST_SAFE_INTEGER&&e===SMALLEST_UNSAFE_INTEGER?add(int53,1):e===SMALLEST_UNSAFE_INTEGER?add(upscaleWithinI53AndLoopCheck(t-1,e-1),1):upscaleWithinI53AndLoopCheck(t,e)}const DEFAULT_STRING_POOL="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-";function string(t=DEFAULT_STRING_POOL){const e=t.length;if(!e)throw new Error("Expected pool not to be an empty string");const r=integer(0,e-1);return(e,n)=>{let i="";for(let o=0;o<n;++o){const n=r(e);i+=t.charAt(n)}return i}}const LOWER_HEX_POOL="0123456789abcdef",lowerHex=string(LOWER_HEX_POOL),upperHex=string(LOWER_HEX_POOL.toUpperCase()),stringRepeat=(()=>{try{if("xxx"==="x".repeat(3))return(t,e)=>t.repeat(e)}catch(t){}return(t,e)=>{let r="";for(;e>0;)1&e&&(r+=t),e>>=1,t+=t;return r}})(),nativeMath={next:()=>Math.random()*UINT32_SIZE|0},I32Array=(()=>{try{const t=new ArrayBuffer(4),e=new Int32Array(t);if(e[0]=INT32_SIZE,e[0]===-INT32_SIZE)return Int32Array}catch(t){}return Array})();function createEntropy(t=nativeMath,e=16){const r=[];r.push(0|(new Date).getTime());for(let n=1;n<e;++n)r[n]=0|t.next();return r}const imul=(()=>{try{if(-5===Math.imul(UINT32_MAX,5))return Math.imul}catch(t){}const t=65535;return(e,r)=>{const n=e&t,i=r&t;return n*i+((e>>>16&t)*i+n*(r>>>16&t)<<16>>>0)|0}})(),ARRAY_SIZE=624,ARRAY_MAX=ARRAY_SIZE-1,M=397,ARRAY_SIZE_MINUS_M=ARRAY_SIZE-M,A=2567483615;class MersenneTwister19937{constructor(){this.data=new I32Array(ARRAY_SIZE),this.index=0,this.uses=0}static seed(t){return(new MersenneTwister19937).seed(t)}static seedWithArray(t){return(new MersenneTwister19937).seedWithArray(t)}static autoSeed(){return MersenneTwister19937.seedWithArray(createEntropy())}next(){(0|this.index)>=ARRAY_SIZE&&(refreshData(this.data),this.index=0);const t=this.data[this.index];return this.index=this.index+1|0,this.uses+=1,0|temper(t)}getUseCount(){return this.uses}discard(t){if(t<=0)return this;for(this.uses+=t,(0|this.index)>=ARRAY_SIZE&&(refreshData(this.data),this.index=0);t+this.index>ARRAY_SIZE;)t-=ARRAY_SIZE-this.index,refreshData(this.data),this.index=0;return this.index=this.index+t|0,this}seed(t){let e=0;this.data[0]=e=0|t;for(let t=1;t<ARRAY_SIZE;t=t+1|0)this.data[t]=e=imul(e^e>>>30,1812433253)+t|0;return this.index=ARRAY_SIZE,this.uses=0,this}seedWithArray(t){return this.seed(19650218),seedWithArray(this.data,t),this}}function refreshData(t){let e=0,r=0;for(;(0|e)<ARRAY_SIZE_MINUS_M;e=e+1|0)r=t[e]&INT32_SIZE|t[e+1|0]&INT32_MAX,t[e]=t[e+M|0]^r>>>1^(1&r?A:0);for(;(0|e)<ARRAY_MAX;e=e+1|0)r=t[e]&INT32_SIZE|t[e+1|0]&INT32_MAX,t[e]=t[e-ARRAY_SIZE_MINUS_M|0]^r>>>1^(1&r?A:0);r=t[ARRAY_MAX]&INT32_SIZE|t[0]&INT32_MAX,t[ARRAY_MAX]=t[M-1]^r>>>1^(1&r?A:0)}function temper(t){return t^=t>>>11,t^=t<<7&2636928640,(t^=t<<15&4022730752)^t>>>18}function seedWithArray(t,e){let r=1,n=0;const i=e.length;let o=0|Math.max(i,ARRAY_SIZE),s=0|t[0];for(;(0|o)>0;--o)t[r]=s=(t[r]^imul(s^s>>>30,1664525))+(0|e[n])+(0|n)|0,r=r+1|0,++n,(0|r)>ARRAY_MAX&&(t[0]=t[ARRAY_MAX],r=1),n>=i&&(n=0);for(o=ARRAY_MAX;(0|o)>0;--o)t[r]=s=(t[r]^imul(s^s>>>30,1566083941))-r|0,r=r+1|0,(0|r)>ARRAY_MAX&&(t[0]=t[ARRAY_MAX],r=1);t[0]=INT32_SIZE}function checkFloat(t){return t>0&&t<=1}function examplesBaggingWithReplacement(t,e,r){let n,i=integer(0,t.rows-1);if(void 0===r)n=MersenneTwister19937.autoSeed();else{if(!Number.isInteger(r))throw new RangeError("Expected seed must be undefined or integer not "+r);n=MersenneTwister19937.seed(r)}let o=new Array(t.rows),s=new Array(t.rows);for(let r=0;r<t.rows;++r){let a=i(n);o[r]=t.getRow(a),s[r]=e[a]}return{X:new Matrix(o),y:s}}function featureBagging(t,e,r,n){if(t.columns<e)throw new RangeError("N should be less or equal to the number of columns of X");let i,o=integer(0,t.columns-1);if(void 0===n)i=MersenneTwister19937.autoSeed();else{if(!Number.isInteger(n))throw new RangeError("Expected seed must be undefined or integer not "+n);i=MersenneTwister19937.seed(n)}let s,a,l=new Matrix(t.rows,e);if(r){s=new Array(e);for(let r=0;r<e;++r)a=o(i),s[r]=a,l.setColumn(r,t.getColumn(a))}else{s=new Set,a=o(i);for(let r=0;r<e;++r){for(;s.has(a);)a=o(i);l.setColumn(r,t.getColumn(a)),s.add(a)}s=Array.from(s)}return{X:l,usedIndex:s}}class RandomForestBase{constructor(t,e){if(!0===t){this.replacement=e.replacement,this.maxFeatures=e.maxFeatures,this.nEstimators=e.nEstimators,this.treeOptions=e.treeOptions,this.isClassifier=e.isClassifier,this.seed=e.seed,this.n=e.n,this.indexes=e.indexes,this.useSampleBagging=e.useSampleBagging;let t=this.isClassifier?DecisionTreeClassifier:DecisionTreeRegression;this.estimators=e.estimators.map((e=>t.load(e)))}else this.replacement=t.replacement,this.maxFeatures=t.maxFeatures,this.nEstimators=t.nEstimators,this.treeOptions=t.treeOptions,this.isClassifier=t.isClassifier,this.seed=t.seed,this.useSampleBagging=t.useSampleBagging}train(t,e){if(t=Matrix.checkMatrix(t),this.maxFeatures=this.maxFeatures||t.columns,checkFloat(this.maxFeatures))this.n=Math.floor(t.columns*this.maxFeatures);else{if(!Number.isInteger(this.maxFeatures))throw new RangeError("Cannot process the maxFeatures parameter "+this.maxFeatures);if(this.maxFeatures>t.columns)throw new RangeError("The maxFeatures parameter should be less than "+t.columns);this.n=this.maxFeatures}let r;r=this.isClassifier?DecisionTreeClassifier:DecisionTreeRegression,this.estimators=new Array(this.nEstimators),this.indexes=new Array(this.nEstimators);for(let n=0;n<this.nEstimators;++n){let i=this.useSampleBagging?examplesBaggingWithReplacement(t,e,this.seed):{X:t,y:e},o=i.X,s=i.y;i=featureBagging(o,this.n,this.replacement,this.seed),o=i.X,this.indexes[n]=i.usedIndex,this.estimators[n]=new r(this.treeOptions),this.estimators[n].train(o,s)}}selection(t){throw new Error("Abstract method 'selection' not implemented!")}predict(t){let e=new Array(this.nEstimators);t=Matrix.checkMatrix(t);for(let r=0;r<this.nEstimators;++r){let n=new MatrixColumnSelectionView(t,this.indexes[r]);e[r]=this.estimators[r].predict(n)}e=new MatrixTransposeView(new WrapperMatrix2D(e));let r=new Array(e.rows);for(let t=0;t<e.rows;++t)r[t]=this.selection(e.getRow(t));return r}toJSON(){return{indexes:this.indexes,n:this.n,replacement:this.replacement,maxFeatures:this.maxFeatures,nEstimators:this.nEstimators,treeOptions:this.treeOptions,isClassifier:this.isClassifier,seed:this.seed,estimators:this.estimators.map((t=>t.toJSON())),useSampleBagging:this.useSampleBagging}}}const defaultOptions$2={maxFeatures:1,replacement:!0,nEstimators:10,seed:42,useSampleBagging:!1};class RandomForestClassifier extends RandomForestBase{constructor(t,e){!0===t?super(!0,e.baseModel):((t=Object.assign({},defaultOptions$2,t)).isClassifier=!0,super(t))}selection(t){return mode(t)}toJSON(){return{baseModel:super.toJSON(),name:"RFClassifier"}}static load(t){if("RFClassifier"!==t.name)throw new RangeError("Invalid model: "+t.name);return new RandomForestClassifier(!0,t)}}function mode(t){return t.sort(((e,r)=>t.filter((t=>t===e)).length-t.filter((t=>t===r)).length)).pop()}const toString$2=Object.prototype.toString;function isAnyArray$2(t){return toString$2.call(t).endsWith("Array]")}var commonjsGlobal="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function createCommonjsModule(t,e,r){return t(r={path:e,exports:{},require:function(t,e){return commonjsRequire(t,null==e?r.path:e)}},r.exports),r.exports}function getAugmentedNamespace(t){if(t.__esModule)return t;var e=Object.defineProperty({},"__esModule",{value:!0});return Object.keys(t).forEach((function(r){var n=Object.getOwnPropertyDescriptor(t,r);Object.defineProperty(e,r,n.get?n:{enumerable:!0,get:function(){return t[r]}})})),e}function commonjsRequire(){throw new Error("Dynamic requires are not currently supported by @rollup/plugin-commonjs")}var medianQuickselect_min=createCommonjsModule((function(t){!function(){function e(t){for(var e=0,i=t.length-1,o=void 0,s=void 0,a=void 0,l=n(e,i);;){if(i<=e)return t[l];if(i==e+1)return t[e]>t[i]&&r(t,e,i),t[l];for(t[o=n(e,i)]>t[i]&&r(t,o,i),t[e]>t[i]&&r(t,e,i),t[o]>t[e]&&r(t,o,e),r(t,o,e+1),s=e+1,a=i;;){do{s++}while(t[e]>t[s]);do{a--}while(t[a]>t[e]);if(a<s)break;r(t,s,a)}r(t,e,a),a<=l&&(e=s),a>=l&&(i=a-1)}}var r=function(t,e,r){var n;return n=[t[r],t[e]],t[e]=n[0],t[r]=n[1],n},n=function(t,e){return~~((t+e)/2)};t.exports?t.exports=e:window.median=e}()}));function median(t){if(!isAnyArray$2(t))throw new TypeError("input must be an array");if(0===t.length)throw new TypeError("input must not be empty");return medianQuickselect_min(t.slice())}const selectionMethods={mean:mean,median:median},defaultOptions$3={maxFeatures:1,replacement:!1,nEstimators:10,treeOptions:{},selectionMethod:"mean",seed:42,useSampleBagging:!1};class RandomForestRegression extends RandomForestBase{constructor(t,e){if(!0===t)super(!0,e.baseModel),this.selectionMethod=e.selectionMethod;else{if("mean"!==(t=Object.assign({},defaultOptions$3,t)).selectionMethod&&"median"!==t.selectionMethod)throw new RangeError("Unsupported selection method "+t.selectionMethod);t.isClassifier=!1,super(t),this.selectionMethod=t.selectionMethod}}selection(t){return selectionMethods[this.selectionMethod](t)}toJSON(){return{baseModel:super.toJSON(),selectionMethod:this.selectionMethod,name:"RFRegression"}}static load(t){if("RFRegression"!==t.name)throw new RangeError("Invalid model: "+t.name);return new RandomForestRegression(!0,t)}}class PCA{constructor(t,e={}){if(!0===t){const t=e;return this.center=t.center,this.scale=t.scale,this.means=t.means,this.stdevs=t.stdevs,this.U=Matrix.checkMatrix(t.U),this.S=t.S,this.R=t.R,void(this.excludedFeatures=t.excludedFeatures||[])}t=new Matrix(t);const{isCovarianceMatrix:r=!1,method:n="SVD",nCompNIPALS:i=2,center:o=!0,scale:s=!1,ignoreZeroVariance:a=!1}=e;if(this.center=o,this.scale=s,this.means=null,this.stdevs=null,this.excludedFeatures=[],r)this._computeFromCovarianceMatrix(t);else switch(this._adjust(t,a),n){case"covarianceMatrix":{const e=new MatrixTransposeView(t).mmul(t).div(t.rows-1);this._computeFromCovarianceMatrix(e);break}case"NIPALS":this._computeWithNIPALS(t,i);break;case"SVD":{const e=new SingularValueDecomposition(t,{computeLeftSingularVectors:!1,computeRightSingularVectors:!0,autoTranspose:!0});this.U=e.rightSingularVectors;const r=e.diagonal,n=[];for(const e of r)n.push(e*e/(t.rows-1));this.S=n;break}default:throw new Error("unknown method: "+n)}}static load(t){if("string"!=typeof t.name)throw new TypeError("model must have a name property");if("PCA"!==t.name)throw new RangeError("invalid model: "+t.name);return new PCA(!0,t)}predict(t,e={}){const{nComponents:r=this.U.columns}=e;if(t=new Matrix(t),this.center&&(t.subRowVector(this.means),this.scale)){for(let e of this.excludedFeatures)t.removeColumn(e);t.divRowVector(this.stdevs)}var n=t.mmul(this.U);return n.subMatrix(0,n.rows-1,0,r-1)}invert(t){var e=(t=Matrix.checkMatrix(t)).mmul(this.U.transpose());return this.center&&(this.scale&&e.mulRowVector(this.stdevs),e.addRowVector(this.means)),e}getExplainedVariance(){var t=0;for(const e of this.S)t+=e;return this.S.map((e=>e/t))}getCumulativeVariance(){for(var t=this.getExplainedVariance(),e=1;e<t.length;e++)t[e]+=t[e-1];return t}getEigenvectors(){return this.U}getEigenvalues(){return this.S}getStandardDeviations(){return this.S.map((t=>Math.sqrt(t)))}getLoadings(){return this.U.transpose()}toJSON(){return{name:"PCA",center:this.center,scale:this.scale,means:this.means,stdevs:this.stdevs,U:this.U,S:this.S,excludedFeatures:this.excludedFeatures}}_adjust(t,e){if(this.center){const r=t.mean("column"),n=this.scale?t.standardDeviation("column",{mean:r}):null;if(this.means=r,t.subRowVector(r),this.scale){for(let r=0;r<n.length;r++)if(0===n[r]){if(!e)throw new RangeError("Cannot scale the dataset (standard deviation is zero at index "+r);t.removeColumn(r),n.splice(r,1),this.excludedFeatures.push(r),r--}this.stdevs=n,t.divRowVector(n)}}}_computeFromCovarianceMatrix(t){const e=new EigenvalueDecomposition(t,{assumeSymmetric:!0});this.U=e.eigenvectorMatrix,this.U.flipRows(),this.S=e.realEigenvalues,this.S.reverse()}_computeWithNIPALS(t,e){this.U=new Matrix(e,t.columns),this.S=[];let r=t;for(let t=0;t<e;t++){let e=new nipals(r);this.U.setRow(t,e.w.transpose()),this.S.push(Math.pow(e.s.get(0,0),2)),r=e.xResidual}this.U=this.U.transpose()}}function squaredEuclidean(t,e){let r=0;for(let n=0;n<t.length;n++)r+=(t[n]-e[n])*(t[n]-e[n]);return r}function euclidean(t,e){return Math.sqrt(squaredEuclidean(t,e))}var euclidean$1=Object.freeze({__proto__:null,squaredEuclidean:squaredEuclidean,euclidean:euclidean});function distanceMatrix(t,e){const r=getMatrix(t.length);for(let n=0;n<t.length;n++)for(let i=0;i<=n;i++)r[n][i]=e(t[n],t[i]),r[i][n]=r[n][i];return r}function getMatrix(t){const e=[];for(let r=0;r<t;r++){const r=[];e.push(r);for(let e=0;e<t;e++)r.push(0)}return e}var heap=createCommonjsModule((function(t,e){(function(){var e,r,n,i,o,s,a,l,h,u,c,f,m,g,p;n=Math.floor,u=Math.min,r=function(t,e){return t<e?-1:t>e?1:0},h=function(t,e,i,o,s){var a;if(null==i&&(i=0),null==s&&(s=r),i<0)throw new Error("lo must be non-negative");for(null==o&&(o=t.length);i<o;)s(e,t[a=n((i+o)/2)])<0?o=a:i=a+1;return[].splice.apply(t,[i,i-i].concat(e)),e},s=function(t,e,n){return null==n&&(n=r),t.push(e),g(t,0,t.length-1,n)},o=function(t,e){var n,i;return null==e&&(e=r),n=t.pop(),t.length?(i=t[0],t[0]=n,p(t,0,e)):i=n,i},l=function(t,e,n){var i;return null==n&&(n=r),i=t[0],t[0]=e,p(t,0,n),i},a=function(t,e,n){var i;return null==n&&(n=r),t.length&&n(t[0],e)<0&&(e=(i=[t[0],e])[0],t[0]=i[1],p(t,0,n)),e},i=function(t,e){var i,o,s,a,l,h;for(null==e&&(e=r),l=[],o=0,s=(a=function(){h=[];for(var e=0,r=n(t.length/2);0<=r?e<r:e>r;0<=r?e++:e--)h.push(e);return h}.apply(this).reverse()).length;o<s;o++)i=a[o],l.push(p(t,i,e));return l},m=function(t,e,n){var i;if(null==n&&(n=r),-1!==(i=t.indexOf(e)))return g(t,0,i,n),p(t,i,n)},c=function(t,e,n){var o,s,l,h,u;if(null==n&&(n=r),!(s=t.slice(0,e)).length)return s;for(i(s,n),l=0,h=(u=t.slice(e)).length;l<h;l++)o=u[l],a(s,o,n);return s.sort(n).reverse()},f=function(t,e,n){var s,a,l,c,f,m,g,p,d;if(null==n&&(n=r),10*e<=t.length){if(!(l=t.slice(0,e).sort(n)).length)return l;for(a=l[l.length-1],c=0,m=(g=t.slice(e)).length;c<m;c++)n(s=g[c],a)<0&&(h(l,s,0,null,n),l.pop(),a=l[l.length-1]);return l}for(i(t,n),d=[],f=0,p=u(e,t.length);0<=p?f<p:f>p;0<=p?++f:--f)d.push(o(t,n));return d},g=function(t,e,n,i){var o,s,a;for(null==i&&(i=r),o=t[n];n>e&&i(o,s=t[a=n-1>>1])<0;)t[n]=s,n=a;return t[n]=o},p=function(t,e,n){var i,o,s,a,l;for(null==n&&(n=r),o=t.length,l=e,s=t[e],i=2*e+1;i<o;)(a=i+1)<o&&!(n(t[i],t[a])<0)&&(i=a),t[e]=t[i],i=2*(e=i)+1;return t[e]=s,g(t,l,e,n)},e=function(){function t(t){this.cmp=null!=t?t:r,this.nodes=[]}return t.push=s,t.pop=o,t.replace=l,t.pushpop=a,t.heapify=i,t.updateItem=m,t.nlargest=c,t.nsmallest=f,t.prototype.push=function(t){return s(this.nodes,t,this.cmp)},t.prototype.pop=function(){return o(this.nodes,this.cmp)},t.prototype.peek=function(){return this.nodes[0]},t.prototype.contains=function(t){return-1!==this.nodes.indexOf(t)},t.prototype.replace=function(t){return l(this.nodes,t,this.cmp)},t.prototype.pushpop=function(t){return a(this.nodes,t,this.cmp)},t.prototype.heapify=function(){return i(this.nodes,this.cmp)},t.prototype.updateItem=function(t){return m(this.nodes,t,this.cmp)},t.prototype.clear=function(){return this.nodes=[]},t.prototype.empty=function(){return 0===this.nodes.length},t.prototype.size=function(){return this.nodes.length},t.prototype.clone=function(){var e;return(e=new t).nodes=this.nodes.slice(0),e},t.prototype.toArray=function(){return this.nodes.slice(0)},t.prototype.insert=t.prototype.push,t.prototype.top=t.prototype.peek,t.prototype.front=t.prototype.peek,t.prototype.has=t.prototype.contains,t.prototype.copy=t.prototype.clone,t}(),t.exports=e}).call(commonjsGlobal)})),heap$1=heap;class Cluster{constructor(){this.children=[],this.height=0,this.size=1,this.index=-1,this.isLeaf=!1}cut(t){if("number"!=typeof t)throw new TypeError("threshold must be a number");if(t<0)throw new RangeError("threshold must be a positive number");let e=[this];const r=[];for(;e.length>0;){const n=e.shift();t>=n.height?r.push(n):e=e.concat(n.children)}return r}group(t){if(!Number.isInteger(t)||t<1)throw new RangeError("groups must be a positive integer");const e=new heap$1(((t,e)=>e.height-t.height));for(e.push(this);e.size()<t;){var r=e.pop();if(0===r.children.length)break;r.children.forEach((t=>e.push(t)))}var n=new Cluster;return n.children=e.toArray(),n.height=this.height,n}traverse(t){!function t(e,r){if(r(e),e.children)for(const n of e.children)t(n,r)}(this,t)}indices(){const t=[];return this.traverse((e=>{e.isLeaf&&t.push(e.index)})),t}}function singleLink(t,e){return Math.min(t,e)}function completeLink(t,e){return Math.max(t,e)}function averageLink(t,e,r,n,i){return n/(n+i)*t+i/(n+i)*e}function weightedAverageLink(t,e){return(t+e)/2}function centroidLink(t,e,r,n,i){return n/(n+i)*t+i/(n+i)*e+-n*i/(n+i)**2*r}function medianLink(t,e,r){return t/2+e/2-r/4}function wardLink(t,e,r,n,i,o){return(n+o)/(n+i+o)*t+(i+o)/(n+i+o)*e+-o/(n+i+o)*r}function wardLink2(t,e,r,n,i,o){const s=(n+o)/(n+i+o),a=(i+o)/(n+i+o),l=-o/(n+i+o);return Math.sqrt(s*t*t+a*e*e+l*r*r)}function agnes(t,e={}){const{distanceFunction:r=euclidean,method:n="complete",isDistanceMatrix:i=!1}=e;let o;i||(t=distanceMatrix(t,r));let s=new Matrix(t);const a=s.rows;if("string"==typeof n)switch(n.toLowerCase()){case"single":o=singleLink;break;case"complete":o=completeLink;break;case"average":case"upgma":o=averageLink;break;case"wpgma":o=weightedAverageLink;break;case"centroid":case"upgmc":o=centroidLink;break;case"median":case"wpgmc":o=medianLink;break;case"ward":o=wardLink;break;case"ward2":o=wardLink2;break;default:throw new RangeError("unknown clustering method: "+n)}else if("function"!=typeof n)throw new TypeError("method must be a string or function");let l=[];for(let t=0;t<a;t++){const e=new Cluster;e.isLeaf=!0,e.index=t,l.push(e)}for(let t=0;t<a-1;t++){const[t,e,r]=getSmallestDistance(s),n=l[t],i=l[e],a=new Cluster;a.size=n.size+i.size,a.children.push(n,i),a.height=r;const h=[a],u=new Matrix(s.rows-1,s.rows-1),c=r=>getPreviousIndex(r,Math.min(t,e),Math.max(t,e));for(let a=1;a<u.rows;a++){const f=c(a),m=l[f];h.push(m);for(let l=0;l<a;l++)if(0===l){const h=o(s.get(t,f),s.get(f,e),r,n.size,i.size,m.size);u.set(a,l,h),u.set(l,a,h)}else{const t=s.get(f,c(l));u.set(a,l,t),u.set(l,a,t)}}l=h,s=u}return l[0]}function getSmallestDistance(t){let e=1/0,r=0,n=0;for(let i=1;i<t.rows;i++)for(let o=0;o<i;o++)t.get(i,o)<e&&(e=t.get(i,o),r=i,n=o);return[r,n,e]}function getPreviousIndex(t,e,r){return(t-=1)>=e&&t++,t>=r&&t++,t}var index=Object.freeze({__proto__:null,agnes:agnes});const defaultOptions$4={distanceFunction:squaredEuclidean};function nearestVector(t,e,r=defaultOptions$4){const n=r.distanceFunction||defaultOptions$4.distanceFunction,i=r.similarityFunction||defaultOptions$4.similarityFunction;let o=-1;if("function"==typeof i){let r=Number.MIN_VALUE;for(let n=0;n<t.length;n++){const s=i(e,t[n]);s>r&&(r=s,o=n)}}else{if("function"!=typeof n)throw new Error("A similarity or distance function it's required");{let r=Number.MAX_VALUE;for(let i=0;i<t.length;i++){const s=n(e,t[i]);s<r&&(r=s,o=i)}}}return o}function calculateDistanceMatrix(t,e){for(var r=new Array(t.length),n=0;n<t.length;++n)for(var i=n;i<t.length;++i){r[n]||(r[n]=new Array(t.length)),r[i]||(r[i]=new Array(t.length));const o=e(t[n],t[i]);r[n][i]=o,r[i][n]=o}return r}function updateClusterID(t,e,r,n){for(var i=0;i<t.length;i++)r[i]=nearestVector(e,t[i],{distanceFunction:n});return r}function updateCenters(t,e,r,n){const i=e[0].length;for(var o=new Array(n),s=new Array(n),a=0;a<n;a++){o[a]=new Array(i),s[a]=0;for(var l=0;l<i;l++)o[a][l]=0}for(var h=0;h<e.length;h++){s[r[h]]++;for(var u=0;u<i;u++)o[r[h]][u]+=e[h][u]}for(var c=0;c<n;c++)for(var f=0;f<i;f++)s[c]?o[c][f]/=s[c]:o[c][f]=t[c][f];return o}function hasConverged(t,e,r,n){for(var i=0;i<t.length;i++)if(r(t[i],e[i])>n)return!1;return!0}const LOOP=8,FLOAT_MUL=1/16777216,sh1=15,sh2=18,sh3=11;function multiply_uint32(t,e){const r=65535&(t>>>=0);return((t-r)*(e>>>=0)>>>0)+r*e>>>0}class XSadd{constructor(t=Date.now()){this.state=new Uint32Array(4),this.init(t),this.random=this.getFloat.bind(this)}getUint32(){return this.nextState(),this.state[3]+this.state[2]>>>0}getFloat(){return(this.getUint32()>>>8)*FLOAT_MUL}init(t){if(!Number.isInteger(t))throw new TypeError("seed must be an integer");this.state[0]=t,this.state[1]=0,this.state[2]=0,this.state[3]=0;for(let t=1;t<LOOP;t++)this.state[3&t]^=t+multiply_uint32(1812433253,this.state[t-1&3]^this.state[t-1&3]>>>30>>>0)>>>0;this.periodCertification();for(let t=0;t<LOOP;t++)this.nextState()}periodCertification(){0===this.state[0]&&0===this.state[1]&&0===this.state[2]&&0===this.state[3]&&(this.state[0]=88,this.state[1]=83,this.state[2]=65,this.state[3]=68)}nextState(){let t=this.state[0];t^=t<<sh1,t^=t>>>sh2,t^=this.state[3]<<sh3,this.state[0]=this.state[1],this.state[1]=this.state[2],this.state[2]=this.state[3],this.state[3]=t}}const PROB_TOLERANCE=1e-8;function randomChoice(t,e={},r=Math.random){const{size:n=1,replace:i=!1,probabilities:o}=e;let s,a;if(s="number"==typeof t?getArray(t):t.slice(),o){if(!i)throw new Error("choice with probabilities and no replacement is not implemented");if(o.length!==s.length)throw new Error("the length of probabilities option should be equal to the number of choices");a=[o[0]];for(let t=1;t<o.length;t++)a[t]=a[t-1]+o[t];if(Math.abs(1-a[a.length-1])>PROB_TOLERANCE)throw new Error("probabilities should sum to 1, but instead sums to "+a[a.length-1])}if(!1===i&&n>s.length)throw new Error("size option is too large");const l=[];for(let t=0;t<n;t++){const t=randomIndex(s.length,r,a);l.push(s[t]),i||s.splice(t,1)}return l}function getArray(t){const e=[];for(let r=0;r<t;r++)e.push(r);return e}function randomIndex(t,e,r){const n=e();if(r){let t=0;for(;n>r[t];)t++;return t}return Math.floor(n*t)}class Random{constructor(t=Math.random){if("number"==typeof t){const e=new XSadd(t);this.randomGenerator=e.random}else this.randomGenerator=t}choice(t,e){return randomChoice(t,e,this.randomGenerator)}random(){return this.randomGenerator()}randInt(t,e){return void 0===e&&(e=t,t=0),t+Math.floor(this.randomGenerator()*(e-t))}randomSample(t){const e=[];for(let r=0;r<t;r++)e.push(this.random());return e}}function random(t,e,r){return new Random(r).choice(t,{size:e})}function mostDistant(t,e,r,n){const i=new Random(n);var o=new Array(e);if(o[0]=Math.floor(i.random()*t.length),e>1){for(var s={dist:-1,index:-1},a=0;a<t.length;++a)r[o[0]][a]>s.dist&&(s.dist=r[o[0]][a],s.index=a);if(o[1]=s.index,e>2)for(var l=2;l<e;++l){for(var h={dist:-1,index:-1},u=0;u<t.length;++u){for(var c={dist:Number.MAX_VALUE,index:-1},f=0;f<l;++f)r[f][u]<c.dist&&-1===o.indexOf(u)&&(c={dist:r[f][u],index:u});c.dist!==Number.MAX_VALUE&&c.dist>h.dist&&(h=Object.assign({},c))}o[l]=h.index}}return o.map((e=>t[e]))}function kmeanspp(t,e,r={}){const n=(t=new Matrix(t)).rows,i=new Random(r.seed),o=[],s=r.localTrials||2+Math.floor(Math.log(e)),a=i.randInt(n);o.push(t.getRow(a));let l=new Matrix(1,t.rows);for(let e=0;e<t.rows;e++)l.set(0,e,squaredEuclidean(t.getRow(e),o[0]));let h=[cumSum(l.getRow(0))];const u=1/h[0][n-1];let c=Matrix.mul(l,u);for(let r=1;r<e;r++){const e=i.choice(n,{replace:!0,size:s,probabilities:c[0]}),a=euclideanDistances(t.selection(e,range(t.columns)),t);let u,f,m;for(let t=0;t<s;t++){const r=Matrix.min(l,[a.getRow(t)]),n=r.sum();(void 0===u||n<f)&&(u=e[t],f=n,m=r)}o[r]=t.getRow(u),l=m,h=[cumSum(l.getRow(0))],c=Matrix.mul(l,1/h[0][n-1])}return o}function euclideanDistances(t,e){const r=new Matrix(t.rows,e.rows);for(let n=0;n<t.rows;n++)for(let i=0;i<e.rows;i++)r.set(n,i,squaredEuclidean(t.getRow(n),e.getRow(i)));return r}function range(t){let e=[];for(let r=0;r<t;r++)e.push(r);return e}function cumSum(t){let e=[t[0]];for(let r=1;r<t.length;r++)e[r]=e[r-1]+t[r];return e}const distanceSymbol=Symbol("distance");class KMeansResult{constructor(t,e,r,n,i){this.clusters=t,this.centroids=e,this.converged=r,this.iterations=n,this[distanceSymbol]=i}nearest(t){const e=new Array(t.length);return updateClusterID(t,this.centroids.map((function(t){return t.centroid})),e,this[distanceSymbol])}computeInformation(t){for(var e=this.centroids.map((function(t){return{centroid:t,error:0,size:0}})),r=0;r<t.length;r++)e[this.clusters[r]].error+=this[distanceSymbol](t[r],this.centroids[this.clusters[r]]),e[this.clusters[r]].size++;for(var n=0;n<this.centroids.length;n++)e[n].size?e[n].error/=e[n].size:e[n].error=null;return new KMeansResult(this.clusters,e,this.converged,this.iterations,this[distanceSymbol])}}const defaultOptions$5={maxIterations:100,tolerance:1e-6,withIterations:!1,initialization:"kmeans++",distanceFunction:squaredEuclidean};function step(t,e,r,n,i,o){var s=updateCenters(t,e,r=updateClusterID(e,t,r,i.distanceFunction),n),a=hasConverged(s,t,i.distanceFunction,i.tolerance);return new KMeansResult(r,s,a,o,i.distanceFunction)}function*kmeansGenerator(t,e,r,n,i){for(var o,s=!1,a=0;!s&&a<i.maxIterations;)o=step(t,e,r,n,i,++a),yield o.computeInformation(e),s=o.converged,t=o.centroids}function kmeans(t,e,r){if(r=Object.assign({},defaultOptions$5,r),e<=0||e>t.length||!Number.isInteger(e))throw new Error("K should be a positive integer smaller than the number of points");var n;if(Array.isArray(r.initialization)){if(r.initialization.length!==e)throw new Error("The initial centers should have the same length as K");n=r.initialization}else switch(r.initialization){case"kmeans++":n=kmeanspp(t,e,r);break;case"random":n=random(t,e,r.seed);break;case"mostDistant":n=mostDistant(t,e,calculateDistanceMatrix(t,r.distanceFunction),r.seed);break;default:throw new Error(`Unknown initialization method: "${r.initialization}"`)}0===r.maxIterations&&(r.maxIterations=Number.MAX_VALUE);var i=new Array(t.length);if(r.withIterations)return kmeansGenerator(n,t,i,e,r);for(var o,s=!1,a=0;!s&&a<r.maxIterations;)s=(o=step(n,t,i,e,r,++a)).converged,n=o.centroids;return o.computeInformation(t)}function separateClasses(t,e){for(var r=t.columns,n=0,i=new Array(1e4),o=0;o<e.length;o++)void 0===i[e[o]]&&(i[e[o]]=0,n++),i[e[o]]++;var s=new Array(n),a=new Array(n);for(o=0;o<n;++o)s[o]=new Matrix(i[o],r),a[o]=0;for(o=0;o<t.rows;++o)s[e[o]].setRow(a[e[o]],t.getRow(o)),a[e[o]]++;return s}class GaussianNB{constructor(t,e){t&&(this.means=e.means,this.calculateProbabilities=e.calculateProbabilities)}train(t,e){var r=Math.sqrt(2*Math.PI);if((t=Matrix.checkMatrix(t)).rows!==e.length)throw new RangeError("the size of the training set and the training labels must be the same.");var n=separateClasses(t,e),i=new Array(n.length);this.means=new Array(n.length);for(var o=0;o<n.length;++o){var s=n[o].mean("column"),a=n[o].standardDeviation("column",{mean:s}),l=Math.log(n[o].rows/t.rows);i[o]=new Array(s.length+1),i[o][0]=l;for(var h=1;h<s.length+1;++h){var u=a[h-1];i[o][h]=[1/(r*u),-2*u*u]}this.means[o]=s}this.calculateProbabilities=i}predict(t){if((t=Matrix.checkMatrix(t)).rows===this.calculateProbabilities[0].length)throw new RangeError("the dataset must have the same features as the training set");for(var e=new Array(t.rows),r=0;r<e.length;++r)e[r]=getCurrentClass(t.getRow(r),this.means,this.calculateProbabilities);return e}toJSON(){return{modelName:"NaiveBayes",means:this.means,calculateProbabilities:this.calculateProbabilities}}static load(t){if("NaiveBayes"!==t.modelName)throw new RangeError("The current model is not a Multinomial Naive Bayes, current model:",t.name);return new GaussianNB(!0,t)}}function getCurrentClass(t,e,r){for(var n=0,i=-1,o=0;o<r.length;++o){for(var s=r[o][0],a=1;a<r[0][1].length+1;++a)s+=calculateLogProbability(t[a-1],e[o][a-1],r[o][a][0],r[o][a][1]);(s=Math.exp(s))>n&&(n=s,i=o)}return i}function calculateLogProbability(t,e,r,n){return t-=e,Math.log(r*Math.exp(t*t/n))}class MultinomialNB{constructor(t){t&&(this.conditionalProbability=Matrix.checkMatrix(t.conditionalProbability),this.priorProbability=Matrix.checkMatrix(t.priorProbability))}train(t,e){if((t=Matrix.checkMatrix(t)).rows!==e.length)throw new RangeError("the size of the training set and the training labels must be the same.");var r=separateClasses(t,e);this.priorProbability=new Matrix(r.length,1);for(var n=0;n<r.length;++n)this.priorProbability.set(n,0,Math.log(r[n].rows/t.rows));var i=t.columns;for(this.conditionalProbability=new Matrix(r.length,i),n=0;n<r.length;++n){var o=Matrix.checkMatrix(r[n]),s=o.sum()+i;this.conditionalProbability.setRow(n,Matrix.rowVector(o.sum("column")).add(1).div(s).apply(matrixLog))}}predict(t){t=Matrix.checkMatrix(t);for(var e=new Array(t.rows),r=0;r<t.rows;++r){var n=t.getRowVector(r);const i=Matrix.columnVector(this.conditionalProbability.clone().mulRowVector(n).sum("row"));e[r]=i.add(this.priorProbability).maxIndex()[0]}return e}toJSON(){return{name:"MultinomialNB",priorProbability:this.priorProbability,conditionalProbability:this.conditionalProbability}}static load(t){if("MultinomialNB"!==t.name)throw new RangeError(t.name+" is not a Multinomial Naive Bayes");return new MultinomialNB(t)}}function matrixLog(t,e){this.set(t,e,Math.log(this.get(t,e)))}var index$1=Object.freeze({__proto__:null,GaussianNB:GaussianNB,MultinomialNB:MultinomialNB});
 /*
    * Original code from:
    *
@@ -10,5 +10,5 @@
    * @author Martin Kleppe <kleppe@ubilabs.net>, 2012
    * @author Ubilabs http://ubilabs.net, 2012
    * @license MIT License <http://www.opensource.org/licenses/mit-license.php>
-   */function Node(t,e,r){this.obj=t,this.left=null,this.right=null,this.parent=r,this.dimension=e}class KDTree{constructor(t,e){if(Array.isArray(t)){this.dimensions=new Array(t[0].length);for(var r=0;r<this.dimensions.length;r++)this.dimensions[r]=r;this.root=buildTree(t,0,null,this.dimensions)}else this.dimensions=t.dimensions,this.root=t,restoreParent(this.root);this.metric=e}toJSON(){const t=toJSONImpl(this.root);return t.dimensions=this.dimensions,t}nearest(t,e,r){const i=this.metric,n=this.dimensions;var s;const o=new BinaryHeap((function(t){return-t[1]}));if(r)for(s=0;s<e;s+=1)o.push([null,r]);this.root&&function r(s){const a=n[s.dimension],h=i(t,s.obj),l={};var u,c,f,m;function g(t,r){o.push([t,r]),o.size()>e&&o.pop()}for(m=0;m<n.length;m+=1)m===s.dimension?l[n[m]]=t[n[m]]:l[n[m]]=s.obj[n[m]];c=i(l,s.obj),null!==s.right||null!==s.left?(r(u=null===s.right?s.left:null===s.left?s.right:t[a]<s.obj[a]?s.left:s.right),(o.size()<e||h<o.peek()[1])&&g(s,h),(o.size()<e||Math.abs(c)<o.peek()[1])&&null!==(f=u===s.left?s.right:s.left)&&r(f)):(o.size()<e||h<o.peek()[1])&&g(s,h)}(this.root);const a=[];for(s=0;s<Math.min(e,o.content.length);s+=1)o.content[s][0]&&a.push([o.content[s][0].obj,o.content[s][1]]);return a}}function toJSONImpl(t){const e=new Node(t.obj,t.dimension,null);return t.left&&(e.left=toJSONImpl(t.left)),t.right&&(e.right=toJSONImpl(t.right)),e}function buildTree(t,e,r,i){const n=e%i.length;if(0===t.length)return null;if(1===t.length)return new Node(t[0],n,r);t.sort((t,e)=>t[i[n]]-e[i[n]]);const s=Math.floor(t.length/2),o=new Node(t[s],n,r);return o.left=buildTree(t.slice(0,s),e+1,o,i),o.right=buildTree(t.slice(s+1),e+1,o,i),o}function restoreParent(t){t.left&&(t.left.parent=t,restoreParent(t.left)),t.right&&(t.right.parent=t,restoreParent(t.right))}class BinaryHeap{constructor(t){this.content=[],this.scoreFunction=t}push(t){this.content.push(t),this.bubbleUp(this.content.length-1)}pop(){var t=this.content[0],e=this.content.pop();return this.content.length>0&&(this.content[0]=e,this.sinkDown(0)),t}peek(){return this.content[0]}size(){return this.content.length}bubbleUp(t){for(var e=this.content[t];t>0;){const r=Math.floor((t+1)/2)-1,i=this.content[r];if(!(this.scoreFunction(e)<this.scoreFunction(i)))break;this.content[r]=e,this.content[t]=i,t=r}}sinkDown(t){for(var e=this.content.length,r=this.content[t],i=this.scoreFunction(r);;){var n=2*(t+1),s=n-1,o=null;if(s<e){var a=this.content[s],h=this.scoreFunction(a);h<i&&(o=s)}if(n<e){var l=this.content[n];this.scoreFunction(l)<(null===o?i:h)&&(o=n)}if(null===o)break;this.content[t]=this.content[o],this.content[o]=r,t=o}}}class KNN{constructor(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(!0===t){const t=e;return this.kdTree=new KDTree(t.kdTree,r),this.k=t.k,this.classes=new Set(t.classes),void(this.isEuclidean=t.isEuclidean)}const i=new Set(e),{distance:n=euclidean,k:s=i.size+1}=r,o=new Array(t.length);for(var a=0;a<o.length;++a)o[a]=t[a].slice();for(a=0;a<e.length;++a)o[a].push(e[a]);this.kdTree=new KDTree(o,n),this.k=s,this.classes=i,this.isEuclidean=n===euclidean}static load(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:euclidean;if("KNN"!==t.name)throw new Error("invalid model: ".concat(t.name));if(!t.isEuclidean&&e===euclidean)throw new Error("a custom distance function was used to create the model. Please provide it again");if(t.isEuclidean&&e!==euclidean)throw new Error("the model was created with the default distance function. Do not load it with another one");return new KNN(!0,t,e)}toJSON(){return{name:"KNN",kdTree:this.kdTree,k:this.k,classes:Array.from(this.classes),isEuclidean:this.isEuclidean}}predict(t){if(Array.isArray(t)){if("number"==typeof t[0])return getSinglePrediction(this,t);if(Array.isArray(t[0])&&"number"==typeof t[0][0]){const r=new Array(t.length);for(var e=0;e<t.length;e++)r[e]=getSinglePrediction(this,t[e]);return r}}throw new TypeError("dataset to predict must be an array or a matrix")}}function getSinglePrediction(t,e){var r=t.kdTree.nearest(e,t.k),i={},n=-1,s=-1,o=r[0][0].length-1;for(var a of t.classes)i[a]=0;for(var h=0;h<r.length;++h){var l=r[h][0][o],u=++i[l];u>s&&(n=l,s=u)}return n}function norm(t){return Math.sqrt(t.clone().apply(pow2array).sum())}function pow2array(t,e){this.set(t,e,this.get(t,e)**2)}function initializeMatrices(t,e){if(e)for(var r=0;r<t.length;++r)for(var i=0;i<t[r].length;++i){var n=t[r][i];t[r][i]=null!==n?new Matrix(t[r][i]):void 0}else for(r=0;r<t.length;++r)t[r]=new Matrix(t[r]);return t}class PLS{constructor(t,e){if(!0===t)this.meanX=e.meanX,this.stdDevX=e.stdDevX,this.meanY=e.meanY,this.stdDevY=e.stdDevY,this.PBQ=Matrix.checkMatrix(e.PBQ),this.R2X=e.R2X,this.scale=e.scale,this.scaleMethod=e.scaleMethod,this.tolerance=e.tolerance;else{var{tolerance:r=1e-5,scale:i=!0}=t;this.tolerance=r,this.scale=i,this.latentVectors=t.latentVectors}}train(t,e){if(t=Matrix.checkMatrix(t),e=Matrix.checkMatrix(e),t.length!==e.length)throw new RangeError("The number of X rows must be equal to the number of Y rows");this.meanX=t.mean("column"),this.stdDevX=t.standardDeviation("column",{mean:this.meanX,unbiased:!0}),this.meanY=e.mean("column"),this.stdDevY=e.standardDeviation("column",{mean:this.meanY,unbiased:!0}),this.scale&&(t=t.clone().subRowVector(this.meanX).divRowVector(this.stdDevX),e=e.clone().subRowVector(this.meanY).divRowVector(this.stdDevY)),void 0===this.latentVectors&&(this.latentVectors=Math.min(t.rows-1,t.columns));for(var r=t.rows,i=t.columns,n=e.rows,s=e.columns,o=t.clone().mul(t).sum(),a=e.clone().mul(e).sum(),h=this.tolerance,l=this.latentVectors,u=Matrix.zeros(r,l),c=Matrix.zeros(i,l),f=Matrix.zeros(n,l),m=Matrix.zeros(s,l),g=Matrix.zeros(l,l),d=c.clone(),p=0;norm(e)>h&&p<l;){for(var w=t.transpose(),x=e.transpose(),M=maxSumColIndex(t.clone().mul(t)),y=maxSumColIndex(e.clone().mul(e)),v=t.getColumnVector(M),b=e.getColumnVector(y),S=Matrix.zeros(r,1);norm(v.clone().sub(S))>h;){var A=w.mmul(b);A.div(norm(A)),S=v,v=t.mmul(A);var E=x.mmul(v);E.div(norm(E)),b=e.mmul(E)}S=v;var R=w.mmul(S),k=S.transpose().mmul(S).get(0,0),C=R.div(k),T=norm(C);C.div(T),S.mul(T),A.mul(T),R=b.transpose().mmul(S),k=S.transpose().mmul(S).get(0,0);var N=R.div(k).get(0,0);t.sub(S.mmul(C.transpose())),e.sub(S.clone().mul(N).mmul(E.transpose())),u.setColumn(p,S),c.setColumn(p,C),f.setColumn(p,b),m.setColumn(p,E),d.setColumn(p,A),g.set(p,p,N),p++}p--,u=u.subMatrix(0,u.rows-1,0,p),c=c.subMatrix(0,c.rows-1,0,p),f=f.subMatrix(0,f.rows-1,0,p),m=m.subMatrix(0,m.rows-1,0,p),d=d.subMatrix(0,d.rows-1,0,p),g=g.subMatrix(0,p,0,p),this.ssqYcal=a,this.E=t,this.F=e,this.T=u,this.P=c,this.U=f,this.Q=m,this.W=d,this.B=g,this.PBQ=c.mmul(g).mmul(m.transpose()),this.R2X=S.transpose().mmul(S).mmul(C.transpose().mmul(C)).div(o).get(0,0)}predict(t){var e=Matrix.checkMatrix(t);this.scale&&(e=e.subRowVector(this.meanX).divRowVector(this.stdDevX));var r=e.mmul(this.PBQ);return r=r.mulRowVector(this.stdDevY).addRowVector(this.meanY)}getExplainedVariance(){return this.R2X}toJSON(){return{name:"PLS",R2X:this.R2X,meanX:this.meanX,stdDevX:this.stdDevX,meanY:this.meanY,stdDevY:this.stdDevY,PBQ:this.PBQ,tolerance:this.tolerance,scale:this.scale}}static load(t){if("PLS"!==t.name)throw new RangeError("Invalid model: ".concat(t.name));return new PLS(!0,t)}}function maxSumColIndex(t){return Matrix.rowVector(t.sum("column")).maxIndex()[0]}class KOPLS{constructor(t,e){if(!0===t)this.trainingSet=new Matrix(e.trainingSet),this.YLoadingMat=new Matrix(e.YLoadingMat),this.SigmaPow=new Matrix(e.SigmaPow),this.YScoreMat=new Matrix(e.YScoreMat),this.predScoreMat=initializeMatrices(e.predScoreMat,!1),this.YOrthLoadingVec=initializeMatrices(e.YOrthLoadingVec,!1),this.YOrthEigen=e.YOrthEigen,this.YOrthScoreMat=initializeMatrices(e.YOrthScoreMat,!1),this.toNorm=initializeMatrices(e.toNorm,!1),this.TURegressionCoeff=initializeMatrices(e.TURegressionCoeff,!1),this.kernelX=initializeMatrices(e.kernelX,!0),this.kernel=e.kernel,this.orthogonalComp=e.orthogonalComp,this.predictiveComp=e.predictiveComp;else{if(void 0===t.predictiveComponents)throw new RangeError("no predictive components found!");if(void 0===t.orthogonalComponents)throw new RangeError("no orthogonal components found!");if(void 0===t.kernel)throw new RangeError("no kernel found!");this.orthogonalComp=t.orthogonalComponents,this.predictiveComp=t.predictiveComponents,this.kernel=t.kernel}}train(t,e){t=Matrix.checkMatrix(t),e=Matrix.checkMatrix(e),this.trainingSet=t.clone();var r=this.kernel.compute(t),i=Matrix.eye(r.rows,r.rows,1),n=r;r=new Array(this.orthogonalComp+1);for(let t=0;t<this.orthogonalComp+1;t++)r[t]=new Array(this.orthogonalComp+1);r[0][0]=n;var s=new SingularValueDecomposition(e.transpose().mmul(r[0][0]).mmul(e),{computeLeftSingularVectors:!0,computeRightSingularVectors:!1}),o=s.leftSingularVectors,a=s.diagonalMatrix;o=o.subMatrix(0,o.rows-1,0,this.predictiveComp-1),a=a.subMatrix(0,this.predictiveComp-1,0,this.predictiveComp-1);var h=e.mmul(o),l=new Array(this.orthogonalComp+1),u=new Array(this.orthogonalComp+1),c=new Array(this.orthogonalComp),f=new Array(this.orthogonalComp),m=new Array(this.orthogonalComp),g=new Array(this.orthogonalComp),d=Matrix.pow(a,-.5);d.apply((function(t,e){this.get(t,e)===1/0&&this.set(t,e,0)}));for(var p=0;p<this.orthogonalComp;++p){l[p]=r[0][p].transpose().mmul(h).mmul(d);var w=l[p].transpose();u[p]=inverse(w.mmul(l[p])).mmul(w).mmul(h);var x=(s=new SingularValueDecomposition(w.mmul(Matrix.sub(r[p][p],l[p].mmul(w))).mmul(l[p]),{computeLeftSingularVectors:!0,computeRightSingularVectors:!1})).leftSingularVectors,M=s.diagonalMatrix;f[p]=x.subMatrix(0,x.rows-1,0,0),m[p]=M.get(0,0),c[p]=Matrix.sub(r[p][p],l[p].mmul(w)).mmul(l[p]).mmul(f[p]).mul(Math.pow(m[p],-.5));var y=c[p].transpose();g[p]=Matrix.sqrt(y.mmul(c[p])),c[p]=c[p].divRowVector(g[p]);var v=Matrix.sub(i,c[p].mmul(c[p].transpose()));r[0][p+1]=r[0][p].mmul(v),r[p+1][p+1]=v.mmul(r[p][p]).mmul(v)}var b=l[this.orthogonalComp]=r[0][this.orthogonalComp].transpose().mmul(h).mmul(d),S=b.transpose();u[this.orthogonalComp]=inverse(S.mmul(b)).mmul(S).mmul(h),this.YLoadingMat=o,this.SigmaPow=d,this.YScoreMat=h,this.predScoreMat=l,this.YOrthLoadingVec=f,this.YOrthEigen=m,this.YOrthScoreMat=c,this.toNorm=g,this.TURegressionCoeff=u,this.kernelX=r}predict(t){var e=this.kernel.compute(t,this.trainingSet),r=e;e=new Array(this.orthogonalComp+1);for(let t=0;t<this.orthogonalComp+1;t++)e[t]=new Array(this.orthogonalComp+1);e[0][0]=r;var i,n=new Array(this.orthogonalComp),s=new Array(this.orthogonalComp);for(i=0;i<this.orthogonalComp;++i){s[i]=e[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow),n[i]=Matrix.sub(e[i][i],s[i].mmul(this.predScoreMat[i].transpose())).mmul(this.predScoreMat[i]).mmul(this.YOrthLoadingVec[i]).mul(Math.pow(this.YOrthEigen[i],-.5)),n[i]=n[i].divRowVector(this.toNorm[i]);var o=this.YOrthScoreMat[i].transpose();e[i+1][0]=Matrix.sub(e[i][0],n[i].mmul(o).mmul(this.kernelX[0][i].transpose()));var a=Matrix.sub(e[i][0],e[i][i].mmul(this.YOrthScoreMat[i]).mmul(o)),h=n[i].mmul(o).mmul(this.kernelX[i][i]),l=h.mmul(this.YOrthScoreMat[i]).mmul(o);e[i+1][i+1]=a.sub(h).add(l)}return s[i]=e[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow),{prediction:s[i].mmul(this.TURegressionCoeff[i]).mmul(this.YLoadingMat.transpose()),predScoreMat:s,predYOrthVectors:n}}toJSON(){return{name:"K-OPLS",YLoadingMat:this.YLoadingMat,SigmaPow:this.SigmaPow,YScoreMat:this.YScoreMat,predScoreMat:this.predScoreMat,YOrthLoadingVec:this.YOrthLoadingVec,YOrthEigen:this.YOrthEigen,YOrthScoreMat:this.YOrthScoreMat,toNorm:this.toNorm,TURegressionCoeff:this.TURegressionCoeff,kernelX:this.kernelX,trainingSet:this.trainingSet,orthogonalComp:this.orthogonalComp,predictiveComp:this.predictiveComp}}static load(t,e){if("K-OPLS"!==t.name)throw new RangeError("Invalid model: ".concat(t.name));if(!e)throw new RangeError("You must provide a kernel for the model!");return t.kernel=e,new KOPLS(!0,t)}}class ConfusionMatrix{constructor(t,e){if(t.length!==t[0].length)throw new Error("Confusion matrix must be square");if(e.length!==t.length)throw new Error("Confusion matrix and labels should have the same length");this.labels=e,this.matrix=t}static fromLabels(t,e){let r,i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(e.length!==t.length)throw new Error("predicted and actual must have the same length");r=i.labels?new Set(i.labels):new Set([...t,...e]),r=Array.from(r),i.sort&&r.sort(i.sort);const n=Array.from({length:r.length});for(let t=0;t<n.length;t++)n[t]=new Array(n.length),n[t].fill(0);for(let i=0;i<e.length;i++){const s=r.indexOf(t[i]),o=r.indexOf(e[i]);s>=0&&o>=0&&n[s][o]++}return new ConfusionMatrix(n,r)}getMatrix(){return this.matrix}getLabels(){return this.labels}getTotalCount(){let t=0;for(var e=0;e<this.matrix.length;e++)for(var r=0;r<this.matrix.length;r++)t+=this.matrix[e][r];return t}getTrueCount(){for(var t=0,e=0;e<this.matrix.length;e++)t+=this.matrix[e][e];return t}getFalseCount(){return this.getTotalCount()-this.getTrueCount()}getTruePositiveCount(t){const e=this.getIndex(t);return this.matrix[e][e]}getTrueNegativeCount(t){const e=this.getIndex(t);for(var r=0,i=0;i<this.matrix.length;i++)for(var n=0;n<this.matrix.length;n++)i!==e&&n!==e&&(r+=this.matrix[i][n]);return r}getFalsePositiveCount(t){const e=this.getIndex(t);for(var r=0,i=0;i<this.matrix.length;i++)i!==e&&(r+=this.matrix[i][e]);return r}getFalseNegativeCount(t){const e=this.getIndex(t);for(var r=0,i=0;i<this.matrix.length;i++)i!==e&&(r+=this.matrix[e][i]);return r}getPositiveCount(t){return this.getTruePositiveCount(t)+this.getFalseNegativeCount(t)}getNegativeCount(t){return this.getTrueNegativeCount(t)+this.getFalsePositiveCount(t)}getIndex(t){const e=this.labels.indexOf(t);if(-1===e)throw new Error("The label does not exist");return e}getTruePositiveRate(t){return this.getTruePositiveCount(t)/this.getPositiveCount(t)}getTrueNegativeRate(t){return this.getTrueNegativeCount(t)/this.getNegativeCount(t)}getPositivePredictiveValue(t){const e=this.getTruePositiveCount(t);return e/(e+this.getFalsePositiveCount(t))}getNegativePredictiveValue(t){const e=this.getTrueNegativeCount(t);return e/(e+this.getFalseNegativeCount(t))}getFalseNegativeRate(t){return 1-this.getTruePositiveRate(t)}getFalsePositiveRate(t){return 1-this.getTrueNegativeRate(t)}getFalseDiscoveryRate(t){const e=this.getFalsePositiveCount(t);return e/(e+this.getTruePositiveCount(t))}getFalseOmissionRate(t){const e=this.getFalseNegativeCount(t);return e/(e+this.getTruePositiveCount(t))}getF1Score(t){const e=this.getTruePositiveCount(t);return 2*e/(2*e+this.getFalsePositiveCount(t)+this.getFalseNegativeCount(t))}getMatthewsCorrelationCoefficient(t){const e=this.getTruePositiveCount(t),r=this.getTrueNegativeCount(t),i=this.getFalsePositiveCount(t),n=this.getFalseNegativeCount(t);return(e*r-i*n)/Math.sqrt((e+i)*(e+n)*(r+i)*(r+n))}getInformedness(t){return this.getTruePositiveRate(t)+this.getTrueNegativeRate(t)-1}getMarkedness(t){return this.getPositivePredictiveValue(t)+this.getNegativePredictiveValue(t)-1}getConfusionTable(t){return[[this.getTruePositiveCount(t),this.getFalseNegativeCount(t)],[this.getFalsePositiveCount(t),this.getTrueNegativeCount(t)]]}getAccuracy(){let t=0,e=0;for(var r=0;r<this.matrix.length;r++)for(var i=0;i<this.matrix.length;i++)r===i?t+=this.matrix[r][i]:e+=this.matrix[r][i];return t/(t+e)}getCount(t,e){const r=this.getIndex(t),i=this.getIndex(e);return this.matrix[r][i]}get accuracy(){return this.getAccuracy()}get total(){return this.getTotalCount()}}var src$1=ConfusionMatrix;const defaultOptions$6={mode:"index"};var src$2=function*(t,e,r){r=Object.assign({},defaultOptions$6,r);for(var i,n,s,o=new Array(e),a=new Array(t),h=new Array(e),l=new Array(e+2),u=0;u<e;u++)o[u]=u,h[u]=u<e-t?0:1;for(u=0;u<t;u++)a[u]=e-t+u;for(u=0;u<l.length;u++)l[u]=0===u?e+1:u<=e-t?0:u<=e?u-e+t:-2;function c(){var t,e,r;for(e=1;l[e]<=0;)e++;if(0===l[e-1]){for(t=e-1;1!==t;t--)l[t]=-1;l[e]=0,i=s=0,l[1]=1,n=e-1}else{e>1&&(l[e-1]=0);do{e++}while(l[e]>0);for(r=e-1,t=e;0===l[t];)l[t++]=-1;if(-1===l[t])l[t]=l[r],s=l[r]-1,i=t-1,n=r-1,l[r]=-1;else{if(t===l[0])return 0;l[e]=l[t],s=l[t]-1,l[t]=0,i=e-1,n=t-1}}return 1}if("index"===r.mode)for(yield a.slice();c();)a[s]=o[i],yield a.slice();else{if("mask"!==r.mode)throw new Error("Invalid mode");for(yield h.slice();c();)h[i]=1,h[n]=0,yield h.slice()}};const CV={};function check(t,e){if(t.length!==e.length)throw new Error("features and labels should have the same length")}function initMatrix(t,e){return new Array(t).fill(0).map(()=>new Array(e).fill(0))}function getDistinct(t){var e=new Set;for(let r=0;r<t.length;r++)e.add(t[r]);return Array.from(e)}function validate(t,e,r,i,n,s,o,a){const{testFeatures:h,trainFeatures:l,testLabels:u,trainLabels:c}=getTrainTest(e,r,n,s);var f;t.prototype.train?(f=new t(i)).train(l,c):f=new t(l,c,i),updateConfusionMatrix(o,u,f.predict(h),a)}function validateWithCallback(t,e,r,i,n,s,o){const{testFeatures:a,trainFeatures:h,testLabels:l,trainLabels:u}=getTrainTest(t,e,r,i);updateConfusionMatrix(n,l,o(h,u,a),s)}function updateConfusionMatrix(t,e,r,i){for(var n=0;n<r.length;n++){const s=i.indexOf(e[n]),o=i.indexOf(r[n]);(s<0||o<0)&&console.warn("ignore unknown predicted label ".concat(r[n])),t[s][o]++}}function getTrainTest(t,e,r,i){return{testFeatures:r.map((function(e){return t[e]})),trainFeatures:i.map((function(e){return t[e]})),testLabels:r.map((function(t){return e[t]})),trainLabels:i.map((function(t){return e[t]}))}}CV.leaveOneOut=function(t,e,r,i){if("function"==typeof r){var n=r;return r=e,e=t,CV.leavePOut(e,r,1,n)}return CV.leavePOut(t,e,r,i,1)},CV.leavePOut=function(t,e,r,i,n){if("function"==typeof i){var s=i;n=r,r=e,e=t}check(e,r);const o=getDistinct(r),a=initMatrix(o.length,o.length);var h=e.length,l=src$2(n,h),u=new Array(h);for(let t=0;t<h;t++)u[t]=t;for(const n of l){var c=u.slice();for(let t=n.length-1;t>=0;t--)c.splice(n[t],1);s?validateWithCallback(e,r,n,c,a,o,s):validate(t,e,r,i,n,c,a,o)}return new src$1(a,o)},CV.kFold=function(t,e,r,i,n){if("function"==typeof i){var s=i;n=r,r=e,e=t}check(e,r);const o=getDistinct(r),a=initMatrix(o.length,o.length);for(var h=e.length,l=new Array(h),u=0;u<h;u++)l[u]=u;for(var c=Math.floor(h/n),f=[],m=[];l.length;){var g=Math.floor(Math.random()*l.length);f.push(l[g]),l.splice(g,1),f.length===c&&(m.push(f),f=[])}for(f.length&&m.push(f),m=m.slice(0,n),u=0;u<m.length;u++){for(var d=m[u],p=[],w=0;w<m.length;w++)w!==u&&(p=p.concat(m[w]));s?validateWithCallback(e,r,d,p,a,o,s):validate(t,e,r,i,d,p,a,o)}return new src$1(a,o)};var src$3=CV;function logistic(t){return 1/(1+Math.exp(-t))}function expELU(t,e){return t<0?e*(Math.exp(t)-1):t}function softExponential(t,e){return e<0?-Math.log(1-e*(t+e))/e:e>0?(Math.exp(e*t)-1)/e+e:t}function softExponentialPrime(t,e){return e<0?1/(1-e*(e+t)):Math.exp(e*t)}const ACTIVATION_FUNCTIONS={tanh:{activation:Math.tanh,derivate:t=>1-t*t},identity:{activation:t=>t,derivate:()=>1},logistic:{activation:logistic,derivate:t=>logistic(t)*(1-logistic(t))},arctan:{activation:Math.atan,derivate:t=>1/(t*t+1)},softsign:{activation:t=>t/(1+Math.abs(t)),derivate:t=>1/((1+Math.abs(t))*(1+Math.abs(t)))},relu:{activation:t=>t<0?0:t,derivate:t=>t<0?0:1},softplus:{activation:t=>Math.log(1+Math.exp(t)),derivate:t=>1/(1+Math.exp(-t))},bent:{activation:t=>(Math.sqrt(t*t+1)-1)/2+t,derivate:t=>t/(2*Math.sqrt(t*t+1))+1},sinusoid:{activation:Math.sin,derivate:Math.cos},sinc:{activation:t=>0===t?1:Math.sin(t)/t,derivate:t=>0===t?0:Math.cos(t)/t-Math.sin(t)/(t*t)},gaussian:{activation:t=>Math.exp(-t*t),derivate:t=>-2*t*Math.exp(-t*t)},"parametric-relu":{activation:(t,e)=>t<0?e*t:t,derivate:(t,e)=>t<0?e:1},"exponential-elu":{activation:expELU,derivate:(t,e)=>t<0?expELU(t,e)+e:1},"soft-exponential":{activation:softExponential,derivate:softExponentialPrime}};class Layer{constructor(t){this.inputSize=t.inputSize,this.outputSize=t.outputSize,this.regularization=t.regularization,this.epsilon=t.epsilon,this.activation=t.activation,this.activationParam=t.activationParam;var e=ACTIVATION_FUNCTIONS[t.activation],r=e.activation.length,i=r>1?r=>e.activation(r,t.activationParam):e.activation,n=r>1?r=>e.derivate(r,t.activationParam):e.derivate;this.activationFunction=function(t,e){this.set(t,e,i(this.get(t,e)))},this.derivate=function(t,e){this.set(t,e,n(this.get(t,e)))},t.model?(this.W=Matrix.Matrix.checkMatrix(t.W),this.b=Matrix.Matrix.checkMatrix(t.b)):(this.W=Matrix.Matrix.rand(this.inputSize,this.outputSize),this.b=Matrix.Matrix.zeros(1,this.outputSize),this.W.apply((function(e,r){this.set(e,r,this.get(e,r)/Math.sqrt(t.inputSize))})))}forward(t){var e=t.mmul(this.W).addRowVector(this.b);return e.apply(this.activationFunction),this.a=e.clone(),e}backpropagation(t,e){this.dW=e.transpose().mmul(t),this.db=Matrix.Matrix.rowVector(t.sum("column"));var r=e.clone();return t.mmul(this.W.transpose()).mul(r.apply(this.derivate))}update(){this.dW.add(this.W.clone().mul(this.regularization)),this.W.add(this.dW.mul(-this.epsilon)),this.b.add(this.db.mul(-this.epsilon))}toJSON(){return{model:"Layer",inputSize:this.inputSize,outputSize:this.outputSize,regularization:this.regularization,epsilon:this.epsilon,activation:this.activation,W:this.W,b:this.b}}static load(t){if("Layer"!==t.model)throw new RangeError("the current model is not a Layer model");return new Layer(t)}}class OutputLayer extends Layer{constructor(t){super(t),this.activationFunction=function(t,e){this.set(t,e,Math.exp(this.get(t,e)))}}static load(t){if("Layer"!==t.model)throw new RangeError("the current model is not a Layer model");return new OutputLayer(t)}}class FeedForwardNeuralNetworks{constructor(t){if((t=t||{}).model){this.hiddenLayers=t.hiddenLayers,this.iterations=t.iterations,this.learningRate=t.learningRate,this.regularization=t.regularization,this.dicts=t.dicts,this.activation=t.activation,this.activationParam=t.activationParam,this.model=new Array(t.layers.length);for(var e=0;e<this.model.length-1;++e)this.model[e]=Layer.load(t.layers[e]);this.model[this.model.length-1]=OutputLayer.load(t.layers[this.model.length-1])}else this.hiddenLayers=t.hiddenLayers||[10],this.iterations=t.iterations||50,this.learningRate=t.learningRate||.01,this.regularization=t.regularization||.01,this.activation=t.activation||"tanh",this.activationParam=t.activationParam||1,this.activation in Object.keys(ACTIVATION_FUNCTIONS)||(this.activation="tanh")}buildNetwork(t,e){var r=this.hiddenLayers.length-1+2;this.model=new Array(r),this.model[0]=new Layer({inputSize:t,outputSize:this.hiddenLayers[0],activation:this.activation,activationParam:this.activationParam,regularization:this.regularization,epsilon:this.learningRate});for(var i=1;i<this.hiddenLayers.length;++i)this.model[i]=new Layer({inputSize:this.hiddenLayers[i-1],outputSize:this.hiddenLayers[i],activation:this.activation,activationParam:this.activationParam,regularization:this.regularization,epsilon:this.learningRate});this.model[r-1]=new OutputLayer({inputSize:this.hiddenLayers[this.hiddenLayers.length-1],outputSize:e,activation:this.activation,activationParam:this.activationParam,regularization:this.regularization,epsilon:this.learningRate})}train(t,e){t=Matrix.Matrix.checkMatrix(t),this.dicts=dictOutputs(e);var r=t.columns,i=Object.keys(this.dicts.inputs).length;this.model||this.buildNetwork(r,i);for(var n=0;n<this.iterations;++n){var s=this.propagate(t);this.backpropagation(t,e,s)}}propagate(t){for(var e=t,r=0;r<this.model.length;++r)e=this.model[r].forward(e);return e.divColumnVector(e.sum("row"))}backpropagation(t,e,r){for(var i=0;i<r.rows;++i)r.set(i,this.dicts.inputs[e[i]],r.get(i,this.dicts.inputs[e[i]])-1);var n=r;for(i=this.model.length-1;i>=0;--i){var s=i>0?this.model[i-1].a:t;n=this.model[i].backpropagation(n,s)}for(i=0;i<this.model.length;++i)this.model[i].update()}predict(t){t=Matrix.Matrix.checkMatrix(t);for(var e=new Array(t.rows),r=this.propagate(t),i=0;i<t.rows;++i)e[i]=this.dicts.outputs[r.maxRowIndex(i)[1]];return e}toJSON(){for(var t={model:"FNN",hiddenLayers:this.hiddenLayers,iterations:this.iterations,learningRate:this.learningRate,regularization:this.regularization,activation:this.activation,activationParam:this.activationParam,dicts:this.dicts,layers:new Array(this.model.length)},e=0;e<this.model.length;++e)t.layers[e]=this.model[e].toJSON();return t}static load(t){if("FNN"!==t.model)throw new RangeError("the current model is not a feed forward network");return new FeedForwardNeuralNetworks(t)}}function dictOutputs(t){for(var e={},r={},i=0,n=0;n<t.length;n+=1)void 0===e[t[n]]&&(e[t[n]]=i,r[i]=t[n],i++);return{inputs:e,outputs:r}}var FeedForwardNeuralNetwork=FeedForwardNeuralNetworks;function NodeSquare(t,e,r,i){this.x=t,this.y=e,this.weights=r,this.som=i,this.neighbors={}}NodeSquare.prototype.adjustWeights=function(t,e,r){for(var i=0,n=this.weights.length;i<n;i++)this.weights[i]+=e*r*(t[i]-this.weights[i])},NodeSquare.prototype.getDistance=function(t){return Math.max(Math.abs(this.x-t.x),Math.abs(this.y-t.y))},NodeSquare.prototype.getDistanceTorus=function(t){var e=Math.abs(this.x-t.x),r=Math.abs(this.y-t.y);return Math.max(Math.min(e,this.som.gridDim.x-e),Math.min(r,this.som.gridDim.y-r))},NodeSquare.prototype.getNeighbors=function(t){if(!this.neighbors[t]){var e,r,i,n;if(this.neighbors[t]=new Array(2),this[t]>0?e=this[t]-1:this.som.torus&&(e=this.som.gridDim[t]-1),void 0!==e)"x"===t?(r=e,i=this.y):(r=this.x,i=e),this.neighbors[t][0]=this.som.nodes[r][i];this[t]<this.som.gridDim[t]-1?n=this[t]+1:this.som.torus&&(n=0),void 0!==n&&("x"===t?(r=n,i=this.y):(r=this.x,i=n),this.neighbors[t][1]=this.som.nodes[r][i])}return this.neighbors[t]},NodeSquare.prototype.getPos=function(t,e){var r,i,n=this.getNeighbors(t),s=this.som.distance;n[0]?n[1]?s(e,n[0].weights)<s(e,n[1].weights)?(r=n[0],i=-1):(r=n[1],i=1):(r=n[0],i=-1):(r=n[1],i=1);var o=1-s(e,this.weights),a=1-s(e,r.weights);return.5+.5*((o-a)/(2-o-a))*i},NodeSquare.prototype.getPosition=function(t){return[this.getPos("x",t),this.getPos("y",t)]};var nodeSquare=NodeSquare;function NodeHexagonal(t,e,r,i){nodeSquare.call(this,t,e,r,i),this.hX=t-Math.floor(e/2),this.z=0-this.hX-e}NodeHexagonal.prototype=new nodeSquare,NodeHexagonal.prototype.constructor=NodeHexagonal,NodeHexagonal.prototype.getDistance=function(t){return Math.max(Math.abs(this.hX-t.hX),Math.abs(this.y-t.y),Math.abs(this.z-t.z))},NodeHexagonal.prototype.getDistanceTorus=function(t){var e=Math.abs(this.hX-t.hX),r=Math.abs(this.y-t.y),i=Math.abs(this.z-t.z);return Math.max(Math.min(e,this.som.gridDim.x-e),Math.min(r,this.som.gridDim.y-r),Math.min(i,this.som.gridDim.z-i))},NodeHexagonal.prototype.getPosition=function(){throw new Error("Unimplemented : cannot get position of the points for hexagonal grid")};var nodeHexagonal=NodeHexagonal,defaultOptions$7={fields:3,randomizer:Math.random,distance:squareEuclidean,iterations:10,learningRate:.1,gridType:"rect",torus:!0,method:"random"};function SOM(t,e,r,i){for(var n in this.x=t,this.y=e,r=r||{},this.options={},defaultOptions$7)r.hasOwnProperty(n)?this.options[n]=r[n]:this.options[n]=defaultOptions$7[n];if("number"==typeof this.options.fields)this.numWeights=this.options.fields;else{if(!Array.isArray(this.options.fields))throw new Error("Invalid fields definition");this.numWeights=this.options.fields.length;var s=getConverters(this.options.fields);this.extractor=s.extractor,this.creator=s.creator}if("rect"===this.options.gridType)this.nodeType=nodeSquare,this.gridDim={x:t,y:e};else{this.nodeType=nodeHexagonal;var o=this.x-Math.floor(this.y/2);this.gridDim={x:o,y:this.y,z:-(0-o-this.y)}}if(this.torus=this.options.torus,this.distanceMethod=this.torus?"getDistanceTorus":"getDistance",this.distance=this.options.distance,this.maxDistance=getMaxDistance(this.distance,this.numWeights),!0!==i){if(!(t>0&&e>0))throw new Error("x and y must be positive");this.times={findBMU:0,adjust:0},this.randomizer=this.options.randomizer,this.iterationCount=0,this.iterations=this.options.iterations,this.startLearningRate=this.learningRate=this.options.learningRate,this.mapRadius=Math.floor(Math.max(t,e)/2),this.algorithmMethod=this.options.method,this._initNodes(),this.done=!1}else this.done=!0}function getConverters(t){for(var e=t.length,r=new Array(e),i=new Array(e),n=0;n<e;n++)r[n]=getNormalizer(t[n].range),i[n]=getDenormalizer(t[n].range);return{extractor:function(i){for(var n=new Array(e),s=0;s<e;s++)n[s]=r[s](i[t[s].name]);return n},creator:function(r){for(var n={},s=0;s<e;s++)n[t[s].name]=i[s](r[s]);return n}}}function getNormalizer(t){return function(e){return(e-t[0])/(t[1]-t[0])}}function getDenormalizer(t){return function(e){return t[0]+e*(t[1]-t[0])}}function squareEuclidean(t,e){for(var r=0,i=0,n=t.length;i<n;i++)r+=(t[i]-e[i])*(t[i]-e[i]);return r}function getRandomValue(t,e){return t[Math.floor(e()*t.length)]}function getMaxDistance(t,e){for(var r=new Array(e),i=new Array(e),n=0;n<e;n++)r[n]=0,i[n]=1;return t(r,i)}SOM.load=function loadModel(model,distance){if("SOM"===model.name){var x=model.data.length,y=model.data[0].length;distance?model.options.distance=distance:model.options.distance&&(model.options.distance=eval("("+model.options.distance+")"));var som=new SOM(x,y,model.options,!0);som.nodes=new Array(x);for(var i=0;i<x;i++){som.nodes[i]=new Array(y);for(var j=0;j<y;j++)som.nodes[i][j]=new som.nodeType(i,j,model.data[i][j],som)}return som}throw new Error("expecting a SOM model")},SOM.prototype.export=function(t){if(!this.done)throw new Error("model is not ready yet");var e={name:"SOM"};e.options={fields:this.options.fields,gridType:this.options.gridType,torus:this.options.torus},e.data=new Array(this.x);for(var r=0;r<this.x;r++){e.data[r]=new Array(this.y);for(var i=0;i<this.y;i++)e.data[r][i]=this.nodes[r][i].weights}return t&&(e.options.distance=this.distance.toString()),e},SOM.prototype._initNodes=function(){var t,e,r,i=Date.now();for(this.nodes=new Array(this.x),t=0;t<this.x;t++)for(this.nodes[t]=new Array(this.y),e=0;e<this.y;e++){var n=new Array(this.numWeights);for(r=0;r<this.numWeights;r++)n[r]=this.randomizer();this.nodes[t][e]=new this.nodeType(t,e,n,this)}this.times.initNodes=Date.now()-i},SOM.prototype.setTraining=function(t){if(this.trainingSet)throw new Error("training set has already been set");var e,r=Date.now(),i=t,n=t.length;if(this.extractor)for(i=new Array(n),e=0;e<n;e++)i[e]=this.extractor(t[e]);this.numIterations=this.iterations*n,"random"===this.algorithmMethod?this.timeConstant=this.numIterations/Math.log(this.mapRadius):this.timeConstant=n/Math.log(this.mapRadius),this.trainingSet=i,this.times.setTraining=Date.now()-r},SOM.prototype.trainOne=function(){return!this.done&&(this.numIterations-- >0?("random"===this.algorithmMethod?(t=this.mapRadius*Math.exp(-this.iterationCount/this.timeConstant),e=getRandomValue(this.trainingSet,this.randomizer),this._adjust(e,t),this.learningRate=this.startLearningRate*Math.exp(-this.iterationCount/this.numIterations)):(r=-Math.floor(this.iterationCount/this.trainingSet.length),t=this.mapRadius*Math.exp(r/this.timeConstant),e=this.trainingSet[this.iterationCount%this.trainingSet.length],this._adjust(e,t),(this.iterationCount+1)%this.trainingSet.length==0&&(this.learningRate=this.startLearningRate*Math.exp(r/Math.floor(this.numIterations/this.trainingSet.length)))),this.iterationCount++,!0):(this.done=!0,!1));var t,e,r},SOM.prototype._adjust=function(t,e){var r,i,n,s,o=Date.now(),a=this._findBestMatchingUnit(t),h=Date.now();this.times.findBMU+=h-o;var l=Math.floor(e),u=a.x-l,c=a.x+l,f=a.y-l,m=a.y+l;for(r=u;r<=c;r++){var g=r;for(r<0?g+=this.x:r>=this.x&&(g-=this.x),i=f;i<=m;i++){var d=i;i<0?d+=this.y:i>=this.y&&(d-=this.y),(n=a[this.distanceMethod](this.nodes[g][d]))<e&&(s=Math.exp(-n/(2*e)),this.nodes[g][d].adjustWeights(t,this.learningRate,s))}}this.times.adjust+=Date.now()-h},SOM.prototype.train=function(t){if(!this.done)for(this.setTraining(t);this.trainOne(););},SOM.prototype.getConvertedNodes=function(){for(var t=new Array(this.x),e=0;e<this.x;e++){t[e]=new Array(this.y);for(var r=0;r<this.y;r++){var i=this.nodes[e][r];t[e][r]=this.creator?this.creator(i.weights):i.weights}}return t},SOM.prototype._findBestMatchingUnit=function(t){for(var e,r,i=1/0,n=0;n<this.x;n++)for(var s=0;s<this.y;s++)(r=this.distance(this.nodes[n][s].weights,t))<i&&(i=r,e=this.nodes[n][s]);return e},SOM.prototype.predict=function(t,e){if("boolean"==typeof t&&(e=t,t=null),t||(t=this.trainingSet),Array.isArray(t)&&(Array.isArray(t[0])||"object"==typeof t[0])){var r=this;return t.map((function(t){return r._predict(t,e)}))}return this._predict(t,e)},SOM.prototype._predict=function(t,e){Array.isArray(t)||(t=this.extractor(t));var r=this._findBestMatchingUnit(t),i=[r.x,r.y];return e&&(i[2]=r.getPosition(t)),i},SOM.prototype.getQuantizationError=function(){for(var t=this.getFit(),e=t.length,r=0,i=0;i<e;i++)r+=t[i];return r/e},SOM.prototype.getFit=function(t){t||(t=this.trainingSet);for(var e,r=t.length,i=new Array(r),n=0;n<r;n++)e=this._findBestMatchingUnit(t[n]),i[n]=Math.sqrt(this.distance(t[n],e.weights));return i};var src$4=SOM;function maybeToPrecision(t,e){return t<0?(t=0-t,"- ".concat("number"==typeof e?t.toPrecision(e):t.toString())):"number"==typeof e?t.toPrecision(e):t.toString()}function checkArraySize(t,e){if(!Array.isArray(t)||!Array.isArray(e))throw new TypeError("x and y must be arrays");if(t.length!==e.length)throw new RangeError("x and y arrays must have the same length")}class BaseRegression{constructor(){if(new.target===BaseRegression)throw new Error("BaseRegression must be subclassed")}predict(t){if("number"==typeof t)return this._predict(t);if(Array.isArray(t)){const e=[];for(let r=0;r<t.length;r++)e.push(this._predict(t[r]));return e}throw new TypeError("x must be a number or array")}_predict(){throw new Error("_predict must be implemented")}train(){}toString(){return""}toLaTeX(){return""}score(t,e){if(!Array.isArray(t)||!Array.isArray(e)||t.length!==e.length)throw new Error("x and y must be arrays of the same length");const r=t.length,i=new Array(r);for(let e=0;e<r;e++)i[e]=this._predict(t[e]);let n=0,s=0,o=0,a=0,h=0,l=0,u=0;for(let t=0;t<r;t++)n+=i[t],s+=e[t],h+=i[t]*i[t],l+=e[t]*e[t],u+=i[t]*e[t],0!==e[t]&&(o+=(e[t]-i[t])*(e[t]-i[t])/e[t]),a+=(e[t]-i[t])*(e[t]-i[t]);const c=(r*u-n*s)/Math.sqrt((r*h-n*n)*(r*l-s*s));return{r:c,r2:c*c,chi2:o,rmsd:Math.sqrt(a/r)}}}class PolynomialRegression extends BaseRegression{constructor(t,e,r){super(),!0===t?(this.degree=e.degree,this.powers=e.powers,this.coefficients=e.coefficients):(checkArraySize(t,e),regress(this,t,e,r))}_predict(t){let e=0;for(let r=0;r<this.powers.length;r++)e+=this.coefficients[r]*Math.pow(t,this.powers[r]);return e}toJSON(){return{name:"polynomialRegression",degree:this.degree,powers:this.powers,coefficients:this.coefficients}}toString(t){return this._toFormula(t,!1)}toLaTeX(t){return this._toFormula(t,!0)}_toFormula(t,e){let r="^",i="",n=" * ";e&&(r="^{",i="}",n="");let s="",o="";for(let e=0;e<this.coefficients.length;e++)o="",0!==this.coefficients[e]&&(o=0===this.powers[e]?maybeToPrecision(this.coefficients[e],t):1===this.powers[e]?"".concat(maybeToPrecision(this.coefficients[e],t)+n,"x"):"".concat(maybeToPrecision(this.coefficients[e],t)+n,"x").concat(r).concat(this.powers[e]).concat(i),this.coefficients[e]>0&&e!==this.coefficients.length-1?o=" + ".concat(o):e!==this.coefficients.length-1&&(o=" ".concat(o))),s=o+s;return"+"===s.charAt(0)&&(s=s.slice(1)),"f(x) = ".concat(s)}static load(t){if("polynomialRegression"!==t.name)throw new TypeError("not a polynomial regression model");return new PolynomialRegression(!0,t)}}function regress(t,e,r,i){const n=e.length;let s;if(Array.isArray(i))i=(s=i).length;else{i++,s=new Array(i);for(let t=0;t<i;t++)s[t]=t}const o=new Matrix(n,i),a=new Matrix([r]);for(let t=0;t<i;t++)for(let r=0;r<n;r++)0===s[t]?o.set(r,t,1):o.set(r,t,Math.pow(e[r],s[t]));const h=new MatrixTransposeView(o),l=h.mmul(o),u=h.mmul(new MatrixTransposeView(a));t.degree=i-1,t.powers=s,t.coefficients=solve(l,u).to1DArray()}class SimpleLinearRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.slope=e.slope,this.intercept=e.intercept,this.coefficients=[e.intercept,e.slope]):(checkArraySize(t,e),regress$1(this,t,e))}toJSON(){return{name:"simpleLinearRegression",slope:this.slope,intercept:this.intercept}}_predict(t){return this.slope*t+this.intercept}computeX(t){return(t-this.intercept)/this.slope}toString(t){let e="f(x) = ";if(0!==this.slope){const r=maybeToPrecision(this.slope,t);if(e+="".concat("1"===r?"":"".concat(r," * "),"x"),0!==this.intercept){const r=Math.abs(this.intercept),i=r===this.intercept?"+":"-";e+=" ".concat(i," ").concat(maybeToPrecision(r,t))}}else e+=maybeToPrecision(this.intercept,t);return e}toLaTeX(t){return this.toString(t)}static load(t){if("simpleLinearRegression"!==t.name)throw new TypeError("not a SLR model");return new SimpleLinearRegression(!0,t)}}function regress$1(t,e,r){const i=e.length;let n=0,s=0,o=0,a=0;for(let t=0;t<i;t++)n+=e[t],s+=r[t],o+=e[t]*e[t],a+=e[t]*r[t];const h=i*a-n*s;t.slope=h/(i*o-n*n),t.intercept=1/i*s-t.slope*(1/i)*n,t.coefficients=[t.intercept,t.slope]}class ExponentialRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.A=e.A,this.B=e.B):(checkArraySize(t,e),regress$2(this,t,e))}_predict(t){return this.B*Math.exp(t*this.A)}toJSON(){return{name:"exponentialRegression",A:this.A,B:this.B}}toString(t){return"f(x) = ".concat(maybeToPrecision(this.B,t)," * e^(").concat(maybeToPrecision(this.A,t)," * x)")}toLaTeX(t){return this.A>=0?"f(x) = ".concat(maybeToPrecision(this.B,t),"e^{").concat(maybeToPrecision(this.A,t),"x}"):"f(x) = \\frac{".concat(maybeToPrecision(this.B,t),"}{e^{").concat(maybeToPrecision(-this.A,t),"x}}")}static load(t){if("exponentialRegression"!==t.name)throw new TypeError("not a exponential regression model");return new ExponentialRegression(!0,t)}}function regress$2(t,e,r){const i=e.length,n=new Array(i);for(let t=0;t<i;t++)n[t]=Math.log(r[t]);const s=new SimpleLinearRegression(e,n);t.A=s.slope,t.B=Math.exp(s.intercept)}class PowerRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.A=e.A,this.B=e.B):(checkArraySize(t,e),regress$3(this,t,e))}_predict(t){return this.A*Math.pow(t,this.B)}toJSON(){return{name:"powerRegression",A:this.A,B:this.B}}toString(t){return"f(x) = ".concat(maybeToPrecision(this.A,t)," * x^").concat(maybeToPrecision(this.B,t))}toLaTeX(t){let e="";return e=(e=this.B>=0?"f(x) = ".concat(maybeToPrecision(this.A,t),"x^{").concat(maybeToPrecision(this.B,t),"}"):"f(x) = \\frac{".concat(maybeToPrecision(this.A,t),"}{x^{").concat(maybeToPrecision(-this.B,t),"}}")).replace(/e([+-]?[0-9]+)/g,"e^{$1}")}static load(t){if("powerRegression"!==t.name)throw new TypeError("not a power regression model");return new PowerRegression(!0,t)}}function regress$3(t,e,r){const i=e.length,n=new Array(i),s=new Array(i);for(let t=0;t<i;t++)n[t]=Math.log(e[t]),s[t]=Math.log(r[t]);const o=new SimpleLinearRegression(n,s);t.A=Math.exp(o.intercept),t.B=o.slope}class MultivariateLinearRegression{constructor(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const{intercept:i=!0,statistics:n=!0}=r;if(this.statistics=n,!0===t)this.weights=e.weights,this.inputs=e.inputs,this.outputs=e.outputs,this.intercept=e.intercept;else{t=new Matrix(t),e=new Matrix(e),i&&t.addColumn(new Array(t.rows).fill(1));let r=t.transpose();const s=r.mmul(t),o=r.mmul(e),a=new SingularValueDecomposition(s).inverse(),h=o.transpose().mmul(a).transpose();if(this.weights=h.to2DArray(),this.inputs=t.columns,this.outputs=e.columns,i&&this.inputs--,this.intercept=i,n){const r=t.mmul(h),i=e.clone().addM(r.neg()).to2DArray().map(t=>Math.pow(t[0],2)).reduce((t,e)=>t+e)/(e.rows-t.columns);this.stdError=Math.sqrt(i),this.stdErrorMatrix=pseudoInverse(s).mul(i),this.stdErrors=this.stdErrorMatrix.diagonal().map(t=>Math.sqrt(t)),this.tStats=this.weights.map((t,e)=>0===this.stdErrors[e]?0:t[0]/this.stdErrors[e])}}}predict(t){if(Array.isArray(t)){if("number"==typeof t[0])return this._predict(t);if(Array.isArray(t[0])){const e=new Array(t.length);for(let r=0;r<t.length;r++)e[r]=this._predict(t[r]);return e}}else if(Matrix.isMatrix(t)){const e=new Matrix(t.rows,this.outputs);for(let r=0;r<t.rows;r++)e.setRow(r,this._predict(t.getRow(r)));return e}throw new TypeError("x must be a matrix or array of numbers")}_predict(t){const e=new Array(this.outputs);if(this.intercept)for(let t=0;t<this.outputs;t++)e[t]=this.weights[this.inputs][t];else e.fill(0);for(let r=0;r<this.inputs;r++)for(let i=0;i<this.outputs;i++)e[i]+=this.weights[r][i]*t[r];return e}score(){throw new Error("score method is not implemented yet")}toJSON(){return{name:"multivariateLinearRegression",weights:this.weights,inputs:this.inputs,outputs:this.outputs,intercept:this.intercept,summary:this.statistics?{regressionStatistics:{standardError:this.stdError,observations:this.outputs},variables:this.weights.map((t,e)=>({label:e===this.weights.length-1?"Intercept":"X Variable ".concat(e+1),coefficients:t,standardError:this.stdErrors[e],tStat:this.tStats[e]}))}:void 0}}static load(t){if("multivariateLinearRegression"!==t.name)throw new Error("not a MLR model");return new MultivariateLinearRegression(!0,t)}}const{squaredEuclidean:squaredEuclidean$1}=euclidean$1,defaultOptions$8={sigma:1};class GaussianKernel{constructor(t){t=Object.assign({},defaultOptions$8,t),this.sigma=t.sigma,this.divisor=2*t.sigma*t.sigma}compute(t,e){const r=squaredEuclidean$1(t,e);return Math.exp(-r/this.divisor)}}var gaussianKernel=GaussianKernel;const defaultOptions$9={degree:1,constant:1,scale:1};class PolynomialKernel{constructor(t){t=Object.assign({},defaultOptions$9,t),this.degree=t.degree,this.constant=t.constant,this.scale=t.scale}compute(t,e){for(var r=0,i=0;i<t.length;i++)r+=t[i]*e[i];return Math.pow(this.scale*r+this.constant,this.degree)}}var polynomialKernel=PolynomialKernel;const defaultOptions$a={alpha:.01,constant:-Math.E};class SigmoidKernel{constructor(t){t=Object.assign({},defaultOptions$a,t),this.alpha=t.alpha,this.constant=t.constant}compute(t,e){for(var r=0,i=0;i<t.length;i++)r+=t[i]*e[i];return Math.tanh(this.alpha*r+this.constant)}}var sigmoidKernel=SigmoidKernel;const defaultOptions$b={sigma:1,degree:1};class ANOVAKernel{constructor(t){t=Object.assign({},defaultOptions$b,t),this.sigma=t.sigma,this.degree=t.degree}compute(t,e){for(var r=0,i=Math.min(t.length,e.length),n=1;n<=i;++n)r+=Math.pow(Math.exp(-this.sigma*Math.pow(Math.pow(t[n-1],n)-Math.pow(e[n-1],n),2)),this.degree);return r}}var anovaKernel=ANOVAKernel;const{squaredEuclidean:squaredEuclidean$2}=euclidean$1,defaultOptions$c={sigma:1};class CauchyKernel{constructor(t){t=Object.assign({},defaultOptions$c,t),this.sigma=t.sigma}compute(t,e){return 1/(1+squaredEuclidean$2(t,e)/(this.sigma*this.sigma))}}var cauchyKernel=CauchyKernel;const{euclidean:euclidean$2}=euclidean$1,defaultOptions$d={sigma:1};class ExponentialKernel{constructor(t){t=Object.assign({},defaultOptions$d,t),this.sigma=t.sigma,this.divisor=2*t.sigma*t.sigma}compute(t,e){const r=euclidean$2(t,e);return Math.exp(-r/this.divisor)}}var exponentialKernel=ExponentialKernel;class HistogramIntersectionKernel{compute(t,e){for(var r=Math.min(t.length,e.length),i=0,n=0;n<r;++n)i+=Math.min(t[n],e[n]);return i}}var histogramIntersectionKernel=HistogramIntersectionKernel;const{euclidean:euclidean$3}=euclidean$1,defaultOptions$e={sigma:1};class LaplacianKernel{constructor(t){t=Object.assign({},defaultOptions$e,t),this.sigma=t.sigma}compute(t,e){const r=euclidean$3(t,e);return Math.exp(-r/this.sigma)}}var laplacianKernel=LaplacianKernel;const{squaredEuclidean:squaredEuclidean$3}=euclidean$1,defaultOptions$f={constant:1};class MultiquadraticKernel{constructor(t){t=Object.assign({},defaultOptions$f,t),this.constant=t.constant}compute(t,e){return Math.sqrt(squaredEuclidean$3(t,e)+this.constant*this.constant)}}var multiquadraticKernel=MultiquadraticKernel;const{squaredEuclidean:squaredEuclidean$4}=euclidean$1,defaultOptions$g={constant:1};class RationalQuadraticKernel{constructor(t){t=Object.assign({},defaultOptions$g,t),this.constant=t.constant}compute(t,e){const r=squaredEuclidean$4(t,e);return 1-r/(r+this.constant)}}var rationalQuadraticKernel=RationalQuadraticKernel;const{Matrix:Matrix$1,MatrixTransposeView:MatrixTransposeView$1}=Matrix,kernelType={gaussian:gaussianKernel,rbf:gaussianKernel,polynomial:polynomialKernel,poly:polynomialKernel,anova:anovaKernel,cauchy:cauchyKernel,exponential:exponentialKernel,histogram:histogramIntersectionKernel,min:histogramIntersectionKernel,laplacian:laplacianKernel,multiquadratic:multiquadraticKernel,rational:rationalQuadraticKernel,sigmoid:sigmoidKernel,mlp:sigmoidKernel};class Kernel{constructor(t,e){if(this.kernelType=t,"linear"!==t)if("string"==typeof t){t=t.toLowerCase();var r=kernelType[t];if(!r)throw new Error("unsupported kernel type: ".concat(t));this.kernelFunction=new r(e)}else{if("object"!=typeof t||"function"!=typeof t.compute)throw new TypeError("first argument must be a valid kernel type or instance");this.kernelFunction=t}}compute(t,e){if(t=Matrix$1.checkMatrix(t),e=void 0===e?t:Matrix$1.checkMatrix(e),"linear"===this.kernelType)return t.mmul(new MatrixTransposeView$1(e));const r=new Matrix$1(t.rows,e.rows);if(t===e)for(let e=0;e<t.rows;e++)for(let i=e;i<t.rows;i++){const n=this.kernelFunction.compute(t.getRow(e),t.getRow(i));r.set(e,i,n),r.set(i,e,n)}else for(let i=0;i<t.rows;i++)for(let n=0;n<e.rows;n++)r.set(i,n,this.kernelFunction.compute(t.getRow(i),e.getRow(n)));return r}}var kernel=Kernel;class TheilSenRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.slope=e.slope,this.intercept=e.intercept,this.coefficients=e.coefficients):(checkArraySize(t,e),theilSen(this,t,e))}toJSON(){return{name:"TheilSenRegression",slope:this.slope,intercept:this.intercept}}_predict(t){return this.slope*t+this.intercept}computeX(t){return(t-this.intercept)/this.slope}toString(t){var e="f(x) = ";if(this.slope){var r=maybeToPrecision(this.slope,t);if(e+="".concat(Math.abs(r-1)<1e-5?"":"".concat(r," * "),"x"),this.intercept){var i=Math.abs(this.intercept),n=i===this.intercept?"+":"-";e+=" ".concat(n," ").concat(maybeToPrecision(i,t))}}else e+=maybeToPrecision(this.intercept,t);return e}toLaTeX(t){return this.toString(t)}static load(t){if("TheilSenRegression"!==t.name)throw new TypeError("not a Theil-Sen model");return new TheilSenRegression(!0,t)}}function theilSen(t,e,r){let i=e.length,n=new Array(i*i),s=0;for(let t=0;t<i;++t)for(let o=t+1;o<i;++o)e[t]!==e[o]&&(n[s++]=(r[o]-r[t])/(e[o]-e[t]));n.length=s;let o=median(n),a=new Array(i);for(let t=0;t<i;++t)a[t]=r[t]-o*e[t];t.slope=o,t.intercept=median(a),t.coefficients=[t.intercept,t.slope]}class RobustPolynomialRegression extends BaseRegression{constructor(t,e,r){super(),!0===t?(this.degree=e.degree,this.powers=e.powers,this.coefficients=e.coefficients):(checkArraySize(t,e),robustPolynomial(this,t,e,r))}toJSON(){return{name:"robustPolynomialRegression",degree:this.degree,powers:this.powers,coefficients:this.coefficients}}_predict(t){return predict(t,this.powers,this.coefficients)}toString(t){return this._toFormula(t,!1)}toLaTeX(t){return this._toFormula(t,!0)}_toFormula(t,e){let r="^",i="",n=" * ";e&&(r="^{",i="}",n="");let s="",o="";for(let e=0;e<this.coefficients.length;e++)o="",0!==this.coefficients[e]&&(o=0===this.powers[e]?maybeToPrecision(this.coefficients[e],t):1===this.powers[e]?"".concat(maybeToPrecision(this.coefficients[e],t)+n,"x"):"".concat(maybeToPrecision(this.coefficients[e],t)+n,"x").concat(r).concat(this.powers[e]).concat(i),this.coefficients[e]>0&&e!==this.coefficients.length-1?o=" + ".concat(o):e!==this.coefficients.length-1&&(o=" ".concat(o))),s=o+s;return"+"===s.charAt(0)&&(s=s.slice(1)),"f(x) = ".concat(s)}static load(t){if("robustPolynomialRegression"!==t.name)throw new TypeError("not a RobustPolynomialRegression model");return new RobustPolynomialRegression(!0,t)}}function robustPolynomial(t,e,r,i){let n=Array(i).fill(0).map((t,e)=>e);const s=getRandomTuples(e,r,i);for(var o,a=0;a<s.length;a++){for(var h=calcCoefficients(s[a],n),l=e.slice(),u=0;u<e.length;u++)l[u]=r[u]-predict(e[u],n,h),l[u]={residual:l[u]*l[u],coefficients:h};var c=residualsMedian(l);(!o||c.residual<o.residual)&&(o=c)}t.degree=i,t.powers=n,t.coefficients=o.coefficients}function getRandomTuples(t,e,r){for(var i=Math.floor(t.length/r),n=new Array(i),s=0;s<t.length;s++){for(var o=Math.floor(Math.random()*i),a=0;a<t.length;){if(!n[o]){n[o]=[{x:t[s],y:e[s]}];break}if(n[o].length<r){n[o].push({x:t[s],y:e[s]});break}a++,o=(o+1)%i}if(a===t.length)return n}return n}function calcCoefficients(t,e){for(var r=t.slice(),i=t.slice(),n=0;n<r.length;n++){i[n]=[t[n].y],r[n]=new Array(e.length);for(var s=0;s<e.length;s++)r[n][s]=Math.pow(t[n].x,e[s])}return solve(r,i).to1DArray()}function predict(t,e,r){let i=0;for(let n=0;n<e.length;n++)i+=r[n]*Math.pow(t,e[n]);return i}function residualsMedian(t){t.sort((t,e)=>t.residual-e.residual);var e=t.length,r=Math.floor(e/2);return e%2==0?t[r-1]:t[r]}function errorCalculation(t,e,r){var i=0;const n=r(e);for(var s=0;s<t.x.length;s++)i+=Math.abs(t.y[s]-n(t.x[s]));return i}function gradientFunction(t,e,r,i,n){const s=r.length,o=t.x.length;for(var a=new Array(s),h=0;h<s;h++){a[h]=new Array(o);var l=r.concat();l[h]+=i;for(var u=n(l),c=0;c<o;c++)a[h][c]=e[c]-u(t.x[c])}return new Matrix(a)}function matrixFunction(t,e){const r=t.x.length;for(var i=new Array(r),n=0;n<r;n++)i[n]=[t.y[n]-e[n]];return new Matrix(i)}function step$1(t,e,r,i,n){var s=r*i*i,o=Matrix.eye(e.length,e.length,s);const a=n(e);var h=t.x.map(t=>a(t)),l=gradientFunction(t,h,e,i,n),u=matrixFunction(t,h),c=inverse(o.add(l.mmul(l.transpose())));return(e=(e=new Matrix([e])).sub(c.mmul(l).mmul(u).mul(i).transpose())).to1DArray()}function levenbergMarquardt(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},{maxIterations:i=100,gradientDifference:n=.1,damping:s=0,errorTolerance:o=.01,minValues:a,maxValues:h,initialValues:l}=r;if(s<=0)throw new Error("The damping option must be a positive number");if(!t.x||!t.y)throw new Error("The data parameter must have x and y elements");if(!Array.isArray(t.x)||t.x.length<2||!Array.isArray(t.y)||t.y.length<2)throw new Error("The data parameter elements must be an array with more than 2 points");if(t.x.length!==t.y.length)throw new Error("The data parameter elements must have the same size");var u=l||new Array(e.length).fill(1);let c=u.length;if(h=h||new Array(c).fill(Number.MAX_SAFE_INTEGER),a=a||new Array(c).fill(Number.MIN_SAFE_INTEGER),h.length!==a.length)throw new Error("minValues and maxValues must be the same size");if(!Array.isArray(u))throw new Error("initialValues must be an array");for(var f=errorCalculation(t,u,e),m=f<=o,g=0;g<i&&!m;g++){u=step$1(t,u,s,n,e);for(let t=0;t<c;t++)u[t]=Math.min(Math.max(a[t],u[t]),h[t]);if(f=errorCalculation(t,u,e),isNaN(f))break;m=f<=o}return{parameterValues:u,parameterError:f,iterations:g}}function selection(t,e){let r=[];for(let i=0;i<e.length;i++)r[i]=t[e[i]];return r}function sortCollectionSet(t){let e,r=t.map((t,e)=>{let r=BigInt(0);return t.forEach(t=>r|=BigInt(1)<<BigInt(t)),{value:t,index:e,key:r}}).sort((t,e)=>t.key-e.key<0?-1:1),i=[],n=[];for(let t of r)t.key!==e&&(e=t.key,n.push([]),i.push(t.value)),n[n.length-1].push(t.index);return{values:i,indices:n}}function cssls(t,e,r,i,n){let s=Matrix.zeros(i,n);if(null===r){let r=new CholeskyDecomposition(t);if(!0===r.isPositiveDefinite())s=r.solve(e);else{let r=new LuDecomposition(t);s=!1===r.isSingular()?r.solve(Matrix.eye(i)).mmul(e):solve(t,e,{useSVD:!0})}}else{let o=sortCollectionSet(r).values,a=sortCollectionSet(r).indices;if(1===o.length&&0===o[0].length&&a[0].length===n)return s;if(1===o.length&&o[0].length===i&&a[0].length===n){let r=new CholeskyDecomposition(t);if(!0===r.isPositiveDefinite())s=r.solve(e);else{let r=new LuDecomposition(t);s=!1===r.isSingular()?r.solve(Matrix.eye(i)).mmul(e):solve(t,e,{useSVD:!0})}}else for(let r=0;r<o.length;r++){let i,n=a[r],h=o[r],l=new CholeskyDecomposition(t.selection(h,h));if(!0===l.isPositiveDefinite())i=l.solve(e.selection(h,n));else{let r=new LuDecomposition(t.selection(h,h));i=!1===r.isSingular()?r.solve(Matrix.eye(h.length)).mmul(e.selection(h,n)):solve(t.selection(h,h),e.selection(h,n),{useSVD:!0})}for(let t=0;t<i.rows;t++)for(let e=0;e<i.columns;e++)s.set(h[t],n[e],i.get(t,e))}}return s}function initialisation(t,e){let r=t.rows,i=t.columns,n=e.columns;if(e.rows!==r)throw new Error("ERROR: matrix size not compatible");let s=Matrix.zeros(i,n),o=t.transpose().mmul(t),a=t.transpose().mmul(e),h=cssls(o,a,null,i,n),l=[];for(let t=0;t<n;t++){l[t]=[];for(let e=0;e<i;e++)h.get(e,t)>0?l[t].push(e):h.set(e,t,0)}let u=[];for(let t=0;t<n;t++)l[t].length!==i&&u.push(t);let c=h.clone();return{n:r,l:i,p:n,iter:0,W:s,XtX:o,XtY:a,K:h,Pset:l,Fset:u,D:c}}function setDifference(t,e){let r=[];for(let i of t)e.includes(i)||r.push(i);return r}function optimality(t,e,r,i,n,s,o,a,h,l,u){if(t===e)throw new Error("Maximum number of iterations exceeded");let c=i.subMatrixColumn(n).subtract(r.mmul(a.subMatrixColumn(n)));for(let t=0;t<n.length;t++)o.setColumn(n[t],c.subMatrixColumn([t]));let f=[],m=[];for(let t=0;t<h;t++)m.push(t);for(let t=0;t<n.length;t++){let e=setDifference(m,s[n[t]]);0===e.length?f.push(n[t]):o.selection(e,[n[t]]).max()<=0&&f.push(n[t])}if(0!==(n=setDifference(n,f)).length){for(let t=0;t<n.length;t++){for(let e=0;e<h;e++)s[n[t]].includes(e)&&o.set(e,n[t],-1/0);s[n[t]].push(o.subMatrixColumn(n).maxColumnIndex(t)[0])}for(let t=0;t<n.length;t++)u.setColumn(n[t],a.getColumn(n[t]))}for(let t=0;t<l;t++)s[t].sort((t,e)=>t-e);return{Pset:s,Fset:n,W:o}}function fcnnls(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};t=Matrix.checkMatrix(t),e=Matrix.checkMatrix(e);let{l:i,p:n,iter:s,W:o,XtX:a,XtY:h,K:l,Pset:u,Fset:c,D:f}=initialisation(t,e);const{maxIterations:m=3*t.columns}=r;for(;c.length>0;){let t=cssls(a,h.subMatrixColumn(c),selection(u,c),i,c.length);for(let e=0;e<i;e++)for(let r=0;r<c.length;r++)l.set(e,c[r],t.get(e,r));let e=[];for(let r=0;r<c.length;r++)for(let n=0;n<i;n++)if(t.get(n,r)<0){e.push(r);break}let r=selection(c,e);if(r.length>0){let e=r.length,n=Matrix.ones(i,e);for(;e>0&&s<m;){s++,n.mul(1/0);let o=[[],[]],c=[[],[]];for(let t=0;t<e;t++)for(let e=0;e<u[r[t]].length;e++)l.get(u[r[t]][e],r[t])<0&&(o[0].push(u[r[t]][e]),o[1].push(t),c[0].push(u[r[t]][e]),c[1].push(r[t]));for(let t=0;t<o[0].length;t++)n.set(o[0][t],o[1][t],f.get(c[0][t],c[1][t])/(f.get(c[0][t],c[1][t])-l.get(c[0][t],c[1][t])));let m=[],g=[];for(let t=0;t<e;t++)m[t]=n.minColumn(t),g[t]=n.minColumnIndex(t)[0];m=Matrix.rowVector(m);for(let t=0;t<i;t++)n.setSubMatrix(m,t,0);let d=new Matrix(i,e);d=f.subMatrixColumn(r).subtract(n.subMatrix(0,i-1,0,e-1).mul(f.subMatrixColumn(r).subtract(l.subMatrixColumn(r))));for(let t=0;t<e;t++)f.setColumn(r[t],d.subMatrixColumn([t]));let p=[g,r];for(let t=0;t<e;t++)f.set(p[0][t],p[1][t],0);for(let t=0;t<e;t++)u[r[t]].splice(u[r[t]].findIndex(e=>e===g[t]),1);t=cssls(a,h.subMatrixColumn(r),selection(u,r),i,e);for(let i=0;i<e;i++)l.setColumn(r[i],t.subMatrixColumn([i]));r=[];for(let t=0;t<l.columns;t++)for(let e=0;e<i;e++)if(l.get(e,t)<0){r.push(t);break}e=r.length}}let g=optimality(s,m,a,h,c,u,o,l,i,n,f);u=g.Pset,c=g.Fset,o=g.W}return l}function fcnnlsVector(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(!1===Array.isArray(e))throw new TypeError("y must be a 1D Array");return fcnnls(t,Matrix.columnVector(e),r).to1DArray()}var index$2=Object.freeze({__proto__:null,fcnnls:fcnnls,fcnnlsVector:fcnnlsVector}),binarySearch=function(t,e,r,i,n){var s,o;if(void 0===i)i=0;else if((i|=0)<0||i>=t.length)throw new RangeError("invalid lower bound");if(void 0===n)n=t.length-1;else if((n|=0)<i||n>=t.length)throw new RangeError("invalid upper bound");for(;i<=n;)if((o=+r(t[s=i+(n-i>>>1)],e,s,t))<0)i=s+1;else{if(!(o>0))return s;n=s-1}return~i};function assertNumber(t){if("number"!=typeof t||Number.isNaN(t))throw new TypeError("Expected a number")}var ascending=(t,e)=>(assertNumber(t),assertNumber(e),t-e),descending=(t,e)=>(assertNumber(t),assertNumber(e),e-t),numSort={ascending:ascending,descending:descending},index$3=Object.freeze({__proto__:null,default:numSort,__moduleExports:numSort,ascending:ascending,descending:descending});const largestPrime=2147483647,primeNumbers=[largestPrime,5,11,23,47,97,197,397,797,1597,3203,6421,12853,25717,51437,102877,205759,411527,823117,1646237,3292489,6584983,13169977,26339969,52679969,105359939,210719881,421439783,842879579,1685759167,433,877,1759,3527,7057,14143,28289,56591,113189,226379,452759,905551,1811107,3622219,7244441,14488931,28977863,57955739,115911563,231823147,463646329,927292699,1854585413,953,1907,3821,7643,15287,30577,61169,122347,244703,489407,978821,1957651,3915341,7830701,15661423,31322867,62645741,125291483,250582987,501165979,1002331963,2004663929,1039,2081,4177,8363,16729,33461,66923,133853,267713,535481,1070981,2141977,4283963,8567929,17135863,34271747,68543509,137087021,274174111,548348231,1096696463,31,67,137,277,557,1117,2237,4481,8963,17929,35863,71741,143483,286973,573953,1147921,2295859,4591721,9183457,18366923,36733847,73467739,146935499,293871013,587742049,1175484103,599,1201,2411,4831,9677,19373,38747,77509,155027,310081,620171,1240361,2480729,4961459,9922933,19845871,39691759,79383533,158767069,317534141,635068283,1270136683,311,631,1277,2557,5119,10243,20507,41017,82037,164089,328213,656429,1312867,2625761,5251529,10503061,21006137,42012281,84024581,168049163,336098327,672196673,1344393353,3,7,17,37,79,163,331,673,1361,2729,5471,10949,21911,43853,87719,175447,350899,701819,1403641,2807303,5614657,11229331,22458671,44917381,89834777,179669557,359339171,718678369,1437356741,43,89,179,359,719,1439,2879,5779,11579,23159,46327,92657,185323,370661,741337,1482707,2965421,5930887,11861791,23723597,47447201,94894427,189788857,379577741,759155483,1518310967,379,761,1523,3049,6101,12203,24407,48817,97649,195311,390647,781301,1562611,3125257,6250537,12501169,25002389,50004791,100009607,200019221,400038451,800076929,1600153859,13,29,59,127,257,521,1049,2099,4201,8419,16843,33703,67409,134837,269683,539389,1078787,2157587,4315183,8630387,17260781,34521589,69043189,138086407,276172823,552345671,1104691373,19,41,83,167,337,677,1361,2729,5471,10949,21911,43853,87719,175447,350899,701819,1403641,2807303,5614657,11229331,22458671,44917381,89834777,179669557,359339171,718678369,1437356741,53,107,223,449,907,1823,3659,7321,14653,29311,58631,117269,234539,469099,938207,1876417,3752839,7505681,15011389,30022781,60045577,120091177,240182359,480364727,960729461,1921458943];function nextPrime(t){let e=binarySearch(primeNumbers,t,ascending);return e<0&&(e=~e),primeNumbers[e]}primeNumbers.sort(ascending);const FREE=0,FULL=1,REMOVED=2,defaultInitialCapacity=150,defaultMinLoadFactor=1/6,defaultMaxLoadFactor=2/3;class HashTable{constructor(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(t instanceof HashTable)return this.table=t.table.slice(),this.values=t.values.slice(),this.state=t.state.slice(),this.minLoadFactor=t.minLoadFactor,this.maxLoadFactor=t.maxLoadFactor,this.distinct=t.distinct,this.freeEntries=t.freeEntries,this.lowWaterMark=t.lowWaterMark,void(this.highWaterMark=t.maxLoadFactor);const e=void 0===t.initialCapacity?defaultInitialCapacity:t.initialCapacity;if(e<0)throw new RangeError("initial capacity must not be less than zero: ".concat(e));const r=void 0===t.minLoadFactor?defaultMinLoadFactor:t.minLoadFactor,i=void 0===t.maxLoadFactor?defaultMaxLoadFactor:t.maxLoadFactor;if(r<0||r>=1)throw new RangeError("invalid minLoadFactor: ".concat(r));if(i<=0||i>=1)throw new RangeError("invalid maxLoadFactor: ".concat(i));if(r>=i)throw new RangeError("minLoadFactor (".concat(r,") must be smaller than maxLoadFactor (").concat(i,")"));let n=e;0===(n=nextPrime(n=n/i|0))&&(n=1),this.table=newArray$1(n),this.values=newArray$1(n),this.state=newArray$1(n),this.minLoadFactor=r,this.maxLoadFactor=n===largestPrime?1:i,this.distinct=0,this.freeEntries=n,this.lowWaterMark=0,this.highWaterMark=chooseHighWaterMark(n,this.maxLoadFactor)}clone(){return new HashTable(this)}get size(){return this.distinct}get(t){const e=this.indexOfKey(t);return e<0?0:this.values[e]}set(t,e){let r=this.indexOfInsertion(t);if(r<0)return r=-r-1,this.values[r]=e,!1;if(this.distinct>this.highWaterMark){const r=chooseGrowCapacity(this.distinct+1,this.minLoadFactor,this.maxLoadFactor);return this.rehash(r),this.set(t,e)}if(this.table[r]=t,this.values[r]=e,this.state[r]===FREE&&this.freeEntries--,this.state[r]=FULL,this.distinct++,this.freeEntries<1){const t=chooseGrowCapacity(this.distinct+1,this.minLoadFactor,this.maxLoadFactor);this.rehash(t)}return!0}remove(t,e){const r=this.indexOfKey(t);return!(r<0)&&(this.state[r]=REMOVED,this.distinct--,e||this.maybeShrinkCapacity(),!0)}delete(t,e){const r=this.indexOfKey(t);return!(r<0)&&(this.state[r]=FREE,this.distinct--,e||this.maybeShrinkCapacity(),!0)}maybeShrinkCapacity(){if(this.distinct<this.lowWaterMark){const t=chooseShrinkCapacity(this.distinct,this.minLoadFactor,this.maxLoadFactor);this.rehash(t)}}containsKey(t){return this.indexOfKey(t)>=0}indexOfKey(t){const e=this.table,r=this.state,i=this.table.length,n=2147483647&t;let s=n%i,o=n%(i-2);for(0===o&&(o=1);r[s]!==FREE&&(r[s]===REMOVED||e[s]!==t);)(s-=o)<0&&(s+=i);return r[s]===FREE?-1:s}containsValue(t){return this.indexOfValue(t)>=0}indexOfValue(t){const e=this.values,r=this.state;for(var i=0;i<r.length;i++)if(r[i]===FULL&&e[i]===t)return i;return-1}indexOfInsertion(t){const e=this.table,r=this.state,i=e.length,n=2147483647&t;let s=n%i,o=n%(i-2);for(0===o&&(o=1);r[s]===FULL&&e[s]!==t;)(s-=o)<0&&(s+=i);if(r[s]===REMOVED){const n=s;for(;r[s]!==FREE&&(r[s]===REMOVED||e[s]!==t);)(s-=o)<0&&(s+=i);r[s]===FREE&&(s=n)}return r[s]===FULL?-s-1:s}ensureCapacity(t){if(this.table.length<t){const e=nextPrime(t);this.rehash(e)}}rehash(t){const e=this.table.length;if(t<=this.distinct)throw new Error("Unexpected");const r=this.table,i=this.values,n=this.state,s=newArray$1(t),o=newArray$1(t),a=newArray$1(t);this.lowWaterMark=chooseLowWaterMark(t,this.minLoadFactor),this.highWaterMark=chooseHighWaterMark(t,this.maxLoadFactor),this.table=s,this.values=o,this.state=a,this.freeEntries=t-this.distinct;for(var h=0;h<e;h++)if(n[h]===FULL){var l=r[h],u=this.indexOfInsertion(l);s[u]=l,o[u]=i[h],a[u]=FULL}}forEachKey(t){for(var e=0;e<this.state.length;e++)if(this.state[e]===FULL&&!t(this.table[e]))return!1;return!0}forEachValue(t){for(var e=0;e<this.state.length;e++)if(this.state[e]===FULL&&!t(this.values[e]))return!1;return!0}forEachPair(t){for(var e=0;e<this.state.length;e++)if(this.state[e]===FULL&&!t(this.table[e],this.values[e]))return!1;return!0}}function chooseLowWaterMark(t,e){return t*e|0}function chooseHighWaterMark(t,e){return Math.min(t-2,t*e|0)}function chooseGrowCapacity(t,e,r){return nextPrime(Math.max(t+1,4*t/(3*e+r)|0))}function chooseShrinkCapacity(t,e,r){return nextPrime(Math.max(t+1,4*t/(e+3*r)|0))}function newArray$1(t){return Array(t).fill(0)}class SparseMatrix{constructor(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(t instanceof SparseMatrix){const e=t;this._init(e.rows,e.columns,e.elements.clone(),e.threshold)}else if(Array.isArray(t)){const o=t;t=o.length,r=e||{},e=o[0].length,this._init(t,e,new HashTable(r),r.threshold);for(var i=0;i<t;i++)for(var n=0;n<e;n++){var s=o[i][n];this.threshold&&Math.abs(s)<this.threshold&&(s=0),0!==s&&this.elements.set(i*e+n,o[i][n])}}else this._init(t,e,new HashTable(r),r.threshold)}_init(t,e,r,i){this.rows=t,this.columns=e,this.elements=r,this.threshold=i||0}static eye(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:t;const r=Math.min(t,e),i=new SparseMatrix(t,e,{initialCapacity:r});for(var n=0;n<r;n++)i.set(n,n,1);return i}clone(){return new SparseMatrix(this)}to2DArray(){const t=new Array(this.rows);for(var e=0;e<this.rows;e++){t[e]=new Array(this.columns);for(var r=0;r<this.columns;r++)t[e][r]=this.get(e,r)}return t}isSquare(){return this.rows===this.columns}isSymmetric(){if(!this.isSquare())return!1;var t=!0;return this.forEachNonZero((e,r,i)=>this.get(r,e)!==i?(t=!1,!1):i),t}bandWidth(){let t=this.columns,e=-1;return this.forEachNonZero((r,i,n)=>{let s=r-i;return t=Math.min(t,s),e=Math.max(e,s),n}),e-t}isBanded(t){return this.bandWidth()<=t}get cardinality(){return this.elements.size}get size(){return this.rows*this.columns}get(t,e){return this.elements.get(t*this.columns+e)}set(t,e,r){return this.threshold&&Math.abs(r)<this.threshold&&(r=0),0===r?this.elements.remove(t*this.columns+e):this.elements.set(t*this.columns+e,r),this}mmul(t){this.columns!==t.rows&&console.warn("Number of columns of left matrix are not equal to number of rows of right matrix.");const e=this.rows,r=t.columns,i=new SparseMatrix(e,r);return this.forEachNonZero((e,r,n)=>(t.forEachNonZero((t,s,o)=>(r===t&&i.set(e,s,i.get(e,s)+n*o),o)),n)),i}kroneckerProduct(t){const e=this.rows,r=this.columns,i=t.rows,n=t.columns,s=new SparseMatrix(e*i,r*n,{initialCapacity:this.cardinality*t.cardinality});return this.forEachNonZero((e,r,o)=>(t.forEachNonZero((t,a,h)=>(s.set(i*e+t,n*r+a,o*h),h)),o)),s}forEachNonZero(t){return this.elements.forEachPair((e,r)=>{const i=e/this.columns|0,n=e%this.columns;let s=t(i,n,r);return!1!==s&&(this.threshold&&Math.abs(s)<this.threshold&&(s=0),s!==r&&(0===s?this.elements.remove(e,!0):this.elements.set(e,s)),!0)}),this.elements.maybeShrinkCapacity(),this}getNonZeros(){const t=this.cardinality,e=new Array(t),r=new Array(t),i=new Array(t);var n=0;return this.forEachNonZero((t,s,o)=>(e[n]=t,r[n]=s,i[n]=o,n++,o)),{rows:e,columns:r,values:i}}setThreshold(t){return 0!==t&&t!==this.threshold&&(this.threshold=t,this.forEachNonZero((t,e,r)=>r)),this}transpose(){let t=new SparseMatrix(this.columns,this.rows,{initialCapacity:this.cardinality});return this.forEachNonZero((e,r,i)=>(t.set(r,e,i),i)),t}}SparseMatrix.prototype.klass="Matrix",SparseMatrix.identity=SparseMatrix.eye,SparseMatrix.prototype.tensorProduct=SparseMatrix.prototype.kroneckerProduct;var inplaceOperator="\n(function %name%(value) {\n    if (typeof value === 'number') return this.%name%S(value);\n    return this.%name%M(value);\n})\n",inplaceOperatorScalar="\n(function %name%S(value) {\n    this.forEachNonZero((i, j, v) => v %op% value);\n    return this;\n})\n",inplaceOperatorMatrix="\n(function %name%M(matrix) {\n    matrix.forEachNonZero((i, j, v) => {\n        this.set(i, j, this.get(i, j) %op% v);\n        return v;\n    });\n    return this;\n})\n",staticOperator="\n(function %name%(matrix, value) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%(value);\n})\n",inplaceMethod="\n(function %name%() {\n    this.forEachNonZero((i, j, v) => %method%(v));\n    return this;\n})\n",staticMethod="\n(function %name%(matrix) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%();\n})\n";const operators=[["+","add"],["-","sub","subtract"],["*","mul","multiply"],["/","div","divide"],["%","mod","modulus"],["&","and"],["|","or"],["^","xor"],["<<","leftShift"],[">>","signPropagatingRightShift"],[">>>","rightShift","zeroFillRightShift"]];for(const operator of operators)for(let i=1;i<operator.length;i++)SparseMatrix.prototype[operator[i]]=eval(fillTemplateFunction(inplaceOperator,{name:operator[i],op:operator[0]})),SparseMatrix.prototype["".concat(operator[i],"S")]=eval(fillTemplateFunction(inplaceOperatorScalar,{name:"".concat(operator[i],"S"),op:operator[0]})),SparseMatrix.prototype["".concat(operator[i],"M")]=eval(fillTemplateFunction(inplaceOperatorMatrix,{name:"".concat(operator[i],"M"),op:operator[0]})),SparseMatrix[operator[i]]=eval(fillTemplateFunction(staticOperator,{name:operator[i]}));var methods=[["~","not"]];["abs","acos","acosh","asin","asinh","atan","atanh","cbrt","ceil","clz32","cos","cosh","exp","expm1","floor","fround","log","log1p","log10","log2","round","sign","sin","sinh","sqrt","tan","tanh","trunc"].forEach((function(t){methods.push(["Math.".concat(t),t])}));for(const method of methods)for(let i=1;i<method.length;i++)SparseMatrix.prototype[method[i]]=eval(fillTemplateFunction(inplaceMethod,{name:method[i],method:method[0]})),SparseMatrix[method[i]]=eval(fillTemplateFunction(staticMethod,{name:method[i]}));function fillTemplateFunction(t,e){for(const r in e)t=t.replace(new RegExp("%".concat(r,"%"),"g"),e[r]);return t}function additiveSymmetric(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=(t[r]-e[r])*(t[r]-e[r])*(t[r]+e[r])/(t[r]*e[r]);return 2*n}function avg(t,e){for(var r=t.length,i=0,n=0,s=0,o=0;o<r;o++)n+=s=Math.abs(t[o]-e[o]),i<s&&(i=s);return(i+n)/2}function bhattacharyya(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.sqrt(t[n]*e[n]);return-Math.log(i)}function canberra(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.abs(t[n]-e[n])/(t[n]+e[n]);return i}function chebyshev(t,e){for(var r=t.length,i=0,n=0,s=0;s<r;s++)i<(n=Math.abs(t[s]-e[s]))&&(i=n);return i}function clark(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=Math.sqrt((t[r]-e[r])*(t[r]-e[r])/((t[r]+e[r])*(t[r]+e[r])));return 2*n}function czekanowskiSimilarity(t,e){for(var r=0,i=0,n=0;n<t.length;n++)r+=Math.min(t[n],e[n]),i+=t[n]+e[n];return 2*r/i}function czekanowskiDistance(t,e){return 1-czekanowskiSimilarity(t,e)}function dice(t,e){for(var r=t.length,i=0,n=0,s=0,o=0;o<r;o++)i+=t[o]*t[o],n+=e[o]*e[o],s+=(t[o]-e[o])*(t[o]-e[o]);return s/(i+n)}function divergence(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=(t[r]-e[r])*(t[r]-e[r])/((t[r]+e[r])*(t[r]+e[r]));return 2*n}function fidelity(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.sqrt(t[n]*e[n]);return i}function gower(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.abs(t[n]-e[n]);return i/r}function harmonicMean(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=t[n]*e[n]/(t[n]+e[n]);return 2*i}function hellinger(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.sqrt(t[n]*e[n]);return 2*Math.sqrt(1-i)}function innerProduct(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=t[n]*e[n];return i}function intersection(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.min(t[n],e[n]);return 1-i}function jaccard(t,e){for(var r=t.length,i=0,n=0,s=0,o=0,a=0;a<r;a++)i+=t[a]*e[a],n+=t[a]*t[a],s+=e[a]*e[a],o+=(t[a]-e[a])*(t[a]-e[a]);return o/(n+s-i)}function jeffreys(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=(t[n]-e[n])*Math.log(t[n]/e[n]);return i}function jensenDifference(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=(t[n]*Math.log(t[n])+e[n]*Math.log(e[n]))/2-(t[n]+e[n])/2*Math.log((t[n]+e[n])/2);return i}function jensenShannon(t,e){for(var r=t.length,i=0,n=0,s=0;s<r;s++)i+=t[s]*Math.log(2*t[s]/(t[s]+e[s])),n+=e[s]*Math.log(2*e[s]/(t[s]+e[s]));return(i+n)/2}function kdivergence(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=t[n]*Math.log(2*t[n]/(t[n]+e[n]));return i}function kulczynski(t,e){for(var r=t.length,i=0,n=0,s=0;s<r;s++)i+=Math.abs(t[s]-e[s]),n+=Math.min(t[s],e[s]);return i/n}function kullbackLeibler(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=t[n]*Math.log(t[n]/e[n]);return i}function kumarHassebrook(t,e){for(var r=t.length,i=0,n=0,s=0,o=0;o<r;o++)i+=t[o]*e[o],n+=t[o]*t[o],s+=e[o]*e[o];return i/(n+s-i)}function kumarJohnson(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.pow(t[n]*t[n]-e[n]*e[n],2)/(2*Math.pow(t[n]*e[n],1.5));return i}function lorentzian(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.log(Math.abs(t[n]-e[n])+1);return i}function manhattan(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=Math.abs(t[r]-e[r]);return n}function matusita(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=Math.sqrt(t[n]*e[n]);return Math.sqrt(2-2*i)}function minkowski(t,e,r){for(var i=0,n=t.length,s=0;i<n;i++)s+=Math.pow(Math.abs(t[i]-e[i]),r);return Math.pow(s,1/r)}function motyka(t,e){for(var r=t.length,i=0,n=0,s=0;s<r;s++)i+=Math.min(t[s],e[s]),n+=t[s]+e[s];return 1-i/n}function neyman(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=(t[r]-e[r])*(t[r]-e[r])/t[r];return n}function pearson(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=(t[r]-e[r])*(t[r]-e[r])/e[r];return n}function probabilisticSymmetric(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=(t[r]-e[r])*(t[r]-e[r])/(t[r]+e[r]);return 2*n}function ruzicka(t,e){for(var r=t.length,i=0,n=0,s=0;s<r;s++)i+=Math.min(t[s],e[s]),n+=Math.max(t[s],e[s]);return i/n}function soergel(t,e){for(var r=t.length,i=0,n=0,s=0;s<r;s++)i+=Math.abs(t[s]-e[s]),n+=Math.max(t[s],e[s]);return i/n}function sorensen(t,e){for(var r=t.length,i=0,n=0,s=0;s<r;s++)i+=Math.abs(t[s]-e[s]),n+=t[s]+e[s];return i/n}function squared(t,e){for(var r=0,i=t.length,n=0;r<i;r++)n+=(t[r]-e[r])*(t[r]-e[r])/(t[r]+e[r]);return n}function squaredChord(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=(Math.sqrt(t[n])-Math.sqrt(e[n]))*(Math.sqrt(t[n])-Math.sqrt(e[n]));return i}function taneja(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=(t[n]+e[n])/2*Math.log((t[n]+e[n])/(2*Math.sqrt(t[n]*e[n])));return i}function tanimoto(t,e,r){if(r){for(var i=0,n=0,s=0;s<t.length;s++)i+=t[s]&&e[s],n+=t[s]||e[s];return 0===n?1:i/n}for(var o=t.length,a=0,h=0,l=0,u=0;u<o;u++)a+=t[u],h+=e[u],l+=Math.min(t[u],e[u]);return 1-(a+h-2*l)/(a+h-l)}function tanimoto$1(t,e,r){if(r)return 1-tanimoto(t,e,r);for(var i=t.length,n=0,s=0,o=0,a=0;a<i;a++)n+=t[a],s+=e[a],o+=Math.min(t[a],e[a]);return(n+s-2*o)/(n+s-o)}function topsoe(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=t[n]*Math.log(2*t[n]/(t[n]+e[n]))+e[n]*Math.log(2*e[n]/(t[n]+e[n]));return i}function waveHedges(t,e){for(var r=t.length,i=0,n=0;n<r;n++)i+=1-Math.min(t[n],e[n])/Math.max(t[n],e[n]);return i}var distances=Object.freeze({__proto__:null,euclidean:euclidean,squaredEuclidean:squaredEuclidean,additiveSymmetric:additiveSymmetric,avg:avg,bhattacharyya:bhattacharyya,canberra:canberra,chebyshev:chebyshev,clark:clark,czekanowski:czekanowskiDistance,dice:dice,divergence:divergence,fidelity:fidelity,gower:gower,harmonicMean:harmonicMean,hellinger:hellinger,innerProduct:innerProduct,intersection:intersection,jaccard:jaccard,jeffreys:jeffreys,jensenDifference:jensenDifference,jensenShannon:jensenShannon,kdivergence:kdivergence,kulczynski:kulczynski,kullbackLeibler:kullbackLeibler,kumarHassebrook:kumarHassebrook,kumarJohnson:kumarJohnson,lorentzian:lorentzian,manhattan:manhattan,matusita:matusita,minkowski:minkowski,motyka:motyka,neyman:neyman,pearson:pearson,probabilisticSymmetric:probabilisticSymmetric,ruzicka:ruzicka,soergel:soergel,sorensen:sorensen,squared:squared,squaredChord:squaredChord,taneja:taneja,tanimoto:tanimoto$1,topsoe:topsoe,waveHedges:waveHedges});function createTree(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};var r=t[0];const{minWindow:i=.16,threshold:n=.01,from:s=r[0],to:o=r[r.length-1]}=e;return mainCreateTree(t[0],t[1],s,o,i,n)}function mainCreateTree(t,e,r,i,n,s){if(i-r<n)return null;var o=binarySearch(t,r,ascending);o<0&&(o=~o);for(var a=0,h=0,l=o;l<t.length&&!(t[l]>=i);l++)a+=e[l],h+=t[l]*e[l];return a<s?null:(h/=a)-r<1e-6||i-h<1e-6?null:h-r<n/4?mainCreateTree(t,e,h,i,n,s):i-h<n/4?mainCreateTree(t,e,r,h,n,s):new Tree(a,h,mainCreateTree(t,e,r,h,n,s),mainCreateTree(t,e,h,i,n,s))}class Tree{constructor(t,e,r,i){this.sum=t,this.center=e,this.left=r,this.right=i}}function getSimilarity(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const{alpha:i=.1,beta:n=.33,gamma:s=.001}=r;return null===t||null===e?0:(Array.isArray(t)&&(t=createTree(t)),Array.isArray(e)&&(e=createTree(e)),n*(i*Math.min(t.sum,e.sum)/Math.max(t.sum,e.sum)+(1-i)*Math.exp(-s*Math.abs(t.center-e.center)))+(1-n)*(getSimilarity(t.left,e.left,r)+getSimilarity(t.right,e.right,r))/2)}function treeSimilarity(t,e){return getSimilarity(t,e,arguments.length>2&&void 0!==arguments[2]?arguments[2]:{})}function getFunction(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};return(e,r)=>getSimilarity(e,r,t)}var index$4=Object.freeze({__proto__:null,treeSimilarity:treeSimilarity,getFunction:getFunction,createTree:createTree});function cosine(t,e){for(var r=t.length,i=0,n=0,s=0,o=0;o<r;o++)i+=t[o]*e[o],n+=t[o]*t[o],s+=e[o]*e[o];return i/(Math.sqrt(n)*Math.sqrt(s))}function dice$1(t,e){return 1-dice(t,e)}function intersection$1(t,e){return 1-intersection(t,e)}function jaccard$1(t,e){return 1-jaccard(t,e)}function kulczynski$1(t,e){return 1/kulczynski(t,e)}function motyka$1(t,e){return 1-motyka(t,e)}function pearson$1(t,e){for(var r=mean(t),i=mean(e),n=new Array(t.length),s=new Array(e.length),o=0;o<n.length;o++)n[o]=t[o]-r,s[o]=e[o]-i;return cosine(n,s)}function squaredChord$1(t,e){return 1-squaredChord(t,e)}var similarities=Object.freeze({__proto__:null,tree:index$4,cosine:cosine,czekanowski:czekanowskiSimilarity,dice:dice$1,intersection:intersection$1,jaccard:jaccard$1,kulczynski:kulczynski$1,motyka:motyka$1,pearson:pearson$1,squaredChord:squaredChord$1,tanimoto:tanimoto}),acc=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=(t.tn[i]+t.tp[i])/(e-1);return r},err=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.fn[i]+t.fp[i]/(e-1);return r},fpr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.fp[i]/t.nNeg;return r},tpr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.tp[i]/t.nPos;return r},fnr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.fn[i]/t.nPos;return r},tnr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.tn[i]/t.nNeg;return r},ppv=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.fp[i]+t.tp[i]!==0?t.tp[i]/(t.fp[i]+t.tp[i]):0;return r},npv=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.fn[i]+t.tn[i]!==0?t.tn[i]/(t.fn[i]+t.tn[i]):0;return r},pcfall=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.fp[i]+t.tp[i]!==0?1-t.tp[i]/(t.fp[i]+t.tp[i]):1;return r},pcmiss=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.fn[i]+t.tn[i]!==0?1-t.tn[i]/(t.fn[i]+t.tn[i]):1;return r},lift=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=0!==t.nPosPred[i]?t.tp[i]/t.nPos/(t.nPosPred[i]/t.nSamples):0;return r},rpp=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.nPosPred[i]/t.nSamples;return r},rnp=t=>{const e=t.cutoffs.length,r=new Array(e);for(var i=0;i<e;i++)r[i]=t.nNegPred[i]/t.nSamples;return r},threshold=t=>{const e=t.cutoffs.slice();return e[0]=e[1],e},measures={acc:acc,err:err,fpr:fpr,tpr:tpr,fnr:fnr,tnr:tnr,ppv:ppv,npv:npv,pcfall:pcfall,pcmiss:pcmiss,lift:lift,rpp:rpp,rnp:rnp,threshold:threshold};class Performance{constructor(t,e,r){if(r=r||{},t.length!==e.length||t[0].length!==e[0].length)throw new Error("dimensions of prediction and target do not match");const i=t.length,n=t[0].length,s=!r.max,o=[];if(r.all)for(var a=0;a<i;a++)for(var h=0;h<n;h++)o.push({pred:t[a][h],targ:e[a][h]});else{if(i<3||i!==n)throw new Error('When "all" option is false, the prediction matrix must be square and have at least 3 columns');for(a=0;a<i-1;a++)for(h=a+1;h<n;h++)o.push({pred:t[a][h],targ:e[a][h]})}s?o.sort((t,e)=>t.pred-e.pred):o.sort((t,e)=>e.pred-t.pred);const l=this.cutoffs=[s?Number.MIN_VALUE:Number.MAX_VALUE],u=this.fp=[0],c=this.tp=[0];var f=0,m=0,g=o[0].pred,d=0,p=0;for(a=0;a<o.length;a++)o[a].pred!==g&&(l.push(g),u.push(p),c.push(d),g=o[a].pred),o[a].targ?(f++,d++):(m++,p++);l.push(g),u.push(p),c.push(d);const w=l.length,x=this.fn=new Array(w),M=this.tn=new Array(w),y=this.nPosPred=new Array(w),v=this.nNegPred=new Array(w);for(a=0;a<w;a++)x[a]=f-c[a],M[a]=m-u[a],y[a]=c[a]+u[a],v[a]=M[a]+x[a];this.nPos=f,this.nNeg=m,this.nSamples=f+m}getMeasure(t){if("string"!=typeof t)throw new Error("No measure specified");if(!measures[t])throw new Error("The specified measure (".concat(t,") does not exist"));return measures[t](this)}getAURC(){const t=this.cutoffs.length,e=new Array(t),r=new Array(t);for(var i=0;i<t;i++)e[i]=this.fp[i]/this.nNeg,r[i]=this.tp[i]/this.nPos;var n=0;for(i=1;i<t;i++)n+=.5*(e[i]-e[i-1])*(r[i]+r[i-1]);return n}getAUDC(){const t=this.cutoffs.length,e=new Array(t),r=new Array(t);for(var i=0;i<t;i++)e[i]=this.fn[i]/this.nPos,r[i]=this.fp[i]/this.nNeg;var n=0;for(i=1;i<t;i++)n+=.5*(e[i]+e[i-1])*(r[i]-r[i-1]);return n}getDistribution(t){t=t||{};for(var e=this.cutoffs.length,r=t.xMin||Math.floor(100*this.cutoffs[e-1])/100,i=t.xMax||Math.ceil(100*this.cutoffs[1])/100,n=t.interval||Math.floor((i-r)/20*1e7-1)/1e7,s=[],o=[],a=[],h=[],l=[],u=this.tp[e-1],c=0,f=this.fp[e-1],m=0,g=r,d=e-1;g<=i;g+=n){for(;this.cutoffs[d]<g;)d--;s.push(g);var p=u-c-this.tp[d],w=f-m-this.fp[d];c+=p,m+=w,o.push(w),a.push(p),h.push(100-(f-this.fp[d])/f*100),l.push(100-(u-this.tp[d])/u*100)}return{xLabels:s,interValues:o,intraValues:a,interCumPercent:h,intraCumPercent:l}}}Performance.names={acc:"Accuracy",err:"Error rate",fpr:"False positive rate",tpr:"True positive rate",fnr:"False negative rate",tnr:"True negative rate",ppv:"Positive predictive value",npv:"Negative predictive value",pcfall:"Prediction-conditioned fallout",pcmiss:"Prediction-conditioned miss",lift:"Lift value",rpp:"Rate of positive predictions",rnp:"Rate of negative predictions",threshold:"Threshold"};var src$5=Performance,defaultOptions$h={size:1,value:0};function arrayCase(t,e){var r=t.length;"number"==typeof e.size&&(e.size=[e.size,e.size]);var i,n,s=r+e.size[0]+e.size[1];if(e.output){if(e.output.length!==s)throw new RangeError("Wrong output size");i=e.output}else i=new Array(s);if("circular"===e.value)for(n=0;n<s;n++)n<e.size[0]?i[n]=t[(r-e.size[0]%r+n)%r]:n<e.size[0]+r?i[n]=t[n-e.size[0]]:i[n]=t[(n-e.size[0])%r];else if("replicate"===e.value)for(n=0;n<s;n++)n<e.size[0]?i[n]=t[0]:n<e.size[0]+r?i[n]=t[n-e.size[0]]:i[n]=t[r-1];else if("symmetric"===e.value){if(e.size[0]>r||e.size[1]>r)throw new RangeError("expanded value should not be bigger than the data length");for(n=0;n<s;n++)n<e.size[0]?i[n]=t[e.size[0]-1-n]:n<e.size[0]+r?i[n]=t[n-e.size[0]]:i[n]=t[2*r+e.size[0]-n-1]}else for(n=0;n<s;n++)n<e.size[0]?i[n]=e.value:n<e.size[0]+r?i[n]=t[n-e.size[0]]:i[n]=e.value;return i}function matrixCase(t,e){throw void 0===e.size[0]&&(e.size=[e.size,e.size,e.size,e.size]),new Error("matrix not supported yet, sorry")}function padArray(t,e){if(e=Object.assign({},defaultOptions$h,e),Array.isArray(t))return Array.isArray(t[0])?matrixCase(t,e):arrayCase(t,e);throw new TypeError("data should be an array")}var src$6=padArray;const defaultOptions$i={windowSize:5,derivative:1,polynomial:2,pad:"none",padValue:"replicate"};function savitzkyGolay(t,e,r){if((r=Object.assign({},defaultOptions$i,r)).windowSize%2==0||r.windowSize<5||!Number.isInteger(r.windowSize))throw new RangeError("Invalid window size (should be odd and at least 5 integer number)");if(r.derivative<0||!Number.isInteger(r.derivative))throw new RangeError("Derivative should be a positive integer");if(r.polynomial<1||!Number.isInteger(r.polynomial))throw new RangeError("Polynomial should be a positive integer");let i,n,s=Math.floor(r.windowSize/2);"pre"===r.pad&&(t=src$6(t,{size:s,value:r.padValue}));let o=new 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size");if(h||(h=l?Math.floor((o-n)/l)+1:o-n+1),!l&&h&&(l=(o-n)/(h-1)),Array.isArray(t)){t.length=0;for(var u=0;u<h;u++)t.push(n),n+=l}else{if(t.length!==h)throw new Error("sequentialFill typed array must have the correct length");for(var c=0;c<h;c++)t[c]=n,n+=l}return t}function variance(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!src(t))throw new TypeError("input must be an array");for(var r=e.unbiased,i=void 0===r||r,n=e.mean,s=void 0===n?mean(t):n,o=0,a=0;a<t.length;a++){var h=t[a]-s;o+=h*h}return i?o/(t.length-1):o/t.length}function standardDeviation(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return Math.sqrt(variance(t,e))}function mergeByCentroids(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const{window:i=.01}=r;for(var n={x:e.slice(),y:new Array(e.length).fill(0)},s=0,o=0;s<t.x.length&&o<e.length;){var a=t.x[s]-e[o];Math.abs(a)<i?n.y[o]+=t.y[s++]:a<0?s++:o++}return n}function closestX(t,e){const{x:r,y:i}=t,{target:n=r[0],reverse:s=!1}=e;let o;return(o=binarySearch(r,n,s?descending:ascending))>=0?{x:r[o],y:i[o]}:0!==(o=~o)&&Math.abs(r[o]-n)>.5||o===r.length?{x:r[o-1],y:i[o-1]}:{x:r[o],y:i[o]}}function covariance$1(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{x:r,y:i}=t,{unbiased:n=!0}=e,s=mean(r),o=mean(i);var a=0;for(let t=0;t<r.length;t++)a+=(r[t]-s)*(i[t]-o);return n?a/(r.length-1):a/r.length}function maxMerge(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{x:r,y:i}=t,{groupWidth:n=.001}=e;for(var s={x:[],y:[]},o={x:[],y:[]},a=0,h=0;h<r.length;)0===a||r[h]-s.x[a-1]>n?(o.x.push(r[h]),o.y.push(i[h]),s.x.push(r[h]),s.y.push(i[h]),h++,a++):(i[h]>o.y[a-1]&&(o.x[a-1]=r[h],o.y[a-1]=i[h]),s.x[a-1]=r[h],s.y[a-1]+=i[h],h++);return s.x=o.x.slice(),s}function maxY(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{x:r,y:i}=t;let{from:n={index:0},to:s={index:r.length},reverse:o=!1}=e;void 0!==n.value&&void 0===n.index&&(n.index=calculateIndex(n.value,r,o)),void 0!==s.value&&void 0===s.index&&(s.index=calculateIndex(s.value,r,o));for(var a,h=Number.MIN_VALUE,l=n.index;l<s.index;l++)h<i[l]&&(h=i[l],a=l);return{index:a,value:h}}function calculateIndex(t,e,r){let i;if((i=binarySearch(e,t,r?descending:ascending))<0)throw new Error("the value ".concat(t," doesn't belongs to the abscissa value"));return i}function sortX(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{x:r,y:i}=t,{reverse:n=!1}=e;var s;s=n?(t,e)=>e.x-t.x:(t,e)=>t.x-e.x;for(var o=r.map((t,e)=>({x:t,y:i[e]})).sort(s),a={x:r.slice(),y:i.slice()},h=0;h<r.length;h++)a.x[h]=o[h].x,a.y[h]=o[h].y;return a}function uniqueX(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};const{x:e,y:r}=t;if(e.length<2)return;if(e.length!==r.length)throw new Error("The X and Y arrays mush have the same length");let i=e[0],n=0;for(let t=1;t<e.length;t++)i!==e[t]&&(n++,i=e[t],e[n]=e[t],t!==n&&(r[n]=0)),t!==n&&(r[n]+=r[t]);e.length=n+1,r.length=n+1}function weightedMerge(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{x:r,y:i}=t,{groupWidth:n=.001}=e;for(var s={x:[],y:[]},o={x:[],y:[]},a=0,h=0;h<r.length;)0===a||r[h]-s.x[a-1]>n?(o.x.push(r[h]*i[h]),o.y.push(i[h]),s.x.push(r[h]),s.y.push(i[h]),h++,a++):(o.x[a-1]+=r[h]*i[h],o.y[a-1]+=i[h],s.x[a-1]=r[h],s.y[a-1]+=i[h],h++);for(var l=0;l<s.x.length;l++)s.x[l]=o.x[l]/o.y[l];return s}function integral(t,e,r,i){return.5*r*e*e+i*e-(.5*r*t*t+i*t)}function equallySpacedSmooth(t,e,r,i,n){var s=t.length,o=(i-r)/(n-1),a=o/2,h=new Array(n),l=t[1]-t[0],u=t[s-1]-t[s-2],c=r-a,f=r+a,m=Number.MIN_VALUE,g=0,d=t[0]-l,p=0,w=0,x=0,M=0,y=0,v=0,b=0,S=0;t:for(;;){for(m<=c&&c<=d&&(y=w+(A=integral(0,c-m,x,g)));d-f>=0;){var A=integral(0,f-m,x,g);if(v=w+A,h[S++]=(v-y)/o,S===n)break t;c=f,f+=o,y=v}w+=integral(m,d,x,M),m=d,g=p,b<s?(d=t[b],p=e[b],b++):b===s&&(d+=u,p=0),M=-(x=(p-g)/(d-m))*m+g}return h}function equallySpacedSlot(t,e,r,i,n){var s=t.length,o=(i-r)/(n-1),a=o/2,h=t[t.length-1]-t[t.length-2],l=r-a,u=new Array(n),c=l,f=l+o,m=-Number.MAX_VALUE,g=0,d=t[0],p=e[0],w=0,x=!0,M=0,y=0,v=1,b=0;t:for(;;){if(m>=d)throw new Error("x must be an increasing serie");for(;m-f>0;){if(x&&(y++,x=!1),u[b]=y<=0?0:M/y,++b===n)break t;c=f,f+=o,M=0,y=0}m>c&&(M+=g,y++),(m===-Number.MAX_VALUE||w>1)&&y--,m=d,g=p,v<s?(d=t[v],p=e[v],v++):(d+=h,p=0,w++)}return u}function getZones(t,e,r){let i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:[];t>e&&([t,e]=[e,t]),i=i.filter(t=>void 0!==t.from&&void 0!==t.to),(i=JSON.parse(JSON.stringify(i))).forEach(t=>{t.from>t.to&&([t.to,t.from]=[t.from,t.to])}),i.sort((t,e)=>t.from-e.from),i.forEach(r=>{r.from<t&&(r.from=t),r.to>e&&(r.to=e)});for(let t=0;t<i.length-1;t++)i[t].to>i[t+1].from&&(i[t].to=i[t+1].from);if(!(i=i.filter(t=>t.from<t.to))||0===i.length)return[{from:t,to:e,numberOfPoints:r}];let n=i.reduce((t,e)=>t+=e.to-e.from,0),s=(e-t-n)/r,o=[],a=t,h=0;for(let t of i){let e=Math.round((t.from-a)/s);h+=e,e>0&&o.push({from:a,to:t.from,numberOfPoints:e}),a=t.to}return r-h>0&&o.push({from:a,to:e,numberOfPoints:r-h}),o}function equallySpaced(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},{x:r,y:i}=t,n=r.length,s=!1;r.length>1&&r[0]>r[1]&&(r=r.slice().reverse(),i=i.slice().reverse(),s=!0);let{from:o=r[0],to:a=r[n-1],variant:h="smooth",numberOfPoints:l=100,exclusions:u=[]}=e;if(n!==i.length)throw new RangeError("the x and y vector doesn't have the same size.");if("number"!=typeof o||isNaN(o))throw new RangeError("'from' option must be a number");if("number"!=typeof a||isNaN(a))throw new RangeError("'to' option must be a number");if("number"!=typeof l||isNaN(l))throw new RangeError("'numberOfPoints' option must be a number");if(l<2)throw new RangeError("'numberOfPoints' option must be greater than 1");let c=getZones(o,a,l,u),f=[],m=[];for(let t of c){let e=processZone(r,i,t.from,t.to,t.numberOfPoints,h);f=f.concat(e.x),m=m.concat(e.y)}return s?o<a?{x:f.reverse(),y:m.reverse()}:{x:f,y:m}:o<a?{x:f,y:m}:{x:f.reverse(),y:m.reverse()}}function processZone(t,e,r,i,n,s){if(n<1)throw new RangeError("the number of points must be at least 1");var o="slot"===s?equallySpacedSlot(t,e,r,i,n):equallySpacedSmooth(t,e,r,i,n);return{x:sequentialFill({from:r,to:i,size:n}),y:o}}function getZones$1(t,e){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:[];t>e&&([t,e]=[e,t]),r=r.filter(t=>void 0!==t.from&&void 0!==t.to),(r=JSON.parse(JSON.stringify(r))).forEach(t=>{t.from>t.to&&([t.to,t.from]=[t.from,t.to])}),r.sort((t,e)=>t.from-e.from),r.forEach(r=>{r.from<t&&(r.from=t),r.to>e&&(r.to=e)});for(let t=0;t<r.length-1;t++)r[t].to>r[t+1].from&&(r[t].to=r[t+1].from);if(!(r=r.filter(t=>t.from<t.to))||0===r.length)return[{from:t,to:e}];let i=[],n=t;for(let t of r)n<t.from&&i.push({from:n,to:t.from}),n=t.to;return n<e&&i.push({from:n,to:e}),i}function filterX(t){let e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{x:r,y:i}=t,{from:n=r[0],to:s=r[r.length-1],exclusions:o=[]}=e;let a=getZones$1(n,s,o),h=0,l=[],u=[],c=0;for(;c<r.length;){if(r[c]<=a[h].to&&r[c]>=a[h].from)l.push(r[c]),u.push(i[c]);else if(r[c]>a[h].to&&!a[++h])break;c++}return{x:l,y:u}}const{Matrix:Matrix$2,SVD:SVD,EVD:EVD,CholeskyDecomposition:CholeskyDecomposition$1,LuDecomposition:LuDecomposition$1,QrDecomposition:QrDecomposition$1}=MatrixLib,Array$1={min:min,max:max,median:median,mean:mean,mode:mode$1,normed:norm$1,rescale:rescale,sequentialFill:sequentialFill,standardDeviation:standardDeviation,sum:sum,variance:variance},ArrayXY={centroidsMerge:mergeByCentroids,closestX:closestX,covariance:covariance$1,maxMerge:maxMerge,maxY:maxY,sortX:sortX,uniqueX:uniqueX,weightedMerge:weightedMerge,equallySpaced:equallySpaced,filterX:filterX};exports.Array=Array$1,exports.ArrayXY=ArrayXY,exports.BitArray=src$7,exports.CholeskyDecomposition=CholeskyDecomposition$1,exports.ConfusionMatrix=src$1,exports.CrossValidation=src$3,exports.DecisionTreeClassifier=DecisionTreeClassifier,exports.DecisionTreeRegression=DecisionTreeRegression,exports.Distance=distances,exports.EVD=EVD,exports.ExponentialRegression=ExponentialRegression,exports.FCNNLS=index$2,exports.FNN=FeedForwardNeuralNetwork,exports.HClust=index,exports.HashTable=HashTable,exports.KMeans=kmeans,exports.KNN=KNN,exports.KOPLS=KOPLS,exports.Kernel=kernel,exports.LuDecomposition=LuDecomposition$1,exports.Matrix=Matrix$2,exports.MatrixLib=MatrixLib,exports.MultivariateLinearRegression=MultivariateLinearRegression,exports.NaiveBayes=index$1,exports.PCA=PCA,exports.PLS=PLS,exports.Performance=src$5,exports.PolynomialRegression=PolynomialRegression,exports.PowerRegression=PowerRegression,exports.QrDecomposition=QrDecomposition$1,exports.Random=Random,exports.RandomForestClassifier=RandomForestClassifier,exports.RandomForestRegression=RandomForestRegression,exports.RobustPolynomialRegression=RobustPolynomialRegression,exports.SOM=src$4,exports.SVD=SVD,exports.Similarity=similarities,exports.SimpleLinearRegression=SimpleLinearRegression,exports.SparseMatrix=SparseMatrix,exports.TheilSenRegression=TheilSenRegression,exports.XSadd=XSadd,exports.binarySearch=binarySearch,exports.distanceMatrix=distanceMatrix,exports.levenbergMarquardt=levenbergMarquardt,exports.numSort=index$3,exports.padArray=src$6,exports.savitzkyGolay=savitzkyGolay,Object.defineProperty(exports,"__esModule",{value:!0})}));
+   */function Node(t,e,r){this.obj=t,this.left=null,this.right=null,this.parent=r,this.dimension=e}class KDTree{constructor(t,e){if(Array.isArray(t)){this.dimensions=new Array(t[0].length);for(var r=0;r<this.dimensions.length;r++)this.dimensions[r]=r;this.root=buildTree(t,0,null,this.dimensions)}else this.dimensions=t.dimensions,this.root=t,restoreParent(this.root);this.metric=e}toJSON(){const t=toJSONImpl(this.root);return t.dimensions=this.dimensions,t}nearest(t,e,r){const n=this.metric,i=this.dimensions;var o;const s=new BinaryHeap((function(t){return-t[1]}));if(r)for(o=0;o<e;o+=1)s.push([null,r]);this.root&&function r(o){const a=i[o.dimension],l=n(t,o.obj),h={};var u,c,f,m;function g(t,r){s.push([t,r]),s.size()>e&&s.pop()}for(m=0;m<i.length;m+=1)m===o.dimension?h[i[m]]=t[i[m]]:h[i[m]]=o.obj[i[m]];c=n(h,o.obj),null!==o.right||null!==o.left?(r(u=null===o.right?o.left:null===o.left?o.right:t[a]<o.obj[a]?o.left:o.right),(s.size()<e||l<s.peek()[1])&&g(o,l),(s.size()<e||Math.abs(c)<s.peek()[1])&&null!==(f=u===o.left?o.right:o.left)&&r(f)):(s.size()<e||l<s.peek()[1])&&g(o,l)}(this.root);const a=[];for(o=0;o<Math.min(e,s.content.length);o+=1)s.content[o][0]&&a.push([s.content[o][0].obj,s.content[o][1]]);return a}}function toJSONImpl(t){const e=new Node(t.obj,t.dimension,null);return t.left&&(e.left=toJSONImpl(t.left)),t.right&&(e.right=toJSONImpl(t.right)),e}function buildTree(t,e,r,n){const i=e%n.length;if(0===t.length)return null;if(1===t.length)return new Node(t[0],i,r);t.sort(((t,e)=>t[n[i]]-e[n[i]]));const o=Math.floor(t.length/2),s=new Node(t[o],i,r);return s.left=buildTree(t.slice(0,o),e+1,s,n),s.right=buildTree(t.slice(o+1),e+1,s,n),s}function restoreParent(t){t.left&&(t.left.parent=t,restoreParent(t.left)),t.right&&(t.right.parent=t,restoreParent(t.right))}class BinaryHeap{constructor(t){this.content=[],this.scoreFunction=t}push(t){this.content.push(t),this.bubbleUp(this.content.length-1)}pop(){var t=this.content[0],e=this.content.pop();return this.content.length>0&&(this.content[0]=e,this.sinkDown(0)),t}peek(){return this.content[0]}size(){return this.content.length}bubbleUp(t){for(var e=this.content[t];t>0;){const r=Math.floor((t+1)/2)-1,n=this.content[r];if(!(this.scoreFunction(e)<this.scoreFunction(n)))break;this.content[r]=e,this.content[t]=n,t=r}}sinkDown(t){for(var e=this.content.length,r=this.content[t],n=this.scoreFunction(r);;){var i=2*(t+1),o=i-1,s=null;if(o<e){var a=this.content[o],l=this.scoreFunction(a);l<n&&(s=o)}if(i<e){var h=this.content[i];this.scoreFunction(h)<(null===s?n:l)&&(s=i)}if(null===s)break;this.content[t]=this.content[s],this.content[s]=r,t=s}}}class KNN{constructor(t,e,r={}){if(!0===t){const t=e;return this.kdTree=new KDTree(t.kdTree,r),this.k=t.k,this.classes=new Set(t.classes),void(this.isEuclidean=t.isEuclidean)}const n=new Set(e),{distance:i=euclidean,k:o=n.size+1}=r,s=new Array(t.length);for(var a=0;a<s.length;++a)s[a]=t[a].slice();for(a=0;a<e.length;++a)s[a].push(e[a]);this.kdTree=new KDTree(s,i),this.k=o,this.classes=n,this.isEuclidean=i===euclidean}static load(t,e=euclidean){if("KNN"!==t.name)throw new Error("invalid model: "+t.name);if(!t.isEuclidean&&e===euclidean)throw new Error("a custom distance function was used to create the model. Please provide it again");if(t.isEuclidean&&e!==euclidean)throw new Error("the model was created with the default distance function. Do not load it with another one");return new KNN(!0,t,e)}toJSON(){return{name:"KNN",kdTree:this.kdTree,k:this.k,classes:Array.from(this.classes),isEuclidean:this.isEuclidean}}predict(t){if(Array.isArray(t)){if("number"==typeof t[0])return getSinglePrediction(this,t);if(Array.isArray(t[0])&&"number"==typeof t[0][0]){const r=new Array(t.length);for(var e=0;e<t.length;e++)r[e]=getSinglePrediction(this,t[e]);return r}}throw new TypeError("dataset to predict must be an array or a matrix")}}function getSinglePrediction(t,e){var r=t.kdTree.nearest(e,t.k),n={},i=-1,o=-1,s=r[0][0].length-1;for(var a of t.classes)n[a]=0;for(var l=0;l<r.length;++l){var h=r[l][0][s],u=++n[h];u>o&&(i=h,o=u)}return i}function norm(t){return Math.sqrt(t.clone().apply(pow2array).sum())}function pow2array(t,e){this.set(t,e,this.get(t,e)**2)}function initializeMatrices(t,e){if(e)for(let e=0;e<t.length;++e)for(let r=0;r<t[e].length;++r){let n=t[e][r];t[e][r]=null!==n?new Matrix(t[e][r]):void 0}else for(let e=0;e<t.length;++e)t[e]=new Matrix(t[e]);return t}class PLS{constructor(t,e){if(!0===t)this.meanX=e.meanX,this.stdDevX=e.stdDevX,this.meanY=e.meanY,this.stdDevY=e.stdDevY,this.PBQ=Matrix.checkMatrix(e.PBQ),this.R2X=e.R2X,this.scale=e.scale,this.scaleMethod=e.scaleMethod,this.tolerance=e.tolerance;else{let{tolerance:e=1e-5,scale:r=!0}=t;this.tolerance=e,this.scale=r,this.latentVectors=t.latentVectors}}train(t,e){if(t=Matrix.checkMatrix(t),e=Matrix.checkMatrix(e),t.length!==e.length)throw new RangeError("The number of X rows must be equal to the number of Y rows");this.meanX=t.mean("column"),this.stdDevX=t.standardDeviation("column",{mean:this.meanX,unbiased:!0}),this.meanY=e.mean("column"),this.stdDevY=e.standardDeviation("column",{mean:this.meanY,unbiased:!0}),this.scale&&(t=t.clone().subRowVector(this.meanX).divRowVector(this.stdDevX),e=e.clone().subRowVector(this.meanY).divRowVector(this.stdDevY)),void 0===this.latentVectors&&(this.latentVectors=Math.min(t.rows-1,t.columns));let r,n,i,o,s=t.rows,a=t.columns,l=e.rows,h=e.columns,u=t.clone().mul(t).sum(),c=e.clone().mul(e).sum(),f=this.tolerance,m=this.latentVectors,g=Matrix.zeros(s,m),p=Matrix.zeros(a,m),d=Matrix.zeros(l,m),w=Matrix.zeros(h,m),y=Matrix.zeros(m,m),x=p.clone(),M=0;for(;norm(e)>f&&M<m;){let a=t.transpose(),l=e.transpose(),h=maxSumColIndex(t.clone().mul(t)),u=maxSumColIndex(e.clone().mul(e)),c=t.getColumnVector(h),m=e.getColumnVector(u);for(r=Matrix.zeros(s,1);norm(c.clone().sub(r))>f;)n=a.mmul(m),n.div(norm(n)),r=c,c=t.mmul(n),i=l.mmul(c),i.div(norm(i)),m=e.mmul(i);r=c;let v=a.mmul(r),b=r.transpose().mmul(r).get(0,0);o=v.div(b);let S=norm(o);o.div(S),r.mul(S),n.mul(S),v=m.transpose().mmul(r),b=r.transpose().mmul(r).get(0,0);let A=v.div(b).get(0,0);t.sub(r.mmul(o.transpose())),e.sub(r.clone().mul(A).mmul(i.transpose())),g.setColumn(M,r),p.setColumn(M,o),d.setColumn(M,m),w.setColumn(M,i),x.setColumn(M,n),y.set(M,M,A),M++}M--,g=g.subMatrix(0,g.rows-1,0,M),p=p.subMatrix(0,p.rows-1,0,M),d=d.subMatrix(0,d.rows-1,0,M),w=w.subMatrix(0,w.rows-1,0,M),x=x.subMatrix(0,x.rows-1,0,M),y=y.subMatrix(0,M,0,M),this.ssqYcal=c,this.E=t,this.F=e,this.T=g,this.P=p,this.U=d,this.Q=w,this.W=x,this.B=y,this.PBQ=p.mmul(y).mmul(w.transpose()),this.R2X=r.transpose().mmul(r).mmul(o.transpose().mmul(o)).div(u).get(0,0)}predict(t){let e=Matrix.checkMatrix(t);this.scale&&(e=e.subRowVector(this.meanX).divRowVector(this.stdDevX));let r=e.mmul(this.PBQ);return r=r.mulRowVector(this.stdDevY).addRowVector(this.meanY),r}getExplainedVariance(){return this.R2X}toJSON(){return{name:"PLS",R2X:this.R2X,meanX:this.meanX,stdDevX:this.stdDevX,meanY:this.meanY,stdDevY:this.stdDevY,PBQ:this.PBQ,tolerance:this.tolerance,scale:this.scale}}static load(t){if("PLS"!==t.name)throw new RangeError("Invalid model: "+t.name);return new PLS(!0,t)}}function maxSumColIndex(t){return Matrix.rowVector(t.sum("column")).maxIndex()[0]}class KOPLS{constructor(t,e){if(!0===t)this.trainingSet=new Matrix(e.trainingSet),this.YLoadingMat=new Matrix(e.YLoadingMat),this.SigmaPow=new Matrix(e.SigmaPow),this.YScoreMat=new Matrix(e.YScoreMat),this.predScoreMat=initializeMatrices(e.predScoreMat,!1),this.YOrthLoadingVec=initializeMatrices(e.YOrthLoadingVec,!1),this.YOrthEigen=e.YOrthEigen,this.YOrthScoreMat=initializeMatrices(e.YOrthScoreMat,!1),this.toNorm=initializeMatrices(e.toNorm,!1),this.TURegressionCoeff=initializeMatrices(e.TURegressionCoeff,!1),this.kernelX=initializeMatrices(e.kernelX,!0),this.kernel=e.kernel,this.orthogonalComp=e.orthogonalComp,this.predictiveComp=e.predictiveComp;else{if(void 0===t.predictiveComponents)throw new RangeError("no predictive components found!");if(void 0===t.orthogonalComponents)throw new RangeError("no orthogonal components found!");if(void 0===t.kernel)throw new RangeError("no kernel found!");this.orthogonalComp=t.orthogonalComponents,this.predictiveComp=t.predictiveComponents,this.kernel=t.kernel}}train(t,e){t=Matrix.checkMatrix(t),e=Matrix.checkMatrix(e),this.trainingSet=t.clone();let r=this.kernel.compute(t),n=Matrix.eye(r.rows,r.rows,1),i=r;r=new Array(this.orthogonalComp+1);for(let t=0;t<this.orthogonalComp+1;t++)r[t]=new Array(this.orthogonalComp+1);r[0][0]=i;let o=new SingularValueDecomposition(e.transpose().mmul(r[0][0]).mmul(e),{computeLeftSingularVectors:!0,computeRightSingularVectors:!1}),s=o.leftSingularVectors,a=o.diagonalMatrix;s=s.subMatrix(0,s.rows-1,0,this.predictiveComp-1),a=a.subMatrix(0,this.predictiveComp-1,0,this.predictiveComp-1);let l=e.mmul(s),h=new Array(this.orthogonalComp+1),u=new Array(this.orthogonalComp+1),c=new Array(this.orthogonalComp),f=new Array(this.orthogonalComp),m=new Array(this.orthogonalComp),g=new Array(this.orthogonalComp),p=Matrix.pow(a,-.5);p.apply((function(t,e){this.get(t,e)===1/0&&this.set(t,e,0)}));for(let t=0;t<this.orthogonalComp;++t){h[t]=r[0][t].transpose().mmul(l).mmul(p);let e=h[t].transpose();u[t]=inverse(e.mmul(h[t])).mmul(e).mmul(l),o=new SingularValueDecomposition(e.mmul(Matrix.sub(r[t][t],h[t].mmul(e))).mmul(h[t]),{computeLeftSingularVectors:!0,computeRightSingularVectors:!1});let i=o.leftSingularVectors,s=o.diagonalMatrix;f[t]=i.subMatrix(0,i.rows-1,0,0),m[t]=s.get(0,0),c[t]=Matrix.sub(r[t][t],h[t].mmul(e)).mmul(h[t]).mmul(f[t]).mul(Math.pow(m[t],-.5));let a=c[t].transpose();g[t]=Matrix.sqrt(a.mmul(c[t])),c[t]=c[t].divRowVector(g[t]);let d=Matrix.sub(n,c[t].mmul(c[t].transpose()));r[0][t+1]=r[0][t].mmul(d),r[t+1][t+1]=d.mmul(r[t][t]).mmul(d)}let d=h[this.orthogonalComp]=r[0][this.orthogonalComp].transpose().mmul(l).mmul(p),w=d.transpose();u[this.orthogonalComp]=inverse(w.mmul(d)).mmul(w).mmul(l),this.YLoadingMat=s,this.SigmaPow=p,this.YScoreMat=l,this.predScoreMat=h,this.YOrthLoadingVec=f,this.YOrthEigen=m,this.YOrthScoreMat=c,this.toNorm=g,this.TURegressionCoeff=u,this.kernelX=r}predict(t){let e=this.kernel.compute(t,this.trainingSet),r=e;e=new Array(this.orthogonalComp+1);for(let t=0;t<this.orthogonalComp+1;t++)e[t]=new Array(this.orthogonalComp+1);e[0][0]=r;let n,i=new Array(this.orthogonalComp),o=new Array(this.orthogonalComp);for(n=0;n<this.orthogonalComp;++n){o[n]=e[n][0].mmul(this.YScoreMat).mmul(this.SigmaPow),i[n]=Matrix.sub(e[n][n],o[n].mmul(this.predScoreMat[n].transpose())).mmul(this.predScoreMat[n]).mmul(this.YOrthLoadingVec[n]).mul(Math.pow(this.YOrthEigen[n],-.5)),i[n]=i[n].divRowVector(this.toNorm[n]);let t=this.YOrthScoreMat[n].transpose();e[n+1][0]=Matrix.sub(e[n][0],i[n].mmul(t).mmul(this.kernelX[0][n].transpose()));let r=Matrix.sub(e[n][0],e[n][n].mmul(this.YOrthScoreMat[n]).mmul(t)),s=i[n].mmul(t).mmul(this.kernelX[n][n]),a=s.mmul(this.YOrthScoreMat[n]).mmul(t);e[n+1][n+1]=r.sub(s).add(a)}return o[n]=e[n][0].mmul(this.YScoreMat).mmul(this.SigmaPow),{prediction:o[n].mmul(this.TURegressionCoeff[n]).mmul(this.YLoadingMat.transpose()),predScoreMat:o,predYOrthVectors:i}}toJSON(){return{name:"K-OPLS",YLoadingMat:this.YLoadingMat,SigmaPow:this.SigmaPow,YScoreMat:this.YScoreMat,predScoreMat:this.predScoreMat,YOrthLoadingVec:this.YOrthLoadingVec,YOrthEigen:this.YOrthEigen,YOrthScoreMat:this.YOrthScoreMat,toNorm:this.toNorm,TURegressionCoeff:this.TURegressionCoeff,kernelX:this.kernelX,trainingSet:this.trainingSet,orthogonalComp:this.orthogonalComp,predictiveComp:this.predictiveComp}}static load(t,e){if("K-OPLS"!==t.name)throw new RangeError("Invalid model: "+t.name);if(!e)throw new RangeError("You must provide a kernel for the model!");return t.kernel=e,new KOPLS(!0,t)}}class ConfusionMatrix{constructor(t,e){if(t.length!==t[0].length)throw new Error("Confusion matrix must be square");if(e.length!==t.length)throw new Error("Confusion matrix and labels should have the same length");this.labels=e,this.matrix=t}static fromLabels(t,e,r={}){if(e.length!==t.length)throw new Error("predicted and actual must have the same length");let n;n=r.labels?new Set(r.labels):new Set([...t,...e]),n=Array.from(n),r.sort&&n.sort(r.sort);const i=Array.from({length:n.length});for(let t=0;t<i.length;t++)i[t]=new Array(i.length),i[t].fill(0);for(let r=0;r<e.length;r++){const o=n.indexOf(t[r]),s=n.indexOf(e[r]);o>=0&&s>=0&&i[o][s]++}return new ConfusionMatrix(i,n)}getMatrix(){return this.matrix}getLabels(){return this.labels}getTotalCount(){let t=0;for(let e=0;e<this.matrix.length;e++)for(let r=0;r<this.matrix.length;r++)t+=this.matrix[e][r];return t}getTrueCount(){let t=0;for(let e=0;e<this.matrix.length;e++)t+=this.matrix[e][e];return t}getFalseCount(){return this.getTotalCount()-this.getTrueCount()}getTruePositiveCount(t){const e=this.getIndex(t);return this.matrix[e][e]}getTrueNegativeCount(t){const e=this.getIndex(t);let r=0;for(let t=0;t<this.matrix.length;t++)for(let n=0;n<this.matrix.length;n++)t!==e&&n!==e&&(r+=this.matrix[t][n]);return r}getFalsePositiveCount(t){const e=this.getIndex(t);let r=0;for(let t=0;t<this.matrix.length;t++)t!==e&&(r+=this.matrix[t][e]);return r}getFalseNegativeCount(t){const e=this.getIndex(t);let r=0;for(let t=0;t<this.matrix.length;t++)t!==e&&(r+=this.matrix[e][t]);return r}getPositiveCount(t){return this.getTruePositiveCount(t)+this.getFalseNegativeCount(t)}getNegativeCount(t){return this.getTrueNegativeCount(t)+this.getFalsePositiveCount(t)}getIndex(t){const e=this.labels.indexOf(t);if(-1===e)throw new Error("The label does not exist");return e}getTruePositiveRate(t){return this.getTruePositiveCount(t)/this.getPositiveCount(t)}getTrueNegativeRate(t){return this.getTrueNegativeCount(t)/this.getNegativeCount(t)}getPositivePredictiveValue(t){const e=this.getTruePositiveCount(t);return e/(e+this.getFalsePositiveCount(t))}getNegativePredictiveValue(t){const e=this.getTrueNegativeCount(t);return e/(e+this.getFalseNegativeCount(t))}getFalseNegativeRate(t){return 1-this.getTruePositiveRate(t)}getFalsePositiveRate(t){return 1-this.getTrueNegativeRate(t)}getFalseDiscoveryRate(t){const e=this.getFalsePositiveCount(t);return e/(e+this.getTruePositiveCount(t))}getFalseOmissionRate(t){const e=this.getFalseNegativeCount(t);return e/(e+this.getTruePositiveCount(t))}getF1Score(t){const e=this.getTruePositiveCount(t);return 2*e/(2*e+this.getFalsePositiveCount(t)+this.getFalseNegativeCount(t))}getMatthewsCorrelationCoefficient(t){const 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nipals(M,{Y:x}),b=M.clone().sub(v.t.mmul(v.p)),S=this.model.map((t=>t.R2x)),A=this.model.map((t=>t.R2y));this.output={Q2y:u,R2x:S,R2y:A,tPred:d.plsC.t,pPred:d.plsC.p,wPred:d.plsC.w,betasPred:d.plsC.betas,Qpc:d.plsC.q,tCV:g,tOrthCV:p,tOrth:d.tOrth,pOrth:d.pOrth,wOrth:d.wOrth,XOrth:w,yHat:d.totalPred,Yres:d.plsC.yResidual,E:b}}getLogs(){return this.output}getScores(){return{scoresX:this.tCV.map((t=>t.to1DArray())),scoresY:this.tOrthCV.map((t=>t.to1DArray()))}}static load(t){if("string"!=typeof t.name)throw new TypeError("model must have a name property");if("OPLS"!==t.name)throw new RangeError("invalid model: "+t.name);return new OPLS(!0,[],t)}toJSON(){return{name:"OPLS",center:this.center,scale:this.scale,means:this.means,stdevs:this.stdevs,model:this.model,tCV:this.tCV,tOrthCV:this.tOrthCV,yHatCV:this.yHatCV}}predict(t,e={}){let{trueLabels:r=[],nc:n=1}=e,i=[];r.length>0&&(r=Matrix.from1DArray(r.length,1,r),i=r.clone());let o=t.clone();this.center&&(o.center("column",{center:this.means}),i.rows>0&&"regression"===this.mode&&i.center("column",{center:this.meansY})),this.scale&&(o.scale("column",{scale:this.stdevs}),i.rows>0&&"regression"===this.mode&&i.scale("column",{scale:this.stdevsY}));let s,a,l,h,u,c=o.clone();for(let t=0;t<n;t++)a=this.model[t].wOrth.transpose(),l=this.model[t].pOrth,s=c.mmul(a),c.sub(s.mmul(l)),u=c.mmul(this.model[t].plsC.w.transpose()),h=u.mmul(this.model[t].plsC.betas);if(!(i.rows>0))return{tPred:u,tOrth:s,yHat:h};if("regression"===this.mode){let t=tss(i);return{tPred:u,tOrth:s,yHat:h,Q2y:1-tss(i.clone().sub(h))/t}}if("discriminantAnalysis"===this.mode){let t=[];return t=ConfusionMatrix.fromLabels(r.to1DArray(),h.to1DArray()),{tPred:u,tOrth:s,yHat:h,confusionMatrix:t}}}_predictAll(t,e,r={}){const{center:n=!0,scale:i=!0}=r;n&&(t.center("column"),e.center("column")),i&&(t.scale("column"),e.scale("column"),this.tssy=tss(e),this.tssx=tss(t));let o=OPLSNipals(t,e),s=new nipals(o.filteredX,{Y:e}),a=o.filteredX.mmul(s.w.transpose()),l=a.mmul(s.betas);return{R2y:1-tss(e.clone().sub(l))/this.tssy,R2x:tss(s.t.mmul(s.p))/this.tssx,xRes:o.filteredX,tOrth:o.scoresXOrtho,pOrth:o.loadingsXOrtho,wOrth:o.weightsXOrtho,tPred:a,totalPred:l,XOrth:o.scoresXOrtho.mmul(o.loadingsXOrtho),oplsC:o,plsC:s}}_getTrainTest(t,e,r){let n=new Matrix(r.testIndex.length,t.columns),i=new Matrix(r.testIndex.length,1);r.testIndex.forEach(((r,o)=>{n.setRow(o,t.getRow(r)),i.setRow(o,e.getRow(r))}));let o=new Matrix(r.trainIndex.length,t.columns),s=new Matrix(r.trainIndex.length,1);return r.trainIndex.forEach(((r,n)=>{o.setRow(n,t.getRow(r)),s.setRow(n,e.getRow(r))})),{trainFeatures:o,testFeatures:n,trainLabels:s,testLabels:i}}}var require$$0=getAugmentedNamespace(MatrixLib);function logistic(t){return 1/(1+Math.exp(-t))}function expELU(t,e){return t<0?e*(Math.exp(t)-1):t}function softExponential(t,e){return e<0?-Math.log(1-e*(t+e))/e:e>0?(Math.exp(e*t)-1)/e+e:t}function softExponentialPrime(t,e){return e<0?1/(1-e*(e+t)):Math.exp(e*t)}const ACTIVATION_FUNCTIONS={tanh:{activation:Math.tanh,derivate:t=>1-t*t},identity:{activation:t=>t,derivate:()=>1},logistic:{activation:logistic,derivate:t=>logistic(t)*(1-logistic(t))},arctan:{activation:Math.atan,derivate:t=>1/(t*t+1)},softsign:{activation:t=>t/(1+Math.abs(t)),derivate:t=>1/((1+Math.abs(t))*(1+Math.abs(t)))},relu:{activation:t=>t<0?0:t,derivate:t=>t<0?0:1},softplus:{activation:t=>Math.log(1+Math.exp(t)),derivate:t=>1/(1+Math.exp(-t))},bent:{activation:t=>(Math.sqrt(t*t+1)-1)/2+t,derivate:t=>t/(2*Math.sqrt(t*t+1))+1},sinusoid:{activation:Math.sin,derivate:Math.cos},sinc:{activation:t=>0===t?1:Math.sin(t)/t,derivate:t=>0===t?0:Math.cos(t)/t-Math.sin(t)/(t*t)},gaussian:{activation:t=>Math.exp(-t*t),derivate:t=>-2*t*Math.exp(-t*t)},"parametric-relu":{activation:(t,e)=>t<0?e*t:t,derivate:(t,e)=>t<0?e:1},"exponential-elu":{activation:expELU,derivate:(t,e)=>t<0?expELU(t,e)+e:1},"soft-exponential":{activation:softExponential,derivate:softExponentialPrime}};class Layer{constructor(t){this.inputSize=t.inputSize,this.outputSize=t.outputSize,this.regularization=t.regularization,this.epsilon=t.epsilon,this.activation=t.activation,this.activationParam=t.activationParam;var e=ACTIVATION_FUNCTIONS[t.activation],r=e.activation.length,n=r>1?r=>e.activation(r,t.activationParam):e.activation,i=r>1?r=>e.derivate(r,t.activationParam):e.derivate;this.activationFunction=function(t,e){this.set(t,e,n(this.get(t,e)))},this.derivate=function(t,e){this.set(t,e,i(this.get(t,e)))},t.model?(this.W=require$$0.Matrix.checkMatrix(t.W),this.b=require$$0.Matrix.checkMatrix(t.b)):(this.W=require$$0.Matrix.rand(this.inputSize,this.outputSize),this.b=require$$0.Matrix.zeros(1,this.outputSize),this.W.apply((function(e,r){this.set(e,r,this.get(e,r)/Math.sqrt(t.inputSize))})))}forward(t){var e=t.mmul(this.W).addRowVector(this.b);return e.apply(this.activationFunction),this.a=e.clone(),e}backpropagation(t,e){this.dW=e.transpose().mmul(t),this.db=require$$0.Matrix.rowVector(t.sum("column"));var r=e.clone();return t.mmul(this.W.transpose()).mul(r.apply(this.derivate))}update(){this.dW.add(this.W.clone().mul(this.regularization)),this.W.add(this.dW.mul(-this.epsilon)),this.b.add(this.db.mul(-this.epsilon))}toJSON(){return{model:"Layer",inputSize:this.inputSize,outputSize:this.outputSize,regularization:this.regularization,epsilon:this.epsilon,activation:this.activation,W:this.W,b:this.b}}static load(t){if("Layer"!==t.model)throw new RangeError("the current model is not a Layer model");return new Layer(t)}}class OutputLayer extends Layer{constructor(t){super(t),this.activationFunction=function(t,e){this.set(t,e,Math.exp(this.get(t,e)))}}static load(t){if("Layer"!==t.model)throw new RangeError("the current model is not a Layer model");return new OutputLayer(t)}}class FeedForwardNeuralNetworks{constructor(t){if((t=t||{}).model){this.hiddenLayers=t.hiddenLayers,this.iterations=t.iterations,this.learningRate=t.learningRate,this.regularization=t.regularization,this.dicts=t.dicts,this.activation=t.activation,this.activationParam=t.activationParam,this.model=new Array(t.layers.length);for(var e=0;e<this.model.length-1;++e)this.model[e]=Layer.load(t.layers[e]);this.model[this.model.length-1]=OutputLayer.load(t.layers[this.model.length-1])}else this.hiddenLayers=t.hiddenLayers||[10],this.iterations=t.iterations||50,this.learningRate=t.learningRate||.01,this.regularization=t.regularization||.01,this.activation=t.activation||"tanh",this.activationParam=t.activationParam||1,this.activation in Object.keys(ACTIVATION_FUNCTIONS)||(this.activation="tanh")}buildNetwork(t,e){var r=this.hiddenLayers.length-1+2;this.model=new Array(r),this.model[0]=new Layer({inputSize:t,outputSize:this.hiddenLayers[0],activation:this.activation,activationParam:this.activationParam,regularization:this.regularization,epsilon:this.learningRate});for(var n=1;n<this.hiddenLayers.length;++n)this.model[n]=new Layer({inputSize:this.hiddenLayers[n-1],outputSize:this.hiddenLayers[n],activation:this.activation,activationParam:this.activationParam,regularization:this.regularization,epsilon:this.learningRate});this.model[r-1]=new OutputLayer({inputSize:this.hiddenLayers[this.hiddenLayers.length-1],outputSize:e,activation:this.activation,activationParam:this.activationParam,regularization:this.regularization,epsilon:this.learningRate})}train(t,e){t=require$$0.Matrix.checkMatrix(t),this.dicts=dictOutputs(e);var r=t.columns,n=Object.keys(this.dicts.inputs).length;this.model||this.buildNetwork(r,n);for(var i=0;i<this.iterations;++i){var o=this.propagate(t);this.backpropagation(t,e,o)}}propagate(t){for(var e=t,r=0;r<this.model.length;++r)e=this.model[r].forward(e);return e.divColumnVector(e.sum("row"))}backpropagation(t,e,r){for(var n=0;n<r.rows;++n)r.set(n,this.dicts.inputs[e[n]],r.get(n,this.dicts.inputs[e[n]])-1);var i=r;for(n=this.model.length-1;n>=0;--n){var o=n>0?this.model[n-1].a:t;i=this.model[n].backpropagation(i,o)}for(n=0;n<this.model.length;++n)this.model[n].update()}predict(t){t=require$$0.Matrix.checkMatrix(t);for(var e=new Array(t.rows),r=this.propagate(t),n=0;n<t.rows;++n)e[n]=this.dicts.outputs[r.maxRowIndex(n)[1]];return e}toJSON(){for(var t={model:"FNN",hiddenLayers:this.hiddenLayers,iterations:this.iterations,learningRate:this.learningRate,regularization:this.regularization,activation:this.activation,activationParam:this.activationParam,dicts:this.dicts,layers:new Array(this.model.length)},e=0;e<this.model.length;++e)t.layers[e]=this.model[e].toJSON();return t}static load(t){if("FNN"!==t.model)throw new RangeError("the current model is not a feed forward network");return new FeedForwardNeuralNetworks(t)}}function dictOutputs(t){for(var e={},r={},n=0,i=0;i<t.length;i+=1)void 0===e[t[i]]&&(e[t[i]]=n,r[n]=t[i],n++);return{inputs:e,outputs:r}}var FeedForwardNeuralNetwork=FeedForwardNeuralNetworks;function NodeSquare(t,e,r,n){this.x=t,this.y=e,this.weights=r,this.som=n,this.neighbors={}}NodeSquare.prototype.adjustWeights=function(t,e,r){for(var n=0,i=this.weights.length;n<i;n++)this.weights[n]+=e*r*(t[n]-this.weights[n])},NodeSquare.prototype.getDistance=function(t){return Math.max(Math.abs(this.x-t.x),Math.abs(this.y-t.y))},NodeSquare.prototype.getDistanceTorus=function(t){var e=Math.abs(this.x-t.x),r=Math.abs(this.y-t.y);return Math.max(Math.min(e,this.som.gridDim.x-e),Math.min(r,this.som.gridDim.y-r))},NodeSquare.prototype.getNeighbors=function(t){if(!this.neighbors[t]){var e,r,n,i;if(this.neighbors[t]=new Array(2),this[t]>0?e=this[t]-1:this.som.torus&&(e=this.som.gridDim[t]-1),void 0!==e)"x"===t?(r=e,n=this.y):(r=this.x,n=e),this.neighbors[t][0]=this.som.nodes[r][n];this[t]<this.som.gridDim[t]-1?i=this[t]+1:this.som.torus&&(i=0),void 0!==i&&("x"===t?(r=i,n=this.y):(r=this.x,n=i),this.neighbors[t][1]=this.som.nodes[r][n])}return this.neighbors[t]},NodeSquare.prototype.getPos=function(t,e){var r,n,i=this.getNeighbors(t),o=this.som.distance;i[0]?i[1]?o(e,i[0].weights)<o(e,i[1].weights)?(r=i[0],n=-1):(r=i[1],n=1):(r=i[0],n=-1):(r=i[1],n=1);var s=1-o(e,this.weights),a=1-o(e,r.weights);return.5+.5*((s-a)/(2-s-a))*n},NodeSquare.prototype.getPosition=function(t){return[this.getPos("x",t),this.getPos("y",t)]};var nodeSquare=NodeSquare;function NodeHexagonal(t,e,r,n){nodeSquare.call(this,t,e,r,n),this.hX=t-Math.floor(e/2),this.z=0-this.hX-e}NodeHexagonal.prototype=new nodeSquare,NodeHexagonal.prototype.constructor=NodeHexagonal,NodeHexagonal.prototype.getDistance=function(t){return Math.max(Math.abs(this.hX-t.hX),Math.abs(this.y-t.y),Math.abs(this.z-t.z))},NodeHexagonal.prototype.getDistanceTorus=function(t){var e=Math.abs(this.hX-t.hX),r=Math.abs(this.y-t.y),n=Math.abs(this.z-t.z);return Math.max(Math.min(e,this.som.gridDim.x-e),Math.min(r,this.som.gridDim.y-r),Math.min(n,this.som.gridDim.z-n))},NodeHexagonal.prototype.getPosition=function(){throw new Error("Unimplemented : cannot get position of the points for hexagonal grid")};var nodeHexagonal=NodeHexagonal,defaultOptions$6={fields:3,randomizer:Math.random,distance:squareEuclidean,iterations:10,learningRate:.1,gridType:"rect",torus:!0,method:"random"};function SOM(t,e,r,n){for(var i in this.x=t,this.y=e,r=r||{},this.options={},defaultOptions$6)r.hasOwnProperty(i)?this.options[i]=r[i]:this.options[i]=defaultOptions$6[i];if("number"==typeof this.options.fields)this.numWeights=this.options.fields;else{if(!Array.isArray(this.options.fields))throw new Error("Invalid fields definition");this.numWeights=this.options.fields.length;var o=getConverters(this.options.fields);this.extractor=o.extractor,this.creator=o.creator}if("rect"===this.options.gridType)this.nodeType=nodeSquare,this.gridDim={x:t,y:e};else{this.nodeType=nodeHexagonal;var s=this.x-Math.floor(this.y/2);this.gridDim={x:s,y:this.y,z:-(0-s-this.y)}}if(this.torus=this.options.torus,this.distanceMethod=this.torus?"getDistanceTorus":"getDistance",this.distance=this.options.distance,this.maxDistance=getMaxDistance(this.distance,this.numWeights),!0!==n){if(!(t>0&&e>0))throw new Error("x and y must be positive");this.times={findBMU:0,adjust:0},this.randomizer=this.options.randomizer,this.iterationCount=0,this.iterations=this.options.iterations,this.startLearningRate=this.learningRate=this.options.learningRate,this.mapRadius=Math.floor(Math.max(t,e)/2),this.algorithmMethod=this.options.method,this._initNodes(),this.done=!1}else this.done=!0}function getConverters(t){for(var e=t.length,r=new Array(e),n=new Array(e),i=0;i<e;i++)r[i]=getNormalizer(t[i].range),n[i]=getDenormalizer(t[i].range);return{extractor:function(n){for(var i=new Array(e),o=0;o<e;o++)i[o]=r[o](n[t[o].name]);return i},creator:function(r){for(var i={},o=0;o<e;o++)i[t[o].name]=n[o](r[o]);return i}}}function getNormalizer(t){return function(e){return(e-t[0])/(t[1]-t[0])}}function getDenormalizer(t){return function(e){return t[0]+e*(t[1]-t[0])}}function squareEuclidean(t,e){for(var r=0,n=0,i=t.length;n<i;n++)r+=(t[n]-e[n])*(t[n]-e[n]);return r}function getRandomValue(t,e){return t[Math.floor(e()*t.length)]}function getMaxDistance(t,e){for(var r=new Array(e),n=new Array(e),i=0;i<e;i++)r[i]=0,n[i]=1;return t(r,n)}SOM.load=function loadModel(model,distance){if("SOM"===model.name){var x=model.data.length,y=model.data[0].length;distance?model.options.distance=distance:model.options.distance&&(model.options.distance=eval("("+model.options.distance+")"));var som=new SOM(x,y,model.options,!0);som.nodes=new Array(x);for(var i=0;i<x;i++){som.nodes[i]=new Array(y);for(var j=0;j<y;j++)som.nodes[i][j]=new som.nodeType(i,j,model.data[i][j],som)}return som}throw new Error("expecting a SOM model")},SOM.prototype.export=function(t){if(!this.done)throw new Error("model is not ready yet");var e={name:"SOM"};e.options={fields:this.options.fields,gridType:this.options.gridType,torus:this.options.torus},e.data=new Array(this.x);for(var r=0;r<this.x;r++){e.data[r]=new Array(this.y);for(var n=0;n<this.y;n++)e.data[r][n]=this.nodes[r][n].weights}return t&&(e.options.distance=this.distance.toString()),e},SOM.prototype._initNodes=function(){var t,e,r,n=Date.now();for(this.nodes=new Array(this.x),t=0;t<this.x;t++)for(this.nodes[t]=new Array(this.y),e=0;e<this.y;e++){var i=new Array(this.numWeights);for(r=0;r<this.numWeights;r++)i[r]=this.randomizer();this.nodes[t][e]=new this.nodeType(t,e,i,this)}this.times.initNodes=Date.now()-n},SOM.prototype.setTraining=function(t){if(this.trainingSet)throw new Error("training set has already been set");var e,r=Date.now(),n=t,i=t.length;if(this.extractor)for(n=new Array(i),e=0;e<i;e++)n[e]=this.extractor(t[e]);this.numIterations=this.iterations*i,"random"===this.algorithmMethod?this.timeConstant=this.numIterations/Math.log(this.mapRadius):this.timeConstant=i/Math.log(this.mapRadius),this.trainingSet=n,this.times.setTraining=Date.now()-r},SOM.prototype.trainOne=function(){return!this.done&&(this.numIterations-- >0?("random"===this.algorithmMethod?(t=this.mapRadius*Math.exp(-this.iterationCount/this.timeConstant),e=getRandomValue(this.trainingSet,this.randomizer),this._adjust(e,t),this.learningRate=this.startLearningRate*Math.exp(-this.iterationCount/this.numIterations)):(r=-Math.floor(this.iterationCount/this.trainingSet.length),t=this.mapRadius*Math.exp(r/this.timeConstant),e=this.trainingSet[this.iterationCount%this.trainingSet.length],this._adjust(e,t),(this.iterationCount+1)%this.trainingSet.length==0&&(this.learningRate=this.startLearningRate*Math.exp(r/Math.floor(this.numIterations/this.trainingSet.length)))),this.iterationCount++,!0):(this.done=!0,!1));var t,e,r},SOM.prototype._adjust=function(t,e){var r,n,i,o,s=Date.now(),a=this._findBestMatchingUnit(t),l=Date.now();this.times.findBMU+=l-s;var h=Math.floor(e),u=a.x-h,c=a.x+h,f=a.y-h,m=a.y+h;for(r=u;r<=c;r++){var g=r;for(r<0?g+=this.x:r>=this.x&&(g-=this.x),n=f;n<=m;n++){var p=n;n<0?p+=this.y:n>=this.y&&(p-=this.y),(i=a[this.distanceMethod](this.nodes[g][p]))<e&&(o=Math.exp(-i/(2*e)),this.nodes[g][p].adjustWeights(t,this.learningRate,o))}}this.times.adjust+=Date.now()-l},SOM.prototype.train=function(t){if(!this.done)for(this.setTraining(t);this.trainOne(););},SOM.prototype.getConvertedNodes=function(){for(var t=new Array(this.x),e=0;e<this.x;e++){t[e]=new Array(this.y);for(var r=0;r<this.y;r++){var n=this.nodes[e][r];t[e][r]=this.creator?this.creator(n.weights):n.weights}}return t},SOM.prototype._findBestMatchingUnit=function(t){for(var e,r,n=1/0,i=0;i<this.x;i++)for(var o=0;o<this.y;o++)(r=this.distance(this.nodes[i][o].weights,t))<n&&(n=r,e=this.nodes[i][o]);return e},SOM.prototype.predict=function(t,e){if("boolean"==typeof t&&(e=t,t=null),t||(t=this.trainingSet),Array.isArray(t)&&(Array.isArray(t[0])||"object"==typeof t[0])){var r=this;return t.map((function(t){return r._predict(t,e)}))}return this._predict(t,e)},SOM.prototype._predict=function(t,e){Array.isArray(t)||(t=this.extractor(t));var r=this._findBestMatchingUnit(t),n=[r.x,r.y];return e&&(n[2]=r.getPosition(t)),n},SOM.prototype.getQuantizationError=function(){for(var t=this.getFit(),e=t.length,r=0,n=0;n<e;n++)r+=t[n];return r/e},SOM.prototype.getFit=function(t){t||(t=this.trainingSet);for(var e,r=t.length,n=new Array(r),i=0;i<r;i++)e=this._findBestMatchingUnit(t[i]),n[i]=Math.sqrt(this.distance(t[i],e.weights));return n};var src=SOM;function maybeToPrecision(t,e){return t<0?(t=0-t,"number"==typeof e?"- "+t.toPrecision(e):"- "+t.toString()):"number"==typeof e?t.toPrecision(e):t.toString()}function checkArraySize(t,e){if(!Array.isArray(t)||!Array.isArray(e))throw new TypeError("x and y must be arrays");if(t.length!==e.length)throw new RangeError("x and y arrays must have the same length")}class BaseRegression{constructor(){if(new.target===BaseRegression)throw new Error("BaseRegression must be subclassed")}predict(t){if("number"==typeof t)return this._predict(t);if(Array.isArray(t)){const e=[];for(let r=0;r<t.length;r++)e.push(this._predict(t[r]));return e}throw new TypeError("x must be a number or array")}_predict(){throw new Error("_predict must be implemented")}train(){}toString(){return""}toLaTeX(){return""}score(t,e){if(!Array.isArray(t)||!Array.isArray(e)||t.length!==e.length)throw new Error("x and y must be arrays of the same length");const r=t.length,n=new Array(r);for(let e=0;e<r;e++)n[e]=this._predict(t[e]);let i=0,o=0,s=0,a=0,l=0,h=0,u=0;for(let t=0;t<r;t++)i+=n[t],o+=e[t],l+=n[t]*n[t],h+=e[t]*e[t],u+=n[t]*e[t],0!==e[t]&&(s+=(e[t]-n[t])*(e[t]-n[t])/e[t]),a+=(e[t]-n[t])*(e[t]-n[t]);const c=(r*u-i*o)/Math.sqrt((r*l-i*i)*(r*h-o*o));return{r:c,r2:c*c,chi2:s,rmsd:Math.sqrt(a/r)}}}class PolynomialRegression extends BaseRegression{constructor(t,e,r){super(),!0===t?(this.degree=e.degree,this.powers=e.powers,this.coefficients=e.coefficients):(checkArraySize(t,e),regress(this,t,e,r))}_predict(t){let e=0;for(let r=0;r<this.powers.length;r++)e+=this.coefficients[r]*Math.pow(t,this.powers[r]);return e}toJSON(){return{name:"polynomialRegression",degree:this.degree,powers:this.powers,coefficients:this.coefficients}}toString(t){return this._toFormula(t,!1)}toLaTeX(t){return this._toFormula(t,!0)}_toFormula(t,e){let r="^",n="",i=" * ";e&&(r="^{",n="}",i="");let o="",s="";for(let e=0;e<this.coefficients.length;e++)s="",0!==this.coefficients[e]&&(s=0===this.powers[e]?maybeToPrecision(this.coefficients[e],t):1===this.powers[e]?maybeToPrecision(this.coefficients[e],t)+i+"x":`${maybeToPrecision(this.coefficients[e],t)+i}x${r}${this.powers[e]}${n}`,this.coefficients[e]>0&&e!==this.coefficients.length-1?s=" + "+s:e!==this.coefficients.length-1&&(s=" "+s)),o=s+o;return"+"===o.charAt(0)&&(o=o.slice(1)),"f(x) = "+o}static load(t){if("polynomialRegression"!==t.name)throw new TypeError("not a polynomial regression model");return new PolynomialRegression(!0,t)}}function regress(t,e,r,n){const i=e.length;let o;if(Array.isArray(n))o=n,n=o.length;else{n++,o=new Array(n);for(let t=0;t<n;t++)o[t]=t}const s=new Matrix(i,n),a=new Matrix([r]);for(let t=0;t<n;t++)for(let r=0;r<i;r++)0===o[t]?s.set(r,t,1):s.set(r,t,Math.pow(e[r],o[t]));const l=new MatrixTransposeView(s),h=l.mmul(s),u=l.mmul(new MatrixTransposeView(a));t.degree=n-1,t.powers=o,t.coefficients=solve(h,u).to1DArray()}class SimpleLinearRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.slope=e.slope,this.intercept=e.intercept,this.coefficients=[e.intercept,e.slope]):(checkArraySize(t,e),regress$1(this,t,e))}toJSON(){return{name:"simpleLinearRegression",slope:this.slope,intercept:this.intercept}}_predict(t){return this.slope*t+this.intercept}computeX(t){return(t-this.intercept)/this.slope}toString(t){let e="f(x) = ";if(0!==this.slope){const r=maybeToPrecision(this.slope,t);if(e+=("1"===r?"":r+" * ")+"x",0!==this.intercept){const r=Math.abs(this.intercept);e+=` ${r===this.intercept?"+":"-"} ${maybeToPrecision(r,t)}`}}else e+=maybeToPrecision(this.intercept,t);return e}toLaTeX(t){return this.toString(t)}static load(t){if("simpleLinearRegression"!==t.name)throw new TypeError("not a SLR model");return new SimpleLinearRegression(!0,t)}}function regress$1(t,e,r){const n=e.length;let i=0,o=0,s=0,a=0;for(let t=0;t<n;t++)i+=e[t],o+=r[t],s+=e[t]*e[t],a+=e[t]*r[t];const l=n*a-i*o;t.slope=l/(n*s-i*i),t.intercept=1/n*o-t.slope*(1/n)*i,t.coefficients=[t.intercept,t.slope]}class ExponentialRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.A=e.A,this.B=e.B):(checkArraySize(t,e),regress$2(this,t,e))}_predict(t){return this.B*Math.exp(t*this.A)}toJSON(){return{name:"exponentialRegression",A:this.A,B:this.B}}toString(t){return`f(x) = ${maybeToPrecision(this.B,t)} * e^(${maybeToPrecision(this.A,t)} * x)`}toLaTeX(t){return this.A>=0?`f(x) = ${maybeToPrecision(this.B,t)}e^{${maybeToPrecision(this.A,t)}x}`:`f(x) = \\frac{${maybeToPrecision(this.B,t)}}{e^{${maybeToPrecision(-this.A,t)}x}}`}static load(t){if("exponentialRegression"!==t.name)throw new TypeError("not a exponential regression model");return new ExponentialRegression(!0,t)}}function regress$2(t,e,r){const n=e.length,i=new Array(n);for(let t=0;t<n;t++)i[t]=Math.log(r[t]);const o=new SimpleLinearRegression(e,i);t.A=o.slope,t.B=Math.exp(o.intercept)}class PowerRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.A=e.A,this.B=e.B):(checkArraySize(t,e),regress$3(this,t,e))}_predict(t){return this.A*Math.pow(t,this.B)}toJSON(){return{name:"powerRegression",A:this.A,B:this.B}}toString(t){return`f(x) = ${maybeToPrecision(this.A,t)} * x^${maybeToPrecision(this.B,t)}`}toLaTeX(t){let e="";return e=this.B>=0?`f(x) = ${maybeToPrecision(this.A,t)}x^{${maybeToPrecision(this.B,t)}}`:`f(x) = \\frac{${maybeToPrecision(this.A,t)}}{x^{${maybeToPrecision(-this.B,t)}}}`,e=e.replace(/e([+-]?[0-9]+)/g,"e^{$1}"),e}static load(t){if("powerRegression"!==t.name)throw new TypeError("not a power regression model");return new PowerRegression(!0,t)}}function regress$3(t,e,r){const n=e.length,i=new Array(n),o=new Array(n);for(let t=0;t<n;t++)i[t]=Math.log(e[t]),o[t]=Math.log(r[t]);const s=new SimpleLinearRegression(i,o);t.A=Math.exp(s.intercept),t.B=s.slope}class MultivariateLinearRegression{constructor(t,e,r={}){const{intercept:n=!0,statistics:i=!0}=r;if(this.statistics=i,!0===t)this.weights=e.weights,this.inputs=e.inputs,this.outputs=e.outputs,this.intercept=e.intercept;else{t=new Matrix(t),e=new Matrix(e),n&&t.addColumn(new Array(t.rows).fill(1));let r=t.transpose();const o=r.mmul(t),s=r.mmul(e),a=new SingularValueDecomposition(o).inverse(),l=s.transpose().mmul(a).transpose();if(this.weights=l.to2DArray(),this.inputs=t.columns,this.outputs=e.columns,n&&this.inputs--,this.intercept=n,i){const r=t.mmul(l),n=e.clone().addM(r.neg()).to2DArray().map((t=>Math.pow(t[0],2))).reduce(((t,e)=>t+e))/(e.rows-t.columns);this.stdError=Math.sqrt(n),this.stdErrorMatrix=pseudoInverse(o).mul(n),this.stdErrors=this.stdErrorMatrix.diagonal().map((t=>Math.sqrt(t))),this.tStats=this.weights.map(((t,e)=>0===this.stdErrors[e]?0:t[0]/this.stdErrors[e]))}}}predict(t){if(Array.isArray(t)){if("number"==typeof t[0])return this._predict(t);if(Array.isArray(t[0])){const e=new Array(t.length);for(let r=0;r<t.length;r++)e[r]=this._predict(t[r]);return e}}else if(Matrix.isMatrix(t)){const e=new Matrix(t.rows,this.outputs);for(let r=0;r<t.rows;r++)e.setRow(r,this._predict(t.getRow(r)));return e}throw new TypeError("x must be a matrix or array of numbers")}_predict(t){const e=new Array(this.outputs);if(this.intercept)for(let t=0;t<this.outputs;t++)e[t]=this.weights[this.inputs][t];else e.fill(0);for(let r=0;r<this.inputs;r++)for(let n=0;n<this.outputs;n++)e[n]+=this.weights[r][n]*t[r];return e}score(){throw new Error("score method is not implemented yet")}toJSON(){return{name:"multivariateLinearRegression",weights:this.weights,inputs:this.inputs,outputs:this.outputs,intercept:this.intercept,summary:this.statistics?{regressionStatistics:{standardError:this.stdError,observations:this.outputs},variables:this.weights.map(((t,e)=>({label:e===this.weights.length-1?"Intercept":"X Variable "+(e+1),coefficients:t,standardError:this.stdErrors[e],tStat:this.tStats[e]})))}:void 0}}static load(t){if("multivariateLinearRegression"!==t.name)throw new Error("not a MLR model");return new MultivariateLinearRegression(!0,t)}}var require$$0$1=getAugmentedNamespace(euclidean$1);const{squaredEuclidean:squaredEuclidean$1}=require$$0$1,defaultOptions$7={sigma:1};class GaussianKernel{constructor(t){t=Object.assign({},defaultOptions$7,t),this.sigma=t.sigma,this.divisor=2*t.sigma*t.sigma}compute(t,e){const r=squaredEuclidean$1(t,e);return Math.exp(-r/this.divisor)}}var gaussianKernel=GaussianKernel;const defaultOptions$8={degree:1,constant:1,scale:1};class PolynomialKernel{constructor(t){t=Object.assign({},defaultOptions$8,t),this.degree=t.degree,this.constant=t.constant,this.scale=t.scale}compute(t,e){for(var r=0,n=0;n<t.length;n++)r+=t[n]*e[n];return Math.pow(this.scale*r+this.constant,this.degree)}}var polynomialKernel=PolynomialKernel;const defaultOptions$9={alpha:.01,constant:-Math.E};class SigmoidKernel{constructor(t){t=Object.assign({},defaultOptions$9,t),this.alpha=t.alpha,this.constant=t.constant}compute(t,e){for(var r=0,n=0;n<t.length;n++)r+=t[n]*e[n];return Math.tanh(this.alpha*r+this.constant)}}var sigmoidKernel=SigmoidKernel;const defaultOptions$a={sigma:1,degree:1};class ANOVAKernel{constructor(t){t=Object.assign({},defaultOptions$a,t),this.sigma=t.sigma,this.degree=t.degree}compute(t,e){for(var r=0,n=Math.min(t.length,e.length),i=1;i<=n;++i)r+=Math.pow(Math.exp(-this.sigma*Math.pow(Math.pow(t[i-1],i)-Math.pow(e[i-1],i),2)),this.degree);return r}}var anovaKernel=ANOVAKernel;const{squaredEuclidean:squaredEuclidean$2}=require$$0$1,defaultOptions$b={sigma:1};class CauchyKernel{constructor(t){t=Object.assign({},defaultOptions$b,t),this.sigma=t.sigma}compute(t,e){return 1/(1+squaredEuclidean$2(t,e)/(this.sigma*this.sigma))}}var cauchyKernel=CauchyKernel;const{euclidean:euclidean$2}=require$$0$1,defaultOptions$c={sigma:1};class ExponentialKernel{constructor(t){t=Object.assign({},defaultOptions$c,t),this.sigma=t.sigma,this.divisor=2*t.sigma*t.sigma}compute(t,e){const r=euclidean$2(t,e);return Math.exp(-r/this.divisor)}}var exponentialKernel=ExponentialKernel;class HistogramIntersectionKernel{compute(t,e){for(var r=Math.min(t.length,e.length),n=0,i=0;i<r;++i)n+=Math.min(t[i],e[i]);return n}}var histogramIntersectionKernel=HistogramIntersectionKernel;const{euclidean:euclidean$3}=require$$0$1,defaultOptions$d={sigma:1};class LaplacianKernel{constructor(t){t=Object.assign({},defaultOptions$d,t),this.sigma=t.sigma}compute(t,e){const r=euclidean$3(t,e);return Math.exp(-r/this.sigma)}}var laplacianKernel=LaplacianKernel;const{squaredEuclidean:squaredEuclidean$3}=require$$0$1,defaultOptions$e={constant:1};class MultiquadraticKernel{constructor(t){t=Object.assign({},defaultOptions$e,t),this.constant=t.constant}compute(t,e){return Math.sqrt(squaredEuclidean$3(t,e)+this.constant*this.constant)}}var multiquadraticKernel=MultiquadraticKernel;const{squaredEuclidean:squaredEuclidean$4}=require$$0$1,defaultOptions$f={constant:1};class RationalQuadraticKernel{constructor(t){t=Object.assign({},defaultOptions$f,t),this.constant=t.constant}compute(t,e){const r=squaredEuclidean$4(t,e);return 1-r/(r+this.constant)}}var rationalQuadraticKernel=RationalQuadraticKernel;const{Matrix:Matrix$1,MatrixTransposeView:MatrixTransposeView$1}=require$$0,kernelType={gaussian:gaussianKernel,rbf:gaussianKernel,polynomial:polynomialKernel,poly:polynomialKernel,anova:anovaKernel,cauchy:cauchyKernel,exponential:exponentialKernel,histogram:histogramIntersectionKernel,min:histogramIntersectionKernel,laplacian:laplacianKernel,multiquadratic:multiquadraticKernel,rational:rationalQuadraticKernel,sigmoid:sigmoidKernel,mlp:sigmoidKernel};class Kernel{constructor(t,e){if(this.kernelType=t,"linear"!==t)if("string"==typeof t){t=t.toLowerCase();var r=kernelType[t];if(!r)throw new Error("unsupported kernel type: "+t);this.kernelFunction=new r(e)}else{if("object"!=typeof t||"function"!=typeof t.compute)throw new TypeError("first argument must be a valid kernel type or instance");this.kernelFunction=t}}compute(t,e){if(t=Matrix$1.checkMatrix(t),e=void 0===e?t:Matrix$1.checkMatrix(e),"linear"===this.kernelType)return t.mmul(new MatrixTransposeView$1(e));const r=new Matrix$1(t.rows,e.rows);if(t===e)for(let e=0;e<t.rows;e++)for(let n=e;n<t.rows;n++){const i=this.kernelFunction.compute(t.getRow(e),t.getRow(n));r.set(e,n,i),r.set(n,e,i)}else for(let n=0;n<t.rows;n++)for(let i=0;i<e.rows;i++)r.set(n,i,this.kernelFunction.compute(t.getRow(n),e.getRow(i)));return r}}var kernel=Kernel;class TheilSenRegression extends BaseRegression{constructor(t,e){super(),!0===t?(this.slope=e.slope,this.intercept=e.intercept,this.coefficients=e.coefficients):(checkArraySize(t,e),theilSen(this,t,e))}toJSON(){return{name:"TheilSenRegression",slope:this.slope,intercept:this.intercept}}_predict(t){return this.slope*t+this.intercept}computeX(t){return(t-this.intercept)/this.slope}toString(t){var e="f(x) = ";if(this.slope){var r=maybeToPrecision(this.slope,t);if(e+=(Math.abs(r-1)<1e-5?"":r+" * ")+"x",this.intercept){var n=Math.abs(this.intercept);e+=` ${n===this.intercept?"+":"-"} ${maybeToPrecision(n,t)}`}}else e+=maybeToPrecision(this.intercept,t);return e}toLaTeX(t){return this.toString(t)}static load(t){if("TheilSenRegression"!==t.name)throw new TypeError("not a Theil-Sen model");return new TheilSenRegression(!0,t)}}function theilSen(t,e,r){let n=e.length,i=new Array(n*n),o=0;for(let t=0;t<n;++t)for(let s=t+1;s<n;++s)e[t]!==e[s]&&(i[o++]=(r[s]-r[t])/(e[s]-e[t]));i.length=o;let s=median(i),a=new Array(n);for(let t=0;t<n;++t)a[t]=r[t]-s*e[t];t.slope=s,t.intercept=median(a),t.coefficients=[t.intercept,t.slope]}class RobustPolynomialRegression extends BaseRegression{constructor(t,e,r){super(),!0===t?(this.degree=e.degree,this.powers=e.powers,this.coefficients=e.coefficients):(checkArraySize(t,e),robustPolynomial(this,t,e,r))}toJSON(){return{name:"robustPolynomialRegression",degree:this.degree,powers:this.powers,coefficients:this.coefficients}}_predict(t){return predict(t,this.powers,this.coefficients)}toString(t){return this._toFormula(t,!1)}toLaTeX(t){return this._toFormula(t,!0)}_toFormula(t,e){let r="^",n="",i=" * ";e&&(r="^{",n="}",i="");let o="",s="";for(let e=0;e<this.coefficients.length;e++)s="",0!==this.coefficients[e]&&(s=0===this.powers[e]?maybeToPrecision(this.coefficients[e],t):1===this.powers[e]?maybeToPrecision(this.coefficients[e],t)+i+"x":`${maybeToPrecision(this.coefficients[e],t)+i}x${r}${this.powers[e]}${n}`,this.coefficients[e]>0&&e!==this.coefficients.length-1?s=" + "+s:e!==this.coefficients.length-1&&(s=" "+s)),o=s+o;return"+"===o.charAt(0)&&(o=o.slice(1)),"f(x) = "+o}static load(t){if("robustPolynomialRegression"!==t.name)throw new TypeError("not a RobustPolynomialRegression model");return new RobustPolynomialRegression(!0,t)}}function robustPolynomial(t,e,r,n){let i=Array(n).fill(0).map(((t,e)=>e));const o=getRandomTuples(e,r,n);for(var s,a=0;a<o.length;a++){for(var l=calcCoefficients(o[a],i),h=e.slice(),u=0;u<e.length;u++)h[u]=r[u]-predict(e[u],i,l),h[u]={residual:h[u]*h[u],coefficients:l};var c=residualsMedian(h);(!s||c.residual<s.residual)&&(s=c)}t.degree=n,t.powers=i,t.coefficients=s.coefficients}function getRandomTuples(t,e,r){for(var n=Math.floor(t.length/r),i=new Array(n),o=0;o<t.length;o++){for(var s=Math.floor(Math.random()*n),a=0;a<t.length;){if(!i[s]){i[s]=[{x:t[o],y:e[o]}];break}if(i[s].length<r){i[s].push({x:t[o],y:e[o]});break}a++,s=(s+1)%n}if(a===t.length)return i}return i}function calcCoefficients(t,e){for(var r=t.slice(),n=t.slice(),i=0;i<r.length;i++){n[i]=[t[i].y],r[i]=new Array(e.length);for(var o=0;o<e.length;o++)r[i][o]=Math.pow(t[i].x,e[o])}return solve(r,n).to1DArray()}function predict(t,e,r){let n=0;for(let i=0;i<e.length;i++)n+=r[i]*Math.pow(t,e[i]);return n}function residualsMedian(t){t.sort(((t,e)=>t.residual-e.residual));var e=t.length,r=Math.floor(e/2);return e%2==0?t[r-1]:t[r]}const toString$3=Object.prototype.toString;function isAnyArray$3(t){return toString$3.call(t).endsWith("Array]")}function errorCalculation(t,e,r){let n=0;const i=r(e);for(let e=0;e<t.x.length;e++)n+=Math.abs(t.y[e]-i(t.x[e]));return n}function gradientFunction(t,e,r,n,i){const o=r.length,s=t.x.length;let a=new Array(o);for(let l=0;l<o;l++){a[l]=new Array(s);let o=r.slice();o[l]+=n;let h=i(o);for(let r=0;r<s;r++)a[l][r]=e[r]-h(t.x[r])}return new Matrix(a)}function matrixFunction(t,e){const r=t.x.length;let n=new Array(r);for(let i=0;i<r;i++)n[i]=[t.y[i]-e[i]];return new Matrix(n)}function step$1(t,e,r,n,i){let o=r*n*n,s=Matrix.eye(e.length,e.length,o);const a=i(e);let l=new Float64Array(t.x.length);for(let e=0;e<t.x.length;e++)l[e]=a(t.x[e]);let h=gradientFunction(t,l,e,n,i),u=matrixFunction(t,l),c=inverse(s.add(h.mmul(h.transpose())));return(e=(e=new Matrix([e])).sub(c.mmul(h).mmul(u).mul(n).transpose())).to1DArray()}function levenbergMarquardt(t,e,r={}){let{maxIterations:n=100,gradientDifference:i=.1,damping:o=0,errorTolerance:s=.01,minValues:a,maxValues:l,initialValues:h}=r;if(o<=0)throw new Error("The damping option must be a positive number");if(!t.x||!t.y)throw new Error("The data parameter must have x and y elements");if(!isAnyArray$3(t.x)||t.x.length<2||!isAnyArray$3(t.y)||t.y.length<2)throw new Error("The data parameter elements must be an array with more than 2 points");if(t.x.length!==t.y.length)throw new Error("The data parameter elements must have the same size");let u=h||new Array(e.length).fill(1),c=u.length;if(l=l||new Array(c).fill(Number.MAX_SAFE_INTEGER),a=a||new Array(c).fill(Number.MIN_SAFE_INTEGER),l.length!==a.length)throw new Error("minValues and maxValues must be the same size");if(!isAnyArray$3(u))throw new Error("initialValues must be an array");let f,m=errorCalculation(t,u,e),g=m<=s;for(f=0;f<n&&!g;f++){u=step$1(t,u,o,i,e);for(let t=0;t<c;t++)u[t]=Math.min(Math.max(a[t],u[t]),l[t]);if(m=errorCalculation(t,u,e),isNaN(m))break;g=m<=s}return{parameterValues:u,parameterError:m,iterations:f}}function selection(t,e){let r=[];for(let n=0;n<e.length;n++)r[n]=t[e[n]];return r}function sortCollectionSet(t){let e,r=t.map(((t,e)=>{let r=BigInt(0);return t.forEach((t=>r|=BigInt(1)<<BigInt(t))),{value:t,index:e,key:r}})).sort(((t,e)=>t.key-e.key<0?-1:1)),n=[],i=[];for(let t of r)t.key!==e&&(e=t.key,i.push([]),n.push(t.value)),i[i.length-1].push(t.index);return{values:n,indices:i}}function cssls(t,e,r,n,i){let o=Matrix.zeros(n,i);if(null===r){let r=new CholeskyDecomposition(t);if(!0===r.isPositiveDefinite())o=r.solve(e);else{let r=new LuDecomposition(t);o=!1===r.isSingular()?r.solve(Matrix.eye(n)).mmul(e):solve(t,e,{useSVD:!0})}}else{let s=sortCollectionSet(r).values,a=sortCollectionSet(r).indices;if(1===s.length&&0===s[0].length&&a[0].length===i)return o;if(1===s.length&&s[0].length===n&&a[0].length===i){let r=new CholeskyDecomposition(t);if(!0===r.isPositiveDefinite())o=r.solve(e);else{let r=new LuDecomposition(t);o=!1===r.isSingular()?r.solve(Matrix.eye(n)).mmul(e):solve(t,e,{useSVD:!0})}}else for(let r=0;r<s.length;r++){let n,i=a[r],l=s[r],h=new CholeskyDecomposition(t.selection(l,l));if(!0===h.isPositiveDefinite())n=h.solve(e.selection(l,i));else{let r=new LuDecomposition(t.selection(l,l));n=!1===r.isSingular()?r.solve(Matrix.eye(l.length)).mmul(e.selection(l,i)):solve(t.selection(l,l),e.selection(l,i),{useSVD:!0})}for(let t=0;t<n.rows;t++)for(let e=0;e<n.columns;e++)o.set(l[t],i[e],n.get(t,e))}}return o}function initialisation(t,e){let r=t.rows,n=t.columns,i=e.columns;if(e.rows!==r)throw new Error("ERROR: matrix size not compatible");let o=Matrix.zeros(n,i),s=t.transpose().mmul(t),a=t.transpose().mmul(e),l=cssls(s,a,null,n,i),h=[];for(let t=0;t<i;t++){h[t]=[];for(let e=0;e<n;e++)l.get(e,t)>0?h[t].push(e):l.set(e,t,0)}let u=[];for(let t=0;t<i;t++)h[t].length!==n&&u.push(t);let c=l.clone();return{n:r,l:n,p:i,iter:0,W:o,XtX:s,XtY:a,K:l,Pset:h,Fset:u,D:c}}function setDifference(t,e){let r=[];for(let n of t)e.includes(n)||r.push(n);return r}function optimality(t,e,r,n,i,o,s,a,l,h,u){if(t===e)throw new Error("Maximum number of iterations exceeded");let c=n.subMatrixColumn(i).subtract(r.mmul(a.subMatrixColumn(i)));for(let t=0;t<i.length;t++)s.setColumn(i[t],c.subMatrixColumn([t]));let f=[],m=[];for(let t=0;t<l;t++)m.push(t);for(let t=0;t<i.length;t++){let e=setDifference(m,o[i[t]]);(0===e.length||s.selection(e,[i[t]]).max()<=0)&&f.push(i[t])}if(0!==(i=setDifference(i,f)).length){for(let t=0;t<i.length;t++){for(let e=0;e<l;e++)o[i[t]].includes(e)&&s.set(e,i[t],-1/0);o[i[t]].push(s.subMatrixColumn(i).maxColumnIndex(t)[0])}for(let t=0;t<i.length;t++)u.setColumn(i[t],a.getColumn(i[t]))}for(let t=0;t<h;t++)o[t].sort(((t,e)=>t-e));return{Pset:o,Fset:i,W:s}}function fcnnls(t,e,r={}){t=Matrix.checkMatrix(t),e=Matrix.checkMatrix(e);let{l:n,p:i,iter:o,W:s,XtX:a,XtY:l,K:h,Pset:u,Fset:c,D:f}=initialisation(t,e);const{maxIterations:m=3*t.columns}=r;for(;c.length>0;){let t=cssls(a,l.subMatrixColumn(c),selection(u,c),n,c.length);for(let e=0;e<n;e++)for(let r=0;r<c.length;r++)h.set(e,c[r],t.get(e,r));let e=[];for(let r=0;r<c.length;r++)for(let i=0;i<n;i++)if(t.get(i,r)<0){e.push(r);break}let r=selection(c,e);if(r.length>0){let e=r.length,i=Matrix.ones(n,e);for(;e>0&&o<m;){o++,i.mul(1/0);let s=[[],[]],c=[[],[]];for(let t=0;t<e;t++)for(let e=0;e<u[r[t]].length;e++)h.get(u[r[t]][e],r[t])<0&&(s[0].push(u[r[t]][e]),s[1].push(t),c[0].push(u[r[t]][e]),c[1].push(r[t]));for(let t=0;t<s[0].length;t++)i.set(s[0][t],s[1][t],f.get(c[0][t],c[1][t])/(f.get(c[0][t],c[1][t])-h.get(c[0][t],c[1][t])));let m=[],g=[];for(let t=0;t<e;t++)m[t]=i.minColumn(t),g[t]=i.minColumnIndex(t)[0];m=Matrix.rowVector(m);for(let t=0;t<n;t++)i.setSubMatrix(m,t,0);let p=new Matrix(n,e);p=f.subMatrixColumn(r).subtract(i.subMatrix(0,n-1,0,e-1).mul(f.subMatrixColumn(r).subtract(h.subMatrixColumn(r))));for(let t=0;t<e;t++)f.setColumn(r[t],p.subMatrixColumn([t]));let d=[g,r];for(let t=0;t<e;t++)f.set(d[0][t],d[1][t],0);for(let t=0;t<e;t++)u[r[t]].splice(u[r[t]].findIndex((e=>e===g[t])),1);t=cssls(a,l.subMatrixColumn(r),selection(u,r),n,e);for(let n=0;n<e;n++)h.setColumn(r[n],t.subMatrixColumn([n]));r=[];for(let t=0;t<h.columns;t++)for(let e=0;e<n;e++)if(h.get(e,t)<0){r.push(t);break}e=r.length}}let g=optimality(o,m,a,l,c,u,s,h,n,i,f);u=g.Pset,c=g.Fset,s=g.W}return h}function fcnnlsVector(t,e,r={}){if(!1===Array.isArray(e))throw new TypeError("y must be a 1D Array");return fcnnls(t,Matrix.columnVector(e),r).to1DArray()}var index$3=Object.freeze({__proto__:null,fcnnls:fcnnls,fcnnlsVector:fcnnlsVector}),binarySearch=function(t,e,r,n,i){var o,s;if(void 0===n)n=0;else if((n|=0)<0||n>=t.length)throw new RangeError("invalid lower bound");if(void 0===i)i=t.length-1;else if((i|=0)<n||i>=t.length)throw new RangeError("invalid upper bound");for(;n<=i;)if((s=+r(t[o=n+(i-n>>>1)],e,o,t))<0)n=o+1;else{if(!(s>0))return o;i=o-1}return~n};function assertNumber(t){if("number"!=typeof t)throw new TypeError("Expected a number")}var ascending=(t,e)=>(assertNumber(t),assertNumber(e),Number.isNaN(t)?-1:Number.isNaN(e)?1:t-e),descending=(t,e)=>(assertNumber(t),assertNumber(e),Number.isNaN(t)?1:Number.isNaN(e)?-1:e-t),numSort={ascending:ascending,descending:descending},index$4=Object.freeze(Object.assign(Object.create(null),numSort,{default:numSort,ascending:ascending,descending:descending}));const largestPrime=2147483647,primeNumbers=[largestPrime,5,11,23,47,97,197,397,797,1597,3203,6421,12853,25717,51437,102877,205759,411527,823117,1646237,3292489,6584983,13169977,26339969,52679969,105359939,210719881,421439783,842879579,1685759167,433,877,1759,3527,7057,14143,28289,56591,113189,226379,452759,905551,1811107,3622219,7244441,14488931,28977863,57955739,115911563,231823147,463646329,927292699,1854585413,953,1907,3821,7643,15287,30577,61169,122347,244703,489407,978821,1957651,3915341,7830701,15661423,31322867,62645741,125291483,250582987,501165979,1002331963,2004663929,1039,2081,4177,8363,16729,33461,66923,133853,267713,535481,1070981,2141977,4283963,8567929,17135863,34271747,68543509,137087021,274174111,548348231,1096696463,31,67,137,277,557,1117,2237,4481,8963,17929,35863,71741,143483,286973,573953,1147921,2295859,4591721,9183457,18366923,36733847,73467739,146935499,293871013,587742049,1175484103,599,1201,2411,4831,9677,19373,38747,77509,155027,310081,620171,1240361,2480729,4961459,9922933,19845871,39691759,79383533,158767069,317534141,635068283,1270136683,311,631,1277,2557,5119,10243,20507,41017,82037,164089,328213,656429,1312867,2625761,5251529,10503061,21006137,42012281,84024581,168049163,336098327,672196673,1344393353,3,7,17,37,79,163,331,673,1361,2729,5471,10949,21911,43853,87719,175447,350899,701819,1403641,2807303,5614657,11229331,22458671,44917381,89834777,179669557,359339171,718678369,1437356741,43,89,179,359,719,1439,2879,5779,11579,23159,46327,92657,185323,370661,741337,1482707,2965421,5930887,11861791,23723597,47447201,94894427,189788857,379577741,759155483,1518310967,379,761,1523,3049,6101,12203,24407,48817,97649,195311,390647,781301,1562611,3125257,6250537,12501169,25002389,50004791,100009607,200019221,400038451,800076929,1600153859,13,29,59,127,257,521,1049,2099,4201,8419,16843,33703,67409,134837,269683,539389,1078787,2157587,4315183,8630387,17260781,34521589,69043189,138086407,276172823,552345671,1104691373,19,41,83,167,337,677,1361,2729,5471,10949,21911,43853,87719,175447,350899,701819,1403641,2807303,5614657,11229331,22458671,44917381,89834777,179669557,359339171,718678369,1437356741,53,107,223,449,907,1823,3659,7321,14653,29311,58631,117269,234539,469099,938207,1876417,3752839,7505681,15011389,30022781,60045577,120091177,240182359,480364727,960729461,1921458943];function nextPrime(t){let e=binarySearch(primeNumbers,t,ascending);return e<0&&(e=~e),primeNumbers[e]}primeNumbers.sort(ascending);const FREE=0,FULL=1,REMOVED=2,defaultInitialCapacity=150,defaultMinLoadFactor=1/6,defaultMaxLoadFactor=2/3;class HashTable{constructor(t={}){if(t instanceof HashTable)return this.table=t.table.slice(),this.values=t.values.slice(),this.state=t.state.slice(),this.minLoadFactor=t.minLoadFactor,this.maxLoadFactor=t.maxLoadFactor,this.distinct=t.distinct,this.freeEntries=t.freeEntries,this.lowWaterMark=t.lowWaterMark,void(this.highWaterMark=t.maxLoadFactor);const e=void 0===t.initialCapacity?defaultInitialCapacity:t.initialCapacity;if(e<0)throw new RangeError("initial capacity must not be less than zero: "+e);const r=void 0===t.minLoadFactor?defaultMinLoadFactor:t.minLoadFactor,n=void 0===t.maxLoadFactor?defaultMaxLoadFactor:t.maxLoadFactor;if(r<0||r>=1)throw new RangeError("invalid minLoadFactor: "+r);if(n<=0||n>=1)throw new RangeError("invalid maxLoadFactor: "+n);if(r>=n)throw new RangeError(`minLoadFactor (${r}) must be smaller than maxLoadFactor (${n})`);let i=e;i=i/n|0,i=nextPrime(i),0===i&&(i=1),this.table=newArray$1(i),this.values=newArray$1(i),this.state=newArray$1(i),this.minLoadFactor=r,this.maxLoadFactor=i===largestPrime?1:n,this.distinct=0,this.freeEntries=i,this.lowWaterMark=0,this.highWaterMark=chooseHighWaterMark(i,this.maxLoadFactor)}clone(){return new HashTable(this)}get size(){return this.distinct}get(t){const e=this.indexOfKey(t);return e<0?0:this.values[e]}set(t,e){let r=this.indexOfInsertion(t);if(r<0)return r=-r-1,this.values[r]=e,!1;if(this.distinct>this.highWaterMark){const r=chooseGrowCapacity(this.distinct+1,this.minLoadFactor,this.maxLoadFactor);return this.rehash(r),this.set(t,e)}if(this.table[r]=t,this.values[r]=e,this.state[r]===FREE&&this.freeEntries--,this.state[r]=FULL,this.distinct++,this.freeEntries<1){const t=chooseGrowCapacity(this.distinct+1,this.minLoadFactor,this.maxLoadFactor);this.rehash(t)}return!0}remove(t,e){const r=this.indexOfKey(t);return!(r<0)&&(this.state[r]=REMOVED,this.distinct--,e||this.maybeShrinkCapacity(),!0)}delete(t,e){const r=this.indexOfKey(t);return!(r<0)&&(this.state[r]=FREE,this.distinct--,e||this.maybeShrinkCapacity(),!0)}maybeShrinkCapacity(){if(this.distinct<this.lowWaterMark){const t=chooseShrinkCapacity(this.distinct,this.minLoadFactor,this.maxLoadFactor);this.rehash(t)}}containsKey(t){return this.indexOfKey(t)>=0}indexOfKey(t){const e=this.table,r=this.state,n=this.table.length,i=2147483647&t;let o=i%n,s=i%(n-2);for(0===s&&(s=1);r[o]!==FREE&&(r[o]===REMOVED||e[o]!==t);)o-=s,o<0&&(o+=n);return r[o]===FREE?-1:o}containsValue(t){return this.indexOfValue(t)>=0}indexOfValue(t){const e=this.values,r=this.state;for(var n=0;n<r.length;n++)if(r[n]===FULL&&e[n]===t)return n;return-1}indexOfInsertion(t){const e=this.table,r=this.state,n=e.length,i=2147483647&t;let o=i%n,s=i%(n-2);for(0===s&&(s=1);r[o]===FULL&&e[o]!==t;)o-=s,o<0&&(o+=n);if(r[o]===REMOVED){const i=o;for(;r[o]!==FREE&&(r[o]===REMOVED||e[o]!==t);)o-=s,o<0&&(o+=n);r[o]===FREE&&(o=i)}return r[o]===FULL?-o-1:o}ensureCapacity(t){if(this.table.length<t){const e=nextPrime(t);this.rehash(e)}}rehash(t){const e=this.table.length;if(t<=this.distinct)throw new Error("Unexpected");const r=this.table,n=this.values,i=this.state,o=newArray$1(t),s=newArray$1(t),a=newArray$1(t);this.lowWaterMark=chooseLowWaterMark(t,this.minLoadFactor),this.highWaterMark=chooseHighWaterMark(t,this.maxLoadFactor),this.table=o,this.values=s,this.state=a,this.freeEntries=t-this.distinct;for(var l=0;l<e;l++)if(i[l]===FULL){var h=r[l],u=this.indexOfInsertion(h);o[u]=h,s[u]=n[l],a[u]=FULL}}forEachKey(t){for(var e=0;e<this.state.length;e++)if(this.state[e]===FULL&&!t(this.table[e]))return!1;return!0}forEachValue(t){for(var e=0;e<this.state.length;e++)if(this.state[e]===FULL&&!t(this.values[e]))return!1;return!0}forEachPair(t){for(var e=0;e<this.state.length;e++)if(this.state[e]===FULL&&!t(this.table[e],this.values[e]))return!1;return!0}}function chooseLowWaterMark(t,e){return t*e|0}function chooseHighWaterMark(t,e){return Math.min(t-2,t*e|0)}function chooseGrowCapacity(t,e,r){return nextPrime(Math.max(t+1,4*t/(3*e+r)|0))}function chooseShrinkCapacity(t,e,r){return nextPrime(Math.max(t+1,4*t/(e+3*r)|0))}function newArray$1(t){return Array(t).fill(0)}class SparseMatrix{constructor(t,e,r={}){if(t instanceof SparseMatrix){const e=t;this._init(e.rows,e.columns,e.elements.clone(),e.threshold)}else if(Array.isArray(t)){const s=t;t=s.length,r=e||{},e=s[0].length,this._init(t,e,new HashTable(r),r.threshold);for(var n=0;n<t;n++)for(var i=0;i<e;i++){var o=s[n][i];this.threshold&&Math.abs(o)<this.threshold&&(o=0),0!==o&&this.elements.set(n*e+i,s[n][i])}}else this._init(t,e,new HashTable(r),r.threshold)}_init(t,e,r,n){this.rows=t,this.columns=e,this.elements=r,this.threshold=n||0}static eye(t=1,e=t){const r=Math.min(t,e),n=new SparseMatrix(t,e,{initialCapacity:r});for(var i=0;i<r;i++)n.set(i,i,1);return n}clone(){return new SparseMatrix(this)}to2DArray(){const t=new Array(this.rows);for(var e=0;e<this.rows;e++){t[e]=new Array(this.columns);for(var r=0;r<this.columns;r++)t[e][r]=this.get(e,r)}return t}isSquare(){return this.rows===this.columns}isSymmetric(){if(!this.isSquare())return!1;var t=!0;return this.forEachNonZero(((e,r,n)=>this.get(r,e)!==n?(t=!1,!1):n)),t}bandWidth(){let t=this.columns,e=-1;return this.forEachNonZero(((r,n,i)=>{let o=r-n;return t=Math.min(t,o),e=Math.max(e,o),i})),e-t}isBanded(t){return this.bandWidth()<=t}get cardinality(){return this.elements.size}get size(){return this.rows*this.columns}get(t,e){return this.elements.get(t*this.columns+e)}set(t,e,r){return this.threshold&&Math.abs(r)<this.threshold&&(r=0),0===r?this.elements.remove(t*this.columns+e):this.elements.set(t*this.columns+e,r),this}mmul(t){this.columns!==t.rows&&console.warn("Number of columns of left matrix are not equal to number of rows of right matrix.");const e=this.rows,r=t.columns,n=new SparseMatrix(e,r);return this.forEachNonZero(((e,r,i)=>(t.forEachNonZero(((t,o,s)=>(r===t&&n.set(e,o,n.get(e,o)+i*s),s))),i))),n}kroneckerProduct(t){const e=this.rows,r=this.columns,n=t.rows,i=t.columns,o=new SparseMatrix(e*n,r*i,{initialCapacity:this.cardinality*t.cardinality});return this.forEachNonZero(((e,r,s)=>(t.forEachNonZero(((t,a,l)=>(o.set(n*e+t,i*r+a,s*l),l))),s))),o}forEachNonZero(t){return this.elements.forEachPair(((e,r)=>{const n=e/this.columns|0,i=e%this.columns;let o=t(n,i,r);return!1!==o&&(this.threshold&&Math.abs(o)<this.threshold&&(o=0),o!==r&&(0===o?this.elements.remove(e,!0):this.elements.set(e,o)),!0)})),this.elements.maybeShrinkCapacity(),this}getNonZeros(){const t=this.cardinality,e=new Array(t),r=new Array(t),n=new Array(t);var i=0;return this.forEachNonZero(((t,o,s)=>(e[i]=t,r[i]=o,n[i]=s,i++,s))),{rows:e,columns:r,values:n}}setThreshold(t){return 0!==t&&t!==this.threshold&&(this.threshold=t,this.forEachNonZero(((t,e,r)=>r))),this}transpose(){let t=new SparseMatrix(this.columns,this.rows,{initialCapacity:this.cardinality});return this.forEachNonZero(((e,r,n)=>(t.set(r,e,n),n))),t}}SparseMatrix.prototype.klass="Matrix",SparseMatrix.identity=SparseMatrix.eye,SparseMatrix.prototype.tensorProduct=SparseMatrix.prototype.kroneckerProduct;var inplaceOperator="\n(function %name%(value) {\n    if (typeof value === 'number') return this.%name%S(value);\n    return this.%name%M(value);\n})\n",inplaceOperatorScalar="\n(function %name%S(value) {\n    this.forEachNonZero((i, j, v) => v %op% value);\n    return this;\n})\n",inplaceOperatorMatrix="\n(function %name%M(matrix) {\n    matrix.forEachNonZero((i, j, v) => {\n        this.set(i, j, this.get(i, j) %op% v);\n        return v;\n    });\n    return this;\n})\n",staticOperator="\n(function %name%(matrix, value) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%(value);\n})\n",inplaceMethod="\n(function %name%() {\n    this.forEachNonZero((i, j, v) => %method%(v));\n    return this;\n})\n",staticMethod="\n(function %name%(matrix) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%();\n})\n";const operators=[["+","add"],["-","sub","subtract"],["*","mul","multiply"],["/","div","divide"],["%","mod","modulus"],["&","and"],["|","or"],["^","xor"],["<<","leftShift"],[">>","signPropagatingRightShift"],[">>>","rightShift","zeroFillRightShift"]];for(const operator of operators)for(let i=1;i<operator.length;i++)SparseMatrix.prototype[operator[i]]=eval(fillTemplateFunction(inplaceOperator,{name:operator[i],op:operator[0]})),SparseMatrix.prototype[operator[i]+"S"]=eval(fillTemplateFunction(inplaceOperatorScalar,{name:operator[i]+"S",op:operator[0]})),SparseMatrix.prototype[operator[i]+"M"]=eval(fillTemplateFunction(inplaceOperatorMatrix,{name:operator[i]+"M",op:operator[0]})),SparseMatrix[operator[i]]=eval(fillTemplateFunction(staticOperator,{name:operator[i]}));var methods=[["~","not"]];["abs","acos","acosh","asin","asinh","atan","atanh","cbrt","ceil","clz32","cos","cosh","exp","expm1","floor","fround","log","log1p","log10","log2","round","sign","sin","sinh","sqrt","tan","tanh","trunc"].forEach((function(t){methods.push(["Math."+t,t])}));for(const method of methods)for(let i=1;i<method.length;i++)SparseMatrix.prototype[method[i]]=eval(fillTemplateFunction(inplaceMethod,{name:method[i],method:method[0]})),SparseMatrix[method[i]]=eval(fillTemplateFunction(staticMethod,{name:method[i]}));function fillTemplateFunction(t,e){for(const r in e)t=t.replace(new RegExp(`%${r}%`,"g"),e[r]);return t}function additiveSymmetric(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=(t[r]-e[r])*(t[r]-e[r])*(t[r]+e[r])/(t[r]*e[r]);return 2*i}function avg(t,e){for(var r=t.length,n=0,i=0,o=0,s=0;s<r;s++)i+=o=Math.abs(t[s]-e[s]),n<o&&(n=o);return(n+i)/2}function bhattacharyya(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.sqrt(t[i]*e[i]);return-Math.log(n)}function canberra(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.abs(t[i]-e[i])/(t[i]+e[i]);return n}function chebyshev(t,e){for(var r=t.length,n=0,i=0,o=0;o<r;o++)n<(i=Math.abs(t[o]-e[o]))&&(n=i);return n}function clark(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=Math.sqrt((t[r]-e[r])*(t[r]-e[r])/((t[r]+e[r])*(t[r]+e[r])));return 2*i}function czekanowskiSimilarity(t,e){for(var r=0,n=0,i=0;i<t.length;i++)r+=Math.min(t[i],e[i]),n+=t[i]+e[i];return 2*r/n}function czekanowskiDistance(t,e){return 1-czekanowskiSimilarity(t,e)}function dice(t,e){for(var r=t.length,n=0,i=0,o=0,s=0;s<r;s++)n+=t[s]*t[s],i+=e[s]*e[s],o+=(t[s]-e[s])*(t[s]-e[s]);return o/(n+i)}function divergence(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=(t[r]-e[r])*(t[r]-e[r])/((t[r]+e[r])*(t[r]+e[r]));return 2*i}function fidelity(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.sqrt(t[i]*e[i]);return n}function gower(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.abs(t[i]-e[i]);return n/r}function harmonicMean(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=t[i]*e[i]/(t[i]+e[i]);return 2*n}function hellinger(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.sqrt(t[i]*e[i]);return 2*Math.sqrt(1-n)}function innerProduct(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=t[i]*e[i];return n}function intersection(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.min(t[i],e[i]);return 1-n}function jaccard(t,e){for(var r=t.length,n=0,i=0,o=0,s=0,a=0;a<r;a++)n+=t[a]*e[a],i+=t[a]*t[a],o+=e[a]*e[a],s+=(t[a]-e[a])*(t[a]-e[a]);return s/(i+o-n)}function jeffreys(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=(t[i]-e[i])*Math.log(t[i]/e[i]);return n}function jensenDifference(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=(t[i]*Math.log(t[i])+e[i]*Math.log(e[i]))/2-(t[i]+e[i])/2*Math.log((t[i]+e[i])/2);return n}function jensenShannon(t,e){for(var r=t.length,n=0,i=0,o=0;o<r;o++)n+=t[o]*Math.log(2*t[o]/(t[o]+e[o])),i+=e[o]*Math.log(2*e[o]/(t[o]+e[o]));return(n+i)/2}function kdivergence(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=t[i]*Math.log(2*t[i]/(t[i]+e[i]));return n}function kulczynski(t,e){for(var r=t.length,n=0,i=0,o=0;o<r;o++)n+=Math.abs(t[o]-e[o]),i+=Math.min(t[o],e[o]);return n/i}function kullbackLeibler(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=t[i]*Math.log(t[i]/e[i]);return n}function kumarHassebrook(t,e){for(var r=t.length,n=0,i=0,o=0,s=0;s<r;s++)n+=t[s]*e[s],i+=t[s]*t[s],o+=e[s]*e[s];return n/(i+o-n)}function kumarJohnson(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.pow(t[i]*t[i]-e[i]*e[i],2)/(2*Math.pow(t[i]*e[i],1.5));return n}function lorentzian(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.log(Math.abs(t[i]-e[i])+1);return n}function manhattan(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=Math.abs(t[r]-e[r]);return i}function matusita(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=Math.sqrt(t[i]*e[i]);return Math.sqrt(2-2*n)}function minkowski(t,e,r){for(var n=0,i=t.length,o=0;n<i;n++)o+=Math.pow(Math.abs(t[n]-e[n]),r);return Math.pow(o,1/r)}function motyka(t,e){for(var r=t.length,n=0,i=0,o=0;o<r;o++)n+=Math.min(t[o],e[o]),i+=t[o]+e[o];return 1-n/i}function neyman(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=(t[r]-e[r])*(t[r]-e[r])/t[r];return i}function pearson(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=(t[r]-e[r])*(t[r]-e[r])/e[r];return i}function probabilisticSymmetric(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=(t[r]-e[r])*(t[r]-e[r])/(t[r]+e[r]);return 2*i}function ruzicka(t,e){for(var r=t.length,n=0,i=0,o=0;o<r;o++)n+=Math.min(t[o],e[o]),i+=Math.max(t[o],e[o]);return n/i}function soergel(t,e){for(var r=t.length,n=0,i=0,o=0;o<r;o++)n+=Math.abs(t[o]-e[o]),i+=Math.max(t[o],e[o]);return n/i}function sorensen(t,e){for(var r=t.length,n=0,i=0,o=0;o<r;o++)n+=Math.abs(t[o]-e[o]),i+=t[o]+e[o];return n/i}function squared(t,e){for(var r=0,n=t.length,i=0;r<n;r++)i+=(t[r]-e[r])*(t[r]-e[r])/(t[r]+e[r]);return i}function squaredChord(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=(Math.sqrt(t[i])-Math.sqrt(e[i]))*(Math.sqrt(t[i])-Math.sqrt(e[i]));return n}function taneja(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=(t[i]+e[i])/2*Math.log((t[i]+e[i])/(2*Math.sqrt(t[i]*e[i])));return n}function tanimoto(t,e,r){if(r){for(var n=0,i=0,o=0;o<t.length;o++)n+=t[o]&&e[o],i+=t[o]||e[o];return 0===i?1:n/i}for(var s=t.length,a=0,l=0,h=0,u=0;u<s;u++)a+=t[u],l+=e[u],h+=Math.min(t[u],e[u]);return 1-(a+l-2*h)/(a+l-h)}function tanimoto$1(t,e,r){if(r)return 1-tanimoto(t,e,r);for(var n=t.length,i=0,o=0,s=0,a=0;a<n;a++)i+=t[a],o+=e[a],s+=Math.min(t[a],e[a]);return(i+o-2*s)/(i+o-s)}function topsoe(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=t[i]*Math.log(2*t[i]/(t[i]+e[i]))+e[i]*Math.log(2*e[i]/(t[i]+e[i]));return n}function waveHedges(t,e){for(var r=t.length,n=0,i=0;i<r;i++)n+=1-Math.min(t[i],e[i])/Math.max(t[i],e[i]);return n}var distances=Object.freeze({__proto__:null,euclidean:euclidean,squaredEuclidean:squaredEuclidean,additiveSymmetric:additiveSymmetric,avg:avg,bhattacharyya:bhattacharyya,canberra:canberra,chebyshev:chebyshev,clark:clark,czekanowski:czekanowskiDistance,dice:dice,divergence:divergence,fidelity:fidelity,gower:gower,harmonicMean:harmonicMean,hellinger:hellinger,innerProduct:innerProduct,intersection:intersection,jaccard:jaccard,jeffreys:jeffreys,jensenDifference:jensenDifference,jensenShannon:jensenShannon,kdivergence:kdivergence,kulczynski:kulczynski,kullbackLeibler:kullbackLeibler,kumarHassebrook:kumarHassebrook,kumarJohnson:kumarJohnson,lorentzian:lorentzian,manhattan:manhattan,matusita:matusita,minkowski:minkowski,motyka:motyka,neyman:neyman,pearson:pearson,probabilisticSymmetric:probabilisticSymmetric,ruzicka:ruzicka,soergel:soergel,sorensen:sorensen,squared:squared,squaredChord:squaredChord,taneja:taneja,tanimoto:tanimoto$1,topsoe:topsoe,waveHedges:waveHedges});function createTree(t,e={}){var r=t[0];const{minWindow:n=.16,threshold:i=.01,from:o=r[0],to:s=r[r.length-1]}=e;return mainCreateTree(t[0],t[1],o,s,n,i)}function mainCreateTree(t,e,r,n,i,o){if(n-r<i)return null;var s=binarySearch(t,r,ascending);s<0&&(s=~s);for(var a=0,l=0,h=s;h<t.length&&!(t[h]>=n);h++)a+=e[h],l+=t[h]*e[h];return a<o||(l/=a)-r<1e-6||n-l<1e-6?null:l-r<i/4?mainCreateTree(t,e,l,n,i,o):n-l<i/4?mainCreateTree(t,e,r,l,i,o):new Tree(a,l,mainCreateTree(t,e,r,l,i,o),mainCreateTree(t,e,l,n,i,o))}class Tree{constructor(t,e,r,n){this.sum=t,this.center=e,this.left=r,this.right=n}}function getSimilarity(t,e,r={}){const{alpha:n=.1,beta:i=.33,gamma:o=.001}=r;return null===t||null===e?0:(Array.isArray(t)&&(t=createTree(t)),Array.isArray(e)&&(e=createTree(e)),i*(n*Math.min(t.sum,e.sum)/Math.max(t.sum,e.sum)+(1-n)*Math.exp(-o*Math.abs(t.center-e.center)))+(1-i)*(getSimilarity(t.left,e.left,r)+getSimilarity(t.right,e.right,r))/2)}function treeSimilarity(t,e,r={}){return getSimilarity(t,e,r)}function getFunction(t={}){return(e,r)=>getSimilarity(e,r,t)}var index$5=Object.freeze({__proto__:null,treeSimilarity:treeSimilarity,getFunction:getFunction,createTree:createTree});function cosine(t,e){for(var r=t.length,n=0,i=0,o=0,s=0;s<r;s++)n+=t[s]*e[s],i+=t[s]*t[s],o+=e[s]*e[s];return n/(Math.sqrt(i)*Math.sqrt(o))}function dice$1(t,e){return 1-dice(t,e)}function intersection$1(t,e){return 1-intersection(t,e)}function jaccard$1(t,e){return 1-jaccard(t,e)}function kulczynski$1(t,e){return 1/kulczynski(t,e)}function motyka$1(t,e){return 1-motyka(t,e)}function pearson$1(t,e){for(var r=mean(t),n=mean(e),i=new Array(t.length),o=new Array(e.length),s=0;s<i.length;s++)i[s]=t[s]-r,o[s]=e[s]-n;return cosine(i,o)}function squaredChord$1(t,e){return 1-squaredChord(t,e)}var similarities=Object.freeze({__proto__:null,tree:index$5,cosine:cosine,czekanowski:czekanowskiSimilarity,dice:dice$1,intersection:intersection$1,jaccard:jaccard$1,kulczynski:kulczynski$1,motyka:motyka$1,pearson:pearson$1,squaredChord:squaredChord$1,tanimoto:tanimoto}),acc=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=(t.tn[n]+t.tp[n])/(e-1);return r},err=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.fn[n]+t.fp[n]/(e-1);return r},fpr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.fp[n]/t.nNeg;return r},tpr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.tp[n]/t.nPos;return r},fnr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.fn[n]/t.nPos;return r},tnr=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.tn[n]/t.nNeg;return r},ppv=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.fp[n]+t.tp[n]!==0?t.tp[n]/(t.fp[n]+t.tp[n]):0;return r},npv=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.fn[n]+t.tn[n]!==0?t.tn[n]/(t.fn[n]+t.tn[n]):0;return r},pcfall=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.fp[n]+t.tp[n]!==0?1-t.tp[n]/(t.fp[n]+t.tp[n]):1;return r},pcmiss=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.fn[n]+t.tn[n]!==0?1-t.tn[n]/(t.fn[n]+t.tn[n]):1;return r},lift=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=0!==t.nPosPred[n]?t.tp[n]/t.nPos/(t.nPosPred[n]/t.nSamples):0;return r},rpp=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.nPosPred[n]/t.nSamples;return r},rnp=t=>{const e=t.cutoffs.length,r=new Array(e);for(var n=0;n<e;n++)r[n]=t.nNegPred[n]/t.nSamples;return r},threshold=t=>{const e=t.cutoffs.slice();return e[0]=e[1],e},measures={acc:acc,err:err,fpr:fpr,tpr:tpr,fnr:fnr,tnr:tnr,ppv:ppv,npv:npv,pcfall:pcfall,pcmiss:pcmiss,lift:lift,rpp:rpp,rnp:rnp,threshold:threshold};class Performance{constructor(t,e,r){if(r=r||{},t.length!==e.length||t[0].length!==e[0].length)throw new Error("dimensions of prediction and target do not match");const n=t.length,i=t[0].length,o=!r.max,s=[];if(r.all)for(var a=0;a<n;a++)for(var l=0;l<i;l++)s.push({pred:t[a][l],targ:e[a][l]});else{if(n<3||n!==i)throw new Error('When "all" option is false, the prediction matrix must be square and have at least 3 columns');for(a=0;a<n-1;a++)for(l=a+1;l<i;l++)s.push({pred:t[a][l],targ:e[a][l]})}o?s.sort(((t,e)=>t.pred-e.pred)):s.sort(((t,e)=>e.pred-t.pred));const h=this.cutoffs=[o?Number.MIN_VALUE:Number.MAX_VALUE],u=this.fp=[0],c=this.tp=[0];var f=0,m=0,g=s[0].pred,p=0,d=0;for(a=0;a<s.length;a++)s[a].pred!==g&&(h.push(g),u.push(d),c.push(p),g=s[a].pred),s[a].targ?(f++,p++):(m++,d++);h.push(g),u.push(d),c.push(p);const w=h.length,y=this.fn=new Array(w),x=this.tn=new Array(w),M=this.nPosPred=new Array(w),v=this.nNegPred=new Array(w);for(a=0;a<w;a++)y[a]=f-c[a],x[a]=m-u[a],M[a]=c[a]+u[a],v[a]=x[a]+y[a];this.nPos=f,this.nNeg=m,this.nSamples=f+m}getMeasure(t){if("string"!=typeof t)throw new Error("No measure specified");if(!measures[t])throw new Error(`The specified measure (${t}) does not exist`);return measures[t](this)}getAURC(){const t=this.cutoffs.length,e=new Array(t),r=new Array(t);for(var n=0;n<t;n++)e[n]=this.fp[n]/this.nNeg,r[n]=this.tp[n]/this.nPos;var i=0;for(n=1;n<t;n++)i+=.5*(e[n]-e[n-1])*(r[n]+r[n-1]);return i}getAUDC(){const t=this.cutoffs.length,e=new Array(t),r=new Array(t);for(var n=0;n<t;n++)e[n]=this.fn[n]/this.nPos,r[n]=this.fp[n]/this.nNeg;var i=0;for(n=1;n<t;n++)i+=.5*(e[n]+e[n-1])*(r[n]-r[n-1]);return i}getDistribution(t){t=t||{};for(var e=this.cutoffs.length,r=t.xMin||Math.floor(100*this.cutoffs[e-1])/100,n=t.xMax||Math.ceil(100*this.cutoffs[1])/100,i=t.interval||Math.floor((n-r)/20*1e7-1)/1e7,o=[],s=[],a=[],l=[],h=[],u=this.tp[e-1],c=0,f=this.fp[e-1],m=0,g=r,p=e-1;g<=n;g+=i){for(;this.cutoffs[p]<g;)p--;o.push(g);var d=u-c-this.tp[p],w=f-m-this.fp[p];c+=d,m+=w,s.push(w),a.push(d),l.push(100-(f-this.fp[p])/f*100),h.push(100-(u-this.tp[p])/u*100)}return{xLabels:o,interValues:s,intraValues:a,interCumPercent:l,intraCumPercent:h}}}Performance.names={acc:"Accuracy",err:"Error rate",fpr:"False positive rate",tpr:"True positive rate",fnr:"False negative rate",tnr:"True negative rate",ppv:"Positive predictive value",npv:"Negative predictive value",pcfall:"Prediction-conditioned fallout",pcmiss:"Prediction-conditioned miss",lift:"Lift value",rpp:"Rate of positive predictions",rnp:"Rate of negative predictions",threshold:"Threshold"};var src$1=Performance,defaultOptions$g={size:1,value:0};function arrayCase(t,e){var r=t.length;"number"==typeof e.size&&(e.size=[e.size,e.size]);var n,i,o=r+e.size[0]+e.size[1];if(e.output){if(e.output.length!==o)throw new RangeError("Wrong output size");n=e.output}else n=new Array(o);if("circular"===e.value)for(i=0;i<o;i++)i<e.size[0]?n[i]=t[(r-e.size[0]%r+i)%r]:i<e.size[0]+r?n[i]=t[i-e.size[0]]:n[i]=t[(i-e.size[0])%r];else if("replicate"===e.value)for(i=0;i<o;i++)i<e.size[0]?n[i]=t[0]:i<e.size[0]+r?n[i]=t[i-e.size[0]]:n[i]=t[r-1];else if("symmetric"===e.value){if(e.size[0]>r||e.size[1]>r)throw new RangeError("expanded value should not be bigger than the data length");for(i=0;i<o;i++)i<e.size[0]?n[i]=t[e.size[0]-1-i]:i<e.size[0]+r?n[i]=t[i-e.size[0]]:n[i]=t[2*r+e.size[0]-i-1]}else for(i=0;i<o;i++)i<e.size[0]?n[i]=e.value:i<e.size[0]+r?n[i]=t[i-e.size[0]]:n[i]=e.value;return n}function matrixCase(t,e){throw void 0===e.size[0]&&(e.size=[e.size,e.size,e.size,e.size]),new Error("matrix not supported yet, sorry")}function padArray(t,e){if(e=Object.assign({},defaultOptions$g,e),Array.isArray(t))return Array.isArray(t[0])?matrixCase(t,e):arrayCase(t,e);throw new TypeError("data should be an array")}var src$2=padArray;function factorial(t){let e=1;for(;t>0;)e*=t--;return e}const defaultOptions$h={windowSize:5,derivative:1,polynomial:2,pad:"none",padValue:"replicate"};function savitzkyGolay(t,e,r){if((r=Object.assign({},defaultOptions$h,r)).windowSize%2==0||r.windowSize<5||!Number.isInteger(r.windowSize))throw new RangeError("Invalid window size (should be odd and at least 5 integer number)");if(r.derivative<0||!Number.isInteger(r.derivative))throw new RangeError("Derivative should be a positive integer");if(r.polynomial<1||!Number.isInteger(r.polynomial))throw new RangeError("Polynomial should be a positive integer");let n,i,o=Math.floor(r.windowSize/2);"pre"===r.pad&&(t=src$2(t,{size:o,value:r.padValue}));let s=new Array(t.length-2*o);if(5!==r.windowSize||2!==r.polynomial||1!==r.derivative&&2!==r.derivative){let t=Matrix.ones(r.windowSize,r.polynomial+1),e=-(r.windowSize-1)/2;for(let r=0;r<t.rows;r++)for(let n=0;n<t.columns;n++)e+1===0&&0===n||t.set(r,n,Math.pow(e+r,n));let o=new MatrixTransposeView(t);n=inverse(o.mmul(t)).mmul(o),n=n.getRow(r.derivative),i=1/factorial(r.derivative)}else 1===r.derivative?(n=[-2,-1,0,1,2],i=10):(n=[2,-1,-2,-1,2],i=7);let a=i*Math.pow(e,r.derivative);for(let e=o;e<t.length-o;e++){let r=0;for(let i=0;i<n.length;i++)r+=n[i]*t[i+e-o]/a;s[e-o]=r}return"post"===r.pad&&(s=src$2(s,{size:o,value:r.padValue})),s}for(var ans=new Array(256),i=0;i<256;i++){for(var num=i,c=0;num;)num&=num-1,c++;ans[i]=c}var creator=ans;function count(t){for(var e=0,r=0;r<t.length;r++)e+=creator[255&t[r]]+creator[t[r]>>8&255]+creator[t[r]>>16&255]+creator[t[r]>>24&255];return e}function and(t,e){for(var r=new Array(t.length),n=0;n<t.length;n++)r[n]=t[n]&e[n];return r}function or(t,e){for(var r=new Array(t.length),n=0;n<t.length;n++)r[n]=t[n]|e[n];return r}function xor(t,e){for(var r=new Array(t.length),n=0;n<t.length;n++)r[n]=t[n]^e[n];return r}function not(t){for(var e=new Array(t.length),r=0;r<e.length;r++)e[r]=~t[r];return e}function getBit(t,e){var r=1<<31-e%32;return Boolean(t[e>>5]&r)}function setBit(t,e,r){var n=e>>5,i=1<<31-e%32;return t[n]=r?i|t[n]:~i&t[n],t}function toBinaryString(t){for(var e="",r=0;r<t.length;r++){var n=(t[r]>>>0).toString(2);e+="00000000000000000000000000000000".substr(n.length)+n}return e}function parseBinaryString(t){for(var e=t.length/32,r=new Array(e),n=0;n<e;n++)r[n]=0|parseInt(t.substr(32*n,32),2);return r}function toHexString(t){for(var e="",r=0;r<t.length;r++){var n=(t[r]>>>0).toString(16);e+="00000000".substr(n.length)+n}return e}function parseHexString(t){for(var e=t.length/8,r=new Array(e),n=0;n<e;n++)r[n]=0|parseInt(t.substr(8*n,8),16);return r}function toDebug(t){for(var e=toBinaryString(t),r="",n=0;n<t.length;n++){r+="0000".substr((32*n).toString(16).length)+(32*n).toString(16)+":";for(var i=0;i<32;i+=4)r+=" "+e.substr(32*n+i,4);n<t.length-1&&(r+="\n")}return r}var src$3={count:count,and:and,or:or,xor:xor,not:not,getBit:getBit,setBit:setBit,toBinaryString:toBinaryString,parseBinaryString:parseBinaryString,toHexString:toHexString,parseHexString:parseHexString,toDebug:toDebug};function SavitzkyGolay(t,e,r={}){let{windowSize:n=9,derivative:i=0,polynomial:o=3}=r;if(n%2==0||n<5||!Number.isInteger(n))throw new RangeError("Invalid window size (should be odd and at least 5 integer number)");if(n>t.length)throw new RangeError(`Window size is higher than the data length ${n}>${t.length}`);if(i<0||!Number.isInteger(i))throw new RangeError("Derivative should be a positive integer");if(o<1||!Number.isInteger(o))throw new RangeError("Polynomial should be a positive integer");o>=6&&console.warn("You should not use polynomial grade higher than 5 if you are not sure that your data arises from such a model. Possible polynomial oscillation problems");let s=Math.floor(n/2),a=t.length,l=new Array(a),h=fullWeights(n,o,i),u=0,c=!0;Array.isArray(e)?c=!1:u=Math.pow(e,i);for(let r=0;r<s;r++){let o=h[s-r-1],f=h[s+r+1],m=0,g=0;for(let e=0;e<n;e++)m+=o[e]*t[e],g+=f[e]*t[a-n+e];c?(l[s-r-1]=m/u,l[a-s+r]=g/u):(u=getHs(e,s-r-1,s,i),l[s-r-1]=m/u,u=getHs(e,a-s+r,s,i),l[a-s+r]=g/u)}let f=h[s];for(let r=n;r<=a;r++){let o=0;for(let e=0;e<n;e++)o+=f[e]*t[e+r-n];c||(u=getHs(e,r-s-1,s,i)),l[r-s-1]=o/u}return l}function getHs(t,e,r,n){let i=0,o=0;for(let n=e-r;n<e+r;n++)n>=0&&n<t.length-1&&(i+=t[n+1]-t[n],o++);return Math.pow(i/o,n)}function GramPoly(t,e,r,n){let i=0;return i=r>0?(4*r-2)/(r*(2*e-r+1))*(t*GramPoly(t,e,r-1,n)+n*GramPoly(t,e,r-1,n-1))-(r-1)*(2*e+r)/(r*(2*e-r+1))*GramPoly(t,e,r-2,n):0===r&&0===n?1:0,i}function GenFact(t,e){let r=1;if(t>=e)for(let n=t-e+1;n<=t;n++)r*=n;return r}function Weight(t,e,r,n,i){let o=0;for(let s=0;s<=n;s++)o+=(2*s+1)*(GenFact(2*r,s)/GenFact(2*r+s+1,s+1))*GramPoly(t,r,s,0)*GramPoly(e,r,s,i);return o}function fullWeights(t,e,r){let n=new Array(t),i=Math.floor(t/2);for(let o=-i;o<=i;o++){n[o+i]=new Array(t);for(let t=-i;t<=i;t++)n[o+i][t+i]=Weight(t,o,i,e,r)}return n}function gsd(t,e,r={}){let{noiseLevel:n,sgOptions:i={windowSize:9,polynomial:3},smoothY:o=!0,heightFactor:s=0,broadRatio:a=0,maxCriteria:l=!0,minMaxRatio:h=25e-5,derivativeThreshold:u=-1,realTopDetection:c=!1}=r;const f=e.slice();let m=isEqualSpaced(t);void 0===n&&(n=m?getNoiseLevel(f):0);const g={m:1,b:n};l||(g.m=-1,g.b*=-1);for(let t=0;t<f.length;t++)f[t]=g.m*f[t]-g.b;for(let t=0;t<f.length;t++)f[t]<0&&(f[t]=0);let p,d,w=f;const{windowSize:y,polynomial:x}=i;m?(o&&(w=SavitzkyGolay(f,t[1]-t[0],{windowSize:y,polynomial:x,derivative:0})),p=SavitzkyGolay(f,t[1]-t[0],{windowSize:y,polynomial:x,derivative:1}),d=SavitzkyGolay(f,t[1]-t[0],{windowSize:y,polynomial:x,derivative:2})):(o&&(w=SavitzkyGolay(f,t,{windowSize:y,polynomial:x,derivative:0})),p=SavitzkyGolay(f,t,{windowSize:y,polynomial:x,derivative:1}),d=SavitzkyGolay(f,t,{windowSize:y,polynomial:x,derivative:2}));const M=t,v=t[1]-t[0];let b=0,S=0;for(let t=0;t<w.length;t++)Math.abs(d[t])>b&&(b=Math.abs(d[t])),Math.abs(w[t])>S&&(S=Math.abs(w[t]));let A=null,E=null,R=new Array(w.length-2),k=new Array(w.length),N=new Array(w.length),T=new Array(w.length-2),C=0,I=0,O=0,L=0;for(let t=1;t<w.length-1;++t)Math.abs(p[t])>u&&((p[t]<p[t-1]&&p[t]<=p[t+1]||p[t]<=p[t-1]&&p[t]<p[t+1])&&(E={x:M[t],index:t},v>0&&null!==A&&(k[I++]=A,N[O++]=E)),(p[t]>=p[t-1]&&p[t]>p[t+1]||p[t]>p[t-1]&&p[t]>=p[t+1])&&(A={x:M[t],index:t},v<0&&null!==E&&(k[I++]=A,N[O++]=E))),d[t]<d[t-1]&&d[t]<d[t+1]&&(R[C++]=t,T[L++]=Math.abs(d[t])<=a*b);R.length=C,k.length=I,N.length=O,T.length=L;let F,P,V,_,D,z=new Array(R.length),$=0,q=-1;for(let t=0;t<R.length;++t){P=M[R[t]],F=-1;let e=q+1;for(_=Number.MAX_VALUE,V=0,D=!0;-1===F&&e<k.length&&D;)V=Math.abs(P-(k[e].x+N[e].x)/2),V<_?_=V:D=!1,V<Math.abs(k[e].x-N[e].x)/2&&(F=e,q=e),++e;if(-1!==F&&Math.abs(w[R[t]])>h*S&&(z[$++]={index:R[t],x:P,y:(w[R[t]]+g.b)/g.m,width:Math.abs(N[F].x-k[F].x),soft:T[t]},z[$-1].left=k[F],z[$-1].right=N[F],s)){let t=w[k[F].index],e=w[N[F].index];z[$-1].height=s*(z[$-1].y-(t+e)/2)}}z.length=$,c&&determineRealTop(z,M,w);for(let t=0;t<z.length;t++)z[t].base=n;return z.sort((function(t,e){return t.x-e.x})),z}const isEqualSpaced=t=>{let e,r=0,n=Number.MAX_SAFE_INTEGER;for(let i=0;i<t.length-1;++i)e=Math.abs(t[i+1]-t[i]),e<n&&(n=e),e>r&&(r=e);return(r-n)/r<.05},getNoiseLevel=t=>{let e=0,r=0,n=t.length;for(let r=0;r<n;++r)e+=t[r];e/=n;let i=new Array(n);for(let r=0;r<n;++r)i[r]=Math.abs(t[r]-e);return i.sort(((t,e)=>t-e)),r=n%2==1?i[(n-1)/2]/.6745:.5*(i[n/2]+i[n/2-1])/.6745,r},determineRealTop=(t,e,r)=>{let n,i,o,s,a;for(let l=0;l<t.length;l++)a=t[l].index,r[a-1]>=r[a-2]&&r[a-1]>=r[a]?a--:r[a+1]>=r[a]&&r[a+1]>=r[a+2]?a++:r[a-2]>=r[a-3]&&r[a-2]>=r[a-1]?a-=2:r[a+2]>=r[a+1]&&r[a+2]>=r[a+3]&&(a+=2),r[a-1]>0&&r[a+1]>0&&r[a]>=r[a-1]&&r[a]>=r[a+1]&&(r[a]!==r[a-1]||r[a]!==r[a+1])&&(n=20*Math.log10(r[a-1]),i=20*Math.log10(r[a]),o=20*Math.log10(r[a+1]),s=.5*(n-o)/(n-2*i+o),t[l].x=e[a]+(e[a]-e[a-1])*s,t[l].y=r[a]-.25*(r[a-1]-r[a+1])*s)};function sumOfGaussians(t){return function(e){let r,n=t.length/3,i=e.length,o=void 0===i?0:new Float64Array(i).fill(0);for(let s=0;s<n;s++)if(r=2*Math.pow(t[s+2*n],2),void 0===i)o+=t[s+n]*Math.exp(-Math.pow(e-t[s],2)/r);else for(let a=0;a<i;a++)o[a]+=t[s+n]*Math.exp(-Math.pow(e[a]-t[s],2)/r);return o}}function optimizeGaussianSum(t,e,r={}){let n=t[0],i=t[1],o=Math.max(...i);i.forEach(((t,e,r)=>r[e]/=o));let s=e.length,a=new Float64Array(3*s),l=new Float64Array(3*s),h=new Float64Array(3*s),u=Math.abs(n[0]-n[1]);for(let t=0;t<s;t++)a[t]=e[t].x,a[t+s]=e[t].y/o,a[t+2*s]=e[t].width,l[t]=e[t].x-u,l[t+s]=0,l[t+2*s]=e[t].width/4,h[t]=e[t].x+u,h[t+s]=1.2*e[t].y/o,h[t+2*s]=4*e[t].width;let c={x:n,y:i},f=new Array(s),m={damping:1.5,initialValues:a,minValues:l,maxValues:h,gradientDifference:u/1e4,maxIterations:100,errorTolerance:1e-4},g=levenbergMarquardt(c,sumOfGaussians,r=Object.assign({},m,r));for(let t=0;t<s;t++)f[t]={parameters:[g.parameterValues[t],g.parameterValues[t+s]*o,g.parameterValues[t+2*s]],error:g.parameterError};return f}function singleGaussian(t){return function(e){let r=t[2]*t[2]/2;if(!e.length)return t[1]*Math.exp(-(e-t[0])*(e-t[0])/r);let n=new Float64Array(e.length);for(let i=0;i<e.length;i++)n[i]=t[1]*Math.exp(-(e[i]-t[0])*(e[i]-t[0])/r);return n}}function optimizeSingleGaussian(t,e,r={}){let n=t[0],i=t[1],o=Math.max(...i);i.forEach(((t,e,r)=>r[e]/=o));let s=Math.abs(n[0]-n[1]),a={damping:1.5,initialValues:new Float64Array([e.x,1,e.width]),minValues:new Float64Array([e.x-s,0,e.width/4]),maxValues:new Float64Array([e.x+s,1.25,4*e.width]),gradientDifference:s/1e4,maxIterations:100,errorTolerance:1e-4},l=levenbergMarquardt({x:n,y:i},singleGaussian,r=Object.assign({},a,r));return{parameters:[l.parameterValues[0],l.parameterValues[1]*o,l.parameterValues[2]],error:l.parameterError}}function sumOfLorentzians(t){return function(e){let r,n,i=t.length/3,o=e.length,s=void 0===o?0:new Float64Array(o).fill(0);for(let a=0;a<i;a++)if(n=Math.pow(t[a+2*i]/2,2),r=t[a+i]*n,void 0===o)s+=r/(Math.pow(e-t[a],2)+n);else for(let i=0;i<o;i++)s[i]+=r/(Math.pow(e[i]-t[a],2)+n);return s}}function optimizeLorentzianSum(t,e,r={}){let n=t[0],i=t[1],o=Math.max(...i);i.forEach(((t,e,r)=>r[e]/=o));let s=e.length,a=new Float64Array(3*s),l=new Float64Array(3*s),h=new Float64Array(3*s),u=Math.abs(n[0]-n[1]);for(let t=0;t<s;t++)a[t]=e[t].x,a[t+s]=1,a[t+2*s]=e[t].width,l[t]=e[t].x-u,l[t+s]=0,l[t+2*s]=e[t].width/4,h[t]=e[t].x+u,h[t+s]=1.5,h[t+2*s]=4*e[t].width;let c={x:n,y:i},f=new Array(s),m={damping:1.5,initialValues:a,minValues:l,maxValues:h,gradientDifference:u/1e4,maxIterations:100,errorTolerance:1e-4},g=levenbergMarquardt(c,sumOfLorentzians,r=Object.assign({},m,r));for(let t=0;t<s;t++)f[t]={parameters:[g.parameterValues[t],g.parameterValues[t+s]*o,g.parameterValues[t+2*s]],error:g.parameterError};return f}function singleLorentzian(t){return function(e){let r=t[1]*Math.pow(t[2]/2,2),n=e.length;if(!n)return r/(Math.pow(e-t[0],2)+Math.pow(t[2]/2,2));let i=new Float64Array(n);for(let o=0;o<n;o++)i[o]=r/(Math.pow(e[o]-t[0],2)+Math.pow(t[2]/2,2));return i}}function optimizeSingleLorentzian(t,e,r={}){let n=t[0],i=t[1],o=Math.max(...i);i.forEach(((t,e,r)=>r[e]/=o));let s=Math.abs(n[0]-n[1]),a={damping:1.5,initialValues:new Float64Array([e.x,1,e.width]),minValues:new Float64Array([e.x-s,.75,e.width/4]),maxValues:new Float64Array([e.x+s,1.25,4*e.width]),gradientDifference:s/1e4,maxIterations:100,errorTolerance:1e-4},l=levenbergMarquardt({x:n,y:i},singleLorentzian,r=Object.assign({},a,r));return{parameters:[l.parameterValues[0],l.parameterValues[1]*o,l.parameterValues[2]],error:l.parameterError}}function optimizePeaks(t,e,r,n={}){const{functionName:i="gaussian",factorWidth:o=4,optimizationOptions:s={damping:1.5,maxIterations:100,errorTolerance:1e-4}}=n;let a,l=[0],h=groupPeaks(t,o),u=[],c=1;"gaussian"===i&&(c=1.17741);for(let t=0;t<h.length;t++){let n=h[t].group;if(n.length>1){if(a=sampleFunction(h[t].limits[0]-h[t].limits[1],h[t].limits[0]+h[t].limits[1],e,r,l),a[0].length>5){let t=[];"gaussian"===i?t=optimizeGaussianSum(a,n,s):"lorentzian"===i&&(t=optimizeLorentzianSum(a,n,s));for(let e=0;e<t.length;e++){let{parameters:r}=t[e];u.push({x:r[0],y:r[1],width:r[2]*c,index:n[e].index})}}}else if(n=n[0],a=sampleFunction(n.x-o*n.width,n.x+o*n.width,e,r,l),a[0].length>5){let t=[];"gaussian"===i?t=optimizeSingleGaussian([a[0],a[1]],n,s):"lorentzian"===i&&(t=optimizeSingleLorentzian([a[0],a[1]],n,s));let{parameters:e}=t;u.push({x:e[0],y:e[1],width:e[2]*c,index:n.index})}}return u}function sampleFunction(t,e,r,n,i){let o=r.length,s=[],a=[],l=Math.sign(r[1]-r[0]);-1===l&&(i[0]=r.length-1);let h=Math.abs(e-t)/2,u=(t+e)/2,c=!1,f=i[0];for(;!c&&f<o&&f>=0;)Math.abs(r[f]-u)<=h?(s.push(r[f]),a.push(n[f]),f+=l):1===Math.sign(u-r[f])?f+=l:c=!0;return i[0]=f,[s,a]}function groupPeaks(t,e){let r,n,i=[],o=[],s=[t[0].x,e*t[0].width];for(let a=0;a<t.length;a++)Math.abs(t[a].x-s[0])<e*t[a].width+s[1]?(i.push(t[a]),r=s[0]+s[1],t[a].x+e*t[a].width>r&&(r=t[a].x+e*t[a].width),n=s[0]-s[1],t[a].x-e*t[a].width<n&&(n=t[a].x-e*t[a].width),s=[(r+n)/2,Math.abs(r-n)/2]):(o.push({limits:s,group:i}),i=[t[a]],s=[t[a].x,e*t[a].width]);o.push({limits:s,group:i});for(let t=o.length-2;t>=0;t--)if(Math.abs(o[t].limits[0]-o[t+1].limits[0])<(o[t].limits[1]+o[t+1].limits[1])/2){for(let e=0;e<o[t+1].group.length;e++)o[t].group.push(o[t+1].group[e]);r=o[t].limits[0]+o[t].limits[1],o[t+1].limits[0]+o[t+1].limits[1]>r&&(r=o[t+1].limits[0]+o[t+1].limits[1]),n=o[t].limits[0]-o[t].limits[1],o[t+1].limits[0]-o[t+1].limits[1]<n&&(n=o[t+1].limits[0]-o[t+1].limits[1]),o[t].limits=[(r+n)/2,Math.abs(r-n)/2],o.splice(t+1,1)}return o}function joinBroadPeaks(t,e={}){let r=e.width,n=[],i=0,o=0,s=1;for(let e=t.length-1;e>=0;e--)t[e].soft&&n.push(t.splice(e,1)[0]);n.push({x:Number.MAX_VALUE});let a=[[n[0].x,n[0].y]],l=[n[0].index];for(let 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p=0;p<t.length;p++)e[p]=t[p]/g;return e;default:throw new Error("norm: unknown algorithm: ".concat(i))}}function absoluteSum(t){for(var e=0,r=0;r<t.length;r++)e+=Math.abs(t[r]);return e}function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function sequentialFill(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if("object"!==_typeof(t)||isAnyArray$4(t)||(e=t,t=[]),!isAnyArray$4(t))throw new TypeError("input must be an array");var r=e,n=r.from,i=void 0===n?0:n,o=r.to,s=void 0===o?10:o,a=r.size,l=void 0===a?t.length:a,h=r.step;if(0!==l&&h)throw new Error("step is defined by the array size");if(l||(l=h?Math.floor((s-i)/h)+1:s-i+1),!h&&l&&(h=(s-i)/(l-1)),Array.isArray(t)){t.length=0;for(var u=0;u<l;u++)t.push(i),i+=h}else{if(t.length!==l)throw new Error("sequentialFill typed array must have the correct length");for(var c=0;c<l;c++)t[c]=i,i+=h}return t}const toString$6=Object.prototype.toString;function isAnyArray$6(t){return toString$6.call(t).endsWith("Array]")}function variance(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!isAnyArray$6(t))throw new TypeError("input must be an array");for(var r=e.unbiased,n=void 0===r||r,i=e.mean,o=void 0===i?mean(t):i,s=0,a=0;a<t.length;a++){var l=t[a]-o;s+=l*l}return n?s/(t.length-1):s/t.length}function standardDeviation(t){var e=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return Math.sqrt(variance(t,e))}function mergeByCentroids(t,e,r={}){const{window:n=.01}=r;for(var i={x:e.slice(),y:new Array(e.length).fill(0)},o=0,s=0;o<t.x.length&&s<e.length;){var a=t.x[o]-e[s];Math.abs(a)<n?i.y[s]+=t.y[o++]:a<0?o++:s++}return i}function closestX(t,e){const{x:r,y:n}=t,{target:i=r[0],reverse:o=!1}=e;let s;return s=binarySearch(r,i,o?descending:ascending),s>=0?{x:r[s],y:n[s]}:(s=~s,0!==s&&Math.abs(r[s]-i)>.5||s===r.length?{x:r[s-1],y:n[s-1]}:{x:r[s],y:n[s]})}function covariance$1(t,e={}){const{x:r,y:n}=t,{unbiased:i=!0}=e,o=mean(r),s=mean(n);var a=0;for(let t=0;t<r.length;t++)a+=(r[t]-o)*(n[t]-s);return i?a/(r.length-1):a/r.length}function maxMerge(t,e={}){const{x:r,y:n}=t,{groupWidth:i=.001}=e;for(var o={x:[],y:[]},s={x:[],y:[]},a=0,l=0;l<r.length;)0===a||r[l]-o.x[a-1]>i?(s.x.push(r[l]),s.y.push(n[l]),o.x.push(r[l]),o.y.push(n[l]),l++,a++):(n[l]>s.y[a-1]&&(s.x[a-1]=r[l],s.y[a-1]=n[l]),o.x[a-1]=r[l],o.y[a-1]+=n[l],l++);return o.x=s.x.slice(),o}function maxY(t,e={}){const{x:r,y:n}=t;let{from:i={index:0},to:o={index:r.length},reverse:s=!1}=e;void 0!==i.value&&void 0===i.index&&(i.index=calculateIndex(i.value,r,s)),void 0!==o.value&&void 0===o.index&&(o.index=calculateIndex(o.value,r,s));for(var a,l=Number.MIN_VALUE,h=i.index;h<o.index;h++)l<n[h]&&(l=n[h],a=h);return{index:a,value:l}}function calculateIndex(t,e,r){let n;if(n=binarySearch(e,t,r?descending:ascending),n<0)throw new Error(`the value ${t} doesn't belongs to the abscissa value`);return n}function sortX(t,e={}){const{x:r,y:n}=t,{reverse:i=!1}=e;var o;o=i?(t,e)=>e.x-t.x:(t,e)=>t.x-e.x;for(var s=r.map(((t,e)=>({x:t,y:n[e]}))).sort(o),a={x:r.slice(),y:n.slice()},l=0;l<r.length;l++)a.x[l]=s[l].x,a.y[l]=s[l].y;return a}function uniqueX(t={}){const{x:e,y:r}=t;if(e.length<2)return;if(e.length!==r.length)throw new Error("The X and Y arrays mush have the same length");let n=e[0],i=0;for(let t=1;t<e.length;t++)n!==e[t]&&(i++,n=e[t],e[i]=e[t],t!==i&&(r[i]=0)),t!==i&&(r[i]+=r[t]);e.length=i+1,r.length=i+1}function weightedMerge(t,e={}){const{x:r,y:n}=t,{groupWidth:i=.001}=e;for(var o={x:[],y:[]},s={x:[],y:[]},a=0,l=0;l<r.length;)0===a||r[l]-o.x[a-1]>i?(s.x.push(r[l]*n[l]),s.y.push(n[l]),o.x.push(r[l]),o.y.push(n[l]),l++,a++):(s.x[a-1]+=r[l]*n[l],s.y[a-1]+=n[l],o.x[a-1]=r[l],o.y[a-1]+=n[l],l++);for(var h=0;h<o.x.length;h++)o.x[h]=s.x[h]/s.y[h];return o}function normalize(t=[],e={}){if(0===t.length)return[];let{from:r=Number.NEGATIVE_INFINITY,to:n=Number.POSITIVE_INFINITY}=e;if(r>n&&([r,n]=[n,r]),(t=(t=JSON.parse(JSON.stringify(t)).map((t=>t.from>t.to?{from:t.to,to:t.from}:t))).sort(((t,e)=>t.from!==e.from?t.from-e.from:t.to-e.to))).forEach((t=>{r>t.from&&(t.from=r),n<t.to&&(t.to=n)})),0===(t=t.filter((t=>t.from<=t.to))).length)return[];let i=t[0],o=[i];for(let e=1;e<t.length;e++){let r=t[e];r.from<=i.to?i.to=r.to:(i=r,o.push(i))}return o}function invert(t=[],e={}){let{from:r=Number.NEGATIVE_INFINITY,to:n=Number.POSITIVE_INFINITY}=e;if(r>n&&([r,n]=[n,r]),0===(t=normalize(t,{from:r,to:n})).length)return[{from:r,to:n}];let i=[];for(let e=0;e<t.length;e++){let o=t[e],s=t[e+1];0===e&&o.from>r&&i.push({from:r,to:o.from}),e===t.length-1?o.to<n&&i.push({from:o.to,to:n}):i.push({from:o.to,to:s.from})}return i}function zonesWithPoints(t,e,r={}){if(0===t.length)return t;let n=(t=normalize(t,r)).reduce(((t,e)=>t+(e.to-e.from)),0)/e,i=0;for(let r=0;r<t.length-1;r++){let o=t[r];o.numberOfPoints=Math.min(Math.round((o.to-o.from)/n),e-i),i+=o.numberOfPoints}return t[t.length-1].numberOfPoints=e-i,t}function integral(t,e,r,n){return.5*r*e*e+n*e-(.5*r*t*t+n*t)}function equallySpacedSmooth(t,e,r,n,i){let o=t.length,s=(n-r)/(i-1),a=s/2,l=new Array(i),h=t[1]-t[0],u=t[o-1]-t[o-2],c=r-a,f=r+a,m=Number.MIN_VALUE,g=0,p=t[0]-h,d=0,w=0,y=0,x=0,M=0,v=0,b=0,S=0;let A=0;t:for(;;){for(m<=c&&c<=p&&(A=integral(0,c-m,y,g),M=w+A);p-f>=0;){if(A=integral(0,f-m,y,g),v=w+A,l[S++]=(v-M)/s,S===i)break t;c=f,f+=s,M=v}w+=integral(m,p,y,x),m=p,g=d,b<o?(p=t[b],d=e[b],b++):b===o&&(p+=u,d=0),y=(d-g)/(p-m),x=-y*m+g}return l}function equallySpacedSlot(t,e,r,n,i){let o=t.length,s=(n-r)/(i-1),a=s/2,l=t[t.length-1]-t[t.length-2],h=r-a,u=new Array(i),c=h,f=h+s,m=-Number.MAX_VALUE,g=0,p=t[0],d=e[0],w=0,y=!0,x=0,M=0,v=1,b=0;t:for(;;){if(m>=p)throw new Error("x must be an increasing serie");for(;m-f>0;){if(y&&(M++,y=!1),u[b]=M<=0?0:x/M,b++,b===i)break t;c=f,f+=s,x=0,M=0}m>c&&(x+=g,M++),(m===-Number.MAX_VALUE||w>1)&&M--,m=p,g=d,v<o?(p=t[v],d=e[v],v++):(p+=l,d=0,w++)}return u}function equallySpaced(t={},e={}){let{x:r,y:n}=t,i=r.length,o=!1;r.length>1&&r[0]>r[1]&&(r=r.slice().reverse(),n=n.slice().reverse(),o=!0);let{from:s=r[0],to:a=r[i-1],variant:l="smooth",numberOfPoints:h=100,exclusions:u=[],zones:c=[]}=e;if(i!==n.length)throw new RangeError("the x and y vector doesn't have the same size.");if("number"!=typeof s||isNaN(s))throw new RangeError("'from' option must be a number");if("number"!=typeof a||isNaN(a))throw new RangeError("'to' option must be a number");if("number"!=typeof h||isNaN(h))throw new RangeError("'numberOfPoints' option must be a number");if(h<2)throw new RangeError("'numberOfPoints' option must be greater than 1");0===c.length&&(c=invert(u,{from:s,to:a})),c=zonesWithPoints(c,h,{from:s,to:a});let f=[],m=[];for(let t of c){let e=processZone(r,n,t.from,t.to,t.numberOfPoints,l);f=f.concat(e.x),m=m.concat(e.y)}return o?s<a?{x:f.reverse(),y:m.reverse()}:{x:f,y:m}:s<a?{x:f,y:m}:{x:f.reverse(),y:m.reverse()}}function processZone(t,e,r,n,i,o){if(i<1)throw new RangeError("the number of points must be at least 1");let s="slot"===o?equallySpacedSlot(t,e,r,n,i):equallySpacedSmooth(t,e,r,n,i);return{x:sequentialFill({from:r,to:n,size:i}),y:s}}function getZones(t,e,r=[]){t>e&&([t,e]=[e,t]),r=r.filter((t=>void 0!==t.from&&void 0!==t.to)),(r=JSON.parse(JSON.stringify(r))).forEach((t=>{t.from>t.to&&([t.to,t.from]=[t.from,t.to])})),r.sort(((t,e)=>t.from-e.from)),r.forEach((r=>{r.from<t&&(r.from=t),r.to>e&&(r.to=e)}));for(let t=0;t<r.length-1;t++)r[t].to>r[t+1].from&&(r[t].to=r[t+1].from);if(!(r=r.filter((t=>t.from<t.to)))||0===r.length)return[{from:t,to:e}];let n=[],i=t;for(let t of r)i<t.from&&n.push({from:i,to:t.from}),i=t.to;return i<e&&n.push({from:i,to:e}),n}function filterX(t,e={}){const{x:r,y:n}=t,{from:i=r[0],to:o=r[r.length-1],exclusions:s=[]}=e;let a=getZones(i,o,s),l=0,h=[],u=[],c=0;for(;c<r.length;){if(r[c]<=a[l].to&&r[c]>=a[l].from)h.push(r[c]),u.push(n[c]);else if(r[c]>a[l].to&&(l++,!a[l]))break;c++}return{x:h,y:u}}const{Matrix:Matrix$2,SVD:SVD,EVD:EVD,CholeskyDecomposition:CholeskyDecomposition$1,LuDecomposition:LuDecomposition$1,QrDecomposition:QrDecomposition$1}=MatrixLib,Array$1={min:min$1,max:max$1,median:median,mean:mean,mode:mode$1,normed:norm$1,rescale:rescale,sequentialFill:sequentialFill,standardDeviation:standardDeviation,sum:sum,variance:variance},ArrayXY={centroidsMerge:mergeByCentroids,closestX:closestX,covariance:covariance$1,maxMerge:maxMerge,maxY:maxY,sortX:sortX,uniqueX:uniqueX,weightedMerge:weightedMerge,equallySpaced:equallySpaced,filterX:filterX};exports.Array=Array$1,exports.ArrayXY=ArrayXY,exports.BitArray=src$3,exports.CholeskyDecomposition=CholeskyDecomposition$1,exports.ConfusionMatrix=ConfusionMatrix,exports.CrossValidation=index$2,exports.DecisionTreeClassifier=DecisionTreeClassifier,exports.DecisionTreeRegression=DecisionTreeRegression,exports.Distance=distances,exports.EVD=EVD,exports.ExponentialRegression=ExponentialRegression,exports.FCNNLS=index$3,exports.FNN=FeedForwardNeuralNetwork,exports.GSD=index$6,exports.HClust=index,exports.HashTable=HashTable,exports.KMeans=kmeans,exports.KNN=KNN,exports.KOPLS=KOPLS,exports.Kernel=kernel,exports.LuDecomposition=LuDecomposition$1,exports.Matrix=Matrix$2,exports.MatrixLib=MatrixLib,exports.MultivariateLinearRegression=MultivariateLinearRegression,exports.NaiveBayes=index$1,exports.OPLS=OPLS,exports.OPLSNipals=OPLSNipals,exports.PCA=PCA,exports.PLS=PLS,exports.Performance=src$1,exports.PolynomialRegression=PolynomialRegression,exports.PowerRegression=PowerRegression,exports.QrDecomposition=QrDecomposition$1,exports.Random=Random,exports.RandomForestClassifier=RandomForestClassifier,exports.RandomForestRegression=RandomForestRegression,exports.RobustPolynomialRegression=RobustPolynomialRegression,exports.SOM=src,exports.SVD=SVD,exports.Similarity=similarities,exports.SimpleLinearRegression=SimpleLinearRegression,exports.SparseMatrix=SparseMatrix,exports.TheilSenRegression=TheilSenRegression,exports.XSadd=XSadd,exports.binarySearch=binarySearch,exports.distanceMatrix=distanceMatrix,exports.levenbergMarquardt=levenbergMarquardt,exports.numSort=index$4,exports.padArray=src$2,exports.savitzkyGolay=savitzkyGolay,Object.defineProperty(exports,"__esModule",{value:!0})}));
 //# sourceMappingURL=ml.min.js.map
diff --git a/dist/ml.min.js.map b/dist/ml.min.js.map
index 7d50fd8..cb45a49 100644
--- a/dist/ml.min.js.map
+++ b/dist/ml.min.js.map
@@ -1 +1 @@
-{"version":3,"file":"ml.min.js","sources":["../node_modules/is-any-array/src/index.js","../node_modules/ml-array-max/lib-es6/index.js","../node_modules/ml-array-min/lib-es6/index.js","../node_modules/ml-array-rescale/lib-es6/index.js","../node_modules/ml-matrix/src/util.js","../node_modules/ml-matrix/src/stat.js","../node_modules/ml-matrix/src/inspect.js","../node_modules/ml-matrix/src/mathOperations.js","../node_modules/ml-matrix/src/matrix.js","../node_modules/ml-matrix/src/views/base.js","../node_modules/ml-matrix/src/views/column.js","../node_modules/ml-matrix/src/views/columnSelection.js","../node_modules/ml-matrix/src/views/flipColumn.js","../node_modules/ml-matrix/src/views/flipRow.js","../node_modules/ml-matrix/src/views/row.js","../node_modules/ml-matrix/src/views/rowSelection.js","../node_modules/ml-matrix/src/views/selection.js","../node_modules/ml-matrix/src/views/sub.js","../node_modules/ml-matrix/src/views/transpose.js","../node_modules/ml-matrix/src/wrap/WrapperMatrix1D.js","../node_modules/ml-matrix/src/wrap/WrapperMatrix2D.js","../node_modules/ml-matrix/src/wrap/wrap.js","../node_modules/ml-matrix/src/dc/lu.js","../node_modules/ml-matrix/src/dc/util.js","../node_modules/ml-matrix/src/dc/qr.js","../node_modules/ml-matrix/src/dc/svd.js","../node_modules/ml-matrix/src/decompositions.js","../node_modules/ml-matrix/src/determinant.js","../node_modules/ml-matrix/src/linearDependencies.js","../node_modules/ml-matrix/src/pseudoInverse.js","../node_modules/ml-matrix/src/covariance.js","../node_modules/ml-matrix/src/correlation.js","../node_modules/ml-matrix/src/dc/evd.js","../node_modules/ml-matrix/src/dc/cholesky.js","../node_modules/ml-matrix/src/dc/nipals.js","../node_modules/ml-array-sum/lib-es6/index.js","../node_modules/ml-array-mean/lib-es6/index.js","../node_modules/ml-cart/src/utils.js","../node_modules/ml-cart/src/TreeNode.js","../node_modules/ml-cart/src/DecisionTreeClassifier.js","../node_modules/ml-cart/src/DecisionTreeRegression.js","../node_modules/random-js/dist/random-js.esm.js","../node_modules/ml-random-forest/src/utils.js","../node_modules/ml-random-forest/src/RandomForestBase.js","../node_modules/ml-random-forest/src/RandomForestClassifier.js","../node_modules/median-quickselect/lib/median-quickselect.min.js","../node_modules/ml-array-median/lib-es6/index.js","../node_modules/ml-random-forest/src/RandomForestRegression.js","../node_modules/ml-pca/src/pca.js","../node_modules/ml-distance-euclidean/lib-es6/euclidean.js","../node_modules/ml-distance-matrix/src/index.js","../node_modules/heap/lib/heap.js","../node_modules/heap/index.js","../node_modules/ml-hclust/src/Cluster.js","../node_modules/ml-hclust/src/agnes.js","../node_modules/ml-nearest-vector/lib-es6/index.js","../node_modules/ml-kmeans/src/utils.js","../node_modules/ml-xsadd/lib-es6/xsadd.js","../node_modules/ml-random/lib-es6/choice.js","../node_modules/ml-random/lib-es6/index.js","../node_modules/ml-kmeans/src/initialization.js","../node_modules/ml-kmeans/src/KMeansResult.js","../node_modules/ml-kmeans/src/kmeans.js","../node_modules/ml-naivebayes/src/utils.js","../node_modules/ml-naivebayes/src/GaussianNB.js","../node_modules/ml-naivebayes/src/MultinomialNB.js","../node_modules/ml-knn/src/KDTree.js","../node_modules/ml-knn/src/index.js","../node_modules/ml-pls/src/utils.js","../node_modules/ml-pls/src/pls.js","../node_modules/ml-pls/src/kopls.js","../node_modules/ml-confusion-matrix/src/index.js","../node_modules/ml-combinations/src/index.js","../node_modules/ml-cross-validation/src/index.js","../node_modules/ml-fnn/FeedForwardNeuralNetwork.js","../node_modules/ml-som/src/node-square.js","../node_modules/ml-som/src/node-hexagonal.js","../node_modules/ml-som/src/index.js","../node_modules/ml-regression-base/src/maybeToPrecision.js","../node_modules/ml-regression-base/src/checkArrayLength.js","../node_modules/ml-regression-base/src/index.js","../node_modules/ml-regression-polynomial/src/index.js","../node_modules/ml-regression-simple-linear/src/index.js","../node_modules/ml-regression-exponential/src/index.js","../node_modules/ml-regression-power/src/index.js","../node_modules/ml-regression-multivariate-linear/src/index.js","../node_modules/ml-kernel-gaussian/gaussian-kernel.js","../node_modules/ml-kernel-polynomial/polynomial-kernel.js","../node_modules/ml-kernel-sigmoid/sigmoid-kernel.js","../node_modules/ml-kernel/src/kernels/anova-kernel.js","../node_modules/ml-kernel/src/kernels/cauchy-kernel.js","../node_modules/ml-kernel/src/kernels/exponential-kernel.js","../node_modules/ml-kernel/src/kernels/histogram-intersection-kernel.js","../node_modules/ml-kernel/src/kernels/laplacian-kernel.js","../node_modules/ml-kernel/src/kernels/multiquadratic-kernel.js","../node_modules/ml-kernel/src/kernels/rational-quadratic-kernel.js","../node_modules/ml-kernel/src/kernel.js","../node_modules/ml-regression-theil-sen/src/index.js","../node_modules/ml-regression-robust-polynomial/src/index.js","../node_modules/ml-levenberg-marquard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y-xy-equally-spaced/src/equallySpacedSlot.js","../node_modules/ml-array-xy-equally-spaced/src/getZones.js","../node_modules/ml-array-xy-equally-spaced/src/index.js","../node_modules/ml-array-xy-filter-x/src/getZones.js","../node_modules/ml-array-xy-filter-x/src/index.js","../src/index.js"],"sourcesContent":["'use strict';\n\nconst toString = Object.prototype.toString;\n\nfunction isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n\nmodule.exports = isAnyArray;\n","import isArray from 'is-any-array';\n\n/**\n * Computes the maximum of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction max(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var maxValue = input[0];\n\n  for (var i = 1; i < input.length; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\n\n/**\n * Computes the minimum of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction min(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var minValue = input[0];\n\n  for (var i = 1; i < input.length; i++) {\n    if (input[i] < minValue) minValue = input[i];\n  }\n\n  return minValue;\n}\n\nexport default min;\n","import max from 'ml-array-max';\nimport min from 'ml-array-min';\nimport isArray from 'is-any-array';\n\nfunction rescale(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  } else if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var output;\n\n  if (options.output !== undefined) {\n    if (!isArray(options.output)) {\n      throw new TypeError('output option must be an array if specified');\n    }\n\n    output = options.output;\n  } else {\n    output = new Array(input.length);\n  }\n\n  var currentMin = min(input);\n  var currentMax = max(input);\n\n  if (currentMin === currentMax) {\n    throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');\n  }\n\n  var _options$min = options.min,\n      minValue = _options$min === void 0 ? options.autoMinMax ? currentMin : 0 : _options$min,\n      _options$max = options.max,\n      maxValue = _options$max === void 0 ? options.autoMinMax ? currentMax : 1 : _options$max;\n\n  if (minValue >= maxValue) {\n    throw new RangeError('min option must be smaller than max option');\n  }\n\n  var factor = (maxValue - minValue) / (currentMax - currentMin);\n\n  for (var i = 0; i < input.length; i++) {\n    output[i] = (input[i] - currentMin) * factor + minValue;\n  }\n\n  return output;\n}\n\nexport default rescale;\n","/**\n * @private\n * Check that a row index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkRowIndex(matrix, index, outer) {\n  let max = outer ? matrix.rows : matrix.rows - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Row index out of range');\n  }\n}\n\n/**\n * @private\n * Check that a column index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkColumnIndex(matrix, index, outer) {\n  let max = outer ? matrix.columns : matrix.columns - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Column index out of range');\n  }\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkRowVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.columns) {\n    throw new RangeError(\n      'vector size must be the same as the number of columns',\n    );\n  }\n  return vector;\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkColumnVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.rows) {\n    throw new RangeError('vector size must be the same as the number of rows');\n  }\n  return vector;\n}\n\nexport function checkIndices(matrix, rowIndices, columnIndices) {\n  return {\n    row: checkRowIndices(matrix, rowIndices),\n    column: checkColumnIndices(matrix, columnIndices),\n  };\n}\n\nexport function checkRowIndices(matrix, rowIndices) {\n  if (typeof rowIndices !== 'object') {\n    throw new TypeError('unexpected type for row indices');\n  }\n\n  let rowOut = rowIndices.some((r) => {\n    return r < 0 || r >= matrix.rows;\n  });\n\n  if (rowOut) {\n    throw new RangeError('row indices are out of range');\n  }\n\n  if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);\n\n  return rowIndices;\n}\n\nexport function checkColumnIndices(matrix, columnIndices) {\n  if (typeof columnIndices !== 'object') {\n    throw new TypeError('unexpected type for column indices');\n  }\n\n  let columnOut = columnIndices.some((c) => {\n    return c < 0 || c >= matrix.columns;\n  });\n\n  if (columnOut) {\n    throw new RangeError('column indices are out of range');\n  }\n  if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);\n\n  return columnIndices;\n}\n\nexport function checkRange(matrix, startRow, endRow, startColumn, endColumn) {\n  if (arguments.length !== 5) {\n    throw new RangeError('expected 4 arguments');\n  }\n  checkNumber('startRow', startRow);\n  checkNumber('endRow', endRow);\n  checkNumber('startColumn', startColumn);\n  checkNumber('endColumn', endColumn);\n  if (\n    startRow > endRow ||\n    startColumn > endColumn ||\n    startRow < 0 ||\n    startRow >= matrix.rows ||\n    endRow < 0 ||\n    endRow >= matrix.rows ||\n    startColumn < 0 ||\n    startColumn >= matrix.columns ||\n    endColumn < 0 ||\n    endColumn >= matrix.columns\n  ) {\n    throw new RangeError('Submatrix indices are out of range');\n  }\n}\n\nexport function newArray(length, value = 0) {\n  let array = [];\n  for (let i = 0; i < length; i++) {\n    array.push(value);\n  }\n  return array;\n}\n\nfunction checkNumber(name, value) {\n  if (typeof value !== 'number') {\n    throw new TypeError(`${name} must be a number`);\n  }\n}\n","import { newArray } from './util';\n\nexport function sumByRow(matrix) {\n  let sum = newArray(matrix.rows);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumByColumn(matrix) {\n  let sum = newArray(matrix.columns);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumAll(matrix) {\n  let v = 0;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v += matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function productByRow(matrix) {\n  let sum = newArray(matrix.rows, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productByColumn(matrix) {\n  let sum = newArray(matrix.columns, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productAll(matrix) {\n  let v = 1;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v *= matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function varianceByRow(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let i = 0; i < rows; i++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean[i];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / cols) / (cols - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / cols) / cols);\n    }\n  }\n  return variance;\n}\n\nexport function varianceByColumn(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let j = 0; j < cols; j++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let i = 0; i < rows; i++) {\n      x = matrix.get(i, j) - mean[j];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / rows) / (rows - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / rows) / rows);\n    }\n  }\n  return variance;\n}\n\nexport function varianceAll(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const size = rows * cols;\n\n  let sum1 = 0;\n  let sum2 = 0;\n  let x = 0;\n  for (let i = 0; i < rows; i++) {\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean;\n      sum1 += x;\n      sum2 += x * x;\n    }\n  }\n  if (unbiased) {\n    return (sum2 - (sum1 * sum1) / size) / (size - 1);\n  } else {\n    return (sum2 - (sum1 * sum1) / size) / size;\n  }\n}\n\nexport function centerByRow(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[i]);\n    }\n  }\n}\n\nexport function centerByColumn(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[j]);\n    }\n  }\n}\n\nexport function centerAll(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean);\n    }\n  }\n}\n\nexport function getScaleByRow(matrix) {\n  const scale = [];\n  for (let i = 0; i < matrix.rows; i++) {\n    let sum = 0;\n    for (let j = 0; j < matrix.columns; j++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.columns - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByRow(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[i]);\n    }\n  }\n}\n\nexport function getScaleByColumn(matrix) {\n  const scale = [];\n  for (let j = 0; j < matrix.columns; j++) {\n    let sum = 0;\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.rows - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByColumn(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[j]);\n    }\n  }\n}\n\nexport function getScaleAll(matrix) {\n  const divider = matrix.size - 1;\n  let sum = 0;\n  for (let j = 0; j < matrix.columns; j++) {\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / divider;\n    }\n  }\n  return Math.sqrt(sum);\n}\n\nexport function scaleAll(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale);\n    }\n  }\n}\n","export function inspectMatrix() {\n  const indent = ' '.repeat(2);\n  const indentData = ' '.repeat(4);\n  return `${this.constructor.name} {\n${indent}[\n${indentData}${inspectData(this, indentData)}\n${indent}]\n${indent}rows: ${this.rows}\n${indent}columns: ${this.columns}\n}`;\n}\n\nconst maxRows = 15;\nconst maxColumns = 10;\nconst maxNumSize = 8;\n\nfunction inspectData(matrix, indent) {\n  const { rows, columns } = matrix;\n  const maxI = Math.min(rows, maxRows);\n  const maxJ = Math.min(columns, maxColumns);\n  const result = [];\n  for (let i = 0; i < maxI; i++) {\n    let line = [];\n    for (let j = 0; j < maxJ; j++) {\n      line.push(formatNumber(matrix.get(i, j)));\n    }\n    result.push(`${line.join(' ')}`);\n  }\n  if (maxJ !== columns) {\n    result[result.length - 1] += ` ... ${columns - maxColumns} more columns`;\n  }\n  if (maxI !== rows) {\n    result.push(`... ${rows - maxRows} more rows`);\n  }\n  return result.join(`\\n${indent}`);\n}\n\nfunction formatNumber(num) {\n  const numStr = String(num);\n  if (numStr.length <= maxNumSize) {\n    return numStr.padEnd(maxNumSize, ' ');\n  }\n  const precise = num.toPrecision(maxNumSize - 2);\n  if (precise.length <= maxNumSize) {\n    return precise;\n  }\n  const exponential = num.toExponential(maxNumSize - 2);\n  const eIndex = exponential.indexOf('e');\n  const e = exponential.substring(eIndex);\n  return exponential.substring(0, maxNumSize - e.length) + e;\n}\n","export function installMathOperations(AbstractMatrix, Matrix) {\n  AbstractMatrix.prototype.add = function add(value) {\n    if (typeof value === 'number') return this.addS(value);\n    return this.addM(value);\n  };\n\n  AbstractMatrix.prototype.addS = function addS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.addM = function addM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.add = function add(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.add(value);\n  };\n\n  AbstractMatrix.prototype.sub = function sub(value) {\n    if (typeof value === 'number') return this.subS(value);\n    return this.subM(value);\n  };\n\n  AbstractMatrix.prototype.subS = function subS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.subM = function subM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sub = function sub(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sub(value);\n  };\n  AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;\n  AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;\n  AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;\n  AbstractMatrix.subtract = AbstractMatrix.sub;\n\n  AbstractMatrix.prototype.mul = function mul(value) {\n    if (typeof value === 'number') return this.mulS(value);\n    return this.mulM(value);\n  };\n\n  AbstractMatrix.prototype.mulS = function mulS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.mulM = function mulM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.mul = function mul(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.mul(value);\n  };\n  AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;\n  AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;\n  AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;\n  AbstractMatrix.multiply = AbstractMatrix.mul;\n\n  AbstractMatrix.prototype.div = function div(value) {\n    if (typeof value === 'number') return this.divS(value);\n    return this.divM(value);\n  };\n\n  AbstractMatrix.prototype.divS = function divS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.divM = function divM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.div = function div(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.div(value);\n  };\n  AbstractMatrix.prototype.divide = AbstractMatrix.prototype.div;\n  AbstractMatrix.prototype.divideS = AbstractMatrix.prototype.divS;\n  AbstractMatrix.prototype.divideM = AbstractMatrix.prototype.divM;\n  AbstractMatrix.divide = AbstractMatrix.div;\n\n  AbstractMatrix.prototype.mod = function mod(value) {\n    if (typeof value === 'number') return this.modS(value);\n    return this.modM(value);\n  };\n\n  AbstractMatrix.prototype.modS = function modS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) % value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.modM = function modM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) % matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.mod = function mod(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.mod(value);\n  };\n  AbstractMatrix.prototype.modulus = AbstractMatrix.prototype.mod;\n  AbstractMatrix.prototype.modulusS = AbstractMatrix.prototype.modS;\n  AbstractMatrix.prototype.modulusM = AbstractMatrix.prototype.modM;\n  AbstractMatrix.modulus = AbstractMatrix.mod;\n\n  AbstractMatrix.prototype.and = function and(value) {\n    if (typeof value === 'number') return this.andS(value);\n    return this.andM(value);\n  };\n\n  AbstractMatrix.prototype.andS = function andS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) & value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.andM = function andM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) & matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.and = function and(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.and(value);\n  };\n\n  AbstractMatrix.prototype.or = function or(value) {\n    if (typeof value === 'number') return this.orS(value);\n    return this.orM(value);\n  };\n\n  AbstractMatrix.prototype.orS = function orS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) | value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.orM = function orM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) | matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.or = function or(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.or(value);\n  };\n\n  AbstractMatrix.prototype.xor = function xor(value) {\n    if (typeof value === 'number') return this.xorS(value);\n    return this.xorM(value);\n  };\n\n  AbstractMatrix.prototype.xorS = function xorS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ^ value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.xorM = function xorM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) ^ matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.xor = function xor(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.xor(value);\n  };\n\n  AbstractMatrix.prototype.leftShift = function leftShift(value) {\n    if (typeof value === 'number') return this.leftShiftS(value);\n    return this.leftShiftM(value);\n  };\n\n  AbstractMatrix.prototype.leftShiftS = function leftShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) << value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.leftShiftM = function leftShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) << matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.leftShift = function leftShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.leftShift(value);\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShift = function signPropagatingRightShift(value) {\n    if (typeof value === 'number') return this.signPropagatingRightShiftS(value);\n    return this.signPropagatingRightShiftM(value);\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShiftS = function signPropagatingRightShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >> value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.signPropagatingRightShiftM = function signPropagatingRightShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >> matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.signPropagatingRightShift = function signPropagatingRightShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.signPropagatingRightShift(value);\n  };\n\n  AbstractMatrix.prototype.rightShift = function rightShift(value) {\n    if (typeof value === 'number') return this.rightShiftS(value);\n    return this.rightShiftM(value);\n  };\n\n  AbstractMatrix.prototype.rightShiftS = function rightShiftS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >>> value);\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.rightShiftM = function rightShiftM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) >>> matrix.get(i, j));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.rightShift = function rightShift(matrix, value) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.rightShift(value);\n  };\n  AbstractMatrix.prototype.zeroFillRightShift = AbstractMatrix.prototype.rightShift;\n  AbstractMatrix.prototype.zeroFillRightShiftS = AbstractMatrix.prototype.rightShiftS;\n  AbstractMatrix.prototype.zeroFillRightShiftM = AbstractMatrix.prototype.rightShiftM;\n  AbstractMatrix.zeroFillRightShift = AbstractMatrix.rightShift;\n\n  AbstractMatrix.prototype.not = function not() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, ~(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.not = function not(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.not();\n  };\n\n  AbstractMatrix.prototype.abs = function abs() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.abs(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.abs = function abs(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.abs();\n  };\n\n  AbstractMatrix.prototype.acos = function acos() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.acos(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.acos = function acos(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.acos();\n  };\n\n  AbstractMatrix.prototype.acosh = function acosh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.acosh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.acosh = function acosh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.acosh();\n  };\n\n  AbstractMatrix.prototype.asin = function asin() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.asin(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.asin = function asin(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.asin();\n  };\n\n  AbstractMatrix.prototype.asinh = function asinh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.asinh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.asinh = function asinh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.asinh();\n  };\n\n  AbstractMatrix.prototype.atan = function atan() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.atan(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.atan = function atan(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.atan();\n  };\n\n  AbstractMatrix.prototype.atanh = function atanh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.atanh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.atanh = function atanh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.atanh();\n  };\n\n  AbstractMatrix.prototype.cbrt = function cbrt() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cbrt(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cbrt = function cbrt(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cbrt();\n  };\n\n  AbstractMatrix.prototype.ceil = function ceil() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.ceil(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.ceil = function ceil(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.ceil();\n  };\n\n  AbstractMatrix.prototype.clz32 = function clz32() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.clz32(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.clz32 = function clz32(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.clz32();\n  };\n\n  AbstractMatrix.prototype.cos = function cos() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cos(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cos = function cos(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cos();\n  };\n\n  AbstractMatrix.prototype.cosh = function cosh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.cosh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.cosh = function cosh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.cosh();\n  };\n\n  AbstractMatrix.prototype.exp = function exp() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.exp(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.exp = function exp(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.exp();\n  };\n\n  AbstractMatrix.prototype.expm1 = function expm1() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.expm1(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.expm1 = function expm1(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.expm1();\n  };\n\n  AbstractMatrix.prototype.floor = function floor() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.floor(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.floor = function floor(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.floor();\n  };\n\n  AbstractMatrix.prototype.fround = function fround() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.fround(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.fround = function fround(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.fround();\n  };\n\n  AbstractMatrix.prototype.log = function log() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log = function log(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log();\n  };\n\n  AbstractMatrix.prototype.log1p = function log1p() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log1p(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log1p = function log1p(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log1p();\n  };\n\n  AbstractMatrix.prototype.log10 = function log10() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log10(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log10 = function log10(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log10();\n  };\n\n  AbstractMatrix.prototype.log2 = function log2() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.log2(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.log2 = function log2(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.log2();\n  };\n\n  AbstractMatrix.prototype.round = function round() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.round(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.round = function round(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.round();\n  };\n\n  AbstractMatrix.prototype.sign = function sign() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sign(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sign = function sign(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sign();\n  };\n\n  AbstractMatrix.prototype.sin = function sin() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sin(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sin = function sin(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sin();\n  };\n\n  AbstractMatrix.prototype.sinh = function sinh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sinh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sinh = function sinh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sinh();\n  };\n\n  AbstractMatrix.prototype.sqrt = function sqrt() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.sqrt(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.sqrt = function sqrt(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.sqrt();\n  };\n\n  AbstractMatrix.prototype.tan = function tan() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.tan(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.tan = function tan(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.tan();\n  };\n\n  AbstractMatrix.prototype.tanh = function tanh() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.tanh(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.tanh = function tanh(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.tanh();\n  };\n\n  AbstractMatrix.prototype.trunc = function trunc() {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.trunc(this.get(i, j)));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.trunc = function trunc(matrix) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.trunc();\n  };\n\n  AbstractMatrix.pow = function pow(matrix, arg0) {\n    const newMatrix = new Matrix(matrix);\n    return newMatrix.pow(arg0);\n  };\n\n  AbstractMatrix.prototype.pow = function pow(value) {\n    if (typeof value === 'number') return this.powS(value);\n    return this.powM(value);\n  };\n\n  AbstractMatrix.prototype.powS = function powS(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.pow(this.get(i, j), value));\n      }\n    }\n    return this;\n  };\n\n  AbstractMatrix.prototype.powM = function powM(matrix) {\n    matrix = Matrix.checkMatrix(matrix);\n    if (this.rows !== matrix.rows ||\n      this.columns !== matrix.columns) {\n      throw new RangeError('Matrices dimensions must be equal');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, Math.pow(this.get(i, j), matrix.get(i, j)));\n      }\n    }\n    return this;\n  };\n}\n","import rescale from 'ml-array-rescale';\n\nimport {\n  checkRowVector,\n  checkRowIndex,\n  checkColumnIndex,\n  checkColumnVector,\n  checkRange,\n  checkIndices,\n} from './util';\nimport {\n  sumByRow,\n  sumByColumn,\n  sumAll,\n  productByRow,\n  productByColumn,\n  productAll,\n  varianceByRow,\n  varianceByColumn,\n  varianceAll,\n  centerByRow,\n  centerByColumn,\n  centerAll,\n  scaleByRow,\n  scaleByColumn,\n  scaleAll,\n  getScaleByRow,\n  getScaleByColumn,\n  getScaleAll,\n} from './stat';\nimport { inspectMatrix } from './inspect';\nimport { installMathOperations } from './mathOperations';\n\nexport class AbstractMatrix {\n  static from1DArray(newRows, newColumns, newData) {\n    let length = newRows * newColumns;\n    if (length !== newData.length) {\n      throw new RangeError('data length does not match given dimensions');\n    }\n    let newMatrix = new Matrix(newRows, newColumns);\n    for (let row = 0; row < newRows; row++) {\n      for (let column = 0; column < newColumns; column++) {\n        newMatrix.set(row, column, newData[row * newColumns + column]);\n      }\n    }\n    return newMatrix;\n  }\n\n  static rowVector(newData) {\n    let vector = new Matrix(1, newData.length);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(0, i, newData[i]);\n    }\n    return vector;\n  }\n\n  static columnVector(newData) {\n    let vector = new Matrix(newData.length, 1);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(i, 0, newData[i]);\n    }\n    return vector;\n  }\n\n  static zeros(rows, columns) {\n    return new Matrix(rows, columns);\n  }\n\n  static ones(rows, columns) {\n    return new Matrix(rows, columns).fill(1);\n  }\n\n  static rand(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { random = Math.random } = options;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.set(i, j, random());\n      }\n    }\n    return matrix;\n  }\n\n  static randInt(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1000, random = Math.random } = options;\n    if (!Number.isInteger(min)) throw new TypeError('min must be an integer');\n    if (!Number.isInteger(max)) throw new TypeError('max must be an integer');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let interval = max - min;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        let value = min + Math.round(random() * interval);\n        matrix.set(i, j, value);\n      }\n    }\n    return matrix;\n  }\n\n  static eye(rows, columns, value) {\n    if (columns === undefined) columns = rows;\n    if (value === undefined) value = 1;\n    let min = Math.min(rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, value);\n    }\n    return matrix;\n  }\n\n  static diag(data, rows, columns) {\n    let l = data.length;\n    if (rows === undefined) rows = l;\n    if (columns === undefined) columns = rows;\n    let min = Math.min(l, rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, data[i]);\n    }\n    return matrix;\n  }\n\n  static min(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static max(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new this(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static checkMatrix(value) {\n    return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);\n  }\n\n  static isMatrix(value) {\n    return value != null && value.klass === 'Matrix';\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  apply(callback) {\n    if (typeof callback !== 'function') {\n      throw new TypeError('callback must be a function');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        callback.call(this, i, j);\n      }\n    }\n    return this;\n  }\n\n  to1DArray() {\n    let array = [];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        array.push(this.get(i, j));\n      }\n    }\n    return array;\n  }\n\n  to2DArray() {\n    let copy = [];\n    for (let i = 0; i < this.rows; i++) {\n      copy.push([]);\n      for (let j = 0; j < this.columns; j++) {\n        copy[i].push(this.get(i, j));\n      }\n    }\n    return copy;\n  }\n\n  toJSON() {\n    return this.to2DArray();\n  }\n\n  isRowVector() {\n    return this.rows === 1;\n  }\n\n  isColumnVector() {\n    return this.columns === 1;\n  }\n\n  isVector() {\n    return this.rows === 1 || this.columns === 1;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (this.isSquare()) {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j <= i; j++) {\n          if (this.get(i, j) !== this.get(j, i)) {\n            return false;\n          }\n        }\n      }\n      return true;\n    }\n    return false;\n  }\n\n  isEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isEchelonForm = false;\n          checked = true;\n        }\n      }\n      i++;\n    }\n    return isEchelonForm;\n  }\n\n  isReducedEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isReducedEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isReducedEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isReducedEchelonForm = false;\n          checked = true;\n        }\n      }\n      for (let k = j + 1; k < this.rows; k++) {\n        if (this.get(i, k) !== 0) {\n          isReducedEchelonForm = false;\n        }\n      }\n      i++;\n    }\n    return isReducedEchelonForm;\n  }\n\n  echelonForm() {\n    let result = this.clone();\n    let h = 0;\n    let k = 0;\n    while (h < result.rows && k < result.columns) {\n      let iMax = h;\n      for (let i = h; i < result.rows; i++) {\n        if (result.get(i, k) > result.get(iMax, k)) {\n          iMax = i;\n        }\n      }\n      if (result.get(iMax, k) === 0) {\n        k++;\n      } else {\n        result.swapRows(h, iMax);\n        let tmp = result.get(h, k);\n        for (let j = k; j < result.columns; j++) {\n          result.set(h, j, result.get(h, j) / tmp);\n        }\n        for (let i = h + 1; i < result.rows; i++) {\n          let factor = result.get(i, k) / result.get(h, k);\n          result.set(i, k, 0);\n          for (let j = k + 1; j < result.columns; j++) {\n            result.set(i, j, result.get(i, j) - result.get(h, j) * factor);\n          }\n        }\n        h++;\n        k++;\n      }\n    }\n    return result;\n  }\n\n  reducedEchelonForm() {\n    let result = this.echelonForm();\n    let m = result.columns;\n    let n = result.rows;\n    let h = n - 1;\n    while (h >= 0) {\n      if (result.maxRow(h) === 0) {\n        h--;\n      } else {\n        let p = 0;\n        let pivot = false;\n        while (p < n && pivot === false) {\n          if (result.get(h, p) === 1) {\n            pivot = true;\n          } else {\n            p++;\n          }\n        }\n        for (let i = 0; i < h; i++) {\n          let factor = result.get(i, p);\n          for (let j = p; j < m; j++) {\n            let tmp = result.get(i, j) - factor * result.get(h, j);\n            result.set(i, j, tmp);\n          }\n        }\n        h--;\n      }\n    }\n    return result;\n  }\n\n  set() {\n    throw new Error('set method is unimplemented');\n  }\n\n  get() {\n    throw new Error('get method is unimplemented');\n  }\n\n  repeat(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { rows = 1, columns = 1 } = options;\n    if (!Number.isInteger(rows) || rows <= 0) {\n      throw new TypeError('rows must be a positive integer');\n    }\n    if (!Number.isInteger(columns) || columns <= 0) {\n      throw new TypeError('columns must be a positive integer');\n    }\n    let matrix = new Matrix(this.rows * rows, this.columns * columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.setSubMatrix(this, this.rows * i, this.columns * j);\n      }\n    }\n    return matrix;\n  }\n\n  fill(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, value);\n      }\n    }\n    return this;\n  }\n\n  neg() {\n    return this.mulS(-1);\n  }\n\n  getRow(index) {\n    checkRowIndex(this, index);\n    let row = [];\n    for (let i = 0; i < this.columns; i++) {\n      row.push(this.get(index, i));\n    }\n    return row;\n  }\n\n  getRowVector(index) {\n    return Matrix.rowVector(this.getRow(index));\n  }\n\n  setRow(index, array) {\n    checkRowIndex(this, index);\n    array = checkRowVector(this, array);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, array[i]);\n    }\n    return this;\n  }\n\n  swapRows(row1, row2) {\n    checkRowIndex(this, row1);\n    checkRowIndex(this, row2);\n    for (let i = 0; i < this.columns; i++) {\n      let temp = this.get(row1, i);\n      this.set(row1, i, this.get(row2, i));\n      this.set(row2, i, temp);\n    }\n    return this;\n  }\n\n  getColumn(index) {\n    checkColumnIndex(this, index);\n    let column = [];\n    for (let i = 0; i < this.rows; i++) {\n      column.push(this.get(i, index));\n    }\n    return column;\n  }\n\n  getColumnVector(index) {\n    return Matrix.columnVector(this.getColumn(index));\n  }\n\n  setColumn(index, array) {\n    checkColumnIndex(this, index);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, array[i]);\n    }\n    return this;\n  }\n\n  swapColumns(column1, column2) {\n    checkColumnIndex(this, column1);\n    checkColumnIndex(this, column2);\n    for (let i = 0; i < this.rows; i++) {\n      let temp = this.get(i, column1);\n      this.set(i, column1, this.get(i, column2));\n      this.set(i, column2, temp);\n    }\n    return this;\n  }\n\n  addRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[j]);\n      }\n    }\n    return this;\n  }\n\n  subRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[j]);\n      }\n    }\n    return this;\n  }\n\n  mulRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[j]);\n      }\n    }\n    return this;\n  }\n\n  divRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[j]);\n      }\n    }\n    return this;\n  }\n\n  addColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[i]);\n      }\n    }\n    return this;\n  }\n\n  subColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[i]);\n      }\n    }\n    return this;\n  }\n\n  divColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulRow(index, value) {\n    checkRowIndex(this, index);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, this.get(index, i) * value);\n    }\n    return this;\n  }\n\n  mulColumn(index, value) {\n    checkColumnIndex(this, index);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, this.get(i, index) * value);\n    }\n    return this;\n  }\n\n  max() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  maxIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  min() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  minIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  maxRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  maxRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  minRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  minRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  maxColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  maxColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  minColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  minColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  diag() {\n    let min = Math.min(this.rows, this.columns);\n    let diag = [];\n    for (let i = 0; i < min; i++) {\n      diag.push(this.get(i, i));\n    }\n    return diag;\n  }\n\n  norm(type = 'frobenius') {\n    let result = 0;\n    if (type === 'max') {\n      return this.max();\n    } else if (type === 'frobenius') {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j < this.columns; j++) {\n          result = result + this.get(i, j) * this.get(i, j);\n        }\n      }\n      return Math.sqrt(result);\n    } else {\n      throw new RangeError(`unknown norm type: ${type}`);\n    }\n  }\n\n  cumulativeSum() {\n    let sum = 0;\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        sum += this.get(i, j);\n        this.set(i, j, sum);\n      }\n    }\n    return this;\n  }\n\n  dot(vector2) {\n    if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();\n    let vector1 = this.to1DArray();\n    if (vector1.length !== vector2.length) {\n      throw new RangeError('vectors do not have the same size');\n    }\n    let dot = 0;\n    for (let i = 0; i < vector1.length; i++) {\n      dot += vector1[i] * vector2[i];\n    }\n    return dot;\n  }\n\n  mmul(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.columns;\n\n    let result = new Matrix(m, p);\n\n    let Bcolj = new Float64Array(n);\n    for (let j = 0; j < p; j++) {\n      for (let k = 0; k < n; k++) {\n        Bcolj[k] = other.get(k, j);\n      }\n\n      for (let i = 0; i < m; i++) {\n        let s = 0;\n        for (let k = 0; k < n; k++) {\n          s += this.get(i, k) * Bcolj[k];\n        }\n\n        result.set(i, j, s);\n      }\n    }\n    return result;\n  }\n\n  strassen2x2(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(2, 2);\n    const a11 = this.get(0, 0);\n    const b11 = other.get(0, 0);\n    const a12 = this.get(0, 1);\n    const b12 = other.get(0, 1);\n    const a21 = this.get(1, 0);\n    const b21 = other.get(1, 0);\n    const a22 = this.get(1, 1);\n    const b22 = other.get(1, 1);\n\n    // Compute intermediate values.\n    const m1 = (a11 + a22) * (b11 + b22);\n    const m2 = (a21 + a22) * b11;\n    const m3 = a11 * (b12 - b22);\n    const m4 = a22 * (b21 - b11);\n    const m5 = (a11 + a12) * b22;\n    const m6 = (a21 - a11) * (b11 + b12);\n    const m7 = (a12 - a22) * (b21 + b22);\n\n    // Combine intermediate values into the output.\n    const c00 = m1 + m4 - m5 + m7;\n    const c01 = m3 + m5;\n    const c10 = m2 + m4;\n    const c11 = m1 - m2 + m3 + m6;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    return result;\n  }\n\n  strassen3x3(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(3, 3);\n\n    const a00 = this.get(0, 0);\n    const a01 = this.get(0, 1);\n    const a02 = this.get(0, 2);\n    const a10 = this.get(1, 0);\n    const a11 = this.get(1, 1);\n    const a12 = this.get(1, 2);\n    const a20 = this.get(2, 0);\n    const a21 = this.get(2, 1);\n    const a22 = this.get(2, 2);\n\n    const b00 = other.get(0, 0);\n    const b01 = other.get(0, 1);\n    const b02 = other.get(0, 2);\n    const b10 = other.get(1, 0);\n    const b11 = other.get(1, 1);\n    const b12 = other.get(1, 2);\n    const b20 = other.get(2, 0);\n    const b21 = other.get(2, 1);\n    const b22 = other.get(2, 2);\n\n    const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;\n    const m2 = (a00 - a10) * (-b01 + b11);\n    const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);\n    const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);\n    const m5 = (a10 + a11) * (-b00 + b01);\n    const m6 = a00 * b00;\n    const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);\n    const m8 = (-a00 + a20) * (b02 - b12);\n    const m9 = (a20 + a21) * (-b00 + b02);\n    const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;\n    const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);\n    const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);\n    const m13 = (a02 - a22) * (b11 - b21);\n    const m14 = a02 * b20;\n    const m15 = (a21 + a22) * (-b20 + b21);\n    const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);\n    const m17 = (a02 - a12) * (b12 - b22);\n    const m18 = (a11 + a12) * (-b20 + b22);\n    const m19 = a01 * b10;\n    const m20 = a12 * b21;\n    const m21 = a10 * b02;\n    const m22 = a20 * b01;\n    const m23 = a22 * b22;\n\n    const c00 = m6 + m14 + m19;\n    const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;\n    const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;\n    const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;\n    const c11 = m2 + m4 + m5 + m6 + m20;\n    const c12 = m14 + m16 + m17 + m18 + m21;\n    const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;\n    const c21 = m12 + m13 + m14 + m15 + m22;\n    const c22 = m6 + m7 + m8 + m9 + m23;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(0, 2, c02);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    result.set(1, 2, c12);\n    result.set(2, 0, c20);\n    result.set(2, 1, c21);\n    result.set(2, 2, c22);\n    return result;\n  }\n\n  mmulStrassen(y) {\n    y = Matrix.checkMatrix(y);\n    let x = this.clone();\n    let r1 = x.rows;\n    let c1 = x.columns;\n    let r2 = y.rows;\n    let c2 = y.columns;\n    if (c1 !== r2) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        `Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`,\n      );\n    }\n\n    // Put a matrix into the top left of a matrix of zeros.\n    // `rows` and `cols` are the dimensions of the output matrix.\n    function embed(mat, rows, cols) {\n      let r = mat.rows;\n      let c = mat.columns;\n      if (r === rows && c === cols) {\n        return mat;\n      } else {\n        let resultat = AbstractMatrix.zeros(rows, cols);\n        resultat = resultat.setSubMatrix(mat, 0, 0);\n        return resultat;\n      }\n    }\n\n    // Make sure both matrices are the same size.\n    // This is exclusively for simplicity:\n    // this algorithm can be implemented with matrices of different sizes.\n\n    let r = Math.max(r1, r2);\n    let c = Math.max(c1, c2);\n    x = embed(x, r, c);\n    y = embed(y, r, c);\n\n    // Our recursive multiplication function.\n    function blockMult(a, b, rows, cols) {\n      // For small matrices, resort to naive multiplication.\n      if (rows <= 512 || cols <= 512) {\n        return a.mmul(b); // a is equivalent to this\n      }\n\n      // Apply dynamic padding.\n      if (rows % 2 === 1 && cols % 2 === 1) {\n        a = embed(a, rows + 1, cols + 1);\n        b = embed(b, rows + 1, cols + 1);\n      } else if (rows % 2 === 1) {\n        a = embed(a, rows + 1, cols);\n        b = embed(b, rows + 1, cols);\n      } else if (cols % 2 === 1) {\n        a = embed(a, rows, cols + 1);\n        b = embed(b, rows, cols + 1);\n      }\n\n      let halfRows = parseInt(a.rows / 2, 10);\n      let halfCols = parseInt(a.columns / 2, 10);\n      // Subdivide input matrices.\n      let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n      let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n\n      let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);\n      let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);\n\n      let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);\n      let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);\n\n      let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);\n      let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);\n\n      // Compute intermediate values.\n      let m1 = blockMult(\n        AbstractMatrix.add(a11, a22),\n        AbstractMatrix.add(b11, b22),\n        halfRows,\n        halfCols,\n      );\n      let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);\n      let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);\n      let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);\n      let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);\n      let m6 = blockMult(\n        AbstractMatrix.sub(a21, a11),\n        AbstractMatrix.add(b11, b12),\n        halfRows,\n        halfCols,\n      );\n      let m7 = blockMult(\n        AbstractMatrix.sub(a12, a22),\n        AbstractMatrix.add(b21, b22),\n        halfRows,\n        halfCols,\n      );\n\n      // Combine intermediate values into the output.\n      let c11 = AbstractMatrix.add(m1, m4);\n      c11.sub(m5);\n      c11.add(m7);\n      let c12 = AbstractMatrix.add(m3, m5);\n      let c21 = AbstractMatrix.add(m2, m4);\n      let c22 = AbstractMatrix.sub(m1, m2);\n      c22.add(m3);\n      c22.add(m6);\n\n      // Crop output to the desired size (undo dynamic padding).\n      let resultat = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);\n      resultat = resultat.setSubMatrix(c11, 0, 0);\n      resultat = resultat.setSubMatrix(c12, c11.rows, 0);\n      resultat = resultat.setSubMatrix(c21, 0, c11.columns);\n      resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);\n      return resultat.subMatrix(0, rows - 1, 0, cols - 1);\n    }\n    return blockMult(x, y, r, c);\n  }\n\n  scaleRows(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.rows; i++) {\n      const row = this.getRow(i);\n      rescale(row, { min, max, output: row });\n      newMatrix.setRow(i, row);\n    }\n    return newMatrix;\n  }\n\n  scaleColumns(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.columns; i++) {\n      const column = this.getColumn(i);\n      rescale(column, {\n        min: min,\n        max: max,\n        output: column,\n      });\n      newMatrix.setColumn(i, column);\n    }\n    return newMatrix;\n  }\n\n  flipRows() {\n    const middle = Math.ceil(this.columns / 2);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < middle; j++) {\n        let first = this.get(i, j);\n        let last = this.get(i, this.columns - 1 - j);\n        this.set(i, j, last);\n        this.set(i, this.columns - 1 - j, first);\n      }\n    }\n    return this;\n  }\n\n  flipColumns() {\n    const middle = Math.ceil(this.rows / 2);\n    for (let j = 0; j < this.columns; j++) {\n      for (let i = 0; i < middle; i++) {\n        let first = this.get(i, j);\n        let last = this.get(this.rows - 1 - i, j);\n        this.set(i, j, last);\n        this.set(this.rows - 1 - i, j, first);\n      }\n    }\n    return this;\n  }\n\n  kroneckerProduct(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.rows;\n    let q = other.columns;\n\n    let result = new Matrix(m * p, n * q);\n    for (let i = 0; i < m; i++) {\n      for (let j = 0; j < n; j++) {\n        for (let k = 0; k < p; k++) {\n          for (let l = 0; l < q; l++) {\n            result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));\n          }\n        }\n      }\n    }\n    return result;\n  }\n\n  transpose() {\n    let result = new Matrix(this.columns, this.rows);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        result.set(j, i, this.get(i, j));\n      }\n    }\n    return result;\n  }\n\n  sortRows(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.rows; i++) {\n      this.setRow(i, this.getRow(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  sortColumns(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.columns; i++) {\n      this.setColumn(i, this.getColumn(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  subMatrix(startRow, endRow, startColumn, endColumn) {\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    let newMatrix = new Matrix(\n      endRow - startRow + 1,\n      endColumn - startColumn + 1,\n    );\n    for (let i = startRow; i <= endRow; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        newMatrix.set(i - startRow, j - startColumn, this.get(i, j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixRow(indices, startColumn, endColumn) {\n    if (startColumn === undefined) startColumn = 0;\n    if (endColumn === undefined) endColumn = this.columns - 1;\n    if (\n      startColumn > endColumn ||\n      startColumn < 0 ||\n      startColumn >= this.columns ||\n      endColumn < 0 ||\n      endColumn >= this.columns\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        if (indices[i] < 0 || indices[i] >= this.rows) {\n          throw new RangeError(`Row index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(i, j - startColumn, this.get(indices[i], j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixColumn(indices, startRow, endRow) {\n    if (startRow === undefined) startRow = 0;\n    if (endRow === undefined) endRow = this.rows - 1;\n    if (\n      startRow > endRow ||\n      startRow < 0 ||\n      startRow >= this.rows ||\n      endRow < 0 ||\n      endRow >= this.rows\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(endRow - startRow + 1, indices.length);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startRow; j <= endRow; j++) {\n        if (indices[i] < 0 || indices[i] >= this.columns) {\n          throw new RangeError(`Column index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(j - startRow, i, this.get(j, indices[i]));\n      }\n    }\n    return newMatrix;\n  }\n\n  setSubMatrix(matrix, startRow, startColumn) {\n    matrix = Matrix.checkMatrix(matrix);\n    let endRow = startRow + matrix.rows - 1;\n    let endColumn = startColumn + matrix.columns - 1;\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    for (let i = 0; i < matrix.rows; i++) {\n      for (let j = 0; j < matrix.columns; j++) {\n        this.set(startRow + i, startColumn + j, matrix.get(i, j));\n      }\n    }\n    return this;\n  }\n\n  selection(rowIndices, columnIndices) {\n    let indices = checkIndices(this, rowIndices, columnIndices);\n    let newMatrix = new Matrix(rowIndices.length, columnIndices.length);\n    for (let i = 0; i < indices.row.length; i++) {\n      let rowIndex = indices.row[i];\n      for (let j = 0; j < indices.column.length; j++) {\n        let columnIndex = indices.column[j];\n        newMatrix.set(i, j, this.get(rowIndex, columnIndex));\n      }\n    }\n    return newMatrix;\n  }\n\n  trace() {\n    let min = Math.min(this.rows, this.columns);\n    let trace = 0;\n    for (let i = 0; i < min; i++) {\n      trace += this.get(i, i);\n    }\n    return trace;\n  }\n\n  clone() {\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let row = 0; row < this.rows; row++) {\n      for (let column = 0; column < this.columns; column++) {\n        newMatrix.set(row, column, this.get(row, column));\n      }\n    }\n    return newMatrix;\n  }\n\n  sum(by) {\n    switch (by) {\n      case 'row':\n        return sumByRow(this);\n      case 'column':\n        return sumByColumn(this);\n      case undefined:\n        return sumAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  product(by) {\n    switch (by) {\n      case 'row':\n        return productByRow(this);\n      case 'column':\n        return productByColumn(this);\n      case undefined:\n        return productAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  mean(by) {\n    const sum = this.sum(by);\n    switch (by) {\n      case 'row': {\n        for (let i = 0; i < this.rows; i++) {\n          sum[i] /= this.columns;\n        }\n        return sum;\n      }\n      case 'column': {\n        for (let i = 0; i < this.columns; i++) {\n          sum[i] /= this.rows;\n        }\n        return sum;\n      }\n      case undefined:\n        return sum / this.size;\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  variance(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { unbiased = true, mean = this.mean(by) } = options;\n    if (typeof unbiased !== 'boolean') {\n      throw new TypeError('unbiased must be a boolean');\n    }\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByRow(this, unbiased, mean);\n      }\n      case 'column': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByColumn(this, unbiased, mean);\n      }\n      case undefined: {\n        if (typeof mean !== 'number') {\n          throw new TypeError('mean must be a number');\n        }\n        return varianceAll(this, unbiased, mean);\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  standardDeviation(by, options) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    const variance = this.variance(by, options);\n    if (by === undefined) {\n      return Math.sqrt(variance);\n    } else {\n      for (let i = 0; i < variance.length; i++) {\n        variance[i] = Math.sqrt(variance[i]);\n      }\n      return variance;\n    }\n  }\n\n  center(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { center = this.mean(by) } = options;\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByRow(this, center);\n        return this;\n      }\n      case 'column': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByColumn(this, center);\n        return this;\n      }\n      case undefined: {\n        if (typeof center !== 'number') {\n          throw new TypeError('center must be a number');\n        }\n        centerAll(this, center);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  scale(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    let scale = options.scale;\n    switch (by) {\n      case 'row': {\n        if (scale === undefined) {\n          scale = getScaleByRow(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByRow(this, scale);\n        return this;\n      }\n      case 'column': {\n        if (scale === undefined) {\n          scale = getScaleByColumn(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByColumn(this, scale);\n        return this;\n      }\n      case undefined: {\n        if (scale === undefined) {\n          scale = getScaleAll(this);\n        } else if (typeof scale !== 'number') {\n          throw new TypeError('scale must be a number');\n        }\n        scaleAll(this, scale);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n}\n\nAbstractMatrix.prototype.klass = 'Matrix';\nif (typeof Symbol !== 'undefined') {\n  AbstractMatrix.prototype[\n    Symbol.for('nodejs.util.inspect.custom')\n  ] = inspectMatrix;\n}\n\nfunction compareNumbers(a, b) {\n  return a - b;\n}\n\n// Synonyms\nAbstractMatrix.random = AbstractMatrix.rand;\nAbstractMatrix.randomInt = AbstractMatrix.randInt;\nAbstractMatrix.diagonal = AbstractMatrix.diag;\nAbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;\nAbstractMatrix.identity = AbstractMatrix.eye;\nAbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;\nAbstractMatrix.prototype.tensorProduct =\n  AbstractMatrix.prototype.kroneckerProduct;\n\nexport default class Matrix extends AbstractMatrix {\n  constructor(nRows, nColumns) {\n    super();\n    if (Matrix.isMatrix(nRows)) {\n      return nRows.clone();\n    } else if (Number.isInteger(nRows) && nRows > 0) {\n      // Create an empty matrix\n      this.data = [];\n      if (Number.isInteger(nColumns) && nColumns > 0) {\n        for (let i = 0; i < nRows; i++) {\n          this.data.push(new Float64Array(nColumns));\n        }\n      } else {\n        throw new TypeError('nColumns must be a positive integer');\n      }\n    } else if (Array.isArray(nRows)) {\n      // Copy the values from the 2D array\n      const arrayData = nRows;\n      nRows = arrayData.length;\n      nColumns = arrayData[0].length;\n      if (typeof nColumns !== 'number' || nColumns === 0) {\n        throw new TypeError(\n          'Data must be a 2D array with at least one element',\n        );\n      }\n      this.data = [];\n      for (let i = 0; i < nRows; i++) {\n        if (arrayData[i].length !== nColumns) {\n          throw new RangeError('Inconsistent array dimensions');\n        }\n        this.data.push(Float64Array.from(arrayData[i]));\n      }\n    } else {\n      throw new TypeError(\n        'First argument must be a positive number or an array',\n      );\n    }\n    this.rows = nRows;\n    this.columns = nColumns;\n    return this;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n\n  removeRow(index) {\n    checkRowIndex(this, index);\n    if (this.rows === 1) {\n      throw new RangeError('A matrix cannot have less than one row');\n    }\n    this.data.splice(index, 1);\n    this.rows -= 1;\n    return this;\n  }\n\n  addRow(index, array) {\n    if (array === undefined) {\n      array = index;\n      index = this.rows;\n    }\n    checkRowIndex(this, index, true);\n    array = Float64Array.from(checkRowVector(this, array, true));\n    this.data.splice(index, 0, array);\n    this.rows += 1;\n    return this;\n  }\n\n  removeColumn(index) {\n    checkColumnIndex(this, index);\n    if (this.columns === 1) {\n      throw new RangeError('A matrix cannot have less than one column');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns - 1);\n      for (let j = 0; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      for (let j = index + 1; j < this.columns; j++) {\n        newRow[j - 1] = this.data[i][j];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns -= 1;\n    return this;\n  }\n\n  addColumn(index, array) {\n    if (typeof array === 'undefined') {\n      array = index;\n      index = this.columns;\n    }\n    checkColumnIndex(this, index, true);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns + 1);\n      let j = 0;\n      for (; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      newRow[j++] = array[i];\n      for (; j < this.columns + 1; j++) {\n        newRow[j] = this.data[i][j - 1];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns += 1;\n    return this;\n  }\n}\n\ninstallMathOperations(AbstractMatrix, Matrix);\n","import { AbstractMatrix } from '../matrix';\n\nexport default class BaseView extends AbstractMatrix {\n  constructor(matrix, rows, columns) {\n    super();\n    this.matrix = matrix;\n    this.rows = rows;\n    this.columns = columns;\n  }\n}\n","import { checkColumnIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixColumnView extends BaseView {\n  constructor(matrix, column) {\n    checkColumnIndex(matrix, column);\n    super(matrix, matrix.rows, 1);\n    this.column = column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.column, value);\n    return this;\n  }\n\n  get(rowIndex) {\n    return this.matrix.get(rowIndex, this.column);\n  }\n}\n","import { checkColumnIndices } from '../util';\r\n\r\nimport BaseView from './base';\r\n\r\nexport default class MatrixColumnSelectionView extends BaseView {\r\n  constructor(matrix, columnIndices) {\r\n    columnIndices = checkColumnIndices(matrix, columnIndices);\r\n    super(matrix, matrix.rows, columnIndices.length);\r\n    this.columnIndices = columnIndices;\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);\r\n  }\r\n}\r\n","import BaseView from './base';\r\n\r\nexport default class MatrixFlipColumnView extends BaseView {\r\n  constructor(matrix) {\r\n    super(matrix, matrix.rows, matrix.columns);\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(rowIndex, this.columns - columnIndex - 1);\r\n  }\r\n}\r\n","import BaseView from './base';\r\n\r\nexport default class MatrixFlipRowView extends BaseView {\r\n  constructor(matrix) {\r\n    super(matrix, matrix.rows, matrix.columns);\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(this.rows - rowIndex - 1, columnIndex);\r\n  }\r\n}\r\n","import { checkRowIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixRowView extends BaseView {\n  constructor(matrix, row) {\n    checkRowIndex(matrix, row);\n    super(matrix, 1, matrix.columns);\n    this.row = row;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.row, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.row, columnIndex);\n  }\n}\n","import { checkRowIndices } from '../util';\r\n\r\nimport BaseView from './base';\r\n\r\nexport default class MatrixRowSelectionView extends BaseView {\r\n  constructor(matrix, rowIndices) {\r\n    rowIndices = checkRowIndices(matrix, rowIndices);\r\n    super(matrix, rowIndices.length, matrix.columns);\r\n    this.rowIndices = rowIndices;\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(this.rowIndices[rowIndex], columnIndex);\r\n  }\r\n}\r\n","import { checkIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSelectionView extends BaseView {\n  constructor(matrix, rowIndices, columnIndices) {\n    let indices = checkIndices(matrix, rowIndices, columnIndices);\n    super(matrix, indices.row.length, indices.column.length);\n    this.rowIndices = indices.row;\n    this.columnIndices = indices.column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n    );\n  }\n}\n","import { checkRange } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSubView extends BaseView {\n  constructor(matrix, startRow, endRow, startColumn, endColumn) {\n    checkRange(matrix, startRow, endRow, startColumn, endColumn);\n    super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);\n    this.startRow = startRow;\n    this.startColumn = startColumn;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n    );\n  }\n}\n","import BaseView from './base';\r\n\r\nexport default class MatrixTransposeView extends BaseView {\r\n  constructor(matrix) {\r\n    super(matrix, matrix.columns, matrix.rows);\r\n  }\r\n\r\n  set(rowIndex, columnIndex, value) {\r\n    this.matrix.set(columnIndex, rowIndex, value);\r\n    return this;\r\n  }\r\n\r\n  get(rowIndex, columnIndex) {\r\n    return this.matrix.get(columnIndex, rowIndex);\r\n  }\r\n}\r\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix1D extends AbstractMatrix {\n  constructor(data, options = {}) {\n    const { rows = 1 } = options;\n\n    if (data.length % rows !== 0) {\n      throw new Error('the data length is not divisible by the number of rows');\n    }\n    super();\n    this.rows = rows;\n    this.columns = data.length / rows;\n    this.data = data;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    this.data[index] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    return this.data[index];\n  }\n\n  _calculateIndex(row, column) {\n    return row * this.columns + column;\n  }\n}\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix2D extends AbstractMatrix {\n  constructor(data) {\n    super();\n    this.data = data;\n    this.rows = data.length;\n    this.columns = data[0].length;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n}\n","import WrapperMatrix1D from './WrapperMatrix1D';\nimport WrapperMatrix2D from './WrapperMatrix2D';\n\nexport function wrap(array, options) {\n  if (Array.isArray(array)) {\n    if (array[0] && Array.isArray(array[0])) {\n      return new WrapperMatrix2D(array);\n    } else {\n      return new WrapperMatrix1D(array, options);\n    }\n  } else {\n    throw new Error('the argument is not an array');\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class LuDecomposition {\n  constructor(matrix) {\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n\n    let lu = matrix.clone();\n    let rows = lu.rows;\n    let columns = lu.columns;\n    let pivotVector = new Float64Array(rows);\n    let pivotSign = 1;\n    let i, j, k, p, s, t, v;\n    let LUcolj, kmax;\n\n    for (i = 0; i < rows; i++) {\n      pivotVector[i] = i;\n    }\n\n    LUcolj = new Float64Array(rows);\n\n    for (j = 0; j < columns; j++) {\n      for (i = 0; i < rows; i++) {\n        LUcolj[i] = lu.get(i, j);\n      }\n\n      for (i = 0; i < rows; i++) {\n        kmax = Math.min(i, j);\n        s = 0;\n        for (k = 0; k < kmax; k++) {\n          s += lu.get(i, k) * LUcolj[k];\n        }\n        LUcolj[i] -= s;\n        lu.set(i, j, LUcolj[i]);\n      }\n\n      p = j;\n      for (i = j + 1; i < rows; i++) {\n        if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {\n          p = i;\n        }\n      }\n\n      if (p !== j) {\n        for (k = 0; k < columns; k++) {\n          t = lu.get(p, k);\n          lu.set(p, k, lu.get(j, k));\n          lu.set(j, k, t);\n        }\n\n        v = pivotVector[p];\n        pivotVector[p] = pivotVector[j];\n        pivotVector[j] = v;\n\n        pivotSign = -pivotSign;\n      }\n\n      if (j < rows && lu.get(j, j) !== 0) {\n        for (i = j + 1; i < rows; i++) {\n          lu.set(i, j, lu.get(i, j) / lu.get(j, j));\n        }\n      }\n    }\n\n    this.LU = lu;\n    this.pivotVector = pivotVector;\n    this.pivotSign = pivotSign;\n  }\n\n  isSingular() {\n    let data = this.LU;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      if (data.get(j, j) === 0) {\n        return true;\n      }\n    }\n    return false;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let lu = this.LU;\n    let rows = lu.rows;\n\n    if (rows !== value.rows) {\n      throw new Error('Invalid matrix dimensions');\n    }\n    if (this.isSingular()) {\n      throw new Error('LU matrix is singular');\n    }\n\n    let count = value.columns;\n    let X = value.subMatrixRow(this.pivotVector, 0, count - 1);\n    let columns = lu.columns;\n    let i, j, k;\n\n    for (k = 0; k < columns; k++) {\n      for (i = k + 1; i < columns; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    for (k = columns - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / lu.get(k, k));\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    return X;\n  }\n\n  get determinant() {\n    let data = this.LU;\n    if (!data.isSquare()) {\n      throw new Error('Matrix must be square');\n    }\n    let determinant = this.pivotSign;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      determinant *= data.get(j, j);\n    }\n    return determinant;\n  }\n\n  get lowerTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i > j) {\n          X.set(i, j, data.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, 1);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get upperTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i <= j) {\n          X.set(i, j, data.get(i, j));\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get pivotPermutationVector() {\n    return Array.from(this.pivotVector);\n  }\n}\n","export function hypotenuse(a, b) {\n  let r = 0;\n  if (Math.abs(a) > Math.abs(b)) {\n    r = b / a;\n    return Math.abs(a) * Math.sqrt(1 + r * r);\n  }\n  if (b !== 0) {\n    r = a / b;\n    return Math.abs(b) * Math.sqrt(1 + r * r);\n  }\n  return 0;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class QrDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let qr = value.clone();\n    let m = value.rows;\n    let n = value.columns;\n    let rdiag = new Float64Array(n);\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      let nrm = 0;\n      for (i = k; i < m; i++) {\n        nrm = hypotenuse(nrm, qr.get(i, k));\n      }\n      if (nrm !== 0) {\n        if (qr.get(k, k) < 0) {\n          nrm = -nrm;\n        }\n        for (i = k; i < m; i++) {\n          qr.set(i, k, qr.get(i, k) / nrm);\n        }\n        qr.set(k, k, qr.get(k, k) + 1);\n        for (j = k + 1; j < n; j++) {\n          s = 0;\n          for (i = k; i < m; i++) {\n            s += qr.get(i, k) * qr.get(i, j);\n          }\n          s = -s / qr.get(k, k);\n          for (i = k; i < m; i++) {\n            qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n      rdiag[k] = -nrm;\n    }\n\n    this.QR = qr;\n    this.Rdiag = rdiag;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let qr = this.QR;\n    let m = qr.rows;\n\n    if (value.rows !== m) {\n      throw new Error('Matrix row dimensions must agree');\n    }\n    if (!this.isFullRank()) {\n      throw new Error('Matrix is rank deficient');\n    }\n\n    let count = value.columns;\n    let X = value.clone();\n    let n = qr.columns;\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      for (j = 0; j < count; j++) {\n        s = 0;\n        for (i = k; i < m; i++) {\n          s += qr.get(i, k) * X.get(i, j);\n        }\n        s = -s / qr.get(k, k);\n        for (i = k; i < m; i++) {\n          X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n        }\n      }\n    }\n    for (k = n - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / this.Rdiag[k]);\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));\n        }\n      }\n    }\n\n    return X.subMatrix(0, n - 1, 0, count - 1);\n  }\n\n  isFullRank() {\n    let columns = this.QR.columns;\n    for (let i = 0; i < columns; i++) {\n      if (this.Rdiag[i] === 0) {\n        return false;\n      }\n    }\n    return true;\n  }\n\n  get upperTriangularMatrix() {\n    let qr = this.QR;\n    let n = qr.columns;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        if (i < j) {\n          X.set(i, j, qr.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, this.Rdiag[i]);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get orthogonalMatrix() {\n    let qr = this.QR;\n    let rows = qr.rows;\n    let columns = qr.columns;\n    let X = new Matrix(rows, columns);\n    let i, j, k, s;\n\n    for (k = columns - 1; k >= 0; k--) {\n      for (i = 0; i < rows; i++) {\n        X.set(i, k, 0);\n      }\n      X.set(k, k, 1);\n      for (j = k; j < columns; j++) {\n        if (qr.get(k, k) !== 0) {\n          s = 0;\n          for (i = k; i < rows; i++) {\n            s += qr.get(i, k) * X.get(i, j);\n          }\n\n          s = -s / qr.get(k, k);\n\n          for (i = k; i < rows; i++) {\n            X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n    }\n    return X;\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class SingularValueDecomposition {\n  constructor(value, options = {}) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let m = value.rows;\n    let n = value.columns;\n\n    const {\n      computeLeftSingularVectors = true,\n      computeRightSingularVectors = true,\n      autoTranspose = false,\n    } = options;\n\n    let wantu = Boolean(computeLeftSingularVectors);\n    let wantv = Boolean(computeRightSingularVectors);\n\n    let swapped = false;\n    let a;\n    if (m < n) {\n      if (!autoTranspose) {\n        a = value.clone();\n        // eslint-disable-next-line no-console\n        console.warn(\n          'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose',\n        );\n      } else {\n        a = value.transpose();\n        m = a.rows;\n        n = a.columns;\n        swapped = true;\n        let aux = wantu;\n        wantu = wantv;\n        wantv = aux;\n      }\n    } else {\n      a = value.clone();\n    }\n\n    let nu = Math.min(m, n);\n    let ni = Math.min(m + 1, n);\n    let s = new Float64Array(ni);\n    let U = new Matrix(m, nu);\n    let V = new Matrix(n, n);\n\n    let e = new Float64Array(n);\n    let work = new Float64Array(m);\n\n    let si = new Float64Array(ni);\n    for (let i = 0; i < ni; i++) si[i] = i;\n\n    let nct = Math.min(m - 1, n);\n    let nrt = Math.max(0, Math.min(n - 2, m));\n    let mrc = Math.max(nct, nrt);\n\n    for (let k = 0; k < mrc; k++) {\n      if (k < nct) {\n        s[k] = 0;\n        for (let i = k; i < m; i++) {\n          s[k] = hypotenuse(s[k], a.get(i, k));\n        }\n        if (s[k] !== 0) {\n          if (a.get(k, k) < 0) {\n            s[k] = -s[k];\n          }\n          for (let i = k; i < m; i++) {\n            a.set(i, k, a.get(i, k) / s[k]);\n          }\n          a.set(k, k, a.get(k, k) + 1);\n        }\n        s[k] = -s[k];\n      }\n\n      for (let j = k + 1; j < n; j++) {\n        if (k < nct && s[k] !== 0) {\n          let t = 0;\n          for (let i = k; i < m; i++) {\n            t += a.get(i, k) * a.get(i, j);\n          }\n          t = -t / a.get(k, k);\n          for (let i = k; i < m; i++) {\n            a.set(i, j, a.get(i, j) + t * a.get(i, k));\n          }\n        }\n        e[j] = a.get(k, j);\n      }\n\n      if (wantu && k < nct) {\n        for (let i = k; i < m; i++) {\n          U.set(i, k, a.get(i, k));\n        }\n      }\n\n      if (k < nrt) {\n        e[k] = 0;\n        for (let i = k + 1; i < n; i++) {\n          e[k] = hypotenuse(e[k], e[i]);\n        }\n        if (e[k] !== 0) {\n          if (e[k + 1] < 0) {\n            e[k] = 0 - e[k];\n          }\n          for (let i = k + 1; i < n; i++) {\n            e[i] /= e[k];\n          }\n          e[k + 1] += 1;\n        }\n        e[k] = -e[k];\n        if (k + 1 < m && e[k] !== 0) {\n          for (let i = k + 1; i < m; i++) {\n            work[i] = 0;\n          }\n          for (let i = k + 1; i < m; i++) {\n            for (let j = k + 1; j < n; j++) {\n              work[i] += e[j] * a.get(i, j);\n            }\n          }\n          for (let j = k + 1; j < n; j++) {\n            let t = -e[j] / e[k + 1];\n            for (let i = k + 1; i < m; i++) {\n              a.set(i, j, a.get(i, j) + t * work[i]);\n            }\n          }\n        }\n        if (wantv) {\n          for (let i = k + 1; i < n; i++) {\n            V.set(i, k, e[i]);\n          }\n        }\n      }\n    }\n\n    let p = Math.min(n, m + 1);\n    if (nct < n) {\n      s[nct] = a.get(nct, nct);\n    }\n    if (m < p) {\n      s[p - 1] = 0;\n    }\n    if (nrt + 1 < p) {\n      e[nrt] = a.get(nrt, p - 1);\n    }\n    e[p - 1] = 0;\n\n    if (wantu) {\n      for (let j = nct; j < nu; j++) {\n        for (let i = 0; i < m; i++) {\n          U.set(i, j, 0);\n        }\n        U.set(j, j, 1);\n      }\n      for (let k = nct - 1; k >= 0; k--) {\n        if (s[k] !== 0) {\n          for (let j = k + 1; j < nu; j++) {\n            let t = 0;\n            for (let i = k; i < m; i++) {\n              t += U.get(i, k) * U.get(i, j);\n            }\n            t = -t / U.get(k, k);\n            for (let i = k; i < m; i++) {\n              U.set(i, j, U.get(i, j) + t * U.get(i, k));\n            }\n          }\n          for (let i = k; i < m; i++) {\n            U.set(i, k, -U.get(i, k));\n          }\n          U.set(k, k, 1 + U.get(k, k));\n          for (let i = 0; i < k - 1; i++) {\n            U.set(i, k, 0);\n          }\n        } else {\n          for (let i = 0; i < m; i++) {\n            U.set(i, k, 0);\n          }\n          U.set(k, k, 1);\n        }\n      }\n    }\n\n    if (wantv) {\n      for (let k = n - 1; k >= 0; k--) {\n        if (k < nrt && e[k] !== 0) {\n          for (let j = k + 1; j < n; j++) {\n            let t = 0;\n            for (let i = k + 1; i < n; i++) {\n              t += V.get(i, k) * V.get(i, j);\n            }\n            t = -t / V.get(k + 1, k);\n            for (let i = k + 1; i < n; i++) {\n              V.set(i, j, V.get(i, j) + t * V.get(i, k));\n            }\n          }\n        }\n        for (let i = 0; i < n; i++) {\n          V.set(i, k, 0);\n        }\n        V.set(k, k, 1);\n      }\n    }\n\n    let pp = p - 1;\n    let iter = 0;\n    let eps = Number.EPSILON;\n    while (p > 0) {\n      let k, kase;\n      for (k = p - 2; k >= -1; k--) {\n        if (k === -1) {\n          break;\n        }\n        const alpha =\n          Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));\n        if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {\n          e[k] = 0;\n          break;\n        }\n      }\n      if (k === p - 2) {\n        kase = 4;\n      } else {\n        let ks;\n        for (ks = p - 1; ks >= k; ks--) {\n          if (ks === k) {\n            break;\n          }\n          let t =\n            (ks !== p ? Math.abs(e[ks]) : 0) +\n            (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);\n          if (Math.abs(s[ks]) <= eps * t) {\n            s[ks] = 0;\n            break;\n          }\n        }\n        if (ks === k) {\n          kase = 3;\n        } else if (ks === p - 1) {\n          kase = 1;\n        } else {\n          kase = 2;\n          k = ks;\n        }\n      }\n\n      k++;\n\n      switch (kase) {\n        case 1: {\n          let f = e[p - 2];\n          e[p - 2] = 0;\n          for (let j = p - 2; j >= k; j--) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            if (j !== k) {\n              f = -sn * e[j - 1];\n              e[j - 1] = cs * e[j - 1];\n            }\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, p - 1);\n                V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));\n                V.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 2: {\n          let f = e[k - 1];\n          e[k - 1] = 0;\n          for (let j = k; j < p; j++) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            f = -sn * e[j];\n            e[j] = cs * e[j];\n            if (wantu) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, k - 1);\n                U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 3: {\n          const scale = Math.max(\n            Math.abs(s[p - 1]),\n            Math.abs(s[p - 2]),\n            Math.abs(e[p - 2]),\n            Math.abs(s[k]),\n            Math.abs(e[k]),\n          );\n          const sp = s[p - 1] / scale;\n          const spm1 = s[p - 2] / scale;\n          const epm1 = e[p - 2] / scale;\n          const sk = s[k] / scale;\n          const ek = e[k] / scale;\n          const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;\n          const c = sp * epm1 * (sp * epm1);\n          let shift = 0;\n          if (b !== 0 || c !== 0) {\n            if (b < 0) {\n              shift = 0 - Math.sqrt(b * b + c);\n            } else {\n              shift = Math.sqrt(b * b + c);\n            }\n            shift = c / (b + shift);\n          }\n          let f = (sk + sp) * (sk - sp) + shift;\n          let g = sk * ek;\n          for (let j = k; j < p - 1; j++) {\n            let t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            let cs = f / t;\n            let sn = g / t;\n            if (j !== k) {\n              e[j - 1] = t;\n            }\n            f = cs * s[j] + sn * e[j];\n            e[j] = cs * e[j] - sn * s[j];\n            g = sn * s[j + 1];\n            s[j + 1] = cs * s[j + 1];\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, j + 1);\n                V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));\n                V.set(i, j, t);\n              }\n            }\n            t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            cs = f / t;\n            sn = g / t;\n            s[j] = t;\n            f = cs * e[j] + sn * s[j + 1];\n            s[j + 1] = -sn * e[j] + cs * s[j + 1];\n            g = sn * e[j + 1];\n            e[j + 1] = cs * e[j + 1];\n            if (wantu && j < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, j + 1);\n                U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          e[p - 2] = f;\n          iter = iter + 1;\n          break;\n        }\n        case 4: {\n          if (s[k] <= 0) {\n            s[k] = s[k] < 0 ? -s[k] : 0;\n            if (wantv) {\n              for (let i = 0; i <= pp; i++) {\n                V.set(i, k, -V.get(i, k));\n              }\n            }\n          }\n          while (k < pp) {\n            if (s[k] >= s[k + 1]) {\n              break;\n            }\n            let t = s[k];\n            s[k] = s[k + 1];\n            s[k + 1] = t;\n            if (wantv && k < n - 1) {\n              for (let i = 0; i < n; i++) {\n                t = V.get(i, k + 1);\n                V.set(i, k + 1, V.get(i, k));\n                V.set(i, k, t);\n              }\n            }\n            if (wantu && k < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = U.get(i, k + 1);\n                U.set(i, k + 1, U.get(i, k));\n                U.set(i, k, t);\n              }\n            }\n            k++;\n          }\n          iter = 0;\n          p--;\n          break;\n        }\n        // no default\n      }\n    }\n\n    if (swapped) {\n      let tmp = V;\n      V = U;\n      U = tmp;\n    }\n\n    this.m = m;\n    this.n = n;\n    this.s = s;\n    this.U = U;\n    this.V = V;\n  }\n\n  solve(value) {\n    let Y = value;\n    let e = this.threshold;\n    let scols = this.s.length;\n    let Ls = Matrix.zeros(scols, scols);\n\n    for (let i = 0; i < scols; i++) {\n      if (Math.abs(this.s[i]) <= e) {\n        Ls.set(i, i, 0);\n      } else {\n        Ls.set(i, i, 1 / this.s[i]);\n      }\n    }\n\n    let U = this.U;\n    let V = this.rightSingularVectors;\n\n    let VL = V.mmul(Ls);\n    let vrows = V.rows;\n    let urows = U.rows;\n    let VLU = Matrix.zeros(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < scols; k++) {\n          sum += VL.get(i, k) * U.get(j, k);\n        }\n        VLU.set(i, j, sum);\n      }\n    }\n\n    return VLU.mmul(Y);\n  }\n\n  solveForDiagonal(value) {\n    return this.solve(Matrix.diag(value));\n  }\n\n  inverse() {\n    let V = this.V;\n    let e = this.threshold;\n    let vrows = V.rows;\n    let vcols = V.columns;\n    let X = new Matrix(vrows, this.s.length);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < vcols; j++) {\n        if (Math.abs(this.s[j]) > e) {\n          X.set(i, j, V.get(i, j) / this.s[j]);\n        }\n      }\n    }\n\n    let U = this.U;\n\n    let urows = U.rows;\n    let ucols = U.columns;\n    let Y = new Matrix(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < ucols; k++) {\n          sum += X.get(i, k) * U.get(j, k);\n        }\n        Y.set(i, j, sum);\n      }\n    }\n\n    return Y;\n  }\n\n  get condition() {\n    return this.s[0] / this.s[Math.min(this.m, this.n) - 1];\n  }\n\n  get norm2() {\n    return this.s[0];\n  }\n\n  get rank() {\n    let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;\n    let r = 0;\n    let s = this.s;\n    for (let i = 0, ii = s.length; i < ii; i++) {\n      if (s[i] > tol) {\n        r++;\n      }\n    }\n    return r;\n  }\n\n  get diagonal() {\n    return Array.from(this.s);\n  }\n\n  get threshold() {\n    return (Number.EPSILON / 2) * Math.max(this.m, this.n) * this.s[0];\n  }\n\n  get leftSingularVectors() {\n    return this.U;\n  }\n\n  get rightSingularVectors() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    return Matrix.diag(this.s);\n  }\n}\n","import LuDecomposition from './dc/lu';\nimport QrDecomposition from './dc/qr';\nimport SingularValueDecomposition from './dc/svd';\nimport Matrix from './matrix';\nimport WrapperMatrix2D from './wrap/WrapperMatrix2D';\n\nexport function inverse(matrix, useSVD = false) {\n  matrix = WrapperMatrix2D.checkMatrix(matrix);\n  if (useSVD) {\n    return new SingularValueDecomposition(matrix).inverse();\n  } else {\n    return solve(matrix, Matrix.eye(matrix.rows));\n  }\n}\n\nexport function solve(leftHandSide, rightHandSide, useSVD = false) {\n  leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);\n  rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);\n  if (useSVD) {\n    return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);\n  } else {\n    return leftHandSide.isSquare()\n      ? new LuDecomposition(leftHandSide).solve(rightHandSide)\n      : new QrDecomposition(leftHandSide).solve(rightHandSide);\n  }\n}\n","import Matrix from './matrix';\nimport LuDecomposition from './dc/lu';\nimport MatrixSelectionView from './views/selection';\n\nexport function determinant(matrix) {\n  matrix = Matrix.checkMatrix(matrix);\n  if (matrix.isSquare()) {\n    let a, b, c, d;\n    if (matrix.columns === 2) {\n      // 2 x 2 matrix\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(1, 0);\n      d = matrix.get(1, 1);\n\n      return a * d - b * c;\n    } else if (matrix.columns === 3) {\n      // 3 x 3 matrix\n      let subMatrix0, subMatrix1, subMatrix2;\n      subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);\n      subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);\n      subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(0, 2);\n\n      return (\n        a * determinant(subMatrix0) -\n        b * determinant(subMatrix1) +\n        c * determinant(subMatrix2)\n      );\n    } else {\n      // general purpose determinant using the LU decomposition\n      return new LuDecomposition(matrix).determinant;\n    }\n  } else {\n    throw Error('determinant can only be calculated for a square matrix');\n  }\n}\n","import Matrix from './matrix';\nimport SingularValueDecomposition from './dc/svd';\n\nfunction xrange(n, exception) {\n  let range = [];\n  for (let i = 0; i < n; i++) {\n    if (i !== exception) {\n      range.push(i);\n    }\n  }\n  return range;\n}\n\nfunction dependenciesOneRow(\n  error,\n  matrix,\n  index,\n  thresholdValue = 10e-10,\n  thresholdError = 10e-10,\n) {\n  if (error > thresholdError) {\n    return new Array(matrix.rows + 1).fill(0);\n  } else {\n    let returnArray = matrix.addRow(index, [0]);\n    for (let i = 0; i < returnArray.rows; i++) {\n      if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {\n        returnArray.set(i, 0, 0);\n      }\n    }\n    return returnArray.to1DArray();\n  }\n}\n\nexport function linearDependencies(matrix, options = {}) {\n  const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;\n  matrix = Matrix.checkMatrix(matrix);\n\n  let n = matrix.rows;\n  let results = new Matrix(n, n);\n\n  for (let i = 0; i < n; i++) {\n    let b = Matrix.columnVector(matrix.getRow(i));\n    let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();\n    let svd = new SingularValueDecomposition(Abis);\n    let x = svd.solve(b);\n    let error = Matrix.sub(b, Abis.mmul(x))\n      .abs()\n      .max();\n    results.setRow(\n      i,\n      dependenciesOneRow(error, x, i, thresholdValue, thresholdError),\n    );\n  }\n  return results;\n}\n","import SVD from './dc/svd';\nimport Matrix from './matrix';\n\nexport function pseudoInverse(matrix, threshold = Number.EPSILON) {\n  matrix = Matrix.checkMatrix(matrix);\n  let svdSolution = new SVD(matrix, { autoTranspose: true });\n\n  let U = svdSolution.leftSingularVectors;\n  let V = svdSolution.rightSingularVectors;\n  let s = svdSolution.diagonal;\n\n  for (let i = 0; i < s.length; i++) {\n    if (Math.abs(s[i]) > threshold) {\n      s[i] = 1.0 / s[i];\n    } else {\n      s[i] = 0.0;\n    }\n  }\n\n  return V.mmul(Matrix.diag(s).mmul(U.transpose()));\n}\n","import Matrix from './matrix';\n\nexport function covariance(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = Matrix.checkMatrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = Matrix.checkMatrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n  const { center = true } = options;\n  if (center) {\n    xMatrix = xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix = yMatrix.center('column');\n    }\n  }\n  const cov = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < cov.rows; i++) {\n    for (let j = 0; j < cov.columns; j++) {\n      cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));\n    }\n  }\n  return cov;\n}\n","import Matrix from './matrix';\n\nexport function correlation(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = Matrix.checkMatrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = Matrix.checkMatrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n\n  const { center = true, scale = true } = options;\n  if (center) {\n    xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix.center('column');\n    }\n  }\n  if (scale) {\n    xMatrix.scale('column');\n    if (!yIsSame) {\n      yMatrix.scale('column');\n    }\n  }\n\n  const sdx = xMatrix.standardDeviation('column', { unbiased: true });\n  const sdy = yIsSame\n    ? sdx\n    : yMatrix.standardDeviation('column', { unbiased: true });\n\n  const corr = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < corr.rows; i++) {\n    for (let j = 0; j < corr.columns; j++) {\n      corr.set(\n        i,\n        j,\n        corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)),\n      );\n    }\n  }\n  return corr;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class EigenvalueDecomposition {\n  constructor(matrix, options = {}) {\n    const { assumeSymmetric = false } = options;\n\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n    if (!matrix.isSquare()) {\n      throw new Error('Matrix is not a square matrix');\n    }\n\n    let n = matrix.columns;\n    let V = new Matrix(n, n);\n    let d = new Float64Array(n);\n    let e = new Float64Array(n);\n    let value = matrix;\n    let i, j;\n\n    let isSymmetric = false;\n    if (assumeSymmetric) {\n      isSymmetric = true;\n    } else {\n      isSymmetric = matrix.isSymmetric();\n    }\n\n    if (isSymmetric) {\n      for (i = 0; i < n; i++) {\n        for (j = 0; j < n; j++) {\n          V.set(i, j, value.get(i, j));\n        }\n      }\n      tred2(n, e, d, V);\n      tql2(n, e, d, V);\n    } else {\n      let H = new Matrix(n, n);\n      let ort = new Float64Array(n);\n      for (j = 0; j < n; j++) {\n        for (i = 0; i < n; i++) {\n          H.set(i, j, value.get(i, j));\n        }\n      }\n      orthes(n, H, ort, V);\n      hqr2(n, e, d, V, H);\n    }\n\n    this.n = n;\n    this.e = e;\n    this.d = d;\n    this.V = V;\n  }\n\n  get realEigenvalues() {\n    return Array.from(this.d);\n  }\n\n  get imaginaryEigenvalues() {\n    return Array.from(this.e);\n  }\n\n  get eigenvectorMatrix() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    let n = this.n;\n    let e = this.e;\n    let d = this.d;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        X.set(i, j, 0);\n      }\n      X.set(i, i, d[i]);\n      if (e[i] > 0) {\n        X.set(i, i + 1, e[i]);\n      } else if (e[i] < 0) {\n        X.set(i, i - 1, e[i]);\n      }\n    }\n    return X;\n  }\n}\n\nfunction tred2(n, e, d, V) {\n  let f, g, h, i, j, k, hh, scale;\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n  }\n\n  for (i = n - 1; i > 0; i--) {\n    scale = 0;\n    h = 0;\n    for (k = 0; k < i; k++) {\n      scale = scale + Math.abs(d[k]);\n    }\n\n    if (scale === 0) {\n      e[i] = d[i - 1];\n      for (j = 0; j < i; j++) {\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n        V.set(j, i, 0);\n      }\n    } else {\n      for (k = 0; k < i; k++) {\n        d[k] /= scale;\n        h += d[k] * d[k];\n      }\n\n      f = d[i - 1];\n      g = Math.sqrt(h);\n      if (f > 0) {\n        g = -g;\n      }\n\n      e[i] = scale * g;\n      h = h - f * g;\n      d[i - 1] = f - g;\n      for (j = 0; j < i; j++) {\n        e[j] = 0;\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        V.set(j, i, f);\n        g = e[j] + V.get(j, j) * f;\n        for (k = j + 1; k <= i - 1; k++) {\n          g += V.get(k, j) * d[k];\n          e[k] += V.get(k, j) * f;\n        }\n        e[j] = g;\n      }\n\n      f = 0;\n      for (j = 0; j < i; j++) {\n        e[j] /= h;\n        f += e[j] * d[j];\n      }\n\n      hh = f / (h + h);\n      for (j = 0; j < i; j++) {\n        e[j] -= hh * d[j];\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        g = e[j];\n        for (k = j; k <= i - 1; k++) {\n          V.set(k, j, V.get(k, j) - (f * e[k] + g * d[k]));\n        }\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n      }\n    }\n    d[i] = h;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    V.set(n - 1, i, V.get(i, i));\n    V.set(i, i, 1);\n    h = d[i + 1];\n    if (h !== 0) {\n      for (k = 0; k <= i; k++) {\n        d[k] = V.get(k, i + 1) / h;\n      }\n\n      for (j = 0; j <= i; j++) {\n        g = 0;\n        for (k = 0; k <= i; k++) {\n          g += V.get(k, i + 1) * V.get(k, j);\n        }\n        for (k = 0; k <= i; k++) {\n          V.set(k, j, V.get(k, j) - g * d[k]);\n        }\n      }\n    }\n\n    for (k = 0; k <= i; k++) {\n      V.set(k, i + 1, 0);\n    }\n  }\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n    V.set(n - 1, j, 0);\n  }\n\n  V.set(n - 1, n - 1, 1);\n  e[0] = 0;\n}\n\nfunction tql2(n, e, d, V) {\n  let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2, iter;\n\n  for (i = 1; i < n; i++) {\n    e[i - 1] = e[i];\n  }\n\n  e[n - 1] = 0;\n\n  let f = 0;\n  let tst1 = 0;\n  let eps = Number.EPSILON;\n\n  for (l = 0; l < n; l++) {\n    tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));\n    m = l;\n    while (m < n) {\n      if (Math.abs(e[m]) <= eps * tst1) {\n        break;\n      }\n      m++;\n    }\n\n    if (m > l) {\n      iter = 0;\n      do {\n        iter = iter + 1;\n\n        g = d[l];\n        p = (d[l + 1] - g) / (2 * e[l]);\n        r = hypotenuse(p, 1);\n        if (p < 0) {\n          r = -r;\n        }\n\n        d[l] = e[l] / (p + r);\n        d[l + 1] = e[l] * (p + r);\n        dl1 = d[l + 1];\n        h = g - d[l];\n        for (i = l + 2; i < n; i++) {\n          d[i] -= h;\n        }\n\n        f = f + h;\n\n        p = d[m];\n        c = 1;\n        c2 = c;\n        c3 = c;\n        el1 = e[l + 1];\n        s = 0;\n        s2 = 0;\n        for (i = m - 1; i >= l; i--) {\n          c3 = c2;\n          c2 = c;\n          s2 = s;\n          g = c * e[i];\n          h = c * p;\n          r = hypotenuse(p, e[i]);\n          e[i + 1] = s * r;\n          s = e[i] / r;\n          c = p / r;\n          p = c * d[i] - s * g;\n          d[i + 1] = h + s * (c * g + s * d[i]);\n\n          for (k = 0; k < n; k++) {\n            h = V.get(k, i + 1);\n            V.set(k, i + 1, s * V.get(k, i) + c * h);\n            V.set(k, i, c * V.get(k, i) - s * h);\n          }\n        }\n\n        p = (-s * s2 * c3 * el1 * e[l]) / dl1;\n        e[l] = s * p;\n        d[l] = c * p;\n      } while (Math.abs(e[l]) > eps * tst1);\n    }\n    d[l] = d[l] + f;\n    e[l] = 0;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    k = i;\n    p = d[i];\n    for (j = i + 1; j < n; j++) {\n      if (d[j] < p) {\n        k = j;\n        p = d[j];\n      }\n    }\n\n    if (k !== i) {\n      d[k] = d[i];\n      d[i] = p;\n      for (j = 0; j < n; j++) {\n        p = V.get(j, i);\n        V.set(j, i, V.get(j, k));\n        V.set(j, k, p);\n      }\n    }\n  }\n}\n\nfunction orthes(n, H, ort, V) {\n  let low = 0;\n  let high = n - 1;\n  let f, g, h, i, j, m;\n  let scale;\n\n  for (m = low + 1; m <= high - 1; m++) {\n    scale = 0;\n    for (i = m; i <= high; i++) {\n      scale = scale + Math.abs(H.get(i, m - 1));\n    }\n\n    if (scale !== 0) {\n      h = 0;\n      for (i = high; i >= m; i--) {\n        ort[i] = H.get(i, m - 1) / scale;\n        h += ort[i] * ort[i];\n      }\n\n      g = Math.sqrt(h);\n      if (ort[m] > 0) {\n        g = -g;\n      }\n\n      h = h - ort[m] * g;\n      ort[m] = ort[m] - g;\n\n      for (j = m; j < n; j++) {\n        f = 0;\n        for (i = high; i >= m; i--) {\n          f += ort[i] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (i = m; i <= high; i++) {\n          H.set(i, j, H.get(i, j) - f * ort[i]);\n        }\n      }\n\n      for (i = 0; i <= high; i++) {\n        f = 0;\n        for (j = high; j >= m; j--) {\n          f += ort[j] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (j = m; j <= high; j++) {\n          H.set(i, j, H.get(i, j) - f * ort[j]);\n        }\n      }\n\n      ort[m] = scale * ort[m];\n      H.set(m, m - 1, scale * g);\n    }\n  }\n\n  for (i = 0; i < n; i++) {\n    for (j = 0; j < n; j++) {\n      V.set(i, j, i === j ? 1 : 0);\n    }\n  }\n\n  for (m = high - 1; m >= low + 1; m--) {\n    if (H.get(m, m - 1) !== 0) {\n      for (i = m + 1; i <= high; i++) {\n        ort[i] = H.get(i, m - 1);\n      }\n\n      for (j = m; j <= high; j++) {\n        g = 0;\n        for (i = m; i <= high; i++) {\n          g += ort[i] * V.get(i, j);\n        }\n\n        g = g / ort[m] / H.get(m, m - 1);\n        for (i = m; i <= high; i++) {\n          V.set(i, j, V.get(i, j) + g * ort[i]);\n        }\n      }\n    }\n  }\n}\n\nfunction hqr2(nn, e, d, V, H) {\n  let n = nn - 1;\n  let low = 0;\n  let high = nn - 1;\n  let eps = Number.EPSILON;\n  let exshift = 0;\n  let norm = 0;\n  let p = 0;\n  let q = 0;\n  let r = 0;\n  let s = 0;\n  let z = 0;\n  let iter = 0;\n  let i, j, k, l, m, t, w, x, y;\n  let ra, sa, vr, vi;\n  let notlast, cdivres;\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      d[i] = H.get(i, i);\n      e[i] = 0;\n    }\n\n    for (j = Math.max(i - 1, 0); j < nn; j++) {\n      norm = norm + Math.abs(H.get(i, j));\n    }\n  }\n\n  while (n >= low) {\n    l = n;\n    while (l > low) {\n      s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));\n      if (s === 0) {\n        s = norm;\n      }\n      if (Math.abs(H.get(l, l - 1)) < eps * s) {\n        break;\n      }\n      l--;\n    }\n\n    if (l === n) {\n      H.set(n, n, H.get(n, n) + exshift);\n      d[n] = H.get(n, n);\n      e[n] = 0;\n      n--;\n      iter = 0;\n    } else if (l === n - 1) {\n      w = H.get(n, n - 1) * H.get(n - 1, n);\n      p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;\n      q = p * p + w;\n      z = Math.sqrt(Math.abs(q));\n      H.set(n, n, H.get(n, n) + exshift);\n      H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);\n      x = H.get(n, n);\n\n      if (q >= 0) {\n        z = p >= 0 ? p + z : p - z;\n        d[n - 1] = x + z;\n        d[n] = d[n - 1];\n        if (z !== 0) {\n          d[n] = x - w / z;\n        }\n        e[n - 1] = 0;\n        e[n] = 0;\n        x = H.get(n, n - 1);\n        s = Math.abs(x) + Math.abs(z);\n        p = x / s;\n        q = z / s;\n        r = Math.sqrt(p * p + q * q);\n        p = p / r;\n        q = q / r;\n\n        for (j = n - 1; j < nn; j++) {\n          z = H.get(n - 1, j);\n          H.set(n - 1, j, q * z + p * H.get(n, j));\n          H.set(n, j, q * H.get(n, j) - p * z);\n        }\n\n        for (i = 0; i <= n; i++) {\n          z = H.get(i, n - 1);\n          H.set(i, n - 1, q * z + p * H.get(i, n));\n          H.set(i, n, q * H.get(i, n) - p * z);\n        }\n\n        for (i = low; i <= high; i++) {\n          z = V.get(i, n - 1);\n          V.set(i, n - 1, q * z + p * V.get(i, n));\n          V.set(i, n, q * V.get(i, n) - p * z);\n        }\n      } else {\n        d[n - 1] = x + p;\n        d[n] = x + p;\n        e[n - 1] = z;\n        e[n] = -z;\n      }\n\n      n = n - 2;\n      iter = 0;\n    } else {\n      x = H.get(n, n);\n      y = 0;\n      w = 0;\n      if (l < n) {\n        y = H.get(n - 1, n - 1);\n        w = H.get(n, n - 1) * H.get(n - 1, n);\n      }\n\n      if (iter === 10) {\n        exshift += x;\n        for (i = low; i <= n; i++) {\n          H.set(i, i, H.get(i, i) - x);\n        }\n        s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));\n        x = y = 0.75 * s;\n        w = -0.4375 * s * s;\n      }\n\n      if (iter === 30) {\n        s = (y - x) / 2;\n        s = s * s + w;\n        if (s > 0) {\n          s = Math.sqrt(s);\n          if (y < x) {\n            s = -s;\n          }\n          s = x - w / ((y - x) / 2 + s);\n          for (i = low; i <= n; i++) {\n            H.set(i, i, H.get(i, i) - s);\n          }\n          exshift += s;\n          x = y = w = 0.964;\n        }\n      }\n\n      iter = iter + 1;\n\n      m = n - 2;\n      while (m >= l) {\n        z = H.get(m, m);\n        r = x - z;\n        s = y - z;\n        p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);\n        q = H.get(m + 1, m + 1) - z - r - s;\n        r = H.get(m + 2, m + 1);\n        s = Math.abs(p) + Math.abs(q) + Math.abs(r);\n        p = p / s;\n        q = q / s;\n        r = r / s;\n        if (m === l) {\n          break;\n        }\n        if (\n          Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) <\n          eps *\n            (Math.abs(p) *\n              (Math.abs(H.get(m - 1, m - 1)) +\n                Math.abs(z) +\n                Math.abs(H.get(m + 1, m + 1))))\n        ) {\n          break;\n        }\n        m--;\n      }\n\n      for (i = m + 2; i <= n; i++) {\n        H.set(i, i - 2, 0);\n        if (i > m + 2) {\n          H.set(i, i - 3, 0);\n        }\n      }\n\n      for (k = m; k <= n - 1; k++) {\n        notlast = k !== n - 1;\n        if (k !== m) {\n          p = H.get(k, k - 1);\n          q = H.get(k + 1, k - 1);\n          r = notlast ? H.get(k + 2, k - 1) : 0;\n          x = Math.abs(p) + Math.abs(q) + Math.abs(r);\n          if (x !== 0) {\n            p = p / x;\n            q = q / x;\n            r = r / x;\n          }\n        }\n\n        if (x === 0) {\n          break;\n        }\n\n        s = Math.sqrt(p * p + q * q + r * r);\n        if (p < 0) {\n          s = -s;\n        }\n\n        if (s !== 0) {\n          if (k !== m) {\n            H.set(k, k - 1, -s * x);\n          } else if (l !== m) {\n            H.set(k, k - 1, -H.get(k, k - 1));\n          }\n\n          p = p + s;\n          x = p / s;\n          y = q / s;\n          z = r / s;\n          q = q / p;\n          r = r / p;\n\n          for (j = k; j < nn; j++) {\n            p = H.get(k, j) + q * H.get(k + 1, j);\n            if (notlast) {\n              p = p + r * H.get(k + 2, j);\n              H.set(k + 2, j, H.get(k + 2, j) - p * z);\n            }\n\n            H.set(k, j, H.get(k, j) - p * x);\n            H.set(k + 1, j, H.get(k + 1, j) - p * y);\n          }\n\n          for (i = 0; i <= Math.min(n, k + 3); i++) {\n            p = x * H.get(i, k) + y * H.get(i, k + 1);\n            if (notlast) {\n              p = p + z * H.get(i, k + 2);\n              H.set(i, k + 2, H.get(i, k + 2) - p * r);\n            }\n\n            H.set(i, k, H.get(i, k) - p);\n            H.set(i, k + 1, H.get(i, k + 1) - p * q);\n          }\n\n          for (i = low; i <= high; i++) {\n            p = x * V.get(i, k) + y * V.get(i, k + 1);\n            if (notlast) {\n              p = p + z * V.get(i, k + 2);\n              V.set(i, k + 2, V.get(i, k + 2) - p * r);\n            }\n\n            V.set(i, k, V.get(i, k) - p);\n            V.set(i, k + 1, V.get(i, k + 1) - p * q);\n          }\n        }\n      }\n    }\n  }\n\n  if (norm === 0) {\n    return;\n  }\n\n  for (n = nn - 1; n >= 0; n--) {\n    p = d[n];\n    q = e[n];\n\n    if (q === 0) {\n      l = n;\n      H.set(n, n, 1);\n      for (i = n - 1; i >= 0; i--) {\n        w = H.get(i, i) - p;\n        r = 0;\n        for (j = l; j <= n; j++) {\n          r = r + H.get(i, j) * H.get(j, n);\n        }\n\n        if (e[i] < 0) {\n          z = w;\n          s = r;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];\n            t = (x * s - z * r) / q;\n            H.set(i, n, t);\n            H.set(\n              i + 1,\n              n,\n              Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z,\n            );\n          }\n\n          t = Math.abs(H.get(i, n));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    } else if (q < 0) {\n      l = n - 1;\n\n      if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {\n        H.set(n - 1, n - 1, q / H.get(n, n - 1));\n        H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));\n      } else {\n        cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);\n        H.set(n - 1, n - 1, cdivres[0]);\n        H.set(n - 1, n, cdivres[1]);\n      }\n\n      H.set(n, n - 1, 0);\n      H.set(n, n, 1);\n      for (i = n - 2; i >= 0; i--) {\n        ra = 0;\n        sa = 0;\n        for (j = l; j <= n; j++) {\n          ra = ra + H.get(i, j) * H.get(j, n - 1);\n          sa = sa + H.get(i, j) * H.get(j, n);\n        }\n\n        w = H.get(i, i) - p;\n\n        if (e[i] < 0) {\n          z = w;\n          r = ra;\n          s = sa;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            cdivres = cdiv(-ra, -sa, w, q);\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;\n            vi = (d[i] - p) * 2 * q;\n            if (vr === 0 && vi === 0) {\n              vr =\n                eps *\n                norm *\n                (Math.abs(w) +\n                  Math.abs(q) +\n                  Math.abs(x) +\n                  Math.abs(y) +\n                  Math.abs(z));\n            }\n            cdivres = cdiv(\n              x * r - z * ra + q * sa,\n              x * s - z * sa - q * ra,\n              vr,\n              vi,\n            );\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n            if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {\n              H.set(\n                i + 1,\n                n - 1,\n                (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x,\n              );\n              H.set(\n                i + 1,\n                n,\n                (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x,\n              );\n            } else {\n              cdivres = cdiv(\n                -r - y * H.get(i, n - 1),\n                -s - y * H.get(i, n),\n                z,\n                q,\n              );\n              H.set(i + 1, n - 1, cdivres[0]);\n              H.set(i + 1, n, cdivres[1]);\n            }\n          }\n\n          t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n - 1, H.get(j, n - 1) / t);\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    }\n  }\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      for (j = i; j < nn; j++) {\n        V.set(i, j, H.get(i, j));\n      }\n    }\n  }\n\n  for (j = nn - 1; j >= low; j--) {\n    for (i = low; i <= high; i++) {\n      z = 0;\n      for (k = low; k <= Math.min(j, high); k++) {\n        z = z + V.get(i, k) * H.get(k, j);\n      }\n      V.set(i, j, z);\n    }\n  }\n}\n\nfunction cdiv(xr, xi, yr, yi) {\n  let r, d;\n  if (Math.abs(yr) > Math.abs(yi)) {\n    r = yi / yr;\n    d = yr + r * yi;\n    return [(xr + r * xi) / d, (xi - r * xr) / d];\n  } else {\n    r = yr / yi;\n    d = yi + r * yr;\n    return [(r * xr + xi) / d, (r * xi - xr) / d];\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class CholeskyDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n    if (!value.isSymmetric()) {\n      throw new Error('Matrix is not symmetric');\n    }\n\n    let a = value;\n    let dimension = a.rows;\n    let l = new Matrix(dimension, dimension);\n    let positiveDefinite = true;\n    let i, j, k;\n\n    for (j = 0; j < dimension; j++) {\n      let d = 0;\n      for (k = 0; k < j; k++) {\n        let s = 0;\n        for (i = 0; i < k; i++) {\n          s += l.get(k, i) * l.get(j, i);\n        }\n        s = (a.get(j, k) - s) / l.get(k, k);\n        l.set(j, k, s);\n        d = d + s * s;\n      }\n\n      d = a.get(j, j) - d;\n\n      positiveDefinite &= d > 0;\n      l.set(j, j, Math.sqrt(Math.max(d, 0)));\n      for (k = j + 1; k < dimension; k++) {\n        l.set(j, k, 0);\n      }\n    }\n\n    this.L = l;\n    this.positiveDefinite = Boolean(positiveDefinite);\n  }\n\n  isPositiveDefinite() {\n    return this.positiveDefinite;\n  }\n\n  solve(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let l = this.L;\n    let dimension = l.rows;\n\n    if (value.rows !== dimension) {\n      throw new Error('Matrix dimensions do not match');\n    }\n    if (this.isPositiveDefinite() === false) {\n      throw new Error('Matrix is not positive definite');\n    }\n\n    let count = value.columns;\n    let B = value.clone();\n    let i, j, k;\n\n    for (k = 0; k < dimension; k++) {\n      for (j = 0; j < count; j++) {\n        for (i = 0; i < k; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    for (k = dimension - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        for (i = k + 1; i < dimension; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    return B;\n  }\n\n  get lowerTriangularMatrix() {\n    return this.L;\n  }\n}\n","import WrapperMatrix2D from '../wrap/WrapperMatrix2D';\nimport Matrix from '../matrix';\n\nexport default class nipals {\n  constructor(X, options = {}) {\n    X = WrapperMatrix2D.checkMatrix(X);\n    let { Y } = options;\n    const {\n      scaleScores = false,\n      maxIterations = 1000,\n      terminationCriteria = 1e-10,\n    } = options;\n\n    let u;\n    if (Y) {\n      if (Array.isArray(Y) && typeof Y[0] === 'number') {\n        Y = Matrix.columnVector(Y);\n      } else {\n        Y = WrapperMatrix2D.checkMatrix(Y);\n      }\n      if (!Y.isColumnVector() || Y.rows !== X.rows) {\n        throw new Error('Y must be a column vector of length X.rows');\n      }\n      u = Y;\n    } else {\n      u = X.getColumnVector(0);\n    }\n\n    let diff = 1;\n    let t, q, w, tOld;\n\n    for (\n      let counter = 0;\n      counter < maxIterations && diff > terminationCriteria;\n      counter++\n    ) {\n      w = X.transpose()\n        .mmul(u)\n        .div(\n          u\n            .transpose()\n            .mmul(u)\n            .get(0, 0),\n        );\n      w = w.div(w.norm());\n\n      t = X.mmul(w).div(\n        w\n          .transpose()\n          .mmul(w)\n          .get(0, 0),\n      );\n\n      if (counter > 0) {\n        diff = t\n          .clone()\n          .sub(tOld)\n          .pow(2)\n          .sum();\n      }\n      tOld = t.clone();\n\n      if (Y) {\n        q = Y.transpose()\n          .mmul(t)\n          .div(\n            t\n              .transpose()\n              .mmul(t)\n              .get(0, 0),\n          );\n        q = q.div(q.norm());\n\n        u = Y.mmul(q).div(\n          q\n            .transpose()\n            .mmul(q)\n            .get(0, 0),\n        );\n      } else {\n        u = t;\n      }\n    }\n\n    if (Y) {\n      let p = X.transpose()\n        .mmul(t)\n        .div(\n          t\n            .transpose()\n            .mmul(t)\n            .get(0, 0),\n        );\n      p = p.div(p.norm());\n      let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));\n      let residual = u\n        .transpose()\n        .mmul(t)\n        .div(\n          t\n            .transpose()\n            .mmul(t)\n            .get(0, 0),\n        );\n      let yResidual = Y.clone().sub(\n        t\n          .clone()\n          .mulS(residual.get(0, 0))\n          .mmul(q.transpose()),\n      );\n\n      this.t = t;\n      this.p = p.transpose();\n      this.w = w.transpose();\n      this.q = q;\n      this.u = u;\n      this.s = t.transpose().mmul(t);\n      this.xResidual = xResidual;\n      this.yResidual = yResidual;\n      this.betas = residual;\n    } else {\n      this.w = w.transpose();\n      this.s = t\n        .transpose()\n        .mmul(t)\n        .sqrt();\n      if (scaleScores) {\n        this.t = t.clone().div(this.s.get(0, 0));\n      } else {\n        this.t = t;\n      }\n      this.xResidual = X.sub(t.mmul(w.transpose()));\n    }\n  }\n}\n","import isArray from 'is-any-array';\n\n/**\n * Computes the mean of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction sum(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += input[i];\n  }\n\n  return sumValue;\n}\n\nexport default sum;\n","import sum from 'ml-array-sum';\n\n/**\n * Computes the mean of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction mean(input) {\n  return sum(input) / input.length;\n}\n\nexport default mean;\n","import Matrix from 'ml-matrix';\nimport meanArray from 'ml-array-mean';\n\n/**\n * @private\n * return an array of probabilities of each class\n * @param {Array} array - contains the classes\n * @param {number} numberOfClasses\n * @return {Matrix} - rowVector of probabilities.\n */\nexport function toDiscreteDistribution(array, numberOfClasses) {\n  let counts = new Array(numberOfClasses).fill(0);\n  for (let i = 0; i < array.length; ++i) {\n    counts[array[i]] += 1 / array.length;\n  }\n\n  return Matrix.rowVector(counts);\n}\n\n/**\n * @private\n * Retrieves the impurity of array of predictions\n * @param {Array} array - predictions.\n * @return {number} Gini impurity\n */\nexport function giniImpurity(array) {\n  if (array.length === 0) {\n    return 0;\n  }\n\n  let probabilities = toDiscreteDistribution(\n    array,\n    getNumberOfClasses(array),\n  ).getRow(0);\n\n  let sum = 0.0;\n  for (let i = 0; i < probabilities.length; ++i) {\n    sum += probabilities[i] * probabilities[i];\n  }\n\n  return 1 - sum;\n}\n\n/**\n * @private\n * Return the number of classes given the array of predictions.\n * @param {Array} array - predictions.\n * @return {number} Number of classes.\n */\nexport function getNumberOfClasses(array) {\n  return array\n    .filter(function(val, i, arr) {\n      return arr.indexOf(val) === i;\n    })\n    .map((val) => val + 1)\n    .reduce((a, b) => Math.max(a, b));\n}\n\n/**\n * @private\n * Calculates the Gini Gain of an array of predictions and those predictions splitted by a feature.\n * @param {Array} array - Predictions\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - Gini Gain.\n */\n\nexport function giniGain(array, splitted) {\n  let splitsImpurity = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    splitsImpurity +=\n      (giniImpurity(currentSplit) * currentSplit.length) / array.length;\n  }\n\n  return giniImpurity(array) - splitsImpurity;\n}\n\n/**\n * @private\n * Calculates the squared error of a predictions values.\n * @param {Array} array - predictions values\n * @return {number} squared error.\n */\nexport function squaredError(array) {\n  let l = array.length;\n\n  let m = meanArray(array);\n  let error = 0.0;\n\n  for (let i = 0; i < l; ++i) {\n    let currentElement = array[i];\n    error += (currentElement - m) * (currentElement - m);\n  }\n\n  return error;\n}\n\n/**\n * @private\n * Calculates the sum of squared error of the two arrays that contains the splitted values.\n * @param {Array} array - this argument is no necessary but is used to fit with the main interface.\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - sum of squared errors.\n */\nexport function regressionError(array, splitted) {\n  let error = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    error += squaredError(currentSplit);\n  }\n  return error;\n}\n\n/**\n * @private\n * Split the training set and values from a given column of the training set if is less than a value\n * @param {Matrix} X - Training set.\n * @param {Array} y - Training values.\n * @param {number} column - Column to split.\n * @param {number} value - value to split the Training set and values.\n * @return {object} - Object that contains the splitted values.\n */\nexport function matrixSplitter(X, y, column, value) {\n  let lesserX = [];\n  let greaterX = [];\n  let lesserY = [];\n  let greaterY = [];\n\n  for (let i = 0; i < X.rows; ++i) {\n    if (X.get(i, column) < value) {\n      lesserX.push(X.getRow(i));\n      lesserY.push(y[i]);\n    } else {\n      greaterX.push(X.getRow(i));\n      greaterY.push(y[i]);\n    }\n  }\n\n  return {\n    greaterX: greaterX,\n    greaterY: greaterY,\n    lesserX: lesserX,\n    lesserY: lesserY,\n  };\n}\n\n/**\n * @private\n * Calculates the mean between two values\n * @param {number} a\n * @param {number} b\n * @return {number}\n */\nexport function mean(a, b) {\n  return (a + b) / 2;\n}\n\n/**\n * @private\n * Returns a list of tuples that contains the i-th element of each array.\n * @param {Array} a\n * @param {Array} b\n * @return {Array} list of tuples.\n */\nexport function zip(a, b) {\n  if (a.length !== b.length) {\n    throw new TypeError(\n      `Error on zip: the size of a: ${a.length} is different from b: ${b.length}`,\n    );\n  }\n\n  let ret = new Array(a.length);\n  for (let i = 0; i < a.length; ++i) {\n    ret[i] = [a[i], b[i]];\n  }\n\n  return ret;\n}\n","import Matrix from 'ml-matrix';\nimport mean from 'ml-array-mean';\n\nimport * as Utils from './utils';\n\nconst gainFunctions = {\n  gini: Utils.giniGain,\n  regression: Utils.regressionError,\n};\n\nconst splitFunctions = {\n  mean: Utils.mean,\n};\n\nexport default class TreeNode {\n  /**\n   * @private\n   * Constructor for a tree node given the options received on the main classes (DecisionTreeClassifier, DecisionTreeRegression)\n   * @param {object|TreeNode} options for loading\n   * @constructor\n   */\n  constructor(options) {\n    // options parameters\n    this.kind = options.kind;\n    this.gainFunction = options.gainFunction;\n    this.splitFunction = options.splitFunction;\n    this.minNumSamples = options.minNumSamples;\n    this.maxDepth = options.maxDepth;\n  }\n\n  /**\n   * @private\n   * Function that retrieve the best feature to make the split.\n   * @param {Matrix} XTranspose - Training set transposed\n   * @param {Array} y - labels or values (depending of the decision tree)\n   * @return {object} - return tree values, the best gain, column and the split value.\n   */\n  bestSplit(XTranspose, y) {\n    // Depending in the node tree class, we set the variables to check information gain (to classify)\n    // or error (for regression)\n\n    let bestGain = this.kind === 'classifier' ? -Infinity : Infinity;\n    let check = this.kind === 'classifier' ? (a, b) => a > b : (a, b) => a < b;\n\n    let maxColumn;\n    let maxValue;\n\n    for (let i = 0; i < XTranspose.rows; ++i) {\n      let currentFeature = XTranspose.getRow(i);\n      let splitValues = this.featureSplit(currentFeature, y);\n      for (let j = 0; j < splitValues.length; ++j) {\n        let currentSplitVal = splitValues[j];\n        let splitted = this.split(currentFeature, y, currentSplitVal);\n\n        let gain = gainFunctions[this.gainFunction](y, splitted);\n        if (check(gain, bestGain)) {\n          maxColumn = i;\n          maxValue = currentSplitVal;\n          bestGain = gain;\n        }\n      }\n    }\n\n    return {\n      maxGain: bestGain,\n      maxColumn: maxColumn,\n      maxValue: maxValue,\n    };\n  }\n\n  /**\n   * @private\n   * Makes the split of the training labels or values from the training set feature given a split value.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @param {number} splitValue\n   * @return {object}\n   */\n  split(x, y, splitValue) {\n    let lesser = [];\n    let greater = [];\n\n    for (let i = 0; i < x.length; ++i) {\n      if (x[i] < splitValue) {\n        lesser.push(y[i]);\n      } else {\n        greater.push(y[i]);\n      }\n    }\n\n    return {\n      greater: greater,\n      lesser: lesser,\n    };\n  }\n\n  /**\n   * @private\n   * Calculates the possible points to split over the tree given a training set feature and corresponding labels or values.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @return {Array} possible split values.\n   */\n  featureSplit(x, y) {\n    let splitValues = [];\n    let arr = Utils.zip(x, y);\n    arr.sort(function(a, b) {\n      return a[0] - b[0];\n    });\n\n    for (let i = 1; i < arr.length; ++i) {\n      if (arr[i - 1][1] !== arr[i][1]) {\n        splitValues.push(\n          splitFunctions[this.splitFunction](arr[i - 1][0], arr[i][0]),\n        );\n      }\n    }\n\n    return splitValues;\n  }\n\n  /**\n   * @private\n   * Calculate the predictions of a leaf tree node given the training labels or values\n   * @param {Array} y\n   */\n  calculatePrediction(y) {\n    if (this.kind === 'classifier') {\n      this.distribution = Utils.toDiscreteDistribution(\n        y,\n        Utils.getNumberOfClasses(y),\n      );\n      if (this.distribution.columns === 0) {\n        throw new TypeError('Error on calculate the prediction');\n      }\n    } else {\n      this.distribution = mean(y);\n    }\n  }\n\n  /**\n   * @private\n   * Train a node given the training set and labels, because it trains recursively, it also receive\n   * the current depth of the node, parent gain to avoid infinite recursion and boolean value to check if\n   * the training set is transposed.\n   * @param {Matrix} X - Training set (could be transposed or not given transposed).\n   * @param {Array} y - Training labels or values.\n   * @param {number} currentDepth - Current depth of the node.\n   * @param {number} parentGain - parent node gain or error.\n   */\n  train(X, y, currentDepth, parentGain) {\n    if (X.rows <= this.minNumSamples) {\n      this.calculatePrediction(y);\n      return;\n    }\n    if (parentGain === undefined) parentGain = 0.0;\n\n    let XTranspose = X.transpose();\n    let split = this.bestSplit(XTranspose, y);\n\n    this.splitValue = split.maxValue;\n    this.splitColumn = split.maxColumn;\n    this.gain = split.maxGain;\n\n    let splittedMatrix = Utils.matrixSplitter(\n      X,\n      y,\n      this.splitColumn,\n      this.splitValue,\n    );\n\n    if (\n      currentDepth < this.maxDepth &&\n      (this.gain > 0.01 && this.gain !== parentGain) &&\n      (splittedMatrix.lesserX.length > 0 && splittedMatrix.greaterX.length > 0)\n    ) {\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      let lesserX = new Matrix(splittedMatrix.lesserX);\n      let greaterX = new Matrix(splittedMatrix.greaterX);\n\n      this.left.train(\n        lesserX,\n        splittedMatrix.lesserY,\n        currentDepth + 1,\n        this.gain,\n      );\n      this.right.train(\n        greaterX,\n        splittedMatrix.greaterY,\n        currentDepth + 1,\n        this.gain,\n      );\n    } else {\n      this.calculatePrediction(y);\n    }\n  }\n\n  /**\n   * @private\n   * Calculates the prediction of a given element.\n   * @param {Array} row\n   * @return {number|Array} prediction\n   *          * if a node is a classifier returns an array of probabilities of each class.\n   *          * if a node is for regression returns a number with the prediction.\n   */\n  classify(row) {\n    if (this.right && this.left) {\n      if (row[this.splitColumn] < this.splitValue) {\n        return this.left.classify(row);\n      } else {\n        return this.right.classify(row);\n      }\n    }\n\n    return this.distribution;\n  }\n\n  /**\n   * @private\n   * Set the parameter of the current node and their children.\n   * @param {object} node - parameters of the current node and the children.\n   */\n  setNodeParameters(node) {\n    if (node.distribution !== undefined) {\n      this.distribution =\n        node.distribution.constructor === Array\n          ? new Matrix(node.distribution)\n          : node.distribution;\n    } else {\n      this.distribution = undefined;\n      this.splitValue = node.splitValue;\n      this.splitColumn = node.splitColumn;\n      this.gain = node.gain;\n\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      if (node.left !== {}) {\n        this.left.setNodeParameters(node.left);\n      }\n      if (node.right !== {}) {\n        this.right.setNodeParameters(node.right);\n      }\n    }\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'gini',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeClassifier {\n  /**\n   * Create new Decision Tree Classifier with CART implementation with the given options\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"gini\"] - gain function to get the best split, \"gini\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   * @constructor\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'classifier';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    this.root = new Tree(this.options);\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    this.root.train(trainingSet, trainingLabels, 0, null);\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    toPredict = Matrix.checkMatrix(toPredict);\n    let predictions = new Array(toPredict.rows);\n\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root\n        .classify(toPredict.getRow(i))\n        .maxRowIndex(0)[1];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {DecisionTreeClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'DTClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new DecisionTreeClassifier(true, model);\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'regression',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeRegression {\n  /**\n   * Create new Decision Tree Regression with CART implementation with the given options.\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"regression\"] - gain function to get the best split, \"regression\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'regression';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and values.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    this.root = new Tree(this.options);\n\n    if (\n      typeof trainingSet[0] !== 'undefined' &&\n      trainingSet[0].length === undefined\n    ) {\n      trainingSet = Matrix.columnVector(trainingSet);\n    } else {\n      trainingSet = Matrix.checkMatrix(trainingSet);\n    }\n    this.root.train(trainingSet, trainingValues, 0);\n  }\n\n  /**\n   * Predicts the values given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    if (\n      typeof toPredict[0] !== 'undefined' &&\n      toPredict[0].length === undefined\n    ) {\n      toPredict = Matrix.columnVector(toPredict);\n    }\n    toPredict = Matrix.checkMatrix(toPredict);\n\n    let predictions = new Array(toPredict.rows);\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root.classify(toPredict.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree regression with the given model.\n   * @param {object} model\n   * @return {DecisionTreeRegression}\n   */\n  static load(model) {\n    if (model.name !== 'DTRegression') {\n      throw new RangeError(`Invalid model:${model.name}`);\n    }\n\n    return new DecisionTreeRegression(true, model);\n  }\n}\n","const SMALLEST_UNSAFE_INTEGER = 0x20000000000000;\r\nconst LARGEST_SAFE_INTEGER = SMALLEST_UNSAFE_INTEGER - 1;\r\nconst UINT32_MAX = -1 >>> 0;\r\nconst UINT32_SIZE = UINT32_MAX + 1;\r\nconst INT32_SIZE = UINT32_SIZE / 2;\r\nconst INT32_MAX = INT32_SIZE - 1;\r\nconst UINT21_SIZE = 1 << 21;\r\nconst UINT21_MAX = UINT21_SIZE - 1;\n\n/**\r\n * Returns a value within [-0x80000000, 0x7fffffff]\r\n */\r\nfunction int32(engine) {\r\n    return engine.next() | 0;\r\n}\n\nfunction add(distribution, addend) {\r\n    if (addend === 0) {\r\n        return distribution;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) + addend;\r\n    }\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n */\r\nfunction int53(engine) {\r\n    const high = engine.next() | 0;\r\n    const low = engine.next() >>> 0;\r\n    return ((high & UINT21_MAX) * UINT32_SIZE +\r\n        low +\r\n        (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n */\r\nfunction int53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & 0x400000) {\r\n            if ((high & 0x7fffff) === 0x400000 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return ((high & UINT21_MAX) * UINT32_SIZE +\r\n                low +\r\n                (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n        }\r\n    }\r\n}\n\n/**\r\n * Returns a value within [0, 0xffffffff]\r\n */\r\nfunction uint32(engine) {\r\n    return engine.next() >>> 0;\r\n}\n\n/**\r\n * Returns a value within [0, 0x1fffffffffffff]\r\n */\r\nfunction uint53(engine) {\r\n    const high = engine.next() & UINT21_MAX;\r\n    const low = engine.next() >>> 0;\r\n    return high * UINT32_SIZE + low;\r\n}\n\n/**\r\n * Returns a value within [0, 0x20000000000000]\r\n */\r\nfunction uint53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & UINT21_SIZE) {\r\n            if ((high & UINT21_MAX) === 0 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return (high & UINT21_MAX) * UINT32_SIZE + low;\r\n        }\r\n    }\r\n}\n\nfunction isPowerOfTwoMinusOne(value) {\r\n    return ((value + 1) & value) === 0;\r\n}\r\nfunction bitmask(masking) {\r\n    return (engine) => engine.next() & masking;\r\n}\r\nfunction downscaleToLoopCheckedRange(range) {\r\n    const extendedRange = range + 1;\r\n    const maximum = extendedRange * Math.floor(UINT32_SIZE / extendedRange);\r\n    return engine => {\r\n        let value = 0;\r\n        do {\r\n            value = engine.next() >>> 0;\r\n        } while (value >= maximum);\r\n        return value % extendedRange;\r\n    };\r\n}\r\nfunction downscaleToRange(range) {\r\n    if (isPowerOfTwoMinusOne(range)) {\r\n        return bitmask(range);\r\n    }\r\n    else {\r\n        return downscaleToLoopCheckedRange(range);\r\n    }\r\n}\r\nfunction isEvenlyDivisibleByMaxInt32(value) {\r\n    return (value | 0) === 0;\r\n}\r\nfunction upscaleWithHighMasking(masking) {\r\n    return engine => {\r\n        const high = engine.next() & masking;\r\n        const low = engine.next() >>> 0;\r\n        return high * UINT32_SIZE + low;\r\n    };\r\n}\r\nfunction upscaleToLoopCheckedRange(extendedRange) {\r\n    const maximum = extendedRange * Math.floor(SMALLEST_UNSAFE_INTEGER / extendedRange);\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() & UINT21_MAX;\r\n            const low = engine.next() >>> 0;\r\n            ret = high * UINT32_SIZE + low;\r\n        } while (ret >= maximum);\r\n        return ret % extendedRange;\r\n    };\r\n}\r\nfunction upscaleWithinU53(range) {\r\n    const extendedRange = range + 1;\r\n    if (isEvenlyDivisibleByMaxInt32(extendedRange)) {\r\n        const highRange = ((extendedRange / UINT32_SIZE) | 0) - 1;\r\n        if (isPowerOfTwoMinusOne(highRange)) {\r\n            return upscaleWithHighMasking(highRange);\r\n        }\r\n    }\r\n    return upscaleToLoopCheckedRange(extendedRange);\r\n}\r\nfunction upscaleWithinI53AndLoopCheck(min, max) {\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() | 0;\r\n            const low = engine.next() >>> 0;\r\n            ret =\r\n                (high & UINT21_MAX) * UINT32_SIZE +\r\n                    low +\r\n                    (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);\r\n        } while (ret < min || ret > max);\r\n        return ret;\r\n    };\r\n}\r\n/**\r\n * Returns a Distribution to return a value within [min, max]\r\n * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n */\r\nfunction integer(min, max) {\r\n    min = Math.floor(min);\r\n    max = Math.floor(max);\r\n    if (min < -SMALLEST_UNSAFE_INTEGER || !isFinite(min)) {\r\n        throw new RangeError(`Expected min to be at least ${-SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    else if (max > SMALLEST_UNSAFE_INTEGER || !isFinite(max)) {\r\n        throw new RangeError(`Expected max to be at most ${SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    const range = max - min;\r\n    if (range <= 0 || !isFinite(range)) {\r\n        return () => min;\r\n    }\r\n    else if (range === UINT32_MAX) {\r\n        if (min === 0) {\r\n            return uint32;\r\n        }\r\n        else {\r\n            return add(int32, min + INT32_SIZE);\r\n        }\r\n    }\r\n    else if (range < UINT32_MAX) {\r\n        return add(downscaleToRange(range), min);\r\n    }\r\n    else if (range === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53, min);\r\n    }\r\n    else if (range < LARGEST_SAFE_INTEGER) {\r\n        return add(upscaleWithinU53(range), min);\r\n    }\r\n    else if (max - 1 - min === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53Full, min);\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER &&\r\n        max === SMALLEST_UNSAFE_INTEGER) {\r\n        return int53Full;\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER && max === LARGEST_SAFE_INTEGER) {\r\n        return int53;\r\n    }\r\n    else if (min === -LARGEST_SAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(int53, 1);\r\n    }\r\n    else if (max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(upscaleWithinI53AndLoopCheck(min - 1, max - 1), 1);\r\n    }\r\n    else {\r\n        return upscaleWithinI53AndLoopCheck(min, max);\r\n    }\r\n}\n\nfunction isLeastBitTrue(engine) {\r\n    return (engine.next() & 1) === 1;\r\n}\r\nfunction lessThan(distribution, value) {\r\n    return engine => distribution(engine) < value;\r\n}\r\nfunction probability(percentage) {\r\n    if (percentage <= 0) {\r\n        return () => false;\r\n    }\r\n    else if (percentage >= 1) {\r\n        return () => true;\r\n    }\r\n    else {\r\n        const scaled = percentage * UINT32_SIZE;\r\n        if (scaled % 1 === 0) {\r\n            return lessThan(int32, (scaled - INT32_SIZE) | 0);\r\n        }\r\n        else {\r\n            return lessThan(uint53, Math.round(percentage * SMALLEST_UNSAFE_INTEGER));\r\n        }\r\n    }\r\n}\r\nfunction bool(numerator, denominator) {\r\n    if (denominator == null) {\r\n        if (numerator == null) {\r\n            return isLeastBitTrue;\r\n        }\r\n        return probability(numerator);\r\n    }\r\n    else {\r\n        if (numerator <= 0) {\r\n            return () => false;\r\n        }\r\n        else if (numerator >= denominator) {\r\n            return () => true;\r\n        }\r\n        return lessThan(integer(0, denominator - 1), numerator);\r\n    }\r\n}\n\n/**\r\n * Returns a Distribution that returns a random `Date` within the inclusive\r\n * range of [`start`, `end`].\r\n * @param start The minimum `Date`\r\n * @param end The maximum `Date`\r\n */\r\nfunction date(start, end) {\r\n    const distribution = integer(+start, +end);\r\n    return engine => new Date(distribution(engine));\r\n}\n\n/**\r\n * Returns a Distribution to return a value within [1, sideCount]\r\n * @param sideCount The number of sides of the die\r\n */\r\nfunction die(sideCount) {\r\n    return integer(1, sideCount);\r\n}\n\n/**\r\n * Returns a distribution that returns an array of length `dieCount` of values\r\n * within [1, `sideCount`]\r\n * @param sideCount The number of sides of each die\r\n * @param dieCount The number of dice\r\n */\r\nfunction dice(sideCount, dieCount) {\r\n    const distribution = die(sideCount);\r\n    return engine => {\r\n        const result = [];\r\n        for (let i = 0; i < dieCount; ++i) {\r\n            result.push(distribution(engine));\r\n        }\r\n        return result;\r\n    };\r\n}\n\n// tslint:disable:unified-signatures\r\n// has 2**x chars, for faster uniform distribution\r\nconst DEFAULT_STRING_POOL = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-\";\r\nfunction string(pool = DEFAULT_STRING_POOL) {\r\n    const poolLength = pool.length;\r\n    if (!poolLength) {\r\n        throw new Error(\"Expected pool not to be an empty string\");\r\n    }\r\n    const distribution = integer(0, poolLength - 1);\r\n    return (engine, length) => {\r\n        let result = \"\";\r\n        for (let i = 0; i < length; ++i) {\r\n            const j = distribution(engine);\r\n            result += pool.charAt(j);\r\n        }\r\n        return result;\r\n    };\r\n}\n\nconst LOWER_HEX_POOL = \"0123456789abcdef\";\r\nconst lowerHex = string(LOWER_HEX_POOL);\r\nconst upperHex = string(LOWER_HEX_POOL.toUpperCase());\r\n/**\r\n * Returns a Distribution that returns a random string comprised of numbers\r\n * or the characters `abcdef` (or `ABCDEF`) of length `length`.\r\n * @param length Length of the result string\r\n * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n */\r\nfunction hex(uppercase) {\r\n    if (uppercase) {\r\n        return upperHex;\r\n    }\r\n    else {\r\n        return lowerHex;\r\n    }\r\n}\n\nfunction convertSliceArgument(value, length) {\r\n    if (value < 0) {\r\n        return Math.max(value + length, 0);\r\n    }\r\n    else {\r\n        return Math.min(value, length);\r\n    }\r\n}\n\nfunction toInteger(value) {\r\n    const num = +value;\r\n    if (num < 0) {\r\n        return Math.ceil(num);\r\n    }\r\n    else {\r\n        return Math.floor(num);\r\n    }\r\n}\n\n/**\r\n * Returns a random value within the provided `source` within the sliced\r\n * bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction pick(engine, source, begin, end) {\r\n    const length = source.length;\r\n    if (length === 0) {\r\n        throw new RangeError(\"Cannot pick from an empty array\");\r\n    }\r\n    const start = begin == null ? 0 : convertSliceArgument(toInteger(begin), length);\r\n    const finish = end === void 0 ? length : convertSliceArgument(toInteger(end), length);\r\n    if (start >= finish) {\r\n        throw new RangeError(`Cannot pick between bounds ${start} and ${finish}`);\r\n    }\r\n    const distribution = integer(start, finish - 1);\r\n    return source[distribution(engine)];\r\n}\n\nfunction multiply(distribution, multiplier) {\r\n    if (multiplier === 1) {\r\n        return distribution;\r\n    }\r\n    else if (multiplier === 0) {\r\n        return () => 0;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) * multiplier;\r\n    }\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0)\r\n */\r\nfunction realZeroToOneExclusive(engine) {\r\n    return uint53(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0]\r\n */\r\nfunction realZeroToOneInclusive(engine) {\r\n    return uint53Full(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [min, max) or [min, max]\r\n * @param min The minimum floating-point value, inclusive.\r\n * @param max The maximum floating-point value.\r\n * @param inclusive If true, `max` will be inclusive.\r\n */\r\nfunction real(min, max, inclusive = false) {\r\n    if (!isFinite(min)) {\r\n        throw new RangeError(\"Expected min to be a finite number\");\r\n    }\r\n    else if (!isFinite(max)) {\r\n        throw new RangeError(\"Expected max to be a finite number\");\r\n    }\r\n    return add(multiply(inclusive ? realZeroToOneInclusive : realZeroToOneExclusive, max - min), min);\r\n}\n\nconst sliceArray = Array.prototype.slice;\n\n/**\r\n * Shuffles an array in-place\r\n * @param engine The Engine to use when choosing random values\r\n * @param array The array to shuffle\r\n * @param downTo minimum index to shuffle. Only used internally.\r\n */\r\nfunction shuffle(engine, array, downTo = 0) {\r\n    const length = array.length;\r\n    if (length) {\r\n        for (let i = (length - 1) >>> 0; i > downTo; --i) {\r\n            const distribution = integer(0, i);\r\n            const j = distribution(engine);\r\n            if (i !== j) {\r\n                const tmp = array[i];\r\n                array[i] = array[j];\r\n                array[j] = tmp;\r\n            }\r\n        }\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * From the population array, produce an array with sampleSize elements that\r\n * are randomly chosen without repeats.\r\n * @param engine The Engine to use when choosing random values\r\n * @param population An array that has items to choose a sample from\r\n * @param sampleSize The size of the result array\r\n */\r\nfunction sample(engine, population, sampleSize) {\r\n    if (sampleSize < 0 ||\r\n        sampleSize > population.length ||\r\n        !isFinite(sampleSize)) {\r\n        throw new RangeError(\"Expected sampleSize to be within 0 and the length of the population\");\r\n    }\r\n    if (sampleSize === 0) {\r\n        return [];\r\n    }\r\n    const clone = sliceArray.call(population);\r\n    const length = clone.length;\r\n    if (length === sampleSize) {\r\n        return shuffle(engine, clone, 0);\r\n    }\r\n    const tailLength = length - sampleSize;\r\n    return shuffle(engine, clone, tailLength - 1).slice(tailLength);\r\n}\n\nconst stringRepeat = (() => {\r\n    try {\r\n        if (\"x\".repeat(3) === \"xxx\") {\r\n            return (pattern, count) => pattern.repeat(count);\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return (pattern, count) => {\r\n        let result = \"\";\r\n        while (count > 0) {\r\n            if (count & 1) {\r\n                result += pattern;\r\n            }\r\n            count >>= 1;\r\n            pattern += pattern;\r\n        }\r\n        return result;\r\n    };\r\n})();\n\nfunction zeroPad(text, zeroCount) {\r\n    return stringRepeat(\"0\", zeroCount - text.length) + text;\r\n}\r\n/**\r\n * Returns a Universally Unique Identifier Version 4.\r\n *\r\n * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n */\r\nfunction uuid4(engine) {\r\n    const a = engine.next() >>> 0;\r\n    const b = engine.next() | 0;\r\n    const c = engine.next() | 0;\r\n    const d = engine.next() >>> 0;\r\n    return (zeroPad(a.toString(16), 8) +\r\n        \"-\" +\r\n        zeroPad((b & 0xffff).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad((((b >> 4) & 0x0fff) | 0x4000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c & 0x3fff) | 0x8000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c >> 4) & 0xffff).toString(16), 4) +\r\n        zeroPad(d.toString(16), 8));\r\n}\n\n/**\r\n * An int32-producing Engine that uses `Math.random()`\r\n */\r\nconst nativeMath = {\r\n    next() {\r\n        return (Math.random() * UINT32_SIZE) | 0;\r\n    }\r\n};\n\n// tslint:disable:unified-signatures\r\n/**\r\n * A wrapper around an Engine that provides easy-to-use methods for\r\n * producing values based on known distributions\r\n */\r\nclass Random {\r\n    /**\r\n     * Creates a new Random wrapper\r\n     * @param engine The engine to use (defaults to a `Math.random`-based implementation)\r\n     */\r\n    constructor(engine = nativeMath) {\r\n        this.engine = engine;\r\n    }\r\n    /**\r\n     * Returns a value within [-0x80000000, 0x7fffffff]\r\n     */\r\n    int32() {\r\n        return int32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0xffffffff]\r\n     */\r\n    uint32() {\r\n        return uint32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x1fffffffffffff]\r\n     */\r\n    uint53() {\r\n        return uint53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x20000000000000]\r\n     */\r\n    uint53Full() {\r\n        return uint53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n     */\r\n    int53() {\r\n        return int53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n     */\r\n    int53Full() {\r\n        return int53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [min, max]\r\n     * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n     * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n     */\r\n    integer(min, max) {\r\n        return integer(min, max)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0]\r\n     */\r\n    realZeroToOneInclusive() {\r\n        return realZeroToOneInclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0)\r\n     */\r\n    realZeroToOneExclusive() {\r\n        return realZeroToOneExclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [min, max) or [min, max]\r\n     * @param min The minimum floating-point value, inclusive.\r\n     * @param max The maximum floating-point value.\r\n     * @param inclusive If true, `max` will be inclusive.\r\n     */\r\n    real(min, max, inclusive = false) {\r\n        return real(min, max, inclusive)(this.engine);\r\n    }\r\n    bool(numerator, denominator) {\r\n        return bool(numerator, denominator)(this.engine);\r\n    }\r\n    /**\r\n     * Return a random value within the provided `source` within the sliced\r\n     * bounds of `begin` and `end`.\r\n     * @param source an array of items to pick from\r\n     * @param begin the beginning slice index (defaults to `0`)\r\n     * @param end the ending slice index (defaults to `source.length`)\r\n     */\r\n    pick(source, begin, end) {\r\n        return pick(this.engine, source, begin, end);\r\n    }\r\n    /**\r\n     * Shuffles an array in-place\r\n     * @param array The array to shuffle\r\n     */\r\n    shuffle(array) {\r\n        return shuffle(this.engine, array);\r\n    }\r\n    /**\r\n     * From the population array, returns an array with sampleSize elements that\r\n     * are randomly chosen without repeats.\r\n     * @param population An array that has items to choose a sample from\r\n     * @param sampleSize The size of the result array\r\n     */\r\n    sample(population, sampleSize) {\r\n        return sample(this.engine, population, sampleSize);\r\n    }\r\n    /**\r\n     * Returns a value within [1, sideCount]\r\n     * @param sideCount The number of sides of the die\r\n     */\r\n    die(sideCount) {\r\n        return die(sideCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns an array of length `dieCount` of values within [1, sideCount]\r\n     * @param sideCount The number of sides of each die\r\n     * @param dieCount The number of dice\r\n     */\r\n    dice(sideCount, dieCount) {\r\n        return dice(sideCount, dieCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a Universally Unique Identifier Version 4.\r\n     *\r\n     * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n     */\r\n    uuid4() {\r\n        return uuid4(this.engine);\r\n    }\r\n    string(length, pool) {\r\n        return string(pool)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random string comprised of numbers or the characters `abcdef`\r\n     * (or `ABCDEF`) of length `length`.\r\n     * @param length Length of the result string\r\n     * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n     */\r\n    hex(length, uppercase) {\r\n        return hex(uppercase)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random `Date` within the inclusive range of [`start`, `end`].\r\n     * @param start The minimum `Date`\r\n     * @param end The maximum `Date`\r\n     */\r\n    date(start, end) {\r\n        return date(start, end)(this.engine);\r\n    }\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array\r\n */\r\nconst I32Array = (() => {\r\n    try {\r\n        const buffer = new ArrayBuffer(4);\r\n        const view = new Int32Array(buffer);\r\n        view[0] = INT32_SIZE;\r\n        if (view[0] === -INT32_SIZE) {\r\n            return Int32Array;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return Array;\r\n})();\n\nlet data = null;\r\nconst COUNT = 128;\r\nlet index = COUNT;\r\n/**\r\n * An Engine that relies on the globally-available `crypto.getRandomValues`,\r\n * which is typically available in modern browsers.\r\n *\r\n * See https://developer.mozilla.org/en-US/docs/Web/API/Crypto/getRandomValues\r\n *\r\n * If unavailable or otherwise non-functioning, then `browserCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst browserCrypto = {\r\n    next() {\r\n        if (index >= COUNT) {\r\n            if (data === null) {\r\n                data = new I32Array(COUNT);\r\n            }\r\n            crypto.getRandomValues(data);\r\n            index = 0;\r\n        }\r\n        return data[index++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns an array of random int32 values, based on current time\r\n * and a random number engine\r\n *\r\n * @param engine an Engine to pull random values from, default `nativeMath`\r\n * @param length the length of the Array, minimum 1, default 16\r\n */\r\nfunction createEntropy(engine = nativeMath, length = 16) {\r\n    const array = [];\r\n    array.push(new Date().getTime() | 0);\r\n    for (let i = 1; i < length; ++i) {\r\n        array[i] = engine.next() | 0;\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/imul\r\n */\r\nconst imul = (() => {\r\n    try {\r\n        if (Math.imul(UINT32_MAX, 5) === -5) {\r\n            return Math.imul;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    const UINT16_MAX = 0xffff;\r\n    return (a, b) => {\r\n        const ah = (a >>> 16) & UINT16_MAX;\r\n        const al = a & UINT16_MAX;\r\n        const bh = (b >>> 16) & UINT16_MAX;\r\n        const bl = b & UINT16_MAX;\r\n        // the shift by 0 fixes the sign on the high part\r\n        // the final |0 converts the unsigned value into a signed value\r\n        return (al * bl + (((ah * bl + al * bh) << 16) >>> 0)) | 0;\r\n    };\r\n})();\n\nconst ARRAY_SIZE = 624;\r\nconst ARRAY_MAX = ARRAY_SIZE - 1;\r\nconst M = 397;\r\nconst ARRAY_SIZE_MINUS_M = ARRAY_SIZE - M;\r\nconst A = 0x9908b0df;\r\n/**\r\n * An Engine that is a pseudorandom number generator using the Mersenne\r\n * Twister algorithm based on the prime 2**19937 − 1\r\n *\r\n * See http://en.wikipedia.org/wiki/Mersenne_twister\r\n */\r\nclass MersenneTwister19937 {\r\n    /**\r\n     * MersenneTwister19937 should not be instantiated directly.\r\n     * Instead, use the static methods `seed`, `seedWithArray`, or `autoSeed`.\r\n     */\r\n    constructor() {\r\n        this.data = new I32Array(ARRAY_SIZE);\r\n        this.index = 0; // integer within [0, 624]\r\n        this.uses = 0;\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with an initial int32 value\r\n     * @param initial the initial seed value\r\n     */\r\n    static seed(initial) {\r\n        return new MersenneTwister19937().seed(initial);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with zero or more int32 values\r\n     * @param source A series of int32 values\r\n     */\r\n    static seedWithArray(source) {\r\n        return new MersenneTwister19937().seedWithArray(source);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with the current time and\r\n     * a series of natively-generated random values\r\n     */\r\n    static autoSeed() {\r\n        return MersenneTwister19937.seedWithArray(createEntropy());\r\n    }\r\n    /**\r\n     * Returns the next int32 value of the sequence\r\n     */\r\n    next() {\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        const value = this.data[this.index];\r\n        this.index = (this.index + 1) | 0;\r\n        this.uses += 1;\r\n        return temper(value) | 0;\r\n    }\r\n    /**\r\n     * Returns the number of times that the Engine has been used.\r\n     *\r\n     * This can be provided to an unused MersenneTwister19937 with the same\r\n     * seed, bringing it to the exact point that was left off.\r\n     */\r\n    getUseCount() {\r\n        return this.uses;\r\n    }\r\n    /**\r\n     * Discards one or more items from the engine\r\n     * @param count The count of items to discard\r\n     */\r\n    discard(count) {\r\n        if (count <= 0) {\r\n            return this;\r\n        }\r\n        this.uses += count;\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        while (count + this.index > ARRAY_SIZE) {\r\n            count -= ARRAY_SIZE - this.index;\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        this.index = (this.index + count) | 0;\r\n        return this;\r\n    }\r\n    seed(initial) {\r\n        let previous = 0;\r\n        this.data[0] = previous = initial | 0;\r\n        for (let i = 1; i < ARRAY_SIZE; i = (i + 1) | 0) {\r\n            this.data[i] = previous =\r\n                (imul(previous ^ (previous >>> 30), 0x6c078965) + i) | 0;\r\n        }\r\n        this.index = ARRAY_SIZE;\r\n        this.uses = 0;\r\n        return this;\r\n    }\r\n    seedWithArray(source) {\r\n        this.seed(0x012bd6aa);\r\n        seedWithArray(this.data, source);\r\n        return this;\r\n    }\r\n}\r\nfunction refreshData(data) {\r\n    let k = 0;\r\n    let tmp = 0;\r\n    for (; (k | 0) < ARRAY_SIZE_MINUS_M; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] = data[(k + M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    for (; (k | 0) < ARRAY_MAX; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] =\r\n            data[(k - ARRAY_SIZE_MINUS_M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    tmp = (data[ARRAY_MAX] & INT32_SIZE) | (data[0] & INT32_MAX);\r\n    data[ARRAY_MAX] = data[M - 1] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n}\r\nfunction temper(value) {\r\n    value ^= value >>> 11;\r\n    value ^= (value << 7) & 0x9d2c5680;\r\n    value ^= (value << 15) & 0xefc60000;\r\n    return value ^ (value >>> 18);\r\n}\r\nfunction seedWithArray(data, source) {\r\n    let i = 1;\r\n    let j = 0;\r\n    const sourceLength = source.length;\r\n    let k = Math.max(sourceLength, ARRAY_SIZE) | 0;\r\n    let previous = data[0] | 0;\r\n    for (; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x0019660d)) +\r\n                (source[j] | 0) +\r\n                (j | 0)) |\r\n                0;\r\n        i = (i + 1) | 0;\r\n        ++j;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n        if (j >= sourceLength) {\r\n            j = 0;\r\n        }\r\n    }\r\n    for (k = ARRAY_MAX; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x5d588b65)) - i) | 0;\r\n        i = (i + 1) | 0;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n    }\r\n    data[0] = INT32_SIZE;\r\n}\n\nlet data$1 = null;\r\nconst COUNT$1 = 128;\r\nlet index$1 = COUNT$1;\r\n/**\r\n * An Engine that relies on the node-available\r\n * `require('crypto').randomBytes`, which has been available since 0.58.\r\n *\r\n * See https://nodejs.org/api/crypto.html#crypto_crypto_randombytes_size_callback\r\n *\r\n * If unavailable or otherwise non-functioning, then `nodeCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst nodeCrypto = {\r\n    next() {\r\n        if (index$1 >= COUNT$1) {\r\n            data$1 = new Int32Array(new Int8Array(require(\"crypto\").randomBytes(4 * COUNT$1)).buffer);\r\n            index$1 = 0;\r\n        }\r\n        return data$1[index$1++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns a Distribution to random value within the provided `source`\r\n * within the sliced bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction picker(source, begin, end) {\r\n    const clone = sliceArray.call(source, begin, end);\r\n    if (clone.length === 0) {\r\n        throw new RangeError(`Cannot pick from a source with no items`);\r\n    }\r\n    const distribution = integer(0, clone.length - 1);\r\n    return engine => clone[distribution(engine)];\r\n}\n\nexport { Random, browserCrypto, nativeMath, MersenneTwister19937, nodeCrypto, bool, date, dice, die, hex, int32, int53, int53Full, integer, pick, picker, real, realZeroToOneExclusive, realZeroToOneInclusive, sample, shuffle, string, uint32, uint53, uint53Full, uuid4, createEntropy };\n//# sourceMappingURL=random-js.esm.js.map\n","import * as Random from 'random-js';\nimport Matrix from 'ml-matrix';\n\nexport function checkFloat(n) {\n  return n > 0.0 && n <= 1.0;\n}\n\n/**\n * Select n with replacement elements on the training set and values, where n is the size of the training set.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {Array} trainingValue\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object} with new X and y.\n */\nexport function examplesBaggingWithReplacement(\n  trainingSet,\n  trainingValue,\n  seed,\n) {\n  let engine;\n  let distribution = Random.integer(0, trainingSet.rows - 1);\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let Xr = new Array(trainingSet.rows);\n  let yr = new Array(trainingSet.rows);\n\n  for (let i = 0; i < trainingSet.rows; ++i) {\n    let index = distribution(engine);\n    Xr[i] = trainingSet.getRow(index);\n    yr[i] = trainingValue[index];\n  }\n\n  return {\n    X: new Matrix(Xr),\n    y: yr,\n  };\n}\n\n/**\n * selects n features from the training set with or without replacement, returns the new training set and the indexes used.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {number} n - features.\n * @param {boolean} replacement\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object}\n */\nexport function featureBagging(trainingSet, n, replacement, seed) {\n  if (trainingSet.columns < n) {\n    throw new RangeError(\n      'N should be less or equal to the number of columns of X',\n    );\n  }\n\n  let distribution = Random.integer(0, trainingSet.columns - 1);\n  let engine;\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let toRet = new Matrix(trainingSet.rows, n);\n\n  let usedIndex;\n  let index;\n  if (replacement) {\n    usedIndex = new Array(n);\n    for (let i = 0; i < n; ++i) {\n      index = distribution(engine);\n      usedIndex[i] = index;\n      toRet.setColumn(i, trainingSet.getColumn(index));\n    }\n  } else {\n    usedIndex = new Set();\n    index = distribution(engine);\n    for (let i = 0; i < n; ++i) {\n      while (usedIndex.has(index)) {\n        index = distribution(engine);\n      }\n      toRet.setColumn(i, trainingSet.getColumn(index));\n      usedIndex.add(index);\n    }\n    usedIndex = Array.from(usedIndex);\n  }\n\n  return {\n    X: toRet,\n    usedIndex: usedIndex,\n  };\n}\n","import {\n  DecisionTreeClassifier as DTClassifier,\n  DecisionTreeRegression as DTRegression,\n} from 'ml-cart';\nimport {\n  Matrix,\n  WrapperMatrix2D,\n  MatrixTransposeView,\n  MatrixColumnSelectionView,\n} from 'ml-matrix';\n\nimport * as Utils from './utils';\n\n/**\n * @class RandomForestBase\n */\nexport class RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number|String} [options.maxFeatures] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement] - use replacement over the sample features.\n   * @param {number} [options.seed] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators] - number of estimator to use.\n   * @param {object} [options.treeOptions] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.isClassifier] - boolean to check if is a classifier or regression model (used by subclasses).\n   * @param {boolean} [options.useSampleBagging] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.replacement = model.replacement;\n      this.maxFeatures = model.maxFeatures;\n      this.nEstimators = model.nEstimators;\n      this.treeOptions = model.treeOptions;\n      this.isClassifier = model.isClassifier;\n      this.seed = model.seed;\n      this.n = model.n;\n      this.indexes = model.indexes;\n      this.useSampleBagging = model.useSampleBagging;\n\n      let Estimator = this.isClassifier ? DTClassifier : DTRegression;\n      this.estimators = model.estimators.map((est) => Estimator.load(est));\n    } else {\n      this.replacement = options.replacement;\n      this.maxFeatures = options.maxFeatures;\n      this.nEstimators = options.nEstimators;\n      this.treeOptions = options.treeOptions;\n      this.isClassifier = options.isClassifier;\n      this.seed = options.seed;\n      this.useSampleBagging = options.useSampleBagging;\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    this.maxFeatures = this.maxFeatures || trainingSet.columns;\n\n    if (Utils.checkFloat(this.maxFeatures)) {\n      this.n = Math.floor(trainingSet.columns * this.maxFeatures);\n    } else if (Number.isInteger(this.maxFeatures)) {\n      if (this.maxFeatures > trainingSet.columns) {\n        throw new RangeError(\n          `The maxFeatures parameter should be less than ${trainingSet.columns}`,\n        );\n      } else {\n        this.n = this.maxFeatures;\n      }\n    } else {\n      throw new RangeError(\n        `Cannot process the maxFeatures parameter ${this.maxFeatures}`,\n      );\n    }\n\n    let Estimator;\n    if (this.isClassifier) {\n      Estimator = DTClassifier;\n    } else {\n      Estimator = DTRegression;\n    }\n\n    this.estimators = new Array(this.nEstimators);\n    this.indexes = new Array(this.nEstimators);\n\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let res = this.useSampleBagging\n        ? Utils.examplesBaggingWithReplacement(\n            trainingSet,\n            trainingValues,\n            this.seed,\n          )\n        : { X: trainingSet, y: trainingValues };\n      let X = res.X;\n      let y = res.y;\n\n      res = Utils.featureBagging(X, this.n, this.replacement, this.seed);\n      X = res.X;\n\n      this.indexes[i] = res.usedIndex;\n      this.estimators[i] = new Estimator(this.treeOptions);\n      this.estimators[i].train(X, y);\n    }\n  }\n\n  /**\n   * Method that returns the way the algorithm generates the predictions, for example, in classification\n   * you can return the mode of all predictions retrieved by the trees, or in case of regression you can\n   * use the mean or the median.\n   * @abstract\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction.\n   */\n  // eslint-disable-next-line no-unused-vars\n  selection(values) {\n    throw new Error(\"Abstract method 'selection' not implemented!\");\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    let predictionValues = new Array(this.nEstimators);\n    toPredict = Matrix.checkMatrix(toPredict);\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let X = new MatrixColumnSelectionView(toPredict, this.indexes[i]); // get features for estimator\n      predictionValues[i] = this.estimators[i].predict(X);\n    }\n\n    predictionValues = new MatrixTransposeView(\n      new WrapperMatrix2D(predictionValues),\n    );\n    let predictions = new Array(predictionValues.rows);\n    for (let i = 0; i < predictionValues.rows; ++i) {\n      predictions[i] = this.selection(predictionValues.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      indexes: this.indexes,\n      n: this.n,\n      replacement: this.replacement,\n      maxFeatures: this.maxFeatures,\n      nEstimators: this.nEstimators,\n      treeOptions: this.treeOptions,\n      isClassifier: this.isClassifier,\n      seed: this.seed,\n      estimators: this.estimators.map((est) => est.toJSON()),\n      useSampleBagging: this.useSampleBagging,\n    };\n  }\n}\n","import { RandomForestBase } from './RandomForestBase';\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: true,\n  nEstimators: 10,\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestClassifier\n * @augments RandomForestBase\n */\nexport class RandomForestClassifier extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n      options.isClassifier = true;\n      super(options);\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return mode(values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      name: 'RFClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'RFClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestClassifier(true, model);\n  }\n}\n\n/**\n * Return the most repeated element on the array.\n * @param {Array} arr\n * @return {number} mode\n */\nfunction mode(arr) {\n  return arr\n    .sort(\n      (a, b) =>\n        arr.filter((v) => v === a).length - arr.filter((v) => v === b).length,\n    )\n    .pop();\n}\n","(function(){function a(d){for(var e=0,f=d.length-1,g=void 0,h=void 0,i=void 0,j=c(e,f);!0;){if(f<=e)return d[j];if(f==e+1)return d[e]>d[f]&&b(d,e,f),d[j];for(g=c(e,f),d[g]>d[f]&&b(d,g,f),d[e]>d[f]&&b(d,e,f),d[g]>d[e]&&b(d,g,e),b(d,g,e+1),h=e+1,i=f;!0;){do h++;while(d[e]>d[h]);do i--;while(d[i]>d[e]);if(i<h)break;b(d,h,i)}b(d,e,i),i<=j&&(e=h),i>=j&&(f=i-1)}}var b=function b(d,e,f){var _ref;return _ref=[d[f],d[e]],d[e]=_ref[0],d[f]=_ref[1],_ref},c=function c(d,e){return~~((d+e)/2)};'undefined'!=typeof module&&module.exports?module.exports=a:window.median=a})();\n","import quickSelectMedian from 'median-quickselect';\nimport isArray from 'is-any-array';\n\n/**\n * Computes the median of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction median(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  return quickSelectMedian(input.slice());\n}\n\nexport default median;\n","import arrayMean from 'ml-array-mean';\nimport arrayMedian from 'ml-array-median';\n\nimport { RandomForestBase } from './RandomForestBase';\n\nconst selectionMethods = {\n  mean: arrayMean,\n  median: arrayMedian,\n};\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: false,\n  nEstimators: 10,\n  treeOptions: {},\n  selectionMethod: 'mean',\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestRegression\n * @augments RandomForestBase\n */\nexport class RandomForestRegression extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {string} [options.selectionMethod=\"mean\"] - the way to calculate the prediction from estimators, \"mean\" and \"median\" are supported.\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n      this.selectionMethod = model.selectionMethod;\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n\n      if (\n        !(\n          options.selectionMethod === 'mean' ||\n          options.selectionMethod === 'median'\n        )\n      ) {\n        throw new RangeError(\n          `Unsupported selection method ${options.selectionMethod}`,\n        );\n      }\n\n      options.isClassifier = false;\n\n      super(options);\n      this.selectionMethod = options.selectionMethod;\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return selectionMethods[this.selectionMethod](values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      selectionMethod: this.selectionMethod,\n      name: 'RFRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestRegression}\n   */\n  static load(model) {\n    if (model.name !== 'RFRegression') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestRegression(true, model);\n  }\n}\n","import { Matrix, MatrixTransposeView, EVD, SVD, NIPALS } from 'ml-matrix';\n\n/**\n * Creates new PCA (Principal Component Analysis) from the dataset\n * @param {Matrix} dataset - dataset or covariance matrix.\n * @param {Object} [options]\n * @param {boolean} [options.isCovarianceMatrix=false] - true if the dataset is a covariance matrix.\n * @param {string} [options.method='SVD'] - select which method to use: SVD (default), covarianceMatrirx or NIPALS.\n * @param {number} [options.nCompNIPALS=2] - number of components to be computed with NIPALS.\n * @param {boolean} [options.center=true] - should the data be centered (subtract the mean).\n * @param {boolean} [options.scale=false] - should the data be scaled (divide by the standard deviation).\n * @param {boolean} [options.ignoreZeroVariance=false] - ignore columns with zero variance if `scale` is `true`.\n * */\nexport class PCA {\n  constructor(dataset, options = {}) {\n    if (dataset === true) {\n      const model = options;\n      this.center = model.center;\n      this.scale = model.scale;\n      this.means = model.means;\n      this.stdevs = model.stdevs;\n      this.U = Matrix.checkMatrix(model.U);\n      this.S = model.S;\n      this.R = model.R;\n      this.excludedFeatures = model.excludedFeatures || [];\n      return;\n    }\n\n    dataset = new Matrix(dataset);\n\n    const {\n      isCovarianceMatrix = false,\n      method = 'SVD',\n      nCompNIPALS = 2,\n      center = true,\n      scale = false,\n      ignoreZeroVariance = false,\n    } = options;\n\n    this.center = center;\n    this.scale = scale;\n    this.means = null;\n    this.stdevs = null;\n    this.excludedFeatures = [];\n\n    if (isCovarianceMatrix) {\n      // User provided a covariance matrix instead of dataset.\n      this._computeFromCovarianceMatrix(dataset);\n      return;\n    }\n\n    this._adjust(dataset, ignoreZeroVariance);\n    switch (method) {\n      case 'covarianceMatrix': {\n        // User provided a dataset but wants us to compute and use the covariance matrix.\n        const covarianceMatrix = new MatrixTransposeView(dataset)\n          .mmul(dataset)\n          .div(dataset.rows - 1);\n        this._computeFromCovarianceMatrix(covarianceMatrix);\n        break;\n      }\n      case 'NIPALS': {\n        this._computeWithNIPALS(dataset, nCompNIPALS);\n        break;\n      }\n      case 'SVD': {\n        const svd = new SVD(dataset, {\n          computeLeftSingularVectors: false,\n          computeRightSingularVectors: true,\n          autoTranspose: true,\n        });\n\n        this.U = svd.rightSingularVectors;\n\n        const singularValues = svd.diagonal;\n        const eigenvalues = [];\n        for (const singularValue of singularValues) {\n          eigenvalues.push((singularValue * singularValue) / (dataset.rows - 1));\n        }\n        this.S = eigenvalues;\n        break;\n      }\n      default: {\n        throw new Error(`unknown method: ${method}`);\n      }\n    }\n  }\n\n  /**\n   * Load a PCA model from JSON\n   * @param {Object} model\n   * @return {PCA}\n   */\n  static load(model) {\n    if (typeof model.name !== 'string') {\n      throw new TypeError('model must have a name property');\n    }\n    if (model.name !== 'PCA') {\n      throw new RangeError(`invalid model: ${model.name}`);\n    }\n    return new PCA(true, model);\n  }\n\n  /**\n   * Project the dataset into the PCA space\n   * @param {Matrix} dataset\n   * @param {Object} options\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  predict(dataset, options = {}) {\n    const { nComponents = this.U.columns } = options;\n    dataset = new Matrix(dataset);\n    if (this.center) {\n      dataset.subRowVector(this.means);\n      if (this.scale) {\n        for (let i of this.excludedFeatures) {\n          dataset.removeColumn(i);\n        }\n        dataset.divRowVector(this.stdevs);\n      }\n    }\n    var predictions = dataset.mmul(this.U);\n    return predictions.subMatrix(0, predictions.rows - 1, 0, nComponents - 1);\n  }\n\n  /**\n   * Calculates the inverse PCA transform\n   * @param {Matrix} dataset\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  invert(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n\n    var inverse = dataset.mmul(this.U.transpose());\n\n    if (this.center) {\n      if (this.scale) {\n        inverse.mulRowVector(this.stdevs);\n      }\n      inverse.addRowVector(this.means);\n    }\n\n    return inverse;\n  }\n\n\n  /**\n   * Returns the proportion of variance for each component\n   * @return {[number]}\n   */\n  getExplainedVariance() {\n    var sum = 0;\n    for (const s of this.S) {\n      sum += s;\n    }\n    return this.S.map((value) => value / sum);\n  }\n\n  /**\n   * Returns the cumulative proportion of variance\n   * @return {[number]}\n   */\n  getCumulativeVariance() {\n    var explained = this.getExplainedVariance();\n    for (var i = 1; i < explained.length; i++) {\n      explained[i] += explained[i - 1];\n    }\n    return explained;\n  }\n\n  /**\n   * Returns the Eigenvectors of the covariance matrix\n   * @returns {Matrix}\n   */\n  getEigenvectors() {\n    return this.U;\n  }\n\n  /**\n   * Returns the Eigenvalues (on the diagonal)\n   * @returns {[number]}\n   */\n  getEigenvalues() {\n    return this.S;\n  }\n\n  /**\n   * Returns the standard deviations of the principal components\n   * @returns {[number]}\n   */\n  getStandardDeviations() {\n    return this.S.map((x) => Math.sqrt(x));\n  }\n\n  /**\n   * Returns the loadings matrix\n   * @return {Matrix}\n   */\n  getLoadings() {\n    return this.U.transpose();\n  }\n\n  /**\n   * Export the current model to a JSON object\n   * @return {Object} model\n   */\n  toJSON() {\n    return {\n      name: 'PCA',\n      center: this.center,\n      scale: this.scale,\n      means: this.means,\n      stdevs: this.stdevs,\n      U: this.U,\n      S: this.S,\n      excludedFeatures: this.excludedFeatures,\n    };\n  }\n\n  _adjust(dataset, ignoreZeroVariance) {\n    if (this.center) {\n      const mean = dataset.mean('column');\n      const stdevs = this.scale\n        ? dataset.standardDeviation('column', { mean })\n        : null;\n      this.means = mean;\n      dataset.subRowVector(mean);\n      if (this.scale) {\n        for (let i = 0; i < stdevs.length; i++) {\n          if (stdevs[i] === 0) {\n            if (ignoreZeroVariance) {\n              dataset.removeColumn(i);\n              stdevs.splice(i, 1);\n              this.excludedFeatures.push(i);\n              i--;\n            } else {\n              throw new RangeError(\n                `Cannot scale the dataset (standard deviation is zero at index ${i}`,\n              );\n            }\n          }\n        }\n        this.stdevs = stdevs;\n        dataset.divRowVector(stdevs);\n      }\n    }\n  }\n\n  _computeFromCovarianceMatrix(dataset) {\n    const evd = new EVD(dataset, { assumeSymmetric: true });\n    this.U = evd.eigenvectorMatrix;\n    this.U.flipRows();\n    this.S = evd.realEigenvalues;\n    this.S.reverse();\n  }\n\n  _computeWithNIPALS(dataset, nCompNIPALS) {\n    this.U = new Matrix(nCompNIPALS, dataset.columns);\n    this.S = [];\n\n    let x = dataset;\n    for (let i = 0; i < nCompNIPALS; i++) {\n      let dc = new NIPALS(x);\n\n      this.U.setRow(i, dc.w.transpose());\n      this.S.push(Math.pow(dc.s.get(0, 0), 2));\n\n      x = dc.xResidual;\n    }\n    this.U = this.U.transpose(); // to be compatible with API\n  }\n}\n","export function squaredEuclidean(p, q) {\r\n    let d = 0;\r\n    for (let i = 0; i < p.length; i++) {\r\n        d += (p[i] - q[i]) * (p[i] - q[i]);\r\n    }\r\n    return d;\r\n}\r\nexport function euclidean(p, q) {\r\n    return Math.sqrt(squaredEuclidean(p, q));\r\n}\r\n","/**\n * Computes a distance/similarity matrix given an array of data and a distance/similarity function.\n * @param {Array} data An array of data\n * @param {function} distanceFn  A function that accepts two arguments and computes a distance/similarity between them\n * @return {Array<Array>} The distance/similarity matrix. The matrix is square and has a size equal to the length of\n * the data array\n */\nexport default function distanceMatrix(data, distanceFn) {\n  const result = getMatrix(data.length);\n\n  // Compute upper distance matrix\n  for (let i = 0; i < data.length; i++) {\n    for (let j = 0; j <= i; j++) {\n      result[i][j] = distanceFn(data[i], data[j]);\n      result[j][i] = result[i][j];\n    }\n  }\n\n  return result;\n}\n\nfunction getMatrix(size) {\n  const matrix = [];\n  for (let i = 0; i < size; i++) {\n    const row = [];\n    matrix.push(row);\n    for (let j = 0; j < size; j++) {\n      row.push(0);\n    }\n  }\n  return matrix;\n}\n","// Generated by CoffeeScript 1.8.0\n(function() {\n  var Heap, defaultCmp, floor, heapify, heappop, heappush, heappushpop, heapreplace, insort, min, nlargest, nsmallest, updateItem, _siftdown, _siftup;\n\n  floor = Math.floor, min = Math.min;\n\n\n  /*\n  Default comparison function to be used\n   */\n\n  defaultCmp = function(x, y) {\n    if (x < y) {\n      return -1;\n    }\n    if (x > y) {\n      return 1;\n    }\n    return 0;\n  };\n\n\n  /*\n  Insert item x in list a, and keep it sorted assuming a is sorted.\n  \n  If x is already in a, insert it to the right of the rightmost x.\n  \n  Optional args lo (default 0) and hi (default a.length) bound the slice\n  of a to be searched.\n   */\n\n  insort = function(a, x, lo, hi, cmp) {\n    var mid;\n    if (lo == null) {\n      lo = 0;\n    }\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (lo < 0) {\n      throw new Error('lo must be non-negative');\n    }\n    if (hi == null) {\n      hi = a.length;\n    }\n    while (lo < hi) {\n      mid = floor((lo + hi) / 2);\n      if (cmp(x, a[mid]) < 0) {\n        hi = mid;\n      } else {\n        lo = mid + 1;\n      }\n    }\n    return ([].splice.apply(a, [lo, lo - lo].concat(x)), x);\n  };\n\n\n  /*\n  Push item onto heap, maintaining the heap invariant.\n   */\n\n  heappush = function(array, item, cmp) {\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    array.push(item);\n    return _siftdown(array, 0, array.length - 1, cmp);\n  };\n\n\n  /*\n  Pop the smallest item off the heap, maintaining the heap invariant.\n   */\n\n  heappop = function(array, cmp) {\n    var lastelt, returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    lastelt = array.pop();\n    if (array.length) {\n      returnitem = array[0];\n      array[0] = lastelt;\n      _siftup(array, 0, cmp);\n    } else {\n      returnitem = lastelt;\n    }\n    return returnitem;\n  };\n\n\n  /*\n  Pop and return the current smallest value, and add the new item.\n  \n  This is more efficient than heappop() followed by heappush(), and can be\n  more appropriate when using a fixed size heap. Note that the value\n  returned may be larger than item! That constrains reasonable use of\n  this routine unless written as part of a conditional replacement:\n      if item > array[0]\n        item = heapreplace(array, item)\n   */\n\n  heapreplace = function(array, item, cmp) {\n    var returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    returnitem = array[0];\n    array[0] = item;\n    _siftup(array, 0, cmp);\n    return returnitem;\n  };\n\n\n  /*\n  Fast version of a heappush followed by a heappop.\n   */\n\n  heappushpop = function(array, item, cmp) {\n    var _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (array.length && cmp(array[0], item) < 0) {\n      _ref = [array[0], item], item = _ref[0], array[0] = _ref[1];\n      _siftup(array, 0, cmp);\n    }\n    return item;\n  };\n\n\n  /*\n  Transform list into a heap, in-place, in O(array.length) time.\n   */\n\n  heapify = function(array, cmp) {\n    var i, _i, _j, _len, _ref, _ref1, _results, _results1;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    _ref1 = (function() {\n      _results1 = [];\n      for (var _j = 0, _ref = floor(array.length / 2); 0 <= _ref ? _j < _ref : _j > _ref; 0 <= _ref ? _j++ : _j--){ _results1.push(_j); }\n      return _results1;\n    }).apply(this).reverse();\n    _results = [];\n    for (_i = 0, _len = _ref1.length; _i < _len; _i++) {\n      i = _ref1[_i];\n      _results.push(_siftup(array, i, cmp));\n    }\n    return _results;\n  };\n\n\n  /*\n  Update the position of the given item in the heap.\n  This function should be called every time the item is being modified.\n   */\n\n  updateItem = function(array, item, cmp) {\n    var pos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    pos = array.indexOf(item);\n    if (pos === -1) {\n      return;\n    }\n    _siftdown(array, 0, pos, cmp);\n    return _siftup(array, pos, cmp);\n  };\n\n\n  /*\n  Find the n largest elements in a dataset.\n   */\n\n  nlargest = function(array, n, cmp) {\n    var elem, result, _i, _len, _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    result = array.slice(0, n);\n    if (!result.length) {\n      return result;\n    }\n    heapify(result, cmp);\n    _ref = array.slice(n);\n    for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n      elem = _ref[_i];\n      heappushpop(result, elem, cmp);\n    }\n    return result.sort(cmp).reverse();\n  };\n\n\n  /*\n  Find the n smallest elements in a dataset.\n   */\n\n  nsmallest = function(array, n, cmp) {\n    var elem, i, los, result, _i, _j, _len, _ref, _ref1, _results;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (n * 10 <= array.length) {\n      result = array.slice(0, n).sort(cmp);\n      if (!result.length) {\n        return result;\n      }\n      los = result[result.length - 1];\n      _ref = array.slice(n);\n      for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n        elem = _ref[_i];\n        if (cmp(elem, los) < 0) {\n          insort(result, elem, 0, null, cmp);\n          result.pop();\n          los = result[result.length - 1];\n        }\n      }\n      return result;\n    }\n    heapify(array, cmp);\n    _results = [];\n    for (i = _j = 0, _ref1 = min(n, array.length); 0 <= _ref1 ? _j < _ref1 : _j > _ref1; i = 0 <= _ref1 ? ++_j : --_j) {\n      _results.push(heappop(array, cmp));\n    }\n    return _results;\n  };\n\n  _siftdown = function(array, startpos, pos, cmp) {\n    var newitem, parent, parentpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    newitem = array[pos];\n    while (pos > startpos) {\n      parentpos = (pos - 1) >> 1;\n      parent = array[parentpos];\n      if (cmp(newitem, parent) < 0) {\n        array[pos] = parent;\n        pos = parentpos;\n        continue;\n      }\n      break;\n    }\n    return array[pos] = newitem;\n  };\n\n  _siftup = function(array, pos, cmp) {\n    var childpos, endpos, newitem, rightpos, startpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    endpos = array.length;\n    startpos = pos;\n    newitem = array[pos];\n    childpos = 2 * pos + 1;\n    while (childpos < endpos) {\n      rightpos = childpos + 1;\n      if (rightpos < endpos && !(cmp(array[childpos], array[rightpos]) < 0)) {\n        childpos = rightpos;\n      }\n      array[pos] = array[childpos];\n      pos = childpos;\n      childpos = 2 * pos + 1;\n    }\n    array[pos] = newitem;\n    return _siftdown(array, startpos, pos, cmp);\n  };\n\n  Heap = (function() {\n    Heap.push = heappush;\n\n    Heap.pop = heappop;\n\n    Heap.replace = heapreplace;\n\n    Heap.pushpop = heappushpop;\n\n    Heap.heapify = heapify;\n\n    Heap.updateItem = updateItem;\n\n    Heap.nlargest = nlargest;\n\n    Heap.nsmallest = nsmallest;\n\n    function Heap(cmp) {\n      this.cmp = cmp != null ? cmp : defaultCmp;\n      this.nodes = [];\n    }\n\n    Heap.prototype.push = function(x) {\n      return heappush(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pop = function() {\n      return heappop(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.peek = function() {\n      return this.nodes[0];\n    };\n\n    Heap.prototype.contains = function(x) {\n      return this.nodes.indexOf(x) !== -1;\n    };\n\n    Heap.prototype.replace = function(x) {\n      return heapreplace(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pushpop = function(x) {\n      return heappushpop(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.heapify = function() {\n      return heapify(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.updateItem = function(x) {\n      return updateItem(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.clear = function() {\n      return this.nodes = [];\n    };\n\n    Heap.prototype.empty = function() {\n      return this.nodes.length === 0;\n    };\n\n    Heap.prototype.size = function() {\n      return this.nodes.length;\n    };\n\n    Heap.prototype.clone = function() {\n      var heap;\n      heap = new Heap();\n      heap.nodes = this.nodes.slice(0);\n      return heap;\n    };\n\n    Heap.prototype.toArray = function() {\n      return this.nodes.slice(0);\n    };\n\n    Heap.prototype.insert = Heap.prototype.push;\n\n    Heap.prototype.top = Heap.prototype.peek;\n\n    Heap.prototype.front = Heap.prototype.peek;\n\n    Heap.prototype.has = Heap.prototype.contains;\n\n    Heap.prototype.copy = Heap.prototype.clone;\n\n    return Heap;\n\n  })();\n\n  (function(root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      return define([], factory);\n    } else if (typeof exports === 'object') {\n      return module.exports = factory();\n    } else {\n      return root.Heap = factory();\n    }\n  })(this, function() {\n    return Heap;\n  });\n\n}).call(this);\n","module.exports = require('./lib/heap');\n","import Heap from 'heap';\n\nexport default class Cluster {\n  constructor() {\n    this.children = [];\n    this.height = 0;\n    this.size = 1;\n    this.index = -1;\n    this.isLeaf = false;\n  }\n\n  /**\n   * Creates an array of clusters where the maximum height is smaller than the threshold\n   * @param {number} threshold\n   * @return {Array<Cluster>}\n   */\n  cut(threshold) {\n    if (typeof threshold !== 'number') {\n      throw new TypeError('threshold must be a number');\n    }\n    if (threshold < 0) {\n      throw new RangeError('threshold must be a positive number');\n    }\n    let list = [this];\n    const ans = [];\n    while (list.length > 0) {\n      const aux = list.shift();\n      if (threshold >= aux.height) {\n        ans.push(aux);\n      } else {\n        list = list.concat(aux.children);\n      }\n    }\n    return ans;\n  }\n\n  /**\n   * Merge the leaves in the minimum way to have `groups` number of clusters.\n   * @param {number} groups - Them number of children the first level of the tree should have.\n   * @return {Cluster}\n   */\n  group(groups) {\n    if (!Number.isInteger(groups) || groups < 1) {\n      throw new RangeError('groups must be a positive integer');\n    }\n\n    const heap = new Heap((a, b) => {\n      return b.height - a.height;\n    });\n\n    heap.push(this);\n\n    while (heap.size() < groups) {\n      var first = heap.pop();\n      if (first.children.length === 0) {\n        break;\n      }\n      first.children.forEach((child) => heap.push(child));\n    }\n\n    var root = new Cluster();\n    root.children = heap.toArray();\n    root.height = this.height;\n\n    return root;\n  }\n\n  /**\n   * Traverses the tree depth-first and calls the provided callback with each individual node\n   * @param {function} cb - The callback to be called on each node encounter\n   */\n  traverse(cb) {\n    function visit(root, callback) {\n      callback(root);\n      if (root.children) {\n        for (const child of root.children) {\n          visit(child, callback);\n        }\n      }\n    }\n    visit(this, cb);\n  }\n\n  /**\n   * Returns a list of indices for all the leaves of this cluster.\n   * The list is ordered in such a way that a dendrogram could be drawn without crossing branches.\n   * @returns {Array<number>}\n   */\n  indices() {\n    const result = [];\n    this.traverse((cluster) => {\n      if (cluster.isLeaf) {\n        result.push(cluster.index);\n      }\n    });\n    return result;\n  }\n}\n","import { euclidean } from 'ml-distance-euclidean';\nimport getDistanceMatrix from 'ml-distance-matrix';\nimport { Matrix } from 'ml-matrix';\n\nimport Cluster from './Cluster';\n\nfunction singleLink(dKI, dKJ) {\n  return Math.min(dKI, dKJ);\n}\n\nfunction completeLink(dKI, dKJ) {\n  return Math.max(dKI, dKJ);\n}\n\nfunction averageLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  return ai * dKI + aj * dKJ;\n}\n\nfunction weightedAverageLink(dKI, dKJ) {\n  return (dKI + dKJ) / 2;\n}\n\nfunction centroidLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  const b = -(ni * nj) / (ni + nj) ** 2;\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction medianLink(dKI, dKJ, dIJ) {\n  return dKI / 2 + dKJ / 2 - dIJ / 4;\n}\n\nfunction wardLink(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction wardLink2(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return Math.sqrt(ai * dKI * dKI + aj * dKJ * dKJ + b * dIJ * dIJ);\n}\n\n/**\n * Continuously merge nodes that have the least dissimilarity\n * @param {Array<Array<number>>} data - Array of points to be clustered\n * @param {object} [options]\n * @param {Function} [options.distanceFunction]\n * @param {string} [options.method] - Default: `'complete'`\n * @param {boolean} [options.isDistanceMatrix] - Is the input already a distance matrix?\n * @constructor\n */\nexport function agnes(data, options = {}) {\n  const {\n    distanceFunction = euclidean,\n    method = 'complete',\n    isDistanceMatrix = false,\n  } = options;\n\n  let updateFunc;\n  if (!isDistanceMatrix) {\n    data = getDistanceMatrix(data, distanceFunction);\n  }\n  let distanceMatrix = new Matrix(data);\n  const numLeaves = distanceMatrix.rows;\n\n  // allows to use a string or a given function\n  if (typeof method === 'string') {\n    switch (method.toLowerCase()) {\n      case 'single':\n        updateFunc = singleLink;\n        break;\n      case 'complete':\n        updateFunc = completeLink;\n        break;\n      case 'average':\n      case 'upgma':\n        updateFunc = averageLink;\n        break;\n      case 'wpgma':\n        updateFunc = weightedAverageLink;\n        break;\n      case 'centroid':\n      case 'upgmc':\n        updateFunc = centroidLink;\n        break;\n      case 'median':\n      case 'wpgmc':\n        updateFunc = medianLink;\n        break;\n      case 'ward':\n        updateFunc = wardLink;\n        break;\n      case 'ward2':\n        updateFunc = wardLink2;\n        break;\n      default:\n        throw new RangeError(`unknown clustering method: ${method}`);\n    }\n  } else if (typeof method !== 'function') {\n    throw new TypeError('method must be a string or function');\n  }\n\n  let clusters = [];\n  for (let i = 0; i < numLeaves; i++) {\n    const cluster = new Cluster();\n    cluster.isLeaf = true;\n    cluster.index = i;\n    clusters.push(cluster);\n  }\n\n  for (let n = 0; n < numLeaves - 1; n++) {\n    const [row, column, distance] = getSmallestDistance(distanceMatrix);\n    const cluster1 = clusters[row];\n    const cluster2 = clusters[column];\n    const newCluster = new Cluster();\n    newCluster.size = cluster1.size + cluster2.size;\n    newCluster.children.push(cluster1, cluster2);\n    newCluster.height = distance;\n\n    const newClusters = [newCluster];\n    const newDistanceMatrix = new Matrix(\n      distanceMatrix.rows - 1,\n      distanceMatrix.rows - 1,\n    );\n    const previous = (newIndex) =>\n      getPreviousIndex(newIndex, Math.min(row, column), Math.max(row, column));\n\n    for (let i = 1; i < newDistanceMatrix.rows; i++) {\n      const prevI = previous(i);\n      const prevICluster = clusters[prevI];\n      newClusters.push(prevICluster);\n      for (let j = 0; j < i; j++) {\n        if (j === 0) {\n          const dKI = distanceMatrix.get(row, prevI);\n          const dKJ = distanceMatrix.get(prevI, column);\n          const val = updateFunc(\n            dKI,\n            dKJ,\n            distance,\n            cluster1.size,\n            cluster2.size,\n            prevICluster.size,\n          );\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        } else {\n          // Just copy distance from previous matrix\n          const val = distanceMatrix.get(prevI, previous(j));\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        }\n      }\n    }\n\n    clusters = newClusters;\n    distanceMatrix = newDistanceMatrix;\n  }\n\n  return clusters[0];\n}\n\nfunction getSmallestDistance(distance) {\n  let smallest = Infinity;\n  let smallestI = 0;\n  let smallestJ = 0;\n  for (let i = 1; i < distance.rows; i++) {\n    for (let j = 0; j < i; j++) {\n      if (distance.get(i, j) < smallest) {\n        smallest = distance.get(i, j);\n        smallestI = i;\n        smallestJ = j;\n      }\n    }\n  }\n  return [smallestI, smallestJ, smallest];\n}\n\nfunction getPreviousIndex(newIndex, prev1, prev2) {\n  newIndex -= 1;\n  if (newIndex >= prev1) newIndex++;\n  if (newIndex >= prev2) newIndex++;\n  return newIndex;\n}\n","'use strict';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nconst defaultOptions = {\n    distanceFunction: squaredEuclidean\n};\nexport default function nearestVector(listVectors, vector, options = defaultOptions) {\n    const distanceFunction = options.distanceFunction || defaultOptions.distanceFunction;\n    const similarityFunction = options.similarityFunction || defaultOptions.similarityFunction;\n    let vectorIndex = -1;\n    if (typeof similarityFunction === 'function') {\n        // maximum similarity\n        let maxSim = Number.MIN_VALUE;\n        for (let j = 0; j < listVectors.length; j++) {\n            const sim = similarityFunction(vector, listVectors[j]);\n            if (sim > maxSim) {\n                maxSim = sim;\n                vectorIndex = j;\n            }\n        }\n    }\n    else if (typeof distanceFunction === 'function') {\n        // minimum distance\n        let minDist = Number.MAX_VALUE;\n        for (let i = 0; i < listVectors.length; i++) {\n            const dist = distanceFunction(vector, listVectors[i]);\n            if (dist < minDist) {\n                minDist = dist;\n                vectorIndex = i;\n            }\n        }\n    }\n    else {\n        throw new Error(\"A similarity or distance function it's required\");\n    }\n    return vectorIndex;\n}\nexport function findNearestVector(vectorList, vector, options = defaultOptions) {\n    const index = nearestVector(vectorList, vector, options);\n    return vectorList[index];\n}\n","import nearestVector from 'ml-nearest-vector';\n\n/**\n * Calculates the distance matrix for a given array of points\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {function} distance - Distance function to use between the points\n * @return {Array<Array<number>>} - matrix with the distance values\n */\nexport function calculateDistanceMatrix(data, distance) {\n  var distanceMatrix = new Array(data.length);\n  for (var i = 0; i < data.length; ++i) {\n    for (var j = i; j < data.length; ++j) {\n      if (!distanceMatrix[i]) {\n        distanceMatrix[i] = new Array(data.length);\n      }\n      if (!distanceMatrix[j]) {\n        distanceMatrix[j] = new Array(data.length);\n      }\n      const dist = distance(data[i], data[j]);\n      distanceMatrix[i][j] = dist;\n      distanceMatrix[j][i] = dist;\n    }\n  }\n  return distanceMatrix;\n}\n\n/**\n * Updates the cluster identifier based in the new data\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {function} distance - Distance function to use between the points\n * @return {Array} the cluster identifier for each data dot\n */\nexport function updateClusterID(data, centers, clusterID, distance) {\n  for (var i = 0; i < data.length; i++) {\n    clusterID[i] = nearestVector(centers, data[i], {\n      distanceFunction: distance\n    });\n  }\n  return clusterID;\n}\n\n/**\n * Update the center values based in the new configurations of the clusters\n * @ignore\n * @param {Array<Array<number>>} prevCenters - Centroids from the previous iteration\n * @param {Array <Array <number>>} data - the [x,y,z,...] points to cluster\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @return {Array} he K centers in format [x,y,z,...]\n */\nexport function updateCenters(prevCenters, data, clusterID, K) {\n  const nDim = data[0].length;\n\n  // copy previous centers\n  var centers = new Array(K);\n  var centersLen = new Array(K);\n  for (var i = 0; i < K; i++) {\n    centers[i] = new Array(nDim);\n    centersLen[i] = 0;\n    for (var j = 0; j < nDim; j++) {\n      centers[i][j] = 0;\n    }\n  }\n\n  // add the value for all dimensions of the point\n  for (var l = 0; l < data.length; l++) {\n    centersLen[clusterID[l]]++;\n    for (var dim = 0; dim < nDim; dim++) {\n      centers[clusterID[l]][dim] += data[l][dim];\n    }\n  }\n\n  // divides by length\n  for (var id = 0; id < K; id++) {\n    for (var d = 0; d < nDim; d++) {\n      if (centersLen[id]) {\n        centers[id][d] /= centersLen[id];\n      } else {\n        centers[id][d] = prevCenters[id][d];\n      }\n    }\n  }\n  return centers;\n}\n\n/**\n * The centers have moved more than the tolerance value?\n * @ignore\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} oldCenters - the K old centers in format [x,y,z,...]\n * @param {function} distanceFunction - Distance function to use between the points\n * @param {number} tolerance - Allowed distance for the centroids to move\n * @return {boolean}\n */\nexport function hasConverged(centers, oldCenters, distanceFunction, tolerance) {\n  for (var i = 0; i < centers.length; i++) {\n    if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {\n      return false;\n    }\n  }\n  return true;\n}\n","const LOOP = 8;\nconst FLOAT_MUL = 1 / 16777216;\nconst sh1 = 15;\nconst sh2 = 18;\nconst sh3 = 11;\nfunction multiply_uint32(n, m) {\n    n >>>= 0;\n    m >>>= 0;\n    const nlo = n & 0xffff;\n    const nhi = n - nlo;\n    return (((nhi * m) >>> 0) + nlo * m) >>> 0;\n}\nexport default class XSadd {\n    constructor(seed = Date.now()) {\n        this.state = new Uint32Array(4);\n        this.init(seed);\n        this.random = this.getFloat.bind(this);\n    }\n    /**\n     * Returns a 32-bit integer r (0 <= r < 2^32)\n     */\n    getUint32() {\n        this.nextState();\n        return (this.state[3] + this.state[2]) >>> 0;\n    }\n    /**\n     * Returns a floating point number r (0.0 <= r < 1.0)\n     */\n    getFloat() {\n        return (this.getUint32() >>> 8) * FLOAT_MUL;\n    }\n    init(seed) {\n        if (!Number.isInteger(seed)) {\n            throw new TypeError('seed must be an integer');\n        }\n        this.state[0] = seed;\n        this.state[1] = 0;\n        this.state[2] = 0;\n        this.state[3] = 0;\n        for (let i = 1; i < LOOP; i++) {\n            this.state[i & 3] ^=\n                (i +\n                    multiply_uint32(1812433253, this.state[(i - 1) & 3] ^ ((this.state[(i - 1) & 3] >>> 30) >>> 0))) >>>\n                    0;\n        }\n        this.periodCertification();\n        for (let i = 0; i < LOOP; i++) {\n            this.nextState();\n        }\n    }\n    periodCertification() {\n        if (this.state[0] === 0 &&\n            this.state[1] === 0 &&\n            this.state[2] === 0 &&\n            this.state[3] === 0) {\n            this.state[0] = 88; // X\n            this.state[1] = 83; // S\n            this.state[2] = 65; // A\n            this.state[3] = 68; // D\n        }\n    }\n    nextState() {\n        let t = this.state[0];\n        t ^= t << sh1;\n        t ^= t >>> sh2;\n        t ^= this.state[3] << sh3;\n        this.state[0] = this.state[1];\n        this.state[1] = this.state[2];\n        this.state[2] = this.state[3];\n        this.state[3] = t;\n    }\n}\n","const PROB_TOLERANCE = 0.00000001;\nfunction randomChoice(values, options = {}, random = Math.random) {\n    const { size = 1, replace = false, probabilities } = options;\n    let valuesArr;\n    let cumSum;\n    if (typeof values === 'number') {\n        valuesArr = getArray(values);\n    }\n    else {\n        valuesArr = values.slice();\n    }\n    if (probabilities) {\n        if (!replace) {\n            throw new Error('choice with probabilities and no replacement is not implemented');\n        }\n        // check input is sane\n        if (probabilities.length !== valuesArr.length) {\n            throw new Error('the length of probabilities option should be equal to the number of choices');\n        }\n        cumSum = [probabilities[0]];\n        for (let i = 1; i < probabilities.length; i++) {\n            cumSum[i] = cumSum[i - 1] + probabilities[i];\n        }\n        if (Math.abs(1 - cumSum[cumSum.length - 1]) > PROB_TOLERANCE) {\n            throw new Error(`probabilities should sum to 1, but instead sums to ${cumSum[cumSum.length - 1]}`);\n        }\n    }\n    if (replace === false && size > valuesArr.length) {\n        throw new Error('size option is too large');\n    }\n    const result = [];\n    for (let i = 0; i < size; i++) {\n        const index = randomIndex(valuesArr.length, random, cumSum);\n        result.push(valuesArr[index]);\n        if (!replace) {\n            valuesArr.splice(index, 1);\n        }\n    }\n    return result;\n}\nfunction getArray(n) {\n    const arr = [];\n    for (let i = 0; i < n; i++) {\n        arr.push(i);\n    }\n    return arr;\n}\nfunction randomIndex(n, random, cumSum) {\n    const rand = random();\n    if (!cumSum) {\n        return Math.floor(rand * n);\n    }\n    else {\n        let idx = 0;\n        while (rand > cumSum[idx]) {\n            idx++;\n        }\n        return idx;\n    }\n}\nexport default randomChoice;\n","// tslint:disable-next-line\nimport XSAdd from 'ml-xsadd';\nimport choice from './choice';\n/**\n * @classdesc Random class\n */\nexport default class Random {\n    /**\n     * @param [seedOrRandom=Math.random] - Control the random number generator used by the Random class instance. Pass a random number generator function with a uniform distribution over the half-open interval [0, 1[. If seed will pass it to ml-xsadd to create a seeded random number generator. If undefined will use Math.random.\n     */\n    constructor(seedOrRandom = Math.random) {\n        if (typeof seedOrRandom === 'number') {\n            const xsadd = new XSAdd(seedOrRandom);\n            this.randomGenerator = xsadd.random;\n        }\n        else {\n            this.randomGenerator = seedOrRandom;\n        }\n    }\n    choice(values, options) {\n        if (typeof values === 'number') {\n            return choice(values, options, this.randomGenerator);\n        }\n        return choice(values, options, this.randomGenerator);\n    }\n    /**\n     * Draw a random number from a uniform distribution on [0,1)\n     * @return The random number\n     */\n    random() {\n        return this.randomGenerator();\n    }\n    /**\n     * Draw a random integer from a uniform distribution on [low, high). If only low is specified, the number is drawn on [0, low)\n     * @param low - The lower bound of the uniform distribution interval.\n     * @param high - The higher bound of the uniform distribution interval.\n     */\n    randInt(low, high) {\n        if (high === undefined) {\n            high = low;\n            low = 0;\n        }\n        return low + Math.floor(this.randomGenerator() * (high - low));\n    }\n    /**\n     * Draw several random number from a uniform distribution on [0, 1)\n     * @param size - The number of number to draw\n     * @return - The list of drawn numbers.\n     */\n    randomSample(size) {\n        const result = [];\n        for (let i = 0; i < size; i++) {\n            result.push(this.random());\n        }\n        return result;\n    }\n}\n","import Random from 'ml-random';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nimport { Matrix } from 'ml-matrix';\n\n/**\n * Choose K different random points from the original data\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function random(data, K, seed) {\n  const random = new Random(seed);\n  return random.choice(data, { size: K });\n}\n\n/**\n * Chooses the most distant points to a first random pick\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {Array<Array<number>>} distanceMatrix - matrix with the distance values\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function mostDistant(data, K, distanceMatrix, seed) {\n  const random = new Random(seed);\n  var ans = new Array(K);\n  // chooses a random point as initial cluster\n  ans[0] = Math.floor(random.random() * data.length);\n\n  if (K > 1) {\n    // chooses the more distant point\n    var maxDist = { dist: -1, index: -1 };\n    for (var l = 0; l < data.length; ++l) {\n      if (distanceMatrix[ans[0]][l] > maxDist.dist) {\n        maxDist.dist = distanceMatrix[ans[0]][l];\n        maxDist.index = l;\n      }\n    }\n    ans[1] = maxDist.index;\n\n    if (K > 2) {\n      // chooses the set of points that maximises the min distance\n      for (var k = 2; k < K; ++k) {\n        var center = { dist: -1, index: -1 };\n        for (var m = 0; m < data.length; ++m) {\n          // minimum distance to centers\n          var minDistCent = { dist: Number.MAX_VALUE, index: -1 };\n          for (var n = 0; n < k; ++n) {\n            if (\n              distanceMatrix[n][m] < minDistCent.dist &&\n              ans.indexOf(m) === -1\n            ) {\n              minDistCent = {\n                dist: distanceMatrix[n][m],\n                index: m\n              };\n            }\n          }\n\n          if (\n            minDistCent.dist !== Number.MAX_VALUE &&\n            minDistCent.dist > center.dist\n          ) {\n            center = Object.assign({}, minDistCent);\n          }\n        }\n\n        ans[k] = center.index;\n      }\n    }\n  }\n\n  return ans.map((index) => data[index]);\n}\n\n// Implementation inspired from scikit\nexport function kmeanspp(X, K, options = {}) {\n  X = new Matrix(X);\n  const nSamples = X.rows;\n  const random = new Random(options.seed);\n  // Set the number of trials\n  const centers = [];\n  const localTrials = options.localTrials || 2 + Math.floor(Math.log(K));\n\n  // Pick the first center at random from the dataset\n  const firstCenterIdx = random.randInt(nSamples);\n  centers.push(X.getRow(firstCenterIdx));\n\n  // Init closest distances\n  let closestDistSquared = new Matrix(1, X.rows);\n  for (let i = 0; i < X.rows; i++) {\n    closestDistSquared.set(0, i, squaredEuclidean(X.getRow(i), centers[0]));\n  }\n  let cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n  const factor = 1 / cumSumClosestDistSquared[0][nSamples - 1];\n  let probabilities = Matrix.mul(closestDistSquared, factor);\n\n  // Iterate over the remaining centers\n  for (let i = 1; i < K; i++) {\n    const candidateIdx = random.choice(nSamples, {\n      replace: true,\n      size: localTrials,\n      probabilities: probabilities[0]\n    });\n\n    const candidates = X.selection(candidateIdx, range(X.columns));\n    const distanceToCandidates = euclideanDistances(candidates, X);\n\n    let bestCandidate;\n    let bestPot;\n    let bestDistSquared;\n\n    for (let j = 0; j < localTrials; j++) {\n      const newDistSquared = Matrix.min(closestDistSquared, [distanceToCandidates.getRow(j)]);\n      const newPot = newDistSquared.sum();\n      if (bestCandidate === undefined || newPot < bestPot) {\n        bestCandidate = candidateIdx[j];\n        bestPot = newPot;\n        bestDistSquared = newDistSquared;\n      }\n    }\n    centers[i] = X.getRow(bestCandidate);\n    closestDistSquared = bestDistSquared;\n    cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n    probabilities = Matrix.mul(\n      closestDistSquared,\n      1 / cumSumClosestDistSquared[0][nSamples - 1]\n    );\n  }\n  return centers;\n}\n\nfunction euclideanDistances(A, B) {\n  const result = new Matrix(A.rows, B.rows);\n  for (let i = 0; i < A.rows; i++) {\n    for (let j = 0; j < B.rows; j++) {\n      result.set(i, j, squaredEuclidean(A.getRow(i), B.getRow(j)));\n    }\n  }\n  return result;\n}\n\nfunction range(l) {\n  let r = [];\n  for (let i = 0; i < l; i++) {\n    r.push(i);\n  }\n  return r;\n}\n\nfunction cumSum(arr) {\n  let cumSum = [arr[0]];\n  for (let i = 1; i < arr.length; i++) {\n    cumSum[i] = cumSum[i - 1] + arr[i];\n  }\n  return cumSum;\n}\n","import { updateClusterID } from './utils';\n\nconst distanceSymbol = Symbol('distance');\n\nexport default class KMeansResult {\n  /**\n   * Result of the kmeans algorithm\n   * @param {Array<number>} clusters - the cluster identifier for each data dot\n   * @param {Array<Array<object>>} centroids - the K centers in format [x,y,z,...], the error and size of the cluster\n   * @param {boolean} converged - Converge criteria satisfied\n   * @param {number} iterations - Current number of iterations\n   * @param {function} distance - (*Private*) Distance function to use between the points\n   * @constructor\n   */\n  constructor(clusters, centroids, converged, iterations, distance) {\n    this.clusters = clusters;\n    this.centroids = centroids;\n    this.converged = converged;\n    this.iterations = iterations;\n    this[distanceSymbol] = distance;\n  }\n\n  /**\n   * Allows to compute for a new array of points their cluster id\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {Array<number>} - cluster id for each point\n   */\n  nearest(data) {\n    const clusterID = new Array(data.length);\n    const centroids = this.centroids.map(function (centroid) {\n      return centroid.centroid;\n    });\n    return updateClusterID(data, centroids, clusterID, this[distanceSymbol]);\n  }\n\n  /**\n   * Returns a KMeansResult with the error and size of the cluster\n   * @ignore\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {KMeansResult}\n   */\n  computeInformation(data) {\n    var enrichedCentroids = this.centroids.map(function (centroid) {\n      return {\n        centroid: centroid,\n        error: 0,\n        size: 0\n      };\n    });\n\n    for (var i = 0; i < data.length; i++) {\n      enrichedCentroids[this.clusters[i]].error += this[distanceSymbol](\n        data[i],\n        this.centroids[this.clusters[i]]\n      );\n      enrichedCentroids[this.clusters[i]].size++;\n    }\n\n    for (var j = 0; j < this.centroids.length; j++) {\n      if (enrichedCentroids[j].size) {\n        enrichedCentroids[j].error /= enrichedCentroids[j].size;\n      } else {\n        enrichedCentroids[j].error = null;\n      }\n    }\n\n    return new KMeansResult(\n      this.clusters,\n      enrichedCentroids,\n      this.converged,\n      this.iterations,\n      this[distanceSymbol]\n    );\n  }\n}\n","import { squaredEuclidean } from 'ml-distance-euclidean';\n\nimport {\n  updateClusterID,\n  updateCenters,\n  hasConverged,\n  calculateDistanceMatrix\n} from './utils';\nimport { mostDistant, random, kmeanspp } from './initialization';\nimport KMeansResult from './KMeansResult';\n\nconst defaultOptions = {\n  maxIterations: 100,\n  tolerance: 1e-6,\n  withIterations: false,\n  initialization: 'kmeans++',\n  distanceFunction: squaredEuclidean\n};\n\n/**\n * Each step operation for kmeans\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} iterations - Current number of iterations\n * @return {KMeansResult}\n */\nfunction step(centers, data, clusterID, K, options, iterations) {\n  clusterID = updateClusterID(\n    data,\n    centers,\n    clusterID,\n    options.distanceFunction\n  );\n  var newCenters = updateCenters(centers, data, clusterID, K);\n  var converged = hasConverged(\n    newCenters,\n    centers,\n    options.distanceFunction,\n    options.tolerance\n  );\n  return new KMeansResult(\n    clusterID,\n    newCenters,\n    converged,\n    iterations,\n    options.distanceFunction\n  );\n}\n\n/**\n * Generator version for the algorithm\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n */\nfunction* kmeansGenerator(centers, data, clusterID, K, options) {\n  var converged = false;\n  var stepNumber = 0;\n  var stepResult;\n  while (!converged && stepNumber < options.maxIterations) {\n    stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n    yield stepResult.computeInformation(data);\n    converged = stepResult.converged;\n    centers = stepResult.centroids;\n  }\n}\n\n/**\n * K-means algorithm\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} [options.maxIterations = 100] - Maximum of iterations allowed\n * @param {number} [options.tolerance = 1e-6] - Error tolerance\n * @param {boolean} [options.withIterations = false] - Store clusters and centroids for each iteration\n * @param {function} [options.distanceFunction = squaredDistance] - Distance function to use between the points\n * @param {number} [options.seed] - Seed for random initialization.\n * @param {string|Array<Array<number>>} [options.initialization = 'kmeans++'] - K centers in format [x,y,z,...] or a method for initialize the data:\n *  * You can either specify your custom start centroids, or select one of the following initialization method:\n *  * `'kmeans++'` will use the kmeans++ method as described by http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf\n *  * `'random'` will choose K random different values.\n *  * `'mostDistant'` will choose the more distant points to a first random pick\n * @return {KMeansResult} - Cluster identifier for each data dot and centroids with the following fields:\n *  * `'clusters'`: Array of indexes for the clusters.\n *  * `'centroids'`: Array with the resulting centroids.\n *  * `'iterations'`: Number of iterations that took to converge\n */\nexport default function kmeans(data, K, options) {\n  options = Object.assign({}, defaultOptions, options);\n\n  if (K <= 0 || K > data.length || !Number.isInteger(K)) {\n    throw new Error(\n      'K should be a positive integer smaller than the number of points'\n    );\n  }\n\n  var centers;\n  if (Array.isArray(options.initialization)) {\n    if (options.initialization.length !== K) {\n      throw new Error('The initial centers should have the same length as K');\n    } else {\n      centers = options.initialization;\n    }\n  } else {\n    switch (options.initialization) {\n      case 'kmeans++':\n        centers = kmeanspp(data, K, options);\n        break;\n      case 'random':\n        centers = random(data, K, options.seed);\n        break;\n      case 'mostDistant':\n        centers = mostDistant(\n          data,\n          K,\n          calculateDistanceMatrix(data, options.distanceFunction),\n          options.seed\n        );\n        break;\n      default:\n        throw new Error(\n          `Unknown initialization method: \"${options.initialization}\"`\n        );\n    }\n  }\n\n  // infinite loop until convergence\n  if (options.maxIterations === 0) {\n    options.maxIterations = Number.MAX_VALUE;\n  }\n\n  var clusterID = new Array(data.length);\n  if (options.withIterations) {\n    return kmeansGenerator(centers, data, clusterID, K, options);\n  } else {\n    var converged = false;\n    var stepNumber = 0;\n    var stepResult;\n    while (!converged && stepNumber < options.maxIterations) {\n      stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n      converged = stepResult.converged;\n      centers = stepResult.centroids;\n    }\n    return stepResult.computeInformation(data);\n  }\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that retuns an array of matrices of the cases that belong to each class.\n * @param {Matrix} X - dataset\n * @param {Array} y - predictions\n * @return {Array}\n */\nexport function separateClasses(X, y) {\n  var features = X.columns;\n\n  var classes = 0;\n  var totalPerClasses = new Array(10000); // max upperbound of classes\n  for (var i = 0; i < y.length; i++) {\n    if (totalPerClasses[y[i]] === undefined) {\n      totalPerClasses[y[i]] = 0;\n      classes++;\n    }\n    totalPerClasses[y[i]]++;\n  }\n  var separatedClasses = new Array(classes);\n  var currentIndex = new Array(classes);\n  for (i = 0; i < classes; ++i) {\n    separatedClasses[i] = new Matrix(totalPerClasses[i], features);\n    currentIndex[i] = 0;\n  }\n  for (i = 0; i < X.rows; ++i) {\n    separatedClasses[y[i]].setRow(currentIndex[y[i]], X.getRow(i));\n    currentIndex[y[i]]++;\n  }\n  return separatedClasses;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class GaussianNB {\n  /**\n   * Constructor for the Gaussian Naive Bayes classifier, the parameters here is just for loading purposes.\n   * @constructor\n   * @param {boolean} reload\n   * @param {object} model\n   */\n  constructor(reload, model) {\n    if (reload) {\n      this.means = model.means;\n      this.calculateProbabilities = model.calculateProbabilities;\n    }\n  }\n\n  /**\n   * Function that trains the classifier with a matrix that represents the training set and an array that\n   * represents the label of each row in the training set. the labels must be numbers between 0 to n-1 where\n   * n represents the number of classes.\n   *\n   * WARNING: in the case that one class, all the cases in one or more features have the same value, the\n   * Naive Bayes classifier will not work well.\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    var C1 = Math.sqrt(2 * Math.PI); // constant to precalculate the squared root\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separatedClasses = separateClasses(trainingSet, trainingLabels);\n    var calculateProbabilities = new Array(separatedClasses.length);\n    this.means = new Array(separatedClasses.length);\n    for (var i = 0; i < separatedClasses.length; ++i) {\n      var means = separatedClasses[i].mean('column');\n      var std = separatedClasses[i].standardDeviation('column', {\n        mean: means\n      });\n\n      var logPriorProbability = Math.log(\n        separatedClasses[i].rows / trainingSet.rows\n      );\n      calculateProbabilities[i] = new Array(means.length + 1);\n\n      calculateProbabilities[i][0] = logPriorProbability;\n      for (var j = 1; j < means.length + 1; ++j) {\n        var currentStd = std[j - 1];\n        calculateProbabilities[i][j] = [\n          1 / (C1 * currentStd),\n          -2 * currentStd * currentStd\n        ];\n      }\n\n      this.means[i] = means;\n    }\n\n    this.calculateProbabilities = calculateProbabilities;\n  }\n\n  /**\n   * function that predicts each row of the dataset (must be a matrix).\n   *\n   * @param {Matrix|Array} dataset\n   * @return {Array}\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    if (dataset.rows === this.calculateProbabilities[0].length) {\n      throw new RangeError(\n        'the dataset must have the same features as the training set'\n      );\n    }\n\n    var predictions = new Array(dataset.rows);\n\n    for (var i = 0; i < predictions.length; ++i) {\n      predictions[i] = getCurrentClass(\n        dataset.getRow(i),\n        this.means,\n        this.calculateProbabilities\n      );\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that export the NaiveBayes model.\n   * @return {object}\n   */\n  toJSON() {\n    return {\n      modelName: 'NaiveBayes',\n      means: this.means,\n      calculateProbabilities: this.calculateProbabilities\n    };\n  }\n\n  /**\n   * Function that create a GaussianNB classifier with the given model.\n   * @param {object} model\n   * @return {GaussianNB}\n   */\n  static load(model) {\n    if (model.modelName !== 'NaiveBayes') {\n      throw new RangeError(\n        'The current model is not a Multinomial Naive Bayes, current model:',\n        model.name\n      );\n    }\n\n    return new GaussianNB(true, model);\n  }\n}\n\n/**\n * @private\n * Function the retrieves a prediction with one case.\n *\n * @param {Array} currentCase\n * @param {Array} mean - Precalculated means of each class trained\n * @param {Array} classes - Precalculated value of each class (Prior probability and probability function of each feature)\n * @return {number}\n */\nfunction getCurrentClass(currentCase, mean, classes) {\n  var maxProbability = 0;\n  var predictedClass = -1;\n\n  // going through all precalculated values for the classes\n  for (var i = 0; i < classes.length; ++i) {\n    var currentProbability = classes[i][0]; // initialize with the prior probability\n    for (var j = 1; j < classes[0][1].length + 1; ++j) {\n      currentProbability += calculateLogProbability(\n        currentCase[j - 1],\n        mean[i][j - 1],\n        classes[i][j][0],\n        classes[i][j][1]\n      );\n    }\n\n    currentProbability = Math.exp(currentProbability);\n    if (currentProbability > maxProbability) {\n      maxProbability = currentProbability;\n      predictedClass = i;\n    }\n  }\n\n  return predictedClass;\n}\n\n/**\n * @private\n * function that retrieves the probability of the feature given the class.\n * @param {number} value - value of the feature.\n * @param {number} mean - mean of the feature for the given class.\n * @param {number} C1 - precalculated value of (1 / (sqrt(2*pi) * std)).\n * @param {number} C2 - precalculated value of (2 * std^2) for the denominator of the exponential.\n * @return {number}\n */\nfunction calculateLogProbability(value, mean, C1, C2) {\n  value = value - mean;\n  return Math.log(C1 * Math.exp((value * value) / C2));\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class MultinomialNB {\n  /**\n   * Constructor for Multinomial Naive Bayes, the model parameter is for load purposes.\n   * @constructor\n   * @param {object} model - for load purposes.\n   */\n  constructor(model) {\n    if (model) {\n      this.conditionalProbability = Matrix.checkMatrix(\n        model.conditionalProbability\n      );\n      this.priorProbability = Matrix.checkMatrix(model.priorProbability);\n    }\n  }\n\n  /**\n   * Train the classifier with the current training set and labels, the labels must be numbers between 0 and n.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separateClass = separateClasses(trainingSet, trainingLabels);\n\n    this.priorProbability = new Matrix(separateClass.length, 1);\n\n    for (var i = 0; i < separateClass.length; ++i) {\n      this.priorProbability.set(i, 0, Math.log(\n        separateClass[i].rows / trainingSet.rows\n      ));\n    }\n\n    var features = trainingSet.columns;\n    this.conditionalProbability = new Matrix(separateClass.length, features);\n    for (i = 0; i < separateClass.length; ++i) {\n      var classValues = Matrix.checkMatrix(separateClass[i]);\n      var total = classValues.sum();\n      var divisor = total + features;\n      this.conditionalProbability.setRow(\n        i,\n        Matrix.rowVector(classValues\n          .sum('column'))\n          .add(1)\n          .div(divisor)\n          .apply(matrixLog)\n      );\n    }\n  }\n\n  /**\n   * Retrieves the predictions for the dataset with the current model.\n   * @param {Matrix|Array} dataset\n   * @return {Array} - predictions from the dataset.\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    var predictions = new Array(dataset.rows);\n    for (var i = 0; i < dataset.rows; ++i) {\n      var currentElement = dataset.getRowVector(i);\n      const v = Matrix.columnVector(this.conditionalProbability\n        .clone()\n        .mulRowVector(currentElement)\n        .sum('row'));\n      predictions[i] = v\n        .add(this.priorProbability)\n        .maxIndex()[0];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that saves the current model.\n   * @return {object} - model in JSON format.\n   */\n  toJSON() {\n    return {\n      name: 'MultinomialNB',\n      priorProbability: this.priorProbability,\n      conditionalProbability: this.conditionalProbability\n    };\n  }\n\n  /**\n   * Creates a new MultinomialNB from the given model\n   * @param {object} model\n   * @return {MultinomialNB}\n   */\n  static load(model) {\n    if (model.name !== 'MultinomialNB') {\n      throw new RangeError(`${model.name} is not a Multinomial Naive Bayes`);\n    }\n\n    return new MultinomialNB(model);\n  }\n}\n\nfunction matrixLog(i, j) {\n  this.set(i, j, Math.log(this.get(i, j)));\n}\n","/*\n * Original code from:\n *\n * k-d Tree JavaScript - V 1.01\n *\n * https://github.com/ubilabs/kd-tree-javascript\n *\n * @author Mircea Pricop <pricop@ubilabs.net>, 2012\n * @author Martin Kleppe <kleppe@ubilabs.net>, 2012\n * @author Ubilabs http://ubilabs.net, 2012\n * @license MIT License <http://www.opensource.org/licenses/mit-license.php>\n */\n\nfunction Node(obj, dimension, parent) {\n  this.obj = obj;\n  this.left = null;\n  this.right = null;\n  this.parent = parent;\n  this.dimension = dimension;\n}\n\nexport default class KDTree {\n  constructor(points, metric) {\n    // If points is not an array, assume we're loading a pre-built tree\n    if (!Array.isArray(points)) {\n      this.dimensions = points.dimensions;\n      this.root = points;\n      restoreParent(this.root);\n    } else {\n      this.dimensions = new Array(points[0].length);\n      for (var i = 0; i < this.dimensions.length; i++) {\n        this.dimensions[i] = i;\n      }\n      this.root = buildTree(points, 0, null, this.dimensions);\n    }\n    this.metric = metric;\n  }\n\n  // Convert to a JSON serializable structure; this just requires removing\n  // the `parent` property\n  toJSON() {\n    const result = toJSONImpl(this.root, true);\n    result.dimensions = this.dimensions;\n    return result;\n  }\n\n  nearest(point, maxNodes, maxDistance) {\n    const metric = this.metric;\n    const dimensions = this.dimensions;\n    var i;\n\n    const bestNodes = new BinaryHeap(function (e) {\n      return -e[1];\n    });\n\n    function nearestSearch(node) {\n      const dimension = dimensions[node.dimension];\n      const ownDistance = metric(point, node.obj);\n      const linearPoint = {};\n      var bestChild, linearDistance, otherChild, i;\n\n      function saveNode(node, distance) {\n        bestNodes.push([node, distance]);\n        if (bestNodes.size() > maxNodes) {\n          bestNodes.pop();\n        }\n      }\n\n      for (i = 0; i < dimensions.length; i += 1) {\n        if (i === node.dimension) {\n          linearPoint[dimensions[i]] = point[dimensions[i]];\n        } else {\n          linearPoint[dimensions[i]] = node.obj[dimensions[i]];\n        }\n      }\n\n      linearDistance = metric(linearPoint, node.obj);\n\n      if (node.right === null && node.left === null) {\n        if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n          saveNode(node, ownDistance);\n        }\n        return;\n      }\n\n      if (node.right === null) {\n        bestChild = node.left;\n      } else if (node.left === null) {\n        bestChild = node.right;\n      } else {\n        if (point[dimension] < node.obj[dimension]) {\n          bestChild = node.left;\n        } else {\n          bestChild = node.right;\n        }\n      }\n\n      nearestSearch(bestChild);\n\n      if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n        saveNode(node, ownDistance);\n      }\n\n      if (\n        bestNodes.size() < maxNodes ||\n        Math.abs(linearDistance) < bestNodes.peek()[1]\n      ) {\n        if (bestChild === node.left) {\n          otherChild = node.right;\n        } else {\n          otherChild = node.left;\n        }\n        if (otherChild !== null) {\n          nearestSearch(otherChild);\n        }\n      }\n    }\n\n    if (maxDistance) {\n      for (i = 0; i < maxNodes; i += 1) {\n        bestNodes.push([null, maxDistance]);\n      }\n    }\n\n    if (this.root) {\n      nearestSearch(this.root);\n    }\n\n    const result = [];\n    for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {\n      if (bestNodes.content[i][0]) {\n        result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);\n      }\n    }\n    return result;\n  }\n}\n\nfunction toJSONImpl(src) {\n  const dest = new Node(src.obj, src.dimension, null);\n  if (src.left) dest.left = toJSONImpl(src.left);\n  if (src.right) dest.right = toJSONImpl(src.right);\n  return dest;\n}\n\nfunction buildTree(points, depth, parent, dimensions) {\n  const dim = depth % dimensions.length;\n\n  if (points.length === 0) {\n    return null;\n  }\n  if (points.length === 1) {\n    return new Node(points[0], dim, parent);\n  }\n\n  points.sort((a, b) => a[dimensions[dim]] - b[dimensions[dim]]);\n\n  const median = Math.floor(points.length / 2);\n  const node = new Node(points[median], dim, parent);\n  node.left = buildTree(points.slice(0, median), depth + 1, node, dimensions);\n  node.right = buildTree(points.slice(median + 1), depth + 1, node, dimensions);\n\n  return node;\n}\n\nfunction restoreParent(root) {\n  if (root.left) {\n    root.left.parent = root;\n    restoreParent(root.left);\n  }\n\n  if (root.right) {\n    root.right.parent = root;\n    restoreParent(root.right);\n  }\n}\n\n// Binary heap implementation from:\n// http://eloquentjavascript.net/appendix2.html\nclass BinaryHeap {\n  constructor(scoreFunction) {\n    this.content = [];\n    this.scoreFunction = scoreFunction;\n  }\n\n  push(element) {\n    // Add the new element to the end of the array.\n    this.content.push(element);\n    // Allow it to bubble up.\n    this.bubbleUp(this.content.length - 1);\n  }\n\n  pop() {\n    // Store the first element so we can return it later.\n    var result = this.content[0];\n    // Get the element at the end of the array.\n    var end = this.content.pop();\n    // If there are any elements left, put the end element at the\n    // start, and let it sink down.\n    if (this.content.length > 0) {\n      this.content[0] = end;\n      this.sinkDown(0);\n    }\n    return result;\n  }\n\n  peek() {\n    return this.content[0];\n  }\n\n  size() {\n    return this.content.length;\n  }\n\n  bubbleUp(n) {\n    // Fetch the element that has to be moved.\n    var element = this.content[n];\n    // When at 0, an element can not go up any further.\n    while (n > 0) {\n      // Compute the parent element's index, and fetch it.\n      const parentN = Math.floor((n + 1) / 2) - 1;\n      const parent = this.content[parentN];\n      // Swap the elements if the parent is greater.\n      if (this.scoreFunction(element) < this.scoreFunction(parent)) {\n        this.content[parentN] = element;\n        this.content[n] = parent;\n        // Update 'n' to continue at the new position.\n        n = parentN;\n      } else {\n        // Found a parent that is less, no need to move it further.\n        break;\n      }\n    }\n  }\n\n  sinkDown(n) {\n    // Look up the target element and its score.\n    var length = this.content.length;\n    var element = this.content[n];\n    var elemScore = this.scoreFunction(element);\n\n    while (true) {\n      // Compute the indices of the child elements.\n      var child2N = (n + 1) * 2;\n      var child1N = child2N - 1;\n      // This is used to store the new position of the element,\n      // if any.\n      var swap = null;\n      // If the first child exists (is inside the array)...\n      if (child1N < length) {\n        // Look it up and compute its score.\n        var child1 = this.content[child1N];\n        var child1Score = this.scoreFunction(child1);\n        // If the score is less than our element's, we need to swap.\n        if (child1Score < elemScore) {\n          swap = child1N;\n        }\n      }\n      // Do the same checks for the other child.\n      if (child2N < length) {\n        var child2 = this.content[child2N];\n        var child2Score = this.scoreFunction(child2);\n        if (child2Score < (swap === null ? elemScore : child1Score)) {\n          swap = child2N;\n        }\n      }\n\n      // If the element needs to be moved, swap it, and continue.\n      if (swap !== null) {\n        this.content[n] = this.content[swap];\n        this.content[swap] = element;\n        n = swap;\n      } else {\n        // Otherwise, we are done.\n        break;\n      }\n    }\n  }\n}\n","import { euclidean as euclideanDistance } from 'ml-distance-euclidean';\n\nimport KDTree from './KDTree';\n\nexport default class KNN {\n  /**\n   * @param {Array} dataset\n   * @param {Array} labels\n   * @param {object} options\n   * @param {number} [options.k=numberOfClasses + 1] - Number of neighbors to classify.\n   * @param {function} [options.distance=euclideanDistance] - Distance function that takes two parameters.\n   */\n  constructor(dataset, labels, options = {}) {\n    if (dataset === true) {\n      const model = labels;\n      this.kdTree = new KDTree(model.kdTree, options);\n      this.k = model.k;\n      this.classes = new Set(model.classes);\n      this.isEuclidean = model.isEuclidean;\n      return;\n    }\n\n    const classes = new Set(labels);\n\n    const { distance = euclideanDistance, k = classes.size + 1 } = options;\n\n    const points = new Array(dataset.length);\n    for (var i = 0; i < points.length; ++i) {\n      points[i] = dataset[i].slice();\n    }\n\n    for (i = 0; i < labels.length; ++i) {\n      points[i].push(labels[i]);\n    }\n\n    this.kdTree = new KDTree(points, distance);\n    this.k = k;\n    this.classes = classes;\n    this.isEuclidean = distance === euclideanDistance;\n  }\n\n  /**\n   * Create a new KNN instance with the given model.\n   * @param {object} model\n   * @param {function} distance=euclideanDistance - distance function must be provided if the model wasn't trained with euclidean distance.\n   * @return {KNN}\n   */\n  static load(model, distance = euclideanDistance) {\n    if (model.name !== 'KNN') {\n      throw new Error(`invalid model: ${model.name}`);\n    }\n    if (!model.isEuclidean && distance === euclideanDistance) {\n      throw new Error(\n        'a custom distance function was used to create the model. Please provide it again'\n      );\n    }\n    if (model.isEuclidean && distance !== euclideanDistance) {\n      throw new Error(\n        'the model was created with the default distance function. Do not load it with another one'\n      );\n    }\n    return new KNN(true, model, distance);\n  }\n\n  /**\n   * Return a JSON containing the kd-tree model.\n   * @return {object} JSON KNN model.\n   */\n  toJSON() {\n    return {\n      name: 'KNN',\n      kdTree: this.kdTree,\n      k: this.k,\n      classes: Array.from(this.classes),\n      isEuclidean: this.isEuclidean\n    };\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Array} dataset\n   * @return {Array} predictions\n   */\n  predict(dataset) {\n    if (Array.isArray(dataset)) {\n      if (typeof dataset[0] === 'number') {\n        return getSinglePrediction(this, dataset);\n      } else if (\n        Array.isArray(dataset[0]) &&\n        typeof dataset[0][0] === 'number'\n      ) {\n        const predictions = new Array(dataset.length);\n        for (var i = 0; i < dataset.length; i++) {\n          predictions[i] = getSinglePrediction(this, dataset[i]);\n        }\n        return predictions;\n      }\n    }\n    throw new TypeError('dataset to predict must be an array or a matrix');\n  }\n}\n\nfunction getSinglePrediction(knn, currentCase) {\n  var nearestPoints = knn.kdTree.nearest(currentCase, knn.k);\n  var pointsPerClass = {};\n  var predictedClass = -1;\n  var maxPoints = -1;\n  var lastElement = nearestPoints[0][0].length - 1;\n\n  for (var element of knn.classes) {\n    pointsPerClass[element] = 0;\n  }\n\n  for (var i = 0; i < nearestPoints.length; ++i) {\n    var currentClass = nearestPoints[i][0][lastElement];\n    var currentPoints = ++pointsPerClass[currentClass];\n    if (currentPoints > maxPoints) {\n      predictedClass = currentClass;\n      maxPoints = currentPoints;\n    }\n  }\n\n  return predictedClass;\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that given vector, returns its norm\n * @param {Vector} X\n * @return {number} Norm of the vector\n */\nexport function norm(X) {\n  return Math.sqrt(X.clone().apply(pow2array).sum());\n}\n\n/**\n * @private\n * Function that pow 2 each element of a Matrix or a Vector,\n * used in the apply method of the Matrix object\n * @param {number} i - index i.\n * @param {number} j - index j.\n * @return {Matrix} The Matrix object modified at the index i, j.\n * */\nexport function pow2array(i, j) {\n  this.set(i, j, this.get(i, j) ** 2);\n}\n\n/**\n * @private\n * Function that normalize the dataset and return the means and\n * standard deviation of each feature.\n * @param {Matrix} dataset\n * @return {object} dataset normalized, means and standard deviations\n */\nexport function featureNormalize(dataset) {\n  var means = dataset.mean('column');\n  var std = dataset.standardDeviation('column', { mean: means, unbiased: true });\n  var result = Matrix.checkMatrix(dataset).subRowVector(means);\n  return { result: result.divRowVector(std), means: means, std: std };\n}\n\n/**\n * @private\n * Function that initialize an array of matrices.\n * @param {Array} array\n * @param {boolean} isMatrix\n * @return {Array} array with the matrices initialized.\n */\nexport function initializeMatrices(array, isMatrix) {\n  if (isMatrix) {\n    for (var i = 0; i < array.length; ++i) {\n      for (var j = 0; j < array[i].length; ++j) {\n        var elem = array[i][j];\n        array[i][j] = elem !== null ? new Matrix(array[i][j]) : undefined;\n      }\n    }\n  } else {\n    for (i = 0; i < array.length; ++i) {\n      array[i] = new Matrix(array[i]);\n    }\n  }\n\n  return array;\n}\n","import Matrix from 'ml-matrix';\n\nimport * as Utils from './utils';\n\n/**\n * @class PLS\n */\nexport class PLS {\n  /**\n   * Constructor for Partial Least Squares (PLS)\n   * @param {object} options\n   * @param {number} [options.latentVectors] - Number of latent vector to get (if the algorithm doesn't find a good model below the tolerance)\n   * @param {number} [options.tolerance=1e-5]\n   * @param {boolean} [options.scale=true] - rescale dataset using mean.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.meanX = model.meanX;\n      this.stdDevX = model.stdDevX;\n      this.meanY = model.meanY;\n      this.stdDevY = model.stdDevY;\n      this.PBQ = Matrix.checkMatrix(model.PBQ);\n      this.R2X = model.R2X;\n      this.scale = model.scale;\n      this.scaleMethod = model.scaleMethod;\n      this.tolerance = model.tolerance;\n    } else {\n      var {\n        tolerance = 1e-5,\n        scale = true,\n      } = options;\n      this.tolerance = tolerance;\n      this.scale = scale;\n      this.latentVectors = options.latentVectors;\n    }\n  }\n\n  /**\n   * Fits the model with the given data and predictions, in this function is calculated the\n   * following outputs:\n   *\n   * T - Score matrix of X\n   * P - Loading matrix of X\n   * U - Score matrix of Y\n   * Q - Loading matrix of Y\n   * B - Matrix of regression coefficient\n   * W - Weight matrix of X\n   *\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    if (trainingSet.length !== trainingValues.length) {\n      throw new RangeError('The number of X rows must be equal to the number of Y rows');\n    }\n\n    this.meanX = trainingSet.mean('column');\n    this.stdDevX = trainingSet.standardDeviation('column', { mean: this.meanX, unbiased: true });\n    this.meanY = trainingValues.mean('column');\n    this.stdDevY = trainingValues.standardDeviation('column', { mean: this.meanY, unbiased: true });\n\n    if (this.scale) {\n      trainingSet = trainingSet.clone().subRowVector(this.meanX).divRowVector(this.stdDevX);\n      trainingValues = trainingValues.clone().subRowVector(this.meanY).divRowVector(this.stdDevY);\n    }\n\n    if (this.latentVectors === undefined) {\n      this.latentVectors = Math.min(trainingSet.rows - 1, trainingSet.columns);\n    }\n\n    var rx = trainingSet.rows;\n    var cx = trainingSet.columns;\n    var ry = trainingValues.rows;\n    var cy = trainingValues.columns;\n\n    var ssqXcal = trainingSet.clone().mul(trainingSet).sum(); // for the r²\n    var sumOfSquaresY = trainingValues.clone().mul(trainingValues).sum();\n\n    var tolerance = this.tolerance;\n    var n = this.latentVectors;\n    var T = Matrix.zeros(rx, n);\n    var P = Matrix.zeros(cx, n);\n    var U = Matrix.zeros(ry, n);\n    var Q = Matrix.zeros(cy, n);\n    var B = Matrix.zeros(n, n);\n    var W = P.clone();\n    var k = 0;\n\n    while (Utils.norm(trainingValues) > tolerance && k < n) {\n      var transposeX = trainingSet.transpose();\n      var transposeY = trainingValues.transpose();\n\n      var tIndex = maxSumColIndex(trainingSet.clone().mul(trainingSet));\n      var uIndex = maxSumColIndex(trainingValues.clone().mul(trainingValues));\n\n      var t1 = trainingSet.getColumnVector(tIndex);\n      var u = trainingValues.getColumnVector(uIndex);\n      var t = Matrix.zeros(rx, 1);\n\n      while (Utils.norm(t1.clone().sub(t)) > tolerance) {\n        var w = transposeX.mmul(u);\n        w.div(Utils.norm(w));\n        t = t1;\n        t1 = trainingSet.mmul(w);\n        var q = transposeY.mmul(t1);\n        q.div(Utils.norm(q));\n        u = trainingValues.mmul(q);\n      }\n\n      t = t1;\n      var num = transposeX.mmul(t);\n      var den = t.transpose().mmul(t).get(0, 0);\n      var p = num.div(den);\n      var pnorm = Utils.norm(p);\n      p.div(pnorm);\n      t.mul(pnorm);\n      w.mul(pnorm);\n\n      num = u.transpose().mmul(t);\n      den = t.transpose().mmul(t).get(0, 0);\n      var b = num.div(den).get(0, 0);\n      trainingSet.sub(t.mmul(p.transpose()));\n      trainingValues.sub(t.clone().mul(b).mmul(q.transpose()));\n\n      T.setColumn(k, t);\n      P.setColumn(k, p);\n      U.setColumn(k, u);\n      Q.setColumn(k, q);\n      W.setColumn(k, w);\n\n      B.set(k, k, b);\n      k++;\n    }\n\n    k--;\n    T = T.subMatrix(0, T.rows - 1, 0, k);\n    P = P.subMatrix(0, P.rows - 1, 0, k);\n    U = U.subMatrix(0, U.rows - 1, 0, k);\n    Q = Q.subMatrix(0, Q.rows - 1, 0, k);\n    W = W.subMatrix(0, W.rows - 1, 0, k);\n    B = B.subMatrix(0, k, 0, k);\n\n    // TODO: review of R2Y\n    // this.R2Y = t.transpose().mmul(t).mul(q[k][0]*q[k][0]).divS(ssqYcal)[0][0];\n    //\n    this.ssqYcal = sumOfSquaresY;\n    this.E = trainingSet;\n    this.F = trainingValues;\n    this.T = T;\n    this.P = P;\n    this.U = U;\n    this.Q = Q;\n    this.W = W;\n    this.B = B;\n    this.PBQ = P.mmul(B).mmul(Q.transpose());\n    this.R2X = t.transpose().mmul(t).mmul(p.transpose().mmul(p)).div(ssqXcal).get(0, 0);\n  }\n\n  /**\n   * Predicts the behavior of the given dataset.\n   * @param {Matrix|Array} dataset - data to be predicted.\n   * @return {Matrix} - predictions of each element of the dataset.\n   */\n  predict(dataset) {\n    var X = Matrix.checkMatrix(dataset);\n    if (this.scale) {\n      X = X.subRowVector(this.meanX).divRowVector(this.stdDevX);\n    }\n    var Y = X.mmul(this.PBQ);\n    Y = Y.mulRowVector(this.stdDevY).addRowVector(this.meanY);\n    return Y;\n  }\n\n  /**\n   * Returns the explained variance on training of the PLS model\n   * @return {number}\n   */\n  getExplainedVariance() {\n    return this.R2X;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      name: 'PLS',\n      R2X: this.R2X,\n      meanX: this.meanX,\n      stdDevX: this.stdDevX,\n      meanY: this.meanY,\n      stdDevY: this.stdDevY,\n      PBQ: this.PBQ,\n      tolerance: this.tolerance,\n      scale: this.scale,\n    };\n  }\n\n  /**\n   * Load a PLS model from a JSON Object\n   * @param {object} model\n   * @return {PLS} - PLS object from the given model\n   */\n  static load(model) {\n    if (model.name !== 'PLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n    return new PLS(true, model);\n  }\n}\n\n/**\n * @private\n * Function that returns the index where the sum of each\n * column vector is maximum.\n * @param {Matrix} data\n * @return {number} index of the maximum\n */\nfunction maxSumColIndex(data) {\n  return Matrix.rowVector(data.sum('column')).maxIndex()[0];\n}\n","import { Matrix, SingularValueDecomposition, inverse } from 'ml-matrix';\n\nimport { initializeMatrices } from './utils';\n\n/**\n * @class KOPLS\n */\nexport class KOPLS {\n  /**\n     * Constructor for Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)\n     * @param {object} options\n     * @param {number} [options.predictiveComponents] - Number of predictive components to use.\n     * @param {number} [options.orthogonalComponents] - Number of Y-Orthogonal components.\n     * @param {Kernel} [options.kernel] - Kernel object to apply, see [ml-kernel](https://github.com/mljs/kernel).\n     * @param {object} model - for load purposes.\n     */\n  constructor(options, model) {\n    if (options === true) {\n      this.trainingSet = new Matrix(model.trainingSet);\n      this.YLoadingMat = new Matrix(model.YLoadingMat);\n      this.SigmaPow = new Matrix(model.SigmaPow);\n      this.YScoreMat = new Matrix(model.YScoreMat);\n      this.predScoreMat = initializeMatrices(model.predScoreMat, false);\n      this.YOrthLoadingVec = initializeMatrices(model.YOrthLoadingVec, false);\n      this.YOrthEigen = model.YOrthEigen;\n      this.YOrthScoreMat = initializeMatrices(model.YOrthScoreMat, false);\n      this.toNorm = initializeMatrices(model.toNorm, false);\n      this.TURegressionCoeff = initializeMatrices(model.TURegressionCoeff, false);\n      this.kernelX = initializeMatrices(model.kernelX, true);\n      this.kernel = model.kernel;\n      this.orthogonalComp = model.orthogonalComp;\n      this.predictiveComp = model.predictiveComp;\n    } else {\n      if (options.predictiveComponents === undefined) {\n        throw new RangeError('no predictive components found!');\n      }\n      if (options.orthogonalComponents === undefined) {\n        throw new RangeError('no orthogonal components found!');\n      }\n      if (options.kernel === undefined) {\n        throw new RangeError('no kernel found!');\n      }\n\n      this.orthogonalComp = options.orthogonalComponents;\n      this.predictiveComp = options.predictiveComponents;\n      this.kernel = options.kernel;\n    }\n  }\n\n  /**\n     * Train the K-OPLS model with the given training set and labels.\n     * @param {Matrix|Array} trainingSet\n     * @param {Matrix|Array} trainingValues\n     */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    // to save and compute kernel with the prediction dataset.\n    this.trainingSet = trainingSet.clone();\n\n    var kernelX = this.kernel.compute(trainingSet);\n\n    var Identity = Matrix.eye(kernelX.rows, kernelX.rows, 1);\n    var temp = kernelX;\n    kernelX = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      kernelX[i] = new Array(this.orthogonalComp + 1);\n    }\n    kernelX[0][0] = temp;\n\n    var result = new SingularValueDecomposition(trainingValues.transpose().mmul(kernelX[0][0]).mmul(trainingValues), {\n      computeLeftSingularVectors: true,\n      computeRightSingularVectors: false\n    });\n    var YLoadingMat = result.leftSingularVectors;\n    var Sigma = result.diagonalMatrix;\n\n    YLoadingMat = YLoadingMat.subMatrix(0, YLoadingMat.rows - 1, 0, this.predictiveComp - 1);\n    Sigma = Sigma.subMatrix(0, this.predictiveComp - 1, 0, this.predictiveComp - 1);\n\n    var YScoreMat = trainingValues.mmul(YLoadingMat);\n\n    var predScoreMat = new Array(this.orthogonalComp + 1);\n    var TURegressionCoeff = new Array(this.orthogonalComp + 1);\n    var YOrthScoreMat = new Array(this.orthogonalComp);\n    var YOrthLoadingVec = new Array(this.orthogonalComp);\n    var YOrthEigen = new Array(this.orthogonalComp);\n    var YOrthScoreNorm = new Array(this.orthogonalComp);\n\n    var SigmaPow = Matrix.pow(Sigma, -0.5);\n    // to avoid errors, check infinity\n    SigmaPow.apply(function (i, j) {\n      if (this.get(i, j) === Infinity) {\n        this.set(i, j, 0);\n      }\n    });\n\n    for (var i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = kernelX[0][i].transpose().mmul(YScoreMat).mmul(SigmaPow);\n\n      var TpiPrime = predScoreMat[i].transpose();\n      TURegressionCoeff[i] = inverse(TpiPrime.mmul(predScoreMat[i])).mmul(TpiPrime).mmul(YScoreMat);\n\n      result = new SingularValueDecomposition(TpiPrime.mmul(Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime))).mmul(predScoreMat[i]), {\n        computeLeftSingularVectors: true,\n        computeRightSingularVectors: false\n      });\n      var CoTemp = result.leftSingularVectors;\n      var SoTemp = result.diagonalMatrix;\n\n      YOrthLoadingVec[i] = CoTemp.subMatrix(0, CoTemp.rows - 1, 0, 0);\n      YOrthEigen[i] = SoTemp.get(0, 0);\n\n      YOrthScoreMat[i] = Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime)).mmul(predScoreMat[i]).mmul(YOrthLoadingVec[i]).mul(Math.pow(YOrthEigen[i], -0.5));\n\n      var toiPrime = YOrthScoreMat[i].transpose();\n      YOrthScoreNorm[i] = Matrix.sqrt(toiPrime.mmul(YOrthScoreMat[i]));\n\n      YOrthScoreMat[i] = YOrthScoreMat[i].divRowVector(YOrthScoreNorm[i]);\n\n      var ITo = Matrix.sub(Identity, YOrthScoreMat[i].mmul(YOrthScoreMat[i].transpose()));\n\n      kernelX[0][i + 1] = kernelX[0][i].mmul(ITo);\n      kernelX[i + 1][i + 1] = ITo.mmul(kernelX[i][i]).mmul(ITo);\n    }\n\n    var lastScoreMat = predScoreMat[this.orthogonalComp] = kernelX[0][this.orthogonalComp].transpose().mmul(YScoreMat).mmul(SigmaPow);\n\n    var lastTpPrime = lastScoreMat.transpose();\n    TURegressionCoeff[this.orthogonalComp] = inverse(lastTpPrime.mmul(lastScoreMat)).mmul(lastTpPrime).mmul(YScoreMat);\n\n    this.YLoadingMat = YLoadingMat;\n    this.SigmaPow = SigmaPow;\n    this.YScoreMat = YScoreMat;\n    this.predScoreMat = predScoreMat;\n    this.YOrthLoadingVec = YOrthLoadingVec;\n    this.YOrthEigen = YOrthEigen;\n    this.YOrthScoreMat = YOrthScoreMat;\n    this.toNorm = YOrthScoreNorm;\n    this.TURegressionCoeff = TURegressionCoeff;\n    this.kernelX = kernelX;\n  }\n\n  /**\n     * Predicts the output given the matrix to predict.\n     * @param {Matrix|Array} toPredict\n     * @return {{y: Matrix, predScoreMat: Array<Matrix>, predYOrthVectors: Array<Matrix>}} predictions\n     */\n  predict(toPredict) {\n    var KTestTrain = this.kernel.compute(toPredict, this.trainingSet);\n\n    var temp = KTestTrain;\n    KTestTrain = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      KTestTrain[i] = new Array(this.orthogonalComp + 1);\n    }\n    KTestTrain[0][0] = temp;\n\n    var YOrthScoreVector = new Array(this.orthogonalComp);\n    var predScoreMat = new Array(this.orthogonalComp);\n\n    var i;\n    for (i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);\n\n      YOrthScoreVector[i] = Matrix.sub(KTestTrain[i][i], predScoreMat[i].mmul(this.predScoreMat[i].transpose())).mmul(this.predScoreMat[i]).mmul(this.YOrthLoadingVec[i]).mul(Math.pow(this.YOrthEigen[i], -0.5));\n\n      YOrthScoreVector[i] = YOrthScoreVector[i].divRowVector(this.toNorm[i]);\n\n      var scoreMatPrime = this.YOrthScoreMat[i].transpose();\n      KTestTrain[i + 1][0] = Matrix.sub(KTestTrain[i][0], YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[0][i].transpose()));\n\n      var p1 = Matrix.sub(KTestTrain[i][0], KTestTrain[i][i].mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime));\n      var p2 = YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[i][i]);\n      var p3 = p2.mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime);\n\n      KTestTrain[i + 1][i + 1] = p1.sub(p2).add(p3);\n    }\n\n    predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);\n    var prediction = predScoreMat[i].mmul(this.TURegressionCoeff[i]).mmul(this.YLoadingMat.transpose());\n\n    return {\n      prediction: prediction,\n      predScoreMat: predScoreMat,\n      predYOrthVectors: YOrthScoreVector\n    };\n  }\n\n  /**\n     * Export the current model to JSON.\n     * @return {object} - Current model.\n     */\n  toJSON() {\n    return {\n      name: 'K-OPLS',\n      YLoadingMat: this.YLoadingMat,\n      SigmaPow: this.SigmaPow,\n      YScoreMat: this.YScoreMat,\n      predScoreMat: this.predScoreMat,\n      YOrthLoadingVec: this.YOrthLoadingVec,\n      YOrthEigen: this.YOrthEigen,\n      YOrthScoreMat: this.YOrthScoreMat,\n      toNorm: this.toNorm,\n      TURegressionCoeff: this.TURegressionCoeff,\n      kernelX: this.kernelX,\n      trainingSet: this.trainingSet,\n      orthogonalComp: this.orthogonalComp,\n      predictiveComp: this.predictiveComp\n    };\n  }\n\n  /**\n     * Load a K-OPLS with the given model.\n     * @param {object} model\n     * @param {Kernel} kernel - kernel used on the model, see [ml-kernel](https://github.com/mljs/kernel).\n     * @return {KOPLS}\n     */\n  static load(model, kernel) {\n    if (model.name !== 'K-OPLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    if (!kernel) {\n      throw new RangeError('You must provide a kernel for the model!');\n    }\n\n    model.kernel = kernel;\n    return new KOPLS(true, model);\n  }\n}\n","/**\n *  Constructs a confusion matrix\n * @class ConfusionMatrix\n * @example\n * const CM = new ConfusionMatrix([[13, 2], [10, 5]], ['cat', 'dog'])\n * @param {Array<Array<number>>} matrix - The confusion matrix, a 2D Array. Rows represent the actual label and columns\n *     the predicted label.\n * @param {Array<any>} labels - Labels of the confusion matrix, a 1D Array\n */\nclass ConfusionMatrix {\n    constructor(matrix, labels) {\n        if (matrix.length !== matrix[0].length) {\n            throw new Error('Confusion matrix must be square');\n        }\n        if (labels.length !== matrix.length) {\n            throw new Error('Confusion matrix and labels should have the same length');\n        }\n        this.labels = labels;\n        this.matrix = matrix;\n    }\n\n\n    /**\n     * Construct confusion matrix from the predicted and actual labels (classes). Be sure to provide the arguments in\n     * the correct order!\n     * @param {Array<any>} actual  - The predicted labels of the classification\n     * @param {Array<any>} predicted     - The actual labels of the classification. Has to be of same length as\n     *     predicted.\n     * @param {object} [options] - Additional options\n     * @param {Array<any>} [options.labels] - The list of labels that should be used. If not provided the distinct set\n     *     of labels present in predicted and actual is used. Labels are compared using the strict equality operator\n     *     '==='\n     * @return {ConfusionMatrix} - Confusion matrix\n     */\n    static fromLabels(actual, predicted, options = {}) {\n        if (predicted.length !== actual.length) {\n            throw new Error('predicted and actual must have the same length');\n        }\n        let distinctLabels;\n        if (options.labels) {\n            distinctLabels = new Set(options.labels);\n        } else {\n            distinctLabels = new Set([...actual, ...predicted]);\n        }\n        distinctLabels = Array.from(distinctLabels);\n        if (options.sort) {\n            distinctLabels.sort(options.sort);\n        }\n\n        // Create confusion matrix and fill with 0's\n        const matrix = Array.from({length: distinctLabels.length});\n        for (let i = 0; i < matrix.length; i++) {\n            matrix[i] = new Array(matrix.length);\n            matrix[i].fill(0);\n        }\n\n        for (let i = 0; i < predicted.length; i++) {\n            const actualIdx = distinctLabels.indexOf(actual[i]);\n            const predictedIdx = distinctLabels.indexOf(predicted[i]);\n            if (actualIdx >= 0 && predictedIdx >= 0) {\n                matrix[actualIdx][predictedIdx]++;\n            }\n        }\n\n        return new ConfusionMatrix(matrix, distinctLabels);\n    }\n\n    /**\n     * Get the confusion matrix\n     * @return {Array<Array<number> >}\n     */\n    getMatrix() {\n        return this.matrix;\n    }\n\n    getLabels() {\n        return this.labels;\n    }\n\n    /**\n     * Get the total number of samples\n     * @return {number}\n     */\n    getTotalCount() {\n        let predicted = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            for (var j = 0; j < this.matrix.length; j++) {\n                predicted += this.matrix[i][j];\n            }\n        }\n        return predicted;\n    }\n\n    /**\n     * Get the total number of true predictions\n     * @return {number}\n     */\n    getTrueCount() {\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            count += this.matrix[i][i];\n        }\n        return count;\n    }\n\n    /**\n     * Get the total number of false predictions.\n     * @return {number}\n     */\n    getFalseCount() {\n        return this.getTotalCount() - this.getTrueCount();\n    }\n\n    /**\n     * Get the number of true positive predictions.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getTruePositiveCount(label) {\n        const index = this.getIndex(label);\n        return this.matrix[index][index];\n    }\n\n    /**\n     * Get the number of true negative predictions\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getTrueNegativeCount(label) {\n        const index = this.getIndex(label);\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            for (var j = 0; j < this.matrix.length; j++) {\n                if (i !== index && j !== index) {\n                    count += this.matrix[i][j];\n                }\n            }\n        }\n        return count;\n    }\n\n    /**\n     * Get the number of false positive predictions.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalsePositiveCount(label) {\n        const index = this.getIndex(label);\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            if (i !== index) {\n                count += this.matrix[i][index];\n            }\n        }\n        return count;\n    }\n\n    /**\n     * Get the number of false negative predictions.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseNegativeCount(label) {\n        const index = this.getIndex(label);\n        var count = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            if (i !== index) {\n                count += this.matrix[index][i];\n            }\n        }\n        return count;\n    }\n\n    /**\n     * Get the number of real positive samples.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getPositiveCount(label) {\n        return this.getTruePositiveCount(label) + this.getFalseNegativeCount(label);\n    }\n\n    /**\n     * Get the number of real negative samples.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getNegativeCount(label) {\n        return this.getTrueNegativeCount(label) + this.getFalsePositiveCount(label);\n    }\n\n    /**\n     * Get the index in the confusion matrix that corresponds to the given label\n     * @param {any} label - The label to search for\n     * @throws if the label is not found\n     * @return {number}\n     */\n    getIndex(label) {\n        const index = this.labels.indexOf(label);\n        if (index === -1) throw new Error('The label does not exist');\n        return index;\n    }\n\n    /**\n     * Get the true positive rate a.k.a. sensitivity. Computes the ratio between the number of true positive predictions and the total number of positive samples.\n     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number} - The true positive rate [0-1]\n     */\n    getTruePositiveRate(label) {\n        return this.getTruePositiveCount(label) / this.getPositiveCount(label);\n    }\n\n    /**\n     * Get the true negative rate a.k.a. specificity. Computes the ration between the number of true negative predictions and the total number of negative samples.\n     * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getTrueNegativeRate(label) {\n        return this.getTrueNegativeCount(label) / this.getNegativeCount(label);\n    }\n\n    /**\n     * Get the positive predictive value a.k.a. precision. Computes TP / (TP + FP)\n     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getPositivePredictiveValue(label) {\n        const TP = this.getTruePositiveCount(label);\n        return TP / (TP + this.getFalsePositiveCount(label));\n    }\n\n    /**\n     * Negative predictive value\n     * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getNegativePredictiveValue(label) {\n        const TN = this.getTrueNegativeCount(label);\n        return TN / (TN + this.getFalseNegativeCount(label));\n    }\n\n    /**\n     * False negative rate a.k.a. miss rate.\n     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseNegativeRate(label) {\n        return 1 - this.getTruePositiveRate(label);\n    }\n\n    /**\n     * False positive rate a.k.a. fall-out rate.\n     * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalsePositiveRate(label) {\n        return 1 - this.getTrueNegativeRate(label);\n    }\n\n    /**\n     * False discovery rate (FDR)\n     * {@link https://en.wikipedia.org/wiki/False_discovery_rate}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseDiscoveryRate(label) {\n        const FP = this.getFalsePositiveCount(label);\n        return FP / (FP + this.getTruePositiveCount(label));\n    }\n\n    /**\n     * False omission rate (FOR)\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getFalseOmissionRate(label) {\n        const FN = this.getFalseNegativeCount(label);\n        return FN / (FN + this.getTruePositiveCount(label));\n    }\n\n    /**\n     * F1 score\n     * {@link https://en.wikipedia.org/wiki/F1_score}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getF1Score(label) {\n        const TP = this.getTruePositiveCount(label);\n        return 2 * TP / (2 * TP + this.getFalsePositiveCount(label) + this.getFalseNegativeCount(label));\n    }\n\n    /**\n     * Matthews correlation coefficient (MCC)\n     * {@link https://en.wikipedia.org/wiki/Matthews_correlation_coefficient}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getMatthewsCorrelationCoefficient(label) {\n        const TP = this.getTruePositiveCount(label);\n        const TN = this.getTrueNegativeCount(label);\n        const FP = this.getFalsePositiveCount(label);\n        const FN = this.getFalseNegativeCount(label);\n        return (TP * TN - FP * FN) / Math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN));\n    }\n\n    /**\n     * Informedness\n     * {@link https://en.wikipedia.org/wiki/Youden%27s_J_statistic}\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getInformedness(label) {\n        return this.getTruePositiveRate(label) + this.getTrueNegativeRate(label) - 1;\n    }\n\n    /**\n     * Markedness\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {number}\n     */\n    getMarkedness(label) {\n        return this.getPositivePredictiveValue(label) + this.getNegativePredictiveValue(label) - 1;\n    }\n\n    /**\n     * Get the confusion table.\n     * @param {any} label - The label that should be considered \"positive\"\n     * @return {Array<Array<number> >} - The 2x2 confusion table. [[TP, FN], [FP, TN]]\n     */\n    getConfusionTable(label) {\n        return [\n            [\n                this.getTruePositiveCount(label),\n                this.getFalseNegativeCount(label)\n            ],\n            [\n                this.getFalsePositiveCount(label),\n                this.getTrueNegativeCount(label)\n            ]\n        ];\n    }\n\n    /**\n     * Get total accuracy.\n     * @return {number} - The ratio between the number of true predictions and total number of classifications ([0-1])\n     */\n    getAccuracy() {\n        let correct = 0;\n        let incorrect = 0;\n        for (var i = 0; i < this.matrix.length; i++) {\n            for (var j = 0; j < this.matrix.length; j++) {\n                if (i === j) correct += this.matrix[i][j];\n                else incorrect += this.matrix[i][j];\n            }\n        }\n        return correct / (correct + incorrect);\n    }\n\n\n    /**\n     * Returns the element in the confusion matrix that corresponds to the given actual and predicted labels.\n     * @param {any} actual - The true label\n     * @param {any} predicted - The predicted label\n     * @return {number} - The element in the confusion matrix\n     */\n    getCount(actual, predicted) {\n        const actualIndex = this.getIndex(actual);\n        const predictedIndex = this.getIndex(predicted);\n        return this.matrix[actualIndex][predictedIndex];\n    }\n\n    /**\n     * Compute the general prediction accuracy\n     * @deprecated Use getAccuracy\n     * @return {number} - The prediction accuracy ([0-1]\n     */\n    get accuracy() {\n        return this.getAccuracy();\n    }\n\n    /**\n     * Compute the number of predicted observations\n     * @deprecated Use getTotalCount\n     * @return {number}\n     */\n    get total() {\n        return this.getTotalCount();\n    }\n}\n\nmodule.exports = ConfusionMatrix;\n","'use strict';\nconst defaultOptions = {\n  mode: 'index'\n};\n\nmodule.exports = function *(M, N, options) {\n  options = Object.assign({}, defaultOptions, options);\n  var a = new Array(N);\n  var c = new Array(M);\n  var b = new Array(N);\n  var p = new Array(N + 2);\n  var x, y, z;\n\n  // init a and b\n  for (var i = 0; i < N; i++) {\n    a[i] = i;\n    if (i < N - M) b[i] = 0;\n    else b[i] = 1;\n  }\n\n  // init c\n  for (i = 0; i < M; i++) {\n    c[i] = N - M + i;\n  }\n\n  // init p\n  for (i = 0; i < p.length; i++) {\n    if (i === 0) p[i] = N + 1;\n    else if (i <= N - M) p[i] = 0;\n    else if (i <= N) p[i] = i - N + M;\n    else p[i] = -2;\n  }\n\n  function twiddle() {\n    var i, j, k;\n    j = 1;\n    while (p[j] <= 0) {\n      j++;\n    }\n    if (p[j - 1] === 0) {\n      for (i = j - 1; i !== 1; i--) {\n        p[i] = -1;\n      }\n      p[j] = 0;\n      x = z = 0;\n      p[1] = 1;\n      y = j - 1;\n    } else {\n      if (j > 1) {\n        p[j - 1] = 0;\n      }\n      do {\n        j++;\n      }\n      while (p[j] > 0);\n      k = j - 1;\n      i = j;\n      while (p[i] === 0) {\n        p[i++] = -1;\n      }\n      if (p[i] === -1) {\n        p[i] = p[k];\n        z = p[k] - 1;\n        x = i - 1;\n        y = k - 1;\n        p[k] = -1;\n      } else {\n        if (i === p[0]) {\n          return 0;\n        } else {\n          p[j] = p[i];\n          z = p[i] - 1;\n          p[i] = 0;\n          x = j - 1;\n          y = i - 1;\n        }\n      }\n    }\n    return 1;\n  }\n\n  if (options.mode === 'index') {\n    yield c.slice();\n    while (twiddle()) {\n      c[z] = a[x];\n      yield c.slice();\n    }\n  } else if (options.mode === 'mask') {\n    yield b.slice();\n    while (twiddle()) {\n      b[x] = 1;\n      b[y] = 0;\n      yield b.slice();\n    }\n  } else {\n    throw new Error('Invalid mode');\n  }\n};\n","'use strict';\n\nconst ConfusionMatrix = require('ml-confusion-matrix');\n\nconst CV = {};\nconst combinations = require('ml-combinations');\n\n/**\n * Performs a leave-one-out cross-validation (LOO-CV) of the given samples. In LOO-CV, 1 observation is used as the\n * validation set while the rest is used as the training set. This is repeated once for each observation. LOO-CV is a\n * special case of LPO-CV. @see leavePout\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nCV.leaveOneOut = function (Classifier, features, labels, classifierOptions) {\n    if (typeof labels === 'function') {\n        var callback = labels;\n        labels = features;\n        features = Classifier;\n        return CV.leavePOut(features, labels, 1, callback);\n    }\n    return CV.leavePOut(Classifier, features, labels, classifierOptions, 1);\n};\n\n\n/**\n * Performs a leave-p-out cross-validation (LPO-CV) of the given samples. In LPO-CV, p observations are used as the\n * validation set while the rest is used as the training set. This is repeated as many times as there are possible\n * ways to combine p observations from the set (unordered without replacement). Be aware that for relatively small\n * data-set size this can require a very large number of training and testing to do!\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} p - The size of the validation sub-samples' set\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nCV.leavePOut = function (Classifier, features, labels, classifierOptions, p) {\n    if (typeof classifierOptions === 'function') {\n        var callback = classifierOptions;\n        p = labels;\n        labels = features;\n        features = Classifier;\n    }\n    check(features, labels);\n    const distinct = getDistinct(labels);\n    const confusionMatrix = initMatrix(distinct.length, distinct.length);\n\n    var N = features.length;\n    var gen = combinations(p, N);\n    var allIdx = new Array(N);\n    for (let i = 0; i < N; i++) {\n        allIdx[i] = i;\n    }\n    for (const testIdx of gen) {\n        var trainIdx = allIdx.slice();\n\n        for (let i = testIdx.length - 1; i >= 0; i--) {\n            trainIdx.splice(testIdx[i], 1);\n        }\n\n        if (callback) {\n            validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);\n        } else {\n            validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);\n        }\n\n    }\n\n    return new ConfusionMatrix(confusionMatrix, distinct);\n};\n\n/**\n * Performs k-fold cross-validation (KF-CV). KF-CV separates the data-set into k random equally sized partitions, and\n * uses each as a validation set, with all other partitions used in the training set. Observations left over from if k\n * does not divide the number of observations are left out of the cross-validation process.\n * @param {function} Classifier - The classifier's to use for the cross validation. Expect ml-classifier api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} k - The number of partitions to create\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nCV.kFold = function (Classifier, features, labels, classifierOptions, k) {\n    if (typeof classifierOptions === 'function') {\n        var callback = classifierOptions;\n        k = labels;\n        labels = features;\n        features = Classifier;\n    }\n    check(features, labels);\n    const distinct = getDistinct(labels);\n    const confusionMatrix = initMatrix(distinct.length, distinct.length);\n    var N = features.length;\n    var allIdx = new Array(N);\n    for (var i = 0; i < N; i++) {\n        allIdx[i] = i;\n    }\n\n    var l = Math.floor(N / k);\n    // create random k-folds\n    var current = [];\n    var folds = [];\n    while (allIdx.length) {\n        var randi = Math.floor(Math.random() * allIdx.length);\n        current.push(allIdx[randi]);\n        allIdx.splice(randi, 1);\n        if (current.length === l) {\n            folds.push(current);\n            current = [];\n        }\n    }\n    if (current.length) folds.push(current);\n    folds = folds.slice(0, k);\n\n\n    for (i = 0; i < folds.length; i++) {\n        var testIdx = folds[i];\n        var trainIdx = [];\n        for (var j = 0; j < folds.length; j++) {\n            if (j !== i) trainIdx = trainIdx.concat(folds[j]);\n        }\n\n        if (callback) {\n            validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback);\n        } else {\n            validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct);\n        }\n    }\n\n    return new ConfusionMatrix(confusionMatrix, distinct);\n};\n\nfunction check(features, labels) {\n    if (features.length !== labels.length) {\n        throw new Error('features and labels should have the same length');\n    }\n}\n\nfunction initMatrix(rows, columns) {\n    return new Array(rows).fill(0).map(() => new Array(columns).fill(0));\n}\n\nfunction getDistinct(arr) {\n    var s = new Set();\n    for (let i = 0; i < arr.length; i++) {\n        s.add(arr[i]);\n    }\n    return Array.from(s);\n}\n\nfunction validate(Classifier, features, labels, classifierOptions, testIdx, trainIdx, confusionMatrix, distinct) {\n    const {testFeatures, trainFeatures, testLabels, trainLabels} = getTrainTest(features, labels, testIdx, trainIdx);\n\n    var classifier;\n    if (Classifier.prototype.train) {\n        classifier = new Classifier(classifierOptions);\n        classifier.train(trainFeatures, trainLabels);\n    } else {\n        classifier = new Classifier(trainFeatures, trainLabels, classifierOptions);\n    }\n\n    var predictedLabels = classifier.predict(testFeatures);\n    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction validateWithCallback(features, labels, testIdx, trainIdx, confusionMatrix, distinct, callback) {\n    const {testFeatures, trainFeatures, testLabels, trainLabels} = getTrainTest(features, labels, testIdx, trainIdx);\n    const predictedLabels = callback(trainFeatures, trainLabels, testFeatures);\n    updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct) {\n\n    for (var i = 0; i < predictedLabels.length; i++) {\n        const actualIdx = distinct.indexOf(testLabels[i]);\n        const predictedIdx = distinct.indexOf(predictedLabels[i]);\n        if (actualIdx < 0 || predictedIdx < 0) {\n            // eslint-disable-next-line no-console\n            console.warn(`ignore unknown predicted label ${predictedLabels[i]}`);\n        }\n        confusionMatrix[actualIdx][predictedIdx]++;\n    }\n}\n\n\nfunction getTrainTest(features, labels, testIdx, trainIdx) {\n    return {\n        testFeatures: testIdx.map(function (index) {\n            return features[index];\n        }),\n        trainFeatures: trainIdx.map(function (index) {\n            return features[index];\n        }),\n        testLabels: testIdx.map(function (index) {\n            return labels[index];\n        }),\n        trainLabels: trainIdx.map(function (index) {\n            return labels[index];\n        })\n    };\n}\n\nmodule.exports = CV;\n","'use strict';\n\nvar mlMatrix = require('ml-matrix');\n\nfunction logistic(val) {\n  return 1 / (1 + Math.exp(-val));\n}\n\nfunction expELU(val, param) {\n  return val < 0 ? param * (Math.exp(val) - 1) : val;\n}\n\nfunction softExponential(val, param) {\n  if (param < 0) {\n    return -Math.log(1 - param * (val + param)) / param;\n  }\n  if (param > 0) {\n    return ((Math.exp(param * val) - 1) / param) + param;\n  }\n  return val;\n}\n\nfunction softExponentialPrime(val, param) {\n  if (param < 0) {\n    return 1 / (1 - param * (param + val));\n  } else {\n    return Math.exp(param * val);\n  }\n}\n\nconst ACTIVATION_FUNCTIONS = {\n  tanh: {\n    activation: Math.tanh,\n    derivate: (val) => 1 - (val * val)\n  },\n  identity: {\n    activation: (val) => val,\n    derivate: () => 1\n  },\n  logistic: {\n    activation: logistic,\n    derivate: (val) => logistic(val) * (1 - logistic(val))\n  },\n  arctan: {\n    activation: Math.atan,\n    derivate: (val) => 1 / (val * val + 1)\n  },\n  softsign: {\n    activation: (val) => val / (1 + Math.abs(val)),\n    derivate: (val) => 1 / ((1 + Math.abs(val)) * (1 + Math.abs(val)))\n  },\n  relu: {\n    activation: (val) => (val < 0 ? 0 : val),\n    derivate: (val) => (val < 0 ? 0 : 1)\n  },\n  softplus: {\n    activation: (val) => Math.log(1 + Math.exp(val)),\n    derivate: (val) => 1 / (1 + Math.exp(-val))\n  },\n  bent: {\n    activation: (val) => ((Math.sqrt(val * val + 1) - 1) / 2) + val,\n    derivate: (val) => (val / (2 * Math.sqrt(val * val + 1))) + 1\n  },\n  sinusoid: {\n    activation: Math.sin,\n    derivate: Math.cos\n  },\n  sinc: {\n    activation: (val) => (val === 0 ? 1 : Math.sin(val) / val),\n    derivate: (val) => (val === 0 ? 0 : (Math.cos(val) / val) - (Math.sin(val) / (val * val)))\n  },\n  gaussian: {\n    activation: (val) => Math.exp(-(val * val)),\n    derivate: (val) => -2 * val * Math.exp(-(val * val))\n  },\n  'parametric-relu': {\n    activation: (val, param) => (val < 0 ? param * val : val),\n    derivate: (val, param) => (val < 0 ? param : 1)\n  },\n  'exponential-elu': {\n    activation: expELU,\n    derivate: (val, param) => (val < 0 ? expELU(val, param) + param : 1)\n  },\n  'soft-exponential': {\n    activation: softExponential,\n    derivate: softExponentialPrime\n  }\n};\n\nclass Layer {\n  /**\n     * @private\n     * Create a new layer with the given options\n     * @param {object} options\n     * @param {number} [options.inputSize] - Number of conections that enter the neurons.\n     * @param {number} [options.outputSize] - Number of conections that leave the neurons.\n     * @param {number} [options.regularization] - Regularization parameter.\n     * @param {number} [options.epsilon] - Learning rate parameter.\n     * @param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.\n     * @param {number} [options.activationParam] - Activation parameter if needed.\n     */\n  constructor(options) {\n    this.inputSize = options.inputSize;\n    this.outputSize = options.outputSize;\n    this.regularization = options.regularization;\n    this.epsilon = options.epsilon;\n    this.activation = options.activation;\n    this.activationParam = options.activationParam;\n\n    var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];\n    var params = selectedFunction.activation.length;\n\n    var actFunction = params > 1 ? (val) => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;\n    var derFunction = params > 1 ? (val) => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, actFunction(this.get(i, j)));\n    };\n    this.derivate = function (i, j) {\n      this.set(i, j, derFunction(this.get(i, j)));\n    };\n\n    if (options.model) {\n      // load model\n      this.W = mlMatrix.Matrix.checkMatrix(options.W);\n      this.b = mlMatrix.Matrix.checkMatrix(options.b);\n    } else {\n      // default constructor\n      this.W = mlMatrix.Matrix.rand(this.inputSize, this.outputSize);\n      this.b = mlMatrix.Matrix.zeros(1, this.outputSize);\n\n      this.W.apply(function (i, j) {\n        this.set(i, j, this.get(i, j) / Math.sqrt(options.inputSize));\n      });\n    }\n  }\n\n  /**\n     * @private\n     * propagate the given input through the current layer.\n     * @param {Matrix} X - input.\n     * @return {Matrix} output at the current layer.\n     */\n  forward(X) {\n    var z = X.mmul(this.W).addRowVector(this.b);\n    z.apply(this.activationFunction);\n    this.a = z.clone();\n    return z;\n  }\n\n  /**\n     * @private\n     * apply backpropagation algorithm at the current layer\n     * @param {Matrix} delta - delta values estimated at the following layer.\n     * @param {Matrix} a - 'a' values from the following layer.\n     * @return {Matrix} the new delta values for the next layer.\n     */\n  backpropagation(delta, a) {\n    this.dW = a.transpose().mmul(delta);\n    this.db = mlMatrix.Matrix.rowVector(delta.sum('column'));\n\n    var aCopy = a.clone();\n    return delta.mmul(this.W.transpose()).mul(aCopy.apply(this.derivate));\n  }\n\n  /**\n     * @private\n     * Function that updates the weights at the current layer with the derivatives.\n     */\n  update() {\n    this.dW.add(this.W.clone().mul(this.regularization));\n    this.W.add(this.dW.mul(-this.epsilon));\n    this.b.add(this.db.mul(-this.epsilon));\n  }\n\n  /**\n     * @private\n     * Export the current layer to JSON.\n     * @return {object} model\n     */\n  toJSON() {\n    return {\n      model: 'Layer',\n      inputSize: this.inputSize,\n      outputSize: this.outputSize,\n      regularization: this.regularization,\n      epsilon: this.epsilon,\n      activation: this.activation,\n      W: this.W,\n      b: this.b\n    };\n  }\n\n  /**\n     * @private\n     * Creates a new Layer with the given model.\n     * @param {object} model\n     * @return {Layer}\n     */\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n    return new Layer(model);\n  }\n}\n\nclass OutputLayer extends Layer {\n  constructor(options) {\n    super(options);\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, Math.exp(this.get(i, j)));\n    };\n  }\n\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n\n    return new OutputLayer(model);\n  }\n}\n\nclass FeedForwardNeuralNetworks {\n  /**\n   * Create a new Feedforward neural network model.\n   * @class FeedForwardNeuralNetworks\n   * @param {object} [options]\n   * @param {Array} [options.hiddenLayers=[10]] - Array that contains the sizes of the hidden layers.\n   * @param {number} [options.iterations=50] - Number of iterations at the training step.\n   * @param {number} [options.learningRate=0.01] - Learning rate of the neural net (also known as epsilon).\n   * @param {number} [options.regularization=0.01] - Regularization parameter af the neural net.\n   * @param {string} [options.activation='tanh'] - activation function to be used. (options: 'tanh'(default),\n   * 'identity', 'logistic', 'arctan', 'softsign', 'relu', 'softplus', 'bent', 'sinusoid', 'sinc', 'gaussian').\n   * (single-parametric options: 'parametric-relu', 'exponential-relu', 'soft-exponential').\n   * @param {number} [options.activationParam=1] - if the selected activation function needs a parameter.\n   */\n  constructor(options) {\n    options = options || {};\n    if (options.model) {\n      // load network\n      this.hiddenLayers = options.hiddenLayers;\n      this.iterations = options.iterations;\n      this.learningRate = options.learningRate;\n      this.regularization = options.regularization;\n      this.dicts = options.dicts;\n      this.activation = options.activation;\n      this.activationParam = options.activationParam;\n      this.model = new Array(options.layers.length);\n\n      for (var i = 0; i < this.model.length - 1; ++i) {\n        this.model[i] = Layer.load(options.layers[i]);\n      }\n      this.model[this.model.length - 1] = OutputLayer.load(options.layers[this.model.length - 1]);\n    } else {\n      // default constructor\n      this.hiddenLayers = options.hiddenLayers || [10];\n      this.iterations = options.iterations || 50;\n\n      this.learningRate = options.learningRate || 0.01;\n      this.regularization = options.regularization || 0.01;\n\n      this.activation = options.activation || 'tanh';\n      this.activationParam = options.activationParam || 1;\n      if (!(this.activation in Object.keys(ACTIVATION_FUNCTIONS))) {\n        this.activation = 'tanh';\n      }\n    }\n  }\n\n  /**\n   * @private\n   * Function that build and initialize the neural net.\n   * @param {number} inputSize - total of features to fit.\n   * @param {number} outputSize - total of labels of the prediction set.\n   */\n  buildNetwork(inputSize, outputSize) {\n    var size = 2 + (this.hiddenLayers.length - 1);\n    this.model = new Array(size);\n\n    // input layer\n    this.model[0] = new Layer({\n      inputSize: inputSize,\n      outputSize: this.hiddenLayers[0],\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n\n    // hidden layers\n    for (var i = 1; i < this.hiddenLayers.length; ++i) {\n      this.model[i] = new Layer({\n        inputSize: this.hiddenLayers[i - 1],\n        outputSize: this.hiddenLayers[i],\n        activation: this.activation,\n        activationParam: this.activationParam,\n        regularization: this.regularization,\n        epsilon: this.learningRate\n      });\n    }\n\n    // output layer\n    this.model[size - 1] = new OutputLayer({\n      inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],\n      outputSize: outputSize,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n  }\n\n  /**\n   * Train the neural net with the given features and labels.\n   * @param {Matrix|Array} features\n   * @param {Matrix|Array} labels\n   */\n  train(features, labels) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    this.dicts = dictOutputs(labels);\n\n    var inputSize = features.columns;\n    var outputSize = Object.keys(this.dicts.inputs).length;\n\n    if (!this.model) {\n      this.buildNetwork(inputSize, outputSize);\n    }\n\n    for (var i = 0; i < this.iterations; ++i) {\n      var probabilities = this.propagate(features);\n      this.backpropagation(features, labels, probabilities);\n    }\n  }\n\n  /**\n   * @private\n   * Propagate the input(training set) and retrives the probabilities of each class.\n   * @param {Matrix} X\n   * @return {Matrix} probabilities of each class.\n   */\n  propagate(X) {\n    var input = X;\n    for (var i = 0; i < this.model.length; ++i) {\n      input = this.model[i].forward(input);\n    }\n\n    // get probabilities\n    return input.divColumnVector(input.sum('row'));\n  }\n\n  /**\n   * @private\n   * Function that applies the backpropagation algorithm on each layer of the network\n   * in order to fit the features and labels.\n   * @param {Matrix} features\n   * @param {Array} labels\n   * @param {Matrix} probabilities - probabilities of each class of the feature set.\n   */\n  backpropagation(features, labels, probabilities) {\n    for (var i = 0; i < probabilities.rows; ++i) {\n      probabilities.set(i, this.dicts.inputs[labels[i]], probabilities.get(i, this.dicts.inputs[labels[i]]) - 1);\n    }\n\n    // remember, the last delta doesn't matter\n    var delta = probabilities;\n    for (i = this.model.length - 1; i >= 0; --i) {\n      var a = i > 0 ? this.model[i - 1].a : features;\n      delta = this.model[i].backpropagation(delta, a);\n    }\n\n    for (i = 0; i < this.model.length; ++i) {\n      this.model[i].update();\n    }\n  }\n\n  /**\n   * Predict the output given the feature set.\n   * @param {Array|Matrix} features\n   * @return {Array}\n   */\n  predict(features) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    var outputs = new Array(features.rows);\n    var probabilities = this.propagate(features);\n    for (var i = 0; i < features.rows; ++i) {\n      outputs[i] = this.dicts.outputs[probabilities.maxRowIndex(i)[1]];\n    }\n\n    return outputs;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} model\n   */\n  toJSON() {\n    var model = {\n      model: 'FNN',\n      hiddenLayers: this.hiddenLayers,\n      iterations: this.iterations,\n      learningRate: this.learningRate,\n      regularization: this.regularization,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      dicts: this.dicts,\n      layers: new Array(this.model.length)\n    };\n\n    for (var i = 0; i < this.model.length; ++i) {\n      model.layers[i] = this.model[i].toJSON();\n    }\n\n    return model;\n  }\n\n  /**\n   * Load a Feedforward Neural Network with the current model.\n   * @param {object} model\n   * @return {FeedForwardNeuralNetworks}\n   */\n  static load(model) {\n    if (model.model !== 'FNN') {\n      throw new RangeError('the current model is not a feed forward network');\n    }\n\n    return new FeedForwardNeuralNetworks(model);\n  }\n}\n\n/**\n * @private\n * Method that given an array of labels(predictions), returns two dictionaries, one to transform from labels to\n * numbers and other in the reverse way\n * @param {Array} array\n * @return {object}\n */\nfunction dictOutputs(array) {\n  var inputs = {};\n  var outputs = {};\n  var index = 0;\n  for (var i = 0; i < array.length; i += 1) {\n    if (inputs[array[i]] === undefined) {\n      inputs[array[i]] = index;\n      outputs[index] = array[i];\n      index++;\n    }\n  }\n\n  return {\n    inputs: inputs,\n    outputs: outputs\n  };\n}\n\nmodule.exports = FeedForwardNeuralNetworks;\n","function NodeSquare(x, y, weights, som) {\n    this.x = x;\n    this.y = y;\n    this.weights = weights;\n    this.som = som;\n    this.neighbors = {};\n}\n\nNodeSquare.prototype.adjustWeights = function adjustWeights(target, learningRate, influence) {\n    for (var i = 0, ii = this.weights.length; i < ii; i++) {\n        this.weights[i] += learningRate * influence * (target[i] - this.weights[i]);\n    }\n};\n\nNodeSquare.prototype.getDistance = function getDistance(otherNode) {\n    return Math.max(Math.abs(this.x - otherNode.x), Math.abs(this.y - otherNode.y));\n};\n\nNodeSquare.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.x - otherNode.x),\n        distY = Math.abs(this.y - otherNode.y);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY));\n};\n\nNodeSquare.prototype.getNeighbors = function getNeighbors(xy) {\n    if (!this.neighbors[xy]) {\n        this.neighbors[xy] = new Array(2);\n\n        // left or bottom neighbor\n        var v;\n        if (this[xy] > 0) {\n            v = this[xy] - 1;\n        } else if (this.som.torus) {\n            v = this.som.gridDim[xy] - 1\n        }\n        if (typeof v !== 'undefined') {\n            var x, y;\n            if (xy === 'x') {\n                x = v;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = v;\n            }\n            this.neighbors[xy][0] = this.som.nodes[x][y];\n        }\n\n        // top or right neighbor\n        var w;\n        if (this[xy] < (this.som.gridDim[xy] - 1)) {\n            w = this[xy] + 1;\n        } else if (this.som.torus) {\n            w = 0;\n        }\n        if (typeof w !== 'undefined') {\n            if (xy === 'x') {\n                x = w;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = w;\n            }\n            this.neighbors[xy][1] = this.som.nodes[x][y];\n        }\n    }\n    return this.neighbors[xy];\n};\n\nNodeSquare.prototype.getPos = function getPos(xy, element) {\n    var neighbors = this.getNeighbors(xy),\n        distance = this.som.distance,\n        bestNeighbor,\n        direction;\n    if(neighbors[0]) {\n        if (neighbors[1]) {\n            var dist1 = distance(element, neighbors[0].weights),\n                dist2 = distance(element, neighbors[1].weights);\n            if(dist1 < dist2) {\n                bestNeighbor = neighbors[0];\n                direction = -1;\n            } else {\n                bestNeighbor = neighbors[1];\n                direction = 1;\n            }\n        } else {\n            bestNeighbor = neighbors[0];\n            direction = -1;\n        }\n    } else {\n        bestNeighbor = neighbors[1];\n        direction = 1;\n    }\n    var simA = 1 - distance(element, this.weights),\n        simB = 1 - distance(element, bestNeighbor.weights);\n    var factor = ((simA - simB) / (2 - simA - simB));\n    return 0.5 + 0.5 * factor * direction;\n};\n\nNodeSquare.prototype.getPosition = function getPosition(element) {\n    return [\n        this.getPos('x', element),\n        this.getPos('y', element)\n    ];\n};\n\nmodule.exports = NodeSquare;","var NodeSquare = require('./node-square');\n\nfunction NodeHexagonal(x, y, weights, som) {\n\n    NodeSquare.call(this, x, y, weights, som);\n\n    this.hX = x - Math.floor(y / 2);\n    this.z = 0 - this.hX - y;\n\n}\n\nNodeHexagonal.prototype = new NodeSquare;\nNodeHexagonal.prototype.constructor = NodeHexagonal;\n\nNodeHexagonal.prototype.getDistance = function getDistanceHexagonal(otherNode) {\n    return Math.max(Math.abs(this.hX - otherNode.hX), Math.abs(this.y - otherNode.y), Math.abs(this.z - otherNode.z));\n};\n\nNodeHexagonal.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.hX - otherNode.hX),\n        distY = Math.abs(this.y - otherNode.y),\n        distZ = Math.abs(this.z - otherNode.z);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY), Math.min(distZ, this.som.gridDim.z - distZ));\n};\n\nNodeHexagonal.prototype.getPosition = function getPosition() {\n    throw new Error('Unimplemented : cannot get position of the points for hexagonal grid');\n};\n\nmodule.exports = NodeHexagonal;","'use strict';\n\nvar NodeSquare = require('./node-square'),\n    NodeHexagonal = require('./node-hexagonal');\n\nvar defaultOptions = {\n    fields: 3,\n    randomizer: Math.random,\n    distance: squareEuclidean,\n    iterations: 10,\n    learningRate: 0.1,\n    gridType: 'rect',\n    torus: true,\n    method: 'random'\n};\n\nfunction SOM(x, y, options, reload) {\n\n    this.x = x;\n    this.y = y;\n\n    options = options || {};\n    this.options = {};\n    for (var i in defaultOptions) {\n        if (options.hasOwnProperty(i)) {\n            this.options[i] = options[i];\n        } else {\n            this.options[i] = defaultOptions[i];\n        }\n    }\n\n    if (typeof this.options.fields === 'number') {\n        this.numWeights = this.options.fields;\n    } else if (Array.isArray(this.options.fields)) {\n        this.numWeights = this.options.fields.length;\n        var converters = getConverters(this.options.fields);\n        this.extractor = converters.extractor;\n        this.creator = converters.creator;\n    } else {\n        throw new Error('Invalid fields definition');\n    }\n\n    if (this.options.gridType === 'rect') {\n        this.nodeType = NodeSquare;\n        this.gridDim = {\n            x: x,\n            y: y\n        };\n    } else {\n        this.nodeType = NodeHexagonal;\n        var hx = this.x - Math.floor(this.y / 2);\n        this.gridDim = {\n            x: hx,\n            y: this.y,\n            z: -(0 - hx - this.y)\n        };\n    }\n\n    this.torus = this.options.torus;\n    this.distanceMethod = this.torus ? 'getDistanceTorus' : 'getDistance';\n\n    this.distance = this.options.distance;\n\n    this.maxDistance = getMaxDistance(this.distance, this.numWeights);\n\n    if (reload === true) { // For model loading\n        this.done = true;\n        return;\n    }\n    if (!(x > 0 && y > 0)) {\n        throw new Error('x and y must be positive');\n    }\n\n    this.times = {\n        findBMU: 0,\n        adjust: 0\n    };\n\n    this.randomizer = this.options.randomizer;\n\n    this.iterationCount = 0;\n    this.iterations = this.options.iterations;\n\n    this.startLearningRate = this.learningRate = this.options.learningRate;\n\n    this.mapRadius = Math.floor(Math.max(x, y) / 2);\n\n    this.algorithmMethod = this.options.method;\n\n    this._initNodes();\n\n    this.done = false;\n}\n\nSOM.load = function loadModel(model, distance) {\n    if (model.name === 'SOM') {\n        var x = model.data.length,\n            y = model.data[0].length;\n        if (distance) {\n            model.options.distance = distance;\n        } else if (model.options.distance) {\n            model.options.distance = eval('(' + model.options.distance + ')');\n        }\n        var som = new SOM(x, y, model.options, true);\n        som.nodes = new Array(x);\n        for (var i = 0; i < x; i++) {\n            som.nodes[i] = new Array(y);\n            for (var j = 0; j < y; j++) {\n                som.nodes[i][j] = new som.nodeType(i, j, model.data[i][j], som);\n            }\n        }\n        return som;\n    } else {\n        throw new Error('expecting a SOM model');\n    }\n};\n\nSOM.prototype.export = function exportModel(includeDistance) {\n    if (!this.done) {\n        throw new Error('model is not ready yet');\n    }\n    var model = {\n        name: 'SOM'\n    };\n    model.options = {\n        fields: this.options.fields,\n        gridType: this.options.gridType,\n        torus: this.options.torus\n    };\n    model.data = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        model.data[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            model.data[i][j] = this.nodes[i][j].weights;\n        }\n    }\n    if (includeDistance) {\n        model.options.distance = this.distance.toString();\n    }\n    return model;\n};\n\nSOM.prototype._initNodes = function initNodes() {\n    var now = Date.now(),\n        i, j, k;\n    this.nodes = new Array(this.x);\n    for (i = 0; i < this.x; i++) {\n        this.nodes[i] = new Array(this.y);\n        for (j = 0; j < this.y; j++) {\n            var weights = new Array(this.numWeights);\n            for (k = 0; k < this.numWeights; k++) {\n                weights[k] = this.randomizer();\n            }\n            this.nodes[i][j] = new this.nodeType(i, j, weights, this);\n        }\n    }\n    this.times.initNodes = Date.now() - now;\n};\n\nSOM.prototype.setTraining = function setTraining(trainingSet) {\n    if (this.trainingSet) {\n        throw new Error('training set has already been set');\n    }\n    var now = Date.now();\n    var convertedSet = trainingSet;\n    var i, l = trainingSet.length;\n    if (this.extractor) {\n        convertedSet = new Array(l);\n        for (i = 0; i < l; i++) {\n            convertedSet[i] = this.extractor(trainingSet[i]);\n        }\n    }\n    this.numIterations = this.iterations * l;\n\n    if (this.algorithmMethod === 'random') {\n        this.timeConstant = this.numIterations / Math.log(this.mapRadius);\n    } else {\n        this.timeConstant = l / Math.log(this.mapRadius);\n    }\n    this.trainingSet = convertedSet;\n    this.times.setTraining = Date.now() - now;\n};\n\nSOM.prototype.trainOne = function trainOne() {\n    if (this.done) {\n\n        return false;\n\n    } else if (this.numIterations-- > 0) {\n\n        var neighbourhoodRadius,\n            trainingValue,\n            trainingSetFactor;\n\n        if (this.algorithmMethod === 'random') { // Pick a random value of the training set at each step\n            neighbourhoodRadius = this.mapRadius * Math.exp(-this.iterationCount / this.timeConstant);\n            trainingValue = getRandomValue(this.trainingSet, this.randomizer);\n            this._adjust(trainingValue, neighbourhoodRadius);\n            this.learningRate = this.startLearningRate * Math.exp(-this.iterationCount / this.numIterations);\n        } else { // Get next input vector\n            trainingSetFactor = -Math.floor(this.iterationCount / this.trainingSet.length);\n            neighbourhoodRadius = this.mapRadius * Math.exp(trainingSetFactor / this.timeConstant);\n            trainingValue = this.trainingSet[this.iterationCount % this.trainingSet.length];\n            this._adjust(trainingValue, neighbourhoodRadius);\n            if (((this.iterationCount + 1) % this.trainingSet.length) === 0) {\n                this.learningRate = this.startLearningRate * Math.exp(trainingSetFactor / Math.floor(this.numIterations / this.trainingSet.length));\n            }\n        }\n\n        this.iterationCount++;\n\n        return true;\n\n    } else {\n\n        this.done = true;\n        return false;\n\n    }\n};\n\nSOM.prototype._adjust = function adjust(trainingValue, neighbourhoodRadius) {\n    var now = Date.now(),\n        x, y, dist, influence;\n\n    var bmu = this._findBestMatchingUnit(trainingValue);\n\n    var now2 = Date.now();\n    this.times.findBMU += now2 - now;\n\n    var radiusLimit = Math.floor(neighbourhoodRadius);\n    var xMin = bmu.x - radiusLimit,\n        xMax = bmu.x + radiusLimit,\n        yMin = bmu.y - radiusLimit,\n        yMax = bmu.y + radiusLimit;\n\n    for (x = xMin; x <= xMax; x++) {\n        var theX = x;\n        if (x < 0) {\n            theX += this.x;\n        } else if (x >= this.x) {\n            theX -= this.x;\n        }\n        for (y = yMin; y <= yMax; y++) {\n            var theY = y;\n            if (y < 0) {\n                theY += this.y;\n            } else if (y >= this.y) {\n                theY -= this.y;\n            }\n\n            dist = bmu[this.distanceMethod](this.nodes[theX][theY]);\n\n            if (dist < neighbourhoodRadius) {\n                influence = Math.exp(-dist / (2 * neighbourhoodRadius));\n                this.nodes[theX][theY].adjustWeights(trainingValue, this.learningRate, influence);\n            }\n\n        }\n    }\n\n    this.times.adjust += (Date.now() - now2);\n\n};\n\nSOM.prototype.train = function train(trainingSet) {\n    if (!this.done) {\n        this.setTraining(trainingSet);\n        while (this.trainOne()) {\n        }\n    }\n};\n\nSOM.prototype.getConvertedNodes = function getConvertedNodes() {\n    var result = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        result[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            var node = this.nodes[i][j];\n            result[i][j] = this.creator ? this.creator(node.weights) : node.weights;\n        }\n    }\n    return result;\n};\n\nSOM.prototype._findBestMatchingUnit = function findBestMatchingUnit(candidate) {\n\n    var bmu,\n        lowest = Infinity,\n        dist;\n\n    for (var i = 0; i < this.x; i++) {\n        for (var j = 0; j < this.y; j++) {\n            dist = this.distance(this.nodes[i][j].weights, candidate);\n            if (dist < lowest) {\n                lowest = dist;\n                bmu = this.nodes[i][j];\n            }\n        }\n    }\n\n    return bmu;\n\n};\n\nSOM.prototype.predict = function predict(data, computePosition) {\n    if (typeof data === 'boolean') {\n        computePosition = data;\n        data = null;\n    }\n    if (!data) {\n        data = this.trainingSet;\n    }\n    if (Array.isArray(data) && (Array.isArray(data[0]) || (typeof data[0] === 'object'))) { // predict a dataset\n        var self = this;\n        return data.map(function (element) {\n            return self._predict(element, computePosition);\n        });\n    } else { // predict a single element\n        return this._predict(data, computePosition);\n    }\n};\n\nSOM.prototype._predict = function _predict(element, computePosition) {\n    if (!Array.isArray(element)) {\n        element = this.extractor(element);\n    }\n    var bmu = this._findBestMatchingUnit(element);\n    var result = [bmu.x, bmu.y];\n    if (computePosition) {\n        result[2] = bmu.getPosition(element);\n    }\n    return result;\n};\n\n// As seen in http://www.scholarpedia.org/article/Kohonen_network\nSOM.prototype.getQuantizationError = function getQuantizationError() {\n    var fit = this.getFit(),\n        l = fit.length,\n        sum = 0;\n    for (var i = 0; i < l; i++) {\n        sum += fit[i];\n    }\n    return sum / l;\n};\n\nSOM.prototype.getFit = function getFit(dataset) {\n    if (!dataset) {\n        dataset = this.trainingSet;\n    }\n    var l = dataset.length,\n        bmu,\n        result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        bmu = this._findBestMatchingUnit(dataset[i]);\n        result[i] = Math.sqrt(this.distance(dataset[i], bmu.weights));\n    }\n    return result;\n};\n\nfunction getConverters(fields) {\n    var l = fields.length,\n        normalizers = new Array(l),\n        denormalizers = new Array(l);\n    for (var i = 0; i < l; i++) {\n        normalizers[i] = getNormalizer(fields[i].range);\n        denormalizers[i] = getDenormalizer(fields[i].range);\n    }\n    return {\n        extractor: function extractor(value) {\n            var result = new Array(l);\n            for (var i = 0; i < l; i++) {\n                result[i] = normalizers[i](value[fields[i].name]);\n            }\n            return result;\n        },\n        creator: function creator(value) {\n            var result = {};\n            for (var i = 0; i < l; i++) {\n                result[fields[i].name] = denormalizers[i](value[i]);\n            }\n            return result;\n        }\n    };\n}\n\nfunction getNormalizer(minMax) {\n    return function normalizer(value) {\n        return (value - minMax[0]) / (minMax[1] - minMax[0]);\n    };\n}\n\nfunction getDenormalizer(minMax) {\n    return function denormalizer(value) {\n        return (minMax[0] + value * (minMax[1] - minMax[0]));\n    };\n}\n\nfunction squareEuclidean(a, b) {\n    var d = 0;\n    for (var i = 0, ii = a.length; i < ii; i++) {\n        d += (a[i] - b[i]) * (a[i] - b[i]);\n    }\n    return d;\n}\n\nfunction getRandomValue(arr, randomizer) {\n    return arr[Math.floor(randomizer() * arr.length)];\n}\n\nfunction getMaxDistance(distance, numWeights) {\n    var zero = new Array(numWeights),\n        one = new Array(numWeights);\n    for (var i = 0; i < numWeights; i++) {\n        zero[i] = 0;\n        one[i] = 1;\n    }\n    return distance(zero, one);\n}\n\nmodule.exports = SOM;","export default function maybeToPrecision(value, digits) {\n  if (value < 0) {\n    value = 0 - value;\n    if (typeof digits === 'number') {\n      return `- ${value.toPrecision(digits)}`;\n    } else {\n      return `- ${value.toString()}`;\n    }\n  } else {\n    if (typeof digits === 'number') {\n      return value.toPrecision(digits);\n    } else {\n      return value.toString();\n    }\n  }\n}\n","export default function checkArraySize(x, y) {\n  if (!Array.isArray(x) || !Array.isArray(y)) {\n    throw new TypeError('x and y must be arrays');\n  }\n  if (x.length !== y.length) {\n    throw new RangeError('x and y arrays must have the same length');\n  }\n}\n","export { default as maybeToPrecision } from './maybeToPrecision';\nexport { default as checkArrayLength } from './checkArrayLength';\n\nexport default class BaseRegression {\n  constructor() {\n    if (new.target === BaseRegression) {\n      throw new Error('BaseRegression must be subclassed');\n    }\n  }\n\n  predict(x) {\n    if (typeof x === 'number') {\n      return this._predict(x);\n    } else if (Array.isArray(x)) {\n      const y = [];\n      for (let i = 0; i < x.length; i++) {\n        y.push(this._predict(x[i]));\n      }\n      return y;\n    } else {\n      throw new TypeError('x must be a number or array');\n    }\n  }\n\n  _predict() {\n    throw new Error('_predict must be implemented');\n  }\n\n  train() {\n    // Do nothing for this package\n  }\n\n  toString() {\n    return '';\n  }\n\n  toLaTeX() {\n    return '';\n  }\n\n  /**\n   * Return the correlation coefficient of determination (r) and chi-square.\n   * @param {Array<number>} x\n   * @param {Array<number>} y\n   * @return {object}\n   */\n  score(x, y) {\n    if (!Array.isArray(x) || !Array.isArray(y) || x.length !== y.length) {\n      throw new Error('x and y must be arrays of the same length');\n    }\n\n    const n = x.length;\n    const y2 = new Array(n);\n    for (let i = 0; i < n; i++) {\n      y2[i] = this._predict(x[i]);\n    }\n\n    let xSum = 0;\n    let ySum = 0;\n    let chi2 = 0;\n    let rmsd = 0;\n    let xSquared = 0;\n    let ySquared = 0;\n    let xY = 0;\n    for (let i = 0; i < n; i++) {\n      xSum += y2[i];\n      ySum += y[i];\n      xSquared += y2[i] * y2[i];\n      ySquared += y[i] * y[i];\n      xY += y2[i] * y[i];\n      if (y[i] !== 0) {\n        chi2 += ((y[i] - y2[i]) * (y[i] - y2[i])) / y[i];\n      }\n      rmsd += (y[i] - y2[i]) * (y[i] - y2[i]);\n    }\n\n    const r =\n      (n * xY - xSum * ySum) /\n      Math.sqrt((n * xSquared - xSum * xSum) * (n * ySquared - ySum * ySum));\n\n    return {\n      r: r,\n      r2: r * r,\n      chi2: chi2,\n      rmsd: Math.sqrt(rmsd / n)\n    };\n  }\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { Matrix, MatrixTransposeView, solve } from 'ml-matrix';\n\nexport default class PolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y, degree);\n    }\n  }\n\n  _predict(x) {\n    let y = 0;\n    for (let k = 0; k < this.powers.length; k++) {\n      y += this.coefficients[k] * Math.pow(x, this.powers[k]);\n    }\n    return y;\n  }\n\n  toJSON() {\n    return {\n      name: 'polynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) + times}x`;\n          } else {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) +\n              times\n              }x${\n                sup\n              }${this.powers[k]\n              }${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'polynomialRegression') {\n      throw new TypeError('not a polynomial regression model');\n    }\n    return new PolynomialRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y, degree) {\n  const n = x.length;\n  let powers;\n  if (Array.isArray(degree)) {\n    powers = degree;\n    degree = powers.length;\n  } else {\n    degree++;\n    powers = new Array(degree);\n    for (let k = 0; k < degree; k++) {\n      powers[k] = k;\n    }\n  }\n  const F = new Matrix(n, degree);\n  const Y = new Matrix([y]);\n  for (let k = 0; k < degree; k++) {\n    for (let i = 0; i < n; i++) {\n      if (powers[k] === 0) {\n        F.set(i, k, 1);\n      } else {\n        F.set(i, k, Math.pow(x[i], powers[k]));\n      }\n    }\n  }\n\n  const FT = new MatrixTransposeView(F);\n  const A = FT.mmul(F);\n  const B = FT.mmul(new MatrixTransposeView(Y));\n\n  pr.degree = degree - 1;\n  pr.powers = powers;\n  pr.coefficients = solve(A, B).to1DArray();\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\n\nexport default class SimpleLinearRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = [y.intercept, y.slope];\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'simpleLinearRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(x) {\n    return this.slope * x + this.intercept;\n  }\n\n  computeX(y) {\n    return (y - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    let result = 'f(x) = ';\n    if (this.slope !== 0) {\n      const xFactor = maybeToPrecision(this.slope, precision);\n      result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;\n      if (this.intercept !== 0) {\n        const absIntercept = Math.abs(this.intercept);\n        const operator = absIntercept === this.intercept ? '+' : '-';\n        result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'simpleLinearRegression') {\n      throw new TypeError('not a SLR model');\n    }\n    return new SimpleLinearRegression(true, json);\n  }\n}\n\nfunction regress(slr, x, y) {\n  const n = x.length;\n  let xSum = 0;\n  let ySum = 0;\n\n  let xSquared = 0;\n  let xY = 0;\n\n  for (let i = 0; i < n; i++) {\n    xSum += x[i];\n    ySum += y[i];\n    xSquared += x[i] * x[i];\n    xY += x[i] * y[i];\n  }\n\n  const numerator = n * xY - xSum * ySum;\n  slr.slope = numerator / (n * xSquared - xSum * xSum);\n  slr.intercept = (1 / n) * ySum - slr.slope * (1 / n) * xSum;\n  slr.coefficients = [slr.intercept, slr.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class ExponentialRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(input) {\n    return this.B * Math.exp(input * this.A);\n  }\n\n  toJSON() {\n    return {\n      name: 'exponentialRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return (\n      `f(x) = ${\n        maybeToPrecision(this.B, precision)\n      } * e^(${\n        maybeToPrecision(this.A, precision)\n      } * x)`\n    );\n  }\n\n  toLaTeX(precision) {\n    if (this.A >= 0) {\n      return (\n        `f(x) = ${\n          maybeToPrecision(this.B, precision)\n        }e^{${\n          maybeToPrecision(this.A, precision)\n        }x}`\n      );\n    } else {\n      return (\n        `f(x) = \\\\frac{${\n          maybeToPrecision(this.B, precision)\n        }}{e^{${\n          maybeToPrecision(-this.A, precision)\n        }x}}`\n      );\n    }\n  }\n\n  static load(json) {\n    if (json.name !== 'exponentialRegression') {\n      throw new TypeError('not a exponential regression model');\n    }\n    return new ExponentialRegression(true, json);\n  }\n}\n\nfunction regress(er, x, y) {\n  const n = x.length;\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(x, yl);\n  er.A = linear.slope;\n  er.B = Math.exp(linear.intercept);\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class PowerRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // reloading model\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(newInputs) {\n    return this.A * Math.pow(newInputs, this.B);\n  }\n\n  toJSON() {\n    return {\n      name: 'powerRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return `f(x) = ${maybeToPrecision(\n      this.A,\n      precision\n    )} * x^${maybeToPrecision(this.B, precision)}`;\n  }\n\n  toLaTeX(precision) {\n    let latex = '';\n    if (this.B >= 0) {\n      latex = `f(x) = ${maybeToPrecision(\n        this.A,\n        precision\n      )}x^{${maybeToPrecision(this.B, precision)}}`;\n    } else {\n      latex = `f(x) = \\\\frac{${maybeToPrecision(\n        this.A,\n        precision\n      )}}{x^{${maybeToPrecision(-this.B, precision)}}}`;\n    }\n    latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');\n    return latex;\n  }\n\n  static load(json) {\n    if (json.name !== 'powerRegression') {\n      throw new TypeError('not a power regression model');\n    }\n    return new PowerRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y) {\n  const n = x.length;\n  const xl = new Array(n);\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    xl[i] = Math.log(x[i]);\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(xl, yl);\n  pr.A = Math.exp(linear.intercept);\n  pr.B = linear.slope;\n}\n","import Matrix, { SVD, pseudoInverse } from 'ml-matrix';\n\nexport default class MultivariateLinearRegression {\n  constructor(x, y, options = {}) {\n    const { intercept = true, statistics = true } = options;\n    this.statistics = statistics;\n    if (x === true) {\n      this.weights = y.weights;\n      this.inputs = y.inputs;\n      this.outputs = y.outputs;\n      this.intercept = y.intercept;\n    } else {\n      x = new Matrix(x);\n      y = new Matrix(y);\n      if (intercept) {\n        x.addColumn(new Array(x.rows).fill(1));\n      }\n      let xt = x.transpose();\n      const xx = xt\n        .mmul(x);\n      const xy = xt\n        .mmul(y);\n      const invxx = new SVD(xx)\n        .inverse();\n      const beta = xy\n        .transpose()\n        .mmul(invxx)\n        .transpose();\n      this.weights = beta.to2DArray();\n      this.inputs = x.columns;\n      this.outputs = y.columns;\n      if (intercept) this.inputs--;\n      this.intercept = intercept;\n      if (statistics) {\n        /*\n         * Let's add some basic statistics about the beta's to be able to interpret them.\n         * source: http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat401/Notes/401-multreg.pdf\n         * validated against Excel Regression AddIn\n         * test: \"datamining statistics test\"\n         */\n        const fittedValues = x.mmul(beta);\n        const residuals = y.clone().addM(fittedValues.neg());\n        const variance =\n          residuals\n            .to2DArray()\n            .map((ri) => Math.pow(ri[0], 2))\n            .reduce((a, b) => a + b) /\n          (y.rows - x.columns);\n        this.stdError = Math.sqrt(variance);\n        this.stdErrorMatrix = pseudoInverse(xx).mul(variance);\n        this.stdErrors = this.stdErrorMatrix\n          .diagonal()\n          .map((d) => Math.sqrt(d));\n        this.tStats = this.weights.map((d, i) =>\n          (this.stdErrors[i] === 0 ? 0 : d[0] / this.stdErrors[i])\n        );\n      }\n    }\n  }\n\n  predict(x) {\n    if (Array.isArray(x)) {\n      if (typeof x[0] === 'number') {\n        return this._predict(x);\n      } else if (Array.isArray(x[0])) {\n        const y = new Array(x.length);\n        for (let i = 0; i < x.length; i++) {\n          y[i] = this._predict(x[i]);\n        }\n        return y;\n      }\n    } else if (Matrix.isMatrix(x)) {\n      const y = new Matrix(x.rows, this.outputs);\n      for (let i = 0; i < x.rows; i++) {\n        y.setRow(i, this._predict(x.getRow(i)));\n      }\n      return y;\n    }\n    throw new TypeError('x must be a matrix or array of numbers');\n  }\n\n  _predict(x) {\n    const result = new Array(this.outputs);\n    if (this.intercept) {\n      for (let i = 0; i < this.outputs; i++) {\n        result[i] = this.weights[this.inputs][i];\n      }\n    } else {\n      result.fill(0);\n    }\n    for (let i = 0; i < this.inputs; i++) {\n      for (let j = 0; j < this.outputs; j++) {\n        result[j] += this.weights[i][j] * x[i];\n      }\n    }\n    return result;\n  }\n\n  score() {\n    throw new Error('score method is not implemented yet');\n  }\n\n  toJSON() {\n    return {\n      name: 'multivariateLinearRegression',\n      weights: this.weights,\n      inputs: this.inputs,\n      outputs: this.outputs,\n      intercept: this.intercept,\n      summary: this.statistics\n        ? {\n          regressionStatistics: {\n            standardError: this.stdError,\n            observations: this.outputs\n          },\n          variables: this.weights.map((d, i) => {\n            return {\n              label:\n                  i === this.weights.length - 1\n                    ? 'Intercept'\n                    : `X Variable ${i + 1}`,\n              coefficients: d,\n              standardError: this.stdErrors[i],\n              tStat: this.tStats[i]\n            };\n          })\n        }\n        : undefined\n    };\n  }\n\n  static load(model) {\n    if (model.name !== 'multivariateLinearRegression') {\n      throw new Error('not a MLR model');\n    }\n    return new MultivariateLinearRegression(true, model);\n  }\n}\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass GaussianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = GaussianKernel;\n","'use strict';\n\nconst defaultOptions = {\n  degree: 1,\n  constant: 1,\n  scale: 1\n};\n\nclass PolynomialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n\n    this.degree = options.degree;\n    this.constant = options.constant;\n    this.scale = options.scale;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.pow(this.scale * sum + this.constant, this.degree);\n  }\n}\n\nmodule.exports = PolynomialKernel;\n","'use strict';\n\nconst defaultOptions = {\n  alpha: 0.01,\n  constant: -Math.E\n};\n\nclass SigmoidKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.alpha = options.alpha;\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.tanh(this.alpha * sum + this.constant);\n  }\n}\n\nmodule.exports = SigmoidKernel;\n","'use strict';\n\nconst defaultOptions = {\n  sigma: 1,\n  degree: 1\n};\n\nclass ANOVAKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.degree = options.degree;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    var len = Math.min(x.length, y.length);\n    for (var i = 1; i <= len; ++i) {\n      sum += Math.pow(\n        Math.exp(\n          -this.sigma *\n            Math.pow(Math.pow(x[i - 1], i) - Math.pow(y[i - 1], i), 2)\n        ),\n        this.degree\n      );\n    }\n    return sum;\n  }\n}\n\nmodule.exports = ANOVAKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass CauchyKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    return 1 / (1 + squaredEuclidean(x, y) / (this.sigma * this.sigma));\n  }\n}\n\nmodule.exports = CauchyKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass ExponentialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = ExponentialKernel;\n","'use strict';\n\nclass HistogramIntersectionKernel {\n  compute(x, y) {\n    var min = Math.min(x.length, y.length);\n    var sum = 0;\n    for (var i = 0; i < min; ++i) {\n      sum += Math.min(x[i], y[i]);\n    }\n\n    return sum;\n  }\n}\n\nmodule.exports = HistogramIntersectionKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass LaplacianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.sigma);\n  }\n}\n\nmodule.exports = LaplacianKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass MultiquadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    return Math.sqrt(squaredEuclidean(x, y) + this.constant * this.constant);\n  }\n}\n\nmodule.exports = MultiquadraticKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass RationalQuadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return 1 - distance / (distance + this.constant);\n  }\n}\n\nmodule.exports = RationalQuadraticKernel;\n","'use strict';\n\nconst { Matrix, MatrixTransposeView } = require('ml-matrix');\nconst GaussianKernel = require('ml-kernel-gaussian');\nconst PolynomialKernel = require('ml-kernel-polynomial');\nconst SigmoidKernel = require('ml-kernel-sigmoid');\n\nconst ANOVAKernel = require('./kernels/anova-kernel');\nconst CauchyKernel = require('./kernels/cauchy-kernel');\nconst ExponentialKernel = require('./kernels/exponential-kernel');\nconst HistogramKernel = require('./kernels/histogram-intersection-kernel');\nconst LaplacianKernel = require('./kernels/laplacian-kernel');\nconst MultiquadraticKernel = require('./kernels/multiquadratic-kernel');\nconst RationalKernel = require('./kernels/rational-quadratic-kernel');\n\nconst kernelType = {\n  gaussian: GaussianKernel,\n  rbf: GaussianKernel,\n  polynomial: PolynomialKernel,\n  poly: PolynomialKernel,\n  anova: ANOVAKernel,\n  cauchy: CauchyKernel,\n  exponential: ExponentialKernel,\n  histogram: HistogramKernel,\n  min: HistogramKernel,\n  laplacian: LaplacianKernel,\n  multiquadratic: MultiquadraticKernel,\n  rational: RationalKernel,\n  sigmoid: SigmoidKernel,\n  mlp: SigmoidKernel\n};\n\nclass Kernel {\n  constructor(type, options) {\n    this.kernelType = type;\n    if (type === 'linear') return;\n\n    if (typeof type === 'string') {\n      type = type.toLowerCase();\n\n      var KernelConstructor = kernelType[type];\n      if (KernelConstructor) {\n        this.kernelFunction = new KernelConstructor(options);\n      } else {\n        throw new Error(`unsupported kernel type: ${type}`);\n      }\n    } else if (typeof type === 'object' && typeof type.compute === 'function') {\n      this.kernelFunction = type;\n    } else {\n      throw new TypeError(\n        'first argument must be a valid kernel type or instance'\n      );\n    }\n  }\n\n  compute(inputs, landmarks) {\n    inputs = Matrix.checkMatrix(inputs);\n    if (landmarks === undefined) {\n      landmarks = inputs;\n    } else {\n      landmarks = Matrix.checkMatrix(landmarks);\n    }\n    if (this.kernelType === 'linear') {\n      return inputs.mmul(new MatrixTransposeView(landmarks));\n    }\n\n    const kernelMatrix = new Matrix(inputs.rows, landmarks.rows);\n    if (inputs === landmarks) {\n      // fast path, matrix is symmetric\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = i; j < inputs.rows; j++) {\n          const value = this.kernelFunction.compute(\n            inputs.getRow(i),\n            inputs.getRow(j)\n          );\n          kernelMatrix.set(i, j, value);\n          kernelMatrix.set(j, i, value);\n        }\n      }\n    } else {\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = 0; j < landmarks.rows; j++) {\n          kernelMatrix.set(\n            i,\n            j,\n            this.kernelFunction.compute(inputs.getRow(i), landmarks.getRow(j))\n          );\n        }\n      }\n    }\n    return kernelMatrix;\n  }\n}\n\nmodule.exports = Kernel;\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport median from 'ml-array-median';\n\nexport default class TheilSenRegression extends BaseRegression {\n  /**\n   * Theil–Sen estimator\n   * https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator\n   * @param {Array<number>|boolean} x\n   * @param {Array<number>|object} y\n   * @constructor\n   */\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // loads the model\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = y.coefficients;\n    } else {\n      // creates the model\n      checkArrayLength(x, y);\n      theilSen(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'TheilSenRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(input) {\n    return this.slope * input + this.intercept;\n  }\n\n  computeX(input) {\n    return (input - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    var result = 'f(x) = ';\n    if (this.slope) {\n      var xFactor = maybeToPrecision(this.slope, precision);\n      result += `${Math.abs(xFactor - 1) < 1e-5 ? '' : `${xFactor} * `}x`;\n      if (this.intercept) {\n        var absIntercept = Math.abs(this.intercept);\n        var operator = absIntercept === this.intercept ? '+' : '-';\n        result +=\n          ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'TheilSenRegression') {\n      throw new TypeError('not a Theil-Sen model');\n    }\n    return new TheilSenRegression(true, json);\n  }\n}\n\nfunction theilSen(regression, x, y) {\n  let len = x.length;\n  let slopes = new Array(len * len);\n  let count = 0;\n  for (let i = 0; i < len; ++i) {\n    for (let j = i + 1; j < len; ++j) {\n      if (x[i] !== x[j]) {\n        slopes[count++] = (y[j] - y[i]) / (x[j] - x[i]);\n      }\n    }\n  }\n  slopes.length = count;\n  let medianSlope = median(slopes);\n\n  let cuts = new Array(len);\n  for (let i = 0; i < len; ++i) {\n    cuts[i] = y[i] - medianSlope * x[i];\n  }\n\n  regression.slope = medianSlope;\n  regression.intercept = median(cuts);\n  regression.coefficients = [regression.intercept, regression.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { solve } from 'ml-matrix';\n\n/**\n * @class RobustPolynomialRegression\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree - polynomial degree\n */\nexport default class RobustPolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      robustPolynomial(this, x, y, degree);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'robustPolynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  _predict(x) {\n    return predict(x, this.powers, this.coefficients);\n  }\n\n  /**\n   * Display the formula\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  /**\n   * Display the formula in LaTeX format\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x`;\n          } else {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x${sup}${this.powers[k]}${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'robustPolynomialRegression') {\n      throw new TypeError('not a RobustPolynomialRegression model');\n    }\n    return new RobustPolynomialRegression(true, json);\n  }\n}\n\nfunction robustPolynomial(regression, x, y, degree) {\n  let powers = Array(degree)\n    .fill(0)\n    .map((_, index) => index);\n\n  const tuples = getRandomTuples(x, y, degree);\n\n  var min;\n  for (var i = 0; i < tuples.length; i++) {\n    var tuple = tuples[i];\n    var coefficients = calcCoefficients(tuple, powers);\n\n    var residuals = x.slice();\n    for (var j = 0; j < x.length; j++) {\n      residuals[j] = y[j] - predict(x[j], powers, coefficients);\n      residuals[j] = {\n        residual: residuals[j] * residuals[j],\n        coefficients\n      };\n    }\n\n    var median = residualsMedian(residuals);\n    if (!min || median.residual < min.residual) {\n      min = median;\n    }\n  }\n\n  regression.degree = degree;\n  regression.powers = powers;\n  regression.coefficients = min.coefficients;\n}\n\n/**\n * @ignore\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree\n * @return {Array<{x:number,y:number}>}\n */\nfunction getRandomTuples(x, y, degree) {\n  var len = Math.floor(x.length / degree);\n  var tuples = new Array(len);\n\n  for (var i = 0; i < x.length; i++) {\n    var pos = Math.floor(Math.random() * len);\n\n    var counter = 0;\n    while (counter < x.length) {\n      if (!tuples[pos]) {\n        tuples[pos] = [\n          {\n            x: x[i],\n            y: y[i]\n          }\n        ];\n        break;\n      } else if (tuples[pos].length < degree) {\n        tuples[pos].push({\n          x: x[i],\n          y: y[i]\n        });\n        break;\n      } else {\n        counter++;\n        pos = (pos + 1) % len;\n      }\n    }\n\n    if (counter === x.length) {\n      return tuples;\n    }\n  }\n  return tuples;\n}\n\n/**\n * @ignore\n * @param {{x:number,y:number}} tuple\n * @param {Array<number>} powers\n * @return {Array<number>}\n */\nfunction calcCoefficients(tuple, powers) {\n  var X = tuple.slice();\n  var Y = tuple.slice();\n  for (var i = 0; i < X.length; i++) {\n    Y[i] = [tuple[i].y];\n    X[i] = new Array(powers.length);\n    for (var j = 0; j < powers.length; j++) {\n      X[i][j] = Math.pow(tuple[i].x, powers[j]);\n    }\n  }\n\n  return solve(X, Y).to1DArray();\n}\n\nfunction predict(x, powers, coefficients) {\n  let y = 0;\n  for (let k = 0; k < powers.length; k++) {\n    y += coefficients[k] * Math.pow(x, powers[k]);\n  }\n  return y;\n}\n\nfunction residualsMedian(residuals) {\n  residuals.sort((a, b) => a.residual - b.residual);\n\n  var l = residuals.length;\n  var half = Math.floor(l / 2);\n  return l % 2 === 0 ? residuals[half - 1] : residuals[half];\n}\n","/**\n * Calculate current error\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} parameters - Array of current parameter values\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {number}\n */\nexport default function errorCalculation(\n  data,\n  parameters,\n  parameterizedFunction\n) {\n  var error = 0;\n  const func = parameterizedFunction(parameters);\n\n  for (var i = 0; i < data.x.length; i++) {\n    error += Math.abs(data.y[i] - func(data.x[i]));\n  }\n\n  return error;\n}\n","import { inverse, Matrix } from 'ml-matrix';\n\n/**\n * Difference of the matrix function over the parameters\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Matrix}\n */\nfunction gradientFunction(\n  data,\n  evaluatedData,\n  params,\n  gradientDifference,\n  paramFunction\n) {\n  const n = params.length;\n  const m = data.x.length;\n\n  var ans = new Array(n);\n\n  for (var param = 0; param < n; param++) {\n    ans[param] = new Array(m);\n    var auxParams = params.concat();\n    auxParams[param] += gradientDifference;\n    var funcParam = paramFunction(auxParams);\n\n    for (var point = 0; point < m; point++) {\n      ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);\n    }\n  }\n  return new Matrix(ans);\n}\n\n/**\n * Matrix function over the samples\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @return {Matrix}\n */\nfunction matrixFunction(data, evaluatedData) {\n  const m = data.x.length;\n\n  var ans = new Array(m);\n\n  for (var point = 0; point < m; point++) {\n    ans[point] = [data.y[point] - evaluatedData[point]];\n  }\n\n  return new Matrix(ans);\n}\n\n/**\n * Iteration for Levenberg-Marquardt\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} damping - Levenberg-Marquardt parameter\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Array<number>}\n */\nexport default function step(\n  data,\n  params,\n  damping,\n  gradientDifference,\n  parameterizedFunction\n) {\n  var value = damping * gradientDifference * gradientDifference;\n  var identity = Matrix.eye(params.length, params.length, value);\n\n  const func = parameterizedFunction(params);\n  var evaluatedData = data.x.map((e) => func(e));\n\n  var gradientFunc = gradientFunction(\n    data,\n    evaluatedData,\n    params,\n    gradientDifference,\n    parameterizedFunction\n  );\n  var matrixFunc = matrixFunction(data, evaluatedData);\n  var inverseMatrix = inverse(\n    identity.add(gradientFunc.mmul(gradientFunc.transpose()))\n  );\n\n  params = new Matrix([params]);\n  params = params.sub(\n    inverseMatrix\n      .mmul(gradientFunc)\n      .mmul(matrixFunc)\n      .mul(gradientDifference)\n      .transpose()\n  );\n\n  return params.to1DArray();\n}\n","import errorCalculation from './errorCalculation';\nimport step from './step';\n\n/**\n * Curve fitting algorithm\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @param {object} [options] - Options object\n * @param {number} [options.damping] - Levenberg-Marquardt parameter\n * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter\n * @param {Array<number>} [options.minValues] - Minimum allowed values for parameters\n * @param {Array<number>} [options.maxValues] - Maximum allowed values for parameters\n * @param {Array<number>} [options.initialValues] - Array of initial parameter values\n * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations\n * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point\n * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}\n */\nexport default function levenbergMarquardt(\n  data,\n  parameterizedFunction,\n  options = {}\n) {\n  let {\n    maxIterations = 100,\n    gradientDifference = 10e-2,\n    damping = 0,\n    errorTolerance = 10e-3,\n    minValues,\n    maxValues,\n    initialValues\n  } = options;\n\n  if (damping <= 0) {\n    throw new Error('The damping option must be a positive number');\n  } else if (!data.x || !data.y) {\n    throw new Error('The data parameter must have x and y elements');\n  } else if (\n    !Array.isArray(data.x) ||\n    data.x.length < 2 ||\n    !Array.isArray(data.y) ||\n    data.y.length < 2\n  ) {\n    throw new Error(\n      'The data parameter elements must be an array with more than 2 points'\n    );\n  } else if (data.x.length !== data.y.length) {\n    throw new Error('The data parameter elements must have the same size');\n  }\n\n  var parameters =\n    initialValues || new Array(parameterizedFunction.length).fill(1);\n  let parLen = parameters.length;\n  maxValues = maxValues || new Array(parLen).fill(Number.MAX_SAFE_INTEGER);\n  minValues = minValues || new Array(parLen).fill(Number.MIN_SAFE_INTEGER);\n\n  if (maxValues.length !== minValues.length) {\n    throw new Error('minValues and maxValues must be the same size');\n  }\n\n  if (!Array.isArray(parameters)) {\n    throw new Error('initialValues must be an array');\n  }\n\n  var error = errorCalculation(data, parameters, parameterizedFunction);\n\n  var converged = error <= errorTolerance;\n\n  for (\n    var iteration = 0;\n    iteration < maxIterations && !converged;\n    iteration++\n  ) {\n    parameters = step(\n      data,\n      parameters,\n      damping,\n      gradientDifference,\n      parameterizedFunction\n    );\n\n    for (let k = 0; k < parLen; k++) {\n      parameters[k] = Math.min(\n        Math.max(minValues[k], parameters[k]),\n        maxValues[k]\n      );\n    }\n\n    error = errorCalculation(data, parameters, parameterizedFunction);\n    if (isNaN(error)) break;\n    converged = error <= errorTolerance;\n  }\n\n  return {\n    parameterValues: parameters,\n    parameterError: error,\n    iterations: iteration\n  };\n}\n","/**\n * Returns a new array based on extraction of specific indices of an array\n * @private\n * @param {Array} vector\n * @param {Array} indices\n */\nexport default function selection(vector, indices) {\n  let u = []; //new Float64Array(indices.length);\n  for (let i = 0; i < indices.length; i++) {\n    u[i] = vector[indices[i]];\n  }\n  return u;\n}\n","/**\n *\n * @private\n * @param {Array of arrays} collection\n */\nexport default function sortCollectionSet(collection) {\n  let objectCollection = collection\n    .map((value, index) => {\n      let key = BigInt(0);\n      value.forEach((item) => (key |= BigInt(1) << BigInt(item)));\n      return { value, index, key };\n    })\n    .sort((a, b) => {\n      if (a.key - b.key < 0) return -1;\n      return 1;\n    });\n\n  let sorted = [];\n  let indices = [];\n\n  let key;\n  for (let set of objectCollection) {\n    if (set.key !== key) {\n      key = set.key;\n      indices.push([]);\n      sorted.push(set.value);\n    }\n    indices[indices.length - 1].push(set.index);\n  }\n\n  let result = {\n    values: sorted,\n    indices: indices,\n  };\n  return result;\n}\n","import {\n  Matrix,\n  LuDecomposition,\n  solve,\n  CholeskyDecomposition,\n} from 'ml-matrix';\n\nimport sortCollectionSet from './util/sortCollectionSet';\n\n/**\n * (Combinatorial Subspace Least Squares) - subfunction for the FC-NNLS\n * @private\n * @param {Matrix} XtX\n * @param {Matrix} XtY\n * @param {Array} Pset\n * @param {Numbers} l\n * @param {Numbers} p\n */\nexport default function cssls(XtX, XtY, Pset, l, p) {\n  // Solves the set of equation XtX*K = XtY for the variables in Pset\n  // if XtX (or XtX(vars,vars)) is singular, performs the svd and find pseudoinverse, otherwise (even if ill-conditioned) finds inverse with LU decomposition and solves the set of equation\n  // it is consistent with matlab results for ill-conditioned matrices (at least consistent with test 'ill-conditionned square X rank 2, Y 3x1' in cssls.test)\n\n  let K = Matrix.zeros(l, p);\n  if (Pset === null) {\n    let choXtX = new CholeskyDecomposition(XtX);\n    if (choXtX.isPositiveDefinite() === true) {\n      K = choXtX.solve(XtY);\n    } else {\n      let luXtX = new LuDecomposition(XtX);\n      if (luXtX.isSingular() === false) {\n        K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n      } else {\n        K = solve(XtX, XtY, { useSVD: true });\n      }\n    }\n  } else {\n    let sortedPset = sortCollectionSet(Pset).values;\n    let sortedEset = sortCollectionSet(Pset).indices;\n    if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === 0 &&\n      sortedEset[0].length === p\n    ) {\n      return K;\n    } else if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === l &&\n      sortedEset[0].length === p\n    ) {\n      let choXtX = new CholeskyDecomposition(XtX);\n      if (choXtX.isPositiveDefinite() === true) {\n        K = choXtX.solve(XtY);\n      } else {\n        let luXtX = new LuDecomposition(XtX);\n        if (luXtX.isSingular() === false) {\n          K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n        } else {\n          K = solve(XtX, XtY, { useSVD: true });\n        }\n      }\n    } else {\n      for (let k = 0; k < sortedPset.length; k++) {\n        let cols2Solve = sortedEset[k];\n        let vars = sortedPset[k];\n        let L;\n        let choXtX = new CholeskyDecomposition(XtX.selection(vars, vars));\n        if (choXtX.isPositiveDefinite() === true) {\n          L = choXtX.solve(XtY.selection(vars, cols2Solve));\n        } else {\n          let luXtX = new LuDecomposition(XtX.selection(vars, vars));\n          if (luXtX.isSingular() === false) {\n            L = luXtX\n              .solve(Matrix.eye(vars.length))\n              .mmul(XtY.selection(vars, cols2Solve));\n          } else {\n            L = solve(\n              XtX.selection(vars, vars),\n              XtY.selection(vars, cols2Solve),\n              { useSVD: true },\n            );\n          }\n        }\n        for (let i = 0; i < L.rows; i++) {\n          for (let j = 0; j < L.columns; j++) {\n            K.set(vars[i], cols2Solve[j], L.get(i, j));\n          }\n        }\n      }\n    }\n  }\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport cssls from './cssls';\n\nexport default function initialisation(X, Y) {\n  let n = X.rows;\n  let l = X.columns;\n  let p = Y.columns;\n  let iter = 0;\n\n  if (Y.rows !== n) throw new Error('ERROR: matrix size not compatible');\n\n  let W = Matrix.zeros(l, p);\n\n  // precomputes part of pseudoinverse\n  let XtX = X.transpose().mmul(X);\n  let XtY = X.transpose().mmul(Y);\n\n  let K = cssls(XtX, XtY, null, l, p); // K is lxp\n  let Pset = [];\n  for (let j = 0; j < p; j++) {\n    Pset[j] = [];\n    for (let i = 0; i < l; i++) {\n      if (K.get(i, j) > 0) {\n        Pset[j].push(i);\n      } else {\n        K.set(i, j, 0);\n      } //This is our initial solution, it's the solution found by overwriting the unconstrained least square solution\n    }\n  }\n  let Fset = [];\n  for (let j = 0; j < p; j++) {\n    if (Pset[j].length !== l) {\n      Fset.push(j);\n    }\n  }\n\n  let D = K.clone();\n\n  return { n, l, p, iter, W, XtX, XtY, K, Pset, Fset, D };\n}\n","/**\n * Computes the set difference A\\B\n * @private\n * @param {A} set A as an array\n * @param {B} set B as an array\n */\nexport default function setDifference(A, B) {\n  let C = [];\n  for (let i of A) {\n    if (!B.includes(i)) C.push(i);\n  }\n  return C;\n}\n","import setDifference from './util/setDifference';\n\n// Makes sure the solution has converged\nexport default function optimality(\n  iter,\n  maxIter,\n  XtX,\n  XtY,\n  Fset,\n  Pset,\n  W,\n  K,\n  l,\n  p,\n  D,\n) {\n  if (iter === maxIter) {\n    throw new Error('Maximum number of iterations exceeded');\n  }\n\n  // Check solution for optimality\n  let V = XtY.subMatrixColumn(Fset).subtract(XtX.mmul(K.subMatrixColumn(Fset)));\n  for (let j = 0; j < Fset.length; j++) {\n    W.setColumn(Fset[j], V.subMatrixColumn([j]));\n  }\n  let Jset = [];\n  let fullSet = [];\n  for (let i = 0; i < l; i++) {\n    fullSet.push(i);\n  }\n  for (let j = 0; j < Fset.length; j++) {\n    let notPset = setDifference(fullSet, Pset[Fset[j]]);\n    if (notPset.length === 0) {\n      Jset.push(Fset[j]);\n    } else if (W.selection(notPset, [Fset[j]]).max() <= 0) {\n      Jset.push(Fset[j]);\n    }\n  }\n  Fset = setDifference(Fset, Jset);\n\n  // For non-optimal solutions, add the appropriate variables to Pset\n  if (Fset.length !== 0) {\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (Pset[Fset[j]].includes(i)) W.set(i, Fset[j], -Infinity);\n      }\n      Pset[Fset[j]].push(W.subMatrixColumn(Fset).maxColumnIndex(j)[0]);\n    }\n    for (let j = 0; j < Fset.length; j++) {\n      D.setColumn(Fset[j], K.getColumn(Fset[j]));\n    }\n  }\n  for (let j = 0; j < p; j++) {\n    Pset[j].sort((a, b) => a - b);\n  }\n  return { Pset, Fset, W };\n}\n","import { Matrix } from 'ml-matrix';\n\nimport selection from './util/selection';\nimport cssls from './cssls';\nimport initialisation from './initialisation';\nimport optimality from './optimality';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with multiple Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {Matrix|number[][]} Y\n * @param {object} [options={}]\n * @param {number} [options.maxIterations] if empty maxIterations is set at 3 times the number of columns of X\n * @returns {Matrix} K\n */\nexport default function fcnnls(X, Y, options = {}) {\n  X = Matrix.checkMatrix(X);\n  Y = Matrix.checkMatrix(Y);\n  let { l, p, iter, W, XtX, XtY, K, Pset, Fset, D } = initialisation(X, Y);\n  const { maxIterations = X.columns * 3 } = options;\n\n  // Active set algorithm for NNLS main loop\n  while (Fset.length > 0) {\n    // Solves for the passive variables (uses subroutine below)\n    let L = cssls(\n      XtX,\n      XtY.subMatrixColumn(Fset),\n      selection(Pset, Fset),\n      l,\n      Fset.length,\n    );\n    for (let i = 0; i < l; i++) {\n      for (let j = 0; j < Fset.length; j++) {\n        K.set(i, Fset[j], L.get(i, j));\n      }\n    }\n\n    // Finds any infeasible solutions\n    let infeasIndex = [];\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (L.get(i, j) < 0) {\n          infeasIndex.push(j);\n          break;\n        }\n      }\n    }\n    let Hset = selection(Fset, infeasIndex);\n\n    // Makes infeasible solutions feasible (standard NNLS inner loop)\n    if (Hset.length > 0) {\n      let m = Hset.length;\n      let alpha = Matrix.ones(l, m);\n\n      while (m > 0 && iter < maxIterations) {\n        iter++;\n\n        alpha.mul(Infinity);\n\n        // Finds indices of negative variables in passive set\n        let hRowColIdx = [[], []]; // Indexes work in pairs, each pair reprensents a single element, first array is row index, second array is column index\n        let negRowColIdx = [[], []]; // Same as before\n        for (let j = 0; j < m; j++) {\n          for (let i = 0; i < Pset[Hset[j]].length; i++) {\n            if (K.get(Pset[Hset[j]][i], Hset[j]) < 0) {\n              hRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              hRowColIdx[1].push(j);\n              negRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              negRowColIdx[1].push(Hset[j]);\n            } // Compared to matlab, here we keep the row/column indexing (we are not taking the linear indexing)\n          }\n        }\n\n        for (let k = 0; k < hRowColIdx[0].length; k++) {\n          // could be hRowColIdx[1].length as well\n          alpha.set(\n            hRowColIdx[0][k],\n            hRowColIdx[1][k],\n            D.get(negRowColIdx[0][k], negRowColIdx[1][k]) /\n              (D.get(negRowColIdx[0][k], negRowColIdx[1][k]) -\n                K.get(negRowColIdx[0][k], negRowColIdx[1][k])),\n          );\n        }\n\n        let alphaMin = [];\n        let minIdx = [];\n        for (let j = 0; j < m; j++) {\n          alphaMin[j] = alpha.minColumn(j);\n          minIdx[j] = alpha.minColumnIndex(j)[0];\n        }\n\n        alphaMin = Matrix.rowVector(alphaMin);\n        for (let i = 0; i < l; i++) {\n          alpha.setSubMatrix(alphaMin, i, 0);\n        }\n\n        let E = new Matrix(l, m);\n        E = D.subMatrixColumn(Hset).subtract(\n          alpha\n            .subMatrix(0, l - 1, 0, m - 1)\n            .mul(D.subMatrixColumn(Hset).subtract(K.subMatrixColumn(Hset))),\n        );\n        for (let j = 0; j < m; j++) {\n          D.setColumn(Hset[j], E.subMatrixColumn([j]));\n        }\n\n        let idx2zero = [minIdx, Hset];\n        for (let k = 0; k < m; k++) {\n          D.set(idx2zero[0][k], idx2zero[1][k], 0);\n        }\n\n        for (let j = 0; j < m; j++) {\n          Pset[Hset[j]].splice(\n            Pset[Hset[j]].findIndex((item) => item === minIdx[j]),\n            1,\n          );\n        }\n\n        L = cssls(XtX, XtY.subMatrixColumn(Hset), selection(Pset, Hset), l, m);\n        for (let j = 0; j < m; j++) {\n          K.setColumn(Hset[j], L.subMatrixColumn([j]));\n        }\n\n        Hset = [];\n        for (let j = 0; j < K.columns; j++) {\n          for (let i = 0; i < l; i++) {\n            if (K.get(i, j) < 0) {\n              Hset.push(j);\n\n              break;\n            }\n          }\n        }\n        m = Hset.length;\n      }\n    }\n\n    let newParam = optimality(\n      iter,\n      maxIterations,\n      XtX,\n      XtY,\n      Fset,\n      Pset,\n      W,\n      K,\n      l,\n      p,\n      D,\n    );\n    Pset = newParam.Pset;\n    Fset = newParam.Fset;\n    W = newParam.W;\n  }\n\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport fcnnls from './fcnnls';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with single Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {number[]} y\n * @param {object} [options={}]\n * @param {boolean} [maxIterations] if true or empty maxIterations is set at 3 times the number of columns of X\n * @returns {Array} k\n */\nexport default function fcnnlsVector(X, y, options = {}) {\n  if (Array.isArray(y) === false) {\n    throw new TypeError('y must be a 1D Array');\n  }\n  let Y = Matrix.columnVector(y);\n  let K = fcnnls(X, Y, options);\n  let k = K.to1DArray();\n  return k;\n}\n","module.exports = function(haystack, needle, comparator, low, high) {\n  var mid, cmp;\n\n  if(low === undefined)\n    low = 0;\n\n  else {\n    low = low|0;\n    if(low < 0 || low >= haystack.length)\n      throw new RangeError(\"invalid lower bound\");\n  }\n\n  if(high === undefined)\n    high = haystack.length - 1;\n\n  else {\n    high = high|0;\n    if(high < low || high >= haystack.length)\n      throw new RangeError(\"invalid upper bound\");\n  }\n\n  while(low <= high) {\n    // The naive `low + high >>> 1` could fail for array lengths > 2**31\n    // because `>>>` converts its operands to int32. `low + (high - low >>> 1)`\n    // works for array lengths <= 2**32-1 which is also Javascript's max array\n    // length.\n    mid = low + ((high - low) >>> 1);\n    cmp = +comparator(haystack[mid], needle, mid, haystack);\n\n    // Too low.\n    if(cmp < 0.0)\n      low  = mid + 1;\n\n    // Too high.\n    else if(cmp > 0.0)\n      high = mid - 1;\n\n    // Key found.\n    else\n      return mid;\n  }\n\n  // Key not found.\n  return ~low;\n}\n","'use strict';\n\nfunction assertNumber(number) {\n\tif (typeof number !== 'number' || Number.isNaN(number)) {\n\t\tthrow new TypeError('Expected a number');\n\t}\n}\n\nexports.ascending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\treturn left - right;\n};\n\nexports.descending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\treturn right - left;\n};\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\nexport const largestPrime = 0x7fffffff;\n\nconst primeNumbers = [\n  // chunk #0\n  largestPrime, // 2^31-1\n\n  // chunk #1\n  5,\n  11,\n  23,\n  47,\n  97,\n  197,\n  397,\n  797,\n  1597,\n  3203,\n  6421,\n  12853,\n  25717,\n  51437,\n  102877,\n  205759,\n  411527,\n  823117,\n  1646237,\n  3292489,\n  6584983,\n  13169977,\n  26339969,\n  52679969,\n  105359939,\n  210719881,\n  421439783,\n  842879579,\n  1685759167,\n\n  // chunk #2\n  433,\n  877,\n  1759,\n  3527,\n  7057,\n  14143,\n  28289,\n  56591,\n  113189,\n  226379,\n  452759,\n  905551,\n  1811107,\n  3622219,\n  7244441,\n  14488931,\n  28977863,\n  57955739,\n  115911563,\n  231823147,\n  463646329,\n  927292699,\n  1854585413,\n\n  // chunk #3\n  953,\n  1907,\n  3821,\n  7643,\n  15287,\n  30577,\n  61169,\n  122347,\n  244703,\n  489407,\n  978821,\n  1957651,\n  3915341,\n  7830701,\n  15661423,\n  31322867,\n  62645741,\n  125291483,\n  250582987,\n  501165979,\n  1002331963,\n  2004663929,\n\n  // chunk #4\n  1039,\n  2081,\n  4177,\n  8363,\n  16729,\n  33461,\n  66923,\n  133853,\n  267713,\n  535481,\n  1070981,\n  2141977,\n  4283963,\n  8567929,\n  17135863,\n  34271747,\n  68543509,\n  137087021,\n  274174111,\n  548348231,\n  1096696463,\n\n  // chunk #5\n  31,\n  67,\n  137,\n  277,\n  557,\n  1117,\n  2237,\n  4481,\n  8963,\n  17929,\n  35863,\n  71741,\n  143483,\n  286973,\n  573953,\n  1147921,\n  2295859,\n  4591721,\n  9183457,\n  18366923,\n  36733847,\n  73467739,\n  146935499,\n  293871013,\n  587742049,\n  1175484103,\n\n  // chunk #6\n  599,\n  1201,\n  2411,\n  4831,\n  9677,\n  19373,\n  38747,\n  77509,\n  155027,\n  310081,\n  620171,\n  1240361,\n  2480729,\n  4961459,\n  9922933,\n  19845871,\n  39691759,\n  79383533,\n  158767069,\n  317534141,\n  635068283,\n  1270136683,\n\n  // chunk #7\n  311,\n  631,\n  1277,\n  2557,\n  5119,\n  10243,\n  20507,\n  41017,\n  82037,\n  164089,\n  328213,\n  656429,\n  1312867,\n  2625761,\n  5251529,\n  10503061,\n  21006137,\n  42012281,\n  84024581,\n  168049163,\n  336098327,\n  672196673,\n  1344393353,\n\n  // chunk #8\n  3,\n  7,\n  17,\n  37,\n  79,\n  163,\n  331,\n  673,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #9\n  43,\n  89,\n  179,\n  359,\n  719,\n  1439,\n  2879,\n  5779,\n  11579,\n  23159,\n  46327,\n  92657,\n  185323,\n  370661,\n  741337,\n  1482707,\n  2965421,\n  5930887,\n  11861791,\n  23723597,\n  47447201,\n  94894427,\n  189788857,\n  379577741,\n  759155483,\n  1518310967,\n\n  // chunk #10\n  379,\n  761,\n  1523,\n  3049,\n  6101,\n  12203,\n  24407,\n  48817,\n  97649,\n  195311,\n  390647,\n  781301,\n  1562611,\n  3125257,\n  6250537,\n  12501169,\n  25002389,\n  50004791,\n  100009607,\n  200019221,\n  400038451,\n  800076929,\n  1600153859,\n\n  // chunk #11\n  13,\n  29,\n  59,\n  127,\n  257,\n  521,\n  1049,\n  2099,\n  4201,\n  8419,\n  16843,\n  33703,\n  67409,\n  134837,\n  269683,\n  539389,\n  1078787,\n  2157587,\n  4315183,\n  8630387,\n  17260781,\n  34521589,\n  69043189,\n  138086407,\n  276172823,\n  552345671,\n  1104691373,\n\n  // chunk #12\n  19,\n  41,\n  83,\n  167,\n  337,\n  677,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #13\n  53,\n  107,\n  223,\n  449,\n  907,\n  1823,\n  3659,\n  7321,\n  14653,\n  29311,\n  58631,\n  117269,\n  234539,\n  469099,\n  938207,\n  1876417,\n  3752839,\n  7505681,\n  15011389,\n  30022781,\n  60045577,\n  120091177,\n  240182359,\n  480364727,\n  960729461,\n  1921458943\n];\n\nprimeNumbers.sort(ascending);\n\nexport function nextPrime(value) {\n  let index = binarySearch(primeNumbers, value, ascending);\n  if (index < 0) {\n    index = ~index;\n  }\n  return primeNumbers[index];\n}\n","import { largestPrime, nextPrime } from './primeFinder';\n\nconst FREE = 0;\nconst FULL = 1;\nconst REMOVED = 2;\n\nconst defaultInitialCapacity = 150;\nconst defaultMinLoadFactor = 1 / 6;\nconst defaultMaxLoadFactor = 2 / 3;\n\nexport default class HashTable {\n  constructor(options = {}) {\n    if (options instanceof HashTable) {\n      this.table = options.table.slice();\n      this.values = options.values.slice();\n      this.state = options.state.slice();\n      this.minLoadFactor = options.minLoadFactor;\n      this.maxLoadFactor = options.maxLoadFactor;\n      this.distinct = options.distinct;\n      this.freeEntries = options.freeEntries;\n      this.lowWaterMark = options.lowWaterMark;\n      this.highWaterMark = options.maxLoadFactor;\n      return;\n    }\n\n    const initialCapacity =\n      options.initialCapacity === undefined\n        ? defaultInitialCapacity\n        : options.initialCapacity;\n    if (initialCapacity < 0) {\n      throw new RangeError(\n        `initial capacity must not be less than zero: ${initialCapacity}`\n      );\n    }\n\n    const minLoadFactor =\n      options.minLoadFactor === undefined\n        ? defaultMinLoadFactor\n        : options.minLoadFactor;\n    const maxLoadFactor =\n      options.maxLoadFactor === undefined\n        ? defaultMaxLoadFactor\n        : options.maxLoadFactor;\n    if (minLoadFactor < 0 || minLoadFactor >= 1) {\n      throw new RangeError(`invalid minLoadFactor: ${minLoadFactor}`);\n    }\n    if (maxLoadFactor <= 0 || maxLoadFactor >= 1) {\n      throw new RangeError(`invalid maxLoadFactor: ${maxLoadFactor}`);\n    }\n    if (minLoadFactor >= maxLoadFactor) {\n      throw new RangeError(\n        `minLoadFactor (${minLoadFactor}) must be smaller than maxLoadFactor (${maxLoadFactor})`\n      );\n    }\n\n    let capacity = initialCapacity;\n    // User wants to put at least capacity elements. We need to choose the size based on the maxLoadFactor to\n    // avoid the need to rehash before this capacity is reached.\n    // actualCapacity * maxLoadFactor >= capacity\n    capacity = (capacity / maxLoadFactor) | 0;\n    capacity = nextPrime(capacity);\n    if (capacity === 0) capacity = 1;\n\n    this.table = newArray(capacity);\n    this.values = newArray(capacity);\n    this.state = newArray(capacity);\n\n    this.minLoadFactor = minLoadFactor;\n    if (capacity === largestPrime) {\n      this.maxLoadFactor = 1;\n    } else {\n      this.maxLoadFactor = maxLoadFactor;\n    }\n\n    this.distinct = 0;\n    this.freeEntries = capacity;\n\n    this.lowWaterMark = 0;\n    this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);\n  }\n\n  clone() {\n    return new HashTable(this);\n  }\n\n  get size() {\n    return this.distinct;\n  }\n\n  get(key) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return 0;\n    return this.values[i];\n  }\n\n  set(key, value) {\n    let i = this.indexOfInsertion(key);\n    if (i < 0) {\n      i = -i - 1;\n      this.values[i] = value;\n      return false;\n    }\n\n    if (this.distinct > this.highWaterMark) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n      return this.set(key, value);\n    }\n\n    this.table[i] = key;\n    this.values[i] = value;\n    if (this.state[i] === FREE) this.freeEntries--;\n    this.state[i] = FULL;\n    this.distinct++;\n\n    if (this.freeEntries < 1) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n\n    return true;\n  }\n\n  remove(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = REMOVED;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  delete(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = FREE;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  maybeShrinkCapacity() {\n    if (this.distinct < this.lowWaterMark) {\n      const newCapacity = chooseShrinkCapacity(\n        this.distinct,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n  }\n\n  containsKey(key) {\n    return this.indexOfKey(key) >= 0;\n  }\n\n  indexOfKey(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = this.table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === FREE) return -1;\n    return i;\n  }\n\n  containsValue(value) {\n    return this.indexOfValue(value) >= 0;\n  }\n\n  indexOfValue(value) {\n    const values = this.values;\n    const state = this.state;\n\n    for (var i = 0; i < state.length; i++) {\n      if (state[i] === FULL && values[i] === value) {\n        return i;\n      }\n    }\n\n    return -1;\n  }\n\n  indexOfInsertion(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] === FULL && table[i] !== key) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === REMOVED) {\n      const j = i;\n      while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n        i -= decrement;\n        if (i < 0) i += length;\n      }\n      if (state[i] === FREE) i = j;\n    }\n\n    if (state[i] === FULL) {\n      return -i - 1;\n    }\n\n    return i;\n  }\n\n  ensureCapacity(minCapacity) {\n    if (this.table.length < minCapacity) {\n      const newCapacity = nextPrime(minCapacity);\n      this.rehash(newCapacity);\n    }\n  }\n\n  rehash(newCapacity) {\n    const oldCapacity = this.table.length;\n\n    if (newCapacity <= this.distinct) throw new Error('Unexpected');\n\n    const oldTable = this.table;\n    const oldValues = this.values;\n    const oldState = this.state;\n\n    const newTable = newArray(newCapacity);\n    const newValues = newArray(newCapacity);\n    const newState = newArray(newCapacity);\n\n    this.lowWaterMark = chooseLowWaterMark(newCapacity, this.minLoadFactor);\n    this.highWaterMark = chooseHighWaterMark(newCapacity, this.maxLoadFactor);\n\n    this.table = newTable;\n    this.values = newValues;\n    this.state = newState;\n    this.freeEntries = newCapacity - this.distinct;\n\n    for (var i = 0; i < oldCapacity; i++) {\n      if (oldState[i] === FULL) {\n        var element = oldTable[i];\n        var index = this.indexOfInsertion(element);\n        newTable[index] = element;\n        newValues[index] = oldValues[i];\n        newState[index] = FULL;\n      }\n    }\n  }\n\n  forEachKey(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachValue(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachPair(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i], this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n}\n\nfunction chooseLowWaterMark(capacity, minLoad) {\n  return (capacity * minLoad) | 0;\n}\n\nfunction chooseHighWaterMark(capacity, maxLoad) {\n  return Math.min(capacity - 2, (capacity * maxLoad) | 0);\n}\n\nfunction chooseGrowCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (3 * minLoad + maxLoad)) | 0)\n  );\n}\n\nfunction chooseShrinkCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (minLoad + 3 * maxLoad)) | 0)\n  );\n}\n\nfunction newArray(size) {\n  return Array(size).fill(0);\n}\n","import HashTable from 'ml-hash-table';\n\nexport class SparseMatrix {\n  constructor(rows, columns, options = {}) {\n    if (rows instanceof SparseMatrix) {\n      // clone\n      const other = rows;\n      this._init(\n        other.rows,\n        other.columns,\n        other.elements.clone(),\n        other.threshold\n      );\n      return;\n    }\n\n    if (Array.isArray(rows)) {\n      const matrix = rows;\n      rows = matrix.length;\n      options = columns || {};\n      columns = matrix[0].length;\n      this._init(rows, columns, new HashTable(options), options.threshold);\n      for (var i = 0; i < rows; i++) {\n        for (var j = 0; j < columns; j++) {\n          var value = matrix[i][j];\n          if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n          if (value !== 0) {\n            this.elements.set(i * columns + j, matrix[i][j]);\n          }\n        }\n      }\n    } else {\n      this._init(rows, columns, new HashTable(options), options.threshold);\n    }\n  }\n\n  _init(rows, columns, elements, threshold) {\n    this.rows = rows;\n    this.columns = columns;\n    this.elements = elements;\n    this.threshold = threshold || 0;\n  }\n\n  static eye(rows = 1, columns = rows) {\n    const min = Math.min(rows, columns);\n    const matrix = new SparseMatrix(rows, columns, { initialCapacity: min });\n    for (var i = 0; i < min; i++) {\n      matrix.set(i, i, 1);\n    }\n    return matrix;\n  }\n\n  clone() {\n    return new SparseMatrix(this);\n  }\n\n  to2DArray() {\n    const copy = new Array(this.rows);\n    for (var i = 0; i < this.rows; i++) {\n      copy[i] = new Array(this.columns);\n      for (var j = 0; j < this.columns; j++) {\n        copy[i][j] = this.get(i, j);\n      }\n    }\n    return copy;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (!this.isSquare()) return false;\n\n    var symmetric = true;\n    this.forEachNonZero((i, j, v) => {\n      if (this.get(j, i) !== v) {\n        symmetric = false;\n        return false;\n      }\n      return v;\n    });\n    return symmetric;\n  }\n\n  /**\n   * Search for the wither band in the main diagonals\n   * @return {number}\n   */\n  bandWidth() {\n    let min = this.columns;\n    let max = -1;\n    this.forEachNonZero((i, j, v) => {\n      let diff = i - j;\n      min = Math.min(min, diff);\n      max = Math.max(max, diff);\n      return v;\n    });\n    return max - min;\n  }\n\n  /**\n   * Test if a matrix is consider banded using a threshold\n   * @param {number} width\n   * @return {boolean}\n   */\n  isBanded(width) {\n    let bandWidth = this.bandWidth();\n    return bandWidth <= width;\n  }\n\n  get cardinality() {\n    return this.elements.size;\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  get(row, column) {\n    return this.elements.get(row * this.columns + column);\n  }\n\n  set(row, column, value) {\n    if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n    if (value === 0) {\n      this.elements.remove(row * this.columns + column);\n    } else {\n      this.elements.set(row * this.columns + column, value);\n    }\n    return this;\n  }\n\n  mmul(other) {\n    if (this.columns !== other.rows) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        'Number of columns of left matrix are not equal to number of rows of right matrix.'\n      );\n    }\n\n    const m = this.rows;\n    const p = other.columns;\n\n    const result = new SparseMatrix(m, p);\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        if (j === k) {\n          result.set(i, l, result.get(i, l) + v1 * v2);\n        }\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  kroneckerProduct(other) {\n    const m = this.rows;\n    const n = this.columns;\n    const p = other.rows;\n    const q = other.columns;\n\n    const result = new SparseMatrix(m * p, n * q, {\n      initialCapacity: this.cardinality * other.cardinality\n    });\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        result.set(p * i + k, q * j + l, v1 * v2);\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  forEachNonZero(callback) {\n    this.elements.forEachPair((key, value) => {\n      const i = (key / this.columns) | 0;\n      const j = key % this.columns;\n      let r = callback(i, j, value);\n      if (r === false) return false; // stop iteration\n      if (this.threshold && Math.abs(r) < this.threshold) r = 0;\n      if (r !== value) {\n        if (r === 0) {\n          this.elements.remove(key, true);\n        } else {\n          this.elements.set(key, r);\n        }\n      }\n      return true;\n    });\n    this.elements.maybeShrinkCapacity();\n    return this;\n  }\n\n  getNonZeros() {\n    const cardinality = this.cardinality;\n    const rows = new Array(cardinality);\n    const columns = new Array(cardinality);\n    const values = new Array(cardinality);\n    var idx = 0;\n    this.forEachNonZero((i, j, value) => {\n      rows[idx] = i;\n      columns[idx] = j;\n      values[idx] = value;\n      idx++;\n      return value;\n    });\n    return { rows, columns, values };\n  }\n\n  setThreshold(newThreshold) {\n    if (newThreshold !== 0 && newThreshold !== this.threshold) {\n      this.threshold = newThreshold;\n      this.forEachNonZero((i, j, v) => v);\n    }\n    return this;\n  }\n\n  /**\n   * @return {SparseMatrix} - New transposed sparse matrix\n   */\n  transpose() {\n    let trans = new SparseMatrix(this.columns, this.rows, {\n      initialCapacity: this.cardinality\n    });\n    this.forEachNonZero((i, j, value) => {\n      trans.set(j, i, value);\n      return value;\n    });\n    return trans;\n  }\n}\n\nSparseMatrix.prototype.klass = 'Matrix';\n\nSparseMatrix.identity = SparseMatrix.eye;\nSparseMatrix.prototype.tensorProduct = SparseMatrix.prototype.kroneckerProduct;\n\n/*\n Add dynamically instance and static methods for mathematical operations\n */\n\nvar inplaceOperator = `\n(function %name%(value) {\n    if (typeof value === 'number') return this.%name%S(value);\n    return this.%name%M(value);\n})\n`;\n\nvar inplaceOperatorScalar = `\n(function %name%S(value) {\n    this.forEachNonZero((i, j, v) => v %op% value);\n    return this;\n})\n`;\n\nvar inplaceOperatorMatrix = `\n(function %name%M(matrix) {\n    matrix.forEachNonZero((i, j, v) => {\n        this.set(i, j, this.get(i, j) %op% v);\n        return v;\n    });\n    return this;\n})\n`;\n\nvar staticOperator = `\n(function %name%(matrix, value) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%(value);\n})\n`;\n\nvar inplaceMethod = `\n(function %name%() {\n    this.forEachNonZero((i, j, v) => %method%(v));\n    return this;\n})\n`;\n\nvar staticMethod = `\n(function %name%(matrix) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%();\n})\n`;\n\nconst operators = [\n  // Arithmetic operators\n  ['+', 'add'],\n  ['-', 'sub', 'subtract'],\n  ['*', 'mul', 'multiply'],\n  ['/', 'div', 'divide'],\n  ['%', 'mod', 'modulus'],\n  // Bitwise operators\n  ['&', 'and'],\n  ['|', 'or'],\n  ['^', 'xor'],\n  ['<<', 'leftShift'],\n  ['>>', 'signPropagatingRightShift'],\n  ['>>>', 'rightShift', 'zeroFillRightShift']\n];\n\nfor (const operator of operators) {\n  for (let i = 1; i < operator.length; i++) {\n    SparseMatrix.prototype[operator[i]] = eval(\n      fillTemplateFunction(inplaceOperator, {\n        name: operator[i],\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}S`] = eval(\n      fillTemplateFunction(inplaceOperatorScalar, {\n        name: `${operator[i]}S`,\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}M`] = eval(\n      fillTemplateFunction(inplaceOperatorMatrix, {\n        name: `${operator[i]}M`,\n        op: operator[0]\n      })\n    );\n\n    SparseMatrix[operator[i]] = eval(\n      fillTemplateFunction(staticOperator, { name: operator[i] })\n    );\n  }\n}\n\nvar methods = [['~', 'not']];\n\n[\n  'abs',\n  'acos',\n  'acosh',\n  'asin',\n  'asinh',\n  'atan',\n  'atanh',\n  'cbrt',\n  'ceil',\n  'clz32',\n  'cos',\n  'cosh',\n  'exp',\n  'expm1',\n  'floor',\n  'fround',\n  'log',\n  'log1p',\n  'log10',\n  'log2',\n  'round',\n  'sign',\n  'sin',\n  'sinh',\n  'sqrt',\n  'tan',\n  'tanh',\n  'trunc'\n].forEach(function (mathMethod) {\n  methods.push([`Math.${mathMethod}`, mathMethod]);\n});\n\nfor (const method of methods) {\n  for (let i = 1; i < method.length; i++) {\n    SparseMatrix.prototype[method[i]] = eval(\n      fillTemplateFunction(inplaceMethod, {\n        name: method[i],\n        method: method[0]\n      })\n    );\n    SparseMatrix[method[i]] = eval(\n      fillTemplateFunction(staticMethod, { name: method[i] })\n    );\n  }\n}\n\nfunction fillTemplateFunction(template, values) {\n  for (const i in values) {\n    template = template.replace(new RegExp(`%${i}%`, 'g'), values[i]);\n  }\n  return template;\n}\n","export default function additiveSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i]) * (a[i] + b[i])) / (a[i] * b[i]);\n  }\n  return 2 * d;\n}\n","export default function avg(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var ans = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    ans += aux;\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return (max + ans) / 2;\n}\n","export default function bhattacharyya(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return -Math.log(ans);\n}\n","export default function canberra(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]) / (a[i] + b[i]);\n  }\n  return ans;\n}\n","export default function chebyshev(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return max;\n}\n","export default function clark(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.sqrt(\n      ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]))\n    );\n  }\n  return 2 * d;\n}\n","export default function czekanowskiSimilarity(a, b) {\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < a.length; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return (2 * up) / down;\n}\n","import czekanowskiSimilarity from '../similarities/czekanowski';\n\nexport default function czekanowskiDistance(a, b) {\n  return 1 - czekanowskiSimilarity(a, b);\n}\n","export default function dice(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p + q1);\n}\n","export default function divergence(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]));\n  }\n  return 2 * d;\n}\n","export default function fidelity(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return ans;\n}\n","export default function gower(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]);\n  }\n  return ans / ii;\n}\n","export default function harmonicMean(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] * b[i]) / (a[i] + b[i]);\n  }\n  return 2 * ans;\n}\n","export default function hellinger(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return 2 * Math.sqrt(1 - ans);\n}\n","export default function innerProduct(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * b[i];\n  }\n  return ans;\n}\n","export default function intersection(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.min(a[i], b[i]);\n  }\n  return 1 - ans;\n}\n","export default function jaccard(a, b) {\n  var ii = a.length;\n  var p1 = 0;\n  var p2 = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p1 += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p2 + q1 - p1);\n}\n","export default function jeffreys(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] - b[i]) * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function jensenDifference(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (a[i] * Math.log(a[i]) + b[i] * Math.log(b[i])) / 2 -\n      ((a[i] + b[i]) / 2) * Math.log((a[i] + b[i]) / 2);\n  }\n  return ans;\n}\n","export default function jensenShannon(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n    q += b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return (p + q) / 2;\n}\n","export default function kdivergence(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function kulczynski(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.min(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function kullbackLeibler(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function kumarHassebrook(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (p2 + q2 - p);\n}\n","export default function kumarJohnson(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      Math.pow(a[i] * a[i] - b[i] * b[i], 2) / (2 * Math.pow(a[i] * b[i], 1.5));\n  }\n  return ans;\n}\n","export default function lorentzian(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.log(Math.abs(a[i] - b[i]) + 1);\n  }\n  return ans;\n}\n","export default function manhattan(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.abs(a[i] - b[i]);\n  }\n  return d;\n}\n","export default function matusita(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return Math.sqrt(2 - 2 * ans);\n}\n","export default function minkowski(a, b, p) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.pow(Math.abs(a[i] - b[i]), p);\n  }\n  return Math.pow(d, 1 / p);\n}\n","export default function motyka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return 1 - up / down;\n}\n","export default function neyman(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / a[i];\n  }\n  return d;\n}\n","export default function pearson(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / b[i];\n  }\n  return d;\n}\n","export default function probabilisticSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return 2 * d;\n}\n","export default function ruzicka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function soergel(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function sorensen(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += a[i] + b[i];\n  }\n  return up / down;\n}\n","export default function squared(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return d;\n}\n","export default function squaredChord(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (Math.sqrt(a[i]) - Math.sqrt(b[i])) * (Math.sqrt(a[i]) - Math.sqrt(b[i]));\n  }\n  return ans;\n}\n","export default function taneja(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      ((a[i] + b[i]) / 2) *\n      Math.log((a[i] + b[i]) / (2 * Math.sqrt(a[i] * b[i])));\n  }\n  return ans;\n}\n","export default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    var inter = 0;\n    var union = 0;\n    for (var j = 0; j < a.length; j++) {\n      inter += a[j] && b[j];\n      union += a[j] || b[j];\n    }\n    if (union === 0) {\n      return 1;\n    }\n    return inter / union;\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return 1 - (p + q - 2 * m) / (p + q - m);\n  }\n}\n","import tanimotoS from '../similarities/tanimoto';\n\nexport default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    return 1 - tanimotoS(a, b, bitvector);\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return (p + q - 2 * m) / (p + q - m);\n  }\n}\n","export default function topsoe(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      a[i] * Math.log((2 * a[i]) / (a[i] + b[i])) +\n      b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function waveHedges(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += 1 - Math.min(a[i], b[i]) / Math.max(a[i], b[i]);\n  }\n  return ans;\n}\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\n/**\n * Function that creates the tree\n * @param {Array<Array<number>>} spectrum\n * @param {object} [options]\n * @return {Tree|null}\n * left and right have the same structure than the parent,\n * or are null if they are leaves\n */\nexport function createTree(spectrum, options = {}) {\n  var X = spectrum[0];\n  const {\n    minWindow = 0.16,\n    threshold = 0.01,\n    from = X[0],\n    to = X[X.length - 1]\n  } = options;\n\n  return mainCreateTree(\n    spectrum[0],\n    spectrum[1],\n    from,\n    to,\n    minWindow,\n    threshold\n  );\n}\n\nfunction mainCreateTree(X, Y, from, to, minWindow, threshold) {\n  if (to - from < minWindow) {\n    return null;\n  }\n\n  // search first point\n  var start = binarySearch(X, from, ascending);\n  if (start < 0) {\n    start = ~start;\n  }\n\n  // stop at last point\n  var sum = 0;\n  var center = 0;\n  for (var i = start; i < X.length; i++) {\n    if (X[i] >= to) {\n      break;\n    }\n    sum += Y[i];\n    center += X[i] * Y[i];\n  }\n\n  if (sum < threshold) {\n    return null;\n  }\n\n  center /= sum;\n  if (center - from < 1e-6 || to - center < 1e-6) {\n    return null;\n  }\n  if (center - from < minWindow / 4) {\n    return mainCreateTree(X, Y, center, to, minWindow, threshold);\n  } else {\n    if (to - center < minWindow / 4) {\n      return mainCreateTree(X, Y, from, center, minWindow, threshold);\n    } else {\n      return new Tree(\n        sum,\n        center,\n        mainCreateTree(X, Y, from, center, minWindow, threshold),\n        mainCreateTree(X, Y, center, to, minWindow, threshold)\n      );\n    }\n  }\n}\n\nclass Tree {\n  constructor(sum, center, left, right) {\n    this.sum = sum;\n    this.center = center;\n    this.left = left;\n    this.right = right;\n  }\n}\n","import { createTree } from './createTree';\n\n/**\n * Similarity between two nodes\n * @param {Tree|Array<Array<number>>} a - tree A node\n * @param {Tree|Array<Array<number>>} b - tree B node\n * @param {object} [options]\n * @return {number} similarity measure between tree nodes\n */\nexport function getSimilarity(a, b, options = {}) {\n  const { alpha = 0.1, beta = 0.33, gamma = 0.001 } = options;\n\n  if (a === null || b === null) {\n    return 0;\n  }\n  if (Array.isArray(a)) {\n    a = createTree(a);\n  }\n  if (Array.isArray(b)) {\n    b = createTree(b);\n  }\n\n  var C =\n    (alpha * Math.min(a.sum, b.sum)) / Math.max(a.sum, b.sum) +\n    (1 - alpha) * Math.exp(-gamma * Math.abs(a.center - b.center));\n\n  return (\n    beta * C +\n    ((1 - beta) *\n      (getSimilarity(a.left, b.left, options) +\n        getSimilarity(a.right, b.right, options))) /\n      2\n  );\n}\n","import { getSimilarity } from './getSimilarity';\n\nexport { createTree } from './createTree';\n\nexport function treeSimilarity(A, B, options = {}) {\n  return getSimilarity(A, B, options);\n}\n\nexport function getFunction(options = {}) {\n  return (A, B) => getSimilarity(A, B, options);\n}\n","export default function cosine(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (Math.sqrt(p2) * Math.sqrt(q2));\n}\n","import diceD from '../distances/dice';\n\nexport default function dice(a, b) {\n  return 1 - diceD(a, b);\n}\n","import intersectionD from '../distances/intersection';\n\nexport default function intersection(a, b) {\n  return 1 - intersectionD(a, b);\n}\n","import jaccardD from '../distances/jaccard';\n\nexport default function jaccard(a, b) {\n  return 1 - jaccardD(a, b);\n}\n","import kulczynskiD from '../distances/kulczynski';\n\nexport default function kulczynski(a, b) {\n  return 1 / kulczynskiD(a, b);\n}\n","import motykaD from '../distances/motyka';\n\nexport default function motyka(a, b) {\n  return 1 - motykaD(a, b);\n}\n","import mean from 'ml-array-mean';\n\nimport cosine from './cosine';\n\nexport default function pearson(a, b) {\n  var avgA = mean(a);\n  var avgB = mean(b);\n\n  var newA = new Array(a.length);\n  var newB = new Array(b.length);\n  for (var i = 0; i < newA.length; i++) {\n    newA[i] = a[i] - avgA;\n    newB[i] = b[i] - avgB;\n  }\n\n  return cosine(newA, newB);\n}\n","import squaredChordD from '../distances/squaredChord';\n\nexport default function squaredChord(a, b) {\n  return 1 - squaredChordD(a, b);\n}\n","'use strict';\n\n// Accuracy\nexports.acc = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.tn[i] + pred.tp[i]) / (l - 1);\n    }\n    return result;\n};\n\n// Error rate\nexports.err = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.fp[i] / (l - 1));\n    }\n    return result;\n};\n\n// False positive rate\nexports.fpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fp[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// True positive rate\nexports.tpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tp[i] / pred.nPos;\n    }\n    return result;\n};\n\n// False negative rate\nexports.fnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fn[i] / pred.nPos;\n    }\n    return result;\n};\n\n// True negative rate\nexports.tnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tn[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// Positive predictive value\nexports.ppv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 0;\n    }\n    return result;\n};\n\n// Negative predictive value\nexports.npv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 0;\n    }\n    return result;\n};\n\n// Prediction conditioned fallout\nexports.pcfall = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? 1 - (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 1;\n    }\n    return result;\n};\n\n// Prediction conditioned miss\nexports.pcmiss = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? 1 - (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 1;\n    }\n    return result;\n};\n\n// Lift value\nexports.lift = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.nPosPred[i] !== 0) ? ((pred.tp[i] / pred.nPos) / (pred.nPosPred[i] / pred.nSamples)) : 0;\n    }\n    return result;\n};\n\n// Rate of positive predictions\nexports.rpp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nPosPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Rate of negative predictions\nexports.rnp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nNegPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Threshold\nexports.threshold = pred => {\n    const clone = pred.cutoffs.slice();\n    clone[0] = clone[1]; // Remove the infinite value\n    return clone;\n};\n","'use strict';\n\nconst measures = require('./measures');\n\nclass Performance {\n    /**\n     *\n     * @param prediction - The prediction matrix\n     * @param target - The target matrix (values: truthy for same class, falsy for different class)\n     * @param options\n     *\n     * @option    all    True if the entire matrix must be used. False to ignore the diagonal and lower part (default is false, for similarity/distance matrices)\n     * @option    max    True if the max value corresponds to a perfect match (like in similarity matrices), false if it is the min value (default is false, like in distance matrices. All values will be multiplied by -1)\n     */\n    constructor(prediction, target, options) {\n        options = options || {};\n        if (prediction.length !== target.length || prediction[0].length !== target[0].length) {\n            throw new Error('dimensions of prediction and target do not match');\n        }\n        const rows = prediction.length;\n        const columns = prediction[0].length;\n        const isDistance = !options.max;\n\n        const predP = [];\n\n        if (options.all) {\n            for (var i = 0; i < rows; i++) {\n                for (var j = 0; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        } else {\n            if (rows < 3 || rows !== columns) {\n                throw new Error('When \"all\" option is false, the prediction matrix must be square and have at least 3 columns');\n            }\n            for (var i = 0; i < rows - 1; i++) {\n                for (var j = i + 1; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        }\n\n        if (isDistance) {\n            predP.sort((a, b) => a.pred - b.pred);\n        } else {\n            predP.sort((a, b) => b.pred - a.pred);\n        }\n        \n        const cutoffs = this.cutoffs = [isDistance ? Number.MIN_VALUE : Number.MAX_VALUE];\n        const fp = this.fp = [0];\n        const tp = this.tp = [0];\n\n        var nPos = 0;\n        var nNeg = 0;\n\n        var currentPred = predP[0].pred;\n        var nTp = 0;\n        var nFp = 0;\n        for (var i = 0; i < predP.length; i++) {\n            if (predP[i].pred !== currentPred) {\n                cutoffs.push(currentPred);\n                fp.push(nFp);\n                tp.push(nTp);\n                currentPred = predP[i].pred;\n            }\n            if (predP[i].targ) {\n                nPos++;\n                nTp++;\n            } else {\n                nNeg++;\n                nFp++;\n            }\n        }\n        cutoffs.push(currentPred);\n        fp.push(nFp);\n        tp.push(nTp);\n\n        const l = cutoffs.length;\n        const fn = this.fn = new Array(l);\n        const tn = this.tn = new Array(l);\n        const nPosPred = this.nPosPred = new Array(l);\n        const nNegPred = this.nNegPred = new Array(l);\n\n        for (var i = 0; i < l; i++) {\n            fn[i] = nPos - tp[i];\n            tn[i] = nNeg - fp[i];\n\n            nPosPred[i] = tp[i] + fp[i];\n            nNegPred[i] = tn[i] + fn[i];\n        }\n\n        this.nPos = nPos;\n        this.nNeg = nNeg;\n        this.nSamples = nPos + nNeg;\n    }\n\n    /**\n     * Computes a measure from the prediction object.\n     *\n     * Many measures are available and can be combined :\n     * To create a ROC curve, you need fpr and tpr\n     * To create a DET curve, you need fnr and fpr\n     * To create a Lift chart, you need rpp and lift\n     *\n     * Possible measures are : threshold (Threshold), acc (Accuracy), err (Error rate),\n     * fpr (False positive rate), tpr (True positive rate), fnr (False negative rate), tnr (True negative rate), ppv (Positive predictive value),\n     * npv (Negative predictive value), pcfall (Prediction-conditioned fallout), pcmiss (Prediction-conditioned miss), lift (Lift value), rpp (Rate of positive predictions), rnp (Rate of negative predictions)\n     *\n     * @param measure - The short name of the measure\n     *\n     * @return [number]\n     */\n    getMeasure(measure) {\n        if (typeof measure !== 'string') {\n            throw new Error('No measure specified');\n        }\n        if (!measures[measure]) {\n            throw new Error(`The specified measure (${measure}) does not exist`);\n        }\n        return measures[measure](this);\n    }\n\n    /**\n     * Returns the area under the ROC curve\n     */\n    getAURC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fp[i] / this.nNeg;\n            y[i] = this.tp[i] / this.nPos;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] - x[i - 1]) * (y[i] + y[i - 1]);\n        }\n        return auc;\n    }\n\n    /**\n     * Returns the area under the DET curve\n     */\n    getAUDC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fn[i] / this.nPos;\n            y[i] = this.fp[i] / this.nNeg;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] + x[i - 1]) * (y[i] - y[i - 1]);\n        }\n        return auc;\n    }\n\n    getDistribution(options) {\n        options = options || {};\n        var cutLength = this.cutoffs.length;\n        var cutLow = options.xMin || Math.floor(this.cutoffs[cutLength - 1] * 100) / 100;\n        var cutHigh = options.xMax || Math.ceil(this.cutoffs[1] * 100) / 100;\n        var interval = options.interval || Math.floor(((cutHigh - cutLow) / 20 * 10000000) - 1) / 10000000; // Trick to avoid the precision problem of float numbers\n\n        var xLabels = [];\n        var interValues = [];\n        var intraValues = [];\n        var interCumPercent = [];\n        var intraCumPercent = [];\n\n        var nTP = this.tp[cutLength - 1], currentTP = 0;\n        var nFP = this.fp[cutLength - 1], currentFP = 0;\n\n        for (var i = cutLow, j = (cutLength - 1); i <= cutHigh; i += interval) {\n            while (this.cutoffs[j] < i)\n                j--;\n\n            xLabels.push(i);\n\n            var thisTP = nTP - currentTP - this.tp[j];\n            var thisFP = nFP - currentFP - this.fp[j];\n\n            currentTP += thisTP;\n            currentFP += thisFP;\n\n            interValues.push(thisFP);\n            intraValues.push(thisTP);\n\n            interCumPercent.push(100 - (nFP - this.fp[j]) / nFP * 100);\n            intraCumPercent.push(100 - (nTP - this.tp[j]) / nTP * 100);\n        }\n\n        return {\n            xLabels: xLabels,\n            interValues: interValues,\n            intraValues: intraValues,\n            interCumPercent: interCumPercent,\n            intraCumPercent: intraCumPercent\n        };\n    }\n}\n\nPerformance.names = {\n    acc: 'Accuracy',\n    err: 'Error rate',\n    fpr: 'False positive rate',\n    tpr: 'True positive rate',\n    fnr: 'False negative rate',\n    tnr: 'True negative rate',\n    ppv: 'Positive predictive value',\n    npv: 'Negative predictive value',\n    pcfall: 'Prediction-conditioned fallout',\n    pcmiss: 'Prediction-conditioned miss',\n    lift: 'Lift value',\n    rpp: 'Rate of positive predictions',\n    rnp: 'Rate of negative predictions',\n    threshold: 'Threshold'\n};\n\nmodule.exports = Performance;\n","'use strict';\n\nvar defaultOptions = {\n  size: 1,\n  value: 0\n};\n\n/**\n * Case when the entry is an array\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction arrayCase(data, options) {\n  var len = data.length;\n  if (typeof options.size === 'number') {\n    options.size = [options.size, options.size];\n  }\n\n  var cond = len + options.size[0] + options.size[1];\n\n  var output;\n  if (options.output) {\n    if (options.output.length !== cond) {\n      throw new RangeError('Wrong output size');\n    }\n    output = options.output;\n  } else {\n    output = new Array(cond);\n  }\n\n  var i;\n  if (options.value === 'circular') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) {\n        output[i] = data[(len - (options.size[0] % len) + i) % len];\n      } else if (i < options.size[0] + len) {\n        output[i] = data[i - options.size[0]];\n      } else {\n        output[i] = data[(i - options.size[0]) % len];\n      }\n    }\n  } else if (options.value === 'replicate') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[0];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[len - 1];\n    }\n  } else if (options.value === 'symmetric') {\n    if (options.size[0] > len || options.size[1] > len) {\n      throw new RangeError(\n        'expanded value should not be bigger than the data length'\n      );\n    }\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[options.size[0] - 1 - i];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[2 * len + options.size[0] - i - 1];\n    }\n  } else {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = options.value;\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = options.value;\n    }\n  }\n\n  return output;\n}\n\n/**\n * Case when the entry is a matrix\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction matrixCase(data, options) {\n  // var row = data.length;\n  // var col = data[0].length;\n  if (options.size[0] === undefined) {\n    options.size = [options.size, options.size, options.size, options.size];\n  }\n  throw new Error('matrix not supported yet, sorry');\n}\n\n/**\n * Pads and array\n * @param {Array <number>} data\n * @param {object} options\n */\nfunction padArray(data, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (Array.isArray(data)) {\n    if (Array.isArray(data[0])) return matrixCase(data, options);\n    else return arrayCase(data, options);\n  } else {\n    throw new TypeError('data should be an array');\n  }\n}\n\nmodule.exports = padArray;\n","import { Matrix, MatrixTransposeView, inverse } from 'ml-matrix';\nimport padArray from 'ml-pad-array';\n\nconst defaultOptions = {\n  windowSize: 5,\n  derivative: 1,\n  polynomial: 2,\n  pad: 'none',\n  padValue: 'replicate',\n};\n\n/**\n * Savitzky-Golay filter\n * @param {Array <number>} data\n * @param {number} h\n * @param {Object} options\n * @returns {Array}\n */\nexport default function savitzkyGolay(data, h, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (\n    options.windowSize % 2 === 0 ||\n    options.windowSize < 5 ||\n    !Number.isInteger(options.windowSize)\n  ) {\n    throw new RangeError(\n      'Invalid window size (should be odd and at least 5 integer number)',\n    );\n  }\n  if (options.derivative < 0 || !Number.isInteger(options.derivative)) {\n    throw new RangeError('Derivative should be a positive integer');\n  }\n  if (options.polynomial < 1 || !Number.isInteger(options.polynomial)) {\n    throw new RangeError('Polynomial should be a positive integer');\n  }\n\n  let C, norm;\n  let step = Math.floor(options.windowSize / 2);\n\n  if (options.pad === 'pre') {\n    data = padArray(data, { size: step, value: options.padValue });\n  }\n\n  let ans = new Array(data.length - 2 * step);\n\n  if (\n    options.windowSize === 5 &&\n    options.polynomial === 2 &&\n    (options.derivative === 1 || options.derivative === 2)\n  ) {\n    if (options.derivative === 1) {\n      C = [-2, -1, 0, 1, 2];\n      norm = 10;\n    } else {\n      C = [2, -1, -2, -1, 2];\n      norm = 7;\n    }\n  } else {\n    let J = Matrix.ones(options.windowSize, options.polynomial + 1);\n    let inic = -(options.windowSize - 1) / 2;\n    for (let i = 0; i < J.rows; i++) {\n      for (let j = 0; j < J.columns; j++) {\n        if (inic + 1 !== 0 || j !== 0) J.set(i, j, Math.pow(inic + i, j));\n      }\n    }\n    let Jtranspose = new MatrixTransposeView(J);\n    let Jinv = inverse(Jtranspose.mmul(J));\n    C = Jinv.mmul(Jtranspose);\n    C = C.getRow(options.derivative);\n    norm = 1;\n  }\n  let det = norm * Math.pow(h, options.derivative);\n  for (let k = step; k < data.length - step; k++) {\n    let d = 0;\n    for (let l = 0; l < C.length; l++) d += (C[l] * data[l + k - step]) / det;\n    ans[k - step] = d;\n  }\n\n  if (options.pad === 'post') {\n    ans = padArray(ans, { size: step, value: options.padValue });\n  }\n\n  return ans;\n}\n","// auxiliary file to create the 256 look at table elements\n\nvar ans = new Array(256);\nfor (var i = 0; i < 256; i++) {\n    var num = i;\n    var c = 0;\n    while (num) {\n        num = num & (num - 1);\n        c++;\n    }\n    ans[i] = c;\n}\n\nmodule.exports = ans;","'use strict';\n\nvar eightBits = require('./creator');\n\n/**\n * Count the number of true values in an array\n * @param {Array} arr\n * @return {number}\n */\nfunction count(arr) {\n    var c = 0;\n    for (var i = 0; i < arr.length; i++) {\n        c += eightBits[arr[i] & 0xff] + eightBits[(arr[i] >> 8) & 0xff] + eightBits[(arr[i] >> 16) & 0xff] + eightBits[(arr[i] >> 24) & 0xff];\n    }\n    return c;\n}\n\n/**\n * Logical AND operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction and(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] & arr2[i];\n    return ans;\n}\n\n/**\n * Logical OR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction or(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] | arr2[i];\n    return ans;\n}\n\n/**\n * Logical XOR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction xor(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] ^ arr2[i];\n    return ans;\n}\n\n/**\n * Logical NOT operation\n * @param {Array} arr\n * @return {Array}\n */\nfunction not(arr) {\n    var ans = new Array(arr.length);\n    for (var i = 0; i < ans.length; i++)\n        ans[i] = ~arr[i];\n    return ans;\n}\n\n/**\n * Gets the n value of array arr\n * @param {Array} arr\n * @param {number} n\n * @return {boolean}\n */\nfunction getBit(arr, n) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    return Boolean(arr[index] & mask);\n}\n\n/**\n * Sets the n value of array arr to the value val\n * @param {Array} arr\n * @param {number} n\n * @param {boolean} val\n * @return {Array}\n */\nfunction setBit(arr, n, val) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    if (val)\n        arr[index] = mask | arr[index];\n    else\n        arr[index] = ~mask & arr[index];\n    return arr;\n}\n\n/**\n * Translates an array of numbers to a string of bits\n * @param {Array} arr\n * @returns {string}\n */\nfunction toBinaryString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(2);\n        str += '00000000000000000000000000000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a string of bits\n * @param {string} str\n * @returns {Array}\n */\nfunction parseBinaryString(str) {\n    var len = str.length / 32;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*32, 32), 2) | 0;\n    }\n    return ans;\n}\n\n/**\n * Translates an array of numbers to a hex string\n * @param {Array} arr\n * @returns {string}\n */\nfunction toHexString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(16);\n        str += '00000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a hex string\n * @param {string} str\n * @returns {Array}\n */\nfunction parseHexString(str) {\n    var len = str.length / 8;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*8, 8), 16) | 0;\n    }\n    return ans;\n}\n\n/**\n * Creates a human readable string of the array\n * @param {Array} arr\n * @returns {string}\n */\nfunction toDebug(arr) {\n    var binary = toBinaryString(arr);\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        str += '0000'.substr((i * 32).toString(16).length) + (i * 32).toString(16) + ':';\n        for (var j = 0; j < 32; j += 4) {\n            str += ' ' + binary.substr(i * 32 + j, 4);\n        }\n        if (i < arr.length - 1) str += '\\n';\n    }\n    return str\n}\n\nmodule.exports = {\n    count: count,\n    and: and,\n    or: or,\n    xor: xor,\n    not: not,\n    getBit: getBit,\n    setBit: setBit,\n    toBinaryString: toBinaryString,\n    parseBinaryString: parseBinaryString,\n    toHexString: toHexString,\n    parseHexString: parseHexString,\n    toDebug: toDebug\n};\n","import isArray from 'is-any-array';\n\n/**\n * Computes the mode of the given values\n * @param {Array<number>} input\n * @return {number}\n */\n\nfunction mode(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var maxValue = 0;\n  var maxCount = 0;\n  var count = 0;\n  var counts = {};\n\n  for (var i = 0; i < input.length; ++i) {\n    var element = input[i];\n    count = counts[element];\n\n    if (count) {\n      counts[element]++;\n      count++;\n    } else {\n      counts[element] = count = 1;\n    }\n\n    if (count > maxCount) {\n      maxCount = count;\n      maxValue = input[i];\n    }\n  }\n\n  return maxValue;\n}\n\nexport default mode;\n","import max from 'ml-array-max';\nimport sum from 'ml-array-sum';\n\n/**\n * Computes the norm of the given values\n * @param {Array<number>} input\n * @param {object} [options={}]\n * @param {string} [options.algorithm='absolute'] absolute, sum or max\n * @return {number}\n */\n\nfunction norm(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  var _options$algorithm = options.algorithm,\n      algorithm = _options$algorithm === void 0 ? 'absolute' : _options$algorithm;\n\n  if (!Array.isArray(input)) {\n    throw new Error('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new Error('input must not be empty');\n  }\n\n  switch (algorithm.toLowerCase()) {\n    case 'absolute':\n      {\n        var absoluteSumValue = absoluteSum(input);\n        if (absoluteSumValue === 0) return input.slice(0);\n        return input.map(function (element) {\n          return element / absoluteSumValue;\n        });\n      }\n\n    case 'max':\n      {\n        var maxValue = max(input);\n        if (maxValue === 0) return input.slice(0);\n        return input.map(function (element) {\n          return element / maxValue;\n        });\n      }\n\n    case 'sum':\n      {\n        var sumValue = sum(input);\n        if (sumValue === 0) return input.slice(0);\n        return input.map(function (element) {\n          return element / sumValue;\n        });\n      }\n\n    default:\n      throw new Error(\"norm: unknown algorithm: \".concat(algorithm));\n  }\n}\n\nfunction absoluteSum(input) {\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += Math.abs(input[i]);\n  }\n\n  return sumValue;\n}\n\nexport default norm;\n","import isArray from 'is-any-array';\n\nfunction _typeof(obj) {\n  if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") {\n    _typeof = function (obj) {\n      return typeof obj;\n    };\n  } else {\n    _typeof = function (obj) {\n      return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj;\n    };\n  }\n\n  return _typeof(obj);\n}\n\n/**\n * Fill an array with sequential numbers\n * @param {Array<number>} [input] - optional destination array (if not provided a new array will be created)\n * @param {object} [options={}]\n * @param {number} [options.from=0] - first value in the array\n * @param {number} [options.to=10] - last value in the array\n * @param {number} [options.size=input.length] - size of the array (if not provided calculated from step)\n * @param {number} [options.step] - if not provided calculated from size\n * @return {Array<number>}\n */\n\nfunction sequentialFill() {\n  var input = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (_typeof(input) === 'object' && !isArray(input)) {\n    options = input;\n    input = [];\n  }\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options = options,\n      _options$from = _options.from,\n      from = _options$from === void 0 ? 0 : _options$from,\n      _options$to = _options.to,\n      to = _options$to === void 0 ? 10 : _options$to,\n      _options$size = _options.size,\n      size = _options$size === void 0 ? input.length : _options$size,\n      step = _options.step;\n\n  if (size && step) {\n    throw new Error('step is defined by the array size');\n  }\n\n  if (!size) {\n    if (step) {\n      size = Math.floor((to - from) / step) + 1;\n    } else {\n      size = to - from + 1;\n    }\n  }\n\n  if (!step && size) {\n    step = (to - from) / (size - 1);\n  }\n\n  if (Array.isArray(input)) {\n    input.length = 0; // only works with normal array\n\n    for (var i = 0; i < size; i++) {\n      input.push(from);\n      from += step;\n    }\n  } else {\n    if (input.length !== size) {\n      throw new Error('sequentialFill typed array must have the correct length');\n    }\n\n    for (var _i = 0; _i < size; _i++) {\n      input[_i] = from;\n      from += step;\n    }\n  }\n\n  return input;\n}\n\nexport default sequentialFill;\n","import arrayMean from 'ml-array-mean';\nimport isArray from 'is-any-array';\n\n/**\n * Computes the variance of the given values\n * @param {Array} values\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @param {number} [options.mean = arrayMean] - precalculated mean, if any.\n * @return {number}\n */\n\nfunction variance(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(values)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options$unbiased = options.unbiased,\n      unbiased = _options$unbiased === void 0 ? true : _options$unbiased,\n      _options$mean = options.mean,\n      mean = _options$mean === void 0 ? arrayMean(values) : _options$mean;\n  var sqrError = 0;\n\n  for (var i = 0; i < values.length; i++) {\n    var x = values[i] - mean;\n    sqrError += x * x;\n  }\n\n  if (unbiased) {\n    return sqrError / (values.length - 1);\n  } else {\n    return sqrError / values.length;\n  }\n}\n\nexport default variance;\n","import variance from 'ml-array-variance';\n\n/**\n * Computes the standard deviation of the given values\n * @param {Array} values\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @param {number} [options.mean = arrayMean] - precalculated mean, if any.\n * @return {number}\n */\n\nfunction standardDeviation(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  return Math.sqrt(variance(values, options));\n}\n\nexport default standardDeviation;\n","/**\n * Merge abscissa values if the ordinate value is in a list of centroids\n * @param {object} originalPoints\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {Array<number>} centroids\n * @param {object} [options]\n * @param {number} [options.window = 0.01] - has to be a positive number\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function mergeByCentroids(\n  originalPoints,\n  centroids,\n  options = {}\n) {\n  const { window = 0.01 } = options;\n\n  var mergedPoints = {\n    x: centroids.slice(),\n    y: new Array(centroids.length).fill(0)\n  };\n\n  var originalIndex = 0;\n  var mergedIndex = 0;\n  while (\n    originalIndex < originalPoints.x.length &&\n    mergedIndex < centroids.length\n  ) {\n    var diff = originalPoints.x[originalIndex] - centroids[mergedIndex];\n    if (Math.abs(diff) < window) {\n      mergedPoints.y[mergedIndex] += originalPoints.y[originalIndex++];\n    } else if (diff < 0) {\n      originalIndex++;\n    } else {\n      mergedIndex++;\n    }\n  }\n\n  return mergedPoints;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {*} options\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function closestX(points, options) {\n  const { x, y } = points;\n  const { target = x[0], reverse = false } = options;\n\n  let index;\n  if (reverse) {\n    index = binarySearch(x, target, descending);\n  } else {\n    index = binarySearch(x, target, ascending);\n  }\n\n  if (index >= 0) {\n    return {\n      x: x[index],\n      y: y[index]\n    };\n  } else {\n    index = ~index;\n    if (\n      (index !== 0 && Math.abs(x[index] - target) > 0.5) ||\n      index === x.length\n    ) {\n      return {\n        x: x[index - 1],\n        y: y[index - 1]\n      };\n    } else {\n      return {\n        x: x[index],\n        y: y[index]\n      };\n    }\n  }\n}\n","import mean from 'ml-array-mean';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} points.x\n * @param {Array<number>} points.y\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @return {number}\n */\nexport default function covariance(points, options = {}) {\n  const { x, y } = points;\n  const { unbiased = true } = options;\n\n  const meanX = mean(x);\n  const meanY = mean(y);\n\n  var error = 0;\n\n  for (let i = 0; i < x.length; i++) {\n    error += (x[i] - meanX) * (y[i] - meanY);\n  }\n\n  if (unbiased) {\n    return error / (x.length - 1);\n  } else {\n    return error / x.length;\n  }\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function maxMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var maxAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      maxAbscissa.x.push(x[index]);\n      maxAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      if (y[index] > maxAbscissa.y[size - 1]) {\n        maxAbscissa.x[size - 1] = x[index];\n        maxAbscissa.y[size - 1] = y[index];\n      }\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  merged.x = maxAbscissa.x.slice();\n\n  return merged;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {object} [options.from = {index: 0}]\n * @param {object} [options.to = {index: x.length-1}]\n * @param {boolean} [options.reverse = false]\n * @return {{index: number, value: number}}\n */\nexport default function maxY(points, options = {}) {\n  const { x, y } = points;\n  let {\n    from = { index: 0 },\n    to = { index: x.length },\n    reverse = false\n  } = options;\n\n  if (from.value !== undefined && from.index === undefined) {\n    from.index = calculateIndex(from.value, x, reverse);\n  }\n\n  if (to.value !== undefined && to.index === undefined) {\n    to.index = calculateIndex(to.value, x, reverse);\n  }\n\n  var currentMax = Number.MIN_VALUE;\n  var currentIndex;\n  for (var i = from.index; i < to.index; i++) {\n    if (currentMax < y[i]) {\n      currentMax = y[i];\n      currentIndex = i;\n    }\n  }\n\n  return {\n    index: currentIndex,\n    value: currentMax\n  };\n}\n\n/**\n * @param {number} value\n * @param {Array<number>} x\n * @param {boolean} reverse\n * @return {number} index of the value in the array\n */\nfunction calculateIndex(value, x, reverse) {\n  let index;\n  if (reverse) {\n    index = binarySearch(x, value, descending);\n  } else {\n    index = binarySearch(x, value, ascending);\n  }\n\n  if (index < 0) {\n    throw new Error(`the value ${value} doesn't belongs to the abscissa value`);\n  }\n\n  return index;\n}\n","export default function sortX(points, options = {}) {\n  const { x, y } = points;\n  const { reverse = false } = options;\n\n  var sortFunc;\n  if (!reverse) {\n    sortFunc = (a, b) => a.x - b.x;\n  } else {\n    sortFunc = (a, b) => b.x - a.x;\n  }\n\n  var grouped = x\n    .map((val, index) => ({\n      x: val,\n      y: y[index]\n    }))\n    .sort(sortFunc);\n\n  var response = { x: x.slice(), y: y.slice() };\n  for (var i = 0; i < x.length; i++) {\n    response.x[i] = grouped[i].x;\n    response.y[i] = grouped[i].y;\n  }\n\n  return response;\n}\n","\n/**\n * In place modification of the 2 arrays to make X unique and sum the Y if X has the same value\n * @param {object} [points={}] : Object of points contains property x (an array) and y (an array)\n * @return points\n */\n\nexport default function uniqueX(points = {}) {\n  const { x, y } = points;\n  if (x.length < 2) return;\n  if (x.length !== y.length) {\n    throw new Error('The X and Y arrays mush have the same length');\n  }\n\n  let current = x[0];\n  let counter = 0;\n\n  for (let i = 1; i < x.length; i++) {\n    if (current !== x[i]) {\n      counter++;\n      current = x[i];\n      x[counter] = x[i];\n      if (i !== counter) {\n        y[counter] = 0;\n      }\n    }\n    if (i !== counter) {\n      y[counter] += y[i];\n    }\n  }\n\n  x.length = counter + 1;\n  y.length = counter + 1;\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function weightedMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var weightedAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      weightedAbscissa.x.push(x[index] * y[index]);\n      weightedAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      weightedAbscissa.x[size - 1] += x[index] * y[index];\n      weightedAbscissa.y[size - 1] += y[index];\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  for (var i = 0; i < merged.x.length; i++) {\n    merged.x[i] = weightedAbscissa.x[i] / weightedAbscissa.y[i];\n  }\n\n  return merged;\n}\n","/**\n * Function that calculates the integral of the line between two\n * x-coordinates, given the slope and intercept of the line.\n * @param {number} x0\n * @param {number} x1\n * @param {number} slope\n * @param {number} intercept\n * @return {number} integral value.\n */\nexport default function integral(x0, x1, slope, intercept) {\n  return (\n    0.5 * slope * x1 * x1 +\n    intercept * x1 -\n    (0.5 * slope * x0 * x0 + intercept * x0)\n  );\n}\n","import integral from './integral';\n\n/**\n * function that retrieves the getEquallySpacedData with the variant \"smooth\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"smooth\"\n */\nexport default function equallySpacedSmooth(x, y, from, to, numberOfPoints) {\n  var xLength = x.length;\n\n  var step = (to - from) / (numberOfPoints - 1);\n  var halfStep = step / 2;\n\n  var output = new Array(numberOfPoints);\n\n  var initialOriginalStep = x[1] - x[0];\n  var lastOriginalStep = x[xLength - 1] - x[xLength - 2];\n\n  // Init main variables\n  var min = from - halfStep;\n  var max = from + halfStep;\n\n  var previousX = Number.MIN_VALUE;\n  var previousY = 0;\n  var nextX = x[0] - initialOriginalStep;\n  var nextY = 0;\n\n  var currentValue = 0;\n  var slope = 0;\n  var intercept = 0;\n  var sumAtMin = 0;\n  var sumAtMax = 0;\n\n  var i = 0; // index of input\n  var j = 0; // index of output\n\n  function getSlope(x0, y0, x1, y1) {\n    return (y1 - y0) / (x1 - x0);\n  }\n\n  main: while (true) {\n    if (previousX <= min && min <= nextX) {\n      add = integral(0, min - previousX, slope, previousY);\n      sumAtMin = currentValue + add;\n    }\n\n    while (nextX - max >= 0) {\n      // no overlap with original point, just consume current value\n      var add = integral(0, max - previousX, slope, previousY);\n      sumAtMax = currentValue + add;\n\n      output[j++] = (sumAtMax - sumAtMin) / step;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      sumAtMin = sumAtMax;\n    }\n\n    currentValue += integral(previousX, nextX, slope, intercept);\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else if (i === xLength) {\n      nextX += lastOriginalStep;\n      nextY = 0;\n    }\n\n    slope = getSlope(previousX, previousY, nextX, nextY);\n    intercept = -slope * previousX + previousY;\n  }\n\n  return output;\n}\n","/**\n * function that retrieves the getEquallySpacedData with the variant \"slot\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"slot\"\n */\nexport default function equallySpacedSlot(x, y, from, to, numberOfPoints) {\n  var xLength = x.length;\n\n  var step = (to - from) / (numberOfPoints - 1);\n  var halfStep = step / 2;\n  var lastStep = x[x.length - 1] - x[x.length - 2];\n\n  var start = from - halfStep;\n  var output = new Array(numberOfPoints);\n\n  // Init main variables\n  var min = start;\n  var max = start + step;\n\n  var previousX = -Number.MAX_VALUE;\n  var previousY = 0;\n  var nextX = x[0];\n  var nextY = y[0];\n  var frontOutsideSpectra = 0;\n  var backOutsideSpectra = true;\n\n  var currentValue = 0;\n\n  // for slot algorithm\n  var currentPoints = 0;\n\n  var i = 1; // index of input\n  var j = 0; // index of output\n\n  main: while (true) {\n    if (previousX >= nextX) throw new Error('x must be an increasing serie');\n    while (previousX - max > 0) {\n      // no overlap with original point, just consume current value\n      if (backOutsideSpectra) {\n        currentPoints++;\n        backOutsideSpectra = false;\n      }\n\n      output[j] = currentPoints <= 0 ? 0 : currentValue / currentPoints;\n      j++;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      currentValue = 0;\n      currentPoints = 0;\n    }\n\n    if (previousX > min) {\n      currentValue += previousY;\n      currentPoints++;\n    }\n\n    if (previousX === -Number.MAX_VALUE || frontOutsideSpectra > 1) {\n      currentPoints--;\n    }\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else {\n      nextX += lastStep;\n      nextY = 0;\n      frontOutsideSpectra++;\n    }\n  }\n\n  return output;\n}\n","export default function getZones(from, to, numberOfPoints, exclusions = []) {\n  if (from > to) {\n    [from, to] = [to, from];\n  }\n\n  // in exclusions from and to have to be defined\n  exclusions = exclusions.filter(\n    (exclusion) => exclusion.from !== undefined && exclusion.to !== undefined\n  );\n\n  exclusions = JSON.parse(JSON.stringify(exclusions));\n  // we ensure that from before to\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from > exclusion.to) {\n      [exclusion.to, exclusion.from] = [exclusion.from, exclusion.to];\n    }\n  });\n\n  exclusions.sort((a, b) => a.from - b.from);\n\n  // we will rework the exclusions in order to remove overlap and outside range (from / to)\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from < from) exclusion.from = from;\n    if (exclusion.to > to) exclusion.to = to;\n  });\n  for (let i = 0; i < exclusions.length - 1; i++) {\n    if (exclusions[i].to > exclusions[i + 1].from) {\n      exclusions[i].to = exclusions[i + 1].from;\n    }\n  }\n  exclusions = exclusions.filter((exclusion) => exclusion.from < exclusion.to);\n\n  if (!exclusions || exclusions.length === 0) {\n    return [{ from, to, numberOfPoints }];\n  }\n\n  // need to deal with overlapping exclusions and out of bound exclusions\n\n  let toRemove = exclusions.reduce(\n    (previous, exclusion) => (previous += exclusion.to - exclusion.from),\n    0\n  );\n  let total = to - from;\n  let unitsPerPoint = (total - toRemove) / numberOfPoints;\n  let zones = [];\n  let currentFrom = from;\n  let totalPoints = 0;\n  for (let exclusion of exclusions) {\n    let currentNbPoints = Math.round(\n      (exclusion.from - currentFrom) / unitsPerPoint\n    );\n    totalPoints += currentNbPoints;\n    if (currentNbPoints > 0) {\n      zones.push({\n        from: currentFrom,\n        to: exclusion.from,\n        numberOfPoints: currentNbPoints\n      });\n    }\n\n    currentFrom = exclusion.to;\n  }\n  if (numberOfPoints - totalPoints > 0) {\n    zones.push({\n      from: currentFrom,\n      to: to,\n      numberOfPoints: numberOfPoints - totalPoints\n    });\n  }\n\n  return zones;\n}\n","import sequentialFill from 'ml-array-sequential-fill';\n\nimport equallySpacedSmooth from './equallySpacedSmooth';\nimport equallySpacedSlot from './equallySpacedSlot';\nimport getZones from './getZones';\n\n/**\n * Function that returns a Number array of equally spaced numberOfPoints\n * containing a representation of intensities of the spectra arguments x\n * and y.\n *\n * The options parameter contains an object in the following form:\n * from: starting point\n * to: last point\n * numberOfPoints: number of points between from and to\n * variant: \"slot\" or \"smooth\" - smooth is the default option\n *\n * The slot variant consist that each point in the new array is calculated\n * averaging the existing points between the slot that belongs to the current\n * value. The smooth variant is the same but takes the integral of the range\n * of the slot and divide by the step size between two points in the new array.\n *\n * @param {object} [arrayXY={}] - object containing 2 properties x and y (both an array)\n * @param {object} [options={}]\n * @param {number} [options.from=x[0]]\n * @param {number} [options.to=x[x.length-1]]\n * @param {string} [options.variant='smooth']\n * @param {number} [options.numberOfPoints=100]\n * @param {Array} [options.exclusions=[]] array of from / to that should be skipped for the generation of the points\n * @return {object<x: Array, y:Array>} new object with x / y array with the equally spaced data.\n */\n\nexport default function equallySpaced(arrayXY = {}, options = {}) {\n  let { x, y } = arrayXY;\n  let xLength = x.length;\n  let reverse = false;\n  if (x.length > 1 && x[0] > x[1]) {\n    x = x.slice().reverse();\n    y = y.slice().reverse();\n    reverse = true;\n  }\n\n  let {\n    from = x[0],\n    to = x[xLength - 1],\n    variant = 'smooth',\n    numberOfPoints = 100,\n    exclusions = []\n  } = options;\n\n  if (xLength !== y.length) {\n    throw new RangeError(\"the x and y vector doesn't have the same size.\");\n  }\n\n  if (typeof from !== 'number' || isNaN(from)) {\n    throw new RangeError(\"'from' option must be a number\");\n  }\n\n  if (typeof to !== 'number' || isNaN(to)) {\n    throw new RangeError(\"'to' option must be a number\");\n  }\n\n  if (typeof numberOfPoints !== 'number' || isNaN(numberOfPoints)) {\n    throw new RangeError(\"'numberOfPoints' option must be a number\");\n  }\n\n  if (numberOfPoints < 2) {\n    throw new RangeError(\"'numberOfPoints' option must be greater than 1\");\n  }\n\n  let zones = getZones(from, to, numberOfPoints, exclusions);\n\n  let xResult = [];\n  let yResult = [];\n  for (let zone of zones) {\n    let zoneResult = processZone(\n      x,\n      y,\n      zone.from,\n      zone.to,\n      zone.numberOfPoints,\n      variant,\n      reverse\n    );\n    xResult = xResult.concat(zoneResult.x);\n    yResult = yResult.concat(zoneResult.y);\n  }\n\n  if (reverse) {\n    if (from < to) {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    } else {\n      return { x: xResult, y: yResult };\n    }\n  } else {\n    if (from < to) {\n      return { x: xResult, y: yResult };\n    } else {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    }\n  }\n}\n\nfunction processZone(x, y, from, to, numberOfPoints, variant) {\n  if (numberOfPoints < 1) {\n    throw new RangeError('the number of points must be at least 1');\n  }\n\n  var output =\n    variant === 'slot'\n      ? equallySpacedSlot(x, y, from, to, numberOfPoints)\n      : equallySpacedSmooth(x, y, from, to, numberOfPoints);\n\n  return {\n    x: sequentialFill({\n      from,\n      to,\n      size: numberOfPoints\n    }),\n    y: output\n  };\n}\n","export default function getZones(from, to, exclusions = []) {\n  if (from > to) {\n    [from, to] = [to, from];\n  }\n\n  // in exclusions from and to have to be defined\n  exclusions = exclusions.filter(\n    (exclusion) => exclusion.from !== undefined && exclusion.to !== undefined\n  );\n\n  exclusions = JSON.parse(JSON.stringify(exclusions));\n  // we ensure that from before to\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from > exclusion.to) {\n      [exclusion.to, exclusion.from] = [exclusion.from, exclusion.to];\n    }\n  });\n\n  exclusions.sort((a, b) => a.from - b.from);\n\n  // we will rework the exclusions in order to remove overlap and outside range (from / to)\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from < from) exclusion.from = from;\n    if (exclusion.to > to) exclusion.to = to;\n  });\n  for (let i = 0; i < exclusions.length - 1; i++) {\n    if (exclusions[i].to > exclusions[i + 1].from) {\n      exclusions[i].to = exclusions[i + 1].from;\n    }\n  }\n  exclusions = exclusions.filter((exclusion) => exclusion.from < exclusion.to);\n\n  if (!exclusions || exclusions.length === 0) {\n    return [{ from, to }];\n  }\n\n  let zones = [];\n  let currentFrom = from;\n  for (let exclusion of exclusions) {\n    if (currentFrom < exclusion.from) {\n      zones.push({\n        from: currentFrom,\n        to: exclusion.from\n      });\n    }\n\n    currentFrom = exclusion.to;\n  }\n  if (currentFrom < to) {\n    zones.push({\n      from: currentFrom,\n      to: to\n    });\n  }\n\n  return zones;\n}\n","import getZones from './getZones';\n\n/**\n * Filter an array x/y based on various criteria\n * x points are expected to be sorted\n *\n * @param {object} points\n * @param {object} [options={}]\n * @param {array} [options.from]\n * @param {array} [options.to]\n * @param {array} [options.exclusions=[]]\n * @return {{x: Array<number>, y: Array<number>}}\n */\n\nexport default function filterX(points, options = {}) {\n  const { x, y } = points;\n  const { from = x[0], to = x[x.length - 1], exclusions = [] } = options;\n\n  let zones = getZones(from, to, exclusions);\n\n\n  let currentZoneIndex = 0;\n  let newX = [];\n  let newY = [];\n  let position = 0;\n  while (position < x.length) {\n    if (\n      x[position] <= zones[currentZoneIndex].to &&\n      x[position] >= zones[currentZoneIndex].from\n    ) {\n      newX.push(x[position]);\n      newY.push(y[position]);\n    } else {\n      if (x[position] > zones[currentZoneIndex].to) {\n        currentZoneIndex++;\n        if (!zones[currentZoneIndex]) break;\n      }\n    }\n    position++;\n  }\n\n  return {\n    x: newX,\n    y: newY\n  };\n}\n","import { DecisionTreeClassifier, DecisionTreeRegression } from 'ml-cart';\nimport {\n  RandomForestClassifier,\n  RandomForestRegression\n} from 'ml-random-forest';\n\n// Try to keep this list in the same structure as the README.\n\n// Unsupervised learning\nexport { PCA } from 'ml-pca';\nimport * as HClust from 'ml-hclust';\nexport { HClust };\nexport { default as KMeans } from 'ml-kmeans';\n\n// Supervised learning\nimport * as NaiveBayes from 'ml-naivebayes';\nexport { NaiveBayes };\nexport { default as KNN } from 'ml-knn';\nexport { PLS, KOPLS } from 'ml-pls';\nexport { default as CrossValidation } from 'ml-cross-validation';\nexport { default as ConfusionMatrix } from 'ml-confusion-matrix';\nexport { DecisionTreeClassifier };\nexport { RandomForestClassifier };\n\n// Artificial neural networks\nexport { default as FNN } from 'ml-fnn';\nexport { default as SOM } from 'ml-som';\n\n// Regression\nexport {\n  SimpleLinearRegression,\n  PolynomialRegression,\n  MultivariateLinearRegression,\n  PowerRegression,\n  ExponentialRegression,\n  TheilSenRegression,\n  RobustPolynomialRegression\n} from 'ml-regression';\nexport { DecisionTreeRegression };\nexport { RandomForestRegression };\n\n// Optimization\nexport { default as levenbergMarquardt } from 'ml-levenberg-marquardt';\nimport * as FCNNLS from 'ml-fcnnls';\nexport { FCNNLS };\n\n// Math\nimport * as MatrixLib from 'ml-matrix';\nconst {\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n} = MatrixLib;\nexport {\n  MatrixLib,\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n};\n\nexport { SparseMatrix } from 'ml-sparse-matrix';\nexport { default as Kernel } from 'ml-kernel';\nimport { distance, similarity } from 'ml-distance';\nexport { distance as Distance, similarity as Similarity };\nexport { default as distanceMatrix } from 'ml-distance-matrix';\nexport { default as XSadd } from 'ml-xsadd';\n\n// Statistics\nexport { default as Performance } from 'ml-performance';\n\n// Data preprocessing\nexport { default as savitzkyGolay } from 'ml-savitzky-golay';\n\n// Utility\nexport { default as BitArray } from 'ml-bit-array';\nexport { default as HashTable } from 'ml-hash-table';\nexport { default as padArray } from 'ml-pad-array';\nexport { default as binarySearch } from 'binary-search';\nimport * as numSort from 'num-sort';\nexport { numSort };\nexport { default as Random } from 'ml-random';\n\nimport min from 'ml-array-min';\nimport max from 'ml-array-max';\nimport median from 'ml-array-median';\nimport mean from 'ml-array-mean';\nimport mode from 'ml-array-mode';\nimport normed from 'ml-array-normed';\nimport rescale from 'ml-array-rescale';\nimport sequentialFill from 'ml-array-sequential-fill';\nimport sum from 'ml-array-sum';\nimport standardDeviation from 'ml-array-standard-deviation';\nimport variance from 'ml-array-variance';\nexport const Array = {\n  min,\n  max,\n  median,\n  mean,\n  mode,\n  normed,\n  rescale,\n  sequentialFill,\n  standardDeviation,\n  sum,\n  variance\n};\n\nimport centroidsMerge from 'ml-array-xy-centroids-merge';\nimport closestX from 'ml-arrayxy-closestx';\nimport covariance from 'ml-array-xy-covariance';\nimport maxMerge from 'ml-array-xy-max-merge';\nimport maxY from 'ml-array-xy-max-y';\nimport sortX from 'ml-array-xy-sort-x';\nimport uniqueX from 'ml-arrayxy-uniquex';\nimport weightedMerge from 'ml-array-xy-weighted-merge';\nimport equallySpaced from 'ml-array-xy-equally-spaced';\nimport filterX from 'ml-array-xy-filter-x';\nexport const ArrayXY = {\n  centroidsMerge,\n  closestX,\n  covariance,\n  maxMerge,\n  maxY,\n  sortX,\n  uniqueX,\n  weightedMerge,\n  equallySpaced,\n  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.js","../node_modules/ml-optimize-lorentzian/src/optimizeSingleGaussian.js","../node_modules/ml-optimize-lorentzian/src/sumOfLorentzians.js","../node_modules/ml-optimize-lorentzian/src/optimizeLorentzianSum.js","../node_modules/ml-optimize-lorentzian/src/singleLorentzian.js","../node_modules/ml-optimize-lorentzian/src/optimizeSingleLorentzian.js","../node_modules/ml-gsd/src/post/optimizePeaks.js","../node_modules/ml-gsd/src/post/joinBroadPeaks.js","../node_modules/ml-gsd/src/post/broadenPeaks.js","../node_modules/is-any-array/src/index.js","../node_modules/ml-array-min/lib-es6/index.js","../node_modules/ml-array-max/lib-es6/index.js","../node_modules/ml-array-mode/lib-es6/index.js","../node_modules/ml-array-normed/node_modules/is-any-array/src/index.js","../node_modules/ml-array-normed/node_modules/ml-array-max/lib-es6/index.js","../node_modules/ml-array-normed/lib-es6/index.js","../node_modules/ml-array-sequential-fill/lib-es6/index.js","../node_modules/ml-array-variance/node_modules/is-any-array/src/index.js","../node_modules/ml-array-variance/lib-es6/index.js","../node_modules/ml-array-standard-deviation/lib-es6/index.js","../node_modules/ml-array-xy-centroids-merge/src/index.js","../node_modules/ml-arrayxy-closestx/src/index.js","../node_modules/ml-array-xy-covariance/src/index.js","../node_modules/ml-array-xy-max-merge/src/index.js","../node_modules/ml-array-xy-max-y/src/index.js","../node_modules/ml-array-xy-sort-x/src/index.js","../node_modules/ml-arrayxy-uniquex/src/index.js","../node_modules/ml-array-xy-weighted-merge/src/index.js","../node_modules/ml-zones/src/normalize.js","../node_modules/ml-zones/src/invert.js","../node_modules/ml-zones/src/zonesWithPoints.js","../node_modules/ml-array-xy-equally-spaced/src/integral.js","../node_modules/ml-array-xy-equally-spaced/src/equallySpacedSmooth.js","../node_modules/ml-array-xy-equally-spaced/src/equallySpacedSlot.js","../node_modules/ml-array-xy-equally-spaced/src/index.js","../node_modules/ml-array-xy-filter-x/src/getZones.js","../node_modules/ml-array-xy-filter-x/src/index.js","../src/index.js"],"sourcesContent":["const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction max(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var maxValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\n\nfunction min(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var minValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] < minValue) minValue = input[i];\n  }\n\n  return minValue;\n}\n\nexport default min;\n","import isArray from 'is-any-array';\nimport max from 'ml-array-max';\nimport min from 'ml-array-min';\n\nfunction rescale(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  } else if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var output;\n\n  if (options.output !== undefined) {\n    if (!isArray(options.output)) {\n      throw new TypeError('output option must be an array if specified');\n    }\n\n    output = options.output;\n  } else {\n    output = new Array(input.length);\n  }\n\n  var currentMin = min(input);\n  var currentMax = max(input);\n\n  if (currentMin === currentMax) {\n    throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');\n  }\n\n  var _options$min = options.min,\n      minValue = _options$min === void 0 ? options.autoMinMax ? currentMin : 0 : _options$min,\n      _options$max = options.max,\n      maxValue = _options$max === void 0 ? options.autoMinMax ? currentMax : 1 : _options$max;\n\n  if (minValue >= maxValue) {\n    throw new RangeError('min option must be smaller than max option');\n  }\n\n  var factor = (maxValue - minValue) / (currentMax - currentMin);\n\n  for (var i = 0; i < input.length; i++) {\n    output[i] = (input[i] - currentMin) * factor + minValue;\n  }\n\n  return output;\n}\n\nexport default rescale;\n","const indent = ' '.repeat(2);\nconst indentData = ' '.repeat(4);\n\nexport function inspectMatrix() {\n  return inspectMatrixWithOptions(this);\n}\n\nexport function inspectMatrixWithOptions(matrix, options = {}) {\n  const { maxRows = 15, maxColumns = 10, maxNumSize = 8 } = options;\n  return `${matrix.constructor.name} {\n${indent}[\n${indentData}${inspectData(matrix, maxRows, maxColumns, maxNumSize)}\n${indent}]\n${indent}rows: ${matrix.rows}\n${indent}columns: ${matrix.columns}\n}`;\n}\n\nfunction inspectData(matrix, maxRows, maxColumns, maxNumSize) {\n  const { rows, columns } = matrix;\n  const maxI = Math.min(rows, maxRows);\n  const maxJ = Math.min(columns, maxColumns);\n  const result = [];\n  for (let i = 0; i < maxI; i++) {\n    let line = [];\n    for (let j = 0; j < maxJ; j++) {\n      line.push(formatNumber(matrix.get(i, j), maxNumSize));\n    }\n    result.push(`${line.join(' ')}`);\n  }\n  if (maxJ !== columns) {\n    result[result.length - 1] += ` ... ${columns - maxColumns} more columns`;\n  }\n  if (maxI !== rows) {\n    result.push(`... ${rows - maxRows} more rows`);\n  }\n  return result.join(`\\n${indentData}`);\n}\n\nfunction formatNumber(num, maxNumSize) {\n  const numStr = String(num);\n  if (numStr.length <= maxNumSize) {\n    return numStr.padEnd(maxNumSize, ' ');\n  }\n  const precise = num.toPrecision(maxNumSize - 2);\n  if (precise.length <= maxNumSize) {\n    return precise;\n  }\n  const exponential = num.toExponential(maxNumSize - 2);\n  const eIndex = exponential.indexOf('e');\n  const e = exponential.slice(eIndex);\n  return exponential.slice(0, maxNumSize - e.length) + e;\n}\n","export function installMathOperations(AbstractMatrix, Matrix) {\r\n  AbstractMatrix.prototype.add = function add(value) {\r\n    if (typeof value === 'number') return this.addS(value);\r\n    return this.addM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.addS = function addS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) + value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.addM = function addM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) + matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.add = function add(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.add(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.sub = function sub(value) {\r\n    if (typeof value === 'number') return this.subS(value);\r\n    return this.subM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.subS = function subS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) - value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.subM = function subM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) - matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sub = function sub(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sub(value);\r\n  };\r\n  AbstractMatrix.prototype.subtract = AbstractMatrix.prototype.sub;\r\n  AbstractMatrix.prototype.subtractS = AbstractMatrix.prototype.subS;\r\n  AbstractMatrix.prototype.subtractM = AbstractMatrix.prototype.subM;\r\n  AbstractMatrix.subtract = AbstractMatrix.sub;\r\n\r\n  AbstractMatrix.prototype.mul = function mul(value) {\r\n    if (typeof value === 'number') return this.mulS(value);\r\n    return this.mulM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.mulS = function mulS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) * value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.mulM = function mulM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) * matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.mul = function mul(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.mul(value);\r\n  };\r\n  AbstractMatrix.prototype.multiply = AbstractMatrix.prototype.mul;\r\n  AbstractMatrix.prototype.multiplyS = AbstractMatrix.prototype.mulS;\r\n  AbstractMatrix.prototype.multiplyM = AbstractMatrix.prototype.mulM;\r\n  AbstractMatrix.multiply = AbstractMatrix.mul;\r\n\r\n  AbstractMatrix.prototype.div = function div(value) {\r\n    if (typeof value === 'number') return this.divS(value);\r\n    return this.divM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.divS = function divS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) / value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.divM = function divM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) / matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.div = function div(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.div(value);\r\n  };\r\n  AbstractMatrix.prototype.divide = AbstractMatrix.prototype.div;\r\n  AbstractMatrix.prototype.divideS = AbstractMatrix.prototype.divS;\r\n  AbstractMatrix.prototype.divideM = AbstractMatrix.prototype.divM;\r\n  AbstractMatrix.divide = AbstractMatrix.div;\r\n\r\n  AbstractMatrix.prototype.mod = function mod(value) {\r\n    if (typeof value === 'number') return this.modS(value);\r\n    return this.modM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.modS = function modS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) % value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.modM = function modM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) % matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.mod = function mod(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.mod(value);\r\n  };\r\n  AbstractMatrix.prototype.modulus = AbstractMatrix.prototype.mod;\r\n  AbstractMatrix.prototype.modulusS = AbstractMatrix.prototype.modS;\r\n  AbstractMatrix.prototype.modulusM = AbstractMatrix.prototype.modM;\r\n  AbstractMatrix.modulus = AbstractMatrix.mod;\r\n\r\n  AbstractMatrix.prototype.and = function and(value) {\r\n    if (typeof value === 'number') return this.andS(value);\r\n    return this.andM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.andS = function andS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) & value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.andM = function andM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) & matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.and = function and(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.and(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.or = function or(value) {\r\n    if (typeof value === 'number') return this.orS(value);\r\n    return this.orM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.orS = function orS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) | value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.orM = function orM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) | matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.or = function or(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.or(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.xor = function xor(value) {\r\n    if (typeof value === 'number') return this.xorS(value);\r\n    return this.xorM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.xorS = function xorS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) ^ value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.xorM = function xorM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) ^ matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.xor = function xor(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.xor(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.leftShift = function leftShift(value) {\r\n    if (typeof value === 'number') return this.leftShiftS(value);\r\n    return this.leftShiftM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.leftShiftS = function leftShiftS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) << value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.leftShiftM = function leftShiftM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) << matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.leftShift = function leftShift(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.leftShift(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.signPropagatingRightShift = function signPropagatingRightShift(value) {\r\n    if (typeof value === 'number') return this.signPropagatingRightShiftS(value);\r\n    return this.signPropagatingRightShiftM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.signPropagatingRightShiftS = function signPropagatingRightShiftS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >> value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.signPropagatingRightShiftM = function signPropagatingRightShiftM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >> matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.signPropagatingRightShift = function signPropagatingRightShift(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.signPropagatingRightShift(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.rightShift = function rightShift(value) {\r\n    if (typeof value === 'number') return this.rightShiftS(value);\r\n    return this.rightShiftM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.rightShiftS = function rightShiftS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >>> value);\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.rightShiftM = function rightShiftM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, this.get(i, j) >>> matrix.get(i, j));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.rightShift = function rightShift(matrix, value) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.rightShift(value);\r\n  };\r\n  AbstractMatrix.prototype.zeroFillRightShift = AbstractMatrix.prototype.rightShift;\r\n  AbstractMatrix.prototype.zeroFillRightShiftS = AbstractMatrix.prototype.rightShiftS;\r\n  AbstractMatrix.prototype.zeroFillRightShiftM = AbstractMatrix.prototype.rightShiftM;\r\n  AbstractMatrix.zeroFillRightShift = AbstractMatrix.rightShift;\r\n\r\n  AbstractMatrix.prototype.not = function not() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, ~(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.not = function not(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.not();\r\n  };\r\n\r\n  AbstractMatrix.prototype.abs = function abs() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.abs(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.abs = function abs(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.abs();\r\n  };\r\n\r\n  AbstractMatrix.prototype.acos = function acos() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.acos(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.acos = function acos(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.acos();\r\n  };\r\n\r\n  AbstractMatrix.prototype.acosh = function acosh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.acosh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.acosh = function acosh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.acosh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.asin = function asin() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.asin(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.asin = function asin(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.asin();\r\n  };\r\n\r\n  AbstractMatrix.prototype.asinh = function asinh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.asinh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.asinh = function asinh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.asinh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.atan = function atan() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.atan(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.atan = function atan(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.atan();\r\n  };\r\n\r\n  AbstractMatrix.prototype.atanh = function atanh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.atanh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.atanh = function atanh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.atanh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.cbrt = function cbrt() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.cbrt(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.cbrt = function cbrt(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.cbrt();\r\n  };\r\n\r\n  AbstractMatrix.prototype.ceil = function ceil() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.ceil(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.ceil = function ceil(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.ceil();\r\n  };\r\n\r\n  AbstractMatrix.prototype.clz32 = function clz32() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.clz32(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.clz32 = function clz32(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.clz32();\r\n  };\r\n\r\n  AbstractMatrix.prototype.cos = function cos() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.cos(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.cos = function cos(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.cos();\r\n  };\r\n\r\n  AbstractMatrix.prototype.cosh = function cosh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.cosh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.cosh = function cosh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.cosh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.exp = function exp() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.exp(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.exp = function exp(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.exp();\r\n  };\r\n\r\n  AbstractMatrix.prototype.expm1 = function expm1() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.expm1(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.expm1 = function expm1(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.expm1();\r\n  };\r\n\r\n  AbstractMatrix.prototype.floor = function floor() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.floor(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.floor = function floor(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.floor();\r\n  };\r\n\r\n  AbstractMatrix.prototype.fround = function fround() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.fround(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.fround = function fround(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.fround();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log = function log() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log = function log(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log1p = function log1p() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log1p(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log1p = function log1p(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log1p();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log10 = function log10() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log10(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log10 = function log10(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log10();\r\n  };\r\n\r\n  AbstractMatrix.prototype.log2 = function log2() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.log2(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.log2 = function log2(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.log2();\r\n  };\r\n\r\n  AbstractMatrix.prototype.round = function round() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.round(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.round = function round(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.round();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sign = function sign() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sign(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sign = function sign(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sign();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sin = function sin() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sin(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sin = function sin(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sin();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sinh = function sinh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sinh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sinh = function sinh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sinh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.sqrt = function sqrt() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.sqrt(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.sqrt = function sqrt(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.sqrt();\r\n  };\r\n\r\n  AbstractMatrix.prototype.tan = function tan() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.tan(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.tan = function tan(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.tan();\r\n  };\r\n\r\n  AbstractMatrix.prototype.tanh = function tanh() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.tanh(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.tanh = function tanh(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.tanh();\r\n  };\r\n\r\n  AbstractMatrix.prototype.trunc = function trunc() {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.trunc(this.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.trunc = function trunc(matrix) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.trunc();\r\n  };\r\n\r\n  AbstractMatrix.pow = function pow(matrix, arg0) {\r\n    const newMatrix = new Matrix(matrix);\r\n    return newMatrix.pow(arg0);\r\n  };\r\n\r\n  AbstractMatrix.prototype.pow = function pow(value) {\r\n    if (typeof value === 'number') return this.powS(value);\r\n    return this.powM(value);\r\n  };\r\n\r\n  AbstractMatrix.prototype.powS = function powS(value) {\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.pow(this.get(i, j), value));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n\r\n  AbstractMatrix.prototype.powM = function powM(matrix) {\r\n    matrix = Matrix.checkMatrix(matrix);\r\n    if (this.rows !== matrix.rows ||\r\n      this.columns !== matrix.columns) {\r\n      throw new RangeError('Matrices dimensions must be equal');\r\n    }\r\n    for (let i = 0; i < this.rows; i++) {\r\n      for (let j = 0; j < this.columns; j++) {\r\n        this.set(i, j, Math.pow(this.get(i, j), matrix.get(i, j)));\r\n      }\r\n    }\r\n    return this;\r\n  };\r\n}\r\n","/**\n * @private\n * Check that a row index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkRowIndex(matrix, index, outer) {\n  let max = outer ? matrix.rows : matrix.rows - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Row index out of range');\n  }\n}\n\n/**\n * @private\n * Check that a column index is not out of bounds\n * @param {Matrix} matrix\n * @param {number} index\n * @param {boolean} [outer]\n */\nexport function checkColumnIndex(matrix, index, outer) {\n  let max = outer ? matrix.columns : matrix.columns - 1;\n  if (index < 0 || index > max) {\n    throw new RangeError('Column index out of range');\n  }\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkRowVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.columns) {\n    throw new RangeError(\n      'vector size must be the same as the number of columns',\n    );\n  }\n  return vector;\n}\n\n/**\n * @private\n * Check that the provided vector is an array with the right length\n * @param {Matrix} matrix\n * @param {Array|Matrix} vector\n * @return {Array}\n * @throws {RangeError}\n */\nexport function checkColumnVector(matrix, vector) {\n  if (vector.to1DArray) {\n    vector = vector.to1DArray();\n  }\n  if (vector.length !== matrix.rows) {\n    throw new RangeError('vector size must be the same as the number of rows');\n  }\n  return vector;\n}\n\nexport function checkIndices(matrix, rowIndices, columnIndices) {\n  return {\n    row: checkRowIndices(matrix, rowIndices),\n    column: checkColumnIndices(matrix, columnIndices),\n  };\n}\n\nexport function checkRowIndices(matrix, rowIndices) {\n  if (typeof rowIndices !== 'object') {\n    throw new TypeError('unexpected type for row indices');\n  }\n\n  let rowOut = rowIndices.some((r) => {\n    return r < 0 || r >= matrix.rows;\n  });\n\n  if (rowOut) {\n    throw new RangeError('row indices are out of range');\n  }\n\n  if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);\n\n  return rowIndices;\n}\n\nexport function checkColumnIndices(matrix, columnIndices) {\n  if (typeof columnIndices !== 'object') {\n    throw new TypeError('unexpected type for column indices');\n  }\n\n  let columnOut = columnIndices.some((c) => {\n    return c < 0 || c >= matrix.columns;\n  });\n\n  if (columnOut) {\n    throw new RangeError('column indices are out of range');\n  }\n  if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);\n\n  return columnIndices;\n}\n\nexport function checkRange(matrix, startRow, endRow, startColumn, endColumn) {\n  if (arguments.length !== 5) {\n    throw new RangeError('expected 4 arguments');\n  }\n  checkNumber('startRow', startRow);\n  checkNumber('endRow', endRow);\n  checkNumber('startColumn', startColumn);\n  checkNumber('endColumn', endColumn);\n  if (\n    startRow > endRow ||\n    startColumn > endColumn ||\n    startRow < 0 ||\n    startRow >= matrix.rows ||\n    endRow < 0 ||\n    endRow >= matrix.rows ||\n    startColumn < 0 ||\n    startColumn >= matrix.columns ||\n    endColumn < 0 ||\n    endColumn >= matrix.columns\n  ) {\n    throw new RangeError('Submatrix indices are out of range');\n  }\n}\n\nexport function newArray(length, value = 0) {\n  let array = [];\n  for (let i = 0; i < length; i++) {\n    array.push(value);\n  }\n  return array;\n}\n\nfunction checkNumber(name, value) {\n  if (typeof value !== 'number') {\n    throw new TypeError(`${name} must be a number`);\n  }\n}\n","import { newArray } from './util';\n\nexport function sumByRow(matrix) {\n  let sum = newArray(matrix.rows);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumByColumn(matrix) {\n  let sum = newArray(matrix.columns);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] += matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function sumAll(matrix) {\n  let v = 0;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v += matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function productByRow(matrix) {\n  let sum = newArray(matrix.rows, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[i] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productByColumn(matrix) {\n  let sum = newArray(matrix.columns, 1);\n  for (let i = 0; i < matrix.rows; ++i) {\n    for (let j = 0; j < matrix.columns; ++j) {\n      sum[j] *= matrix.get(i, j);\n    }\n  }\n  return sum;\n}\n\nexport function productAll(matrix) {\n  let v = 1;\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      v *= matrix.get(i, j);\n    }\n  }\n  return v;\n}\n\nexport function varianceByRow(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let i = 0; i < rows; i++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean[i];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / cols) / (cols - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / cols) / cols);\n    }\n  }\n  return variance;\n}\n\nexport function varianceByColumn(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const variance = [];\n\n  for (let j = 0; j < cols; j++) {\n    let sum1 = 0;\n    let sum2 = 0;\n    let x = 0;\n    for (let i = 0; i < rows; i++) {\n      x = matrix.get(i, j) - mean[j];\n      sum1 += x;\n      sum2 += x * x;\n    }\n    if (unbiased) {\n      variance.push((sum2 - (sum1 * sum1) / rows) / (rows - 1));\n    } else {\n      variance.push((sum2 - (sum1 * sum1) / rows) / rows);\n    }\n  }\n  return variance;\n}\n\nexport function varianceAll(matrix, unbiased, mean) {\n  const rows = matrix.rows;\n  const cols = matrix.columns;\n  const size = rows * cols;\n\n  let sum1 = 0;\n  let sum2 = 0;\n  let x = 0;\n  for (let i = 0; i < rows; i++) {\n    for (let j = 0; j < cols; j++) {\n      x = matrix.get(i, j) - mean;\n      sum1 += x;\n      sum2 += x * x;\n    }\n  }\n  if (unbiased) {\n    return (sum2 - (sum1 * sum1) / size) / (size - 1);\n  } else {\n    return (sum2 - (sum1 * sum1) / size) / size;\n  }\n}\n\nexport function centerByRow(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[i]);\n    }\n  }\n}\n\nexport function centerByColumn(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean[j]);\n    }\n  }\n}\n\nexport function centerAll(matrix, mean) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) - mean);\n    }\n  }\n}\n\nexport function getScaleByRow(matrix) {\n  const scale = [];\n  for (let i = 0; i < matrix.rows; i++) {\n    let sum = 0;\n    for (let j = 0; j < matrix.columns; j++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.columns - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByRow(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[i]);\n    }\n  }\n}\n\nexport function getScaleByColumn(matrix) {\n  const scale = [];\n  for (let j = 0; j < matrix.columns; j++) {\n    let sum = 0;\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / (matrix.rows - 1);\n    }\n    scale.push(Math.sqrt(sum));\n  }\n  return scale;\n}\n\nexport function scaleByColumn(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale[j]);\n    }\n  }\n}\n\nexport function getScaleAll(matrix) {\n  const divider = matrix.size - 1;\n  let sum = 0;\n  for (let j = 0; j < matrix.columns; j++) {\n    for (let i = 0; i < matrix.rows; i++) {\n      sum += Math.pow(matrix.get(i, j), 2) / divider;\n    }\n  }\n  return Math.sqrt(sum);\n}\n\nexport function scaleAll(matrix, scale) {\n  for (let i = 0; i < matrix.rows; i++) {\n    for (let j = 0; j < matrix.columns; j++) {\n      matrix.set(i, j, matrix.get(i, j) / scale);\n    }\n  }\n}\n","import rescale from 'ml-array-rescale';\n\nimport { inspectMatrix, inspectMatrixWithOptions } from './inspect';\nimport { installMathOperations } from './mathOperations';\nimport {\n  sumByRow,\n  sumByColumn,\n  sumAll,\n  productByRow,\n  productByColumn,\n  productAll,\n  varianceByRow,\n  varianceByColumn,\n  varianceAll,\n  centerByRow,\n  centerByColumn,\n  centerAll,\n  scaleByRow,\n  scaleByColumn,\n  scaleAll,\n  getScaleByRow,\n  getScaleByColumn,\n  getScaleAll,\n} from './stat';\nimport {\n  checkRowVector,\n  checkRowIndex,\n  checkColumnIndex,\n  checkColumnVector,\n  checkRange,\n  checkIndices,\n} from './util';\n\nexport class AbstractMatrix {\n  static from1DArray(newRows, newColumns, newData) {\n    let length = newRows * newColumns;\n    if (length !== newData.length) {\n      throw new RangeError('data length does not match given dimensions');\n    }\n    let newMatrix = new Matrix(newRows, newColumns);\n    for (let row = 0; row < newRows; row++) {\n      for (let column = 0; column < newColumns; column++) {\n        newMatrix.set(row, column, newData[row * newColumns + column]);\n      }\n    }\n    return newMatrix;\n  }\n\n  static rowVector(newData) {\n    let vector = new Matrix(1, newData.length);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(0, i, newData[i]);\n    }\n    return vector;\n  }\n\n  static columnVector(newData) {\n    let vector = new Matrix(newData.length, 1);\n    for (let i = 0; i < newData.length; i++) {\n      vector.set(i, 0, newData[i]);\n    }\n    return vector;\n  }\n\n  static zeros(rows, columns) {\n    return new Matrix(rows, columns);\n  }\n\n  static ones(rows, columns) {\n    return new Matrix(rows, columns).fill(1);\n  }\n\n  static rand(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { random = Math.random } = options;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.set(i, j, random());\n      }\n    }\n    return matrix;\n  }\n\n  static randInt(rows, columns, options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1000, random = Math.random } = options;\n    if (!Number.isInteger(min)) throw new TypeError('min must be an integer');\n    if (!Number.isInteger(max)) throw new TypeError('max must be an integer');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let interval = max - min;\n    let matrix = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        let value = min + Math.round(random() * interval);\n        matrix.set(i, j, value);\n      }\n    }\n    return matrix;\n  }\n\n  static eye(rows, columns, value) {\n    if (columns === undefined) columns = rows;\n    if (value === undefined) value = 1;\n    let min = Math.min(rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, value);\n    }\n    return matrix;\n  }\n\n  static diag(data, rows, columns) {\n    let l = data.length;\n    if (rows === undefined) rows = l;\n    if (columns === undefined) columns = rows;\n    let min = Math.min(l, rows, columns);\n    let matrix = this.zeros(rows, columns);\n    for (let i = 0; i < min; i++) {\n      matrix.set(i, i, data[i]);\n    }\n    return matrix;\n  }\n\n  static min(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static max(matrix1, matrix2) {\n    matrix1 = this.checkMatrix(matrix1);\n    matrix2 = this.checkMatrix(matrix2);\n    let rows = matrix1.rows;\n    let columns = matrix1.columns;\n    let result = new this(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));\n      }\n    }\n    return result;\n  }\n\n  static checkMatrix(value) {\n    return AbstractMatrix.isMatrix(value) ? value : new Matrix(value);\n  }\n\n  static isMatrix(value) {\n    return value != null && value.klass === 'Matrix';\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  apply(callback) {\n    if (typeof callback !== 'function') {\n      throw new TypeError('callback must be a function');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        callback.call(this, i, j);\n      }\n    }\n    return this;\n  }\n\n  to1DArray() {\n    let array = [];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        array.push(this.get(i, j));\n      }\n    }\n    return array;\n  }\n\n  to2DArray() {\n    let copy = [];\n    for (let i = 0; i < this.rows; i++) {\n      copy.push([]);\n      for (let j = 0; j < this.columns; j++) {\n        copy[i].push(this.get(i, j));\n      }\n    }\n    return copy;\n  }\n\n  toJSON() {\n    return this.to2DArray();\n  }\n\n  isRowVector() {\n    return this.rows === 1;\n  }\n\n  isColumnVector() {\n    return this.columns === 1;\n  }\n\n  isVector() {\n    return this.rows === 1 || this.columns === 1;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (this.isSquare()) {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j <= i; j++) {\n          if (this.get(i, j) !== this.get(j, i)) {\n            return false;\n          }\n        }\n      }\n      return true;\n    }\n    return false;\n  }\n\n  isEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isEchelonForm = false;\n          checked = true;\n        }\n      }\n      i++;\n    }\n    return isEchelonForm;\n  }\n\n  isReducedEchelonForm() {\n    let i = 0;\n    let j = 0;\n    let previousColumn = -1;\n    let isReducedEchelonForm = true;\n    let checked = false;\n    while (i < this.rows && isReducedEchelonForm) {\n      j = 0;\n      checked = false;\n      while (j < this.columns && checked === false) {\n        if (this.get(i, j) === 0) {\n          j++;\n        } else if (this.get(i, j) === 1 && j > previousColumn) {\n          checked = true;\n          previousColumn = j;\n        } else {\n          isReducedEchelonForm = false;\n          checked = true;\n        }\n      }\n      for (let k = j + 1; k < this.rows; k++) {\n        if (this.get(i, k) !== 0) {\n          isReducedEchelonForm = false;\n        }\n      }\n      i++;\n    }\n    return isReducedEchelonForm;\n  }\n\n  echelonForm() {\n    let result = this.clone();\n    let h = 0;\n    let k = 0;\n    while (h < result.rows && k < result.columns) {\n      let iMax = h;\n      for (let i = h; i < result.rows; i++) {\n        if (result.get(i, k) > result.get(iMax, k)) {\n          iMax = i;\n        }\n      }\n      if (result.get(iMax, k) === 0) {\n        k++;\n      } else {\n        result.swapRows(h, iMax);\n        let tmp = result.get(h, k);\n        for (let j = k; j < result.columns; j++) {\n          result.set(h, j, result.get(h, j) / tmp);\n        }\n        for (let i = h + 1; i < result.rows; i++) {\n          let factor = result.get(i, k) / result.get(h, k);\n          result.set(i, k, 0);\n          for (let j = k + 1; j < result.columns; j++) {\n            result.set(i, j, result.get(i, j) - result.get(h, j) * factor);\n          }\n        }\n        h++;\n        k++;\n      }\n    }\n    return result;\n  }\n\n  reducedEchelonForm() {\n    let result = this.echelonForm();\n    let m = result.columns;\n    let n = result.rows;\n    let h = n - 1;\n    while (h >= 0) {\n      if (result.maxRow(h) === 0) {\n        h--;\n      } else {\n        let p = 0;\n        let pivot = false;\n        while (p < n && pivot === false) {\n          if (result.get(h, p) === 1) {\n            pivot = true;\n          } else {\n            p++;\n          }\n        }\n        for (let i = 0; i < h; i++) {\n          let factor = result.get(i, p);\n          for (let j = p; j < m; j++) {\n            let tmp = result.get(i, j) - factor * result.get(h, j);\n            result.set(i, j, tmp);\n          }\n        }\n        h--;\n      }\n    }\n    return result;\n  }\n\n  set() {\n    throw new Error('set method is unimplemented');\n  }\n\n  get() {\n    throw new Error('get method is unimplemented');\n  }\n\n  repeat(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { rows = 1, columns = 1 } = options;\n    if (!Number.isInteger(rows) || rows <= 0) {\n      throw new TypeError('rows must be a positive integer');\n    }\n    if (!Number.isInteger(columns) || columns <= 0) {\n      throw new TypeError('columns must be a positive integer');\n    }\n    let matrix = new Matrix(this.rows * rows, this.columns * columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        matrix.setSubMatrix(this, this.rows * i, this.columns * j);\n      }\n    }\n    return matrix;\n  }\n\n  fill(value) {\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, value);\n      }\n    }\n    return this;\n  }\n\n  neg() {\n    return this.mulS(-1);\n  }\n\n  getRow(index) {\n    checkRowIndex(this, index);\n    let row = [];\n    for (let i = 0; i < this.columns; i++) {\n      row.push(this.get(index, i));\n    }\n    return row;\n  }\n\n  getRowVector(index) {\n    return Matrix.rowVector(this.getRow(index));\n  }\n\n  setRow(index, array) {\n    checkRowIndex(this, index);\n    array = checkRowVector(this, array);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, array[i]);\n    }\n    return this;\n  }\n\n  swapRows(row1, row2) {\n    checkRowIndex(this, row1);\n    checkRowIndex(this, row2);\n    for (let i = 0; i < this.columns; i++) {\n      let temp = this.get(row1, i);\n      this.set(row1, i, this.get(row2, i));\n      this.set(row2, i, temp);\n    }\n    return this;\n  }\n\n  getColumn(index) {\n    checkColumnIndex(this, index);\n    let column = [];\n    for (let i = 0; i < this.rows; i++) {\n      column.push(this.get(i, index));\n    }\n    return column;\n  }\n\n  getColumnVector(index) {\n    return Matrix.columnVector(this.getColumn(index));\n  }\n\n  setColumn(index, array) {\n    checkColumnIndex(this, index);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, array[i]);\n    }\n    return this;\n  }\n\n  swapColumns(column1, column2) {\n    checkColumnIndex(this, column1);\n    checkColumnIndex(this, column2);\n    for (let i = 0; i < this.rows; i++) {\n      let temp = this.get(i, column1);\n      this.set(i, column1, this.get(i, column2));\n      this.set(i, column2, temp);\n    }\n    return this;\n  }\n\n  addRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[j]);\n      }\n    }\n    return this;\n  }\n\n  subRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[j]);\n      }\n    }\n    return this;\n  }\n\n  mulRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[j]);\n      }\n    }\n    return this;\n  }\n\n  divRowVector(vector) {\n    vector = checkRowVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[j]);\n      }\n    }\n    return this;\n  }\n\n  addColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) + vector[i]);\n      }\n    }\n    return this;\n  }\n\n  subColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) - vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) * vector[i]);\n      }\n    }\n    return this;\n  }\n\n  divColumnVector(vector) {\n    vector = checkColumnVector(this, vector);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        this.set(i, j, this.get(i, j) / vector[i]);\n      }\n    }\n    return this;\n  }\n\n  mulRow(index, value) {\n    checkRowIndex(this, index);\n    for (let i = 0; i < this.columns; i++) {\n      this.set(index, i, this.get(index, i) * value);\n    }\n    return this;\n  }\n\n  mulColumn(index, value) {\n    checkColumnIndex(this, index);\n    for (let i = 0; i < this.rows; i++) {\n      this.set(i, index, this.get(i, index) * value);\n    }\n    return this;\n  }\n\n  max() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  maxIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) > v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  min() {\n    let v = this.get(0, 0);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n        }\n      }\n    }\n    return v;\n  }\n\n  minIndex() {\n    let v = this.get(0, 0);\n    let idx = [0, 0];\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        if (this.get(i, j) < v) {\n          v = this.get(i, j);\n          idx[0] = i;\n          idx[1] = j;\n        }\n      }\n    }\n    return idx;\n  }\n\n  maxRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  maxRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) > v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  minRow(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n      }\n    }\n    return v;\n  }\n\n  minRowIndex(row) {\n    checkRowIndex(this, row);\n    let v = this.get(row, 0);\n    let idx = [row, 0];\n    for (let i = 1; i < this.columns; i++) {\n      if (this.get(row, i) < v) {\n        v = this.get(row, i);\n        idx[1] = i;\n      }\n    }\n    return idx;\n  }\n\n  maxColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  maxColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) > v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  minColumn(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n      }\n    }\n    return v;\n  }\n\n  minColumnIndex(column) {\n    checkColumnIndex(this, column);\n    let v = this.get(0, column);\n    let idx = [0, column];\n    for (let i = 1; i < this.rows; i++) {\n      if (this.get(i, column) < v) {\n        v = this.get(i, column);\n        idx[0] = i;\n      }\n    }\n    return idx;\n  }\n\n  diag() {\n    let min = Math.min(this.rows, this.columns);\n    let diag = [];\n    for (let i = 0; i < min; i++) {\n      diag.push(this.get(i, i));\n    }\n    return diag;\n  }\n\n  norm(type = 'frobenius') {\n    let result = 0;\n    if (type === 'max') {\n      return this.max();\n    } else if (type === 'frobenius') {\n      for (let i = 0; i < this.rows; i++) {\n        for (let j = 0; j < this.columns; j++) {\n          result = result + this.get(i, j) * this.get(i, j);\n        }\n      }\n      return Math.sqrt(result);\n    } else {\n      throw new RangeError(`unknown norm type: ${type}`);\n    }\n  }\n\n  cumulativeSum() {\n    let sum = 0;\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        sum += this.get(i, j);\n        this.set(i, j, sum);\n      }\n    }\n    return this;\n  }\n\n  dot(vector2) {\n    if (AbstractMatrix.isMatrix(vector2)) vector2 = vector2.to1DArray();\n    let vector1 = this.to1DArray();\n    if (vector1.length !== vector2.length) {\n      throw new RangeError('vectors do not have the same size');\n    }\n    let dot = 0;\n    for (let i = 0; i < vector1.length; i++) {\n      dot += vector1[i] * vector2[i];\n    }\n    return dot;\n  }\n\n  mmul(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.columns;\n\n    let result = new Matrix(m, p);\n\n    let Bcolj = new Float64Array(n);\n    for (let j = 0; j < p; j++) {\n      for (let k = 0; k < n; k++) {\n        Bcolj[k] = other.get(k, j);\n      }\n\n      for (let i = 0; i < m; i++) {\n        let s = 0;\n        for (let k = 0; k < n; k++) {\n          s += this.get(i, k) * Bcolj[k];\n        }\n\n        result.set(i, j, s);\n      }\n    }\n    return result;\n  }\n\n  strassen2x2(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(2, 2);\n    const a11 = this.get(0, 0);\n    const b11 = other.get(0, 0);\n    const a12 = this.get(0, 1);\n    const b12 = other.get(0, 1);\n    const a21 = this.get(1, 0);\n    const b21 = other.get(1, 0);\n    const a22 = this.get(1, 1);\n    const b22 = other.get(1, 1);\n\n    // Compute intermediate values.\n    const m1 = (a11 + a22) * (b11 + b22);\n    const m2 = (a21 + a22) * b11;\n    const m3 = a11 * (b12 - b22);\n    const m4 = a22 * (b21 - b11);\n    const m5 = (a11 + a12) * b22;\n    const m6 = (a21 - a11) * (b11 + b12);\n    const m7 = (a12 - a22) * (b21 + b22);\n\n    // Combine intermediate values into the output.\n    const c00 = m1 + m4 - m5 + m7;\n    const c01 = m3 + m5;\n    const c10 = m2 + m4;\n    const c11 = m1 - m2 + m3 + m6;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    return result;\n  }\n\n  strassen3x3(other) {\n    other = Matrix.checkMatrix(other);\n    let result = new Matrix(3, 3);\n\n    const a00 = this.get(0, 0);\n    const a01 = this.get(0, 1);\n    const a02 = this.get(0, 2);\n    const a10 = this.get(1, 0);\n    const a11 = this.get(1, 1);\n    const a12 = this.get(1, 2);\n    const a20 = this.get(2, 0);\n    const a21 = this.get(2, 1);\n    const a22 = this.get(2, 2);\n\n    const b00 = other.get(0, 0);\n    const b01 = other.get(0, 1);\n    const b02 = other.get(0, 2);\n    const b10 = other.get(1, 0);\n    const b11 = other.get(1, 1);\n    const b12 = other.get(1, 2);\n    const b20 = other.get(2, 0);\n    const b21 = other.get(2, 1);\n    const b22 = other.get(2, 2);\n\n    const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;\n    const m2 = (a00 - a10) * (-b01 + b11);\n    const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);\n    const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);\n    const m5 = (a10 + a11) * (-b00 + b01);\n    const m6 = a00 * b00;\n    const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);\n    const m8 = (-a00 + a20) * (b02 - b12);\n    const m9 = (a20 + a21) * (-b00 + b02);\n    const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;\n    const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);\n    const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);\n    const m13 = (a02 - a22) * (b11 - b21);\n    const m14 = a02 * b20;\n    const m15 = (a21 + a22) * (-b20 + b21);\n    const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);\n    const m17 = (a02 - a12) * (b12 - b22);\n    const m18 = (a11 + a12) * (-b20 + b22);\n    const m19 = a01 * b10;\n    const m20 = a12 * b21;\n    const m21 = a10 * b02;\n    const m22 = a20 * b01;\n    const m23 = a22 * b22;\n\n    const c00 = m6 + m14 + m19;\n    const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;\n    const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;\n    const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;\n    const c11 = m2 + m4 + m5 + m6 + m20;\n    const c12 = m14 + m16 + m17 + m18 + m21;\n    const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;\n    const c21 = m12 + m13 + m14 + m15 + m22;\n    const c22 = m6 + m7 + m8 + m9 + m23;\n\n    result.set(0, 0, c00);\n    result.set(0, 1, c01);\n    result.set(0, 2, c02);\n    result.set(1, 0, c10);\n    result.set(1, 1, c11);\n    result.set(1, 2, c12);\n    result.set(2, 0, c20);\n    result.set(2, 1, c21);\n    result.set(2, 2, c22);\n    return result;\n  }\n\n  mmulStrassen(y) {\n    y = Matrix.checkMatrix(y);\n    let x = this.clone();\n    let r1 = x.rows;\n    let c1 = x.columns;\n    let r2 = y.rows;\n    let c2 = y.columns;\n    if (c1 !== r2) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        `Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`,\n      );\n    }\n\n    // Put a matrix into the top left of a matrix of zeros.\n    // `rows` and `cols` are the dimensions of the output matrix.\n    function embed(mat, rows, cols) {\n      let r = mat.rows;\n      let c = mat.columns;\n      if (r === rows && c === cols) {\n        return mat;\n      } else {\n        let resultat = AbstractMatrix.zeros(rows, cols);\n        resultat = resultat.setSubMatrix(mat, 0, 0);\n        return resultat;\n      }\n    }\n\n    // Make sure both matrices are the same size.\n    // This is exclusively for simplicity:\n    // this algorithm can be implemented with matrices of different sizes.\n\n    let r = Math.max(r1, r2);\n    let c = Math.max(c1, c2);\n    x = embed(x, r, c);\n    y = embed(y, r, c);\n\n    // Our recursive multiplication function.\n    function blockMult(a, b, rows, cols) {\n      // For small matrices, resort to naive multiplication.\n      if (rows <= 512 || cols <= 512) {\n        return a.mmul(b); // a is equivalent to this\n      }\n\n      // Apply dynamic padding.\n      if (rows % 2 === 1 && cols % 2 === 1) {\n        a = embed(a, rows + 1, cols + 1);\n        b = embed(b, rows + 1, cols + 1);\n      } else if (rows % 2 === 1) {\n        a = embed(a, rows + 1, cols);\n        b = embed(b, rows + 1, cols);\n      } else if (cols % 2 === 1) {\n        a = embed(a, rows, cols + 1);\n        b = embed(b, rows, cols + 1);\n      }\n\n      let halfRows = parseInt(a.rows / 2, 10);\n      let halfCols = parseInt(a.columns / 2, 10);\n      // Subdivide input matrices.\n      let a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n      let b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);\n\n      let a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);\n      let b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);\n\n      let a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);\n      let b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);\n\n      let a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);\n      let b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);\n\n      // Compute intermediate values.\n      let m1 = blockMult(\n        AbstractMatrix.add(a11, a22),\n        AbstractMatrix.add(b11, b22),\n        halfRows,\n        halfCols,\n      );\n      let m2 = blockMult(AbstractMatrix.add(a21, a22), b11, halfRows, halfCols);\n      let m3 = blockMult(a11, AbstractMatrix.sub(b12, b22), halfRows, halfCols);\n      let m4 = blockMult(a22, AbstractMatrix.sub(b21, b11), halfRows, halfCols);\n      let m5 = blockMult(AbstractMatrix.add(a11, a12), b22, halfRows, halfCols);\n      let m6 = blockMult(\n        AbstractMatrix.sub(a21, a11),\n        AbstractMatrix.add(b11, b12),\n        halfRows,\n        halfCols,\n      );\n      let m7 = blockMult(\n        AbstractMatrix.sub(a12, a22),\n        AbstractMatrix.add(b21, b22),\n        halfRows,\n        halfCols,\n      );\n\n      // Combine intermediate values into the output.\n      let c11 = AbstractMatrix.add(m1, m4);\n      c11.sub(m5);\n      c11.add(m7);\n      let c12 = AbstractMatrix.add(m3, m5);\n      let c21 = AbstractMatrix.add(m2, m4);\n      let c22 = AbstractMatrix.sub(m1, m2);\n      c22.add(m3);\n      c22.add(m6);\n\n      // Crop output to the desired size (undo dynamic padding).\n      let resultat = AbstractMatrix.zeros(2 * c11.rows, 2 * c11.columns);\n      resultat = resultat.setSubMatrix(c11, 0, 0);\n      resultat = resultat.setSubMatrix(c12, c11.rows, 0);\n      resultat = resultat.setSubMatrix(c21, 0, c11.columns);\n      resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);\n      return resultat.subMatrix(0, rows - 1, 0, cols - 1);\n    }\n    return blockMult(x, y, r, c);\n  }\n\n  scaleRows(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.rows; i++) {\n      const row = this.getRow(i);\n      rescale(row, { min, max, output: row });\n      newMatrix.setRow(i, row);\n    }\n    return newMatrix;\n  }\n\n  scaleColumns(options = {}) {\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { min = 0, max = 1 } = options;\n    if (!Number.isFinite(min)) throw new TypeError('min must be a number');\n    if (!Number.isFinite(max)) throw new TypeError('max must be a number');\n    if (min >= max) throw new RangeError('min must be smaller than max');\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let i = 0; i < this.columns; i++) {\n      const column = this.getColumn(i);\n      rescale(column, {\n        min: min,\n        max: max,\n        output: column,\n      });\n      newMatrix.setColumn(i, column);\n    }\n    return newMatrix;\n  }\n\n  flipRows() {\n    const middle = Math.ceil(this.columns / 2);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < middle; j++) {\n        let first = this.get(i, j);\n        let last = this.get(i, this.columns - 1 - j);\n        this.set(i, j, last);\n        this.set(i, this.columns - 1 - j, first);\n      }\n    }\n    return this;\n  }\n\n  flipColumns() {\n    const middle = Math.ceil(this.rows / 2);\n    for (let j = 0; j < this.columns; j++) {\n      for (let i = 0; i < middle; i++) {\n        let first = this.get(i, j);\n        let last = this.get(this.rows - 1 - i, j);\n        this.set(i, j, last);\n        this.set(this.rows - 1 - i, j, first);\n      }\n    }\n    return this;\n  }\n\n  kroneckerProduct(other) {\n    other = Matrix.checkMatrix(other);\n\n    let m = this.rows;\n    let n = this.columns;\n    let p = other.rows;\n    let q = other.columns;\n\n    let result = new Matrix(m * p, n * q);\n    for (let i = 0; i < m; i++) {\n      for (let j = 0; j < n; j++) {\n        for (let k = 0; k < p; k++) {\n          for (let l = 0; l < q; l++) {\n            result.set(p * i + k, q * j + l, this.get(i, j) * other.get(k, l));\n          }\n        }\n      }\n    }\n    return result;\n  }\n\n  transpose() {\n    let result = new Matrix(this.columns, this.rows);\n    for (let i = 0; i < this.rows; i++) {\n      for (let j = 0; j < this.columns; j++) {\n        result.set(j, i, this.get(i, j));\n      }\n    }\n    return result;\n  }\n\n  sortRows(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.rows; i++) {\n      this.setRow(i, this.getRow(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  sortColumns(compareFunction = compareNumbers) {\n    for (let i = 0; i < this.columns; i++) {\n      this.setColumn(i, this.getColumn(i).sort(compareFunction));\n    }\n    return this;\n  }\n\n  subMatrix(startRow, endRow, startColumn, endColumn) {\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    let newMatrix = new Matrix(\n      endRow - startRow + 1,\n      endColumn - startColumn + 1,\n    );\n    for (let i = startRow; i <= endRow; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        newMatrix.set(i - startRow, j - startColumn, this.get(i, j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixRow(indices, startColumn, endColumn) {\n    if (startColumn === undefined) startColumn = 0;\n    if (endColumn === undefined) endColumn = this.columns - 1;\n    if (\n      startColumn > endColumn ||\n      startColumn < 0 ||\n      startColumn >= this.columns ||\n      endColumn < 0 ||\n      endColumn >= this.columns\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(indices.length, endColumn - startColumn + 1);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startColumn; j <= endColumn; j++) {\n        if (indices[i] < 0 || indices[i] >= this.rows) {\n          throw new RangeError(`Row index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(i, j - startColumn, this.get(indices[i], j));\n      }\n    }\n    return newMatrix;\n  }\n\n  subMatrixColumn(indices, startRow, endRow) {\n    if (startRow === undefined) startRow = 0;\n    if (endRow === undefined) endRow = this.rows - 1;\n    if (\n      startRow > endRow ||\n      startRow < 0 ||\n      startRow >= this.rows ||\n      endRow < 0 ||\n      endRow >= this.rows\n    ) {\n      throw new RangeError('Argument out of range');\n    }\n\n    let newMatrix = new Matrix(endRow - startRow + 1, indices.length);\n    for (let i = 0; i < indices.length; i++) {\n      for (let j = startRow; j <= endRow; j++) {\n        if (indices[i] < 0 || indices[i] >= this.columns) {\n          throw new RangeError(`Column index out of range: ${indices[i]}`);\n        }\n        newMatrix.set(j - startRow, i, this.get(j, indices[i]));\n      }\n    }\n    return newMatrix;\n  }\n\n  setSubMatrix(matrix, startRow, startColumn) {\n    matrix = Matrix.checkMatrix(matrix);\n    let endRow = startRow + matrix.rows - 1;\n    let endColumn = startColumn + matrix.columns - 1;\n    checkRange(this, startRow, endRow, startColumn, endColumn);\n    for (let i = 0; i < matrix.rows; i++) {\n      for (let j = 0; j < matrix.columns; j++) {\n        this.set(startRow + i, startColumn + j, matrix.get(i, j));\n      }\n    }\n    return this;\n  }\n\n  selection(rowIndices, columnIndices) {\n    let indices = checkIndices(this, rowIndices, columnIndices);\n    let newMatrix = new Matrix(rowIndices.length, columnIndices.length);\n    for (let i = 0; i < indices.row.length; i++) {\n      let rowIndex = indices.row[i];\n      for (let j = 0; j < indices.column.length; j++) {\n        let columnIndex = indices.column[j];\n        newMatrix.set(i, j, this.get(rowIndex, columnIndex));\n      }\n    }\n    return newMatrix;\n  }\n\n  trace() {\n    let min = Math.min(this.rows, this.columns);\n    let trace = 0;\n    for (let i = 0; i < min; i++) {\n      trace += this.get(i, i);\n    }\n    return trace;\n  }\n\n  clone() {\n    let newMatrix = new Matrix(this.rows, this.columns);\n    for (let row = 0; row < this.rows; row++) {\n      for (let column = 0; column < this.columns; column++) {\n        newMatrix.set(row, column, this.get(row, column));\n      }\n    }\n    return newMatrix;\n  }\n\n  sum(by) {\n    switch (by) {\n      case 'row':\n        return sumByRow(this);\n      case 'column':\n        return sumByColumn(this);\n      case undefined:\n        return sumAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  product(by) {\n    switch (by) {\n      case 'row':\n        return productByRow(this);\n      case 'column':\n        return productByColumn(this);\n      case undefined:\n        return productAll(this);\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  mean(by) {\n    const sum = this.sum(by);\n    switch (by) {\n      case 'row': {\n        for (let i = 0; i < this.rows; i++) {\n          sum[i] /= this.columns;\n        }\n        return sum;\n      }\n      case 'column': {\n        for (let i = 0; i < this.columns; i++) {\n          sum[i] /= this.rows;\n        }\n        return sum;\n      }\n      case undefined:\n        return sum / this.size;\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  variance(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { unbiased = true, mean = this.mean(by) } = options;\n    if (typeof unbiased !== 'boolean') {\n      throw new TypeError('unbiased must be a boolean');\n    }\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByRow(this, unbiased, mean);\n      }\n      case 'column': {\n        if (!Array.isArray(mean)) {\n          throw new TypeError('mean must be an array');\n        }\n        return varianceByColumn(this, unbiased, mean);\n      }\n      case undefined: {\n        if (typeof mean !== 'number') {\n          throw new TypeError('mean must be a number');\n        }\n        return varianceAll(this, unbiased, mean);\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  standardDeviation(by, options) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    const variance = this.variance(by, options);\n    if (by === undefined) {\n      return Math.sqrt(variance);\n    } else {\n      for (let i = 0; i < variance.length; i++) {\n        variance[i] = Math.sqrt(variance[i]);\n      }\n      return variance;\n    }\n  }\n\n  center(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    const { center = this.mean(by) } = options;\n    switch (by) {\n      case 'row': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByRow(this, center);\n        return this;\n      }\n      case 'column': {\n        if (!Array.isArray(center)) {\n          throw new TypeError('center must be an array');\n        }\n        centerByColumn(this, center);\n        return this;\n      }\n      case undefined: {\n        if (typeof center !== 'number') {\n          throw new TypeError('center must be a number');\n        }\n        centerAll(this, center);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  scale(by, options = {}) {\n    if (typeof by === 'object') {\n      options = by;\n      by = undefined;\n    }\n    if (typeof options !== 'object') {\n      throw new TypeError('options must be an object');\n    }\n    let scale = options.scale;\n    switch (by) {\n      case 'row': {\n        if (scale === undefined) {\n          scale = getScaleByRow(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByRow(this, scale);\n        return this;\n      }\n      case 'column': {\n        if (scale === undefined) {\n          scale = getScaleByColumn(this);\n        } else if (!Array.isArray(scale)) {\n          throw new TypeError('scale must be an array');\n        }\n        scaleByColumn(this, scale);\n        return this;\n      }\n      case undefined: {\n        if (scale === undefined) {\n          scale = getScaleAll(this);\n        } else if (typeof scale !== 'number') {\n          throw new TypeError('scale must be a number');\n        }\n        scaleAll(this, scale);\n        return this;\n      }\n      default:\n        throw new Error(`invalid option: ${by}`);\n    }\n  }\n\n  toString(options) {\n    return inspectMatrixWithOptions(this, options);\n  }\n}\n\nAbstractMatrix.prototype.klass = 'Matrix';\nif (typeof Symbol !== 'undefined') {\n  AbstractMatrix.prototype[\n    Symbol.for('nodejs.util.inspect.custom')\n  ] = inspectMatrix;\n}\n\nfunction compareNumbers(a, b) {\n  return a - b;\n}\n\n// Synonyms\nAbstractMatrix.random = AbstractMatrix.rand;\nAbstractMatrix.randomInt = AbstractMatrix.randInt;\nAbstractMatrix.diagonal = AbstractMatrix.diag;\nAbstractMatrix.prototype.diagonal = AbstractMatrix.prototype.diag;\nAbstractMatrix.identity = AbstractMatrix.eye;\nAbstractMatrix.prototype.negate = AbstractMatrix.prototype.neg;\nAbstractMatrix.prototype.tensorProduct =\n  AbstractMatrix.prototype.kroneckerProduct;\n\nexport default class Matrix extends AbstractMatrix {\n  constructor(nRows, nColumns) {\n    super();\n    if (Matrix.isMatrix(nRows)) {\n      return nRows.clone();\n    } else if (Number.isInteger(nRows) && nRows > 0) {\n      // Create an empty matrix\n      this.data = [];\n      if (Number.isInteger(nColumns) && nColumns > 0) {\n        for (let i = 0; i < nRows; i++) {\n          this.data.push(new Float64Array(nColumns));\n        }\n      } else {\n        throw new TypeError('nColumns must be a positive integer');\n      }\n    } else if (Array.isArray(nRows)) {\n      // Copy the values from the 2D array\n      const arrayData = nRows;\n      nRows = arrayData.length;\n      nColumns = arrayData[0].length;\n      if (typeof nColumns !== 'number' || nColumns === 0) {\n        throw new TypeError(\n          'Data must be a 2D array with at least one element',\n        );\n      }\n      this.data = [];\n      for (let i = 0; i < nRows; i++) {\n        if (arrayData[i].length !== nColumns) {\n          throw new RangeError('Inconsistent array dimensions');\n        }\n        this.data.push(Float64Array.from(arrayData[i]));\n      }\n    } else {\n      throw new TypeError(\n        'First argument must be a positive number or an array',\n      );\n    }\n    this.rows = nRows;\n    this.columns = nColumns;\n    return this;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n\n  removeRow(index) {\n    checkRowIndex(this, index);\n    if (this.rows === 1) {\n      throw new RangeError('A matrix cannot have less than one row');\n    }\n    this.data.splice(index, 1);\n    this.rows -= 1;\n    return this;\n  }\n\n  addRow(index, array) {\n    if (array === undefined) {\n      array = index;\n      index = this.rows;\n    }\n    checkRowIndex(this, index, true);\n    array = Float64Array.from(checkRowVector(this, array, true));\n    this.data.splice(index, 0, array);\n    this.rows += 1;\n    return this;\n  }\n\n  removeColumn(index) {\n    checkColumnIndex(this, index);\n    if (this.columns === 1) {\n      throw new RangeError('A matrix cannot have less than one column');\n    }\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns - 1);\n      for (let j = 0; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      for (let j = index + 1; j < this.columns; j++) {\n        newRow[j - 1] = this.data[i][j];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns -= 1;\n    return this;\n  }\n\n  addColumn(index, array) {\n    if (typeof array === 'undefined') {\n      array = index;\n      index = this.columns;\n    }\n    checkColumnIndex(this, index, true);\n    array = checkColumnVector(this, array);\n    for (let i = 0; i < this.rows; i++) {\n      const newRow = new Float64Array(this.columns + 1);\n      let j = 0;\n      for (; j < index; j++) {\n        newRow[j] = this.data[i][j];\n      }\n      newRow[j++] = array[i];\n      for (; j < this.columns + 1; j++) {\n        newRow[j] = this.data[i][j - 1];\n      }\n      this.data[i] = newRow;\n    }\n    this.columns += 1;\n    return this;\n  }\n}\n\ninstallMathOperations(AbstractMatrix, Matrix);\n","import { AbstractMatrix } from '../matrix';\n\nexport default class BaseView extends AbstractMatrix {\n  constructor(matrix, rows, columns) {\n    super();\n    this.matrix = matrix;\n    this.rows = rows;\n    this.columns = columns;\n  }\n}\n","import { checkColumnIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixColumnView extends BaseView {\n  constructor(matrix, column) {\n    checkColumnIndex(matrix, column);\n    super(matrix, matrix.rows, 1);\n    this.column = column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.column, value);\n    return this;\n  }\n\n  get(rowIndex) {\n    return this.matrix.get(rowIndex, this.column);\n  }\n}\n","import { checkColumnIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixColumnSelectionView extends BaseView {\n  constructor(matrix, columnIndices) {\n    columnIndices = checkColumnIndices(matrix, columnIndices);\n    super(matrix, matrix.rows, columnIndices.length);\n    this.columnIndices = columnIndices;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);\n  }\n}\n","import BaseView from './base';\n\nexport default class MatrixFlipColumnView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.rows, matrix.columns);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(rowIndex, this.columns - columnIndex - 1);\n  }\n}\n","import BaseView from './base';\n\nexport default class MatrixFlipRowView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.rows, matrix.columns);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.rows - rowIndex - 1, columnIndex);\n  }\n}\n","import { checkRowIndex } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixRowView extends BaseView {\n  constructor(matrix, row) {\n    checkRowIndex(matrix, row);\n    super(matrix, 1, matrix.columns);\n    this.row = row;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.row, columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.row, columnIndex);\n  }\n}\n","import { checkRowIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixRowSelectionView extends BaseView {\n  constructor(matrix, rowIndices) {\n    rowIndices = checkRowIndices(matrix, rowIndices);\n    super(matrix, rowIndices.length, matrix.columns);\n    this.rowIndices = rowIndices;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(this.rowIndices[rowIndex], columnIndex);\n  }\n}\n","import { checkIndices } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSelectionView extends BaseView {\n  constructor(matrix, rowIndices, columnIndices) {\n    let indices = checkIndices(matrix, rowIndices, columnIndices);\n    super(matrix, indices.row.length, indices.column.length);\n    this.rowIndices = indices.row;\n    this.columnIndices = indices.column;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.rowIndices[rowIndex],\n      this.columnIndices[columnIndex],\n    );\n  }\n}\n","import { checkRange } from '../util';\n\nimport BaseView from './base';\n\nexport default class MatrixSubView extends BaseView {\n  constructor(matrix, startRow, endRow, startColumn, endColumn) {\n    checkRange(matrix, startRow, endRow, startColumn, endColumn);\n    super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);\n    this.startRow = startRow;\n    this.startColumn = startColumn;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n      value,\n    );\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(\n      this.startRow + rowIndex,\n      this.startColumn + columnIndex,\n    );\n  }\n}\n","import BaseView from './base';\n\nexport default class MatrixTransposeView extends BaseView {\n  constructor(matrix) {\n    super(matrix, matrix.columns, matrix.rows);\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.matrix.set(columnIndex, rowIndex, value);\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.matrix.get(columnIndex, rowIndex);\n  }\n}\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix1D extends AbstractMatrix {\n  constructor(data, options = {}) {\n    const { rows = 1 } = options;\n\n    if (data.length % rows !== 0) {\n      throw new Error('the data length is not divisible by the number of rows');\n    }\n    super();\n    this.rows = rows;\n    this.columns = data.length / rows;\n    this.data = data;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    this.data[index] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    let index = this._calculateIndex(rowIndex, columnIndex);\n    return this.data[index];\n  }\n\n  _calculateIndex(row, column) {\n    return row * this.columns + column;\n  }\n}\n","import { AbstractMatrix } from '../matrix';\n\nexport default class WrapperMatrix2D extends AbstractMatrix {\n  constructor(data) {\n    super();\n    this.data = data;\n    this.rows = data.length;\n    this.columns = data[0].length;\n  }\n\n  set(rowIndex, columnIndex, value) {\n    this.data[rowIndex][columnIndex] = value;\n    return this;\n  }\n\n  get(rowIndex, columnIndex) {\n    return this.data[rowIndex][columnIndex];\n  }\n}\n","import WrapperMatrix1D from './WrapperMatrix1D';\nimport WrapperMatrix2D from './WrapperMatrix2D';\n\nexport function wrap(array, options) {\n  if (Array.isArray(array)) {\n    if (array[0] && Array.isArray(array[0])) {\n      return new WrapperMatrix2D(array);\n    } else {\n      return new WrapperMatrix1D(array, options);\n    }\n  } else {\n    throw new Error('the argument is not an array');\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class LuDecomposition {\n  constructor(matrix) {\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n\n    let lu = matrix.clone();\n    let rows = lu.rows;\n    let columns = lu.columns;\n    let pivotVector = new Float64Array(rows);\n    let pivotSign = 1;\n    let i, j, k, p, s, t, v;\n    let LUcolj, kmax;\n\n    for (i = 0; i < rows; i++) {\n      pivotVector[i] = i;\n    }\n\n    LUcolj = new Float64Array(rows);\n\n    for (j = 0; j < columns; j++) {\n      for (i = 0; i < rows; i++) {\n        LUcolj[i] = lu.get(i, j);\n      }\n\n      for (i = 0; i < rows; i++) {\n        kmax = Math.min(i, j);\n        s = 0;\n        for (k = 0; k < kmax; k++) {\n          s += lu.get(i, k) * LUcolj[k];\n        }\n        LUcolj[i] -= s;\n        lu.set(i, j, LUcolj[i]);\n      }\n\n      p = j;\n      for (i = j + 1; i < rows; i++) {\n        if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {\n          p = i;\n        }\n      }\n\n      if (p !== j) {\n        for (k = 0; k < columns; k++) {\n          t = lu.get(p, k);\n          lu.set(p, k, lu.get(j, k));\n          lu.set(j, k, t);\n        }\n\n        v = pivotVector[p];\n        pivotVector[p] = pivotVector[j];\n        pivotVector[j] = v;\n\n        pivotSign = -pivotSign;\n      }\n\n      if (j < rows && lu.get(j, j) !== 0) {\n        for (i = j + 1; i < rows; i++) {\n          lu.set(i, j, lu.get(i, j) / lu.get(j, j));\n        }\n      }\n    }\n\n    this.LU = lu;\n    this.pivotVector = pivotVector;\n    this.pivotSign = pivotSign;\n  }\n\n  isSingular() {\n    let data = this.LU;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      if (data.get(j, j) === 0) {\n        return true;\n      }\n    }\n    return false;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let lu = this.LU;\n    let rows = lu.rows;\n\n    if (rows !== value.rows) {\n      throw new Error('Invalid matrix dimensions');\n    }\n    if (this.isSingular()) {\n      throw new Error('LU matrix is singular');\n    }\n\n    let count = value.columns;\n    let X = value.subMatrixRow(this.pivotVector, 0, count - 1);\n    let columns = lu.columns;\n    let i, j, k;\n\n    for (k = 0; k < columns; k++) {\n      for (i = k + 1; i < columns; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    for (k = columns - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / lu.get(k, k));\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * lu.get(i, k));\n        }\n      }\n    }\n    return X;\n  }\n\n  get determinant() {\n    let data = this.LU;\n    if (!data.isSquare()) {\n      throw new Error('Matrix must be square');\n    }\n    let determinant = this.pivotSign;\n    let col = data.columns;\n    for (let j = 0; j < col; j++) {\n      determinant *= data.get(j, j);\n    }\n    return determinant;\n  }\n\n  get lowerTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i > j) {\n          X.set(i, j, data.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, 1);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get upperTriangularMatrix() {\n    let data = this.LU;\n    let rows = data.rows;\n    let columns = data.columns;\n    let X = new Matrix(rows, columns);\n    for (let i = 0; i < rows; i++) {\n      for (let j = 0; j < columns; j++) {\n        if (i <= j) {\n          X.set(i, j, data.get(i, j));\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get pivotPermutationVector() {\n    return Array.from(this.pivotVector);\n  }\n}\n","export function hypotenuse(a, b) {\n  let r = 0;\n  if (Math.abs(a) > Math.abs(b)) {\n    r = b / a;\n    return Math.abs(a) * Math.sqrt(1 + r * r);\n  }\n  if (b !== 0) {\n    r = a / b;\n    return Math.abs(b) * Math.sqrt(1 + r * r);\n  }\n  return 0;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class QrDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let qr = value.clone();\n    let m = value.rows;\n    let n = value.columns;\n    let rdiag = new Float64Array(n);\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      let nrm = 0;\n      for (i = k; i < m; i++) {\n        nrm = hypotenuse(nrm, qr.get(i, k));\n      }\n      if (nrm !== 0) {\n        if (qr.get(k, k) < 0) {\n          nrm = -nrm;\n        }\n        for (i = k; i < m; i++) {\n          qr.set(i, k, qr.get(i, k) / nrm);\n        }\n        qr.set(k, k, qr.get(k, k) + 1);\n        for (j = k + 1; j < n; j++) {\n          s = 0;\n          for (i = k; i < m; i++) {\n            s += qr.get(i, k) * qr.get(i, j);\n          }\n          s = -s / qr.get(k, k);\n          for (i = k; i < m; i++) {\n            qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n      rdiag[k] = -nrm;\n    }\n\n    this.QR = qr;\n    this.Rdiag = rdiag;\n  }\n\n  solve(value) {\n    value = Matrix.checkMatrix(value);\n\n    let qr = this.QR;\n    let m = qr.rows;\n\n    if (value.rows !== m) {\n      throw new Error('Matrix row dimensions must agree');\n    }\n    if (!this.isFullRank()) {\n      throw new Error('Matrix is rank deficient');\n    }\n\n    let count = value.columns;\n    let X = value.clone();\n    let n = qr.columns;\n    let i, j, k, s;\n\n    for (k = 0; k < n; k++) {\n      for (j = 0; j < count; j++) {\n        s = 0;\n        for (i = k; i < m; i++) {\n          s += qr.get(i, k) * X.get(i, j);\n        }\n        s = -s / qr.get(k, k);\n        for (i = k; i < m; i++) {\n          X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n        }\n      }\n    }\n    for (k = n - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        X.set(k, j, X.get(k, j) / this.Rdiag[k]);\n      }\n      for (i = 0; i < k; i++) {\n        for (j = 0; j < count; j++) {\n          X.set(i, j, X.get(i, j) - X.get(k, j) * qr.get(i, k));\n        }\n      }\n    }\n\n    return X.subMatrix(0, n - 1, 0, count - 1);\n  }\n\n  isFullRank() {\n    let columns = this.QR.columns;\n    for (let i = 0; i < columns; i++) {\n      if (this.Rdiag[i] === 0) {\n        return false;\n      }\n    }\n    return true;\n  }\n\n  get upperTriangularMatrix() {\n    let qr = this.QR;\n    let n = qr.columns;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        if (i < j) {\n          X.set(i, j, qr.get(i, j));\n        } else if (i === j) {\n          X.set(i, j, this.Rdiag[i]);\n        } else {\n          X.set(i, j, 0);\n        }\n      }\n    }\n    return X;\n  }\n\n  get orthogonalMatrix() {\n    let qr = this.QR;\n    let rows = qr.rows;\n    let columns = qr.columns;\n    let X = new Matrix(rows, columns);\n    let i, j, k, s;\n\n    for (k = columns - 1; k >= 0; k--) {\n      for (i = 0; i < rows; i++) {\n        X.set(i, k, 0);\n      }\n      X.set(k, k, 1);\n      for (j = k; j < columns; j++) {\n        if (qr.get(k, k) !== 0) {\n          s = 0;\n          for (i = k; i < rows; i++) {\n            s += qr.get(i, k) * X.get(i, j);\n          }\n\n          s = -s / qr.get(k, k);\n\n          for (i = k; i < rows; i++) {\n            X.set(i, j, X.get(i, j) + s * qr.get(i, k));\n          }\n        }\n      }\n    }\n    return X;\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class SingularValueDecomposition {\n  constructor(value, options = {}) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let m = value.rows;\n    let n = value.columns;\n\n    const {\n      computeLeftSingularVectors = true,\n      computeRightSingularVectors = true,\n      autoTranspose = false,\n    } = options;\n\n    let wantu = Boolean(computeLeftSingularVectors);\n    let wantv = Boolean(computeRightSingularVectors);\n\n    let swapped = false;\n    let a;\n    if (m < n) {\n      if (!autoTranspose) {\n        a = value.clone();\n        // eslint-disable-next-line no-console\n        console.warn(\n          'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose',\n        );\n      } else {\n        a = value.transpose();\n        m = a.rows;\n        n = a.columns;\n        swapped = true;\n        let aux = wantu;\n        wantu = wantv;\n        wantv = aux;\n      }\n    } else {\n      a = value.clone();\n    }\n\n    let nu = Math.min(m, n);\n    let ni = Math.min(m + 1, n);\n    let s = new Float64Array(ni);\n    let U = new Matrix(m, nu);\n    let V = new Matrix(n, n);\n\n    let e = new Float64Array(n);\n    let work = new Float64Array(m);\n\n    let si = new Float64Array(ni);\n    for (let i = 0; i < ni; i++) si[i] = i;\n\n    let nct = Math.min(m - 1, n);\n    let nrt = Math.max(0, Math.min(n - 2, m));\n    let mrc = Math.max(nct, nrt);\n\n    for (let k = 0; k < mrc; k++) {\n      if (k < nct) {\n        s[k] = 0;\n        for (let i = k; i < m; i++) {\n          s[k] = hypotenuse(s[k], a.get(i, k));\n        }\n        if (s[k] !== 0) {\n          if (a.get(k, k) < 0) {\n            s[k] = -s[k];\n          }\n          for (let i = k; i < m; i++) {\n            a.set(i, k, a.get(i, k) / s[k]);\n          }\n          a.set(k, k, a.get(k, k) + 1);\n        }\n        s[k] = -s[k];\n      }\n\n      for (let j = k + 1; j < n; j++) {\n        if (k < nct && s[k] !== 0) {\n          let t = 0;\n          for (let i = k; i < m; i++) {\n            t += a.get(i, k) * a.get(i, j);\n          }\n          t = -t / a.get(k, k);\n          for (let i = k; i < m; i++) {\n            a.set(i, j, a.get(i, j) + t * a.get(i, k));\n          }\n        }\n        e[j] = a.get(k, j);\n      }\n\n      if (wantu && k < nct) {\n        for (let i = k; i < m; i++) {\n          U.set(i, k, a.get(i, k));\n        }\n      }\n\n      if (k < nrt) {\n        e[k] = 0;\n        for (let i = k + 1; i < n; i++) {\n          e[k] = hypotenuse(e[k], e[i]);\n        }\n        if (e[k] !== 0) {\n          if (e[k + 1] < 0) {\n            e[k] = 0 - e[k];\n          }\n          for (let i = k + 1; i < n; i++) {\n            e[i] /= e[k];\n          }\n          e[k + 1] += 1;\n        }\n        e[k] = -e[k];\n        if (k + 1 < m && e[k] !== 0) {\n          for (let i = k + 1; i < m; i++) {\n            work[i] = 0;\n          }\n          for (let i = k + 1; i < m; i++) {\n            for (let j = k + 1; j < n; j++) {\n              work[i] += e[j] * a.get(i, j);\n            }\n          }\n          for (let j = k + 1; j < n; j++) {\n            let t = -e[j] / e[k + 1];\n            for (let i = k + 1; i < m; i++) {\n              a.set(i, j, a.get(i, j) + t * work[i]);\n            }\n          }\n        }\n        if (wantv) {\n          for (let i = k + 1; i < n; i++) {\n            V.set(i, k, e[i]);\n          }\n        }\n      }\n    }\n\n    let p = Math.min(n, m + 1);\n    if (nct < n) {\n      s[nct] = a.get(nct, nct);\n    }\n    if (m < p) {\n      s[p - 1] = 0;\n    }\n    if (nrt + 1 < p) {\n      e[nrt] = a.get(nrt, p - 1);\n    }\n    e[p - 1] = 0;\n\n    if (wantu) {\n      for (let j = nct; j < nu; j++) {\n        for (let i = 0; i < m; i++) {\n          U.set(i, j, 0);\n        }\n        U.set(j, j, 1);\n      }\n      for (let k = nct - 1; k >= 0; k--) {\n        if (s[k] !== 0) {\n          for (let j = k + 1; j < nu; j++) {\n            let t = 0;\n            for (let i = k; i < m; i++) {\n              t += U.get(i, k) * U.get(i, j);\n            }\n            t = -t / U.get(k, k);\n            for (let i = k; i < m; i++) {\n              U.set(i, j, U.get(i, j) + t * U.get(i, k));\n            }\n          }\n          for (let i = k; i < m; i++) {\n            U.set(i, k, -U.get(i, k));\n          }\n          U.set(k, k, 1 + U.get(k, k));\n          for (let i = 0; i < k - 1; i++) {\n            U.set(i, k, 0);\n          }\n        } else {\n          for (let i = 0; i < m; i++) {\n            U.set(i, k, 0);\n          }\n          U.set(k, k, 1);\n        }\n      }\n    }\n\n    if (wantv) {\n      for (let k = n - 1; k >= 0; k--) {\n        if (k < nrt && e[k] !== 0) {\n          for (let j = k + 1; j < n; j++) {\n            let t = 0;\n            for (let i = k + 1; i < n; i++) {\n              t += V.get(i, k) * V.get(i, j);\n            }\n            t = -t / V.get(k + 1, k);\n            for (let i = k + 1; i < n; i++) {\n              V.set(i, j, V.get(i, j) + t * V.get(i, k));\n            }\n          }\n        }\n        for (let i = 0; i < n; i++) {\n          V.set(i, k, 0);\n        }\n        V.set(k, k, 1);\n      }\n    }\n\n    let pp = p - 1;\n    let iter = 0;\n    let eps = Number.EPSILON;\n    while (p > 0) {\n      let k, kase;\n      for (k = p - 2; k >= -1; k--) {\n        if (k === -1) {\n          break;\n        }\n        const alpha =\n          Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));\n        if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {\n          e[k] = 0;\n          break;\n        }\n      }\n      if (k === p - 2) {\n        kase = 4;\n      } else {\n        let ks;\n        for (ks = p - 1; ks >= k; ks--) {\n          if (ks === k) {\n            break;\n          }\n          let t =\n            (ks !== p ? Math.abs(e[ks]) : 0) +\n            (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);\n          if (Math.abs(s[ks]) <= eps * t) {\n            s[ks] = 0;\n            break;\n          }\n        }\n        if (ks === k) {\n          kase = 3;\n        } else if (ks === p - 1) {\n          kase = 1;\n        } else {\n          kase = 2;\n          k = ks;\n        }\n      }\n\n      k++;\n\n      switch (kase) {\n        case 1: {\n          let f = e[p - 2];\n          e[p - 2] = 0;\n          for (let j = p - 2; j >= k; j--) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            if (j !== k) {\n              f = -sn * e[j - 1];\n              e[j - 1] = cs * e[j - 1];\n            }\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, p - 1);\n                V.set(i, p - 1, -sn * V.get(i, j) + cs * V.get(i, p - 1));\n                V.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 2: {\n          let f = e[k - 1];\n          e[k - 1] = 0;\n          for (let j = k; j < p; j++) {\n            let t = hypotenuse(s[j], f);\n            let cs = s[j] / t;\n            let sn = f / t;\n            s[j] = t;\n            f = -sn * e[j];\n            e[j] = cs * e[j];\n            if (wantu) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, k - 1);\n                U.set(i, k - 1, -sn * U.get(i, j) + cs * U.get(i, k - 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          break;\n        }\n        case 3: {\n          const scale = Math.max(\n            Math.abs(s[p - 1]),\n            Math.abs(s[p - 2]),\n            Math.abs(e[p - 2]),\n            Math.abs(s[k]),\n            Math.abs(e[k]),\n          );\n          const sp = s[p - 1] / scale;\n          const spm1 = s[p - 2] / scale;\n          const epm1 = e[p - 2] / scale;\n          const sk = s[k] / scale;\n          const ek = e[k] / scale;\n          const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;\n          const c = sp * epm1 * (sp * epm1);\n          let shift = 0;\n          if (b !== 0 || c !== 0) {\n            if (b < 0) {\n              shift = 0 - Math.sqrt(b * b + c);\n            } else {\n              shift = Math.sqrt(b * b + c);\n            }\n            shift = c / (b + shift);\n          }\n          let f = (sk + sp) * (sk - sp) + shift;\n          let g = sk * ek;\n          for (let j = k; j < p - 1; j++) {\n            let t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            let cs = f / t;\n            let sn = g / t;\n            if (j !== k) {\n              e[j - 1] = t;\n            }\n            f = cs * s[j] + sn * e[j];\n            e[j] = cs * e[j] - sn * s[j];\n            g = sn * s[j + 1];\n            s[j + 1] = cs * s[j + 1];\n            if (wantv) {\n              for (let i = 0; i < n; i++) {\n                t = cs * V.get(i, j) + sn * V.get(i, j + 1);\n                V.set(i, j + 1, -sn * V.get(i, j) + cs * V.get(i, j + 1));\n                V.set(i, j, t);\n              }\n            }\n            t = hypotenuse(f, g);\n            if (t === 0) t = Number.MIN_VALUE;\n            cs = f / t;\n            sn = g / t;\n            s[j] = t;\n            f = cs * e[j] + sn * s[j + 1];\n            s[j + 1] = -sn * e[j] + cs * s[j + 1];\n            g = sn * e[j + 1];\n            e[j + 1] = cs * e[j + 1];\n            if (wantu && j < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = cs * U.get(i, j) + sn * U.get(i, j + 1);\n                U.set(i, j + 1, -sn * U.get(i, j) + cs * U.get(i, j + 1));\n                U.set(i, j, t);\n              }\n            }\n          }\n          e[p - 2] = f;\n          iter = iter + 1;\n          break;\n        }\n        case 4: {\n          if (s[k] <= 0) {\n            s[k] = s[k] < 0 ? -s[k] : 0;\n            if (wantv) {\n              for (let i = 0; i <= pp; i++) {\n                V.set(i, k, -V.get(i, k));\n              }\n            }\n          }\n          while (k < pp) {\n            if (s[k] >= s[k + 1]) {\n              break;\n            }\n            let t = s[k];\n            s[k] = s[k + 1];\n            s[k + 1] = t;\n            if (wantv && k < n - 1) {\n              for (let i = 0; i < n; i++) {\n                t = V.get(i, k + 1);\n                V.set(i, k + 1, V.get(i, k));\n                V.set(i, k, t);\n              }\n            }\n            if (wantu && k < m - 1) {\n              for (let i = 0; i < m; i++) {\n                t = U.get(i, k + 1);\n                U.set(i, k + 1, U.get(i, k));\n                U.set(i, k, t);\n              }\n            }\n            k++;\n          }\n          iter = 0;\n          p--;\n          break;\n        }\n        // no default\n      }\n    }\n\n    if (swapped) {\n      let tmp = V;\n      V = U;\n      U = tmp;\n    }\n\n    this.m = m;\n    this.n = n;\n    this.s = s;\n    this.U = U;\n    this.V = V;\n  }\n\n  solve(value) {\n    let Y = value;\n    let e = this.threshold;\n    let scols = this.s.length;\n    let Ls = Matrix.zeros(scols, scols);\n\n    for (let i = 0; i < scols; i++) {\n      if (Math.abs(this.s[i]) <= e) {\n        Ls.set(i, i, 0);\n      } else {\n        Ls.set(i, i, 1 / this.s[i]);\n      }\n    }\n\n    let U = this.U;\n    let V = this.rightSingularVectors;\n\n    let VL = V.mmul(Ls);\n    let vrows = V.rows;\n    let urows = U.rows;\n    let VLU = Matrix.zeros(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < scols; k++) {\n          sum += VL.get(i, k) * U.get(j, k);\n        }\n        VLU.set(i, j, sum);\n      }\n    }\n\n    return VLU.mmul(Y);\n  }\n\n  solveForDiagonal(value) {\n    return this.solve(Matrix.diag(value));\n  }\n\n  inverse() {\n    let V = this.V;\n    let e = this.threshold;\n    let vrows = V.rows;\n    let vcols = V.columns;\n    let X = new Matrix(vrows, this.s.length);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < vcols; j++) {\n        if (Math.abs(this.s[j]) > e) {\n          X.set(i, j, V.get(i, j) / this.s[j]);\n        }\n      }\n    }\n\n    let U = this.U;\n\n    let urows = U.rows;\n    let ucols = U.columns;\n    let Y = new Matrix(vrows, urows);\n\n    for (let i = 0; i < vrows; i++) {\n      for (let j = 0; j < urows; j++) {\n        let sum = 0;\n        for (let k = 0; k < ucols; k++) {\n          sum += X.get(i, k) * U.get(j, k);\n        }\n        Y.set(i, j, sum);\n      }\n    }\n\n    return Y;\n  }\n\n  get condition() {\n    return this.s[0] / this.s[Math.min(this.m, this.n) - 1];\n  }\n\n  get norm2() {\n    return this.s[0];\n  }\n\n  get rank() {\n    let tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;\n    let r = 0;\n    let s = this.s;\n    for (let i = 0, ii = s.length; i < ii; i++) {\n      if (s[i] > tol) {\n        r++;\n      }\n    }\n    return r;\n  }\n\n  get diagonal() {\n    return Array.from(this.s);\n  }\n\n  get threshold() {\n    return (Number.EPSILON / 2) * Math.max(this.m, this.n) * this.s[0];\n  }\n\n  get leftSingularVectors() {\n    return this.U;\n  }\n\n  get rightSingularVectors() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    return Matrix.diag(this.s);\n  }\n}\n","import LuDecomposition from './dc/lu';\nimport QrDecomposition from './dc/qr';\nimport SingularValueDecomposition from './dc/svd';\nimport Matrix from './matrix';\nimport WrapperMatrix2D from './wrap/WrapperMatrix2D';\n\nexport function inverse(matrix, useSVD = false) {\n  matrix = WrapperMatrix2D.checkMatrix(matrix);\n  if (useSVD) {\n    return new SingularValueDecomposition(matrix).inverse();\n  } else {\n    return solve(matrix, Matrix.eye(matrix.rows));\n  }\n}\n\nexport function solve(leftHandSide, rightHandSide, useSVD = false) {\n  leftHandSide = WrapperMatrix2D.checkMatrix(leftHandSide);\n  rightHandSide = WrapperMatrix2D.checkMatrix(rightHandSide);\n  if (useSVD) {\n    return new SingularValueDecomposition(leftHandSide).solve(rightHandSide);\n  } else {\n    return leftHandSide.isSquare()\n      ? new LuDecomposition(leftHandSide).solve(rightHandSide)\n      : new QrDecomposition(leftHandSide).solve(rightHandSide);\n  }\n}\n","import LuDecomposition from './dc/lu';\nimport Matrix from './matrix';\nimport MatrixSelectionView from './views/selection';\n\nexport function determinant(matrix) {\n  matrix = Matrix.checkMatrix(matrix);\n  if (matrix.isSquare()) {\n    let a, b, c, d;\n    if (matrix.columns === 2) {\n      // 2 x 2 matrix\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(1, 0);\n      d = matrix.get(1, 1);\n\n      return a * d - b * c;\n    } else if (matrix.columns === 3) {\n      // 3 x 3 matrix\n      let subMatrix0, subMatrix1, subMatrix2;\n      subMatrix0 = new MatrixSelectionView(matrix, [1, 2], [1, 2]);\n      subMatrix1 = new MatrixSelectionView(matrix, [1, 2], [0, 2]);\n      subMatrix2 = new MatrixSelectionView(matrix, [1, 2], [0, 1]);\n      a = matrix.get(0, 0);\n      b = matrix.get(0, 1);\n      c = matrix.get(0, 2);\n\n      return (\n        a * determinant(subMatrix0) -\n        b * determinant(subMatrix1) +\n        c * determinant(subMatrix2)\n      );\n    } else {\n      // general purpose determinant using the LU decomposition\n      return new LuDecomposition(matrix).determinant;\n    }\n  } else {\n    throw Error('determinant can only be calculated for a square matrix');\n  }\n}\n","import SingularValueDecomposition from './dc/svd';\nimport Matrix from './matrix';\n\nfunction xrange(n, exception) {\n  let range = [];\n  for (let i = 0; i < n; i++) {\n    if (i !== exception) {\n      range.push(i);\n    }\n  }\n  return range;\n}\n\nfunction dependenciesOneRow(\n  error,\n  matrix,\n  index,\n  thresholdValue = 10e-10,\n  thresholdError = 10e-10,\n) {\n  if (error > thresholdError) {\n    return new Array(matrix.rows + 1).fill(0);\n  } else {\n    let returnArray = matrix.addRow(index, [0]);\n    for (let i = 0; i < returnArray.rows; i++) {\n      if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {\n        returnArray.set(i, 0, 0);\n      }\n    }\n    return returnArray.to1DArray();\n  }\n}\n\nexport function linearDependencies(matrix, options = {}) {\n  const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;\n  matrix = Matrix.checkMatrix(matrix);\n\n  let n = matrix.rows;\n  let results = new Matrix(n, n);\n\n  for (let i = 0; i < n; i++) {\n    let b = Matrix.columnVector(matrix.getRow(i));\n    let Abis = matrix.subMatrixRow(xrange(n, i)).transpose();\n    let svd = new SingularValueDecomposition(Abis);\n    let x = svd.solve(b);\n    let error = Matrix.sub(b, Abis.mmul(x)).abs().max();\n    results.setRow(\n      i,\n      dependenciesOneRow(error, x, i, thresholdValue, thresholdError),\n    );\n  }\n  return results;\n}\n","import SVD from './dc/svd';\nimport Matrix from './matrix';\n\nexport function pseudoInverse(matrix, threshold = Number.EPSILON) {\n  matrix = Matrix.checkMatrix(matrix);\n  let svdSolution = new SVD(matrix, { autoTranspose: true });\n\n  let U = svdSolution.leftSingularVectors;\n  let V = svdSolution.rightSingularVectors;\n  let s = svdSolution.diagonal;\n\n  for (let i = 0; i < s.length; i++) {\n    if (Math.abs(s[i]) > threshold) {\n      s[i] = 1.0 / s[i];\n    } else {\n      s[i] = 0.0;\n    }\n  }\n\n  return V.mmul(Matrix.diag(s).mmul(U.transpose()));\n}\n","import Matrix from './matrix';\n\nexport function covariance(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = new Matrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = new Matrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n  const { center = true } = options;\n  if (center) {\n    xMatrix = xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix = yMatrix.center('column');\n    }\n  }\n  const cov = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < cov.rows; i++) {\n    for (let j = 0; j < cov.columns; j++) {\n      cov.set(i, j, cov.get(i, j) * (1 / (xMatrix.rows - 1)));\n    }\n  }\n  return cov;\n}\n","import Matrix from './matrix';\n\nexport function correlation(xMatrix, yMatrix = xMatrix, options = {}) {\n  xMatrix = new Matrix(xMatrix);\n  let yIsSame = false;\n  if (\n    typeof yMatrix === 'object' &&\n    !Matrix.isMatrix(yMatrix) &&\n    !Array.isArray(yMatrix)\n  ) {\n    options = yMatrix;\n    yMatrix = xMatrix;\n    yIsSame = true;\n  } else {\n    yMatrix = new Matrix(yMatrix);\n  }\n  if (xMatrix.rows !== yMatrix.rows) {\n    throw new TypeError('Both matrices must have the same number of rows');\n  }\n\n  const { center = true, scale = true } = options;\n  if (center) {\n    xMatrix.center('column');\n    if (!yIsSame) {\n      yMatrix.center('column');\n    }\n  }\n  if (scale) {\n    xMatrix.scale('column');\n    if (!yIsSame) {\n      yMatrix.scale('column');\n    }\n  }\n\n  const sdx = xMatrix.standardDeviation('column', { unbiased: true });\n  const sdy = yIsSame\n    ? sdx\n    : yMatrix.standardDeviation('column', { unbiased: true });\n\n  const corr = xMatrix.transpose().mmul(yMatrix);\n  for (let i = 0; i < corr.rows; i++) {\n    for (let j = 0; j < corr.columns; j++) {\n      corr.set(\n        i,\n        j,\n        corr.get(i, j) * (1 / (sdx[i] * sdy[j])) * (1 / (xMatrix.rows - 1)),\n      );\n    }\n  }\n  return corr;\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nimport { hypotenuse } from './util';\n\nexport default class EigenvalueDecomposition {\n  constructor(matrix, options = {}) {\n    const { assumeSymmetric = false } = options;\n\n    matrix = WrapperMatrix2D.checkMatrix(matrix);\n    if (!matrix.isSquare()) {\n      throw new Error('Matrix is not a square matrix');\n    }\n\n    let n = matrix.columns;\n    let V = new Matrix(n, n);\n    let d = new Float64Array(n);\n    let e = new Float64Array(n);\n    let value = matrix;\n    let i, j;\n\n    let isSymmetric = false;\n    if (assumeSymmetric) {\n      isSymmetric = true;\n    } else {\n      isSymmetric = matrix.isSymmetric();\n    }\n\n    if (isSymmetric) {\n      for (i = 0; i < n; i++) {\n        for (j = 0; j < n; j++) {\n          V.set(i, j, value.get(i, j));\n        }\n      }\n      tred2(n, e, d, V);\n      tql2(n, e, d, V);\n    } else {\n      let H = new Matrix(n, n);\n      let ort = new Float64Array(n);\n      for (j = 0; j < n; j++) {\n        for (i = 0; i < n; i++) {\n          H.set(i, j, value.get(i, j));\n        }\n      }\n      orthes(n, H, ort, V);\n      hqr2(n, e, d, V, H);\n    }\n\n    this.n = n;\n    this.e = e;\n    this.d = d;\n    this.V = V;\n  }\n\n  get realEigenvalues() {\n    return Array.from(this.d);\n  }\n\n  get imaginaryEigenvalues() {\n    return Array.from(this.e);\n  }\n\n  get eigenvectorMatrix() {\n    return this.V;\n  }\n\n  get diagonalMatrix() {\n    let n = this.n;\n    let e = this.e;\n    let d = this.d;\n    let X = new Matrix(n, n);\n    let i, j;\n    for (i = 0; i < n; i++) {\n      for (j = 0; j < n; j++) {\n        X.set(i, j, 0);\n      }\n      X.set(i, i, d[i]);\n      if (e[i] > 0) {\n        X.set(i, i + 1, e[i]);\n      } else if (e[i] < 0) {\n        X.set(i, i - 1, e[i]);\n      }\n    }\n    return X;\n  }\n}\n\nfunction tred2(n, e, d, V) {\n  let f, g, h, i, j, k, hh, scale;\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n  }\n\n  for (i = n - 1; i > 0; i--) {\n    scale = 0;\n    h = 0;\n    for (k = 0; k < i; k++) {\n      scale = scale + Math.abs(d[k]);\n    }\n\n    if (scale === 0) {\n      e[i] = d[i - 1];\n      for (j = 0; j < i; j++) {\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n        V.set(j, i, 0);\n      }\n    } else {\n      for (k = 0; k < i; k++) {\n        d[k] /= scale;\n        h += d[k] * d[k];\n      }\n\n      f = d[i - 1];\n      g = Math.sqrt(h);\n      if (f > 0) {\n        g = -g;\n      }\n\n      e[i] = scale * g;\n      h = h - f * g;\n      d[i - 1] = f - g;\n      for (j = 0; j < i; j++) {\n        e[j] = 0;\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        V.set(j, i, f);\n        g = e[j] + V.get(j, j) * f;\n        for (k = j + 1; k <= i - 1; k++) {\n          g += V.get(k, j) * d[k];\n          e[k] += V.get(k, j) * f;\n        }\n        e[j] = g;\n      }\n\n      f = 0;\n      for (j = 0; j < i; j++) {\n        e[j] /= h;\n        f += e[j] * d[j];\n      }\n\n      hh = f / (h + h);\n      for (j = 0; j < i; j++) {\n        e[j] -= hh * d[j];\n      }\n\n      for (j = 0; j < i; j++) {\n        f = d[j];\n        g = e[j];\n        for (k = j; k <= i - 1; k++) {\n          V.set(k, j, V.get(k, j) - (f * e[k] + g * d[k]));\n        }\n        d[j] = V.get(i - 1, j);\n        V.set(i, j, 0);\n      }\n    }\n    d[i] = h;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    V.set(n - 1, i, V.get(i, i));\n    V.set(i, i, 1);\n    h = d[i + 1];\n    if (h !== 0) {\n      for (k = 0; k <= i; k++) {\n        d[k] = V.get(k, i + 1) / h;\n      }\n\n      for (j = 0; j <= i; j++) {\n        g = 0;\n        for (k = 0; k <= i; k++) {\n          g += V.get(k, i + 1) * V.get(k, j);\n        }\n        for (k = 0; k <= i; k++) {\n          V.set(k, j, V.get(k, j) - g * d[k]);\n        }\n      }\n    }\n\n    for (k = 0; k <= i; k++) {\n      V.set(k, i + 1, 0);\n    }\n  }\n\n  for (j = 0; j < n; j++) {\n    d[j] = V.get(n - 1, j);\n    V.set(n - 1, j, 0);\n  }\n\n  V.set(n - 1, n - 1, 1);\n  e[0] = 0;\n}\n\nfunction tql2(n, e, d, V) {\n  let g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2, iter;\n\n  for (i = 1; i < n; i++) {\n    e[i - 1] = e[i];\n  }\n\n  e[n - 1] = 0;\n\n  let f = 0;\n  let tst1 = 0;\n  let eps = Number.EPSILON;\n\n  for (l = 0; l < n; l++) {\n    tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));\n    m = l;\n    while (m < n) {\n      if (Math.abs(e[m]) <= eps * tst1) {\n        break;\n      }\n      m++;\n    }\n\n    if (m > l) {\n      iter = 0;\n      do {\n        iter = iter + 1;\n\n        g = d[l];\n        p = (d[l + 1] - g) / (2 * e[l]);\n        r = hypotenuse(p, 1);\n        if (p < 0) {\n          r = -r;\n        }\n\n        d[l] = e[l] / (p + r);\n        d[l + 1] = e[l] * (p + r);\n        dl1 = d[l + 1];\n        h = g - d[l];\n        for (i = l + 2; i < n; i++) {\n          d[i] -= h;\n        }\n\n        f = f + h;\n\n        p = d[m];\n        c = 1;\n        c2 = c;\n        c3 = c;\n        el1 = e[l + 1];\n        s = 0;\n        s2 = 0;\n        for (i = m - 1; i >= l; i--) {\n          c3 = c2;\n          c2 = c;\n          s2 = s;\n          g = c * e[i];\n          h = c * p;\n          r = hypotenuse(p, e[i]);\n          e[i + 1] = s * r;\n          s = e[i] / r;\n          c = p / r;\n          p = c * d[i] - s * g;\n          d[i + 1] = h + s * (c * g + s * d[i]);\n\n          for (k = 0; k < n; k++) {\n            h = V.get(k, i + 1);\n            V.set(k, i + 1, s * V.get(k, i) + c * h);\n            V.set(k, i, c * V.get(k, i) - s * h);\n          }\n        }\n\n        p = (-s * s2 * c3 * el1 * e[l]) / dl1;\n        e[l] = s * p;\n        d[l] = c * p;\n      } while (Math.abs(e[l]) > eps * tst1);\n    }\n    d[l] = d[l] + f;\n    e[l] = 0;\n  }\n\n  for (i = 0; i < n - 1; i++) {\n    k = i;\n    p = d[i];\n    for (j = i + 1; j < n; j++) {\n      if (d[j] < p) {\n        k = j;\n        p = d[j];\n      }\n    }\n\n    if (k !== i) {\n      d[k] = d[i];\n      d[i] = p;\n      for (j = 0; j < n; j++) {\n        p = V.get(j, i);\n        V.set(j, i, V.get(j, k));\n        V.set(j, k, p);\n      }\n    }\n  }\n}\n\nfunction orthes(n, H, ort, V) {\n  let low = 0;\n  let high = n - 1;\n  let f, g, h, i, j, m;\n  let scale;\n\n  for (m = low + 1; m <= high - 1; m++) {\n    scale = 0;\n    for (i = m; i <= high; i++) {\n      scale = scale + Math.abs(H.get(i, m - 1));\n    }\n\n    if (scale !== 0) {\n      h = 0;\n      for (i = high; i >= m; i--) {\n        ort[i] = H.get(i, m - 1) / scale;\n        h += ort[i] * ort[i];\n      }\n\n      g = Math.sqrt(h);\n      if (ort[m] > 0) {\n        g = -g;\n      }\n\n      h = h - ort[m] * g;\n      ort[m] = ort[m] - g;\n\n      for (j = m; j < n; j++) {\n        f = 0;\n        for (i = high; i >= m; i--) {\n          f += ort[i] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (i = m; i <= high; i++) {\n          H.set(i, j, H.get(i, j) - f * ort[i]);\n        }\n      }\n\n      for (i = 0; i <= high; i++) {\n        f = 0;\n        for (j = high; j >= m; j--) {\n          f += ort[j] * H.get(i, j);\n        }\n\n        f = f / h;\n        for (j = m; j <= high; j++) {\n          H.set(i, j, H.get(i, j) - f * ort[j]);\n        }\n      }\n\n      ort[m] = scale * ort[m];\n      H.set(m, m - 1, scale * g);\n    }\n  }\n\n  for (i = 0; i < n; i++) {\n    for (j = 0; j < n; j++) {\n      V.set(i, j, i === j ? 1 : 0);\n    }\n  }\n\n  for (m = high - 1; m >= low + 1; m--) {\n    if (H.get(m, m - 1) !== 0) {\n      for (i = m + 1; i <= high; i++) {\n        ort[i] = H.get(i, m - 1);\n      }\n\n      for (j = m; j <= high; j++) {\n        g = 0;\n        for (i = m; i <= high; i++) {\n          g += ort[i] * V.get(i, j);\n        }\n\n        g = g / ort[m] / H.get(m, m - 1);\n        for (i = m; i <= high; i++) {\n          V.set(i, j, V.get(i, j) + g * ort[i]);\n        }\n      }\n    }\n  }\n}\n\nfunction hqr2(nn, e, d, V, H) {\n  let n = nn - 1;\n  let low = 0;\n  let high = nn - 1;\n  let eps = Number.EPSILON;\n  let exshift = 0;\n  let norm = 0;\n  let p = 0;\n  let q = 0;\n  let r = 0;\n  let s = 0;\n  let z = 0;\n  let iter = 0;\n  let i, j, k, l, m, t, w, x, y;\n  let ra, sa, vr, vi;\n  let notlast, cdivres;\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      d[i] = H.get(i, i);\n      e[i] = 0;\n    }\n\n    for (j = Math.max(i - 1, 0); j < nn; j++) {\n      norm = norm + Math.abs(H.get(i, j));\n    }\n  }\n\n  while (n >= low) {\n    l = n;\n    while (l > low) {\n      s = Math.abs(H.get(l - 1, l - 1)) + Math.abs(H.get(l, l));\n      if (s === 0) {\n        s = norm;\n      }\n      if (Math.abs(H.get(l, l - 1)) < eps * s) {\n        break;\n      }\n      l--;\n    }\n\n    if (l === n) {\n      H.set(n, n, H.get(n, n) + exshift);\n      d[n] = H.get(n, n);\n      e[n] = 0;\n      n--;\n      iter = 0;\n    } else if (l === n - 1) {\n      w = H.get(n, n - 1) * H.get(n - 1, n);\n      p = (H.get(n - 1, n - 1) - H.get(n, n)) / 2;\n      q = p * p + w;\n      z = Math.sqrt(Math.abs(q));\n      H.set(n, n, H.get(n, n) + exshift);\n      H.set(n - 1, n - 1, H.get(n - 1, n - 1) + exshift);\n      x = H.get(n, n);\n\n      if (q >= 0) {\n        z = p >= 0 ? p + z : p - z;\n        d[n - 1] = x + z;\n        d[n] = d[n - 1];\n        if (z !== 0) {\n          d[n] = x - w / z;\n        }\n        e[n - 1] = 0;\n        e[n] = 0;\n        x = H.get(n, n - 1);\n        s = Math.abs(x) + Math.abs(z);\n        p = x / s;\n        q = z / s;\n        r = Math.sqrt(p * p + q * q);\n        p = p / r;\n        q = q / r;\n\n        for (j = n - 1; j < nn; j++) {\n          z = H.get(n - 1, j);\n          H.set(n - 1, j, q * z + p * H.get(n, j));\n          H.set(n, j, q * H.get(n, j) - p * z);\n        }\n\n        for (i = 0; i <= n; i++) {\n          z = H.get(i, n - 1);\n          H.set(i, n - 1, q * z + p * H.get(i, n));\n          H.set(i, n, q * H.get(i, n) - p * z);\n        }\n\n        for (i = low; i <= high; i++) {\n          z = V.get(i, n - 1);\n          V.set(i, n - 1, q * z + p * V.get(i, n));\n          V.set(i, n, q * V.get(i, n) - p * z);\n        }\n      } else {\n        d[n - 1] = x + p;\n        d[n] = x + p;\n        e[n - 1] = z;\n        e[n] = -z;\n      }\n\n      n = n - 2;\n      iter = 0;\n    } else {\n      x = H.get(n, n);\n      y = 0;\n      w = 0;\n      if (l < n) {\n        y = H.get(n - 1, n - 1);\n        w = H.get(n, n - 1) * H.get(n - 1, n);\n      }\n\n      if (iter === 10) {\n        exshift += x;\n        for (i = low; i <= n; i++) {\n          H.set(i, i, H.get(i, i) - x);\n        }\n        s = Math.abs(H.get(n, n - 1)) + Math.abs(H.get(n - 1, n - 2));\n        x = y = 0.75 * s;\n        w = -0.4375 * s * s;\n      }\n\n      if (iter === 30) {\n        s = (y - x) / 2;\n        s = s * s + w;\n        if (s > 0) {\n          s = Math.sqrt(s);\n          if (y < x) {\n            s = -s;\n          }\n          s = x - w / ((y - x) / 2 + s);\n          for (i = low; i <= n; i++) {\n            H.set(i, i, H.get(i, i) - s);\n          }\n          exshift += s;\n          x = y = w = 0.964;\n        }\n      }\n\n      iter = iter + 1;\n\n      m = n - 2;\n      while (m >= l) {\n        z = H.get(m, m);\n        r = x - z;\n        s = y - z;\n        p = (r * s - w) / H.get(m + 1, m) + H.get(m, m + 1);\n        q = H.get(m + 1, m + 1) - z - r - s;\n        r = H.get(m + 2, m + 1);\n        s = Math.abs(p) + Math.abs(q) + Math.abs(r);\n        p = p / s;\n        q = q / s;\n        r = r / s;\n        if (m === l) {\n          break;\n        }\n        if (\n          Math.abs(H.get(m, m - 1)) * (Math.abs(q) + Math.abs(r)) <\n          eps *\n            (Math.abs(p) *\n              (Math.abs(H.get(m - 1, m - 1)) +\n                Math.abs(z) +\n                Math.abs(H.get(m + 1, m + 1))))\n        ) {\n          break;\n        }\n        m--;\n      }\n\n      for (i = m + 2; i <= n; i++) {\n        H.set(i, i - 2, 0);\n        if (i > m + 2) {\n          H.set(i, i - 3, 0);\n        }\n      }\n\n      for (k = m; k <= n - 1; k++) {\n        notlast = k !== n - 1;\n        if (k !== m) {\n          p = H.get(k, k - 1);\n          q = H.get(k + 1, k - 1);\n          r = notlast ? H.get(k + 2, k - 1) : 0;\n          x = Math.abs(p) + Math.abs(q) + Math.abs(r);\n          if (x !== 0) {\n            p = p / x;\n            q = q / x;\n            r = r / x;\n          }\n        }\n\n        if (x === 0) {\n          break;\n        }\n\n        s = Math.sqrt(p * p + q * q + r * r);\n        if (p < 0) {\n          s = -s;\n        }\n\n        if (s !== 0) {\n          if (k !== m) {\n            H.set(k, k - 1, -s * x);\n          } else if (l !== m) {\n            H.set(k, k - 1, -H.get(k, k - 1));\n          }\n\n          p = p + s;\n          x = p / s;\n          y = q / s;\n          z = r / s;\n          q = q / p;\n          r = r / p;\n\n          for (j = k; j < nn; j++) {\n            p = H.get(k, j) + q * H.get(k + 1, j);\n            if (notlast) {\n              p = p + r * H.get(k + 2, j);\n              H.set(k + 2, j, H.get(k + 2, j) - p * z);\n            }\n\n            H.set(k, j, H.get(k, j) - p * x);\n            H.set(k + 1, j, H.get(k + 1, j) - p * y);\n          }\n\n          for (i = 0; i <= Math.min(n, k + 3); i++) {\n            p = x * H.get(i, k) + y * H.get(i, k + 1);\n            if (notlast) {\n              p = p + z * H.get(i, k + 2);\n              H.set(i, k + 2, H.get(i, k + 2) - p * r);\n            }\n\n            H.set(i, k, H.get(i, k) - p);\n            H.set(i, k + 1, H.get(i, k + 1) - p * q);\n          }\n\n          for (i = low; i <= high; i++) {\n            p = x * V.get(i, k) + y * V.get(i, k + 1);\n            if (notlast) {\n              p = p + z * V.get(i, k + 2);\n              V.set(i, k + 2, V.get(i, k + 2) - p * r);\n            }\n\n            V.set(i, k, V.get(i, k) - p);\n            V.set(i, k + 1, V.get(i, k + 1) - p * q);\n          }\n        }\n      }\n    }\n  }\n\n  if (norm === 0) {\n    return;\n  }\n\n  for (n = nn - 1; n >= 0; n--) {\n    p = d[n];\n    q = e[n];\n\n    if (q === 0) {\n      l = n;\n      H.set(n, n, 1);\n      for (i = n - 1; i >= 0; i--) {\n        w = H.get(i, i) - p;\n        r = 0;\n        for (j = l; j <= n; j++) {\n          r = r + H.get(i, j) * H.get(j, n);\n        }\n\n        if (e[i] < 0) {\n          z = w;\n          s = r;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            H.set(i, n, w !== 0 ? -r / w : -r / (eps * norm));\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];\n            t = (x * s - z * r) / q;\n            H.set(i, n, t);\n            H.set(\n              i + 1,\n              n,\n              Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z,\n            );\n          }\n\n          t = Math.abs(H.get(i, n));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    } else if (q < 0) {\n      l = n - 1;\n\n      if (Math.abs(H.get(n, n - 1)) > Math.abs(H.get(n - 1, n))) {\n        H.set(n - 1, n - 1, q / H.get(n, n - 1));\n        H.set(n - 1, n, -(H.get(n, n) - p) / H.get(n, n - 1));\n      } else {\n        cdivres = cdiv(0, -H.get(n - 1, n), H.get(n - 1, n - 1) - p, q);\n        H.set(n - 1, n - 1, cdivres[0]);\n        H.set(n - 1, n, cdivres[1]);\n      }\n\n      H.set(n, n - 1, 0);\n      H.set(n, n, 1);\n      for (i = n - 2; i >= 0; i--) {\n        ra = 0;\n        sa = 0;\n        for (j = l; j <= n; j++) {\n          ra = ra + H.get(i, j) * H.get(j, n - 1);\n          sa = sa + H.get(i, j) * H.get(j, n);\n        }\n\n        w = H.get(i, i) - p;\n\n        if (e[i] < 0) {\n          z = w;\n          r = ra;\n          s = sa;\n        } else {\n          l = i;\n          if (e[i] === 0) {\n            cdivres = cdiv(-ra, -sa, w, q);\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n          } else {\n            x = H.get(i, i + 1);\n            y = H.get(i + 1, i);\n            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;\n            vi = (d[i] - p) * 2 * q;\n            if (vr === 0 && vi === 0) {\n              vr =\n                eps *\n                norm *\n                (Math.abs(w) +\n                  Math.abs(q) +\n                  Math.abs(x) +\n                  Math.abs(y) +\n                  Math.abs(z));\n            }\n            cdivres = cdiv(\n              x * r - z * ra + q * sa,\n              x * s - z * sa - q * ra,\n              vr,\n              vi,\n            );\n            H.set(i, n - 1, cdivres[0]);\n            H.set(i, n, cdivres[1]);\n            if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {\n              H.set(\n                i + 1,\n                n - 1,\n                (-ra - w * H.get(i, n - 1) + q * H.get(i, n)) / x,\n              );\n              H.set(\n                i + 1,\n                n,\n                (-sa - w * H.get(i, n) - q * H.get(i, n - 1)) / x,\n              );\n            } else {\n              cdivres = cdiv(\n                -r - y * H.get(i, n - 1),\n                -s - y * H.get(i, n),\n                z,\n                q,\n              );\n              H.set(i + 1, n - 1, cdivres[0]);\n              H.set(i + 1, n, cdivres[1]);\n            }\n          }\n\n          t = Math.max(Math.abs(H.get(i, n - 1)), Math.abs(H.get(i, n)));\n          if (eps * t * t > 1) {\n            for (j = i; j <= n; j++) {\n              H.set(j, n - 1, H.get(j, n - 1) / t);\n              H.set(j, n, H.get(j, n) / t);\n            }\n          }\n        }\n      }\n    }\n  }\n\n  for (i = 0; i < nn; i++) {\n    if (i < low || i > high) {\n      for (j = i; j < nn; j++) {\n        V.set(i, j, H.get(i, j));\n      }\n    }\n  }\n\n  for (j = nn - 1; j >= low; j--) {\n    for (i = low; i <= high; i++) {\n      z = 0;\n      for (k = low; k <= Math.min(j, high); k++) {\n        z = z + V.get(i, k) * H.get(k, j);\n      }\n      V.set(i, j, z);\n    }\n  }\n}\n\nfunction cdiv(xr, xi, yr, yi) {\n  let r, d;\n  if (Math.abs(yr) > Math.abs(yi)) {\n    r = yi / yr;\n    d = yr + r * yi;\n    return [(xr + r * xi) / d, (xi - r * xr) / d];\n  } else {\n    r = yr / yi;\n    d = yi + r * yr;\n    return [(r * xr + xi) / d, (r * xi - xr) / d];\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class CholeskyDecomposition {\n  constructor(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n    if (!value.isSymmetric()) {\n      throw new Error('Matrix is not symmetric');\n    }\n\n    let a = value;\n    let dimension = a.rows;\n    let l = new Matrix(dimension, dimension);\n    let positiveDefinite = true;\n    let i, j, k;\n\n    for (j = 0; j < dimension; j++) {\n      let d = 0;\n      for (k = 0; k < j; k++) {\n        let s = 0;\n        for (i = 0; i < k; i++) {\n          s += l.get(k, i) * l.get(j, i);\n        }\n        s = (a.get(j, k) - s) / l.get(k, k);\n        l.set(j, k, s);\n        d = d + s * s;\n      }\n\n      d = a.get(j, j) - d;\n\n      positiveDefinite &= d > 0;\n      l.set(j, j, Math.sqrt(Math.max(d, 0)));\n      for (k = j + 1; k < dimension; k++) {\n        l.set(j, k, 0);\n      }\n    }\n\n    this.L = l;\n    this.positiveDefinite = Boolean(positiveDefinite);\n  }\n\n  isPositiveDefinite() {\n    return this.positiveDefinite;\n  }\n\n  solve(value) {\n    value = WrapperMatrix2D.checkMatrix(value);\n\n    let l = this.L;\n    let dimension = l.rows;\n\n    if (value.rows !== dimension) {\n      throw new Error('Matrix dimensions do not match');\n    }\n    if (this.isPositiveDefinite() === false) {\n      throw new Error('Matrix is not positive definite');\n    }\n\n    let count = value.columns;\n    let B = value.clone();\n    let i, j, k;\n\n    for (k = 0; k < dimension; k++) {\n      for (j = 0; j < count; j++) {\n        for (i = 0; i < k; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(k, i));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    for (k = dimension - 1; k >= 0; k--) {\n      for (j = 0; j < count; j++) {\n        for (i = k + 1; i < dimension; i++) {\n          B.set(k, j, B.get(k, j) - B.get(i, j) * l.get(i, k));\n        }\n        B.set(k, j, B.get(k, j) / l.get(k, k));\n      }\n    }\n\n    return B;\n  }\n\n  get lowerTriangularMatrix() {\n    return this.L;\n  }\n}\n","import Matrix from '../matrix';\nimport WrapperMatrix2D from '../wrap/WrapperMatrix2D';\n\nexport default class nipals {\n  constructor(X, options = {}) {\n    X = WrapperMatrix2D.checkMatrix(X);\n    let { Y } = options;\n    const {\n      scaleScores = false,\n      maxIterations = 1000,\n      terminationCriteria = 1e-10,\n    } = options;\n\n    let u;\n    if (Y) {\n      if (Array.isArray(Y) && typeof Y[0] === 'number') {\n        Y = Matrix.columnVector(Y);\n      } else {\n        Y = WrapperMatrix2D.checkMatrix(Y);\n      }\n      if (!Y.isColumnVector() || Y.rows !== X.rows) {\n        throw new Error('Y must be a column vector of length X.rows');\n      }\n      u = Y;\n    } else {\n      u = X.getColumnVector(0);\n    }\n\n    let diff = 1;\n    let t, q, w, tOld;\n\n    for (\n      let counter = 0;\n      counter < maxIterations && diff > terminationCriteria;\n      counter++\n    ) {\n      w = X.transpose().mmul(u).div(u.transpose().mmul(u).get(0, 0));\n      w = w.div(w.norm());\n\n      t = X.mmul(w).div(w.transpose().mmul(w).get(0, 0));\n\n      if (counter > 0) {\n        diff = t.clone().sub(tOld).pow(2).sum();\n      }\n      tOld = t.clone();\n\n      if (Y) {\n        q = Y.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n        q = q.div(q.norm());\n\n        u = Y.mmul(q).div(q.transpose().mmul(q).get(0, 0));\n      } else {\n        u = t;\n      }\n    }\n\n    if (Y) {\n      let p = X.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n      p = p.div(p.norm());\n      let xResidual = X.clone().sub(t.clone().mmul(p.transpose()));\n      let residual = u.transpose().mmul(t).div(t.transpose().mmul(t).get(0, 0));\n      let yResidual = Y.clone().sub(\n        t.clone().mulS(residual.get(0, 0)).mmul(q.transpose()),\n      );\n\n      this.t = t;\n      this.p = p.transpose();\n      this.w = w.transpose();\n      this.q = q;\n      this.u = u;\n      this.s = t.transpose().mmul(t);\n      this.xResidual = xResidual;\n      this.yResidual = yResidual;\n      this.betas = residual;\n    } else {\n      this.w = w.transpose();\n      this.s = t.transpose().mmul(t).sqrt();\n      if (scaleScores) {\n        this.t = t.clone().div(this.s.get(0, 0));\n      } else {\n        this.t = t;\n      }\n      this.xResidual = X.sub(t.mmul(w.transpose()));\n    }\n  }\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction sum(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += input[i];\n  }\n\n  return sumValue;\n}\n\nexport default sum;\n","import sum from 'ml-array-sum';\n\nfunction mean(input) {\n  return sum(input) / input.length;\n}\n\nexport default mean;\n","import Matrix from 'ml-matrix';\nimport meanArray from 'ml-array-mean';\n\n/**\n * @private\n * return an array of probabilities of each class\n * @param {Array} array - contains the classes\n * @param {number} numberOfClasses\n * @return {Matrix} - rowVector of probabilities.\n */\nexport function toDiscreteDistribution(array, numberOfClasses) {\n  let counts = new Array(numberOfClasses).fill(0);\n  for (let i = 0; i < array.length; ++i) {\n    counts[array[i]] += 1 / array.length;\n  }\n\n  return Matrix.rowVector(counts);\n}\n\n/**\n * @private\n * Retrieves the impurity of array of predictions\n * @param {Array} array - predictions.\n * @return {number} Gini impurity\n */\nexport function giniImpurity(array) {\n  if (array.length === 0) {\n    return 0;\n  }\n\n  let probabilities = toDiscreteDistribution(\n    array,\n    getNumberOfClasses(array),\n  ).getRow(0);\n\n  let sum = 0.0;\n  for (let i = 0; i < probabilities.length; ++i) {\n    sum += probabilities[i] * probabilities[i];\n  }\n\n  return 1 - sum;\n}\n\n/**\n * @private\n * Return the number of classes given the array of predictions.\n * @param {Array} array - predictions.\n * @return {number} Number of classes.\n */\nexport function getNumberOfClasses(array) {\n  return array\n    .filter(function(val, i, arr) {\n      return arr.indexOf(val) === i;\n    })\n    .map((val) => val + 1)\n    .reduce((a, b) => Math.max(a, b));\n}\n\n/**\n * @private\n * Calculates the Gini Gain of an array of predictions and those predictions splitted by a feature.\n * @param {Array} array - Predictions\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - Gini Gain.\n */\n\nexport function giniGain(array, splitted) {\n  let splitsImpurity = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    splitsImpurity +=\n      (giniImpurity(currentSplit) * currentSplit.length) / array.length;\n  }\n\n  return giniImpurity(array) - splitsImpurity;\n}\n\n/**\n * @private\n * Calculates the squared error of a predictions values.\n * @param {Array} array - predictions values\n * @return {number} squared error.\n */\nexport function squaredError(array) {\n  let l = array.length;\n\n  let m = meanArray(array);\n  let error = 0.0;\n\n  for (let i = 0; i < l; ++i) {\n    let currentElement = array[i];\n    error += (currentElement - m) * (currentElement - m);\n  }\n\n  return error;\n}\n\n/**\n * @private\n * Calculates the sum of squared error of the two arrays that contains the splitted values.\n * @param {Array} array - this argument is no necessary but is used to fit with the main interface.\n * @param {object} splitted - Object with elements \"greater\" and \"lesser\" that contains an array of predictions splitted.\n * @return {number} - sum of squared errors.\n */\nexport function regressionError(array, splitted) {\n  let error = 0.0;\n  let splits = ['greater', 'lesser'];\n\n  for (let i = 0; i < splits.length; ++i) {\n    let currentSplit = splitted[splits[i]];\n    error += squaredError(currentSplit);\n  }\n  return error;\n}\n\n/**\n * @private\n * Split the training set and values from a given column of the training set if is less than a value\n * @param {Matrix} X - Training set.\n * @param {Array} y - Training values.\n * @param {number} column - Column to split.\n * @param {number} value - value to split the Training set and values.\n * @return {object} - Object that contains the splitted values.\n */\nexport function matrixSplitter(X, y, column, value) {\n  let lesserX = [];\n  let greaterX = [];\n  let lesserY = [];\n  let greaterY = [];\n\n  for (let i = 0; i < X.rows; ++i) {\n    if (X.get(i, column) < value) {\n      lesserX.push(X.getRow(i));\n      lesserY.push(y[i]);\n    } else {\n      greaterX.push(X.getRow(i));\n      greaterY.push(y[i]);\n    }\n  }\n\n  return {\n    greaterX: greaterX,\n    greaterY: greaterY,\n    lesserX: lesserX,\n    lesserY: lesserY,\n  };\n}\n\n/**\n * @private\n * Calculates the mean between two values\n * @param {number} a\n * @param {number} b\n * @return {number}\n */\nexport function mean(a, b) {\n  return (a + b) / 2;\n}\n\n/**\n * @private\n * Returns a list of tuples that contains the i-th element of each array.\n * @param {Array} a\n * @param {Array} b\n * @return {Array} list of tuples.\n */\nexport function zip(a, b) {\n  if (a.length !== b.length) {\n    throw new TypeError(\n      `Error on zip: the size of a: ${a.length} is different from b: ${b.length}`,\n    );\n  }\n\n  let ret = new Array(a.length);\n  for (let i = 0; i < a.length; ++i) {\n    ret[i] = [a[i], b[i]];\n  }\n\n  return ret;\n}\n","import Matrix from 'ml-matrix';\nimport mean from 'ml-array-mean';\n\nimport * as Utils from './utils';\n\nconst gainFunctions = {\n  gini: Utils.giniGain,\n  regression: Utils.regressionError,\n};\n\nconst splitFunctions = {\n  mean: Utils.mean,\n};\n\nexport default class TreeNode {\n  /**\n   * @private\n   * Constructor for a tree node given the options received on the main classes (DecisionTreeClassifier, DecisionTreeRegression)\n   * @param {object|TreeNode} options for loading\n   * @constructor\n   */\n  constructor(options) {\n    // options parameters\n    this.kind = options.kind;\n    this.gainFunction = options.gainFunction;\n    this.splitFunction = options.splitFunction;\n    this.minNumSamples = options.minNumSamples;\n    this.maxDepth = options.maxDepth;\n  }\n\n  /**\n   * @private\n   * Function that retrieve the best feature to make the split.\n   * @param {Matrix} XTranspose - Training set transposed\n   * @param {Array} y - labels or values (depending of the decision tree)\n   * @return {object} - return tree values, the best gain, column and the split value.\n   */\n  bestSplit(XTranspose, y) {\n    // Depending in the node tree class, we set the variables to check information gain (to classify)\n    // or error (for regression)\n\n    let bestGain = this.kind === 'classifier' ? -Infinity : Infinity;\n    let check = this.kind === 'classifier' ? (a, b) => a > b : (a, b) => a < b;\n\n    let maxColumn;\n    let maxValue;\n\n    for (let i = 0; i < XTranspose.rows; ++i) {\n      let currentFeature = XTranspose.getRow(i);\n      let splitValues = this.featureSplit(currentFeature, y);\n      for (let j = 0; j < splitValues.length; ++j) {\n        let currentSplitVal = splitValues[j];\n        let splitted = this.split(currentFeature, y, currentSplitVal);\n\n        let gain = gainFunctions[this.gainFunction](y, splitted);\n        if (check(gain, bestGain)) {\n          maxColumn = i;\n          maxValue = currentSplitVal;\n          bestGain = gain;\n        }\n      }\n    }\n\n    return {\n      maxGain: bestGain,\n      maxColumn: maxColumn,\n      maxValue: maxValue,\n    };\n  }\n\n  /**\n   * @private\n   * Makes the split of the training labels or values from the training set feature given a split value.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @param {number} splitValue\n   * @return {object}\n   */\n  split(x, y, splitValue) {\n    let lesser = [];\n    let greater = [];\n\n    for (let i = 0; i < x.length; ++i) {\n      if (x[i] < splitValue) {\n        lesser.push(y[i]);\n      } else {\n        greater.push(y[i]);\n      }\n    }\n\n    return {\n      greater: greater,\n      lesser: lesser,\n    };\n  }\n\n  /**\n   * @private\n   * Calculates the possible points to split over the tree given a training set feature and corresponding labels or values.\n   * @param {Array} x - Training set feature\n   * @param {Array} y - Training set value or label\n   * @return {Array} possible split values.\n   */\n  featureSplit(x, y) {\n    let splitValues = [];\n    let arr = Utils.zip(x, y);\n    arr.sort(function(a, b) {\n      return a[0] - b[0];\n    });\n\n    for (let i = 1; i < arr.length; ++i) {\n      if (arr[i - 1][1] !== arr[i][1]) {\n        splitValues.push(\n          splitFunctions[this.splitFunction](arr[i - 1][0], arr[i][0]),\n        );\n      }\n    }\n\n    return splitValues;\n  }\n\n  /**\n   * @private\n   * Calculate the predictions of a leaf tree node given the training labels or values\n   * @param {Array} y\n   */\n  calculatePrediction(y) {\n    if (this.kind === 'classifier') {\n      this.distribution = Utils.toDiscreteDistribution(\n        y,\n        Utils.getNumberOfClasses(y),\n      );\n      if (this.distribution.columns === 0) {\n        throw new TypeError('Error on calculate the prediction');\n      }\n    } else {\n      this.distribution = mean(y);\n    }\n  }\n\n  /**\n   * @private\n   * Train a node given the training set and labels, because it trains recursively, it also receive\n   * the current depth of the node, parent gain to avoid infinite recursion and boolean value to check if\n   * the training set is transposed.\n   * @param {Matrix} X - Training set (could be transposed or not given transposed).\n   * @param {Array} y - Training labels or values.\n   * @param {number} currentDepth - Current depth of the node.\n   * @param {number} parentGain - parent node gain or error.\n   */\n  train(X, y, currentDepth, parentGain) {\n    if (X.rows <= this.minNumSamples) {\n      this.calculatePrediction(y);\n      return;\n    }\n    if (parentGain === undefined) parentGain = 0.0;\n\n    let XTranspose = X.transpose();\n    let split = this.bestSplit(XTranspose, y);\n\n    this.splitValue = split.maxValue;\n    this.splitColumn = split.maxColumn;\n    this.gain = split.maxGain;\n\n    let splittedMatrix = Utils.matrixSplitter(\n      X,\n      y,\n      this.splitColumn,\n      this.splitValue,\n    );\n\n    if (\n      currentDepth < this.maxDepth &&\n      (this.gain > 0.01 && this.gain !== parentGain) &&\n      (splittedMatrix.lesserX.length > 0 && splittedMatrix.greaterX.length > 0)\n    ) {\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      let lesserX = new Matrix(splittedMatrix.lesserX);\n      let greaterX = new Matrix(splittedMatrix.greaterX);\n\n      this.left.train(\n        lesserX,\n        splittedMatrix.lesserY,\n        currentDepth + 1,\n        this.gain,\n      );\n      this.right.train(\n        greaterX,\n        splittedMatrix.greaterY,\n        currentDepth + 1,\n        this.gain,\n      );\n    } else {\n      this.calculatePrediction(y);\n    }\n  }\n\n  /**\n   * @private\n   * Calculates the prediction of a given element.\n   * @param {Array} row\n   * @return {number|Array} prediction\n   *          * if a node is a classifier returns an array of probabilities of each class.\n   *          * if a node is for regression returns a number with the prediction.\n   */\n  classify(row) {\n    if (this.right && this.left) {\n      if (row[this.splitColumn] < this.splitValue) {\n        return this.left.classify(row);\n      } else {\n        return this.right.classify(row);\n      }\n    }\n\n    return this.distribution;\n  }\n\n  /**\n   * @private\n   * Set the parameter of the current node and their children.\n   * @param {object} node - parameters of the current node and the children.\n   */\n  setNodeParameters(node) {\n    if (node.distribution !== undefined) {\n      this.distribution =\n        node.distribution.constructor === Array\n          ? new Matrix(node.distribution)\n          : node.distribution;\n    } else {\n      this.distribution = undefined;\n      this.splitValue = node.splitValue;\n      this.splitColumn = node.splitColumn;\n      this.gain = node.gain;\n\n      this.left = new TreeNode(this);\n      this.right = new TreeNode(this);\n\n      if (node.left !== {}) {\n        this.left.setNodeParameters(node.left);\n      }\n      if (node.right !== {}) {\n        this.right.setNodeParameters(node.right);\n      }\n    }\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'gini',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeClassifier {\n  /**\n   * Create new Decision Tree Classifier with CART implementation with the given options\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"gini\"] - gain function to get the best split, \"gini\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   * @constructor\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'classifier';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    this.root = new Tree(this.options);\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    this.root.train(trainingSet, trainingLabels, 0, null);\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    toPredict = Matrix.checkMatrix(toPredict);\n    let predictions = new Array(toPredict.rows);\n\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root\n        .classify(toPredict.getRow(i))\n        .maxRowIndex(0)[1];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {DecisionTreeClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'DTClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new DecisionTreeClassifier(true, model);\n  }\n}\n","import Matrix from 'ml-matrix';\n\nimport Tree from './TreeNode';\n\nconst defaultOptions = {\n  gainFunction: 'regression',\n  splitFunction: 'mean',\n  minNumSamples: 3,\n  maxDepth: Infinity,\n};\n\nexport class DecisionTreeRegression {\n  /**\n   * Create new Decision Tree Regression with CART implementation with the given options.\n   * @param {object} options\n   * @param {string} [options.gainFunction=\"regression\"] - gain function to get the best split, \"regression\" the only one supported.\n   * @param {string} [options.splitFunction=\"mean\"] - given two integers from a split feature, get the value to split, \"mean\" the only one supported.\n   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.\n   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.options = model.options;\n      this.root = new Tree(model.options);\n      this.root.setNodeParameters(model.root);\n    } else {\n      this.options = Object.assign({}, defaultOptions, options);\n      this.options.kind = 'regression';\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and values.\n   * @param {Matrix|MatrixTransposeView|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    this.root = new Tree(this.options);\n\n    if (\n      typeof trainingSet[0] !== 'undefined' &&\n      trainingSet[0].length === undefined\n    ) {\n      trainingSet = Matrix.columnVector(trainingSet);\n    } else {\n      trainingSet = Matrix.checkMatrix(trainingSet);\n    }\n    this.root.train(trainingSet, trainingValues, 0);\n  }\n\n  /**\n   * Predicts the values given the matrix to predict.\n   * @param {Matrix|MatrixTransposeView|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    if (\n      typeof toPredict[0] !== 'undefined' &&\n      toPredict[0].length === undefined\n    ) {\n      toPredict = Matrix.columnVector(toPredict);\n    }\n    toPredict = Matrix.checkMatrix(toPredict);\n\n    let predictions = new Array(toPredict.rows);\n    for (let i = 0; i < toPredict.rows; ++i) {\n      predictions[i] = this.root.classify(toPredict.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      options: this.options,\n      root: this.root,\n      name: 'DTRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree regression with the given model.\n   * @param {object} model\n   * @return {DecisionTreeRegression}\n   */\n  static load(model) {\n    if (model.name !== 'DTRegression') {\n      throw new RangeError(`Invalid model:${model.name}`);\n    }\n\n    return new DecisionTreeRegression(true, model);\n  }\n}\n","const SMALLEST_UNSAFE_INTEGER = 0x20000000000000;\r\nconst LARGEST_SAFE_INTEGER = SMALLEST_UNSAFE_INTEGER - 1;\r\nconst UINT32_MAX = -1 >>> 0;\r\nconst UINT32_SIZE = UINT32_MAX + 1;\r\nconst INT32_SIZE = UINT32_SIZE / 2;\r\nconst INT32_MAX = INT32_SIZE - 1;\r\nconst UINT21_SIZE = 1 << 21;\r\nconst UINT21_MAX = UINT21_SIZE - 1;\n\n/**\r\n * Returns a value within [-0x80000000, 0x7fffffff]\r\n */\r\nfunction int32(engine) {\r\n    return engine.next() | 0;\r\n}\n\nfunction add(distribution, addend) {\r\n    if (addend === 0) {\r\n        return distribution;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) + addend;\r\n    }\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n */\r\nfunction int53(engine) {\r\n    const high = engine.next() | 0;\r\n    const low = engine.next() >>> 0;\r\n    return ((high & UINT21_MAX) * UINT32_SIZE +\r\n        low +\r\n        (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n}\n\n/**\r\n * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n */\r\nfunction int53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & 0x400000) {\r\n            if ((high & 0x7fffff) === 0x400000 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return ((high & UINT21_MAX) * UINT32_SIZE +\r\n                low +\r\n                (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0));\r\n        }\r\n    }\r\n}\n\n/**\r\n * Returns a value within [0, 0xffffffff]\r\n */\r\nfunction uint32(engine) {\r\n    return engine.next() >>> 0;\r\n}\n\n/**\r\n * Returns a value within [0, 0x1fffffffffffff]\r\n */\r\nfunction uint53(engine) {\r\n    const high = engine.next() & UINT21_MAX;\r\n    const low = engine.next() >>> 0;\r\n    return high * UINT32_SIZE + low;\r\n}\n\n/**\r\n * Returns a value within [0, 0x20000000000000]\r\n */\r\nfunction uint53Full(engine) {\r\n    while (true) {\r\n        const high = engine.next() | 0;\r\n        if (high & UINT21_SIZE) {\r\n            if ((high & UINT21_MAX) === 0 && (engine.next() | 0) === 0) {\r\n                return SMALLEST_UNSAFE_INTEGER;\r\n            }\r\n        }\r\n        else {\r\n            const low = engine.next() >>> 0;\r\n            return (high & UINT21_MAX) * UINT32_SIZE + low;\r\n        }\r\n    }\r\n}\n\nfunction isPowerOfTwoMinusOne(value) {\r\n    return ((value + 1) & value) === 0;\r\n}\r\nfunction bitmask(masking) {\r\n    return (engine) => engine.next() & masking;\r\n}\r\nfunction downscaleToLoopCheckedRange(range) {\r\n    const extendedRange = range + 1;\r\n    const maximum = extendedRange * Math.floor(UINT32_SIZE / extendedRange);\r\n    return engine => {\r\n        let value = 0;\r\n        do {\r\n            value = engine.next() >>> 0;\r\n        } while (value >= maximum);\r\n        return value % extendedRange;\r\n    };\r\n}\r\nfunction downscaleToRange(range) {\r\n    if (isPowerOfTwoMinusOne(range)) {\r\n        return bitmask(range);\r\n    }\r\n    else {\r\n        return downscaleToLoopCheckedRange(range);\r\n    }\r\n}\r\nfunction isEvenlyDivisibleByMaxInt32(value) {\r\n    return (value | 0) === 0;\r\n}\r\nfunction upscaleWithHighMasking(masking) {\r\n    return engine => {\r\n        const high = engine.next() & masking;\r\n        const low = engine.next() >>> 0;\r\n        return high * UINT32_SIZE + low;\r\n    };\r\n}\r\nfunction upscaleToLoopCheckedRange(extendedRange) {\r\n    const maximum = extendedRange * Math.floor(SMALLEST_UNSAFE_INTEGER / extendedRange);\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() & UINT21_MAX;\r\n            const low = engine.next() >>> 0;\r\n            ret = high * UINT32_SIZE + low;\r\n        } while (ret >= maximum);\r\n        return ret % extendedRange;\r\n    };\r\n}\r\nfunction upscaleWithinU53(range) {\r\n    const extendedRange = range + 1;\r\n    if (isEvenlyDivisibleByMaxInt32(extendedRange)) {\r\n        const highRange = ((extendedRange / UINT32_SIZE) | 0) - 1;\r\n        if (isPowerOfTwoMinusOne(highRange)) {\r\n            return upscaleWithHighMasking(highRange);\r\n        }\r\n    }\r\n    return upscaleToLoopCheckedRange(extendedRange);\r\n}\r\nfunction upscaleWithinI53AndLoopCheck(min, max) {\r\n    return engine => {\r\n        let ret = 0;\r\n        do {\r\n            const high = engine.next() | 0;\r\n            const low = engine.next() >>> 0;\r\n            ret =\r\n                (high & UINT21_MAX) * UINT32_SIZE +\r\n                    low +\r\n                    (high & UINT21_SIZE ? -SMALLEST_UNSAFE_INTEGER : 0);\r\n        } while (ret < min || ret > max);\r\n        return ret;\r\n    };\r\n}\r\n/**\r\n * Returns a Distribution to return a value within [min, max]\r\n * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n */\r\nfunction integer(min, max) {\r\n    min = Math.floor(min);\r\n    max = Math.floor(max);\r\n    if (min < -SMALLEST_UNSAFE_INTEGER || !isFinite(min)) {\r\n        throw new RangeError(`Expected min to be at least ${-SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    else if (max > SMALLEST_UNSAFE_INTEGER || !isFinite(max)) {\r\n        throw new RangeError(`Expected max to be at most ${SMALLEST_UNSAFE_INTEGER}`);\r\n    }\r\n    const range = max - min;\r\n    if (range <= 0 || !isFinite(range)) {\r\n        return () => min;\r\n    }\r\n    else if (range === UINT32_MAX) {\r\n        if (min === 0) {\r\n            return uint32;\r\n        }\r\n        else {\r\n            return add(int32, min + INT32_SIZE);\r\n        }\r\n    }\r\n    else if (range < UINT32_MAX) {\r\n        return add(downscaleToRange(range), min);\r\n    }\r\n    else if (range === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53, min);\r\n    }\r\n    else if (range < LARGEST_SAFE_INTEGER) {\r\n        return add(upscaleWithinU53(range), min);\r\n    }\r\n    else if (max - 1 - min === LARGEST_SAFE_INTEGER) {\r\n        return add(uint53Full, min);\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER &&\r\n        max === SMALLEST_UNSAFE_INTEGER) {\r\n        return int53Full;\r\n    }\r\n    else if (min === -SMALLEST_UNSAFE_INTEGER && max === LARGEST_SAFE_INTEGER) {\r\n        return int53;\r\n    }\r\n    else if (min === -LARGEST_SAFE_INTEGER && max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(int53, 1);\r\n    }\r\n    else if (max === SMALLEST_UNSAFE_INTEGER) {\r\n        return add(upscaleWithinI53AndLoopCheck(min - 1, max - 1), 1);\r\n    }\r\n    else {\r\n        return upscaleWithinI53AndLoopCheck(min, max);\r\n    }\r\n}\n\nfunction isLeastBitTrue(engine) {\r\n    return (engine.next() & 1) === 1;\r\n}\r\nfunction lessThan(distribution, value) {\r\n    return engine => distribution(engine) < value;\r\n}\r\nfunction probability(percentage) {\r\n    if (percentage <= 0) {\r\n        return () => false;\r\n    }\r\n    else if (percentage >= 1) {\r\n        return () => true;\r\n    }\r\n    else {\r\n        const scaled = percentage * UINT32_SIZE;\r\n        if (scaled % 1 === 0) {\r\n            return lessThan(int32, (scaled - INT32_SIZE) | 0);\r\n        }\r\n        else {\r\n            return lessThan(uint53, Math.round(percentage * SMALLEST_UNSAFE_INTEGER));\r\n        }\r\n    }\r\n}\r\nfunction bool(numerator, denominator) {\r\n    if (denominator == null) {\r\n        if (numerator == null) {\r\n            return isLeastBitTrue;\r\n        }\r\n        return probability(numerator);\r\n    }\r\n    else {\r\n        if (numerator <= 0) {\r\n            return () => false;\r\n        }\r\n        else if (numerator >= denominator) {\r\n            return () => true;\r\n        }\r\n        return lessThan(integer(0, denominator - 1), numerator);\r\n    }\r\n}\n\n/**\r\n * Returns a Distribution that returns a random `Date` within the inclusive\r\n * range of [`start`, `end`].\r\n * @param start The minimum `Date`\r\n * @param end The maximum `Date`\r\n */\r\nfunction date(start, end) {\r\n    const distribution = integer(+start, +end);\r\n    return engine => new Date(distribution(engine));\r\n}\n\n/**\r\n * Returns a Distribution to return a value within [1, sideCount]\r\n * @param sideCount The number of sides of the die\r\n */\r\nfunction die(sideCount) {\r\n    return integer(1, sideCount);\r\n}\n\n/**\r\n * Returns a distribution that returns an array of length `dieCount` of values\r\n * within [1, `sideCount`]\r\n * @param sideCount The number of sides of each die\r\n * @param dieCount The number of dice\r\n */\r\nfunction dice(sideCount, dieCount) {\r\n    const distribution = die(sideCount);\r\n    return engine => {\r\n        const result = [];\r\n        for (let i = 0; i < dieCount; ++i) {\r\n            result.push(distribution(engine));\r\n        }\r\n        return result;\r\n    };\r\n}\n\n// tslint:disable:unified-signatures\r\n// has 2**x chars, for faster uniform distribution\r\nconst DEFAULT_STRING_POOL = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-\";\r\nfunction string(pool = DEFAULT_STRING_POOL) {\r\n    const poolLength = pool.length;\r\n    if (!poolLength) {\r\n        throw new Error(\"Expected pool not to be an empty string\");\r\n    }\r\n    const distribution = integer(0, poolLength - 1);\r\n    return (engine, length) => {\r\n        let result = \"\";\r\n        for (let i = 0; i < length; ++i) {\r\n            const j = distribution(engine);\r\n            result += pool.charAt(j);\r\n        }\r\n        return result;\r\n    };\r\n}\n\nconst LOWER_HEX_POOL = \"0123456789abcdef\";\r\nconst lowerHex = string(LOWER_HEX_POOL);\r\nconst upperHex = string(LOWER_HEX_POOL.toUpperCase());\r\n/**\r\n * Returns a Distribution that returns a random string comprised of numbers\r\n * or the characters `abcdef` (or `ABCDEF`) of length `length`.\r\n * @param length Length of the result string\r\n * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n */\r\nfunction hex(uppercase) {\r\n    if (uppercase) {\r\n        return upperHex;\r\n    }\r\n    else {\r\n        return lowerHex;\r\n    }\r\n}\n\nfunction convertSliceArgument(value, length) {\r\n    if (value < 0) {\r\n        return Math.max(value + length, 0);\r\n    }\r\n    else {\r\n        return Math.min(value, length);\r\n    }\r\n}\n\nfunction toInteger(value) {\r\n    const num = +value;\r\n    if (num < 0) {\r\n        return Math.ceil(num);\r\n    }\r\n    else {\r\n        return Math.floor(num);\r\n    }\r\n}\n\n/**\r\n * Returns a random value within the provided `source` within the sliced\r\n * bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction pick(engine, source, begin, end) {\r\n    const length = source.length;\r\n    if (length === 0) {\r\n        throw new RangeError(\"Cannot pick from an empty array\");\r\n    }\r\n    const start = begin == null ? 0 : convertSliceArgument(toInteger(begin), length);\r\n    const finish = end === void 0 ? length : convertSliceArgument(toInteger(end), length);\r\n    if (start >= finish) {\r\n        throw new RangeError(`Cannot pick between bounds ${start} and ${finish}`);\r\n    }\r\n    const distribution = integer(start, finish - 1);\r\n    return source[distribution(engine)];\r\n}\n\nfunction multiply(distribution, multiplier) {\r\n    if (multiplier === 1) {\r\n        return distribution;\r\n    }\r\n    else if (multiplier === 0) {\r\n        return () => 0;\r\n    }\r\n    else {\r\n        return engine => distribution(engine) * multiplier;\r\n    }\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0)\r\n */\r\nfunction realZeroToOneExclusive(engine) {\r\n    return uint53(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [0.0, 1.0]\r\n */\r\nfunction realZeroToOneInclusive(engine) {\r\n    return uint53Full(engine) / SMALLEST_UNSAFE_INTEGER;\r\n}\n\n/**\r\n * Returns a floating-point value within [min, max) or [min, max]\r\n * @param min The minimum floating-point value, inclusive.\r\n * @param max The maximum floating-point value.\r\n * @param inclusive If true, `max` will be inclusive.\r\n */\r\nfunction real(min, max, inclusive = false) {\r\n    if (!isFinite(min)) {\r\n        throw new RangeError(\"Expected min to be a finite number\");\r\n    }\r\n    else if (!isFinite(max)) {\r\n        throw new RangeError(\"Expected max to be a finite number\");\r\n    }\r\n    return add(multiply(inclusive ? realZeroToOneInclusive : realZeroToOneExclusive, max - min), min);\r\n}\n\nconst sliceArray = Array.prototype.slice;\n\n/**\r\n * Shuffles an array in-place\r\n * @param engine The Engine to use when choosing random values\r\n * @param array The array to shuffle\r\n * @param downTo minimum index to shuffle. Only used internally.\r\n */\r\nfunction shuffle(engine, array, downTo = 0) {\r\n    const length = array.length;\r\n    if (length) {\r\n        for (let i = (length - 1) >>> 0; i > downTo; --i) {\r\n            const distribution = integer(0, i);\r\n            const j = distribution(engine);\r\n            if (i !== j) {\r\n                const tmp = array[i];\r\n                array[i] = array[j];\r\n                array[j] = tmp;\r\n            }\r\n        }\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * From the population array, produce an array with sampleSize elements that\r\n * are randomly chosen without repeats.\r\n * @param engine The Engine to use when choosing random values\r\n * @param population An array that has items to choose a sample from\r\n * @param sampleSize The size of the result array\r\n */\r\nfunction sample(engine, population, sampleSize) {\r\n    if (sampleSize < 0 ||\r\n        sampleSize > population.length ||\r\n        !isFinite(sampleSize)) {\r\n        throw new RangeError(\"Expected sampleSize to be within 0 and the length of the population\");\r\n    }\r\n    if (sampleSize === 0) {\r\n        return [];\r\n    }\r\n    const clone = sliceArray.call(population);\r\n    const length = clone.length;\r\n    if (length === sampleSize) {\r\n        return shuffle(engine, clone, 0);\r\n    }\r\n    const tailLength = length - sampleSize;\r\n    return shuffle(engine, clone, tailLength - 1).slice(tailLength);\r\n}\n\nconst stringRepeat = (() => {\r\n    try {\r\n        if (\"x\".repeat(3) === \"xxx\") {\r\n            return (pattern, count) => pattern.repeat(count);\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return (pattern, count) => {\r\n        let result = \"\";\r\n        while (count > 0) {\r\n            if (count & 1) {\r\n                result += pattern;\r\n            }\r\n            count >>= 1;\r\n            pattern += pattern;\r\n        }\r\n        return result;\r\n    };\r\n})();\n\nfunction zeroPad(text, zeroCount) {\r\n    return stringRepeat(\"0\", zeroCount - text.length) + text;\r\n}\r\n/**\r\n * Returns a Universally Unique Identifier Version 4.\r\n *\r\n * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n */\r\nfunction uuid4(engine) {\r\n    const a = engine.next() >>> 0;\r\n    const b = engine.next() | 0;\r\n    const c = engine.next() | 0;\r\n    const d = engine.next() >>> 0;\r\n    return (zeroPad(a.toString(16), 8) +\r\n        \"-\" +\r\n        zeroPad((b & 0xffff).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad((((b >> 4) & 0x0fff) | 0x4000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c & 0x3fff) | 0x8000).toString(16), 4) +\r\n        \"-\" +\r\n        zeroPad(((c >> 4) & 0xffff).toString(16), 4) +\r\n        zeroPad(d.toString(16), 8));\r\n}\n\n/**\r\n * An int32-producing Engine that uses `Math.random()`\r\n */\r\nconst nativeMath = {\r\n    next() {\r\n        return (Math.random() * UINT32_SIZE) | 0;\r\n    }\r\n};\n\n// tslint:disable:unified-signatures\r\n/**\r\n * A wrapper around an Engine that provides easy-to-use methods for\r\n * producing values based on known distributions\r\n */\r\nclass Random {\r\n    /**\r\n     * Creates a new Random wrapper\r\n     * @param engine The engine to use (defaults to a `Math.random`-based implementation)\r\n     */\r\n    constructor(engine = nativeMath) {\r\n        this.engine = engine;\r\n    }\r\n    /**\r\n     * Returns a value within [-0x80000000, 0x7fffffff]\r\n     */\r\n    int32() {\r\n        return int32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0xffffffff]\r\n     */\r\n    uint32() {\r\n        return uint32(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x1fffffffffffff]\r\n     */\r\n    uint53() {\r\n        return uint53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [0, 0x20000000000000]\r\n     */\r\n    uint53Full() {\r\n        return uint53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x1fffffffffffff]\r\n     */\r\n    int53() {\r\n        return int53(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [-0x20000000000000, 0x20000000000000]\r\n     */\r\n    int53Full() {\r\n        return int53Full(this.engine);\r\n    }\r\n    /**\r\n     * Returns a value within [min, max]\r\n     * @param min The minimum integer value, inclusive. No less than -0x20000000000000.\r\n     * @param max The maximum integer value, inclusive. No greater than 0x20000000000000.\r\n     */\r\n    integer(min, max) {\r\n        return integer(min, max)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0]\r\n     */\r\n    realZeroToOneInclusive() {\r\n        return realZeroToOneInclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [0.0, 1.0)\r\n     */\r\n    realZeroToOneExclusive() {\r\n        return realZeroToOneExclusive(this.engine);\r\n    }\r\n    /**\r\n     * Returns a floating-point value within [min, max) or [min, max]\r\n     * @param min The minimum floating-point value, inclusive.\r\n     * @param max The maximum floating-point value.\r\n     * @param inclusive If true, `max` will be inclusive.\r\n     */\r\n    real(min, max, inclusive = false) {\r\n        return real(min, max, inclusive)(this.engine);\r\n    }\r\n    bool(numerator, denominator) {\r\n        return bool(numerator, denominator)(this.engine);\r\n    }\r\n    /**\r\n     * Return a random value within the provided `source` within the sliced\r\n     * bounds of `begin` and `end`.\r\n     * @param source an array of items to pick from\r\n     * @param begin the beginning slice index (defaults to `0`)\r\n     * @param end the ending slice index (defaults to `source.length`)\r\n     */\r\n    pick(source, begin, end) {\r\n        return pick(this.engine, source, begin, end);\r\n    }\r\n    /**\r\n     * Shuffles an array in-place\r\n     * @param array The array to shuffle\r\n     */\r\n    shuffle(array) {\r\n        return shuffle(this.engine, array);\r\n    }\r\n    /**\r\n     * From the population array, returns an array with sampleSize elements that\r\n     * are randomly chosen without repeats.\r\n     * @param population An array that has items to choose a sample from\r\n     * @param sampleSize The size of the result array\r\n     */\r\n    sample(population, sampleSize) {\r\n        return sample(this.engine, population, sampleSize);\r\n    }\r\n    /**\r\n     * Returns a value within [1, sideCount]\r\n     * @param sideCount The number of sides of the die\r\n     */\r\n    die(sideCount) {\r\n        return die(sideCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns an array of length `dieCount` of values within [1, sideCount]\r\n     * @param sideCount The number of sides of each die\r\n     * @param dieCount The number of dice\r\n     */\r\n    dice(sideCount, dieCount) {\r\n        return dice(sideCount, dieCount)(this.engine);\r\n    }\r\n    /**\r\n     * Returns a Universally Unique Identifier Version 4.\r\n     *\r\n     * See http://en.wikipedia.org/wiki/Universally_unique_identifier\r\n     */\r\n    uuid4() {\r\n        return uuid4(this.engine);\r\n    }\r\n    string(length, pool) {\r\n        return string(pool)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random string comprised of numbers or the characters `abcdef`\r\n     * (or `ABCDEF`) of length `length`.\r\n     * @param length Length of the result string\r\n     * @param uppercase Whether the string should use `ABCDEF` instead of `abcdef`\r\n     */\r\n    hex(length, uppercase) {\r\n        return hex(uppercase)(this.engine, length);\r\n    }\r\n    /**\r\n     * Returns a random `Date` within the inclusive range of [`start`, `end`].\r\n     * @param start The minimum `Date`\r\n     * @param end The maximum `Date`\r\n     */\r\n    date(start, end) {\r\n        return date(start, end)(this.engine);\r\n    }\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array\r\n */\r\nconst I32Array = (() => {\r\n    try {\r\n        const buffer = new ArrayBuffer(4);\r\n        const view = new Int32Array(buffer);\r\n        view[0] = INT32_SIZE;\r\n        if (view[0] === -INT32_SIZE) {\r\n            return Int32Array;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    return Array;\r\n})();\n\nlet data = null;\r\nconst COUNT = 128;\r\nlet index = COUNT;\r\n/**\r\n * An Engine that relies on the globally-available `crypto.getRandomValues`,\r\n * which is typically available in modern browsers.\r\n *\r\n * See https://developer.mozilla.org/en-US/docs/Web/API/Crypto/getRandomValues\r\n *\r\n * If unavailable or otherwise non-functioning, then `browserCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst browserCrypto = {\r\n    next() {\r\n        if (index >= COUNT) {\r\n            if (data === null) {\r\n                data = new I32Array(COUNT);\r\n            }\r\n            crypto.getRandomValues(data);\r\n            index = 0;\r\n        }\r\n        return data[index++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns an array of random int32 values, based on current time\r\n * and a random number engine\r\n *\r\n * @param engine an Engine to pull random values from, default `nativeMath`\r\n * @param length the length of the Array, minimum 1, default 16\r\n */\r\nfunction createEntropy(engine = nativeMath, length = 16) {\r\n    const array = [];\r\n    array.push(new Date().getTime() | 0);\r\n    for (let i = 1; i < length; ++i) {\r\n        array[i] = engine.next() | 0;\r\n    }\r\n    return array;\r\n}\n\n/**\r\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/imul\r\n */\r\nconst imul = (() => {\r\n    try {\r\n        if (Math.imul(UINT32_MAX, 5) === -5) {\r\n            return Math.imul;\r\n        }\r\n    }\r\n    catch (_) {\r\n        // nothing to do here\r\n    }\r\n    const UINT16_MAX = 0xffff;\r\n    return (a, b) => {\r\n        const ah = (a >>> 16) & UINT16_MAX;\r\n        const al = a & UINT16_MAX;\r\n        const bh = (b >>> 16) & UINT16_MAX;\r\n        const bl = b & UINT16_MAX;\r\n        // the shift by 0 fixes the sign on the high part\r\n        // the final |0 converts the unsigned value into a signed value\r\n        return (al * bl + (((ah * bl + al * bh) << 16) >>> 0)) | 0;\r\n    };\r\n})();\n\nconst ARRAY_SIZE = 624;\r\nconst ARRAY_MAX = ARRAY_SIZE - 1;\r\nconst M = 397;\r\nconst ARRAY_SIZE_MINUS_M = ARRAY_SIZE - M;\r\nconst A = 0x9908b0df;\r\n/**\r\n * An Engine that is a pseudorandom number generator using the Mersenne\r\n * Twister algorithm based on the prime 2**19937 − 1\r\n *\r\n * See http://en.wikipedia.org/wiki/Mersenne_twister\r\n */\r\nclass MersenneTwister19937 {\r\n    /**\r\n     * MersenneTwister19937 should not be instantiated directly.\r\n     * Instead, use the static methods `seed`, `seedWithArray`, or `autoSeed`.\r\n     */\r\n    constructor() {\r\n        this.data = new I32Array(ARRAY_SIZE);\r\n        this.index = 0; // integer within [0, 624]\r\n        this.uses = 0;\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with an initial int32 value\r\n     * @param initial the initial seed value\r\n     */\r\n    static seed(initial) {\r\n        return new MersenneTwister19937().seed(initial);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with zero or more int32 values\r\n     * @param source A series of int32 values\r\n     */\r\n    static seedWithArray(source) {\r\n        return new MersenneTwister19937().seedWithArray(source);\r\n    }\r\n    /**\r\n     * Returns a MersenneTwister19937 seeded with the current time and\r\n     * a series of natively-generated random values\r\n     */\r\n    static autoSeed() {\r\n        return MersenneTwister19937.seedWithArray(createEntropy());\r\n    }\r\n    /**\r\n     * Returns the next int32 value of the sequence\r\n     */\r\n    next() {\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        const value = this.data[this.index];\r\n        this.index = (this.index + 1) | 0;\r\n        this.uses += 1;\r\n        return temper(value) | 0;\r\n    }\r\n    /**\r\n     * Returns the number of times that the Engine has been used.\r\n     *\r\n     * This can be provided to an unused MersenneTwister19937 with the same\r\n     * seed, bringing it to the exact point that was left off.\r\n     */\r\n    getUseCount() {\r\n        return this.uses;\r\n    }\r\n    /**\r\n     * Discards one or more items from the engine\r\n     * @param count The count of items to discard\r\n     */\r\n    discard(count) {\r\n        if (count <= 0) {\r\n            return this;\r\n        }\r\n        this.uses += count;\r\n        if ((this.index | 0) >= ARRAY_SIZE) {\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        while (count + this.index > ARRAY_SIZE) {\r\n            count -= ARRAY_SIZE - this.index;\r\n            refreshData(this.data);\r\n            this.index = 0;\r\n        }\r\n        this.index = (this.index + count) | 0;\r\n        return this;\r\n    }\r\n    seed(initial) {\r\n        let previous = 0;\r\n        this.data[0] = previous = initial | 0;\r\n        for (let i = 1; i < ARRAY_SIZE; i = (i + 1) | 0) {\r\n            this.data[i] = previous =\r\n                (imul(previous ^ (previous >>> 30), 0x6c078965) + i) | 0;\r\n        }\r\n        this.index = ARRAY_SIZE;\r\n        this.uses = 0;\r\n        return this;\r\n    }\r\n    seedWithArray(source) {\r\n        this.seed(0x012bd6aa);\r\n        seedWithArray(this.data, source);\r\n        return this;\r\n    }\r\n}\r\nfunction refreshData(data) {\r\n    let k = 0;\r\n    let tmp = 0;\r\n    for (; (k | 0) < ARRAY_SIZE_MINUS_M; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] = data[(k + M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    for (; (k | 0) < ARRAY_MAX; k = (k + 1) | 0) {\r\n        tmp = (data[k] & INT32_SIZE) | (data[(k + 1) | 0] & INT32_MAX);\r\n        data[k] =\r\n            data[(k - ARRAY_SIZE_MINUS_M) | 0] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n    }\r\n    tmp = (data[ARRAY_MAX] & INT32_SIZE) | (data[0] & INT32_MAX);\r\n    data[ARRAY_MAX] = data[M - 1] ^ (tmp >>> 1) ^ (tmp & 0x1 ? A : 0);\r\n}\r\nfunction temper(value) {\r\n    value ^= value >>> 11;\r\n    value ^= (value << 7) & 0x9d2c5680;\r\n    value ^= (value << 15) & 0xefc60000;\r\n    return value ^ (value >>> 18);\r\n}\r\nfunction seedWithArray(data, source) {\r\n    let i = 1;\r\n    let j = 0;\r\n    const sourceLength = source.length;\r\n    let k = Math.max(sourceLength, ARRAY_SIZE) | 0;\r\n    let previous = data[0] | 0;\r\n    for (; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x0019660d)) +\r\n                (source[j] | 0) +\r\n                (j | 0)) |\r\n                0;\r\n        i = (i + 1) | 0;\r\n        ++j;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n        if (j >= sourceLength) {\r\n            j = 0;\r\n        }\r\n    }\r\n    for (k = ARRAY_MAX; (k | 0) > 0; --k) {\r\n        data[i] = previous =\r\n            ((data[i] ^ imul(previous ^ (previous >>> 30), 0x5d588b65)) - i) | 0;\r\n        i = (i + 1) | 0;\r\n        if ((i | 0) > ARRAY_MAX) {\r\n            data[0] = data[ARRAY_MAX];\r\n            i = 1;\r\n        }\r\n    }\r\n    data[0] = INT32_SIZE;\r\n}\n\nlet data$1 = null;\r\nconst COUNT$1 = 128;\r\nlet index$1 = COUNT$1;\r\n/**\r\n * An Engine that relies on the node-available\r\n * `require('crypto').randomBytes`, which has been available since 0.58.\r\n *\r\n * See https://nodejs.org/api/crypto.html#crypto_crypto_randombytes_size_callback\r\n *\r\n * If unavailable or otherwise non-functioning, then `nodeCrypto` will\r\n * likely `throw` on the first call to `next()`.\r\n */\r\nconst nodeCrypto = {\r\n    next() {\r\n        if (index$1 >= COUNT$1) {\r\n            data$1 = new Int32Array(new Int8Array(require(\"crypto\").randomBytes(4 * COUNT$1)).buffer);\r\n            index$1 = 0;\r\n        }\r\n        return data$1[index$1++] | 0;\r\n    }\r\n};\n\n/**\r\n * Returns a Distribution to random value within the provided `source`\r\n * within the sliced bounds of `begin` and `end`.\r\n * @param source an array of items to pick from\r\n * @param begin the beginning slice index (defaults to `0`)\r\n * @param end the ending slice index (defaults to `source.length`)\r\n */\r\nfunction picker(source, begin, end) {\r\n    const clone = sliceArray.call(source, begin, end);\r\n    if (clone.length === 0) {\r\n        throw new RangeError(`Cannot pick from a source with no items`);\r\n    }\r\n    const distribution = integer(0, clone.length - 1);\r\n    return engine => clone[distribution(engine)];\r\n}\n\nexport { Random, browserCrypto, nativeMath, MersenneTwister19937, nodeCrypto, bool, date, dice, die, hex, int32, int53, int53Full, integer, pick, picker, real, realZeroToOneExclusive, realZeroToOneInclusive, sample, shuffle, string, uint32, uint53, uint53Full, uuid4, createEntropy };\n//# sourceMappingURL=random-js.esm.js.map\n","import * as Random from 'random-js';\nimport Matrix from 'ml-matrix';\n\nexport function checkFloat(n) {\n  return n > 0.0 && n <= 1.0;\n}\n\n/**\n * Select n with replacement elements on the training set and values, where n is the size of the training set.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {Array} trainingValue\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object} with new X and y.\n */\nexport function examplesBaggingWithReplacement(\n  trainingSet,\n  trainingValue,\n  seed,\n) {\n  let engine;\n  let distribution = Random.integer(0, trainingSet.rows - 1);\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let Xr = new Array(trainingSet.rows);\n  let yr = new Array(trainingSet.rows);\n\n  for (let i = 0; i < trainingSet.rows; ++i) {\n    let index = distribution(engine);\n    Xr[i] = trainingSet.getRow(index);\n    yr[i] = trainingValue[index];\n  }\n\n  return {\n    X: new Matrix(Xr),\n    y: yr,\n  };\n}\n\n/**\n * selects n features from the training set with or without replacement, returns the new training set and the indexes used.\n * @ignore\n * @param {Matrix} trainingSet\n * @param {number} n - features.\n * @param {boolean} replacement\n * @param {number} seed - seed for the random selection, must be a 32-bit integer.\n * @return {object}\n */\nexport function featureBagging(trainingSet, n, replacement, seed) {\n  if (trainingSet.columns < n) {\n    throw new RangeError(\n      'N should be less or equal to the number of columns of X',\n    );\n  }\n\n  let distribution = Random.integer(0, trainingSet.columns - 1);\n  let engine;\n  if (seed === undefined) {\n    engine = Random.MersenneTwister19937.autoSeed();\n  } else if (Number.isInteger(seed)) {\n    engine = Random.MersenneTwister19937.seed(seed);\n  } else {\n    throw new RangeError(\n      `Expected seed must be undefined or integer not ${seed}`,\n    );\n  }\n\n  let toRet = new Matrix(trainingSet.rows, n);\n\n  let usedIndex;\n  let index;\n  if (replacement) {\n    usedIndex = new Array(n);\n    for (let i = 0; i < n; ++i) {\n      index = distribution(engine);\n      usedIndex[i] = index;\n      toRet.setColumn(i, trainingSet.getColumn(index));\n    }\n  } else {\n    usedIndex = new Set();\n    index = distribution(engine);\n    for (let i = 0; i < n; ++i) {\n      while (usedIndex.has(index)) {\n        index = distribution(engine);\n      }\n      toRet.setColumn(i, trainingSet.getColumn(index));\n      usedIndex.add(index);\n    }\n    usedIndex = Array.from(usedIndex);\n  }\n\n  return {\n    X: toRet,\n    usedIndex: usedIndex,\n  };\n}\n","import {\n  DecisionTreeClassifier as DTClassifier,\n  DecisionTreeRegression as DTRegression,\n} from 'ml-cart';\nimport {\n  Matrix,\n  WrapperMatrix2D,\n  MatrixTransposeView,\n  MatrixColumnSelectionView,\n} from 'ml-matrix';\n\nimport * as Utils from './utils';\n\n/**\n * @class RandomForestBase\n */\nexport class RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number|String} [options.maxFeatures] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement] - use replacement over the sample features.\n   * @param {number} [options.seed] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators] - number of estimator to use.\n   * @param {object} [options.treeOptions] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.isClassifier] - boolean to check if is a classifier or regression model (used by subclasses).\n   * @param {boolean} [options.useSampleBagging] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.replacement = model.replacement;\n      this.maxFeatures = model.maxFeatures;\n      this.nEstimators = model.nEstimators;\n      this.treeOptions = model.treeOptions;\n      this.isClassifier = model.isClassifier;\n      this.seed = model.seed;\n      this.n = model.n;\n      this.indexes = model.indexes;\n      this.useSampleBagging = model.useSampleBagging;\n\n      let Estimator = this.isClassifier ? DTClassifier : DTRegression;\n      this.estimators = model.estimators.map((est) => Estimator.load(est));\n    } else {\n      this.replacement = options.replacement;\n      this.maxFeatures = options.maxFeatures;\n      this.nEstimators = options.nEstimators;\n      this.treeOptions = options.treeOptions;\n      this.isClassifier = options.isClassifier;\n      this.seed = options.seed;\n      this.useSampleBagging = options.useSampleBagging;\n    }\n  }\n\n  /**\n   * Train the decision tree with the given training set and labels.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    this.maxFeatures = this.maxFeatures || trainingSet.columns;\n\n    if (Utils.checkFloat(this.maxFeatures)) {\n      this.n = Math.floor(trainingSet.columns * this.maxFeatures);\n    } else if (Number.isInteger(this.maxFeatures)) {\n      if (this.maxFeatures > trainingSet.columns) {\n        throw new RangeError(\n          `The maxFeatures parameter should be less than ${trainingSet.columns}`,\n        );\n      } else {\n        this.n = this.maxFeatures;\n      }\n    } else {\n      throw new RangeError(\n        `Cannot process the maxFeatures parameter ${this.maxFeatures}`,\n      );\n    }\n\n    let Estimator;\n    if (this.isClassifier) {\n      Estimator = DTClassifier;\n    } else {\n      Estimator = DTRegression;\n    }\n\n    this.estimators = new Array(this.nEstimators);\n    this.indexes = new Array(this.nEstimators);\n\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let res = this.useSampleBagging\n        ? Utils.examplesBaggingWithReplacement(\n            trainingSet,\n            trainingValues,\n            this.seed,\n          )\n        : { X: trainingSet, y: trainingValues };\n      let X = res.X;\n      let y = res.y;\n\n      res = Utils.featureBagging(X, this.n, this.replacement, this.seed);\n      X = res.X;\n\n      this.indexes[i] = res.usedIndex;\n      this.estimators[i] = new Estimator(this.treeOptions);\n      this.estimators[i].train(X, y);\n    }\n  }\n\n  /**\n   * Method that returns the way the algorithm generates the predictions, for example, in classification\n   * you can return the mode of all predictions retrieved by the trees, or in case of regression you can\n   * use the mean or the median.\n   * @abstract\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction.\n   */\n  // eslint-disable-next-line no-unused-vars\n  selection(values) {\n    throw new Error(\"Abstract method 'selection' not implemented!\");\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|Array} toPredict\n   * @return {Array} predictions\n   */\n  predict(toPredict) {\n    let predictionValues = new Array(this.nEstimators);\n    toPredict = Matrix.checkMatrix(toPredict);\n    for (let i = 0; i < this.nEstimators; ++i) {\n      let X = new MatrixColumnSelectionView(toPredict, this.indexes[i]); // get features for estimator\n      predictionValues[i] = this.estimators[i].predict(X);\n    }\n\n    predictionValues = new MatrixTransposeView(\n      new WrapperMatrix2D(predictionValues),\n    );\n    let predictions = new Array(predictionValues.rows);\n    for (let i = 0; i < predictionValues.rows; ++i) {\n      predictions[i] = this.selection(predictionValues.getRow(i));\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      indexes: this.indexes,\n      n: this.n,\n      replacement: this.replacement,\n      maxFeatures: this.maxFeatures,\n      nEstimators: this.nEstimators,\n      treeOptions: this.treeOptions,\n      isClassifier: this.isClassifier,\n      seed: this.seed,\n      estimators: this.estimators.map((est) => est.toJSON()),\n      useSampleBagging: this.useSampleBagging,\n    };\n  }\n}\n","import { RandomForestBase } from './RandomForestBase';\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: true,\n  nEstimators: 10,\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestClassifier\n * @augments RandomForestBase\n */\nexport class RandomForestClassifier extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n      options.isClassifier = true;\n      super(options);\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return mode(values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      name: 'RFClassifier',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestClassifier}\n   */\n  static load(model) {\n    if (model.name !== 'RFClassifier') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestClassifier(true, model);\n  }\n}\n\n/**\n * Return the most repeated element on the array.\n * @param {Array} arr\n * @return {number} mode\n */\nfunction mode(arr) {\n  return arr\n    .sort(\n      (a, b) =>\n        arr.filter((v) => v === a).length - arr.filter((v) => v === b).length,\n    )\n    .pop();\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","(function(){function a(d){for(var e=0,f=d.length-1,g=void 0,h=void 0,i=void 0,j=c(e,f);!0;){if(f<=e)return d[j];if(f==e+1)return d[e]>d[f]&&b(d,e,f),d[j];for(g=c(e,f),d[g]>d[f]&&b(d,g,f),d[e]>d[f]&&b(d,e,f),d[g]>d[e]&&b(d,g,e),b(d,g,e+1),h=e+1,i=f;!0;){do h++;while(d[e]>d[h]);do i--;while(d[i]>d[e]);if(i<h)break;b(d,h,i)}b(d,e,i),i<=j&&(e=h),i>=j&&(f=i-1)}}var b=function b(d,e,f){var _ref;return _ref=[d[f],d[e]],d[e]=_ref[0],d[f]=_ref[1],_ref},c=function c(d,e){return~~((d+e)/2)};'undefined'!=typeof module&&module.exports?module.exports=a:window.median=a})();\n","import isArray from 'is-any-array';\nimport quickSelectMedian from 'median-quickselect';\n\nfunction median(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  return quickSelectMedian(input.slice());\n}\n\nexport default median;\n","import arrayMean from 'ml-array-mean';\nimport arrayMedian from 'ml-array-median';\n\nimport { RandomForestBase } from './RandomForestBase';\n\nconst selectionMethods = {\n  mean: arrayMean,\n  median: arrayMedian,\n};\n\nconst defaultOptions = {\n  maxFeatures: 1.0,\n  replacement: false,\n  nEstimators: 10,\n  treeOptions: {},\n  selectionMethod: 'mean',\n  seed: 42,\n  useSampleBagging: false,\n};\n\n/**\n * @class RandomForestRegression\n * @augments RandomForestBase\n */\nexport class RandomForestRegression extends RandomForestBase {\n  /**\n   * Create a new base random forest for a classifier or regression model.\n   * @constructor\n   * @param {object} options\n   * @param {number} [options.maxFeatures=1.0] - the number of features used on each estimator.\n   *        * if is an integer it selects maxFeatures elements over the sample features.\n   *        * if is a float between (0, 1), it takes the percentage of features.\n   * @param {boolean} [options.replacement=true] - use replacement over the sample features.\n   * @param {number} [options.seed=42] - seed for feature and samples selection, must be a 32-bit integer.\n   * @param {number} [options.nEstimators=10] - number of estimator to use.\n   * @param {object} [options.treeOptions={}] - options for the tree classifier, see [ml-cart]{@link https://mljs.github.io/decision-tree-cart/}\n   * @param {string} [options.selectionMethod=\"mean\"] - the way to calculate the prediction from estimators, \"mean\" and \"median\" are supported.\n   * @param {boolean} [options.useSampleBagging=false] - use bagging over training samples.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      super(true, model.baseModel);\n      this.selectionMethod = model.selectionMethod;\n    } else {\n      options = Object.assign({}, defaultOptions, options);\n\n      if (\n        !(\n          options.selectionMethod === 'mean' ||\n          options.selectionMethod === 'median'\n        )\n      ) {\n        throw new RangeError(\n          `Unsupported selection method ${options.selectionMethod}`,\n        );\n      }\n\n      options.isClassifier = false;\n\n      super(options);\n      this.selectionMethod = options.selectionMethod;\n    }\n  }\n\n  /**\n   * retrieve the prediction given the selection method.\n   * @param {Array} values - predictions of the estimators.\n   * @return {number} prediction\n   */\n  selection(values) {\n    return selectionMethods[this.selectionMethod](values);\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    let baseModel = super.toJSON();\n    return {\n      baseModel: baseModel,\n      selectionMethod: this.selectionMethod,\n      name: 'RFRegression',\n    };\n  }\n\n  /**\n   * Load a Decision tree classifier with the given model.\n   * @param {object} model\n   * @return {RandomForestRegression}\n   */\n  static load(model) {\n    if (model.name !== 'RFRegression') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    return new RandomForestRegression(true, model);\n  }\n}\n","import { Matrix, MatrixTransposeView, EVD, SVD, NIPALS } from 'ml-matrix';\n\n/**\n * Creates new PCA (Principal Component Analysis) from the dataset\n * @param {Matrix} dataset - dataset or covariance matrix.\n * @param {Object} [options]\n * @param {boolean} [options.isCovarianceMatrix=false] - true if the dataset is a covariance matrix.\n * @param {string} [options.method='SVD'] - select which method to use: SVD (default), covarianceMatrirx or NIPALS.\n * @param {number} [options.nCompNIPALS=2] - number of components to be computed with NIPALS.\n * @param {boolean} [options.center=true] - should the data be centered (subtract the mean).\n * @param {boolean} [options.scale=false] - should the data be scaled (divide by the standard deviation).\n * @param {boolean} [options.ignoreZeroVariance=false] - ignore columns with zero variance if `scale` is `true`.\n * */\nexport class PCA {\n  constructor(dataset, options = {}) {\n    if (dataset === true) {\n      const model = options;\n      this.center = model.center;\n      this.scale = model.scale;\n      this.means = model.means;\n      this.stdevs = model.stdevs;\n      this.U = Matrix.checkMatrix(model.U);\n      this.S = model.S;\n      this.R = model.R;\n      this.excludedFeatures = model.excludedFeatures || [];\n      return;\n    }\n\n    dataset = new Matrix(dataset);\n\n    const {\n      isCovarianceMatrix = false,\n      method = 'SVD',\n      nCompNIPALS = 2,\n      center = true,\n      scale = false,\n      ignoreZeroVariance = false,\n    } = options;\n\n    this.center = center;\n    this.scale = scale;\n    this.means = null;\n    this.stdevs = null;\n    this.excludedFeatures = [];\n\n    if (isCovarianceMatrix) {\n      // User provided a covariance matrix instead of dataset.\n      this._computeFromCovarianceMatrix(dataset);\n      return;\n    }\n\n    this._adjust(dataset, ignoreZeroVariance);\n    switch (method) {\n      case 'covarianceMatrix': {\n        // User provided a dataset but wants us to compute and use the covariance matrix.\n        const covarianceMatrix = new MatrixTransposeView(dataset)\n          .mmul(dataset)\n          .div(dataset.rows - 1);\n        this._computeFromCovarianceMatrix(covarianceMatrix);\n        break;\n      }\n      case 'NIPALS': {\n        this._computeWithNIPALS(dataset, nCompNIPALS);\n        break;\n      }\n      case 'SVD': {\n        const svd = new SVD(dataset, {\n          computeLeftSingularVectors: false,\n          computeRightSingularVectors: true,\n          autoTranspose: true,\n        });\n\n        this.U = svd.rightSingularVectors;\n\n        const singularValues = svd.diagonal;\n        const eigenvalues = [];\n        for (const singularValue of singularValues) {\n          eigenvalues.push((singularValue * singularValue) / (dataset.rows - 1));\n        }\n        this.S = eigenvalues;\n        break;\n      }\n      default: {\n        throw new Error(`unknown method: ${method}`);\n      }\n    }\n  }\n\n  /**\n   * Load a PCA model from JSON\n   * @param {Object} model\n   * @return {PCA}\n   */\n  static load(model) {\n    if (typeof model.name !== 'string') {\n      throw new TypeError('model must have a name property');\n    }\n    if (model.name !== 'PCA') {\n      throw new RangeError(`invalid model: ${model.name}`);\n    }\n    return new PCA(true, model);\n  }\n\n  /**\n   * Project the dataset into the PCA space\n   * @param {Matrix} dataset\n   * @param {Object} options\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  predict(dataset, options = {}) {\n    const { nComponents = this.U.columns } = options;\n    dataset = new Matrix(dataset);\n    if (this.center) {\n      dataset.subRowVector(this.means);\n      if (this.scale) {\n        for (let i of this.excludedFeatures) {\n          dataset.removeColumn(i);\n        }\n        dataset.divRowVector(this.stdevs);\n      }\n    }\n    var predictions = dataset.mmul(this.U);\n    return predictions.subMatrix(0, predictions.rows - 1, 0, nComponents - 1);\n  }\n\n  /**\n   * Calculates the inverse PCA transform\n   * @param {Matrix} dataset\n   * @return {Matrix} dataset projected in the PCA space\n   */\n  invert(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n\n    var inverse = dataset.mmul(this.U.transpose());\n\n    if (this.center) {\n      if (this.scale) {\n        inverse.mulRowVector(this.stdevs);\n      }\n      inverse.addRowVector(this.means);\n    }\n\n    return inverse;\n  }\n\n\n  /**\n   * Returns the proportion of variance for each component\n   * @return {[number]}\n   */\n  getExplainedVariance() {\n    var sum = 0;\n    for (const s of this.S) {\n      sum += s;\n    }\n    return this.S.map((value) => value / sum);\n  }\n\n  /**\n   * Returns the cumulative proportion of variance\n   * @return {[number]}\n   */\n  getCumulativeVariance() {\n    var explained = this.getExplainedVariance();\n    for (var i = 1; i < explained.length; i++) {\n      explained[i] += explained[i - 1];\n    }\n    return explained;\n  }\n\n  /**\n   * Returns the Eigenvectors of the covariance matrix\n   * @returns {Matrix}\n   */\n  getEigenvectors() {\n    return this.U;\n  }\n\n  /**\n   * Returns the Eigenvalues (on the diagonal)\n   * @returns {[number]}\n   */\n  getEigenvalues() {\n    return this.S;\n  }\n\n  /**\n   * Returns the standard deviations of the principal components\n   * @returns {[number]}\n   */\n  getStandardDeviations() {\n    return this.S.map((x) => Math.sqrt(x));\n  }\n\n  /**\n   * Returns the loadings matrix\n   * @return {Matrix}\n   */\n  getLoadings() {\n    return this.U.transpose();\n  }\n\n  /**\n   * Export the current model to a JSON object\n   * @return {Object} model\n   */\n  toJSON() {\n    return {\n      name: 'PCA',\n      center: this.center,\n      scale: this.scale,\n      means: this.means,\n      stdevs: this.stdevs,\n      U: this.U,\n      S: this.S,\n      excludedFeatures: this.excludedFeatures,\n    };\n  }\n\n  _adjust(dataset, ignoreZeroVariance) {\n    if (this.center) {\n      const mean = dataset.mean('column');\n      const stdevs = this.scale\n        ? dataset.standardDeviation('column', { mean })\n        : null;\n      this.means = mean;\n      dataset.subRowVector(mean);\n      if (this.scale) {\n        for (let i = 0; i < stdevs.length; i++) {\n          if (stdevs[i] === 0) {\n            if (ignoreZeroVariance) {\n              dataset.removeColumn(i);\n              stdevs.splice(i, 1);\n              this.excludedFeatures.push(i);\n              i--;\n            } else {\n              throw new RangeError(\n                `Cannot scale the dataset (standard deviation is zero at index ${i}`,\n              );\n            }\n          }\n        }\n        this.stdevs = stdevs;\n        dataset.divRowVector(stdevs);\n      }\n    }\n  }\n\n  _computeFromCovarianceMatrix(dataset) {\n    const evd = new EVD(dataset, { assumeSymmetric: true });\n    this.U = evd.eigenvectorMatrix;\n    this.U.flipRows();\n    this.S = evd.realEigenvalues;\n    this.S.reverse();\n  }\n\n  _computeWithNIPALS(dataset, nCompNIPALS) {\n    this.U = new Matrix(nCompNIPALS, dataset.columns);\n    this.S = [];\n\n    let x = dataset;\n    for (let i = 0; i < nCompNIPALS; i++) {\n      let dc = new NIPALS(x);\n\n      this.U.setRow(i, dc.w.transpose());\n      this.S.push(Math.pow(dc.s.get(0, 0), 2));\n\n      x = dc.xResidual;\n    }\n    this.U = this.U.transpose(); // to be compatible with API\n  }\n}\n","export function squaredEuclidean(p, q) {\r\n    let d = 0;\r\n    for (let i = 0; i < p.length; i++) {\r\n        d += (p[i] - q[i]) * (p[i] - q[i]);\r\n    }\r\n    return d;\r\n}\r\nexport function euclidean(p, q) {\r\n    return Math.sqrt(squaredEuclidean(p, q));\r\n}\r\n","/**\n * Computes a distance/similarity matrix given an array of data and a distance/similarity function.\n * @param {Array} data An array of data\n * @param {function} distanceFn  A function that accepts two arguments and computes a distance/similarity between them\n * @return {Array<Array>} The distance/similarity matrix. The matrix is square and has a size equal to the length of\n * the data array\n */\nexport default function distanceMatrix(data, distanceFn) {\n  const result = getMatrix(data.length);\n\n  // Compute upper distance matrix\n  for (let i = 0; i < data.length; i++) {\n    for (let j = 0; j <= i; j++) {\n      result[i][j] = distanceFn(data[i], data[j]);\n      result[j][i] = result[i][j];\n    }\n  }\n\n  return result;\n}\n\nfunction getMatrix(size) {\n  const matrix = [];\n  for (let i = 0; i < size; i++) {\n    const row = [];\n    matrix.push(row);\n    for (let j = 0; j < size; j++) {\n      row.push(0);\n    }\n  }\n  return matrix;\n}\n","// Generated by CoffeeScript 1.8.0\n(function() {\n  var Heap, defaultCmp, floor, heapify, heappop, heappush, heappushpop, heapreplace, insort, min, nlargest, nsmallest, updateItem, _siftdown, _siftup;\n\n  floor = Math.floor, min = Math.min;\n\n\n  /*\n  Default comparison function to be used\n   */\n\n  defaultCmp = function(x, y) {\n    if (x < y) {\n      return -1;\n    }\n    if (x > y) {\n      return 1;\n    }\n    return 0;\n  };\n\n\n  /*\n  Insert item x in list a, and keep it sorted assuming a is sorted.\n  \n  If x is already in a, insert it to the right of the rightmost x.\n  \n  Optional args lo (default 0) and hi (default a.length) bound the slice\n  of a to be searched.\n   */\n\n  insort = function(a, x, lo, hi, cmp) {\n    var mid;\n    if (lo == null) {\n      lo = 0;\n    }\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (lo < 0) {\n      throw new Error('lo must be non-negative');\n    }\n    if (hi == null) {\n      hi = a.length;\n    }\n    while (lo < hi) {\n      mid = floor((lo + hi) / 2);\n      if (cmp(x, a[mid]) < 0) {\n        hi = mid;\n      } else {\n        lo = mid + 1;\n      }\n    }\n    return ([].splice.apply(a, [lo, lo - lo].concat(x)), x);\n  };\n\n\n  /*\n  Push item onto heap, maintaining the heap invariant.\n   */\n\n  heappush = function(array, item, cmp) {\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    array.push(item);\n    return _siftdown(array, 0, array.length - 1, cmp);\n  };\n\n\n  /*\n  Pop the smallest item off the heap, maintaining the heap invariant.\n   */\n\n  heappop = function(array, cmp) {\n    var lastelt, returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    lastelt = array.pop();\n    if (array.length) {\n      returnitem = array[0];\n      array[0] = lastelt;\n      _siftup(array, 0, cmp);\n    } else {\n      returnitem = lastelt;\n    }\n    return returnitem;\n  };\n\n\n  /*\n  Pop and return the current smallest value, and add the new item.\n  \n  This is more efficient than heappop() followed by heappush(), and can be\n  more appropriate when using a fixed size heap. Note that the value\n  returned may be larger than item! That constrains reasonable use of\n  this routine unless written as part of a conditional replacement:\n      if item > array[0]\n        item = heapreplace(array, item)\n   */\n\n  heapreplace = function(array, item, cmp) {\n    var returnitem;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    returnitem = array[0];\n    array[0] = item;\n    _siftup(array, 0, cmp);\n    return returnitem;\n  };\n\n\n  /*\n  Fast version of a heappush followed by a heappop.\n   */\n\n  heappushpop = function(array, item, cmp) {\n    var _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (array.length && cmp(array[0], item) < 0) {\n      _ref = [array[0], item], item = _ref[0], array[0] = _ref[1];\n      _siftup(array, 0, cmp);\n    }\n    return item;\n  };\n\n\n  /*\n  Transform list into a heap, in-place, in O(array.length) time.\n   */\n\n  heapify = function(array, cmp) {\n    var i, _i, _j, _len, _ref, _ref1, _results, _results1;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    _ref1 = (function() {\n      _results1 = [];\n      for (var _j = 0, _ref = floor(array.length / 2); 0 <= _ref ? _j < _ref : _j > _ref; 0 <= _ref ? _j++ : _j--){ _results1.push(_j); }\n      return _results1;\n    }).apply(this).reverse();\n    _results = [];\n    for (_i = 0, _len = _ref1.length; _i < _len; _i++) {\n      i = _ref1[_i];\n      _results.push(_siftup(array, i, cmp));\n    }\n    return _results;\n  };\n\n\n  /*\n  Update the position of the given item in the heap.\n  This function should be called every time the item is being modified.\n   */\n\n  updateItem = function(array, item, cmp) {\n    var pos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    pos = array.indexOf(item);\n    if (pos === -1) {\n      return;\n    }\n    _siftdown(array, 0, pos, cmp);\n    return _siftup(array, pos, cmp);\n  };\n\n\n  /*\n  Find the n largest elements in a dataset.\n   */\n\n  nlargest = function(array, n, cmp) {\n    var elem, result, _i, _len, _ref;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    result = array.slice(0, n);\n    if (!result.length) {\n      return result;\n    }\n    heapify(result, cmp);\n    _ref = array.slice(n);\n    for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n      elem = _ref[_i];\n      heappushpop(result, elem, cmp);\n    }\n    return result.sort(cmp).reverse();\n  };\n\n\n  /*\n  Find the n smallest elements in a dataset.\n   */\n\n  nsmallest = function(array, n, cmp) {\n    var elem, i, los, result, _i, _j, _len, _ref, _ref1, _results;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    if (n * 10 <= array.length) {\n      result = array.slice(0, n).sort(cmp);\n      if (!result.length) {\n        return result;\n      }\n      los = result[result.length - 1];\n      _ref = array.slice(n);\n      for (_i = 0, _len = _ref.length; _i < _len; _i++) {\n        elem = _ref[_i];\n        if (cmp(elem, los) < 0) {\n          insort(result, elem, 0, null, cmp);\n          result.pop();\n          los = result[result.length - 1];\n        }\n      }\n      return result;\n    }\n    heapify(array, cmp);\n    _results = [];\n    for (i = _j = 0, _ref1 = min(n, array.length); 0 <= _ref1 ? _j < _ref1 : _j > _ref1; i = 0 <= _ref1 ? ++_j : --_j) {\n      _results.push(heappop(array, cmp));\n    }\n    return _results;\n  };\n\n  _siftdown = function(array, startpos, pos, cmp) {\n    var newitem, parent, parentpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    newitem = array[pos];\n    while (pos > startpos) {\n      parentpos = (pos - 1) >> 1;\n      parent = array[parentpos];\n      if (cmp(newitem, parent) < 0) {\n        array[pos] = parent;\n        pos = parentpos;\n        continue;\n      }\n      break;\n    }\n    return array[pos] = newitem;\n  };\n\n  _siftup = function(array, pos, cmp) {\n    var childpos, endpos, newitem, rightpos, startpos;\n    if (cmp == null) {\n      cmp = defaultCmp;\n    }\n    endpos = array.length;\n    startpos = pos;\n    newitem = array[pos];\n    childpos = 2 * pos + 1;\n    while (childpos < endpos) {\n      rightpos = childpos + 1;\n      if (rightpos < endpos && !(cmp(array[childpos], array[rightpos]) < 0)) {\n        childpos = rightpos;\n      }\n      array[pos] = array[childpos];\n      pos = childpos;\n      childpos = 2 * pos + 1;\n    }\n    array[pos] = newitem;\n    return _siftdown(array, startpos, pos, cmp);\n  };\n\n  Heap = (function() {\n    Heap.push = heappush;\n\n    Heap.pop = heappop;\n\n    Heap.replace = heapreplace;\n\n    Heap.pushpop = heappushpop;\n\n    Heap.heapify = heapify;\n\n    Heap.updateItem = updateItem;\n\n    Heap.nlargest = nlargest;\n\n    Heap.nsmallest = nsmallest;\n\n    function Heap(cmp) {\n      this.cmp = cmp != null ? cmp : defaultCmp;\n      this.nodes = [];\n    }\n\n    Heap.prototype.push = function(x) {\n      return heappush(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pop = function() {\n      return heappop(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.peek = function() {\n      return this.nodes[0];\n    };\n\n    Heap.prototype.contains = function(x) {\n      return this.nodes.indexOf(x) !== -1;\n    };\n\n    Heap.prototype.replace = function(x) {\n      return heapreplace(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.pushpop = function(x) {\n      return heappushpop(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.heapify = function() {\n      return heapify(this.nodes, this.cmp);\n    };\n\n    Heap.prototype.updateItem = function(x) {\n      return updateItem(this.nodes, x, this.cmp);\n    };\n\n    Heap.prototype.clear = function() {\n      return this.nodes = [];\n    };\n\n    Heap.prototype.empty = function() {\n      return this.nodes.length === 0;\n    };\n\n    Heap.prototype.size = function() {\n      return this.nodes.length;\n    };\n\n    Heap.prototype.clone = function() {\n      var heap;\n      heap = new Heap();\n      heap.nodes = this.nodes.slice(0);\n      return heap;\n    };\n\n    Heap.prototype.toArray = function() {\n      return this.nodes.slice(0);\n    };\n\n    Heap.prototype.insert = Heap.prototype.push;\n\n    Heap.prototype.top = Heap.prototype.peek;\n\n    Heap.prototype.front = Heap.prototype.peek;\n\n    Heap.prototype.has = Heap.prototype.contains;\n\n    Heap.prototype.copy = Heap.prototype.clone;\n\n    return Heap;\n\n  })();\n\n  (function(root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      return define([], factory);\n    } else if (typeof exports === 'object') {\n      return module.exports = factory();\n    } else {\n      return root.Heap = factory();\n    }\n  })(this, function() {\n    return Heap;\n  });\n\n}).call(this);\n","module.exports = require('./lib/heap');\n","import Heap from 'heap';\n\nexport default class Cluster {\n  constructor() {\n    this.children = [];\n    this.height = 0;\n    this.size = 1;\n    this.index = -1;\n    this.isLeaf = false;\n  }\n\n  /**\n   * Creates an array of clusters where the maximum height is smaller than the threshold\n   * @param {number} threshold\n   * @return {Array<Cluster>}\n   */\n  cut(threshold) {\n    if (typeof threshold !== 'number') {\n      throw new TypeError('threshold must be a number');\n    }\n    if (threshold < 0) {\n      throw new RangeError('threshold must be a positive number');\n    }\n    let list = [this];\n    const ans = [];\n    while (list.length > 0) {\n      const aux = list.shift();\n      if (threshold >= aux.height) {\n        ans.push(aux);\n      } else {\n        list = list.concat(aux.children);\n      }\n    }\n    return ans;\n  }\n\n  /**\n   * Merge the leaves in the minimum way to have `groups` number of clusters.\n   * @param {number} groups - Them number of children the first level of the tree should have.\n   * @return {Cluster}\n   */\n  group(groups) {\n    if (!Number.isInteger(groups) || groups < 1) {\n      throw new RangeError('groups must be a positive integer');\n    }\n\n    const heap = new Heap((a, b) => {\n      return b.height - a.height;\n    });\n\n    heap.push(this);\n\n    while (heap.size() < groups) {\n      var first = heap.pop();\n      if (first.children.length === 0) {\n        break;\n      }\n      first.children.forEach((child) => heap.push(child));\n    }\n\n    var root = new Cluster();\n    root.children = heap.toArray();\n    root.height = this.height;\n\n    return root;\n  }\n\n  /**\n   * Traverses the tree depth-first and calls the provided callback with each individual node\n   * @param {function} cb - The callback to be called on each node encounter\n   */\n  traverse(cb) {\n    function visit(root, callback) {\n      callback(root);\n      if (root.children) {\n        for (const child of root.children) {\n          visit(child, callback);\n        }\n      }\n    }\n    visit(this, cb);\n  }\n\n  /**\n   * Returns a list of indices for all the leaves of this cluster.\n   * The list is ordered in such a way that a dendrogram could be drawn without crossing branches.\n   * @returns {Array<number>}\n   */\n  indices() {\n    const result = [];\n    this.traverse((cluster) => {\n      if (cluster.isLeaf) {\n        result.push(cluster.index);\n      }\n    });\n    return result;\n  }\n}\n","import { euclidean } from 'ml-distance-euclidean';\nimport getDistanceMatrix from 'ml-distance-matrix';\nimport { Matrix } from 'ml-matrix';\n\nimport Cluster from './Cluster';\n\nfunction singleLink(dKI, dKJ) {\n  return Math.min(dKI, dKJ);\n}\n\nfunction completeLink(dKI, dKJ) {\n  return Math.max(dKI, dKJ);\n}\n\nfunction averageLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  return ai * dKI + aj * dKJ;\n}\n\nfunction weightedAverageLink(dKI, dKJ) {\n  return (dKI + dKJ) / 2;\n}\n\nfunction centroidLink(dKI, dKJ, dIJ, ni, nj) {\n  const ai = ni / (ni + nj);\n  const aj = nj / (ni + nj);\n  const b = -(ni * nj) / (ni + nj) ** 2;\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction medianLink(dKI, dKJ, dIJ) {\n  return dKI / 2 + dKJ / 2 - dIJ / 4;\n}\n\nfunction wardLink(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return ai * dKI + aj * dKJ + b * dIJ;\n}\n\nfunction wardLink2(dKI, dKJ, dIJ, ni, nj, nk) {\n  const ai = (ni + nk) / (ni + nj + nk);\n  const aj = (nj + nk) / (ni + nj + nk);\n  const b = -nk / (ni + nj + nk);\n  return Math.sqrt(ai * dKI * dKI + aj * dKJ * dKJ + b * dIJ * dIJ);\n}\n\n/**\n * Continuously merge nodes that have the least dissimilarity\n * @param {Array<Array<number>>} data - Array of points to be clustered\n * @param {object} [options]\n * @param {Function} [options.distanceFunction]\n * @param {string} [options.method] - Default: `'complete'`\n * @param {boolean} [options.isDistanceMatrix] - Is the input already a distance matrix?\n * @constructor\n */\nexport function agnes(data, options = {}) {\n  const {\n    distanceFunction = euclidean,\n    method = 'complete',\n    isDistanceMatrix = false,\n  } = options;\n\n  let updateFunc;\n  if (!isDistanceMatrix) {\n    data = getDistanceMatrix(data, distanceFunction);\n  }\n  let distanceMatrix = new Matrix(data);\n  const numLeaves = distanceMatrix.rows;\n\n  // allows to use a string or a given function\n  if (typeof method === 'string') {\n    switch (method.toLowerCase()) {\n      case 'single':\n        updateFunc = singleLink;\n        break;\n      case 'complete':\n        updateFunc = completeLink;\n        break;\n      case 'average':\n      case 'upgma':\n        updateFunc = averageLink;\n        break;\n      case 'wpgma':\n        updateFunc = weightedAverageLink;\n        break;\n      case 'centroid':\n      case 'upgmc':\n        updateFunc = centroidLink;\n        break;\n      case 'median':\n      case 'wpgmc':\n        updateFunc = medianLink;\n        break;\n      case 'ward':\n        updateFunc = wardLink;\n        break;\n      case 'ward2':\n        updateFunc = wardLink2;\n        break;\n      default:\n        throw new RangeError(`unknown clustering method: ${method}`);\n    }\n  } else if (typeof method !== 'function') {\n    throw new TypeError('method must be a string or function');\n  }\n\n  let clusters = [];\n  for (let i = 0; i < numLeaves; i++) {\n    const cluster = new Cluster();\n    cluster.isLeaf = true;\n    cluster.index = i;\n    clusters.push(cluster);\n  }\n\n  for (let n = 0; n < numLeaves - 1; n++) {\n    const [row, column, distance] = getSmallestDistance(distanceMatrix);\n    const cluster1 = clusters[row];\n    const cluster2 = clusters[column];\n    const newCluster = new Cluster();\n    newCluster.size = cluster1.size + cluster2.size;\n    newCluster.children.push(cluster1, cluster2);\n    newCluster.height = distance;\n\n    const newClusters = [newCluster];\n    const newDistanceMatrix = new Matrix(\n      distanceMatrix.rows - 1,\n      distanceMatrix.rows - 1,\n    );\n    const previous = (newIndex) =>\n      getPreviousIndex(newIndex, Math.min(row, column), Math.max(row, column));\n\n    for (let i = 1; i < newDistanceMatrix.rows; i++) {\n      const prevI = previous(i);\n      const prevICluster = clusters[prevI];\n      newClusters.push(prevICluster);\n      for (let j = 0; j < i; j++) {\n        if (j === 0) {\n          const dKI = distanceMatrix.get(row, prevI);\n          const dKJ = distanceMatrix.get(prevI, column);\n          const val = updateFunc(\n            dKI,\n            dKJ,\n            distance,\n            cluster1.size,\n            cluster2.size,\n            prevICluster.size,\n          );\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        } else {\n          // Just copy distance from previous matrix\n          const val = distanceMatrix.get(prevI, previous(j));\n          newDistanceMatrix.set(i, j, val);\n          newDistanceMatrix.set(j, i, val);\n        }\n      }\n    }\n\n    clusters = newClusters;\n    distanceMatrix = newDistanceMatrix;\n  }\n\n  return clusters[0];\n}\n\nfunction getSmallestDistance(distance) {\n  let smallest = Infinity;\n  let smallestI = 0;\n  let smallestJ = 0;\n  for (let i = 1; i < distance.rows; i++) {\n    for (let j = 0; j < i; j++) {\n      if (distance.get(i, j) < smallest) {\n        smallest = distance.get(i, j);\n        smallestI = i;\n        smallestJ = j;\n      }\n    }\n  }\n  return [smallestI, smallestJ, smallest];\n}\n\nfunction getPreviousIndex(newIndex, prev1, prev2) {\n  newIndex -= 1;\n  if (newIndex >= prev1) newIndex++;\n  if (newIndex >= prev2) newIndex++;\n  return newIndex;\n}\n","'use strict';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nconst defaultOptions = {\n    distanceFunction: squaredEuclidean\n};\nexport default function nearestVector(listVectors, vector, options = defaultOptions) {\n    const distanceFunction = options.distanceFunction || defaultOptions.distanceFunction;\n    const similarityFunction = options.similarityFunction || defaultOptions.similarityFunction;\n    let vectorIndex = -1;\n    if (typeof similarityFunction === 'function') {\n        // maximum similarity\n        let maxSim = Number.MIN_VALUE;\n        for (let j = 0; j < listVectors.length; j++) {\n            const sim = similarityFunction(vector, listVectors[j]);\n            if (sim > maxSim) {\n                maxSim = sim;\n                vectorIndex = j;\n            }\n        }\n    }\n    else if (typeof distanceFunction === 'function') {\n        // minimum distance\n        let minDist = Number.MAX_VALUE;\n        for (let i = 0; i < listVectors.length; i++) {\n            const dist = distanceFunction(vector, listVectors[i]);\n            if (dist < minDist) {\n                minDist = dist;\n                vectorIndex = i;\n            }\n        }\n    }\n    else {\n        throw new Error(\"A similarity or distance function it's required\");\n    }\n    return vectorIndex;\n}\nexport function findNearestVector(vectorList, vector, options = defaultOptions) {\n    const index = nearestVector(vectorList, vector, options);\n    return vectorList[index];\n}\n","import nearestVector from 'ml-nearest-vector';\n\n/**\n * Calculates the distance matrix for a given array of points\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {function} distance - Distance function to use between the points\n * @return {Array<Array<number>>} - matrix with the distance values\n */\nexport function calculateDistanceMatrix(data, distance) {\n  var distanceMatrix = new Array(data.length);\n  for (var i = 0; i < data.length; ++i) {\n    for (var j = i; j < data.length; ++j) {\n      if (!distanceMatrix[i]) {\n        distanceMatrix[i] = new Array(data.length);\n      }\n      if (!distanceMatrix[j]) {\n        distanceMatrix[j] = new Array(data.length);\n      }\n      const dist = distance(data[i], data[j]);\n      distanceMatrix[i][j] = dist;\n      distanceMatrix[j][i] = dist;\n    }\n  }\n  return distanceMatrix;\n}\n\n/**\n * Updates the cluster identifier based in the new data\n * @ignore\n * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {function} distance - Distance function to use between the points\n * @return {Array} the cluster identifier for each data dot\n */\nexport function updateClusterID(data, centers, clusterID, distance) {\n  for (var i = 0; i < data.length; i++) {\n    clusterID[i] = nearestVector(centers, data[i], {\n      distanceFunction: distance\n    });\n  }\n  return clusterID;\n}\n\n/**\n * Update the center values based in the new configurations of the clusters\n * @ignore\n * @param {Array<Array<number>>} prevCenters - Centroids from the previous iteration\n * @param {Array <Array <number>>} data - the [x,y,z,...] points to cluster\n * @param {Array <number>} clusterID - the cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @return {Array} he K centers in format [x,y,z,...]\n */\nexport function updateCenters(prevCenters, data, clusterID, K) {\n  const nDim = data[0].length;\n\n  // copy previous centers\n  var centers = new Array(K);\n  var centersLen = new Array(K);\n  for (var i = 0; i < K; i++) {\n    centers[i] = new Array(nDim);\n    centersLen[i] = 0;\n    for (var j = 0; j < nDim; j++) {\n      centers[i][j] = 0;\n    }\n  }\n\n  // add the value for all dimensions of the point\n  for (var l = 0; l < data.length; l++) {\n    centersLen[clusterID[l]]++;\n    for (var dim = 0; dim < nDim; dim++) {\n      centers[clusterID[l]][dim] += data[l][dim];\n    }\n  }\n\n  // divides by length\n  for (var id = 0; id < K; id++) {\n    for (var d = 0; d < nDim; d++) {\n      if (centersLen[id]) {\n        centers[id][d] /= centersLen[id];\n      } else {\n        centers[id][d] = prevCenters[id][d];\n      }\n    }\n  }\n  return centers;\n}\n\n/**\n * The centers have moved more than the tolerance value?\n * @ignore\n * @param {Array<Array<number>>} centers - the K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} oldCenters - the K old centers in format [x,y,z,...]\n * @param {function} distanceFunction - Distance function to use between the points\n * @param {number} tolerance - Allowed distance for the centroids to move\n * @return {boolean}\n */\nexport function hasConverged(centers, oldCenters, distanceFunction, tolerance) {\n  for (var i = 0; i < centers.length; i++) {\n    if (distanceFunction(centers[i], oldCenters[i]) > tolerance) {\n      return false;\n    }\n  }\n  return true;\n}\n","const LOOP = 8;\nconst FLOAT_MUL = 1 / 16777216;\nconst sh1 = 15;\nconst sh2 = 18;\nconst sh3 = 11;\nfunction multiply_uint32(n, m) {\n    n >>>= 0;\n    m >>>= 0;\n    const nlo = n & 0xffff;\n    const nhi = n - nlo;\n    return (((nhi * m) >>> 0) + nlo * m) >>> 0;\n}\nexport default class XSadd {\n    constructor(seed = Date.now()) {\n        this.state = new Uint32Array(4);\n        this.init(seed);\n        this.random = this.getFloat.bind(this);\n    }\n    /**\n     * Returns a 32-bit integer r (0 <= r < 2^32)\n     */\n    getUint32() {\n        this.nextState();\n        return (this.state[3] + this.state[2]) >>> 0;\n    }\n    /**\n     * Returns a floating point number r (0.0 <= r < 1.0)\n     */\n    getFloat() {\n        return (this.getUint32() >>> 8) * FLOAT_MUL;\n    }\n    init(seed) {\n        if (!Number.isInteger(seed)) {\n            throw new TypeError('seed must be an integer');\n        }\n        this.state[0] = seed;\n        this.state[1] = 0;\n        this.state[2] = 0;\n        this.state[3] = 0;\n        for (let i = 1; i < LOOP; i++) {\n            this.state[i & 3] ^=\n                (i +\n                    multiply_uint32(1812433253, this.state[(i - 1) & 3] ^ ((this.state[(i - 1) & 3] >>> 30) >>> 0))) >>>\n                    0;\n        }\n        this.periodCertification();\n        for (let i = 0; i < LOOP; i++) {\n            this.nextState();\n        }\n    }\n    periodCertification() {\n        if (this.state[0] === 0 &&\n            this.state[1] === 0 &&\n            this.state[2] === 0 &&\n            this.state[3] === 0) {\n            this.state[0] = 88; // X\n            this.state[1] = 83; // S\n            this.state[2] = 65; // A\n            this.state[3] = 68; // D\n        }\n    }\n    nextState() {\n        let t = this.state[0];\n        t ^= t << sh1;\n        t ^= t >>> sh2;\n        t ^= this.state[3] << sh3;\n        this.state[0] = this.state[1];\n        this.state[1] = this.state[2];\n        this.state[2] = this.state[3];\n        this.state[3] = t;\n    }\n}\n","const PROB_TOLERANCE = 0.00000001;\nfunction randomChoice(values, options = {}, random = Math.random) {\n    const { size = 1, replace = false, probabilities } = options;\n    let valuesArr;\n    let cumSum;\n    if (typeof values === 'number') {\n        valuesArr = getArray(values);\n    }\n    else {\n        valuesArr = values.slice();\n    }\n    if (probabilities) {\n        if (!replace) {\n            throw new Error('choice with probabilities and no replacement is not implemented');\n        }\n        // check input is sane\n        if (probabilities.length !== valuesArr.length) {\n            throw new Error('the length of probabilities option should be equal to the number of choices');\n        }\n        cumSum = [probabilities[0]];\n        for (let i = 1; i < probabilities.length; i++) {\n            cumSum[i] = cumSum[i - 1] + probabilities[i];\n        }\n        if (Math.abs(1 - cumSum[cumSum.length - 1]) > PROB_TOLERANCE) {\n            throw new Error(`probabilities should sum to 1, but instead sums to ${cumSum[cumSum.length - 1]}`);\n        }\n    }\n    if (replace === false && size > valuesArr.length) {\n        throw new Error('size option is too large');\n    }\n    const result = [];\n    for (let i = 0; i < size; i++) {\n        const index = randomIndex(valuesArr.length, random, cumSum);\n        result.push(valuesArr[index]);\n        if (!replace) {\n            valuesArr.splice(index, 1);\n        }\n    }\n    return result;\n}\nfunction getArray(n) {\n    const arr = [];\n    for (let i = 0; i < n; i++) {\n        arr.push(i);\n    }\n    return arr;\n}\nfunction randomIndex(n, random, cumSum) {\n    const rand = random();\n    if (!cumSum) {\n        return Math.floor(rand * n);\n    }\n    else {\n        let idx = 0;\n        while (rand > cumSum[idx]) {\n            idx++;\n        }\n        return idx;\n    }\n}\nexport default randomChoice;\n","// tslint:disable-next-line\nimport XSAdd from 'ml-xsadd';\nimport choice from './choice';\n/**\n * @classdesc Random class\n */\nexport default class Random {\n    /**\n     * @param [seedOrRandom=Math.random] - Control the random number generator used by the Random class instance. Pass a random number generator function with a uniform distribution over the half-open interval [0, 1[. If seed will pass it to ml-xsadd to create a seeded random number generator. If undefined will use Math.random.\n     */\n    constructor(seedOrRandom = Math.random) {\n        if (typeof seedOrRandom === 'number') {\n            const xsadd = new XSAdd(seedOrRandom);\n            this.randomGenerator = xsadd.random;\n        }\n        else {\n            this.randomGenerator = seedOrRandom;\n        }\n    }\n    choice(values, options) {\n        if (typeof values === 'number') {\n            return choice(values, options, this.randomGenerator);\n        }\n        return choice(values, options, this.randomGenerator);\n    }\n    /**\n     * Draw a random number from a uniform distribution on [0,1)\n     * @return The random number\n     */\n    random() {\n        return this.randomGenerator();\n    }\n    /**\n     * Draw a random integer from a uniform distribution on [low, high). If only low is specified, the number is drawn on [0, low)\n     * @param low - The lower bound of the uniform distribution interval.\n     * @param high - The higher bound of the uniform distribution interval.\n     */\n    randInt(low, high) {\n        if (high === undefined) {\n            high = low;\n            low = 0;\n        }\n        return low + Math.floor(this.randomGenerator() * (high - low));\n    }\n    /**\n     * Draw several random number from a uniform distribution on [0, 1)\n     * @param size - The number of number to draw\n     * @return - The list of drawn numbers.\n     */\n    randomSample(size) {\n        const result = [];\n        for (let i = 0; i < size; i++) {\n            result.push(this.random());\n        }\n        return result;\n    }\n}\n","import Random from 'ml-random';\nimport { squaredEuclidean } from 'ml-distance-euclidean';\nimport { Matrix } from 'ml-matrix';\n\n/**\n * Choose K different random points from the original data\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function random(data, K, seed) {\n  const random = new Random(seed);\n  return random.choice(data, { size: K });\n}\n\n/**\n * Chooses the most distant points to a first random pick\n * @ignore\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - number of clusters\n * @param {Array<Array<number>>} distanceMatrix - matrix with the distance values\n * @param {number} seed - seed for random number generation\n * @return {Array<Array<number>>} - Initial random points\n */\nexport function mostDistant(data, K, distanceMatrix, seed) {\n  const random = new Random(seed);\n  var ans = new Array(K);\n  // chooses a random point as initial cluster\n  ans[0] = Math.floor(random.random() * data.length);\n\n  if (K > 1) {\n    // chooses the more distant point\n    var maxDist = { dist: -1, index: -1 };\n    for (var l = 0; l < data.length; ++l) {\n      if (distanceMatrix[ans[0]][l] > maxDist.dist) {\n        maxDist.dist = distanceMatrix[ans[0]][l];\n        maxDist.index = l;\n      }\n    }\n    ans[1] = maxDist.index;\n\n    if (K > 2) {\n      // chooses the set of points that maximises the min distance\n      for (var k = 2; k < K; ++k) {\n        var center = { dist: -1, index: -1 };\n        for (var m = 0; m < data.length; ++m) {\n          // minimum distance to centers\n          var minDistCent = { dist: Number.MAX_VALUE, index: -1 };\n          for (var n = 0; n < k; ++n) {\n            if (\n              distanceMatrix[n][m] < minDistCent.dist &&\n              ans.indexOf(m) === -1\n            ) {\n              minDistCent = {\n                dist: distanceMatrix[n][m],\n                index: m\n              };\n            }\n          }\n\n          if (\n            minDistCent.dist !== Number.MAX_VALUE &&\n            minDistCent.dist > center.dist\n          ) {\n            center = Object.assign({}, minDistCent);\n          }\n        }\n\n        ans[k] = center.index;\n      }\n    }\n  }\n\n  return ans.map((index) => data[index]);\n}\n\n// Implementation inspired from scikit\nexport function kmeanspp(X, K, options = {}) {\n  X = new Matrix(X);\n  const nSamples = X.rows;\n  const random = new Random(options.seed);\n  // Set the number of trials\n  const centers = [];\n  const localTrials = options.localTrials || 2 + Math.floor(Math.log(K));\n\n  // Pick the first center at random from the dataset\n  const firstCenterIdx = random.randInt(nSamples);\n  centers.push(X.getRow(firstCenterIdx));\n\n  // Init closest distances\n  let closestDistSquared = new Matrix(1, X.rows);\n  for (let i = 0; i < X.rows; i++) {\n    closestDistSquared.set(0, i, squaredEuclidean(X.getRow(i), centers[0]));\n  }\n  let cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n  const factor = 1 / cumSumClosestDistSquared[0][nSamples - 1];\n  let probabilities = Matrix.mul(closestDistSquared, factor);\n\n  // Iterate over the remaining centers\n  for (let i = 1; i < K; i++) {\n    const candidateIdx = random.choice(nSamples, {\n      replace: true,\n      size: localTrials,\n      probabilities: probabilities[0]\n    });\n\n    const candidates = X.selection(candidateIdx, range(X.columns));\n    const distanceToCandidates = euclideanDistances(candidates, X);\n\n    let bestCandidate;\n    let bestPot;\n    let bestDistSquared;\n\n    for (let j = 0; j < localTrials; j++) {\n      const newDistSquared = Matrix.min(closestDistSquared, [distanceToCandidates.getRow(j)]);\n      const newPot = newDistSquared.sum();\n      if (bestCandidate === undefined || newPot < bestPot) {\n        bestCandidate = candidateIdx[j];\n        bestPot = newPot;\n        bestDistSquared = newDistSquared;\n      }\n    }\n    centers[i] = X.getRow(bestCandidate);\n    closestDistSquared = bestDistSquared;\n    cumSumClosestDistSquared = [cumSum(closestDistSquared.getRow(0))];\n    probabilities = Matrix.mul(\n      closestDistSquared,\n      1 / cumSumClosestDistSquared[0][nSamples - 1]\n    );\n  }\n  return centers;\n}\n\nfunction euclideanDistances(A, B) {\n  const result = new Matrix(A.rows, B.rows);\n  for (let i = 0; i < A.rows; i++) {\n    for (let j = 0; j < B.rows; j++) {\n      result.set(i, j, squaredEuclidean(A.getRow(i), B.getRow(j)));\n    }\n  }\n  return result;\n}\n\nfunction range(l) {\n  let r = [];\n  for (let i = 0; i < l; i++) {\n    r.push(i);\n  }\n  return r;\n}\n\nfunction cumSum(arr) {\n  let cumSum = [arr[0]];\n  for (let i = 1; i < arr.length; i++) {\n    cumSum[i] = cumSum[i - 1] + arr[i];\n  }\n  return cumSum;\n}\n","import { updateClusterID } from './utils';\n\nconst distanceSymbol = Symbol('distance');\n\nexport default class KMeansResult {\n  /**\n   * Result of the kmeans algorithm\n   * @param {Array<number>} clusters - the cluster identifier for each data dot\n   * @param {Array<Array<object>>} centroids - the K centers in format [x,y,z,...], the error and size of the cluster\n   * @param {boolean} converged - Converge criteria satisfied\n   * @param {number} iterations - Current number of iterations\n   * @param {function} distance - (*Private*) Distance function to use between the points\n   * @constructor\n   */\n  constructor(clusters, centroids, converged, iterations, distance) {\n    this.clusters = clusters;\n    this.centroids = centroids;\n    this.converged = converged;\n    this.iterations = iterations;\n    this[distanceSymbol] = distance;\n  }\n\n  /**\n   * Allows to compute for a new array of points their cluster id\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {Array<number>} - cluster id for each point\n   */\n  nearest(data) {\n    const clusterID = new Array(data.length);\n    const centroids = this.centroids.map(function (centroid) {\n      return centroid.centroid;\n    });\n    return updateClusterID(data, centroids, clusterID, this[distanceSymbol]);\n  }\n\n  /**\n   * Returns a KMeansResult with the error and size of the cluster\n   * @ignore\n   * @param {Array<Array<number>>} data - the [x,y,z,...] points to cluster\n   * @return {KMeansResult}\n   */\n  computeInformation(data) {\n    var enrichedCentroids = this.centroids.map(function (centroid) {\n      return {\n        centroid: centroid,\n        error: 0,\n        size: 0\n      };\n    });\n\n    for (var i = 0; i < data.length; i++) {\n      enrichedCentroids[this.clusters[i]].error += this[distanceSymbol](\n        data[i],\n        this.centroids[this.clusters[i]]\n      );\n      enrichedCentroids[this.clusters[i]].size++;\n    }\n\n    for (var j = 0; j < this.centroids.length; j++) {\n      if (enrichedCentroids[j].size) {\n        enrichedCentroids[j].error /= enrichedCentroids[j].size;\n      } else {\n        enrichedCentroids[j].error = null;\n      }\n    }\n\n    return new KMeansResult(\n      this.clusters,\n      enrichedCentroids,\n      this.converged,\n      this.iterations,\n      this[distanceSymbol]\n    );\n  }\n}\n","import { squaredEuclidean } from 'ml-distance-euclidean';\n\nimport {\n  updateClusterID,\n  updateCenters,\n  hasConverged,\n  calculateDistanceMatrix\n} from './utils';\nimport { mostDistant, random, kmeanspp } from './initialization';\nimport KMeansResult from './KMeansResult';\n\nconst defaultOptions = {\n  maxIterations: 100,\n  tolerance: 1e-6,\n  withIterations: false,\n  initialization: 'kmeans++',\n  distanceFunction: squaredEuclidean\n};\n\n/**\n * Each step operation for kmeans\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} iterations - Current number of iterations\n * @return {KMeansResult}\n */\nfunction step(centers, data, clusterID, K, options, iterations) {\n  clusterID = updateClusterID(\n    data,\n    centers,\n    clusterID,\n    options.distanceFunction\n  );\n  var newCenters = updateCenters(centers, data, clusterID, K);\n  var converged = hasConverged(\n    newCenters,\n    centers,\n    options.distanceFunction,\n    options.tolerance\n  );\n  return new KMeansResult(\n    clusterID,\n    newCenters,\n    converged,\n    iterations,\n    options.distanceFunction\n  );\n}\n\n/**\n * Generator version for the algorithm\n * @ignore\n * @param {Array<Array<number>>} centers - K centers in format [x,y,z,...]\n * @param {Array<Array<number>>} data - Points [x,y,z,...] to cluster\n * @param {Array<number>} clusterID - Cluster identifier for each data dot\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n */\nfunction* kmeansGenerator(centers, data, clusterID, K, options) {\n  var converged = false;\n  var stepNumber = 0;\n  var stepResult;\n  while (!converged && stepNumber < options.maxIterations) {\n    stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n    yield stepResult.computeInformation(data);\n    converged = stepResult.converged;\n    centers = stepResult.centroids;\n  }\n}\n\n/**\n * K-means algorithm\n * @param {Array<Array<number>>} data - Points in the format to cluster [x,y,z,...]\n * @param {number} K - Number of clusters\n * @param {object} [options] - Option object\n * @param {number} [options.maxIterations = 100] - Maximum of iterations allowed\n * @param {number} [options.tolerance = 1e-6] - Error tolerance\n * @param {boolean} [options.withIterations = false] - Store clusters and centroids for each iteration\n * @param {function} [options.distanceFunction = squaredDistance] - Distance function to use between the points\n * @param {number} [options.seed] - Seed for random initialization.\n * @param {string|Array<Array<number>>} [options.initialization = 'kmeans++'] - K centers in format [x,y,z,...] or a method for initialize the data:\n *  * You can either specify your custom start centroids, or select one of the following initialization method:\n *  * `'kmeans++'` will use the kmeans++ method as described by http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf\n *  * `'random'` will choose K random different values.\n *  * `'mostDistant'` will choose the more distant points to a first random pick\n * @return {KMeansResult} - Cluster identifier for each data dot and centroids with the following fields:\n *  * `'clusters'`: Array of indexes for the clusters.\n *  * `'centroids'`: Array with the resulting centroids.\n *  * `'iterations'`: Number of iterations that took to converge\n */\nexport default function kmeans(data, K, options) {\n  options = Object.assign({}, defaultOptions, options);\n\n  if (K <= 0 || K > data.length || !Number.isInteger(K)) {\n    throw new Error(\n      'K should be a positive integer smaller than the number of points'\n    );\n  }\n\n  var centers;\n  if (Array.isArray(options.initialization)) {\n    if (options.initialization.length !== K) {\n      throw new Error('The initial centers should have the same length as K');\n    } else {\n      centers = options.initialization;\n    }\n  } else {\n    switch (options.initialization) {\n      case 'kmeans++':\n        centers = kmeanspp(data, K, options);\n        break;\n      case 'random':\n        centers = random(data, K, options.seed);\n        break;\n      case 'mostDistant':\n        centers = mostDistant(\n          data,\n          K,\n          calculateDistanceMatrix(data, options.distanceFunction),\n          options.seed\n        );\n        break;\n      default:\n        throw new Error(\n          `Unknown initialization method: \"${options.initialization}\"`\n        );\n    }\n  }\n\n  // infinite loop until convergence\n  if (options.maxIterations === 0) {\n    options.maxIterations = Number.MAX_VALUE;\n  }\n\n  var clusterID = new Array(data.length);\n  if (options.withIterations) {\n    return kmeansGenerator(centers, data, clusterID, K, options);\n  } else {\n    var converged = false;\n    var stepNumber = 0;\n    var stepResult;\n    while (!converged && stepNumber < options.maxIterations) {\n      stepResult = step(centers, data, clusterID, K, options, ++stepNumber);\n      converged = stepResult.converged;\n      centers = stepResult.centroids;\n    }\n    return stepResult.computeInformation(data);\n  }\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that retuns an array of matrices of the cases that belong to each class.\n * @param {Matrix} X - dataset\n * @param {Array} y - predictions\n * @return {Array}\n */\nexport function separateClasses(X, y) {\n  var features = X.columns;\n\n  var classes = 0;\n  var totalPerClasses = new Array(10000); // max upperbound of classes\n  for (var i = 0; i < y.length; i++) {\n    if (totalPerClasses[y[i]] === undefined) {\n      totalPerClasses[y[i]] = 0;\n      classes++;\n    }\n    totalPerClasses[y[i]]++;\n  }\n  var separatedClasses = new Array(classes);\n  var currentIndex = new Array(classes);\n  for (i = 0; i < classes; ++i) {\n    separatedClasses[i] = new Matrix(totalPerClasses[i], features);\n    currentIndex[i] = 0;\n  }\n  for (i = 0; i < X.rows; ++i) {\n    separatedClasses[y[i]].setRow(currentIndex[y[i]], X.getRow(i));\n    currentIndex[y[i]]++;\n  }\n  return separatedClasses;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class GaussianNB {\n  /**\n   * Constructor for the Gaussian Naive Bayes classifier, the parameters here is just for loading purposes.\n   * @constructor\n   * @param {boolean} reload\n   * @param {object} model\n   */\n  constructor(reload, model) {\n    if (reload) {\n      this.means = model.means;\n      this.calculateProbabilities = model.calculateProbabilities;\n    }\n  }\n\n  /**\n   * Function that trains the classifier with a matrix that represents the training set and an array that\n   * represents the label of each row in the training set. the labels must be numbers between 0 to n-1 where\n   * n represents the number of classes.\n   *\n   * WARNING: in the case that one class, all the cases in one or more features have the same value, the\n   * Naive Bayes classifier will not work well.\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    var C1 = Math.sqrt(2 * Math.PI); // constant to precalculate the squared root\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separatedClasses = separateClasses(trainingSet, trainingLabels);\n    var calculateProbabilities = new Array(separatedClasses.length);\n    this.means = new Array(separatedClasses.length);\n    for (var i = 0; i < separatedClasses.length; ++i) {\n      var means = separatedClasses[i].mean('column');\n      var std = separatedClasses[i].standardDeviation('column', {\n        mean: means\n      });\n\n      var logPriorProbability = Math.log(\n        separatedClasses[i].rows / trainingSet.rows\n      );\n      calculateProbabilities[i] = new Array(means.length + 1);\n\n      calculateProbabilities[i][0] = logPriorProbability;\n      for (var j = 1; j < means.length + 1; ++j) {\n        var currentStd = std[j - 1];\n        calculateProbabilities[i][j] = [\n          1 / (C1 * currentStd),\n          -2 * currentStd * currentStd\n        ];\n      }\n\n      this.means[i] = means;\n    }\n\n    this.calculateProbabilities = calculateProbabilities;\n  }\n\n  /**\n   * function that predicts each row of the dataset (must be a matrix).\n   *\n   * @param {Matrix|Array} dataset\n   * @return {Array}\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    if (dataset.rows === this.calculateProbabilities[0].length) {\n      throw new RangeError(\n        'the dataset must have the same features as the training set'\n      );\n    }\n\n    var predictions = new Array(dataset.rows);\n\n    for (var i = 0; i < predictions.length; ++i) {\n      predictions[i] = getCurrentClass(\n        dataset.getRow(i),\n        this.means,\n        this.calculateProbabilities\n      );\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that export the NaiveBayes model.\n   * @return {object}\n   */\n  toJSON() {\n    return {\n      modelName: 'NaiveBayes',\n      means: this.means,\n      calculateProbabilities: this.calculateProbabilities\n    };\n  }\n\n  /**\n   * Function that create a GaussianNB classifier with the given model.\n   * @param {object} model\n   * @return {GaussianNB}\n   */\n  static load(model) {\n    if (model.modelName !== 'NaiveBayes') {\n      throw new RangeError(\n        'The current model is not a Multinomial Naive Bayes, current model:',\n        model.name\n      );\n    }\n\n    return new GaussianNB(true, model);\n  }\n}\n\n/**\n * @private\n * Function the retrieves a prediction with one case.\n *\n * @param {Array} currentCase\n * @param {Array} mean - Precalculated means of each class trained\n * @param {Array} classes - Precalculated value of each class (Prior probability and probability function of each feature)\n * @return {number}\n */\nfunction getCurrentClass(currentCase, mean, classes) {\n  var maxProbability = 0;\n  var predictedClass = -1;\n\n  // going through all precalculated values for the classes\n  for (var i = 0; i < classes.length; ++i) {\n    var currentProbability = classes[i][0]; // initialize with the prior probability\n    for (var j = 1; j < classes[0][1].length + 1; ++j) {\n      currentProbability += calculateLogProbability(\n        currentCase[j - 1],\n        mean[i][j - 1],\n        classes[i][j][0],\n        classes[i][j][1]\n      );\n    }\n\n    currentProbability = Math.exp(currentProbability);\n    if (currentProbability > maxProbability) {\n      maxProbability = currentProbability;\n      predictedClass = i;\n    }\n  }\n\n  return predictedClass;\n}\n\n/**\n * @private\n * function that retrieves the probability of the feature given the class.\n * @param {number} value - value of the feature.\n * @param {number} mean - mean of the feature for the given class.\n * @param {number} C1 - precalculated value of (1 / (sqrt(2*pi) * std)).\n * @param {number} C2 - precalculated value of (2 * std^2) for the denominator of the exponential.\n * @return {number}\n */\nfunction calculateLogProbability(value, mean, C1, C2) {\n  value = value - mean;\n  return Math.log(C1 * Math.exp((value * value) / C2));\n}\n","import { Matrix } from 'ml-matrix';\n\nimport { separateClasses } from './utils';\n\nexport class MultinomialNB {\n  /**\n   * Constructor for Multinomial Naive Bayes, the model parameter is for load purposes.\n   * @constructor\n   * @param {object} model - for load purposes.\n   */\n  constructor(model) {\n    if (model) {\n      this.conditionalProbability = Matrix.checkMatrix(\n        model.conditionalProbability\n      );\n      this.priorProbability = Matrix.checkMatrix(model.priorProbability);\n    }\n  }\n\n  /**\n   * Train the classifier with the current training set and labels, the labels must be numbers between 0 and n.\n   * @param {Matrix|Array} trainingSet\n   * @param {Array} trainingLabels\n   */\n  train(trainingSet, trainingLabels) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n\n    if (trainingSet.rows !== trainingLabels.length) {\n      throw new RangeError(\n        'the size of the training set and the training labels must be the same.'\n      );\n    }\n\n    var separateClass = separateClasses(trainingSet, trainingLabels);\n\n    this.priorProbability = new Matrix(separateClass.length, 1);\n\n    for (var i = 0; i < separateClass.length; ++i) {\n      this.priorProbability.set(i, 0, Math.log(\n        separateClass[i].rows / trainingSet.rows\n      ));\n    }\n\n    var features = trainingSet.columns;\n    this.conditionalProbability = new Matrix(separateClass.length, features);\n    for (i = 0; i < separateClass.length; ++i) {\n      var classValues = Matrix.checkMatrix(separateClass[i]);\n      var total = classValues.sum();\n      var divisor = total + features;\n      this.conditionalProbability.setRow(\n        i,\n        Matrix.rowVector(classValues\n          .sum('column'))\n          .add(1)\n          .div(divisor)\n          .apply(matrixLog)\n      );\n    }\n  }\n\n  /**\n   * Retrieves the predictions for the dataset with the current model.\n   * @param {Matrix|Array} dataset\n   * @return {Array} - predictions from the dataset.\n   */\n  predict(dataset) {\n    dataset = Matrix.checkMatrix(dataset);\n    var predictions = new Array(dataset.rows);\n    for (var i = 0; i < dataset.rows; ++i) {\n      var currentElement = dataset.getRowVector(i);\n      const v = Matrix.columnVector(this.conditionalProbability\n        .clone()\n        .mulRowVector(currentElement)\n        .sum('row'));\n      predictions[i] = v\n        .add(this.priorProbability)\n        .maxIndex()[0];\n    }\n\n    return predictions;\n  }\n\n  /**\n   * Function that saves the current model.\n   * @return {object} - model in JSON format.\n   */\n  toJSON() {\n    return {\n      name: 'MultinomialNB',\n      priorProbability: this.priorProbability,\n      conditionalProbability: this.conditionalProbability\n    };\n  }\n\n  /**\n   * Creates a new MultinomialNB from the given model\n   * @param {object} model\n   * @return {MultinomialNB}\n   */\n  static load(model) {\n    if (model.name !== 'MultinomialNB') {\n      throw new RangeError(`${model.name} is not a Multinomial Naive Bayes`);\n    }\n\n    return new MultinomialNB(model);\n  }\n}\n\nfunction matrixLog(i, j) {\n  this.set(i, j, Math.log(this.get(i, j)));\n}\n","/*\n * Original code from:\n *\n * k-d Tree JavaScript - V 1.01\n *\n * https://github.com/ubilabs/kd-tree-javascript\n *\n * @author Mircea Pricop <pricop@ubilabs.net>, 2012\n * @author Martin Kleppe <kleppe@ubilabs.net>, 2012\n * @author Ubilabs http://ubilabs.net, 2012\n * @license MIT License <http://www.opensource.org/licenses/mit-license.php>\n */\n\nfunction Node(obj, dimension, parent) {\n  this.obj = obj;\n  this.left = null;\n  this.right = null;\n  this.parent = parent;\n  this.dimension = dimension;\n}\n\nexport default class KDTree {\n  constructor(points, metric) {\n    // If points is not an array, assume we're loading a pre-built tree\n    if (!Array.isArray(points)) {\n      this.dimensions = points.dimensions;\n      this.root = points;\n      restoreParent(this.root);\n    } else {\n      this.dimensions = new Array(points[0].length);\n      for (var i = 0; i < this.dimensions.length; i++) {\n        this.dimensions[i] = i;\n      }\n      this.root = buildTree(points, 0, null, this.dimensions);\n    }\n    this.metric = metric;\n  }\n\n  // Convert to a JSON serializable structure; this just requires removing\n  // the `parent` property\n  toJSON() {\n    const result = toJSONImpl(this.root, true);\n    result.dimensions = this.dimensions;\n    return result;\n  }\n\n  nearest(point, maxNodes, maxDistance) {\n    const metric = this.metric;\n    const dimensions = this.dimensions;\n    var i;\n\n    const bestNodes = new BinaryHeap(function (e) {\n      return -e[1];\n    });\n\n    function nearestSearch(node) {\n      const dimension = dimensions[node.dimension];\n      const ownDistance = metric(point, node.obj);\n      const linearPoint = {};\n      var bestChild, linearDistance, otherChild, i;\n\n      function saveNode(node, distance) {\n        bestNodes.push([node, distance]);\n        if (bestNodes.size() > maxNodes) {\n          bestNodes.pop();\n        }\n      }\n\n      for (i = 0; i < dimensions.length; i += 1) {\n        if (i === node.dimension) {\n          linearPoint[dimensions[i]] = point[dimensions[i]];\n        } else {\n          linearPoint[dimensions[i]] = node.obj[dimensions[i]];\n        }\n      }\n\n      linearDistance = metric(linearPoint, node.obj);\n\n      if (node.right === null && node.left === null) {\n        if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n          saveNode(node, ownDistance);\n        }\n        return;\n      }\n\n      if (node.right === null) {\n        bestChild = node.left;\n      } else if (node.left === null) {\n        bestChild = node.right;\n      } else {\n        if (point[dimension] < node.obj[dimension]) {\n          bestChild = node.left;\n        } else {\n          bestChild = node.right;\n        }\n      }\n\n      nearestSearch(bestChild);\n\n      if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n        saveNode(node, ownDistance);\n      }\n\n      if (\n        bestNodes.size() < maxNodes ||\n        Math.abs(linearDistance) < bestNodes.peek()[1]\n      ) {\n        if (bestChild === node.left) {\n          otherChild = node.right;\n        } else {\n          otherChild = node.left;\n        }\n        if (otherChild !== null) {\n          nearestSearch(otherChild);\n        }\n      }\n    }\n\n    if (maxDistance) {\n      for (i = 0; i < maxNodes; i += 1) {\n        bestNodes.push([null, maxDistance]);\n      }\n    }\n\n    if (this.root) {\n      nearestSearch(this.root);\n    }\n\n    const result = [];\n    for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {\n      if (bestNodes.content[i][0]) {\n        result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);\n      }\n    }\n    return result;\n  }\n}\n\nfunction toJSONImpl(src) {\n  const dest = new Node(src.obj, src.dimension, null);\n  if (src.left) dest.left = toJSONImpl(src.left);\n  if (src.right) dest.right = toJSONImpl(src.right);\n  return dest;\n}\n\nfunction buildTree(points, depth, parent, dimensions) {\n  const dim = depth % dimensions.length;\n\n  if (points.length === 0) {\n    return null;\n  }\n  if (points.length === 1) {\n    return new Node(points[0], dim, parent);\n  }\n\n  points.sort((a, b) => a[dimensions[dim]] - b[dimensions[dim]]);\n\n  const median = Math.floor(points.length / 2);\n  const node = new Node(points[median], dim, parent);\n  node.left = buildTree(points.slice(0, median), depth + 1, node, dimensions);\n  node.right = buildTree(points.slice(median + 1), depth + 1, node, dimensions);\n\n  return node;\n}\n\nfunction restoreParent(root) {\n  if (root.left) {\n    root.left.parent = root;\n    restoreParent(root.left);\n  }\n\n  if (root.right) {\n    root.right.parent = root;\n    restoreParent(root.right);\n  }\n}\n\n// Binary heap implementation from:\n// http://eloquentjavascript.net/appendix2.html\nclass BinaryHeap {\n  constructor(scoreFunction) {\n    this.content = [];\n    this.scoreFunction = scoreFunction;\n  }\n\n  push(element) {\n    // Add the new element to the end of the array.\n    this.content.push(element);\n    // Allow it to bubble up.\n    this.bubbleUp(this.content.length - 1);\n  }\n\n  pop() {\n    // Store the first element so we can return it later.\n    var result = this.content[0];\n    // Get the element at the end of the array.\n    var end = this.content.pop();\n    // If there are any elements left, put the end element at the\n    // start, and let it sink down.\n    if (this.content.length > 0) {\n      this.content[0] = end;\n      this.sinkDown(0);\n    }\n    return result;\n  }\n\n  peek() {\n    return this.content[0];\n  }\n\n  size() {\n    return this.content.length;\n  }\n\n  bubbleUp(n) {\n    // Fetch the element that has to be moved.\n    var element = this.content[n];\n    // When at 0, an element can not go up any further.\n    while (n > 0) {\n      // Compute the parent element's index, and fetch it.\n      const parentN = Math.floor((n + 1) / 2) - 1;\n      const parent = this.content[parentN];\n      // Swap the elements if the parent is greater.\n      if (this.scoreFunction(element) < this.scoreFunction(parent)) {\n        this.content[parentN] = element;\n        this.content[n] = parent;\n        // Update 'n' to continue at the new position.\n        n = parentN;\n      } else {\n        // Found a parent that is less, no need to move it further.\n        break;\n      }\n    }\n  }\n\n  sinkDown(n) {\n    // Look up the target element and its score.\n    var length = this.content.length;\n    var element = this.content[n];\n    var elemScore = this.scoreFunction(element);\n\n    while (true) {\n      // Compute the indices of the child elements.\n      var child2N = (n + 1) * 2;\n      var child1N = child2N - 1;\n      // This is used to store the new position of the element,\n      // if any.\n      var swap = null;\n      // If the first child exists (is inside the array)...\n      if (child1N < length) {\n        // Look it up and compute its score.\n        var child1 = this.content[child1N];\n        var child1Score = this.scoreFunction(child1);\n        // If the score is less than our element's, we need to swap.\n        if (child1Score < elemScore) {\n          swap = child1N;\n        }\n      }\n      // Do the same checks for the other child.\n      if (child2N < length) {\n        var child2 = this.content[child2N];\n        var child2Score = this.scoreFunction(child2);\n        if (child2Score < (swap === null ? elemScore : child1Score)) {\n          swap = child2N;\n        }\n      }\n\n      // If the element needs to be moved, swap it, and continue.\n      if (swap !== null) {\n        this.content[n] = this.content[swap];\n        this.content[swap] = element;\n        n = swap;\n      } else {\n        // Otherwise, we are done.\n        break;\n      }\n    }\n  }\n}\n","import { euclidean as euclideanDistance } from 'ml-distance-euclidean';\n\nimport KDTree from './KDTree';\n\nexport default class KNN {\n  /**\n   * @param {Array} dataset\n   * @param {Array} labels\n   * @param {object} options\n   * @param {number} [options.k=numberOfClasses + 1] - Number of neighbors to classify.\n   * @param {function} [options.distance=euclideanDistance] - Distance function that takes two parameters.\n   */\n  constructor(dataset, labels, options = {}) {\n    if (dataset === true) {\n      const model = labels;\n      this.kdTree = new KDTree(model.kdTree, options);\n      this.k = model.k;\n      this.classes = new Set(model.classes);\n      this.isEuclidean = model.isEuclidean;\n      return;\n    }\n\n    const classes = new Set(labels);\n\n    const { distance = euclideanDistance, k = classes.size + 1 } = options;\n\n    const points = new Array(dataset.length);\n    for (var i = 0; i < points.length; ++i) {\n      points[i] = dataset[i].slice();\n    }\n\n    for (i = 0; i < labels.length; ++i) {\n      points[i].push(labels[i]);\n    }\n\n    this.kdTree = new KDTree(points, distance);\n    this.k = k;\n    this.classes = classes;\n    this.isEuclidean = distance === euclideanDistance;\n  }\n\n  /**\n   * Create a new KNN instance with the given model.\n   * @param {object} model\n   * @param {function} distance=euclideanDistance - distance function must be provided if the model wasn't trained with euclidean distance.\n   * @return {KNN}\n   */\n  static load(model, distance = euclideanDistance) {\n    if (model.name !== 'KNN') {\n      throw new Error(`invalid model: ${model.name}`);\n    }\n    if (!model.isEuclidean && distance === euclideanDistance) {\n      throw new Error(\n        'a custom distance function was used to create the model. Please provide it again'\n      );\n    }\n    if (model.isEuclidean && distance !== euclideanDistance) {\n      throw new Error(\n        'the model was created with the default distance function. Do not load it with another one'\n      );\n    }\n    return new KNN(true, model, distance);\n  }\n\n  /**\n   * Return a JSON containing the kd-tree model.\n   * @return {object} JSON KNN model.\n   */\n  toJSON() {\n    return {\n      name: 'KNN',\n      kdTree: this.kdTree,\n      k: this.k,\n      classes: Array.from(this.classes),\n      isEuclidean: this.isEuclidean\n    };\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Array} dataset\n   * @return {Array} predictions\n   */\n  predict(dataset) {\n    if (Array.isArray(dataset)) {\n      if (typeof dataset[0] === 'number') {\n        return getSinglePrediction(this, dataset);\n      } else if (\n        Array.isArray(dataset[0]) &&\n        typeof dataset[0][0] === 'number'\n      ) {\n        const predictions = new Array(dataset.length);\n        for (var i = 0; i < dataset.length; i++) {\n          predictions[i] = getSinglePrediction(this, dataset[i]);\n        }\n        return predictions;\n      }\n    }\n    throw new TypeError('dataset to predict must be an array or a matrix');\n  }\n}\n\nfunction getSinglePrediction(knn, currentCase) {\n  var nearestPoints = knn.kdTree.nearest(currentCase, knn.k);\n  var pointsPerClass = {};\n  var predictedClass = -1;\n  var maxPoints = -1;\n  var lastElement = nearestPoints[0][0].length - 1;\n\n  for (var element of knn.classes) {\n    pointsPerClass[element] = 0;\n  }\n\n  for (var i = 0; i < nearestPoints.length; ++i) {\n    var currentClass = nearestPoints[i][0][lastElement];\n    var currentPoints = ++pointsPerClass[currentClass];\n    if (currentPoints > maxPoints) {\n      predictedClass = currentClass;\n      maxPoints = currentPoints;\n    }\n  }\n\n  return predictedClass;\n}\n","import Matrix from 'ml-matrix';\n\n/**\n * @private\n * Function that given vector, returns its norm\n * @param {Vector} X\n * @return {number} Norm of the vector\n */\nexport function norm(X) {\n  return Math.sqrt(\n    X.clone()\n      .apply(pow2array)\n      .sum(),\n  );\n}\n\n/**\n * @private\n * Function that pow 2 each element of a Matrix or a Vector,\n * used in the apply method of the Matrix object\n * @param {number} i - index i.\n * @param {number} j - index j.\n * @return {Matrix} The Matrix object modified at the index i, j.\n * */\nexport function pow2array(i, j) {\n  this.set(i, j, this.get(i, j) ** 2);\n}\n\n/**\n * @private\n * Function that normalize the dataset and return the means and\n * standard deviation of each feature.\n * @param {Matrix} dataset\n * @return {object} dataset normalized, means and standard deviations\n */\nexport function featureNormalize(dataset) {\n  let means = dataset.mean('column');\n  let std = dataset.standardDeviation('column', {\n    mean: means,\n    unbiased: true,\n  });\n  let result = Matrix.checkMatrix(dataset).subRowVector(means);\n  return { result: result.divRowVector(std), means: means, std: std };\n}\n\n/**\n * @private\n * Function that initialize an array of matrices.\n * @param {Array} array\n * @param {boolean} isMatrix\n * @return {Array} array with the matrices initialized.\n */\nexport function initializeMatrices(array, isMatrix) {\n  if (isMatrix) {\n    for (let i = 0; i < array.length; ++i) {\n      for (let j = 0; j < array[i].length; ++j) {\n        let elem = array[i][j];\n        array[i][j] = elem !== null ? new Matrix(array[i][j]) : undefined;\n      }\n    }\n  } else {\n    for (let i = 0; i < array.length; ++i) {\n      array[i] = new Matrix(array[i]);\n    }\n  }\n\n  return array;\n}\n","import Matrix from 'ml-matrix';\n\nimport * as Utils from './util/utils';\n\n/**\n * @class PLS\n */\nexport class PLS {\n  /**\n   * Constructor for Partial Least Squares (PLS)\n   * @param {object} options\n   * @param {number} [options.latentVectors] - Number of latent vector to get (if the algorithm doesn't find a good model below the tolerance)\n   * @param {number} [options.tolerance=1e-5]\n   * @param {boolean} [options.scale=true] - rescale dataset using mean.\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.meanX = model.meanX;\n      this.stdDevX = model.stdDevX;\n      this.meanY = model.meanY;\n      this.stdDevY = model.stdDevY;\n      this.PBQ = Matrix.checkMatrix(model.PBQ);\n      this.R2X = model.R2X;\n      this.scale = model.scale;\n      this.scaleMethod = model.scaleMethod;\n      this.tolerance = model.tolerance;\n    } else {\n      let { tolerance = 1e-5, scale = true } = options;\n      this.tolerance = tolerance;\n      this.scale = scale;\n      this.latentVectors = options.latentVectors;\n    }\n  }\n\n  /**\n   * Fits the model with the given data and predictions, in this function is calculated the\n   * following outputs:\n   *\n   * T - Score matrix of X\n   * P - Loading matrix of X\n   * U - Score matrix of Y\n   * Q - Loading matrix of Y\n   * B - Matrix of regression coefficient\n   * W - Weight matrix of X\n   *\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    if (trainingSet.length !== trainingValues.length) {\n      throw new RangeError(\n        'The number of X rows must be equal to the number of Y rows',\n      );\n    }\n\n    this.meanX = trainingSet.mean('column');\n    this.stdDevX = trainingSet.standardDeviation('column', {\n      mean: this.meanX,\n      unbiased: true,\n    });\n    this.meanY = trainingValues.mean('column');\n    this.stdDevY = trainingValues.standardDeviation('column', {\n      mean: this.meanY,\n      unbiased: true,\n    });\n\n    if (this.scale) {\n      trainingSet = trainingSet\n        .clone()\n        .subRowVector(this.meanX)\n        .divRowVector(this.stdDevX);\n      trainingValues = trainingValues\n        .clone()\n        .subRowVector(this.meanY)\n        .divRowVector(this.stdDevY);\n    }\n\n    if (this.latentVectors === undefined) {\n      this.latentVectors = Math.min(trainingSet.rows - 1, trainingSet.columns);\n    }\n\n    let rx = trainingSet.rows;\n    let cx = trainingSet.columns;\n    let ry = trainingValues.rows;\n    let cy = trainingValues.columns;\n\n    let ssqXcal = trainingSet\n      .clone()\n      .mul(trainingSet)\n      .sum(); // for the r²\n    let sumOfSquaresY = trainingValues\n      .clone()\n      .mul(trainingValues)\n      .sum();\n\n    let tolerance = this.tolerance;\n    let n = this.latentVectors;\n    let T = Matrix.zeros(rx, n);\n    let P = Matrix.zeros(cx, n);\n    let U = Matrix.zeros(ry, n);\n    let Q = Matrix.zeros(cy, n);\n    let B = Matrix.zeros(n, n);\n    let W = P.clone();\n    let k = 0;\n    let t;\n    let w;\n    let q;\n    let p;\n\n    while (Utils.norm(trainingValues) > tolerance && k < n) {\n      let transposeX = trainingSet.transpose();\n      let transposeY = trainingValues.transpose();\n\n      let tIndex = maxSumColIndex(trainingSet.clone().mul(trainingSet));\n      let uIndex = maxSumColIndex(trainingValues.clone().mul(trainingValues));\n\n      let t1 = trainingSet.getColumnVector(tIndex);\n      let u = trainingValues.getColumnVector(uIndex);\n      t = Matrix.zeros(rx, 1);\n\n      while (Utils.norm(t1.clone().sub(t)) > tolerance) {\n        w = transposeX.mmul(u);\n        w.div(Utils.norm(w));\n        t = t1;\n        t1 = trainingSet.mmul(w);\n        q = transposeY.mmul(t1);\n        q.div(Utils.norm(q));\n        u = trainingValues.mmul(q);\n      }\n\n      t = t1;\n      let num = transposeX.mmul(t);\n      let den = t\n        .transpose()\n        .mmul(t)\n        .get(0, 0);\n      p = num.div(den);\n      let pnorm = Utils.norm(p);\n      p.div(pnorm);\n      t.mul(pnorm);\n      w.mul(pnorm);\n\n      num = u.transpose().mmul(t);\n      den = t\n        .transpose()\n        .mmul(t)\n        .get(0, 0);\n      let b = num.div(den).get(0, 0);\n      trainingSet.sub(t.mmul(p.transpose()));\n      trainingValues.sub(\n        t\n          .clone()\n          .mul(b)\n          .mmul(q.transpose()),\n      );\n\n      T.setColumn(k, t);\n      P.setColumn(k, p);\n      U.setColumn(k, u);\n      Q.setColumn(k, q);\n      W.setColumn(k, w);\n\n      B.set(k, k, b);\n      k++;\n    }\n\n    k--;\n    T = T.subMatrix(0, T.rows - 1, 0, k);\n    P = P.subMatrix(0, P.rows - 1, 0, k);\n    U = U.subMatrix(0, U.rows - 1, 0, k);\n    Q = Q.subMatrix(0, Q.rows - 1, 0, k);\n    W = W.subMatrix(0, W.rows - 1, 0, k);\n    B = B.subMatrix(0, k, 0, k);\n\n    this.ssqYcal = sumOfSquaresY;\n    this.E = trainingSet;\n    this.F = trainingValues;\n    this.T = T;\n    this.P = P;\n    this.U = U;\n    this.Q = Q;\n    this.W = W;\n    this.B = B;\n    this.PBQ = P.mmul(B).mmul(Q.transpose());\n    this.R2X = t\n      .transpose()\n      .mmul(t)\n      .mmul(p.transpose().mmul(p))\n      .div(ssqXcal)\n      .get(0, 0);\n  }\n\n  /**\n   * Predicts the behavior of the given dataset.\n   * @param {Matrix|Array} dataset - data to be predicted.\n   * @return {Matrix} - predictions of each element of the dataset.\n   */\n  predict(dataset) {\n    let X = Matrix.checkMatrix(dataset);\n    if (this.scale) {\n      X = X.subRowVector(this.meanX).divRowVector(this.stdDevX);\n    }\n    let Y = X.mmul(this.PBQ);\n    Y = Y.mulRowVector(this.stdDevY).addRowVector(this.meanY);\n    return Y;\n  }\n\n  /**\n   * Returns the explained variance on training of the PLS model\n   * @return {number}\n   */\n  getExplainedVariance() {\n    return this.R2X;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      name: 'PLS',\n      R2X: this.R2X,\n      meanX: this.meanX,\n      stdDevX: this.stdDevX,\n      meanY: this.meanY,\n      stdDevY: this.stdDevY,\n      PBQ: this.PBQ,\n      tolerance: this.tolerance,\n      scale: this.scale,\n    };\n  }\n\n  /**\n   * Load a PLS model from a JSON Object\n   * @param {object} model\n   * @return {PLS} - PLS object from the given model\n   */\n  static load(model) {\n    if (model.name !== 'PLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n    return new PLS(true, model);\n  }\n}\n\n/**\n * @private\n * Function that returns the index where the sum of each\n * column vector is maximum.\n * @param {Matrix} data\n * @return {number} index of the maximum\n */\nfunction maxSumColIndex(data) {\n  return Matrix.rowVector(data.sum('column')).maxIndex()[0];\n}\n","import { Matrix, SingularValueDecomposition, inverse } from 'ml-matrix';\n\nimport { initializeMatrices } from './util/utils';\n\n/**\n * @class KOPLS\n */\nexport class KOPLS {\n  /**\n   * Constructor for Kernel-based Orthogonal Projections to Latent Structures (K-OPLS)\n   * @param {object} options\n   * @param {number} [options.predictiveComponents] - Number of predictive components to use.\n   * @param {number} [options.orthogonalComponents] - Number of Y-Orthogonal components.\n   * @param {Kernel} [options.kernel] - Kernel object to apply, see [ml-kernel](https://github.com/mljs/kernel).\n   * @param {object} model - for load purposes.\n   */\n  constructor(options, model) {\n    if (options === true) {\n      this.trainingSet = new Matrix(model.trainingSet);\n      this.YLoadingMat = new Matrix(model.YLoadingMat);\n      this.SigmaPow = new Matrix(model.SigmaPow);\n      this.YScoreMat = new Matrix(model.YScoreMat);\n      this.predScoreMat = initializeMatrices(model.predScoreMat, false);\n      this.YOrthLoadingVec = initializeMatrices(model.YOrthLoadingVec, false);\n      this.YOrthEigen = model.YOrthEigen;\n      this.YOrthScoreMat = initializeMatrices(model.YOrthScoreMat, false);\n      this.toNorm = initializeMatrices(model.toNorm, false);\n      this.TURegressionCoeff = initializeMatrices(\n        model.TURegressionCoeff,\n        false,\n      );\n      this.kernelX = initializeMatrices(model.kernelX, true);\n      this.kernel = model.kernel;\n      this.orthogonalComp = model.orthogonalComp;\n      this.predictiveComp = model.predictiveComp;\n    } else {\n      if (options.predictiveComponents === undefined) {\n        throw new RangeError('no predictive components found!');\n      }\n      if (options.orthogonalComponents === undefined) {\n        throw new RangeError('no orthogonal components found!');\n      }\n      if (options.kernel === undefined) {\n        throw new RangeError('no kernel found!');\n      }\n\n      this.orthogonalComp = options.orthogonalComponents;\n      this.predictiveComp = options.predictiveComponents;\n      this.kernel = options.kernel;\n    }\n  }\n\n  /**\n   * Train the K-OPLS model with the given training set and labels.\n   * @param {Matrix|Array} trainingSet\n   * @param {Matrix|Array} trainingValues\n   */\n  train(trainingSet, trainingValues) {\n    trainingSet = Matrix.checkMatrix(trainingSet);\n    trainingValues = Matrix.checkMatrix(trainingValues);\n\n    // to save and compute kernel with the prediction dataset.\n    this.trainingSet = trainingSet.clone();\n\n    let kernelX = this.kernel.compute(trainingSet);\n\n    let Identity = Matrix.eye(kernelX.rows, kernelX.rows, 1);\n    let temp = kernelX;\n    kernelX = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      kernelX[i] = new Array(this.orthogonalComp + 1);\n    }\n    kernelX[0][0] = temp;\n\n    let result = new SingularValueDecomposition(\n      trainingValues\n        .transpose()\n        .mmul(kernelX[0][0])\n        .mmul(trainingValues),\n      {\n        computeLeftSingularVectors: true,\n        computeRightSingularVectors: false,\n      },\n    );\n    let YLoadingMat = result.leftSingularVectors;\n    let Sigma = result.diagonalMatrix;\n\n    YLoadingMat = YLoadingMat.subMatrix(\n      0,\n      YLoadingMat.rows - 1,\n      0,\n      this.predictiveComp - 1,\n    );\n    Sigma = Sigma.subMatrix(\n      0,\n      this.predictiveComp - 1,\n      0,\n      this.predictiveComp - 1,\n    );\n\n    let YScoreMat = trainingValues.mmul(YLoadingMat);\n\n    let predScoreMat = new Array(this.orthogonalComp + 1);\n    let TURegressionCoeff = new Array(this.orthogonalComp + 1);\n    let YOrthScoreMat = new Array(this.orthogonalComp);\n    let YOrthLoadingVec = new Array(this.orthogonalComp);\n    let YOrthEigen = new Array(this.orthogonalComp);\n    let YOrthScoreNorm = new Array(this.orthogonalComp);\n\n    let SigmaPow = Matrix.pow(Sigma, -0.5);\n    // to avoid errors, check infinity\n    SigmaPow.apply(function(i, j) {\n      if (this.get(i, j) === Infinity) {\n        this.set(i, j, 0);\n      }\n    });\n\n    for (let i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = kernelX[0][i]\n        .transpose()\n        .mmul(YScoreMat)\n        .mmul(SigmaPow);\n\n      let TpiPrime = predScoreMat[i].transpose();\n      TURegressionCoeff[i] = inverse(TpiPrime.mmul(predScoreMat[i]))\n        .mmul(TpiPrime)\n        .mmul(YScoreMat);\n\n      result = new SingularValueDecomposition(\n        TpiPrime.mmul(\n          Matrix.sub(kernelX[i][i], predScoreMat[i].mmul(TpiPrime)),\n        ).mmul(predScoreMat[i]),\n        {\n          computeLeftSingularVectors: true,\n          computeRightSingularVectors: false,\n        },\n      );\n      let CoTemp = result.leftSingularVectors;\n      let SoTemp = result.diagonalMatrix;\n\n      YOrthLoadingVec[i] = CoTemp.subMatrix(0, CoTemp.rows - 1, 0, 0);\n      YOrthEigen[i] = SoTemp.get(0, 0);\n\n      YOrthScoreMat[i] = Matrix.sub(\n        kernelX[i][i],\n        predScoreMat[i].mmul(TpiPrime),\n      )\n        .mmul(predScoreMat[i])\n        .mmul(YOrthLoadingVec[i])\n        .mul(Math.pow(YOrthEigen[i], -0.5));\n\n      let toiPrime = YOrthScoreMat[i].transpose();\n      YOrthScoreNorm[i] = Matrix.sqrt(toiPrime.mmul(YOrthScoreMat[i]));\n\n      YOrthScoreMat[i] = YOrthScoreMat[i].divRowVector(YOrthScoreNorm[i]);\n\n      let ITo = Matrix.sub(\n        Identity,\n        YOrthScoreMat[i].mmul(YOrthScoreMat[i].transpose()),\n      );\n\n      kernelX[0][i + 1] = kernelX[0][i].mmul(ITo);\n      kernelX[i + 1][i + 1] = ITo.mmul(kernelX[i][i]).mmul(ITo);\n    }\n\n    let lastScoreMat = (predScoreMat[this.orthogonalComp] = kernelX[0][\n      this.orthogonalComp\n    ]\n      .transpose()\n      .mmul(YScoreMat)\n      .mmul(SigmaPow));\n\n    let lastTpPrime = lastScoreMat.transpose();\n    TURegressionCoeff[this.orthogonalComp] = inverse(\n      lastTpPrime.mmul(lastScoreMat),\n    )\n      .mmul(lastTpPrime)\n      .mmul(YScoreMat);\n\n    this.YLoadingMat = YLoadingMat;\n    this.SigmaPow = SigmaPow;\n    this.YScoreMat = YScoreMat;\n    this.predScoreMat = predScoreMat;\n    this.YOrthLoadingVec = YOrthLoadingVec;\n    this.YOrthEigen = YOrthEigen;\n    this.YOrthScoreMat = YOrthScoreMat;\n    this.toNorm = YOrthScoreNorm;\n    this.TURegressionCoeff = TURegressionCoeff;\n    this.kernelX = kernelX;\n  }\n\n  /**\n   * Predicts the output given the matrix to predict.\n   * @param {Matrix|Array} toPredict\n   * @return {{y: Matrix, predScoreMat: Array<Matrix>, predYOrthVectors: Array<Matrix>}} predictions\n   */\n  predict(toPredict) {\n    let KTestTrain = this.kernel.compute(toPredict, this.trainingSet);\n\n    let temp = KTestTrain;\n    KTestTrain = new Array(this.orthogonalComp + 1);\n    for (let i = 0; i < this.orthogonalComp + 1; i++) {\n      KTestTrain[i] = new Array(this.orthogonalComp + 1);\n    }\n    KTestTrain[0][0] = temp;\n\n    let YOrthScoreVector = new Array(this.orthogonalComp);\n    let predScoreMat = new Array(this.orthogonalComp);\n\n    let i;\n    for (i = 0; i < this.orthogonalComp; ++i) {\n      predScoreMat[i] = KTestTrain[i][0]\n        .mmul(this.YScoreMat)\n        .mmul(this.SigmaPow);\n\n      YOrthScoreVector[i] = Matrix.sub(\n        KTestTrain[i][i],\n        predScoreMat[i].mmul(this.predScoreMat[i].transpose()),\n      )\n        .mmul(this.predScoreMat[i])\n        .mmul(this.YOrthLoadingVec[i])\n        .mul(Math.pow(this.YOrthEigen[i], -0.5));\n\n      YOrthScoreVector[i] = YOrthScoreVector[i].divRowVector(this.toNorm[i]);\n\n      let scoreMatPrime = this.YOrthScoreMat[i].transpose();\n      KTestTrain[i + 1][0] = Matrix.sub(\n        KTestTrain[i][0],\n        YOrthScoreVector[i]\n          .mmul(scoreMatPrime)\n          .mmul(this.kernelX[0][i].transpose()),\n      );\n\n      let p1 = Matrix.sub(\n        KTestTrain[i][0],\n        KTestTrain[i][i].mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime),\n      );\n      let p2 = YOrthScoreVector[i].mmul(scoreMatPrime).mmul(this.kernelX[i][i]);\n      let p3 = p2.mmul(this.YOrthScoreMat[i]).mmul(scoreMatPrime);\n\n      KTestTrain[i + 1][i + 1] = p1.sub(p2).add(p3);\n    }\n\n    predScoreMat[i] = KTestTrain[i][0].mmul(this.YScoreMat).mmul(this.SigmaPow);\n    let prediction = predScoreMat[i]\n      .mmul(this.TURegressionCoeff[i])\n      .mmul(this.YLoadingMat.transpose());\n\n    return {\n      prediction: prediction,\n      predScoreMat: predScoreMat,\n      predYOrthVectors: YOrthScoreVector,\n    };\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} - Current model.\n   */\n  toJSON() {\n    return {\n      name: 'K-OPLS',\n      YLoadingMat: this.YLoadingMat,\n      SigmaPow: this.SigmaPow,\n      YScoreMat: this.YScoreMat,\n      predScoreMat: this.predScoreMat,\n      YOrthLoadingVec: this.YOrthLoadingVec,\n      YOrthEigen: this.YOrthEigen,\n      YOrthScoreMat: this.YOrthScoreMat,\n      toNorm: this.toNorm,\n      TURegressionCoeff: this.TURegressionCoeff,\n      kernelX: this.kernelX,\n      trainingSet: this.trainingSet,\n      orthogonalComp: this.orthogonalComp,\n      predictiveComp: this.predictiveComp,\n    };\n  }\n\n  /**\n   * Load a K-OPLS with the given model.\n   * @param {object} model\n   * @param {Kernel} kernel - kernel used on the model, see [ml-kernel](https://github.com/mljs/kernel).\n   * @return {KOPLS}\n   */\n  static load(model, kernel) {\n    if (model.name !== 'K-OPLS') {\n      throw new RangeError(`Invalid model: ${model.name}`);\n    }\n\n    if (!kernel) {\n      throw new RangeError('You must provide a kernel for the model!');\n    }\n\n    model.kernel = kernel;\n    return new KOPLS(true, model);\n  }\n}\n","/**\n *  Constructs a confusion matrix\n * @class ConfusionMatrix\n * @example\n * const CM = new ConfusionMatrix([[13, 2], [10, 5]], ['cat', 'dog'])\n * @param {Array<Array<number>>} matrix - The confusion matrix, a 2D Array. Rows represent the actual label and columns\n *     the predicted label.\n * @param {Array<any>} labels - Labels of the confusion matrix, a 1D Array\n */\nexport default class ConfusionMatrix {\n  constructor(matrix, labels) {\n    if (matrix.length !== matrix[0].length) {\n      throw new Error('Confusion matrix must be square');\n    }\n    if (labels.length !== matrix.length) {\n      throw new Error(\n        'Confusion matrix and labels should have the same length',\n      );\n    }\n    this.labels = labels;\n    this.matrix = matrix;\n  }\n\n  /**\n   * Construct confusion matrix from the predicted and actual labels (classes). Be sure to provide the arguments in\n   * the correct order!\n   * @param {Array<any>} actual  - The predicted labels of the classification\n   * @param {Array<any>} predicted     - The actual labels of the classification. Has to be of same length as\n   *     predicted.\n   * @param {object} [options] - Additional options\n   * @param {Array<any>} [options.labels] - The list of labels that should be used. If not provided the distinct set\n   *     of labels present in predicted and actual is used. Labels are compared using the strict equality operator\n   *     '==='\n   * @return {ConfusionMatrix} - Confusion matrix\n   */\n  static fromLabels(actual, predicted, options = {}) {\n    if (predicted.length !== actual.length) {\n      throw new Error('predicted and actual must have the same length');\n    }\n    let distinctLabels;\n    if (options.labels) {\n      distinctLabels = new Set(options.labels);\n    } else {\n      distinctLabels = new Set([...actual, ...predicted]);\n    }\n    distinctLabels = Array.from(distinctLabels);\n    if (options.sort) {\n      distinctLabels.sort(options.sort);\n    }\n\n    // Create confusion matrix and fill with 0's\n    const matrix = Array.from({ length: distinctLabels.length });\n    for (let i = 0; i < matrix.length; i++) {\n      matrix[i] = new Array(matrix.length);\n      matrix[i].fill(0);\n    }\n\n    for (let i = 0; i < predicted.length; i++) {\n      const actualIdx = distinctLabels.indexOf(actual[i]);\n      const predictedIdx = distinctLabels.indexOf(predicted[i]);\n      if (actualIdx >= 0 && predictedIdx >= 0) {\n        matrix[actualIdx][predictedIdx]++;\n      }\n    }\n\n    return new ConfusionMatrix(matrix, distinctLabels);\n  }\n\n  /**\n   * Get the confusion matrix\n   * @return {Array<Array<number> >}\n   */\n  getMatrix() {\n    return this.matrix;\n  }\n\n  getLabels() {\n    return this.labels;\n  }\n\n  /**\n   * Get the total number of samples\n   * @return {number}\n   */\n  getTotalCount() {\n    let predicted = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      for (let j = 0; j < this.matrix.length; j++) {\n        predicted += this.matrix[i][j];\n      }\n    }\n    return predicted;\n  }\n\n  /**\n   * Get the total number of true predictions\n   * @return {number}\n   */\n  getTrueCount() {\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      count += this.matrix[i][i];\n    }\n    return count;\n  }\n\n  /**\n   * Get the total number of false predictions.\n   * @return {number}\n   */\n  getFalseCount() {\n    return this.getTotalCount() - this.getTrueCount();\n  }\n\n  /**\n   * Get the number of true positive predictions.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getTruePositiveCount(label) {\n    const index = this.getIndex(label);\n    return this.matrix[index][index];\n  }\n\n  /**\n   * Get the number of true negative predictions\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getTrueNegativeCount(label) {\n    const index = this.getIndex(label);\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      for (let j = 0; j < this.matrix.length; j++) {\n        if (i !== index && j !== index) {\n          count += this.matrix[i][j];\n        }\n      }\n    }\n    return count;\n  }\n\n  /**\n   * Get the number of false positive predictions.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalsePositiveCount(label) {\n    const index = this.getIndex(label);\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      if (i !== index) {\n        count += this.matrix[i][index];\n      }\n    }\n    return count;\n  }\n\n  /**\n   * Get the number of false negative predictions.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseNegativeCount(label) {\n    const index = this.getIndex(label);\n    let count = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      if (i !== index) {\n        count += this.matrix[index][i];\n      }\n    }\n    return count;\n  }\n\n  /**\n   * Get the number of real positive samples.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getPositiveCount(label) {\n    return this.getTruePositiveCount(label) + this.getFalseNegativeCount(label);\n  }\n\n  /**\n   * Get the number of real negative samples.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getNegativeCount(label) {\n    return this.getTrueNegativeCount(label) + this.getFalsePositiveCount(label);\n  }\n\n  /**\n   * Get the index in the confusion matrix that corresponds to the given label\n   * @param {any} label - The label to search for\n   * @throws if the label is not found\n   * @return {number}\n   */\n  getIndex(label) {\n    const index = this.labels.indexOf(label);\n    if (index === -1) throw new Error('The label does not exist');\n    return index;\n  }\n\n  /**\n   * Get the true positive rate a.k.a. sensitivity. Computes the ratio between the number of true positive predictions and the total number of positive samples.\n   * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number} - The true positive rate [0-1]\n   */\n  getTruePositiveRate(label) {\n    return this.getTruePositiveCount(label) / this.getPositiveCount(label);\n  }\n\n  /**\n   * Get the true negative rate a.k.a. specificity. Computes the ration between the number of true negative predictions and the total number of negative samples.\n   * {@link https://en.wikipedia.org/wiki/Sensitivity_and_specificity}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getTrueNegativeRate(label) {\n    return this.getTrueNegativeCount(label) / this.getNegativeCount(label);\n  }\n\n  /**\n   * Get the positive predictive value a.k.a. precision. Computes TP / (TP + FP)\n   * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getPositivePredictiveValue(label) {\n    const TP = this.getTruePositiveCount(label);\n    return TP / (TP + this.getFalsePositiveCount(label));\n  }\n\n  /**\n   * Negative predictive value\n   * {@link https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getNegativePredictiveValue(label) {\n    const TN = this.getTrueNegativeCount(label);\n    return TN / (TN + this.getFalseNegativeCount(label));\n  }\n\n  /**\n   * False negative rate a.k.a. miss rate.\n   * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseNegativeRate(label) {\n    return 1 - this.getTruePositiveRate(label);\n  }\n\n  /**\n   * False positive rate a.k.a. fall-out rate.\n   * {@link https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#False_positive_and_false_negative_rates}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalsePositiveRate(label) {\n    return 1 - this.getTrueNegativeRate(label);\n  }\n\n  /**\n   * False discovery rate (FDR)\n   * {@link https://en.wikipedia.org/wiki/False_discovery_rate}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseDiscoveryRate(label) {\n    const FP = this.getFalsePositiveCount(label);\n    return FP / (FP + this.getTruePositiveCount(label));\n  }\n\n  /**\n   * False omission rate (FOR)\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getFalseOmissionRate(label) {\n    const FN = this.getFalseNegativeCount(label);\n    return FN / (FN + this.getTruePositiveCount(label));\n  }\n\n  /**\n   * F1 score\n   * {@link https://en.wikipedia.org/wiki/F1_score}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getF1Score(label) {\n    const TP = this.getTruePositiveCount(label);\n    return (\n      (2 * TP) /\n      (2 * TP +\n        this.getFalsePositiveCount(label) +\n        this.getFalseNegativeCount(label))\n    );\n  }\n\n  /**\n   * Matthews correlation coefficient (MCC)\n   * {@link https://en.wikipedia.org/wiki/Matthews_correlation_coefficient}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getMatthewsCorrelationCoefficient(label) {\n    const TP = this.getTruePositiveCount(label);\n    const TN = this.getTrueNegativeCount(label);\n    const FP = this.getFalsePositiveCount(label);\n    const FN = this.getFalseNegativeCount(label);\n    return (\n      (TP * TN - FP * FN) /\n      Math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))\n    );\n  }\n\n  /**\n   * Informedness\n   * {@link https://en.wikipedia.org/wiki/Youden%27s_J_statistic}\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getInformedness(label) {\n    return (\n      this.getTruePositiveRate(label) + this.getTrueNegativeRate(label) - 1\n    );\n  }\n\n  /**\n   * Markedness\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {number}\n   */\n  getMarkedness(label) {\n    return (\n      this.getPositivePredictiveValue(label) +\n      this.getNegativePredictiveValue(label) -\n      1\n    );\n  }\n\n  /**\n   * Get the confusion table.\n   * @param {any} label - The label that should be considered \"positive\"\n   * @return {Array<Array<number> >} - The 2x2 confusion table. [[TP, FN], [FP, TN]]\n   */\n  getConfusionTable(label) {\n    return [\n      [this.getTruePositiveCount(label), this.getFalseNegativeCount(label)],\n      [this.getFalsePositiveCount(label), this.getTrueNegativeCount(label)],\n    ];\n  }\n\n  /**\n   * Get total accuracy.\n   * @return {number} - The ratio between the number of true predictions and total number of classifications ([0-1])\n   */\n  getAccuracy() {\n    let correct = 0;\n    let incorrect = 0;\n    for (let i = 0; i < this.matrix.length; i++) {\n      for (let j = 0; j < this.matrix.length; j++) {\n        if (i === j) correct += this.matrix[i][j];\n        else incorrect += this.matrix[i][j];\n      }\n    }\n    return correct / (correct + incorrect);\n  }\n\n  /**\n   * Returns the element in the confusion matrix that corresponds to the given actual and predicted labels.\n   * @param {any} actual - The true label\n   * @param {any} predicted - The predicted label\n   * @return {number} - The element in the confusion matrix\n   */\n  getCount(actual, predicted) {\n    const actualIndex = this.getIndex(actual);\n    const predictedIndex = this.getIndex(predicted);\n    return this.matrix[actualIndex][predictedIndex];\n  }\n\n  /**\n   * Compute the general prediction accuracy\n   * @deprecated Use getAccuracy\n   * @return {number} - The prediction accuracy ([0-1]\n   */\n  get accuracy() {\n    return this.getAccuracy();\n  }\n\n  /**\n   * Compute the number of predicted observations\n   * @deprecated Use getTotalCount\n   * @return {number}\n   */\n  get total() {\n    return this.getTotalCount();\n  }\n}\n","(function (global, factory) {\n\ttypeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n\ttypeof define === 'function' && define.amd ? define(factory) :\n\t(factory());\n}(this, (function () { 'use strict';\n\n\tfunction createCommonjsModule(fn, module) {\n\t\treturn module = { exports: {} }, fn(module, module.exports), module.exports;\n\t}\n\n\tvar runtime = createCommonjsModule(function (module) {\n\t/**\n\t * Copyright (c) 2014-present, Facebook, Inc.\n\t *\n\t * This source code is licensed under the MIT license found in the\n\t * LICENSE file in the root directory of this source tree.\n\t */\n\n\t!(function(global) {\n\n\t  var Op = Object.prototype;\n\t  var hasOwn = Op.hasOwnProperty;\n\t  var undefined; // More compressible than void 0.\n\t  var $Symbol = typeof Symbol === \"function\" ? Symbol : {};\n\t  var iteratorSymbol = $Symbol.iterator || \"@@iterator\";\n\t  var asyncIteratorSymbol = $Symbol.asyncIterator || \"@@asyncIterator\";\n\t  var toStringTagSymbol = $Symbol.toStringTag || \"@@toStringTag\";\n\t  var runtime = global.regeneratorRuntime;\n\t  if (runtime) {\n\t    {\n\t      // If regeneratorRuntime is defined globally and we're in a module,\n\t      // make the exports object identical to regeneratorRuntime.\n\t      module.exports = runtime;\n\t    }\n\t    // Don't bother evaluating the rest of this file if the runtime was\n\t    // already defined globally.\n\t    return;\n\t  }\n\n\t  // Define the runtime globally (as expected by generated code) as either\n\t  // module.exports (if we're in a module) or a new, empty object.\n\t  runtime = global.regeneratorRuntime = module.exports;\n\n\t  function wrap(innerFn, outerFn, self, tryLocsList) {\n\t    // If outerFn provided and outerFn.prototype is a Generator, then outerFn.prototype instanceof Generator.\n\t    var protoGenerator = outerFn && outerFn.prototype instanceof Generator ? outerFn : Generator;\n\t    var generator = Object.create(protoGenerator.prototype);\n\t    var context = new Context(tryLocsList || []);\n\n\t    // The ._invoke method unifies the implementations of the .next,\n\t    // .throw, and .return methods.\n\t    generator._invoke = makeInvokeMethod(innerFn, self, context);\n\n\t    return generator;\n\t  }\n\t  runtime.wrap = wrap;\n\n\t  // Try/catch helper to minimize deoptimizations. Returns a completion\n\t  // record like context.tryEntries[i].completion. This interface could\n\t  // have been (and was previously) designed to take a closure to be\n\t  // invoked without arguments, but in all the cases we care about we\n\t  // already have an existing method we want to call, so there's no need\n\t  // to create a new function object. We can even get away with assuming\n\t  // the method takes exactly one argument, since that happens to be true\n\t  // in every case, so we don't have to touch the arguments object. The\n\t  // only additional allocation required is the completion record, which\n\t  // has a stable shape and so hopefully should be cheap to allocate.\n\t  function tryCatch(fn, obj, arg) {\n\t    try {\n\t      return { type: \"normal\", arg: fn.call(obj, arg) };\n\t    } catch (err) {\n\t      return { type: \"throw\", arg: err };\n\t    }\n\t  }\n\n\t  var GenStateSuspendedStart = \"suspendedStart\";\n\t  var GenStateSuspendedYield = \"suspendedYield\";\n\t  var GenStateExecuting = \"executing\";\n\t  var GenStateCompleted = \"completed\";\n\n\t  // Returning this object from the innerFn has the same effect as\n\t  // breaking out of the dispatch switch statement.\n\t  var ContinueSentinel = {};\n\n\t  // Dummy constructor functions that we use as the .constructor and\n\t  // .constructor.prototype properties for functions that return Generator\n\t  // objects. For full spec compliance, you may wish to configure your\n\t  // minifier not to mangle the names of these two functions.\n\t  function Generator() {}\n\t  function GeneratorFunction() {}\n\t  function GeneratorFunctionPrototype() {}\n\n\t  // This is a polyfill for %IteratorPrototype% for environments that\n\t  // don't natively support it.\n\t  var IteratorPrototype = {};\n\t  IteratorPrototype[iteratorSymbol] = function () {\n\t    return this;\n\t  };\n\n\t  var getProto = Object.getPrototypeOf;\n\t  var NativeIteratorPrototype = getProto && getProto(getProto(values([])));\n\t  if (NativeIteratorPrototype &&\n\t      NativeIteratorPrototype !== Op &&\n\t      hasOwn.call(NativeIteratorPrototype, iteratorSymbol)) {\n\t    // This environment has a native %IteratorPrototype%; use it instead\n\t    // of the polyfill.\n\t    IteratorPrototype = NativeIteratorPrototype;\n\t  }\n\n\t  var Gp = GeneratorFunctionPrototype.prototype =\n\t    Generator.prototype = Object.create(IteratorPrototype);\n\t  GeneratorFunction.prototype = Gp.constructor = GeneratorFunctionPrototype;\n\t  GeneratorFunctionPrototype.constructor = GeneratorFunction;\n\t  GeneratorFunctionPrototype[toStringTagSymbol] =\n\t    GeneratorFunction.displayName = \"GeneratorFunction\";\n\n\t  // Helper for defining the .next, .throw, and .return methods of the\n\t  // Iterator interface in terms of a single ._invoke method.\n\t  function defineIteratorMethods(prototype) {\n\t    [\"next\", \"throw\", \"return\"].forEach(function(method) {\n\t      prototype[method] = function(arg) {\n\t        return this._invoke(method, arg);\n\t      };\n\t    });\n\t  }\n\n\t  runtime.isGeneratorFunction = function(genFun) {\n\t    var ctor = typeof genFun === \"function\" && genFun.constructor;\n\t    return ctor\n\t      ? ctor === GeneratorFunction ||\n\t        // For the native GeneratorFunction constructor, the best we can\n\t        // do is to check its .name property.\n\t        (ctor.displayName || ctor.name) === \"GeneratorFunction\"\n\t      : false;\n\t  };\n\n\t  runtime.mark = function(genFun) {\n\t    if (Object.setPrototypeOf) {\n\t      Object.setPrototypeOf(genFun, GeneratorFunctionPrototype);\n\t    } else {\n\t      genFun.__proto__ = GeneratorFunctionPrototype;\n\t      if (!(toStringTagSymbol in genFun)) {\n\t        genFun[toStringTagSymbol] = \"GeneratorFunction\";\n\t      }\n\t    }\n\t    genFun.prototype = Object.create(Gp);\n\t    return genFun;\n\t  };\n\n\t  // Within the body of any async function, `await x` is transformed to\n\t  // `yield regeneratorRuntime.awrap(x)`, so that the runtime can test\n\t  // `hasOwn.call(value, \"__await\")` to determine if the yielded value is\n\t  // meant to be awaited.\n\t  runtime.awrap = function(arg) {\n\t    return { __await: arg };\n\t  };\n\n\t  function AsyncIterator(generator) {\n\t    function invoke(method, arg, resolve, reject) {\n\t      var record = tryCatch(generator[method], generator, arg);\n\t      if (record.type === \"throw\") {\n\t        reject(record.arg);\n\t      } else {\n\t        var result = record.arg;\n\t        var value = result.value;\n\t        if (value &&\n\t            typeof value === \"object\" &&\n\t            hasOwn.call(value, \"__await\")) {\n\t          return Promise.resolve(value.__await).then(function(value) {\n\t            invoke(\"next\", value, resolve, reject);\n\t          }, function(err) {\n\t            invoke(\"throw\", err, resolve, reject);\n\t          });\n\t        }\n\n\t        return Promise.resolve(value).then(function(unwrapped) {\n\t          // When a yielded Promise is resolved, its final value becomes\n\t          // the .value of the Promise<{value,done}> result for the\n\t          // current iteration. If the Promise is rejected, however, the\n\t          // result for this iteration will be rejected with the same\n\t          // reason. Note that rejections of yielded Promises are not\n\t          // thrown back into the generator function, as is the case\n\t          // when an awaited Promise is rejected. This difference in\n\t          // behavior between yield and await is important, because it\n\t          // allows the consumer to decide what to do with the yielded\n\t          // rejection (swallow it and continue, manually .throw it back\n\t          // into the generator, abandon iteration, whatever). With\n\t          // await, by contrast, there is no opportunity to examine the\n\t          // rejection reason outside the generator function, so the\n\t          // only option is to throw it from the await expression, and\n\t          // let the generator function handle the exception.\n\t          result.value = unwrapped;\n\t          resolve(result);\n\t        }, reject);\n\t      }\n\t    }\n\n\t    var previousPromise;\n\n\t    function enqueue(method, arg) {\n\t      function callInvokeWithMethodAndArg() {\n\t        return new Promise(function(resolve, reject) {\n\t          invoke(method, arg, resolve, reject);\n\t        });\n\t      }\n\n\t      return previousPromise =\n\t        // If enqueue has been called before, then we want to wait until\n\t        // all previous Promises have been resolved before calling invoke,\n\t        // so that results are always delivered in the correct order. If\n\t        // enqueue has not been called before, then it is important to\n\t        // call invoke immediately, without waiting on a callback to fire,\n\t        // so that the async generator function has the opportunity to do\n\t        // any necessary setup in a predictable way. This predictability\n\t        // is why the Promise constructor synchronously invokes its\n\t        // executor callback, and why async functions synchronously\n\t        // execute code before the first await. Since we implement simple\n\t        // async functions in terms of async generators, it is especially\n\t        // important to get this right, even though it requires care.\n\t        previousPromise ? previousPromise.then(\n\t          callInvokeWithMethodAndArg,\n\t          // Avoid propagating failures to Promises returned by later\n\t          // invocations of the iterator.\n\t          callInvokeWithMethodAndArg\n\t        ) : callInvokeWithMethodAndArg();\n\t    }\n\n\t    // Define the unified helper method that is used to implement .next,\n\t    // .throw, and .return (see defineIteratorMethods).\n\t    this._invoke = enqueue;\n\t  }\n\n\t  defineIteratorMethods(AsyncIterator.prototype);\n\t  AsyncIterator.prototype[asyncIteratorSymbol] = function () {\n\t    return this;\n\t  };\n\t  runtime.AsyncIterator = AsyncIterator;\n\n\t  // Note that simple async functions are implemented on top of\n\t  // AsyncIterator objects; they just return a Promise for the value of\n\t  // the final result produced by the iterator.\n\t  runtime.async = function(innerFn, outerFn, self, tryLocsList) {\n\t    var iter = new AsyncIterator(\n\t      wrap(innerFn, outerFn, self, tryLocsList)\n\t    );\n\n\t    return runtime.isGeneratorFunction(outerFn)\n\t      ? iter // If outerFn is a generator, return the full iterator.\n\t      : iter.next().then(function(result) {\n\t          return result.done ? result.value : iter.next();\n\t        });\n\t  };\n\n\t  function makeInvokeMethod(innerFn, self, context) {\n\t    var state = GenStateSuspendedStart;\n\n\t    return function invoke(method, arg) {\n\t      if (state === GenStateExecuting) {\n\t        throw new Error(\"Generator is already running\");\n\t      }\n\n\t      if (state === GenStateCompleted) {\n\t        if (method === \"throw\") {\n\t          throw arg;\n\t        }\n\n\t        // Be forgiving, per 25.3.3.3.3 of the spec:\n\t        // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-generatorresume\n\t        return doneResult();\n\t      }\n\n\t      context.method = method;\n\t      context.arg = arg;\n\n\t      while (true) {\n\t        var delegate = context.delegate;\n\t        if (delegate) {\n\t          var delegateResult = maybeInvokeDelegate(delegate, context);\n\t          if (delegateResult) {\n\t            if (delegateResult === ContinueSentinel) continue;\n\t            return delegateResult;\n\t          }\n\t        }\n\n\t        if (context.method === \"next\") {\n\t          // Setting context._sent for legacy support of Babel's\n\t          // function.sent implementation.\n\t          context.sent = context._sent = context.arg;\n\n\t        } else if (context.method === \"throw\") {\n\t          if (state === GenStateSuspendedStart) {\n\t            state = GenStateCompleted;\n\t            throw context.arg;\n\t          }\n\n\t          context.dispatchException(context.arg);\n\n\t        } else if (context.method === \"return\") {\n\t          context.abrupt(\"return\", context.arg);\n\t        }\n\n\t        state = GenStateExecuting;\n\n\t        var record = tryCatch(innerFn, self, context);\n\t        if (record.type === \"normal\") {\n\t          // If an exception is thrown from innerFn, we leave state ===\n\t          // GenStateExecuting and loop back for another invocation.\n\t          state = context.done\n\t            ? GenStateCompleted\n\t            : GenStateSuspendedYield;\n\n\t          if (record.arg === ContinueSentinel) {\n\t            continue;\n\t          }\n\n\t          return {\n\t            value: record.arg,\n\t            done: context.done\n\t          };\n\n\t        } else if (record.type === \"throw\") {\n\t          state = GenStateCompleted;\n\t          // Dispatch the exception by looping back around to the\n\t          // context.dispatchException(context.arg) call above.\n\t          context.method = \"throw\";\n\t          context.arg = record.arg;\n\t        }\n\t      }\n\t    };\n\t  }\n\n\t  // Call delegate.iterator[context.method](context.arg) and handle the\n\t  // result, either by returning a { value, done } result from the\n\t  // delegate iterator, or by modifying context.method and context.arg,\n\t  // setting context.delegate to null, and returning the ContinueSentinel.\n\t  function maybeInvokeDelegate(delegate, context) {\n\t    var method = delegate.iterator[context.method];\n\t    if (method === undefined) {\n\t      // A .throw or .return when the delegate iterator has no .throw\n\t      // method always terminates the yield* loop.\n\t      context.delegate = null;\n\n\t      if (context.method === \"throw\") {\n\t        if (delegate.iterator.return) {\n\t          // If the delegate iterator has a return method, give it a\n\t          // chance to clean up.\n\t          context.method = \"return\";\n\t          context.arg = undefined;\n\t          maybeInvokeDelegate(delegate, context);\n\n\t          if (context.method === \"throw\") {\n\t            // If maybeInvokeDelegate(context) changed context.method from\n\t            // \"return\" to \"throw\", let that override the TypeError below.\n\t            return ContinueSentinel;\n\t          }\n\t        }\n\n\t        context.method = \"throw\";\n\t        context.arg = new TypeError(\n\t          \"The iterator does not provide a 'throw' method\");\n\t      }\n\n\t      return ContinueSentinel;\n\t    }\n\n\t    var record = tryCatch(method, delegate.iterator, context.arg);\n\n\t    if (record.type === \"throw\") {\n\t      context.method = \"throw\";\n\t      context.arg = record.arg;\n\t      context.delegate = null;\n\t      return ContinueSentinel;\n\t    }\n\n\t    var info = record.arg;\n\n\t    if (! info) {\n\t      context.method = \"throw\";\n\t      context.arg = new TypeError(\"iterator result is not an object\");\n\t      context.delegate = null;\n\t      return ContinueSentinel;\n\t    }\n\n\t    if (info.done) {\n\t      // Assign the result of the finished delegate to the temporary\n\t      // variable specified by delegate.resultName (see delegateYield).\n\t      context[delegate.resultName] = info.value;\n\n\t      // Resume execution at the desired location (see delegateYield).\n\t      context.next = delegate.nextLoc;\n\n\t      // If context.method was \"throw\" but the delegate handled the\n\t      // exception, let the outer generator proceed normally. If\n\t      // context.method was \"next\", forget context.arg since it has been\n\t      // \"consumed\" by the delegate iterator. If context.method was\n\t      // \"return\", allow the original .return call to continue in the\n\t      // outer generator.\n\t      if (context.method !== \"return\") {\n\t        context.method = \"next\";\n\t        context.arg = undefined;\n\t      }\n\n\t    } else {\n\t      // Re-yield the result returned by the delegate method.\n\t      return info;\n\t    }\n\n\t    // The delegate iterator is finished, so forget it and continue with\n\t    // the outer generator.\n\t    context.delegate = null;\n\t    return ContinueSentinel;\n\t  }\n\n\t  // Define Generator.prototype.{next,throw,return} in terms of the\n\t  // unified ._invoke helper method.\n\t  defineIteratorMethods(Gp);\n\n\t  Gp[toStringTagSymbol] = \"Generator\";\n\n\t  // A Generator should always return itself as the iterator object when the\n\t  // @@iterator function is called on it. Some browsers' implementations of the\n\t  // iterator prototype chain incorrectly implement this, causing the Generator\n\t  // object to not be returned from this call. This ensures that doesn't happen.\n\t  // See https://github.com/facebook/regenerator/issues/274 for more details.\n\t  Gp[iteratorSymbol] = function() {\n\t    return this;\n\t  };\n\n\t  Gp.toString = function() {\n\t    return \"[object Generator]\";\n\t  };\n\n\t  function pushTryEntry(locs) {\n\t    var entry = { tryLoc: locs[0] };\n\n\t    if (1 in locs) {\n\t      entry.catchLoc = locs[1];\n\t    }\n\n\t    if (2 in locs) {\n\t      entry.finallyLoc = locs[2];\n\t      entry.afterLoc = locs[3];\n\t    }\n\n\t    this.tryEntries.push(entry);\n\t  }\n\n\t  function resetTryEntry(entry) {\n\t    var record = entry.completion || {};\n\t    record.type = \"normal\";\n\t    delete record.arg;\n\t    entry.completion = record;\n\t  }\n\n\t  function Context(tryLocsList) {\n\t    // The root entry object (effectively a try statement without a catch\n\t    // or a finally block) gives us a place to store values thrown from\n\t    // locations where there is no enclosing try statement.\n\t    this.tryEntries = [{ tryLoc: \"root\" }];\n\t    tryLocsList.forEach(pushTryEntry, this);\n\t    this.reset(true);\n\t  }\n\n\t  runtime.keys = function(object) {\n\t    var keys = [];\n\t    for (var key in object) {\n\t      keys.push(key);\n\t    }\n\t    keys.reverse();\n\n\t    // Rather than returning an object with a next method, we keep\n\t    // things simple and return the next function itself.\n\t    return function next() {\n\t      while (keys.length) {\n\t        var key = keys.pop();\n\t        if (key in object) {\n\t          next.value = key;\n\t          next.done = false;\n\t          return next;\n\t        }\n\t      }\n\n\t      // To avoid creating an additional object, we just hang the .value\n\t      // and .done properties off the next function object itself. This\n\t      // also ensures that the minifier will not anonymize the function.\n\t      next.done = true;\n\t      return next;\n\t    };\n\t  };\n\n\t  function values(iterable) {\n\t    if (iterable) {\n\t      var iteratorMethod = iterable[iteratorSymbol];\n\t      if (iteratorMethod) {\n\t        return iteratorMethod.call(iterable);\n\t      }\n\n\t      if (typeof iterable.next === \"function\") {\n\t        return iterable;\n\t      }\n\n\t      if (!isNaN(iterable.length)) {\n\t        var i = -1, next = function next() {\n\t          while (++i < iterable.length) {\n\t            if (hasOwn.call(iterable, i)) {\n\t              next.value = iterable[i];\n\t              next.done = false;\n\t              return next;\n\t            }\n\t          }\n\n\t          next.value = undefined;\n\t          next.done = true;\n\n\t          return next;\n\t        };\n\n\t        return next.next = next;\n\t      }\n\t    }\n\n\t    // Return an iterator with no values.\n\t    return { next: doneResult };\n\t  }\n\t  runtime.values = values;\n\n\t  function doneResult() {\n\t    return { value: undefined, done: true };\n\t  }\n\n\t  Context.prototype = {\n\t    constructor: Context,\n\n\t    reset: function(skipTempReset) {\n\t      this.prev = 0;\n\t      this.next = 0;\n\t      // Resetting context._sent for legacy support of Babel's\n\t      // function.sent implementation.\n\t      this.sent = this._sent = undefined;\n\t      this.done = false;\n\t      this.delegate = null;\n\n\t      this.method = \"next\";\n\t      this.arg = undefined;\n\n\t      this.tryEntries.forEach(resetTryEntry);\n\n\t      if (!skipTempReset) {\n\t        for (var name in this) {\n\t          // Not sure about the optimal order of these conditions:\n\t          if (name.charAt(0) === \"t\" &&\n\t              hasOwn.call(this, name) &&\n\t              !isNaN(+name.slice(1))) {\n\t            this[name] = undefined;\n\t          }\n\t        }\n\t      }\n\t    },\n\n\t    stop: function() {\n\t      this.done = true;\n\n\t      var rootEntry = this.tryEntries[0];\n\t      var rootRecord = rootEntry.completion;\n\t      if (rootRecord.type === \"throw\") {\n\t        throw rootRecord.arg;\n\t      }\n\n\t      return this.rval;\n\t    },\n\n\t    dispatchException: function(exception) {\n\t      if (this.done) {\n\t        throw exception;\n\t      }\n\n\t      var context = this;\n\t      function handle(loc, caught) {\n\t        record.type = \"throw\";\n\t        record.arg = exception;\n\t        context.next = loc;\n\n\t        if (caught) {\n\t          // If the dispatched exception was caught by a catch block,\n\t          // then let that catch block handle the exception normally.\n\t          context.method = \"next\";\n\t          context.arg = undefined;\n\t        }\n\n\t        return !! caught;\n\t      }\n\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        var record = entry.completion;\n\n\t        if (entry.tryLoc === \"root\") {\n\t          // Exception thrown outside of any try block that could handle\n\t          // it, so set the completion value of the entire function to\n\t          // throw the exception.\n\t          return handle(\"end\");\n\t        }\n\n\t        if (entry.tryLoc <= this.prev) {\n\t          var hasCatch = hasOwn.call(entry, \"catchLoc\");\n\t          var hasFinally = hasOwn.call(entry, \"finallyLoc\");\n\n\t          if (hasCatch && hasFinally) {\n\t            if (this.prev < entry.catchLoc) {\n\t              return handle(entry.catchLoc, true);\n\t            } else if (this.prev < entry.finallyLoc) {\n\t              return handle(entry.finallyLoc);\n\t            }\n\n\t          } else if (hasCatch) {\n\t            if (this.prev < entry.catchLoc) {\n\t              return handle(entry.catchLoc, true);\n\t            }\n\n\t          } else if (hasFinally) {\n\t            if (this.prev < entry.finallyLoc) {\n\t              return handle(entry.finallyLoc);\n\t            }\n\n\t          } else {\n\t            throw new Error(\"try statement without catch or finally\");\n\t          }\n\t        }\n\t      }\n\t    },\n\n\t    abrupt: function(type, arg) {\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        if (entry.tryLoc <= this.prev &&\n\t            hasOwn.call(entry, \"finallyLoc\") &&\n\t            this.prev < entry.finallyLoc) {\n\t          var finallyEntry = entry;\n\t          break;\n\t        }\n\t      }\n\n\t      if (finallyEntry &&\n\t          (type === \"break\" ||\n\t           type === \"continue\") &&\n\t          finallyEntry.tryLoc <= arg &&\n\t          arg <= finallyEntry.finallyLoc) {\n\t        // Ignore the finally entry if control is not jumping to a\n\t        // location outside the try/catch block.\n\t        finallyEntry = null;\n\t      }\n\n\t      var record = finallyEntry ? finallyEntry.completion : {};\n\t      record.type = type;\n\t      record.arg = arg;\n\n\t      if (finallyEntry) {\n\t        this.method = \"next\";\n\t        this.next = finallyEntry.finallyLoc;\n\t        return ContinueSentinel;\n\t      }\n\n\t      return this.complete(record);\n\t    },\n\n\t    complete: function(record, afterLoc) {\n\t      if (record.type === \"throw\") {\n\t        throw record.arg;\n\t      }\n\n\t      if (record.type === \"break\" ||\n\t          record.type === \"continue\") {\n\t        this.next = record.arg;\n\t      } else if (record.type === \"return\") {\n\t        this.rval = this.arg = record.arg;\n\t        this.method = \"return\";\n\t        this.next = \"end\";\n\t      } else if (record.type === \"normal\" && afterLoc) {\n\t        this.next = afterLoc;\n\t      }\n\n\t      return ContinueSentinel;\n\t    },\n\n\t    finish: function(finallyLoc) {\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        if (entry.finallyLoc === finallyLoc) {\n\t          this.complete(entry.completion, entry.afterLoc);\n\t          resetTryEntry(entry);\n\t          return ContinueSentinel;\n\t        }\n\t      }\n\t    },\n\n\t    \"catch\": function(tryLoc) {\n\t      for (var i = this.tryEntries.length - 1; i >= 0; --i) {\n\t        var entry = this.tryEntries[i];\n\t        if (entry.tryLoc === tryLoc) {\n\t          var record = entry.completion;\n\t          if (record.type === \"throw\") {\n\t            var thrown = record.arg;\n\t            resetTryEntry(entry);\n\t          }\n\t          return thrown;\n\t        }\n\t      }\n\n\t      // The context.catch method must only be called with a location\n\t      // argument that corresponds to a known catch block.\n\t      throw new Error(\"illegal catch attempt\");\n\t    },\n\n\t    delegateYield: function(iterable, resultName, nextLoc) {\n\t      this.delegate = {\n\t        iterator: values(iterable),\n\t        resultName: resultName,\n\t        nextLoc: nextLoc\n\t      };\n\n\t      if (this.method === \"next\") {\n\t        // Deliberately forget the last sent value so that we don't\n\t        // accidentally pass it on to the delegate.\n\t        this.arg = undefined;\n\t      }\n\n\t      return ContinueSentinel;\n\t    }\n\t  };\n\t})(\n\t  // In sloppy mode, unbound `this` refers to the global object, fallback to\n\t  // Function constructor if we're in global strict mode. That is sadly a form\n\t  // of indirect eval which violates Content Security Policy.\n\t  (function() { return this })() || Function(\"return this\")()\n\t);\n\t});\n\n\t/**\n\t * Copyright (c) 2014-present, Facebook, Inc.\n\t *\n\t * This source code is licensed under the MIT license found in the\n\t * LICENSE file in the root directory of this source tree.\n\t */\n\n\t// This method of obtaining a reference to the global object needs to be\n\t// kept identical to the way it is obtained in runtime.js\n\tvar g = (function() { return this })() || Function(\"return this\")();\n\n\t// Use `getOwnPropertyNames` because not all browsers support calling\n\t// `hasOwnProperty` on the global `self` object in a worker. 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i < N; i++) {\n    allIdx[i] = i;\n  }\n\n  let l = Math.floor(N / k);\n  // create random k-folds\n  let current = [];\n  let folds = [];\n  while (allIdx.length) {\n    let randi = Math.floor(Math.random() * allIdx.length);\n    current.push(allIdx[randi]);\n    allIdx.splice(randi, 1);\n    if (current.length === l) {\n      folds.push(current);\n      current = [];\n    }\n  }\n  // we push the remaining to the last fold so that the total length is\n  // preserved. Otherwise the Q2 will fail.\n  if (current.length) current.forEach((e) => folds[k - 1].push(e));\n  folds = folds.slice(0, k);\n\n  let foldsIndex = folds.map((x, idx) => ({\n    testIndex: x,\n    trainIndex: [].concat(...folds.filter((el, idx2) => idx2 !== idx)),\n  }));\n  return foldsIndex;\n}\n","/**\n * A function to sample a dataset maintaining classes equilibrated\n * @param {Array} classVector - an array containing class or group information\n * @param {Number} fraction - a fraction of the class to sample\n * @return {Object} - an object with indexes\n */\n\nexport function sampleAClass(classVector, fraction) {\n  // sort the vector\n  let classVectorSorted = JSON.parse(JSON.stringify(classVector));\n  let result = Array.from(Array(classVectorSorted.length).keys()).sort((a, b) =>\n    classVectorSorted[a] < classVectorSorted[b]\n      ? -1\n      : (classVectorSorted[b] < classVectorSorted[a]) | 0,\n  );\n  classVectorSorted.sort((a, b) => (a < b ? -1 : (b < a) | 0));\n\n  // counts the class elements\n  let counts = {};\n  classVectorSorted.forEach((x) => (counts[x] = (counts[x] || 0) + 1));\n\n  // pick a few per class\n  let indexOfSelected = [];\n\n  Object.keys(counts).forEach((e, i) => {\n    let shift = [];\n    Object.values(counts).reduce((a, c, item) => (shift[item] = a + c), 0);\n\n    let arr = [...Array(counts[e]).keys()];\n\n    let r = [];\n    for (let j = 0; j < Math.floor(counts[e] * fraction); j++) {\n      let n = arr[Math.floor(Math.random() * arr.length)];\n      r.push(n);\n      let ind = arr.indexOf(n);\n      arr.splice(ind, 1);\n    }\n\n    if (i === 0) {\n      indexOfSelected = indexOfSelected.concat(r);\n    } else {\n      indexOfSelected = indexOfSelected.concat(r.map((x) => x + shift[i - 1]));\n    }\n  });\n\n  // sort back the index\n  let trainIndex = [];\n  indexOfSelected.forEach((e) => trainIndex.push(result[e]));\n\n  let testIndex = [];\n  let mask = [];\n  classVector.forEach((el, idx) => {\n    if (trainIndex.includes(idx)) {\n      mask.push(true);\n    } else {\n      mask.push(false);\n      testIndex.push(idx);\n    }\n  });\n  return { trainIndex, testIndex, mask };\n}\n","import ConfusionMatrix from 'ml-confusion-matrix';\nimport combinations from 'ml-combinations';\n\nimport { getFolds } from './getFolds.js';\n\nexport { sampleAClass } from './sampleAClass.js';\nexport { getFolds } from './getFolds.js';\n\n/**\n * Performs a leave-one-out cross-validation (LOO-CV) of the given samples. In LOO-CV, 1 observation is used as the\n * validation set while the rest is used as the training set. This is repeated once for each observation. LOO-CV is a\n * special case of LPO-CV. @see leavePout\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\n\nexport function leaveOneOut(Classifier, features, labels, classifierOptions) {\n  if (typeof labels === 'function') {\n    let callback = labels;\n    labels = features;\n    features = Classifier;\n    return leavePOut(features, labels, 1, callback);\n  }\n  return leavePOut(Classifier, features, labels, classifierOptions, 1);\n}\n\n/**\n * Performs a leave-p-out cross-validation (LPO-CV) of the given samples. In LPO-CV, p observations are used as the\n * validation set while the rest is used as the training set. This is repeated as many times as there are possible\n * ways to combine p observations from the set (unordered without replacement). Be aware that for relatively small\n * data-set size this can require a very large number of training and testing to do!\n * @param {function} Classifier - The classifier's constructor to use for the cross validation. Expect ml-classifier\n *     api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} p - The size of the validation sub-samples' set\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nexport function leavePOut(Classifier, features, labels, classifierOptions, p) {\n  let callback;\n  if (typeof classifierOptions === 'function') {\n    callback = classifierOptions;\n    p = labels;\n    labels = features;\n    features = Classifier;\n  }\n  check(features, labels);\n  const distinct = getDistinct(labels);\n  const confusionMatrix = initMatrix(distinct.length, distinct.length);\n\n  let N = features.length;\n  let gen = combinations(p, N);\n  let allIdx = new Array(N);\n  for (let i = 0; i < N; i++) {\n    allIdx[i] = i;\n  }\n  for (const testIdx of gen) {\n    let trainIdx = allIdx.slice();\n\n    for (let i = testIdx.length - 1; i >= 0; i--) {\n      trainIdx.splice(testIdx[i], 1);\n    }\n\n    if (callback) {\n      validateWithCallback(\n        features,\n        labels,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n        callback,\n      );\n    } else {\n      validate(\n        Classifier,\n        features,\n        labels,\n        classifierOptions,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n      );\n    }\n  }\n\n  return new ConfusionMatrix(confusionMatrix, distinct);\n}\n\n/**\n * Performs k-fold cross-validation (KF-CV). KF-CV separates the data-set into k random equally sized partitions, and\n * uses each as a validation set, with all other partitions used in the training set. Observations left over from if k\n * does not divide the number of observations are left out of the cross-validation process.\n * @param {function} Classifier - The classifier's to use for the cross validation. Expect ml-classifier api.\n * @param {Array} features - The features for all samples of the data-set\n * @param {Array} labels - The classification class of all samples of the data-set\n * @param {object} classifierOptions - The classifier options with which the classifier should be instantiated.\n * @param {number} k - The number of partitions to create\n * @return {ConfusionMatrix} - The cross-validation confusion matrix\n */\nexport function kFold(Classifier, features, labels, classifierOptions, k) {\n  let callback;\n  if (typeof classifierOptions === 'function') {\n    callback = classifierOptions;\n    k = labels;\n    labels = features;\n    features = Classifier;\n  }\n  check(features, labels);\n  const distinct = getDistinct(labels);\n  const confusionMatrix = initMatrix(distinct.length, distinct.length);\n\n  let folds = getFolds(features, k);\n\n  for (let i = 0; i < folds.length; i++) {\n    let testIdx = folds[i].testIndex;\n    let trainIdx = folds[i].trainIndex;\n\n    if (callback) {\n      validateWithCallback(\n        features,\n        labels,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n        callback,\n      );\n    } else {\n      validate(\n        Classifier,\n        features,\n        labels,\n        classifierOptions,\n        testIdx,\n        trainIdx,\n        confusionMatrix,\n        distinct,\n      );\n    }\n  }\n\n  return new ConfusionMatrix(confusionMatrix, distinct);\n}\n\nfunction check(features, labels) {\n  if (features.length !== labels.length) {\n    throw new Error('features and labels should have the same length');\n  }\n}\n\nfunction initMatrix(rows, columns) {\n  return new Array(rows).fill(0).map(() => new Array(columns).fill(0));\n}\n\nfunction getDistinct(arr) {\n  let s = new Set();\n  for (let i = 0; i < arr.length; i++) {\n    s.add(arr[i]);\n  }\n  return Array.from(s);\n}\n\nfunction validate(\n  Classifier,\n  features,\n  labels,\n  classifierOptions,\n  testIdx,\n  trainIdx,\n  confusionMatrix,\n  distinct,\n) {\n  const { testFeatures, trainFeatures, testLabels, trainLabels } = getTrainTest(\n    features,\n    labels,\n    testIdx,\n    trainIdx,\n  );\n\n  let classifier;\n  if (Classifier.prototype.train) {\n    classifier = new Classifier(classifierOptions);\n    classifier.train(trainFeatures, trainLabels);\n  } else {\n    classifier = new Classifier(trainFeatures, trainLabels, classifierOptions);\n  }\n\n  let predictedLabels = classifier.predict(testFeatures);\n  updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction validateWithCallback(\n  features,\n  labels,\n  testIdx,\n  trainIdx,\n  confusionMatrix,\n  distinct,\n  callback,\n) {\n  const { testFeatures, trainFeatures, testLabels, trainLabels } = getTrainTest(\n    features,\n    labels,\n    testIdx,\n    trainIdx,\n  );\n  const predictedLabels = callback(trainFeatures, trainLabels, testFeatures);\n  updateConfusionMatrix(confusionMatrix, testLabels, predictedLabels, distinct);\n}\n\nfunction updateConfusionMatrix(\n  confusionMatrix,\n  testLabels,\n  predictedLabels,\n  distinct,\n) {\n  for (let i = 0; i < predictedLabels.length; i++) {\n    const actualIdx = distinct.indexOf(testLabels[i]);\n    const predictedIdx = distinct.indexOf(predictedLabels[i]);\n    if (actualIdx < 0 || predictedIdx < 0) {\n      // eslint-disable-next-line no-console\n      console.warn(`ignore unknown predicted label ${predictedLabels[i]}`);\n    }\n    confusionMatrix[actualIdx][predictedIdx]++;\n  }\n}\n\nexport function getTrainTest(features, labels, testIdx, trainIdx) {\n  return {\n    testFeatures: testIdx.map(function(index) {\n      return features[index];\n    }),\n    trainFeatures: trainIdx.map(function(index) {\n      return features[index];\n    }),\n    testLabels: testIdx.map(function(index) {\n      return labels[index];\n    }),\n    trainLabels: trainIdx.map(function(index) {\n      return labels[index];\n    }),\n  };\n}\n","import Matrix from 'ml-matrix';\n\nimport { norm } from './util/utils.js';\n\n/**\n * OPLS loop\n * @param {Array} x a matrix with features\n * @param {Array} y an array of labels (dependent variable)\n * @param {Object} options an object with options\n * @return {Object} an object with model (filteredX: err,\n    loadingsXOrtho: pOrtho,\n    scoresXOrtho: tOrtho,\n    weightsXOrtho: wOrtho,\n    weightsPred: w,\n    loadingsXpred: p,\n    scoresXpred: t,\n    loadingsY:)\n */\nexport function OPLSNipals(x, y, options = {}) {\n  const { numberOSC = 100 } = options;\n\n  let X = Matrix.checkMatrix(x);\n  let Y = Matrix.checkMatrix(y);\n\n  let u = Y.getColumnVector(0);\n\n  let diff = 1;\n  let t, c, w, uNew;\n  for (let i = 0; i < numberOSC && diff > 1e-10; i++) {\n    w = u\n      .transpose()\n      .mmul(X)\n      .div(\n        u\n          .transpose()\n          .mmul(u)\n          .get(0, 0),\n      );\n    w = w.transpose().div(norm(w));\n\n    t = X.mmul(w).div(\n      w\n        .transpose()\n        .mmul(w)\n        .get(0, 0),\n    ); // t_h paso 3\n\n    // calc loading\n    c = t\n      .transpose()\n      .mmul(Y)\n      .div(\n        t\n          .transpose()\n          .mmul(t)\n          .get(0, 0),\n      );\n\n    // calc new u and compare with one in previus iteration (stop criterion)\n    uNew = Y.mmul(c.transpose());\n    uNew = uNew.div(\n      c\n        .transpose()\n        .mmul(c)\n        .get(0, 0),\n    );\n\n    if (i > 0) {\n      diff =\n        uNew\n          .clone()\n          .sub(u)\n          .pow(2)\n          .sum() /\n        uNew\n          .clone()\n          .pow(2)\n          .sum();\n    }\n\n    u = uNew.clone();\n  }\n\n  // calc loadings\n  let p = t\n    .transpose()\n    .mmul(X)\n    .div(\n      t\n        .transpose()\n        .mmul(t)\n        .get(0, 0),\n    );\n\n  let wOrtho = p.clone().sub(\n    w\n      .transpose()\n      .mmul(p.transpose())\n      .div(\n        w\n          .transpose()\n          .mmul(w)\n          .get(0, 0),\n      )\n      .mmul(w.transpose()),\n  );\n  wOrtho.div(norm(wOrtho));\n\n  // orthogonal scores\n  let tOrtho = X.mmul(wOrtho.transpose()).div(\n    wOrtho.mmul(wOrtho.transpose()).get(0, 0),\n  );\n\n  // orthogonal loadings\n  let pOrtho = tOrtho\n    .transpose()\n    .mmul(X)\n    .div(\n      tOrtho\n        .transpose()\n        .mmul(tOrtho)\n        .get(0, 0),\n    );\n\n  // filtered data\n  let err = X.clone().sub(tOrtho.mmul(pOrtho));\n  return {\n    filteredX: err,\n    weightsXOrtho: wOrtho,\n    loadingsXOrtho: pOrtho,\n    scoresXOrtho: tOrtho,\n    weightsXPred: w,\n    loadingsXpred: p,\n    scoresXpred: t,\n    loadingsY: c,\n  };\n}\n","import { Matrix } from 'ml-matrix';\n\n/**\n * Get total sum of square\n * @param {Array} x an array\n * @return {Number} - the sum of the squares\n */\nexport function tss(x) {\n  return Matrix.mul(x, x).sum();\n}\n","import { Matrix, NIPALS } from 'ml-matrix';\nimport ConfusionMatrix from 'ml-confusion-matrix';\nimport { getFolds } from 'ml-cross-validation';\n\nimport { OPLSNipals } from './OPLSNipals.js';\nimport { tss } from './util/tss.js';\n\n/**\n * Creates new OPLS (orthogonal partial latent structures) from features and labels.\n * @param {Matrix} data - matrix containing data (X).\n * @param {Array} labels - 1D Array containing metadata (Y).\n * @param {Object} [options]\n * @param {number} [options.nComp = 3] - number of latent structures computed.\n * @param {boolean} [options.center = true] - should the data be centered (subtract the mean).\n * @param {boolean} [options.scale = false] - should the data be scaled (divide by the standard deviation).\n * @param {Array} [options.cvFolds = []] - allows to provide folds as 2D array for testing purpose.\n * */\n\nexport class OPLS {\n  constructor(data, labels, options = {}) {\n    if (data === true) {\n      const opls = options;\n      this.center = opls.center;\n      this.scale = opls.scale;\n      this.means = opls.means;\n      this.meansY = opls.meansY;\n      this.stdevs = opls.stdevs;\n      this.stdevs = opls.stdevsY;\n      this.model = opls.model;\n      this.tCV = opls.tCV;\n      this.tOrthCV = opls.tOrthCV;\n      this.yHatCV = opls.yHatCV;\n      this.mode = opls.mode;\n      return;\n    }\n\n    let features = data.clone();\n    // set default values\n    // cvFolds allows to define folds for testing purpose\n    const { nComp = 3, center = true, scale = true, cvFolds = [] } = options;\n\n    let group;\n    if (typeof labels[0] === 'number') {\n      // numeric labels: OPLS regression is used\n      this.mode = 'regression';\n      group = Matrix.from1DArray(labels.length, 1, labels);\n    } else if (typeof labels[0] === 'string') {\n      // non-numeric labels: OPLS-DA is used\n      this.mode = 'discriminantAnalysis';\n      group = labels;\n      throw new Error('discriminant analysis is not yet supported');\n    }\n\n    // check types of features and labels\n    if (features.constructor.name !== 'Matrix') {\n      throw new TypeError('features must be of class Matrix');\n    }\n    // getting center and scale the features (all)\n    this.center = center;\n    if (this.center) {\n      this.means = features.mean('column');\n      this.meansY = group.mean('column');\n    } else {\n      this.stdevs = null;\n    }\n    this.scale = scale;\n    if (this.scale) {\n      this.stdevs = features.standardDeviation('column');\n      this.stdevsY = group.standardDeviation('column');\n    } else {\n      this.means = null;\n    }\n\n    // check and remove for features with sd = 0 TODO here\n    // check opls.R line 70\n\n    let folds;\n    if (cvFolds.length > 0) {\n      folds = cvFolds;\n    } else {\n      folds = getFolds(labels, 5);\n    }\n\n    let Q2 = [];\n    this.model = [];\n\n    this.tCV = [];\n    this.tOrthCV = [];\n    this.yHatCV = [];\n    let oplsCV = [];\n\n    let modelNC = [];\n\n    // this code could be made more efficient by reverting the order of the loops\n    // this is a legacy loop to be consistent with R code from MetaboMate package\n    // this allows for having statistic (R2) from CV to decide wether to continue\n    // with more latent structures\n    let nc;\n    for (nc = 0; nc < nComp; nc++) {\n      let yHatk = new Matrix(group.rows, 1);\n      let tPredk = new Matrix(group.rows, 1);\n      let tOrthk = new Matrix(group.rows, 1);\n      let oplsk = [];\n\n      let f = 0;\n      for (let fold of folds) {\n        let trainTest = this._getTrainTest(features, group, fold);\n        let testXk = trainTest.testFeatures;\n        let Xk = trainTest.trainFeatures;\n        let Yk = trainTest.trainLabels;\n\n        // determine center and scale of training set\n        let dataCenter = Xk.mean('column');\n        let dataSD = Xk.standardDeviation('column');\n\n        // center and scale training set\n        if (center) {\n          Xk.center('column');\n          Yk.center('column');\n        }\n\n        if (scale) {\n          Xk.scale('column');\n          Yk.scale('column');\n        }\n\n        // perform opls\n        if (nc === 0) {\n          oplsk[f] = OPLSNipals(Xk, Yk);\n        } else {\n          oplsk[f] = OPLSNipals(oplsCV[nc - 1][f].filteredX, Yk);\n        }\n        // store model for next component\n        oplsCV[nc] = oplsk;\n\n        let plsCV = new NIPALS(oplsk[f].filteredX, { Y: Yk });\n\n        // scaling the test dataset with respect to the train\n        testXk.center('column', { center: dataCenter });\n        testXk.scale('column', { scale: dataSD });\n\n        let Eh = testXk;\n        // removing the orthogonal components from PLS\n        let scores;\n        for (let idx = 0; idx < nc + 1; idx++) {\n          scores = Eh.mmul(oplsCV[idx][f].weightsXOrtho.transpose()); // ok\n          Eh.sub(scores.mmul(oplsCV[idx][f].loadingsXOrtho));\n        }\n\n        // prediction\n        let tPred = Eh.mmul(plsCV.w.transpose());\n        // this should be summed over ncomp (pls_prediction.R line 23)\n        let yHat = tPred.mmul(plsCV.betas); // ok\n\n        // adding all prediction from all folds\n        for (let i = 0; i < fold.testIndex.length; i++) {\n          yHatk.setRow(fold.testIndex[i], [yHat.get(i, 0)]);\n          tPredk.setRow(fold.testIndex[i], [tPred.get(i, 0)]);\n          tOrthk.setRow(fold.testIndex[i], [scores.get(i, 0)]);\n        }\n        f++;\n      } // end of loop over folds\n\n      this.tCV.push(tPredk);\n      this.tOrthCV.push(tOrthk);\n      this.yHatCV.push(yHatk);\n\n      // calculate Q2y for all the prediction (all folds)\n      // ROC for DA is not implemented (check opls.R line 183) TODO\n      if (this.mode === 'regression') {\n        let tssy = tss(group.center('column').scale('column'));\n        let press = tss(group.clone().sub(yHatk));\n        let Q2y = 1 - press / tssy;\n        Q2.push(Q2y);\n      } else if (this.mode === 'discriminantAnalysis') {\n        throw new Error('discriminant analysis is not yet supported');\n      }\n\n      // calculate the R2y for the complete data\n      if (nc === 0) {\n        modelNC = this._predictAll(features, group);\n      } else {\n        modelNC = this._predictAll(\n          modelNC.xRes,\n          group,\n          (options = { scale: false, center: false }),\n        );\n      }\n\n      // adding the predictive statistics from CV\n      modelNC.Q2y = Q2;\n      // store the model for each component\n      this.model.push(modelNC);\n      // console.warn(`OPLS iteration over # of Components: ${nc + 1}`);\n    } // end of loop over nc\n\n    // store scores from CV\n    let tCV = this.tCV;\n    let tOrthCV = this.tOrthCV;\n\n    let m = this.model[nc - 1];\n    let XOrth = m.XOrth;\n    let FeaturesCS = features.center('column').scale('column');\n    let labelsCS = group.center('column').scale('column');\n    let Xres = FeaturesCS.clone().sub(XOrth);\n    let plsCall = new NIPALS(Xres, { Y: labelsCS });\n    let E = Xres.clone().sub(plsCall.t.mmul(plsCall.p));\n\n    let R2x = this.model.map((x) => x.R2x);\n    let R2y = this.model.map((x) => x.R2y);\n\n    this.output = {\n      Q2y: Q2,\n      R2x,\n      R2y,\n      tPred: m.plsC.t,\n      pPred: m.plsC.p,\n      wPred: m.plsC.w,\n      betasPred: m.plsC.betas,\n      Qpc: m.plsC.q,\n      tCV,\n      tOrthCV,\n      tOrth: m.tOrth,\n      pOrth: m.pOrth,\n      wOrth: m.wOrth,\n      XOrth,\n      yHat: m.totalPred,\n      Yres: m.plsC.yResidual,\n      E,\n    };\n  }\n\n  /**\n   * get access to all the computed elements\n   * Mainly for debug and testing\n   * @return {Object} output object\n   */\n  getLogs() {\n    return this.output;\n  }\n\n  getScores() {\n    let scoresX = this.tCV.map((x) => x.to1DArray());\n    let scoresY = this.tOrthCV.map((x) => x.to1DArray());\n    return { scoresX, scoresY };\n  }\n\n  /**\n   * Load an OPLS model from JSON\n   * @param {Object} model\n   * @return {OPLS}\n   */\n  static load(model) {\n    if (typeof model.name !== 'string') {\n      throw new TypeError('model must have a name property');\n    }\n    if (model.name !== 'OPLS') {\n      throw new RangeError(`invalid model: ${model.name}`);\n    }\n    return new OPLS(true, [], model);\n  }\n\n  /**\n   * Export the current model to a JSON object\n   * @return {Object} model\n   */\n  toJSON() {\n    return {\n      name: 'OPLS',\n      center: this.center,\n      scale: this.scale,\n      means: this.means,\n      stdevs: this.stdevs,\n      model: this.model,\n      tCV: this.tCV,\n      tOrthCV: this.tOrthCV,\n      yHatCV: this.yHatCV,\n    };\n  }\n\n  /**\n   * Predict scores for new data\n   * @param {Matrix} features - a matrix containing new data\n   * @param {Object} [options]\n   * @param {Array} [options.trueLabel] - an array with true values to compute confusion matrix\n   * @param {Number} [options.nc] - the number of components to be used\n   * @return {Object} - predictions\n   */\n  predict(newData, options = {}) {\n    let { trueLabels = [], nc = 1 } = options;\n    let labels = [];\n    if (trueLabels.length > 0) {\n      trueLabels = Matrix.from1DArray(trueLabels.length, 1, trueLabels);\n      labels = trueLabels.clone();\n    }\n\n    let features = newData.clone();\n\n    // scaling the test dataset with respect to the train\n    if (this.center) {\n      features.center('column', { center: this.means });\n      if (labels.rows > 0 && this.mode === 'regression') {\n        labels.center('column', { center: this.meansY });\n      }\n    }\n    if (this.scale) {\n      features.scale('column', { scale: this.stdevs });\n      if (labels.rows > 0 && this.mode === 'regression') {\n        labels.scale('column', { scale: this.stdevsY });\n      }\n    }\n\n    let Eh = features.clone();\n    // removing the orthogonal components from PLS\n    let tOrth;\n    let wOrth;\n    let pOrth;\n    let yHat;\n    let tPred;\n\n    for (let idx = 0; idx < nc; idx++) {\n      wOrth = this.model[idx].wOrth.transpose();\n      pOrth = this.model[idx].pOrth;\n      tOrth = Eh.mmul(wOrth);\n      Eh.sub(tOrth.mmul(pOrth));\n      // prediction\n      tPred = Eh.mmul(this.model[idx].plsC.w.transpose());\n      // this should be summed over ncomp (pls_prediction.R line 23)\n      yHat = tPred.mmul(this.model[idx].plsC.betas);\n    }\n\n    if (labels.rows > 0) {\n      if (this.mode === 'regression') {\n        let tssy = tss(labels);\n        let press = tss(labels.clone().sub(yHat));\n        let Q2y = 1 - press / tssy;\n\n        return { tPred, tOrth, yHat, Q2y };\n      } else if (this.mode === 'discriminantAnalysis') {\n        let confusionMatrix = [];\n        confusionMatrix = ConfusionMatrix.fromLabels(\n          trueLabels.to1DArray(),\n          yHat.to1DArray(),\n        );\n\n        return { tPred, tOrth, yHat, confusionMatrix };\n      }\n    } else {\n      return { tPred, tOrth, yHat };\n    }\n  }\n\n  _predictAll(features, labels, options = {}) {\n    // cannot use the global this.center here\n    // since it is used in the NC loop and\n    // centering and scaling should only be\n    // performed once\n    const { center = true, scale = true } = options;\n\n    if (center) {\n      features.center('column');\n      labels.center('column');\n    }\n\n    if (scale) {\n      features.scale('column');\n      labels.scale('column');\n      // reevaluate tssy and tssx after scaling\n      // must be global because re-used for next nc iteration\n      // tssx is only evaluate the first time\n      this.tssy = tss(labels);\n      this.tssx = tss(features);\n    }\n\n    let oplsC = OPLSNipals(features, labels);\n    let plsC = new NIPALS(oplsC.filteredX, { Y: labels });\n\n    let tPred = oplsC.filteredX.mmul(plsC.w.transpose());\n    let yHat = tPred.mmul(plsC.betas);\n\n    let rss = tss(labels.clone().sub(yHat));\n    let R2y = 1 - rss / this.tssy;\n\n    let xEx = plsC.t.mmul(plsC.p);\n    let rssx = tss(xEx);\n    let R2x = rssx / this.tssx;\n\n    return {\n      R2y,\n      R2x,\n      xRes: oplsC.filteredX,\n      tOrth: oplsC.scoresXOrtho,\n      pOrth: oplsC.loadingsXOrtho,\n      wOrth: oplsC.weightsXOrtho,\n      tPred: tPred,\n      totalPred: yHat,\n      XOrth: oplsC.scoresXOrtho.mmul(oplsC.loadingsXOrtho),\n      oplsC,\n      plsC,\n    };\n  }\n  /**\n   *\n   * @param {*} X - dataset matrix object\n   * @param {*} group - labels matrix object\n   * @param {*} index - train and test index (output from getFold())\n   */\n  _getTrainTest(X, group, index) {\n    let testFeatures = new Matrix(index.testIndex.length, X.columns);\n    let testLabels = new Matrix(index.testIndex.length, 1);\n    index.testIndex.forEach((el, idx) => {\n      testFeatures.setRow(idx, X.getRow(el));\n      testLabels.setRow(idx, group.getRow(el));\n    });\n\n    let trainFeatures = new Matrix(index.trainIndex.length, X.columns);\n    let trainLabels = new Matrix(index.trainIndex.length, 1);\n    index.trainIndex.forEach((el, idx) => {\n      trainFeatures.setRow(idx, X.getRow(el));\n      trainLabels.setRow(idx, group.getRow(el));\n    });\n\n    return {\n      trainFeatures,\n      testFeatures,\n      trainLabels,\n      testLabels,\n    };\n  }\n}\n","'use strict';\n\nvar mlMatrix = require('ml-matrix');\n\nfunction logistic(val) {\n  return 1 / (1 + Math.exp(-val));\n}\n\nfunction expELU(val, param) {\n  return val < 0 ? param * (Math.exp(val) - 1) : val;\n}\n\nfunction softExponential(val, param) {\n  if (param < 0) {\n    return -Math.log(1 - param * (val + param)) / param;\n  }\n  if (param > 0) {\n    return ((Math.exp(param * val) - 1) / param) + param;\n  }\n  return val;\n}\n\nfunction softExponentialPrime(val, param) {\n  if (param < 0) {\n    return 1 / (1 - param * (param + val));\n  } else {\n    return Math.exp(param * val);\n  }\n}\n\nconst ACTIVATION_FUNCTIONS = {\n  tanh: {\n    activation: Math.tanh,\n    derivate: (val) => 1 - (val * val)\n  },\n  identity: {\n    activation: (val) => val,\n    derivate: () => 1\n  },\n  logistic: {\n    activation: logistic,\n    derivate: (val) => logistic(val) * (1 - logistic(val))\n  },\n  arctan: {\n    activation: Math.atan,\n    derivate: (val) => 1 / (val * val + 1)\n  },\n  softsign: {\n    activation: (val) => val / (1 + Math.abs(val)),\n    derivate: (val) => 1 / ((1 + Math.abs(val)) * (1 + Math.abs(val)))\n  },\n  relu: {\n    activation: (val) => (val < 0 ? 0 : val),\n    derivate: (val) => (val < 0 ? 0 : 1)\n  },\n  softplus: {\n    activation: (val) => Math.log(1 + Math.exp(val)),\n    derivate: (val) => 1 / (1 + Math.exp(-val))\n  },\n  bent: {\n    activation: (val) => ((Math.sqrt(val * val + 1) - 1) / 2) + val,\n    derivate: (val) => (val / (2 * Math.sqrt(val * val + 1))) + 1\n  },\n  sinusoid: {\n    activation: Math.sin,\n    derivate: Math.cos\n  },\n  sinc: {\n    activation: (val) => (val === 0 ? 1 : Math.sin(val) / val),\n    derivate: (val) => (val === 0 ? 0 : (Math.cos(val) / val) - (Math.sin(val) / (val * val)))\n  },\n  gaussian: {\n    activation: (val) => Math.exp(-(val * val)),\n    derivate: (val) => -2 * val * Math.exp(-(val * val))\n  },\n  'parametric-relu': {\n    activation: (val, param) => (val < 0 ? param * val : val),\n    derivate: (val, param) => (val < 0 ? param : 1)\n  },\n  'exponential-elu': {\n    activation: expELU,\n    derivate: (val, param) => (val < 0 ? expELU(val, param) + param : 1)\n  },\n  'soft-exponential': {\n    activation: softExponential,\n    derivate: softExponentialPrime\n  }\n};\n\nclass Layer {\n  /**\n     * @private\n     * Create a new layer with the given options\n     * @param {object} options\n     * @param {number} [options.inputSize] - Number of conections that enter the neurons.\n     * @param {number} [options.outputSize] - Number of conections that leave the neurons.\n     * @param {number} [options.regularization] - Regularization parameter.\n     * @param {number} [options.epsilon] - Learning rate parameter.\n     * @param {string} [options.activation] - Activation function parameter from the FeedForwardNeuralNetwork class.\n     * @param {number} [options.activationParam] - Activation parameter if needed.\n     */\n  constructor(options) {\n    this.inputSize = options.inputSize;\n    this.outputSize = options.outputSize;\n    this.regularization = options.regularization;\n    this.epsilon = options.epsilon;\n    this.activation = options.activation;\n    this.activationParam = options.activationParam;\n\n    var selectedFunction = ACTIVATION_FUNCTIONS[options.activation];\n    var params = selectedFunction.activation.length;\n\n    var actFunction = params > 1 ? (val) => selectedFunction.activation(val, options.activationParam) : selectedFunction.activation;\n    var derFunction = params > 1 ? (val) => selectedFunction.derivate(val, options.activationParam) : selectedFunction.derivate;\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, actFunction(this.get(i, j)));\n    };\n    this.derivate = function (i, j) {\n      this.set(i, j, derFunction(this.get(i, j)));\n    };\n\n    if (options.model) {\n      // load model\n      this.W = mlMatrix.Matrix.checkMatrix(options.W);\n      this.b = mlMatrix.Matrix.checkMatrix(options.b);\n    } else {\n      // default constructor\n      this.W = mlMatrix.Matrix.rand(this.inputSize, this.outputSize);\n      this.b = mlMatrix.Matrix.zeros(1, this.outputSize);\n\n      this.W.apply(function (i, j) {\n        this.set(i, j, this.get(i, j) / Math.sqrt(options.inputSize));\n      });\n    }\n  }\n\n  /**\n     * @private\n     * propagate the given input through the current layer.\n     * @param {Matrix} X - input.\n     * @return {Matrix} output at the current layer.\n     */\n  forward(X) {\n    var z = X.mmul(this.W).addRowVector(this.b);\n    z.apply(this.activationFunction);\n    this.a = z.clone();\n    return z;\n  }\n\n  /**\n     * @private\n     * apply backpropagation algorithm at the current layer\n     * @param {Matrix} delta - delta values estimated at the following layer.\n     * @param {Matrix} a - 'a' values from the following layer.\n     * @return {Matrix} the new delta values for the next layer.\n     */\n  backpropagation(delta, a) {\n    this.dW = a.transpose().mmul(delta);\n    this.db = mlMatrix.Matrix.rowVector(delta.sum('column'));\n\n    var aCopy = a.clone();\n    return delta.mmul(this.W.transpose()).mul(aCopy.apply(this.derivate));\n  }\n\n  /**\n     * @private\n     * Function that updates the weights at the current layer with the derivatives.\n     */\n  update() {\n    this.dW.add(this.W.clone().mul(this.regularization));\n    this.W.add(this.dW.mul(-this.epsilon));\n    this.b.add(this.db.mul(-this.epsilon));\n  }\n\n  /**\n     * @private\n     * Export the current layer to JSON.\n     * @return {object} model\n     */\n  toJSON() {\n    return {\n      model: 'Layer',\n      inputSize: this.inputSize,\n      outputSize: this.outputSize,\n      regularization: this.regularization,\n      epsilon: this.epsilon,\n      activation: this.activation,\n      W: this.W,\n      b: this.b\n    };\n  }\n\n  /**\n     * @private\n     * Creates a new Layer with the given model.\n     * @param {object} model\n     * @return {Layer}\n     */\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n    return new Layer(model);\n  }\n}\n\nclass OutputLayer extends Layer {\n  constructor(options) {\n    super(options);\n\n    this.activationFunction = function (i, j) {\n      this.set(i, j, Math.exp(this.get(i, j)));\n    };\n  }\n\n  static load(model) {\n    if (model.model !== 'Layer') {\n      throw new RangeError('the current model is not a Layer model');\n    }\n\n    return new OutputLayer(model);\n  }\n}\n\nclass FeedForwardNeuralNetworks {\n  /**\n   * Create a new Feedforward neural network model.\n   * @class FeedForwardNeuralNetworks\n   * @param {object} [options]\n   * @param {Array} [options.hiddenLayers=[10]] - Array that contains the sizes of the hidden layers.\n   * @param {number} [options.iterations=50] - Number of iterations at the training step.\n   * @param {number} [options.learningRate=0.01] - Learning rate of the neural net (also known as epsilon).\n   * @param {number} [options.regularization=0.01] - Regularization parameter af the neural net.\n   * @param {string} [options.activation='tanh'] - activation function to be used. (options: 'tanh'(default),\n   * 'identity', 'logistic', 'arctan', 'softsign', 'relu', 'softplus', 'bent', 'sinusoid', 'sinc', 'gaussian').\n   * (single-parametric options: 'parametric-relu', 'exponential-relu', 'soft-exponential').\n   * @param {number} [options.activationParam=1] - if the selected activation function needs a parameter.\n   */\n  constructor(options) {\n    options = options || {};\n    if (options.model) {\n      // load network\n      this.hiddenLayers = options.hiddenLayers;\n      this.iterations = options.iterations;\n      this.learningRate = options.learningRate;\n      this.regularization = options.regularization;\n      this.dicts = options.dicts;\n      this.activation = options.activation;\n      this.activationParam = options.activationParam;\n      this.model = new Array(options.layers.length);\n\n      for (var i = 0; i < this.model.length - 1; ++i) {\n        this.model[i] = Layer.load(options.layers[i]);\n      }\n      this.model[this.model.length - 1] = OutputLayer.load(options.layers[this.model.length - 1]);\n    } else {\n      // default constructor\n      this.hiddenLayers = options.hiddenLayers || [10];\n      this.iterations = options.iterations || 50;\n\n      this.learningRate = options.learningRate || 0.01;\n      this.regularization = options.regularization || 0.01;\n\n      this.activation = options.activation || 'tanh';\n      this.activationParam = options.activationParam || 1;\n      if (!(this.activation in Object.keys(ACTIVATION_FUNCTIONS))) {\n        this.activation = 'tanh';\n      }\n    }\n  }\n\n  /**\n   * @private\n   * Function that build and initialize the neural net.\n   * @param {number} inputSize - total of features to fit.\n   * @param {number} outputSize - total of labels of the prediction set.\n   */\n  buildNetwork(inputSize, outputSize) {\n    var size = 2 + (this.hiddenLayers.length - 1);\n    this.model = new Array(size);\n\n    // input layer\n    this.model[0] = new Layer({\n      inputSize: inputSize,\n      outputSize: this.hiddenLayers[0],\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n\n    // hidden layers\n    for (var i = 1; i < this.hiddenLayers.length; ++i) {\n      this.model[i] = new Layer({\n        inputSize: this.hiddenLayers[i - 1],\n        outputSize: this.hiddenLayers[i],\n        activation: this.activation,\n        activationParam: this.activationParam,\n        regularization: this.regularization,\n        epsilon: this.learningRate\n      });\n    }\n\n    // output layer\n    this.model[size - 1] = new OutputLayer({\n      inputSize: this.hiddenLayers[this.hiddenLayers.length - 1],\n      outputSize: outputSize,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      regularization: this.regularization,\n      epsilon: this.learningRate\n    });\n  }\n\n  /**\n   * Train the neural net with the given features and labels.\n   * @param {Matrix|Array} features\n   * @param {Matrix|Array} labels\n   */\n  train(features, labels) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    this.dicts = dictOutputs(labels);\n\n    var inputSize = features.columns;\n    var outputSize = Object.keys(this.dicts.inputs).length;\n\n    if (!this.model) {\n      this.buildNetwork(inputSize, outputSize);\n    }\n\n    for (var i = 0; i < this.iterations; ++i) {\n      var probabilities = this.propagate(features);\n      this.backpropagation(features, labels, probabilities);\n    }\n  }\n\n  /**\n   * @private\n   * Propagate the input(training set) and retrives the probabilities of each class.\n   * @param {Matrix} X\n   * @return {Matrix} probabilities of each class.\n   */\n  propagate(X) {\n    var input = X;\n    for (var i = 0; i < this.model.length; ++i) {\n      input = this.model[i].forward(input);\n    }\n\n    // get probabilities\n    return input.divColumnVector(input.sum('row'));\n  }\n\n  /**\n   * @private\n   * Function that applies the backpropagation algorithm on each layer of the network\n   * in order to fit the features and labels.\n   * @param {Matrix} features\n   * @param {Array} labels\n   * @param {Matrix} probabilities - probabilities of each class of the feature set.\n   */\n  backpropagation(features, labels, probabilities) {\n    for (var i = 0; i < probabilities.rows; ++i) {\n      probabilities.set(i, this.dicts.inputs[labels[i]], probabilities.get(i, this.dicts.inputs[labels[i]]) - 1);\n    }\n\n    // remember, the last delta doesn't matter\n    var delta = probabilities;\n    for (i = this.model.length - 1; i >= 0; --i) {\n      var a = i > 0 ? this.model[i - 1].a : features;\n      delta = this.model[i].backpropagation(delta, a);\n    }\n\n    for (i = 0; i < this.model.length; ++i) {\n      this.model[i].update();\n    }\n  }\n\n  /**\n   * Predict the output given the feature set.\n   * @param {Array|Matrix} features\n   * @return {Array}\n   */\n  predict(features) {\n    features = mlMatrix.Matrix.checkMatrix(features);\n    var outputs = new Array(features.rows);\n    var probabilities = this.propagate(features);\n    for (var i = 0; i < features.rows; ++i) {\n      outputs[i] = this.dicts.outputs[probabilities.maxRowIndex(i)[1]];\n    }\n\n    return outputs;\n  }\n\n  /**\n   * Export the current model to JSON.\n   * @return {object} model\n   */\n  toJSON() {\n    var model = {\n      model: 'FNN',\n      hiddenLayers: this.hiddenLayers,\n      iterations: this.iterations,\n      learningRate: this.learningRate,\n      regularization: this.regularization,\n      activation: this.activation,\n      activationParam: this.activationParam,\n      dicts: this.dicts,\n      layers: new Array(this.model.length)\n    };\n\n    for (var i = 0; i < this.model.length; ++i) {\n      model.layers[i] = this.model[i].toJSON();\n    }\n\n    return model;\n  }\n\n  /**\n   * Load a Feedforward Neural Network with the current model.\n   * @param {object} model\n   * @return {FeedForwardNeuralNetworks}\n   */\n  static load(model) {\n    if (model.model !== 'FNN') {\n      throw new RangeError('the current model is not a feed forward network');\n    }\n\n    return new FeedForwardNeuralNetworks(model);\n  }\n}\n\n/**\n * @private\n * Method that given an array of labels(predictions), returns two dictionaries, one to transform from labels to\n * numbers and other in the reverse way\n * @param {Array} array\n * @return {object}\n */\nfunction dictOutputs(array) {\n  var inputs = {};\n  var outputs = {};\n  var index = 0;\n  for (var i = 0; i < array.length; i += 1) {\n    if (inputs[array[i]] === undefined) {\n      inputs[array[i]] = index;\n      outputs[index] = array[i];\n      index++;\n    }\n  }\n\n  return {\n    inputs: inputs,\n    outputs: outputs\n  };\n}\n\nmodule.exports = FeedForwardNeuralNetworks;\n","function NodeSquare(x, y, weights, som) {\n    this.x = x;\n    this.y = y;\n    this.weights = weights;\n    this.som = som;\n    this.neighbors = {};\n}\n\nNodeSquare.prototype.adjustWeights = function adjustWeights(target, learningRate, influence) {\n    for (var i = 0, ii = this.weights.length; i < ii; i++) {\n        this.weights[i] += learningRate * influence * (target[i] - this.weights[i]);\n    }\n};\n\nNodeSquare.prototype.getDistance = function getDistance(otherNode) {\n    return Math.max(Math.abs(this.x - otherNode.x), Math.abs(this.y - otherNode.y));\n};\n\nNodeSquare.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.x - otherNode.x),\n        distY = Math.abs(this.y - otherNode.y);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY));\n};\n\nNodeSquare.prototype.getNeighbors = function getNeighbors(xy) {\n    if (!this.neighbors[xy]) {\n        this.neighbors[xy] = new Array(2);\n\n        // left or bottom neighbor\n        var v;\n        if (this[xy] > 0) {\n            v = this[xy] - 1;\n        } else if (this.som.torus) {\n            v = this.som.gridDim[xy] - 1\n        }\n        if (typeof v !== 'undefined') {\n            var x, y;\n            if (xy === 'x') {\n                x = v;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = v;\n            }\n            this.neighbors[xy][0] = this.som.nodes[x][y];\n        }\n\n        // top or right neighbor\n        var w;\n        if (this[xy] < (this.som.gridDim[xy] - 1)) {\n            w = this[xy] + 1;\n        } else if (this.som.torus) {\n            w = 0;\n        }\n        if (typeof w !== 'undefined') {\n            if (xy === 'x') {\n                x = w;\n                y = this.y;\n            } else {\n                x = this.x;\n                y = w;\n            }\n            this.neighbors[xy][1] = this.som.nodes[x][y];\n        }\n    }\n    return this.neighbors[xy];\n};\n\nNodeSquare.prototype.getPos = function getPos(xy, element) {\n    var neighbors = this.getNeighbors(xy),\n        distance = this.som.distance,\n        bestNeighbor,\n        direction;\n    if(neighbors[0]) {\n        if (neighbors[1]) {\n            var dist1 = distance(element, neighbors[0].weights),\n                dist2 = distance(element, neighbors[1].weights);\n            if(dist1 < dist2) {\n                bestNeighbor = neighbors[0];\n                direction = -1;\n            } else {\n                bestNeighbor = neighbors[1];\n                direction = 1;\n            }\n        } else {\n            bestNeighbor = neighbors[0];\n            direction = -1;\n        }\n    } else {\n        bestNeighbor = neighbors[1];\n        direction = 1;\n    }\n    var simA = 1 - distance(element, this.weights),\n        simB = 1 - distance(element, bestNeighbor.weights);\n    var factor = ((simA - simB) / (2 - simA - simB));\n    return 0.5 + 0.5 * factor * direction;\n};\n\nNodeSquare.prototype.getPosition = function getPosition(element) {\n    return [\n        this.getPos('x', element),\n        this.getPos('y', element)\n    ];\n};\n\nmodule.exports = NodeSquare;","var NodeSquare = require('./node-square');\n\nfunction NodeHexagonal(x, y, weights, som) {\n\n    NodeSquare.call(this, x, y, weights, som);\n\n    this.hX = x - Math.floor(y / 2);\n    this.z = 0 - this.hX - y;\n\n}\n\nNodeHexagonal.prototype = new NodeSquare;\nNodeHexagonal.prototype.constructor = NodeHexagonal;\n\nNodeHexagonal.prototype.getDistance = function getDistanceHexagonal(otherNode) {\n    return Math.max(Math.abs(this.hX - otherNode.hX), Math.abs(this.y - otherNode.y), Math.abs(this.z - otherNode.z));\n};\n\nNodeHexagonal.prototype.getDistanceTorus = function getDistanceTorus(otherNode) {\n    var distX = Math.abs(this.hX - otherNode.hX),\n        distY = Math.abs(this.y - otherNode.y),\n        distZ = Math.abs(this.z - otherNode.z);\n    return Math.max(Math.min(distX, this.som.gridDim.x - distX), Math.min(distY, this.som.gridDim.y - distY), Math.min(distZ, this.som.gridDim.z - distZ));\n};\n\nNodeHexagonal.prototype.getPosition = function getPosition() {\n    throw new Error('Unimplemented : cannot get position of the points for hexagonal grid');\n};\n\nmodule.exports = NodeHexagonal;","'use strict';\n\nvar NodeSquare = require('./node-square'),\n    NodeHexagonal = require('./node-hexagonal');\n\nvar defaultOptions = {\n    fields: 3,\n    randomizer: Math.random,\n    distance: squareEuclidean,\n    iterations: 10,\n    learningRate: 0.1,\n    gridType: 'rect',\n    torus: true,\n    method: 'random'\n};\n\nfunction SOM(x, y, options, reload) {\n\n    this.x = x;\n    this.y = y;\n\n    options = options || {};\n    this.options = {};\n    for (var i in defaultOptions) {\n        if (options.hasOwnProperty(i)) {\n            this.options[i] = options[i];\n        } else {\n            this.options[i] = defaultOptions[i];\n        }\n    }\n\n    if (typeof this.options.fields === 'number') {\n        this.numWeights = this.options.fields;\n    } else if (Array.isArray(this.options.fields)) {\n        this.numWeights = this.options.fields.length;\n        var converters = getConverters(this.options.fields);\n        this.extractor = converters.extractor;\n        this.creator = converters.creator;\n    } else {\n        throw new Error('Invalid fields definition');\n    }\n\n    if (this.options.gridType === 'rect') {\n        this.nodeType = NodeSquare;\n        this.gridDim = {\n            x: x,\n            y: y\n        };\n    } else {\n        this.nodeType = NodeHexagonal;\n        var hx = this.x - Math.floor(this.y / 2);\n        this.gridDim = {\n            x: hx,\n            y: this.y,\n            z: -(0 - hx - this.y)\n        };\n    }\n\n    this.torus = this.options.torus;\n    this.distanceMethod = this.torus ? 'getDistanceTorus' : 'getDistance';\n\n    this.distance = this.options.distance;\n\n    this.maxDistance = getMaxDistance(this.distance, this.numWeights);\n\n    if (reload === true) { // For model loading\n        this.done = true;\n        return;\n    }\n    if (!(x > 0 && y > 0)) {\n        throw new Error('x and y must be positive');\n    }\n\n    this.times = {\n        findBMU: 0,\n        adjust: 0\n    };\n\n    this.randomizer = this.options.randomizer;\n\n    this.iterationCount = 0;\n    this.iterations = this.options.iterations;\n\n    this.startLearningRate = this.learningRate = this.options.learningRate;\n\n    this.mapRadius = Math.floor(Math.max(x, y) / 2);\n\n    this.algorithmMethod = this.options.method;\n\n    this._initNodes();\n\n    this.done = false;\n}\n\nSOM.load = function loadModel(model, distance) {\n    if (model.name === 'SOM') {\n        var x = model.data.length,\n            y = model.data[0].length;\n        if (distance) {\n            model.options.distance = distance;\n        } else if (model.options.distance) {\n            model.options.distance = eval('(' + model.options.distance + ')');\n        }\n        var som = new SOM(x, y, model.options, true);\n        som.nodes = new Array(x);\n        for (var i = 0; i < x; i++) {\n            som.nodes[i] = new Array(y);\n            for (var j = 0; j < y; j++) {\n                som.nodes[i][j] = new som.nodeType(i, j, model.data[i][j], som);\n            }\n        }\n        return som;\n    } else {\n        throw new Error('expecting a SOM model');\n    }\n};\n\nSOM.prototype.export = function exportModel(includeDistance) {\n    if (!this.done) {\n        throw new Error('model is not ready yet');\n    }\n    var model = {\n        name: 'SOM'\n    };\n    model.options = {\n        fields: this.options.fields,\n        gridType: this.options.gridType,\n        torus: this.options.torus\n    };\n    model.data = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        model.data[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            model.data[i][j] = this.nodes[i][j].weights;\n        }\n    }\n    if (includeDistance) {\n        model.options.distance = this.distance.toString();\n    }\n    return model;\n};\n\nSOM.prototype._initNodes = function initNodes() {\n    var now = Date.now(),\n        i, j, k;\n    this.nodes = new Array(this.x);\n    for (i = 0; i < this.x; i++) {\n        this.nodes[i] = new Array(this.y);\n        for (j = 0; j < this.y; j++) {\n            var weights = new Array(this.numWeights);\n            for (k = 0; k < this.numWeights; k++) {\n                weights[k] = this.randomizer();\n            }\n            this.nodes[i][j] = new this.nodeType(i, j, weights, this);\n        }\n    }\n    this.times.initNodes = Date.now() - now;\n};\n\nSOM.prototype.setTraining = function setTraining(trainingSet) {\n    if (this.trainingSet) {\n        throw new Error('training set has already been set');\n    }\n    var now = Date.now();\n    var convertedSet = trainingSet;\n    var i, l = trainingSet.length;\n    if (this.extractor) {\n        convertedSet = new Array(l);\n        for (i = 0; i < l; i++) {\n            convertedSet[i] = this.extractor(trainingSet[i]);\n        }\n    }\n    this.numIterations = this.iterations * l;\n\n    if (this.algorithmMethod === 'random') {\n        this.timeConstant = this.numIterations / Math.log(this.mapRadius);\n    } else {\n        this.timeConstant = l / Math.log(this.mapRadius);\n    }\n    this.trainingSet = convertedSet;\n    this.times.setTraining = Date.now() - now;\n};\n\nSOM.prototype.trainOne = function trainOne() {\n    if (this.done) {\n\n        return false;\n\n    } else if (this.numIterations-- > 0) {\n\n        var neighbourhoodRadius,\n            trainingValue,\n            trainingSetFactor;\n\n        if (this.algorithmMethod === 'random') { // Pick a random value of the training set at each step\n            neighbourhoodRadius = this.mapRadius * Math.exp(-this.iterationCount / this.timeConstant);\n            trainingValue = getRandomValue(this.trainingSet, this.randomizer);\n            this._adjust(trainingValue, neighbourhoodRadius);\n            this.learningRate = this.startLearningRate * Math.exp(-this.iterationCount / this.numIterations);\n        } else { // Get next input vector\n            trainingSetFactor = -Math.floor(this.iterationCount / this.trainingSet.length);\n            neighbourhoodRadius = this.mapRadius * Math.exp(trainingSetFactor / this.timeConstant);\n            trainingValue = this.trainingSet[this.iterationCount % this.trainingSet.length];\n            this._adjust(trainingValue, neighbourhoodRadius);\n            if (((this.iterationCount + 1) % this.trainingSet.length) === 0) {\n                this.learningRate = this.startLearningRate * Math.exp(trainingSetFactor / Math.floor(this.numIterations / this.trainingSet.length));\n            }\n        }\n\n        this.iterationCount++;\n\n        return true;\n\n    } else {\n\n        this.done = true;\n        return false;\n\n    }\n};\n\nSOM.prototype._adjust = function adjust(trainingValue, neighbourhoodRadius) {\n    var now = Date.now(),\n        x, y, dist, influence;\n\n    var bmu = this._findBestMatchingUnit(trainingValue);\n\n    var now2 = Date.now();\n    this.times.findBMU += now2 - now;\n\n    var radiusLimit = Math.floor(neighbourhoodRadius);\n    var xMin = bmu.x - radiusLimit,\n        xMax = bmu.x + radiusLimit,\n        yMin = bmu.y - radiusLimit,\n        yMax = bmu.y + radiusLimit;\n\n    for (x = xMin; x <= xMax; x++) {\n        var theX = x;\n        if (x < 0) {\n            theX += this.x;\n        } else if (x >= this.x) {\n            theX -= this.x;\n        }\n        for (y = yMin; y <= yMax; y++) {\n            var theY = y;\n            if (y < 0) {\n                theY += this.y;\n            } else if (y >= this.y) {\n                theY -= this.y;\n            }\n\n            dist = bmu[this.distanceMethod](this.nodes[theX][theY]);\n\n            if (dist < neighbourhoodRadius) {\n                influence = Math.exp(-dist / (2 * neighbourhoodRadius));\n                this.nodes[theX][theY].adjustWeights(trainingValue, this.learningRate, influence);\n            }\n\n        }\n    }\n\n    this.times.adjust += (Date.now() - now2);\n\n};\n\nSOM.prototype.train = function train(trainingSet) {\n    if (!this.done) {\n        this.setTraining(trainingSet);\n        while (this.trainOne()) {\n        }\n    }\n};\n\nSOM.prototype.getConvertedNodes = function getConvertedNodes() {\n    var result = new Array(this.x);\n    for (var i = 0; i < this.x; i++) {\n        result[i] = new Array(this.y);\n        for (var j = 0; j < this.y; j++) {\n            var node = this.nodes[i][j];\n            result[i][j] = this.creator ? this.creator(node.weights) : node.weights;\n        }\n    }\n    return result;\n};\n\nSOM.prototype._findBestMatchingUnit = function findBestMatchingUnit(candidate) {\n\n    var bmu,\n        lowest = Infinity,\n        dist;\n\n    for (var i = 0; i < this.x; i++) {\n        for (var j = 0; j < this.y; j++) {\n            dist = this.distance(this.nodes[i][j].weights, candidate);\n            if (dist < lowest) {\n                lowest = dist;\n                bmu = this.nodes[i][j];\n            }\n        }\n    }\n\n    return bmu;\n\n};\n\nSOM.prototype.predict = function predict(data, computePosition) {\n    if (typeof data === 'boolean') {\n        computePosition = data;\n        data = null;\n    }\n    if (!data) {\n        data = this.trainingSet;\n    }\n    if (Array.isArray(data) && (Array.isArray(data[0]) || (typeof data[0] === 'object'))) { // predict a dataset\n        var self = this;\n        return data.map(function (element) {\n            return self._predict(element, computePosition);\n        });\n    } else { // predict a single element\n        return this._predict(data, computePosition);\n    }\n};\n\nSOM.prototype._predict = function _predict(element, computePosition) {\n    if (!Array.isArray(element)) {\n        element = this.extractor(element);\n    }\n    var bmu = this._findBestMatchingUnit(element);\n    var result = [bmu.x, bmu.y];\n    if (computePosition) {\n        result[2] = bmu.getPosition(element);\n    }\n    return result;\n};\n\n// As seen in http://www.scholarpedia.org/article/Kohonen_network\nSOM.prototype.getQuantizationError = function getQuantizationError() {\n    var fit = this.getFit(),\n        l = fit.length,\n        sum = 0;\n    for (var i = 0; i < l; i++) {\n        sum += fit[i];\n    }\n    return sum / l;\n};\n\nSOM.prototype.getFit = function getFit(dataset) {\n    if (!dataset) {\n        dataset = this.trainingSet;\n    }\n    var l = dataset.length,\n        bmu,\n        result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        bmu = this._findBestMatchingUnit(dataset[i]);\n        result[i] = Math.sqrt(this.distance(dataset[i], bmu.weights));\n    }\n    return result;\n};\n\nfunction getConverters(fields) {\n    var l = fields.length,\n        normalizers = new Array(l),\n        denormalizers = new Array(l);\n    for (var i = 0; i < l; i++) {\n        normalizers[i] = getNormalizer(fields[i].range);\n        denormalizers[i] = getDenormalizer(fields[i].range);\n    }\n    return {\n        extractor: function extractor(value) {\n            var result = new Array(l);\n            for (var i = 0; i < l; i++) {\n                result[i] = normalizers[i](value[fields[i].name]);\n            }\n            return result;\n        },\n        creator: function creator(value) {\n            var result = {};\n            for (var i = 0; i < l; i++) {\n                result[fields[i].name] = denormalizers[i](value[i]);\n            }\n            return result;\n        }\n    };\n}\n\nfunction getNormalizer(minMax) {\n    return function normalizer(value) {\n        return (value - minMax[0]) / (minMax[1] - minMax[0]);\n    };\n}\n\nfunction getDenormalizer(minMax) {\n    return function denormalizer(value) {\n        return (minMax[0] + value * (minMax[1] - minMax[0]));\n    };\n}\n\nfunction squareEuclidean(a, b) {\n    var d = 0;\n    for (var i = 0, ii = a.length; i < ii; i++) {\n        d += (a[i] - b[i]) * (a[i] - b[i]);\n    }\n    return d;\n}\n\nfunction getRandomValue(arr, randomizer) {\n    return arr[Math.floor(randomizer() * arr.length)];\n}\n\nfunction getMaxDistance(distance, numWeights) {\n    var zero = new Array(numWeights),\n        one = new Array(numWeights);\n    for (var i = 0; i < numWeights; i++) {\n        zero[i] = 0;\n        one[i] = 1;\n    }\n    return distance(zero, one);\n}\n\nmodule.exports = SOM;","export default function maybeToPrecision(value, digits) {\n  if (value < 0) {\n    value = 0 - value;\n    if (typeof digits === 'number') {\n      return `- ${value.toPrecision(digits)}`;\n    } else {\n      return `- ${value.toString()}`;\n    }\n  } else {\n    if (typeof digits === 'number') {\n      return value.toPrecision(digits);\n    } else {\n      return value.toString();\n    }\n  }\n}\n","export default function checkArraySize(x, y) {\n  if (!Array.isArray(x) || !Array.isArray(y)) {\n    throw new TypeError('x and y must be arrays');\n  }\n  if (x.length !== y.length) {\n    throw new RangeError('x and y arrays must have the same length');\n  }\n}\n","export { default as maybeToPrecision } from './maybeToPrecision';\nexport { default as checkArrayLength } from './checkArrayLength';\n\nexport default class BaseRegression {\n  constructor() {\n    if (new.target === BaseRegression) {\n      throw new Error('BaseRegression must be subclassed');\n    }\n  }\n\n  predict(x) {\n    if (typeof x === 'number') {\n      return this._predict(x);\n    } else if (Array.isArray(x)) {\n      const y = [];\n      for (let i = 0; i < x.length; i++) {\n        y.push(this._predict(x[i]));\n      }\n      return y;\n    } else {\n      throw new TypeError('x must be a number or array');\n    }\n  }\n\n  _predict() {\n    throw new Error('_predict must be implemented');\n  }\n\n  train() {\n    // Do nothing for this package\n  }\n\n  toString() {\n    return '';\n  }\n\n  toLaTeX() {\n    return '';\n  }\n\n  /**\n   * Return the correlation coefficient of determination (r) and chi-square.\n   * @param {Array<number>} x\n   * @param {Array<number>} y\n   * @return {object}\n   */\n  score(x, y) {\n    if (!Array.isArray(x) || !Array.isArray(y) || x.length !== y.length) {\n      throw new Error('x and y must be arrays of the same length');\n    }\n\n    const n = x.length;\n    const y2 = new Array(n);\n    for (let i = 0; i < n; i++) {\n      y2[i] = this._predict(x[i]);\n    }\n\n    let xSum = 0;\n    let ySum = 0;\n    let chi2 = 0;\n    let rmsd = 0;\n    let xSquared = 0;\n    let ySquared = 0;\n    let xY = 0;\n    for (let i = 0; i < n; i++) {\n      xSum += y2[i];\n      ySum += y[i];\n      xSquared += y2[i] * y2[i];\n      ySquared += y[i] * y[i];\n      xY += y2[i] * y[i];\n      if (y[i] !== 0) {\n        chi2 += ((y[i] - y2[i]) * (y[i] - y2[i])) / y[i];\n      }\n      rmsd += (y[i] - y2[i]) * (y[i] - y2[i]);\n    }\n\n    const r =\n      (n * xY - xSum * ySum) /\n      Math.sqrt((n * xSquared - xSum * xSum) * (n * ySquared - ySum * ySum));\n\n    return {\n      r: r,\n      r2: r * r,\n      chi2: chi2,\n      rmsd: Math.sqrt(rmsd / n)\n    };\n  }\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { Matrix, MatrixTransposeView, solve } from 'ml-matrix';\n\nexport default class PolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y, degree);\n    }\n  }\n\n  _predict(x) {\n    let y = 0;\n    for (let k = 0; k < this.powers.length; k++) {\n      y += this.coefficients[k] * Math.pow(x, this.powers[k]);\n    }\n    return y;\n  }\n\n  toJSON() {\n    return {\n      name: 'polynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) + times}x`;\n          } else {\n            str =\n              `${maybeToPrecision(this.coefficients[k], precision) +\n              times\n              }x${\n                sup\n              }${this.powers[k]\n              }${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'polynomialRegression') {\n      throw new TypeError('not a polynomial regression model');\n    }\n    return new PolynomialRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y, degree) {\n  const n = x.length;\n  let powers;\n  if (Array.isArray(degree)) {\n    powers = degree;\n    degree = powers.length;\n  } else {\n    degree++;\n    powers = new Array(degree);\n    for (let k = 0; k < degree; k++) {\n      powers[k] = k;\n    }\n  }\n  const F = new Matrix(n, degree);\n  const Y = new Matrix([y]);\n  for (let k = 0; k < degree; k++) {\n    for (let i = 0; i < n; i++) {\n      if (powers[k] === 0) {\n        F.set(i, k, 1);\n      } else {\n        F.set(i, k, Math.pow(x[i], powers[k]));\n      }\n    }\n  }\n\n  const FT = new MatrixTransposeView(F);\n  const A = FT.mmul(F);\n  const B = FT.mmul(new MatrixTransposeView(Y));\n\n  pr.degree = degree - 1;\n  pr.powers = powers;\n  pr.coefficients = solve(A, B).to1DArray();\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\n\nexport default class SimpleLinearRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = [y.intercept, y.slope];\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'simpleLinearRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(x) {\n    return this.slope * x + this.intercept;\n  }\n\n  computeX(y) {\n    return (y - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    let result = 'f(x) = ';\n    if (this.slope !== 0) {\n      const xFactor = maybeToPrecision(this.slope, precision);\n      result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;\n      if (this.intercept !== 0) {\n        const absIntercept = Math.abs(this.intercept);\n        const operator = absIntercept === this.intercept ? '+' : '-';\n        result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'simpleLinearRegression') {\n      throw new TypeError('not a SLR model');\n    }\n    return new SimpleLinearRegression(true, json);\n  }\n}\n\nfunction regress(slr, x, y) {\n  const n = x.length;\n  let xSum = 0;\n  let ySum = 0;\n\n  let xSquared = 0;\n  let xY = 0;\n\n  for (let i = 0; i < n; i++) {\n    xSum += x[i];\n    ySum += y[i];\n    xSquared += x[i] * x[i];\n    xY += x[i] * y[i];\n  }\n\n  const numerator = n * xY - xSum * ySum;\n  slr.slope = numerator / (n * xSquared - xSum * xSum);\n  slr.intercept = (1 / n) * ySum - slr.slope * (1 / n) * xSum;\n  slr.coefficients = [slr.intercept, slr.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class ExponentialRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(input) {\n    return this.B * Math.exp(input * this.A);\n  }\n\n  toJSON() {\n    return {\n      name: 'exponentialRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return (\n      `f(x) = ${\n        maybeToPrecision(this.B, precision)\n      } * e^(${\n        maybeToPrecision(this.A, precision)\n      } * x)`\n    );\n  }\n\n  toLaTeX(precision) {\n    if (this.A >= 0) {\n      return (\n        `f(x) = ${\n          maybeToPrecision(this.B, precision)\n        }e^{${\n          maybeToPrecision(this.A, precision)\n        }x}`\n      );\n    } else {\n      return (\n        `f(x) = \\\\frac{${\n          maybeToPrecision(this.B, precision)\n        }}{e^{${\n          maybeToPrecision(-this.A, precision)\n        }x}}`\n      );\n    }\n  }\n\n  static load(json) {\n    if (json.name !== 'exponentialRegression') {\n      throw new TypeError('not a exponential regression model');\n    }\n    return new ExponentialRegression(true, json);\n  }\n}\n\nfunction regress(er, x, y) {\n  const n = x.length;\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(x, yl);\n  er.A = linear.slope;\n  er.B = Math.exp(linear.intercept);\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport SimpleLinearRegression from 'ml-regression-simple-linear';\n\nexport default class PowerRegression extends BaseRegression {\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // reloading model\n      this.A = y.A;\n      this.B = y.B;\n    } else {\n      checkArrayLength(x, y);\n      regress(this, x, y);\n    }\n  }\n\n  _predict(newInputs) {\n    return this.A * Math.pow(newInputs, this.B);\n  }\n\n  toJSON() {\n    return {\n      name: 'powerRegression',\n      A: this.A,\n      B: this.B\n    };\n  }\n\n  toString(precision) {\n    return `f(x) = ${maybeToPrecision(\n      this.A,\n      precision\n    )} * x^${maybeToPrecision(this.B, precision)}`;\n  }\n\n  toLaTeX(precision) {\n    let latex = '';\n    if (this.B >= 0) {\n      latex = `f(x) = ${maybeToPrecision(\n        this.A,\n        precision\n      )}x^{${maybeToPrecision(this.B, precision)}}`;\n    } else {\n      latex = `f(x) = \\\\frac{${maybeToPrecision(\n        this.A,\n        precision\n      )}}{x^{${maybeToPrecision(-this.B, precision)}}}`;\n    }\n    latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');\n    return latex;\n  }\n\n  static load(json) {\n    if (json.name !== 'powerRegression') {\n      throw new TypeError('not a power regression model');\n    }\n    return new PowerRegression(true, json);\n  }\n}\n\nfunction regress(pr, x, y) {\n  const n = x.length;\n  const xl = new Array(n);\n  const yl = new Array(n);\n  for (let i = 0; i < n; i++) {\n    xl[i] = Math.log(x[i]);\n    yl[i] = Math.log(y[i]);\n  }\n\n  const linear = new SimpleLinearRegression(xl, yl);\n  pr.A = Math.exp(linear.intercept);\n  pr.B = linear.slope;\n}\n","import Matrix, { SVD, pseudoInverse } from 'ml-matrix';\n\nexport default class MultivariateLinearRegression {\n  constructor(x, y, options = {}) {\n    const { intercept = true, statistics = true } = options;\n    this.statistics = statistics;\n    if (x === true) {\n      this.weights = y.weights;\n      this.inputs = y.inputs;\n      this.outputs = y.outputs;\n      this.intercept = y.intercept;\n    } else {\n      x = new Matrix(x);\n      y = new Matrix(y);\n      if (intercept) {\n        x.addColumn(new Array(x.rows).fill(1));\n      }\n      let xt = x.transpose();\n      const xx = xt\n        .mmul(x);\n      const xy = xt\n        .mmul(y);\n      const invxx = new SVD(xx)\n        .inverse();\n      const beta = xy\n        .transpose()\n        .mmul(invxx)\n        .transpose();\n      this.weights = beta.to2DArray();\n      this.inputs = x.columns;\n      this.outputs = y.columns;\n      if (intercept) this.inputs--;\n      this.intercept = intercept;\n      if (statistics) {\n        /*\n         * Let's add some basic statistics about the beta's to be able to interpret them.\n         * source: http://dept.stat.lsa.umich.edu/~kshedden/Courses/Stat401/Notes/401-multreg.pdf\n         * validated against Excel Regression AddIn\n         * test: \"datamining statistics test\"\n         */\n        const fittedValues = x.mmul(beta);\n        const residuals = y.clone().addM(fittedValues.neg());\n        const variance =\n          residuals\n            .to2DArray()\n            .map((ri) => Math.pow(ri[0], 2))\n            .reduce((a, b) => a + b) /\n          (y.rows - x.columns);\n        this.stdError = Math.sqrt(variance);\n        this.stdErrorMatrix = pseudoInverse(xx).mul(variance);\n        this.stdErrors = this.stdErrorMatrix\n          .diagonal()\n          .map((d) => Math.sqrt(d));\n        this.tStats = this.weights.map((d, i) =>\n          (this.stdErrors[i] === 0 ? 0 : d[0] / this.stdErrors[i])\n        );\n      }\n    }\n  }\n\n  predict(x) {\n    if (Array.isArray(x)) {\n      if (typeof x[0] === 'number') {\n        return this._predict(x);\n      } else if (Array.isArray(x[0])) {\n        const y = new Array(x.length);\n        for (let i = 0; i < x.length; i++) {\n          y[i] = this._predict(x[i]);\n        }\n        return y;\n      }\n    } else if (Matrix.isMatrix(x)) {\n      const y = new Matrix(x.rows, this.outputs);\n      for (let i = 0; i < x.rows; i++) {\n        y.setRow(i, this._predict(x.getRow(i)));\n      }\n      return y;\n    }\n    throw new TypeError('x must be a matrix or array of numbers');\n  }\n\n  _predict(x) {\n    const result = new Array(this.outputs);\n    if (this.intercept) {\n      for (let i = 0; i < this.outputs; i++) {\n        result[i] = this.weights[this.inputs][i];\n      }\n    } else {\n      result.fill(0);\n    }\n    for (let i = 0; i < this.inputs; i++) {\n      for (let j = 0; j < this.outputs; j++) {\n        result[j] += this.weights[i][j] * x[i];\n      }\n    }\n    return result;\n  }\n\n  score() {\n    throw new Error('score method is not implemented yet');\n  }\n\n  toJSON() {\n    return {\n      name: 'multivariateLinearRegression',\n      weights: this.weights,\n      inputs: this.inputs,\n      outputs: this.outputs,\n      intercept: this.intercept,\n      summary: this.statistics\n        ? {\n          regressionStatistics: {\n            standardError: this.stdError,\n            observations: this.outputs\n          },\n          variables: this.weights.map((d, i) => {\n            return {\n              label:\n                  i === this.weights.length - 1\n                    ? 'Intercept'\n                    : `X Variable ${i + 1}`,\n              coefficients: d,\n              standardError: this.stdErrors[i],\n              tStat: this.tStats[i]\n            };\n          })\n        }\n        : undefined\n    };\n  }\n\n  static load(model) {\n    if (model.name !== 'multivariateLinearRegression') {\n      throw new Error('not a MLR model');\n    }\n    return new MultivariateLinearRegression(true, model);\n  }\n}\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass GaussianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = GaussianKernel;\n","'use strict';\n\nconst defaultOptions = {\n  degree: 1,\n  constant: 1,\n  scale: 1\n};\n\nclass PolynomialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n\n    this.degree = options.degree;\n    this.constant = options.constant;\n    this.scale = options.scale;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.pow(this.scale * sum + this.constant, this.degree);\n  }\n}\n\nmodule.exports = PolynomialKernel;\n","'use strict';\n\nconst defaultOptions = {\n  alpha: 0.01,\n  constant: -Math.E\n};\n\nclass SigmoidKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.alpha = options.alpha;\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    for (var i = 0; i < x.length; i++) {\n      sum += x[i] * y[i];\n    }\n    return Math.tanh(this.alpha * sum + this.constant);\n  }\n}\n\nmodule.exports = SigmoidKernel;\n","'use strict';\n\nconst defaultOptions = {\n  sigma: 1,\n  degree: 1\n};\n\nclass ANOVAKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.degree = options.degree;\n  }\n\n  compute(x, y) {\n    var sum = 0;\n    var len = Math.min(x.length, y.length);\n    for (var i = 1; i <= len; ++i) {\n      sum += Math.pow(\n        Math.exp(\n          -this.sigma *\n            Math.pow(Math.pow(x[i - 1], i) - Math.pow(y[i - 1], i), 2)\n        ),\n        this.degree\n      );\n    }\n    return sum;\n  }\n}\n\nmodule.exports = ANOVAKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass CauchyKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    return 1 / (1 + squaredEuclidean(x, y) / (this.sigma * this.sigma));\n  }\n}\n\nmodule.exports = CauchyKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass ExponentialKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n    this.divisor = 2 * options.sigma * options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.divisor);\n  }\n}\n\nmodule.exports = ExponentialKernel;\n","'use strict';\n\nclass HistogramIntersectionKernel {\n  compute(x, y) {\n    var min = Math.min(x.length, y.length);\n    var sum = 0;\n    for (var i = 0; i < min; ++i) {\n      sum += Math.min(x[i], y[i]);\n    }\n\n    return sum;\n  }\n}\n\nmodule.exports = HistogramIntersectionKernel;\n","'use strict';\n\nconst { euclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  sigma: 1\n};\n\nclass LaplacianKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.sigma = options.sigma;\n  }\n\n  compute(x, y) {\n    const distance = euclidean(x, y);\n    return Math.exp(-distance / this.sigma);\n  }\n}\n\nmodule.exports = LaplacianKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass MultiquadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    return Math.sqrt(squaredEuclidean(x, y) + this.constant * this.constant);\n  }\n}\n\nmodule.exports = MultiquadraticKernel;\n","'use strict';\n\nconst { squaredEuclidean } = require('ml-distance-euclidean');\n\nconst defaultOptions = {\n  constant: 1\n};\n\nclass RationalQuadraticKernel {\n  constructor(options) {\n    options = Object.assign({}, defaultOptions, options);\n    this.constant = options.constant;\n  }\n\n  compute(x, y) {\n    const distance = squaredEuclidean(x, y);\n    return 1 - distance / (distance + this.constant);\n  }\n}\n\nmodule.exports = RationalQuadraticKernel;\n","'use strict';\n\nconst { Matrix, MatrixTransposeView } = require('ml-matrix');\nconst GaussianKernel = require('ml-kernel-gaussian');\nconst PolynomialKernel = require('ml-kernel-polynomial');\nconst SigmoidKernel = require('ml-kernel-sigmoid');\n\nconst ANOVAKernel = require('./kernels/anova-kernel');\nconst CauchyKernel = require('./kernels/cauchy-kernel');\nconst ExponentialKernel = require('./kernels/exponential-kernel');\nconst HistogramKernel = require('./kernels/histogram-intersection-kernel');\nconst LaplacianKernel = require('./kernels/laplacian-kernel');\nconst MultiquadraticKernel = require('./kernels/multiquadratic-kernel');\nconst RationalKernel = require('./kernels/rational-quadratic-kernel');\n\nconst kernelType = {\n  gaussian: GaussianKernel,\n  rbf: GaussianKernel,\n  polynomial: PolynomialKernel,\n  poly: PolynomialKernel,\n  anova: ANOVAKernel,\n  cauchy: CauchyKernel,\n  exponential: ExponentialKernel,\n  histogram: HistogramKernel,\n  min: HistogramKernel,\n  laplacian: LaplacianKernel,\n  multiquadratic: MultiquadraticKernel,\n  rational: RationalKernel,\n  sigmoid: SigmoidKernel,\n  mlp: SigmoidKernel\n};\n\nclass Kernel {\n  constructor(type, options) {\n    this.kernelType = type;\n    if (type === 'linear') return;\n\n    if (typeof type === 'string') {\n      type = type.toLowerCase();\n\n      var KernelConstructor = kernelType[type];\n      if (KernelConstructor) {\n        this.kernelFunction = new KernelConstructor(options);\n      } else {\n        throw new Error(`unsupported kernel type: ${type}`);\n      }\n    } else if (typeof type === 'object' && typeof type.compute === 'function') {\n      this.kernelFunction = type;\n    } else {\n      throw new TypeError(\n        'first argument must be a valid kernel type or instance'\n      );\n    }\n  }\n\n  compute(inputs, landmarks) {\n    inputs = Matrix.checkMatrix(inputs);\n    if (landmarks === undefined) {\n      landmarks = inputs;\n    } else {\n      landmarks = Matrix.checkMatrix(landmarks);\n    }\n    if (this.kernelType === 'linear') {\n      return inputs.mmul(new MatrixTransposeView(landmarks));\n    }\n\n    const kernelMatrix = new Matrix(inputs.rows, landmarks.rows);\n    if (inputs === landmarks) {\n      // fast path, matrix is symmetric\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = i; j < inputs.rows; j++) {\n          const value = this.kernelFunction.compute(\n            inputs.getRow(i),\n            inputs.getRow(j)\n          );\n          kernelMatrix.set(i, j, value);\n          kernelMatrix.set(j, i, value);\n        }\n      }\n    } else {\n      for (let i = 0; i < inputs.rows; i++) {\n        for (let j = 0; j < landmarks.rows; j++) {\n          kernelMatrix.set(\n            i,\n            j,\n            this.kernelFunction.compute(inputs.getRow(i), landmarks.getRow(j))\n          );\n        }\n      }\n    }\n    return kernelMatrix;\n  }\n}\n\nmodule.exports = Kernel;\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport median from 'ml-array-median';\n\nexport default class TheilSenRegression extends BaseRegression {\n  /**\n   * Theil–Sen estimator\n   * https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator\n   * @param {Array<number>|boolean} x\n   * @param {Array<number>|object} y\n   * @constructor\n   */\n  constructor(x, y) {\n    super();\n    if (x === true) {\n      // loads the model\n      this.slope = y.slope;\n      this.intercept = y.intercept;\n      this.coefficients = y.coefficients;\n    } else {\n      // creates the model\n      checkArrayLength(x, y);\n      theilSen(this, x, y);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'TheilSenRegression',\n      slope: this.slope,\n      intercept: this.intercept\n    };\n  }\n\n  _predict(input) {\n    return this.slope * input + this.intercept;\n  }\n\n  computeX(input) {\n    return (input - this.intercept) / this.slope;\n  }\n\n  toString(precision) {\n    var result = 'f(x) = ';\n    if (this.slope) {\n      var xFactor = maybeToPrecision(this.slope, precision);\n      result += `${Math.abs(xFactor - 1) < 1e-5 ? '' : `${xFactor} * `}x`;\n      if (this.intercept) {\n        var absIntercept = Math.abs(this.intercept);\n        var operator = absIntercept === this.intercept ? '+' : '-';\n        result +=\n          ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;\n      }\n    } else {\n      result += maybeToPrecision(this.intercept, precision);\n    }\n    return result;\n  }\n\n  toLaTeX(precision) {\n    return this.toString(precision);\n  }\n\n  static load(json) {\n    if (json.name !== 'TheilSenRegression') {\n      throw new TypeError('not a Theil-Sen model');\n    }\n    return new TheilSenRegression(true, json);\n  }\n}\n\nfunction theilSen(regression, x, y) {\n  let len = x.length;\n  let slopes = new Array(len * len);\n  let count = 0;\n  for (let i = 0; i < len; ++i) {\n    for (let j = i + 1; j < len; ++j) {\n      if (x[i] !== x[j]) {\n        slopes[count++] = (y[j] - y[i]) / (x[j] - x[i]);\n      }\n    }\n  }\n  slopes.length = count;\n  let medianSlope = median(slopes);\n\n  let cuts = new Array(len);\n  for (let i = 0; i < len; ++i) {\n    cuts[i] = y[i] - medianSlope * x[i];\n  }\n\n  regression.slope = medianSlope;\n  regression.intercept = median(cuts);\n  regression.coefficients = [regression.intercept, regression.slope];\n}\n","import BaseRegression, {\n  checkArrayLength,\n  maybeToPrecision\n} from 'ml-regression-base';\nimport { solve } from 'ml-matrix';\n\n/**\n * @class RobustPolynomialRegression\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree - polynomial degree\n */\nexport default class RobustPolynomialRegression extends BaseRegression {\n  constructor(x, y, degree) {\n    super();\n    if (x === true) {\n      this.degree = y.degree;\n      this.powers = y.powers;\n      this.coefficients = y.coefficients;\n    } else {\n      checkArrayLength(x, y);\n      robustPolynomial(this, x, y, degree);\n    }\n  }\n\n  toJSON() {\n    return {\n      name: 'robustPolynomialRegression',\n      degree: this.degree,\n      powers: this.powers,\n      coefficients: this.coefficients\n    };\n  }\n\n  _predict(x) {\n    return predict(x, this.powers, this.coefficients);\n  }\n\n  /**\n   * Display the formula\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toString(precision) {\n    return this._toFormula(precision, false);\n  }\n\n  /**\n   * Display the formula in LaTeX format\n   * @param {number} precision - precision for the numbers\n   * @return {string}\n   */\n  toLaTeX(precision) {\n    return this._toFormula(precision, true);\n  }\n\n  _toFormula(precision, isLaTeX) {\n    let sup = '^';\n    let closeSup = '';\n    let times = ' * ';\n    if (isLaTeX) {\n      sup = '^{';\n      closeSup = '}';\n      times = '';\n    }\n\n    let fn = '';\n    let str = '';\n    for (let k = 0; k < this.coefficients.length; k++) {\n      str = '';\n      if (this.coefficients[k] !== 0) {\n        if (this.powers[k] === 0) {\n          str = maybeToPrecision(this.coefficients[k], precision);\n        } else {\n          if (this.powers[k] === 1) {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x`;\n          } else {\n            str = `${maybeToPrecision(this.coefficients[k], precision) +\n              times}x${sup}${this.powers[k]}${closeSup}`;\n          }\n        }\n\n        if (this.coefficients[k] > 0 && k !== this.coefficients.length - 1) {\n          str = ` + ${str}`;\n        } else if (k !== this.coefficients.length - 1) {\n          str = ` ${str}`;\n        }\n      }\n      fn = str + fn;\n    }\n    if (fn.charAt(0) === '+') {\n      fn = fn.slice(1);\n    }\n\n    return `f(x) = ${fn}`;\n  }\n\n  static load(json) {\n    if (json.name !== 'robustPolynomialRegression') {\n      throw new TypeError('not a RobustPolynomialRegression model');\n    }\n    return new RobustPolynomialRegression(true, json);\n  }\n}\n\nfunction robustPolynomial(regression, x, y, degree) {\n  let powers = Array(degree)\n    .fill(0)\n    .map((_, index) => index);\n\n  const tuples = getRandomTuples(x, y, degree);\n\n  var min;\n  for (var i = 0; i < tuples.length; i++) {\n    var tuple = tuples[i];\n    var coefficients = calcCoefficients(tuple, powers);\n\n    var residuals = x.slice();\n    for (var j = 0; j < x.length; j++) {\n      residuals[j] = y[j] - predict(x[j], powers, coefficients);\n      residuals[j] = {\n        residual: residuals[j] * residuals[j],\n        coefficients\n      };\n    }\n\n    var median = residualsMedian(residuals);\n    if (!min || median.residual < min.residual) {\n      min = median;\n    }\n  }\n\n  regression.degree = degree;\n  regression.powers = powers;\n  regression.coefficients = min.coefficients;\n}\n\n/**\n * @ignore\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} degree\n * @return {Array<{x:number,y:number}>}\n */\nfunction getRandomTuples(x, y, degree) {\n  var len = Math.floor(x.length / degree);\n  var tuples = new Array(len);\n\n  for (var i = 0; i < x.length; i++) {\n    var pos = Math.floor(Math.random() * len);\n\n    var counter = 0;\n    while (counter < x.length) {\n      if (!tuples[pos]) {\n        tuples[pos] = [\n          {\n            x: x[i],\n            y: y[i]\n          }\n        ];\n        break;\n      } else if (tuples[pos].length < degree) {\n        tuples[pos].push({\n          x: x[i],\n          y: y[i]\n        });\n        break;\n      } else {\n        counter++;\n        pos = (pos + 1) % len;\n      }\n    }\n\n    if (counter === x.length) {\n      return tuples;\n    }\n  }\n  return tuples;\n}\n\n/**\n * @ignore\n * @param {{x:number,y:number}} tuple\n * @param {Array<number>} powers\n * @return {Array<number>}\n */\nfunction calcCoefficients(tuple, powers) {\n  var X = tuple.slice();\n  var Y = tuple.slice();\n  for (var i = 0; i < X.length; i++) {\n    Y[i] = [tuple[i].y];\n    X[i] = new Array(powers.length);\n    for (var j = 0; j < powers.length; j++) {\n      X[i][j] = Math.pow(tuple[i].x, powers[j]);\n    }\n  }\n\n  return solve(X, Y).to1DArray();\n}\n\nfunction predict(x, powers, coefficients) {\n  let y = 0;\n  for (let k = 0; k < powers.length; k++) {\n    y += coefficients[k] * Math.pow(x, powers[k]);\n  }\n  return y;\n}\n\nfunction residualsMedian(residuals) {\n  residuals.sort((a, b) => a.residual - b.residual);\n\n  var l = residuals.length;\n  var half = Math.floor(l / 2);\n  return l % 2 === 0 ? residuals[half - 1] : residuals[half];\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","/**\n * Calculate current error\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} parameters - Array of current parameter values\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {number}\n */\nexport default function errorCalculation(\n  data,\n  parameters,\n  parameterizedFunction,\n) {\n  let error = 0;\n  const func = parameterizedFunction(parameters);\n\n  for (let i = 0; i < data.x.length; i++) {\n    error += Math.abs(data.y[i] - func(data.x[i]));\n  }\n\n  return error;\n}\n","import { inverse, Matrix } from 'ml-matrix';\n\n/**\n * Difference of the matrix function over the parameters\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Matrix}\n */\nfunction gradientFunction(\n  data,\n  evaluatedData,\n  params,\n  gradientDifference,\n  paramFunction,\n) {\n  const n = params.length;\n  const m = data.x.length;\n\n  let ans = new Array(n);\n\n  for (let param = 0; param < n; param++) {\n    ans[param] = new Array(m);\n    let auxParams = params.slice();\n    auxParams[param] += gradientDifference;\n    let funcParam = paramFunction(auxParams);\n\n    for (let point = 0; point < m; point++) {\n      ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);\n    }\n  }\n  return new Matrix(ans);\n}\n\n/**\n * Matrix function over the samples\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} evaluatedData - Array of previous evaluated function values\n * @return {Matrix}\n */\nfunction matrixFunction(data, evaluatedData) {\n  const m = data.x.length;\n\n  let ans = new Array(m);\n\n  for (let point = 0; point < m; point++) {\n    ans[point] = [data.y[point] - evaluatedData[point]];\n  }\n\n  return new Matrix(ans);\n}\n\n/**\n * Iteration for Levenberg-Marquardt\n * @ignore\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {Array<number>} params - Array of previous parameter values\n * @param {number} damping - Levenberg-Marquardt parameter\n * @param {number} gradientDifference - Adjustment for decrease the damping parameter\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @return {Array<number>}\n */\nexport default function step(\n  data,\n  params,\n  damping,\n  gradientDifference,\n  parameterizedFunction,\n) {\n  let value = damping * gradientDifference * gradientDifference;\n  let identity = Matrix.eye(params.length, params.length, value);\n\n  const func = parameterizedFunction(params);\n\n  let evaluatedData = new Float64Array(data.x.length);\n  for (let i = 0; i < data.x.length; i++) {\n    evaluatedData[i] = func(data.x[i]);\n  }\n\n  let gradientFunc = gradientFunction(\n    data,\n    evaluatedData,\n    params,\n    gradientDifference,\n    parameterizedFunction,\n  );\n  let matrixFunc = matrixFunction(data, evaluatedData);\n  let inverseMatrix = inverse(\n    identity.add(gradientFunc.mmul(gradientFunc.transpose())),\n  );\n\n  params = new Matrix([params]);\n  params = params.sub(\n    inverseMatrix\n      .mmul(gradientFunc)\n      .mmul(matrixFunc)\n      .mul(gradientDifference)\n      .transpose(),\n  );\n\n  return params.to1DArray();\n}\n","import isArray from 'is-any-array';\n\nimport errorCalculation from './errorCalculation';\nimport step from './step';\n\n/**\n * Curve fitting algorithm\n * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]\n * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter\n * @param {object} [options] - Options object\n * @param {number} [options.damping] - Levenberg-Marquardt parameter\n * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter\n * @param {Array<number>} [options.minValues] - Minimum allowed values for parameters\n * @param {Array<number>} [options.maxValues] - Maximum allowed values for parameters\n * @param {Array<number>} [options.initialValues] - Array of initial parameter values\n * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations\n * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point\n * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}\n */\nexport default function levenbergMarquardt(\n  data,\n  parameterizedFunction,\n  options = {},\n) {\n  let {\n    maxIterations = 100,\n    gradientDifference = 10e-2,\n    damping = 0,\n    errorTolerance = 10e-3,\n    minValues,\n    maxValues,\n    initialValues,\n  } = options;\n\n  if (damping <= 0) {\n    throw new Error('The damping option must be a positive number');\n  } else if (!data.x || !data.y) {\n    throw new Error('The data parameter must have x and y elements');\n  } else if (\n    !isArray(data.x) ||\n    data.x.length < 2 ||\n    !isArray(data.y) ||\n    data.y.length < 2\n  ) {\n    throw new Error(\n      'The data parameter elements must be an array with more than 2 points',\n    );\n  } else if (data.x.length !== data.y.length) {\n    throw new Error('The data parameter elements must have the same size');\n  }\n\n  let parameters =\n    initialValues || new Array(parameterizedFunction.length).fill(1);\n  let parLen = parameters.length;\n  maxValues = maxValues || new Array(parLen).fill(Number.MAX_SAFE_INTEGER);\n  minValues = minValues || new Array(parLen).fill(Number.MIN_SAFE_INTEGER);\n\n  if (maxValues.length !== minValues.length) {\n    throw new Error('minValues and maxValues must be the same size');\n  }\n\n  if (!isArray(parameters)) {\n    throw new Error('initialValues must be an array');\n  }\n\n  let error = errorCalculation(data, parameters, parameterizedFunction);\n\n  let converged = error <= errorTolerance;\n\n  let iteration;\n  for (iteration = 0; iteration < maxIterations && !converged; iteration++) {\n    parameters = step(\n      data,\n      parameters,\n      damping,\n      gradientDifference,\n      parameterizedFunction,\n    );\n\n    for (let k = 0; k < parLen; k++) {\n      parameters[k] = Math.min(\n        Math.max(minValues[k], parameters[k]),\n        maxValues[k],\n      );\n    }\n\n    error = errorCalculation(data, parameters, parameterizedFunction);\n    if (isNaN(error)) break;\n    converged = error <= errorTolerance;\n  }\n\n  return {\n    parameterValues: parameters,\n    parameterError: error,\n    iterations: iteration,\n  };\n}\n","/**\n * Returns a new array based on extraction of specific indices of an array\n * @private\n * @param {Array} vector\n * @param {Array} indices\n */\nexport default function selection(vector, indices) {\n  let u = []; //new Float64Array(indices.length);\n  for (let i = 0; i < indices.length; i++) {\n    u[i] = vector[indices[i]];\n  }\n  return u;\n}\n","/**\n *\n * @private\n * @param {Array of arrays} collection\n */\nexport default function sortCollectionSet(collection) {\n  let objectCollection = collection\n    .map((value, index) => {\n      let key = BigInt(0);\n      value.forEach((item) => (key |= BigInt(1) << BigInt(item)));\n      return { value, index, key };\n    })\n    .sort((a, b) => {\n      if (a.key - b.key < 0) return -1;\n      return 1;\n    });\n\n  let sorted = [];\n  let indices = [];\n\n  let key;\n  for (let set of objectCollection) {\n    if (set.key !== key) {\n      key = set.key;\n      indices.push([]);\n      sorted.push(set.value);\n    }\n    indices[indices.length - 1].push(set.index);\n  }\n\n  let result = {\n    values: sorted,\n    indices: indices,\n  };\n  return result;\n}\n","import {\n  Matrix,\n  LuDecomposition,\n  solve,\n  CholeskyDecomposition,\n} from 'ml-matrix';\n\nimport sortCollectionSet from './util/sortCollectionSet';\n\n/**\n * (Combinatorial Subspace Least Squares) - subfunction for the FC-NNLS\n * @private\n * @param {Matrix} XtX\n * @param {Matrix} XtY\n * @param {Array} Pset\n * @param {Numbers} l\n * @param {Numbers} p\n */\nexport default function cssls(XtX, XtY, Pset, l, p) {\n  // Solves the set of equation XtX*K = XtY for the variables in Pset\n  // if XtX (or XtX(vars,vars)) is singular, performs the svd and find pseudoinverse, otherwise (even if ill-conditioned) finds inverse with LU decomposition and solves the set of equation\n  // it is consistent with matlab results for ill-conditioned matrices (at least consistent with test 'ill-conditionned square X rank 2, Y 3x1' in cssls.test)\n\n  let K = Matrix.zeros(l, p);\n  if (Pset === null) {\n    let choXtX = new CholeskyDecomposition(XtX);\n    if (choXtX.isPositiveDefinite() === true) {\n      K = choXtX.solve(XtY);\n    } else {\n      let luXtX = new LuDecomposition(XtX);\n      if (luXtX.isSingular() === false) {\n        K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n      } else {\n        K = solve(XtX, XtY, { useSVD: true });\n      }\n    }\n  } else {\n    let sortedPset = sortCollectionSet(Pset).values;\n    let sortedEset = sortCollectionSet(Pset).indices;\n    if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === 0 &&\n      sortedEset[0].length === p\n    ) {\n      return K;\n    } else if (\n      sortedPset.length === 1 &&\n      sortedPset[0].length === l &&\n      sortedEset[0].length === p\n    ) {\n      let choXtX = new CholeskyDecomposition(XtX);\n      if (choXtX.isPositiveDefinite() === true) {\n        K = choXtX.solve(XtY);\n      } else {\n        let luXtX = new LuDecomposition(XtX);\n        if (luXtX.isSingular() === false) {\n          K = luXtX.solve(Matrix.eye(l)).mmul(XtY);\n        } else {\n          K = solve(XtX, XtY, { useSVD: true });\n        }\n      }\n    } else {\n      for (let k = 0; k < sortedPset.length; k++) {\n        let cols2Solve = sortedEset[k];\n        let vars = sortedPset[k];\n        let L;\n        let choXtX = new CholeskyDecomposition(XtX.selection(vars, vars));\n        if (choXtX.isPositiveDefinite() === true) {\n          L = choXtX.solve(XtY.selection(vars, cols2Solve));\n        } else {\n          let luXtX = new LuDecomposition(XtX.selection(vars, vars));\n          if (luXtX.isSingular() === false) {\n            L = luXtX\n              .solve(Matrix.eye(vars.length))\n              .mmul(XtY.selection(vars, cols2Solve));\n          } else {\n            L = solve(\n              XtX.selection(vars, vars),\n              XtY.selection(vars, cols2Solve),\n              { useSVD: true },\n            );\n          }\n        }\n        for (let i = 0; i < L.rows; i++) {\n          for (let j = 0; j < L.columns; j++) {\n            K.set(vars[i], cols2Solve[j], L.get(i, j));\n          }\n        }\n      }\n    }\n  }\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport cssls from './cssls';\n\nexport default function initialisation(X, Y) {\n  let n = X.rows;\n  let l = X.columns;\n  let p = Y.columns;\n  let iter = 0;\n\n  if (Y.rows !== n) throw new Error('ERROR: matrix size not compatible');\n\n  let W = Matrix.zeros(l, p);\n\n  // precomputes part of pseudoinverse\n  let XtX = X.transpose().mmul(X);\n  let XtY = X.transpose().mmul(Y);\n\n  let K = cssls(XtX, XtY, null, l, p); // K is lxp\n  let Pset = [];\n  for (let j = 0; j < p; j++) {\n    Pset[j] = [];\n    for (let i = 0; i < l; i++) {\n      if (K.get(i, j) > 0) {\n        Pset[j].push(i);\n      } else {\n        K.set(i, j, 0);\n      } //This is our initial solution, it's the solution found by overwriting the unconstrained least square solution\n    }\n  }\n  let Fset = [];\n  for (let j = 0; j < p; j++) {\n    if (Pset[j].length !== l) {\n      Fset.push(j);\n    }\n  }\n\n  let D = K.clone();\n\n  return { n, l, p, iter, W, XtX, XtY, K, Pset, Fset, D };\n}\n","/**\n * Computes the set difference A\\B\n * @private\n * @param {A} set A as an array\n * @param {B} set B as an array\n */\nexport default function setDifference(A, B) {\n  let C = [];\n  for (let i of A) {\n    if (!B.includes(i)) C.push(i);\n  }\n  return C;\n}\n","import setDifference from './util/setDifference';\n\n// Makes sure the solution has converged\nexport default function optimality(\n  iter,\n  maxIter,\n  XtX,\n  XtY,\n  Fset,\n  Pset,\n  W,\n  K,\n  l,\n  p,\n  D,\n) {\n  if (iter === maxIter) {\n    throw new Error('Maximum number of iterations exceeded');\n  }\n\n  // Check solution for optimality\n  let V = XtY.subMatrixColumn(Fset).subtract(XtX.mmul(K.subMatrixColumn(Fset)));\n  for (let j = 0; j < Fset.length; j++) {\n    W.setColumn(Fset[j], V.subMatrixColumn([j]));\n  }\n  let Jset = [];\n  let fullSet = [];\n  for (let i = 0; i < l; i++) {\n    fullSet.push(i);\n  }\n  for (let j = 0; j < Fset.length; j++) {\n    let notPset = setDifference(fullSet, Pset[Fset[j]]);\n    if (notPset.length === 0) {\n      Jset.push(Fset[j]);\n    } else if (W.selection(notPset, [Fset[j]]).max() <= 0) {\n      Jset.push(Fset[j]);\n    }\n  }\n  Fset = setDifference(Fset, Jset);\n\n  // For non-optimal solutions, add the appropriate variables to Pset\n  if (Fset.length !== 0) {\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (Pset[Fset[j]].includes(i)) W.set(i, Fset[j], -Infinity);\n      }\n      Pset[Fset[j]].push(W.subMatrixColumn(Fset).maxColumnIndex(j)[0]);\n    }\n    for (let j = 0; j < Fset.length; j++) {\n      D.setColumn(Fset[j], K.getColumn(Fset[j]));\n    }\n  }\n  for (let j = 0; j < p; j++) {\n    Pset[j].sort((a, b) => a - b);\n  }\n  return { Pset, Fset, W };\n}\n","import { Matrix } from 'ml-matrix';\n\nimport selection from './util/selection';\nimport cssls from './cssls';\nimport initialisation from './initialisation';\nimport optimality from './optimality';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with multiple Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {Matrix|number[][]} Y\n * @param {object} [options={}]\n * @param {number} [options.maxIterations] if empty maxIterations is set at 3 times the number of columns of X\n * @returns {Matrix} K\n */\nexport default function fcnnls(X, Y, options = {}) {\n  X = Matrix.checkMatrix(X);\n  Y = Matrix.checkMatrix(Y);\n  let { l, p, iter, W, XtX, XtY, K, Pset, Fset, D } = initialisation(X, Y);\n  const { maxIterations = X.columns * 3 } = options;\n\n  // Active set algorithm for NNLS main loop\n  while (Fset.length > 0) {\n    // Solves for the passive variables (uses subroutine below)\n    let L = cssls(\n      XtX,\n      XtY.subMatrixColumn(Fset),\n      selection(Pset, Fset),\n      l,\n      Fset.length,\n    );\n    for (let i = 0; i < l; i++) {\n      for (let j = 0; j < Fset.length; j++) {\n        K.set(i, Fset[j], L.get(i, j));\n      }\n    }\n\n    // Finds any infeasible solutions\n    let infeasIndex = [];\n    for (let j = 0; j < Fset.length; j++) {\n      for (let i = 0; i < l; i++) {\n        if (L.get(i, j) < 0) {\n          infeasIndex.push(j);\n          break;\n        }\n      }\n    }\n    let Hset = selection(Fset, infeasIndex);\n\n    // Makes infeasible solutions feasible (standard NNLS inner loop)\n    if (Hset.length > 0) {\n      let m = Hset.length;\n      let alpha = Matrix.ones(l, m);\n\n      while (m > 0 && iter < maxIterations) {\n        iter++;\n\n        alpha.mul(Infinity);\n\n        // Finds indices of negative variables in passive set\n        let hRowColIdx = [[], []]; // Indexes work in pairs, each pair reprensents a single element, first array is row index, second array is column index\n        let negRowColIdx = [[], []]; // Same as before\n        for (let j = 0; j < m; j++) {\n          for (let i = 0; i < Pset[Hset[j]].length; i++) {\n            if (K.get(Pset[Hset[j]][i], Hset[j]) < 0) {\n              hRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              hRowColIdx[1].push(j);\n              negRowColIdx[0].push(Pset[Hset[j]][i]); // i\n              negRowColIdx[1].push(Hset[j]);\n            } // Compared to matlab, here we keep the row/column indexing (we are not taking the linear indexing)\n          }\n        }\n\n        for (let k = 0; k < hRowColIdx[0].length; k++) {\n          // could be hRowColIdx[1].length as well\n          alpha.set(\n            hRowColIdx[0][k],\n            hRowColIdx[1][k],\n            D.get(negRowColIdx[0][k], negRowColIdx[1][k]) /\n              (D.get(negRowColIdx[0][k], negRowColIdx[1][k]) -\n                K.get(negRowColIdx[0][k], negRowColIdx[1][k])),\n          );\n        }\n\n        let alphaMin = [];\n        let minIdx = [];\n        for (let j = 0; j < m; j++) {\n          alphaMin[j] = alpha.minColumn(j);\n          minIdx[j] = alpha.minColumnIndex(j)[0];\n        }\n\n        alphaMin = Matrix.rowVector(alphaMin);\n        for (let i = 0; i < l; i++) {\n          alpha.setSubMatrix(alphaMin, i, 0);\n        }\n\n        let E = new Matrix(l, m);\n        E = D.subMatrixColumn(Hset).subtract(\n          alpha\n            .subMatrix(0, l - 1, 0, m - 1)\n            .mul(D.subMatrixColumn(Hset).subtract(K.subMatrixColumn(Hset))),\n        );\n        for (let j = 0; j < m; j++) {\n          D.setColumn(Hset[j], E.subMatrixColumn([j]));\n        }\n\n        let idx2zero = [minIdx, Hset];\n        for (let k = 0; k < m; k++) {\n          D.set(idx2zero[0][k], idx2zero[1][k], 0);\n        }\n\n        for (let j = 0; j < m; j++) {\n          Pset[Hset[j]].splice(\n            Pset[Hset[j]].findIndex((item) => item === minIdx[j]),\n            1,\n          );\n        }\n\n        L = cssls(XtX, XtY.subMatrixColumn(Hset), selection(Pset, Hset), l, m);\n        for (let j = 0; j < m; j++) {\n          K.setColumn(Hset[j], L.subMatrixColumn([j]));\n        }\n\n        Hset = [];\n        for (let j = 0; j < K.columns; j++) {\n          for (let i = 0; i < l; i++) {\n            if (K.get(i, j) < 0) {\n              Hset.push(j);\n\n              break;\n            }\n          }\n        }\n        m = Hset.length;\n      }\n    }\n\n    let newParam = optimality(\n      iter,\n      maxIterations,\n      XtX,\n      XtY,\n      Fset,\n      Pset,\n      W,\n      K,\n      l,\n      p,\n      D,\n    );\n    Pset = newParam.Pset;\n    Fset = newParam.Fset;\n    W = newParam.W;\n  }\n\n  return K;\n}\n","import { Matrix } from 'ml-matrix';\n\nimport fcnnls from './fcnnls';\n\n/**\n * Fast Combinatorial Non-negative Least Squares with single Right Hand Side\n * @param {Matrix|number[][]} X\n * @param {number[]} y\n * @param {object} [options={}]\n * @param {boolean} [maxIterations] if true or empty maxIterations is set at 3 times the number of columns of X\n * @returns {Array} k\n */\nexport default function fcnnlsVector(X, y, options = {}) {\n  if (Array.isArray(y) === false) {\n    throw new TypeError('y must be a 1D Array');\n  }\n  let Y = Matrix.columnVector(y);\n  let K = fcnnls(X, Y, options);\n  let k = K.to1DArray();\n  return k;\n}\n","module.exports = function(haystack, needle, comparator, low, high) {\n  var mid, cmp;\n\n  if(low === undefined)\n    low = 0;\n\n  else {\n    low = low|0;\n    if(low < 0 || low >= haystack.length)\n      throw new RangeError(\"invalid lower bound\");\n  }\n\n  if(high === undefined)\n    high = haystack.length - 1;\n\n  else {\n    high = high|0;\n    if(high < low || high >= haystack.length)\n      throw new RangeError(\"invalid upper bound\");\n  }\n\n  while(low <= high) {\n    // The naive `low + high >>> 1` could fail for array lengths > 2**31\n    // because `>>>` converts its operands to int32. `low + (high - low >>> 1)`\n    // works for array lengths <= 2**32-1 which is also Javascript's max array\n    // length.\n    mid = low + ((high - low) >>> 1);\n    cmp = +comparator(haystack[mid], needle, mid, haystack);\n\n    // Too low.\n    if(cmp < 0.0)\n      low  = mid + 1;\n\n    // Too high.\n    else if(cmp > 0.0)\n      high = mid - 1;\n\n    // Key found.\n    else\n      return mid;\n  }\n\n  // Key not found.\n  return ~low;\n}\n","'use strict';\n\nfunction assertNumber(number) {\n\tif (typeof number !== 'number') {\n\t\tthrow new TypeError('Expected a number');\n\t}\n}\n\nexports.ascending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\n\tif (Number.isNaN(left)) {\n\t\treturn -1;\n\t}\n\n\tif (Number.isNaN(right)) {\n\t\treturn 1;\n\t}\n\n\treturn left - right;\n};\n\nexports.descending = (left, right) => {\n\tassertNumber(left);\n\tassertNumber(right);\n\n\tif (Number.isNaN(left)) {\n\t\treturn 1;\n\t}\n\n\tif (Number.isNaN(right)) {\n\t\treturn -1;\n\t}\n\n\treturn right - left;\n};\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\nexport const largestPrime = 0x7fffffff;\n\nconst primeNumbers = [\n  // chunk #0\n  largestPrime, // 2^31-1\n\n  // chunk #1\n  5,\n  11,\n  23,\n  47,\n  97,\n  197,\n  397,\n  797,\n  1597,\n  3203,\n  6421,\n  12853,\n  25717,\n  51437,\n  102877,\n  205759,\n  411527,\n  823117,\n  1646237,\n  3292489,\n  6584983,\n  13169977,\n  26339969,\n  52679969,\n  105359939,\n  210719881,\n  421439783,\n  842879579,\n  1685759167,\n\n  // chunk #2\n  433,\n  877,\n  1759,\n  3527,\n  7057,\n  14143,\n  28289,\n  56591,\n  113189,\n  226379,\n  452759,\n  905551,\n  1811107,\n  3622219,\n  7244441,\n  14488931,\n  28977863,\n  57955739,\n  115911563,\n  231823147,\n  463646329,\n  927292699,\n  1854585413,\n\n  // chunk #3\n  953,\n  1907,\n  3821,\n  7643,\n  15287,\n  30577,\n  61169,\n  122347,\n  244703,\n  489407,\n  978821,\n  1957651,\n  3915341,\n  7830701,\n  15661423,\n  31322867,\n  62645741,\n  125291483,\n  250582987,\n  501165979,\n  1002331963,\n  2004663929,\n\n  // chunk #4\n  1039,\n  2081,\n  4177,\n  8363,\n  16729,\n  33461,\n  66923,\n  133853,\n  267713,\n  535481,\n  1070981,\n  2141977,\n  4283963,\n  8567929,\n  17135863,\n  34271747,\n  68543509,\n  137087021,\n  274174111,\n  548348231,\n  1096696463,\n\n  // chunk #5\n  31,\n  67,\n  137,\n  277,\n  557,\n  1117,\n  2237,\n  4481,\n  8963,\n  17929,\n  35863,\n  71741,\n  143483,\n  286973,\n  573953,\n  1147921,\n  2295859,\n  4591721,\n  9183457,\n  18366923,\n  36733847,\n  73467739,\n  146935499,\n  293871013,\n  587742049,\n  1175484103,\n\n  // chunk #6\n  599,\n  1201,\n  2411,\n  4831,\n  9677,\n  19373,\n  38747,\n  77509,\n  155027,\n  310081,\n  620171,\n  1240361,\n  2480729,\n  4961459,\n  9922933,\n  19845871,\n  39691759,\n  79383533,\n  158767069,\n  317534141,\n  635068283,\n  1270136683,\n\n  // chunk #7\n  311,\n  631,\n  1277,\n  2557,\n  5119,\n  10243,\n  20507,\n  41017,\n  82037,\n  164089,\n  328213,\n  656429,\n  1312867,\n  2625761,\n  5251529,\n  10503061,\n  21006137,\n  42012281,\n  84024581,\n  168049163,\n  336098327,\n  672196673,\n  1344393353,\n\n  // chunk #8\n  3,\n  7,\n  17,\n  37,\n  79,\n  163,\n  331,\n  673,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #9\n  43,\n  89,\n  179,\n  359,\n  719,\n  1439,\n  2879,\n  5779,\n  11579,\n  23159,\n  46327,\n  92657,\n  185323,\n  370661,\n  741337,\n  1482707,\n  2965421,\n  5930887,\n  11861791,\n  23723597,\n  47447201,\n  94894427,\n  189788857,\n  379577741,\n  759155483,\n  1518310967,\n\n  // chunk #10\n  379,\n  761,\n  1523,\n  3049,\n  6101,\n  12203,\n  24407,\n  48817,\n  97649,\n  195311,\n  390647,\n  781301,\n  1562611,\n  3125257,\n  6250537,\n  12501169,\n  25002389,\n  50004791,\n  100009607,\n  200019221,\n  400038451,\n  800076929,\n  1600153859,\n\n  // chunk #11\n  13,\n  29,\n  59,\n  127,\n  257,\n  521,\n  1049,\n  2099,\n  4201,\n  8419,\n  16843,\n  33703,\n  67409,\n  134837,\n  269683,\n  539389,\n  1078787,\n  2157587,\n  4315183,\n  8630387,\n  17260781,\n  34521589,\n  69043189,\n  138086407,\n  276172823,\n  552345671,\n  1104691373,\n\n  // chunk #12\n  19,\n  41,\n  83,\n  167,\n  337,\n  677,\n  1361,\n  2729,\n  5471,\n  10949,\n  21911,\n  43853,\n  87719,\n  175447,\n  350899,\n  701819,\n  1403641,\n  2807303,\n  5614657,\n  11229331,\n  22458671,\n  44917381,\n  89834777,\n  179669557,\n  359339171,\n  718678369,\n  1437356741,\n\n  // chunk #13\n  53,\n  107,\n  223,\n  449,\n  907,\n  1823,\n  3659,\n  7321,\n  14653,\n  29311,\n  58631,\n  117269,\n  234539,\n  469099,\n  938207,\n  1876417,\n  3752839,\n  7505681,\n  15011389,\n  30022781,\n  60045577,\n  120091177,\n  240182359,\n  480364727,\n  960729461,\n  1921458943\n];\n\nprimeNumbers.sort(ascending);\n\nexport function nextPrime(value) {\n  let index = binarySearch(primeNumbers, value, ascending);\n  if (index < 0) {\n    index = ~index;\n  }\n  return primeNumbers[index];\n}\n","import { largestPrime, nextPrime } from './primeFinder';\n\nconst FREE = 0;\nconst FULL = 1;\nconst REMOVED = 2;\n\nconst defaultInitialCapacity = 150;\nconst defaultMinLoadFactor = 1 / 6;\nconst defaultMaxLoadFactor = 2 / 3;\n\nexport default class HashTable {\n  constructor(options = {}) {\n    if (options instanceof HashTable) {\n      this.table = options.table.slice();\n      this.values = options.values.slice();\n      this.state = options.state.slice();\n      this.minLoadFactor = options.minLoadFactor;\n      this.maxLoadFactor = options.maxLoadFactor;\n      this.distinct = options.distinct;\n      this.freeEntries = options.freeEntries;\n      this.lowWaterMark = options.lowWaterMark;\n      this.highWaterMark = options.maxLoadFactor;\n      return;\n    }\n\n    const initialCapacity =\n      options.initialCapacity === undefined\n        ? defaultInitialCapacity\n        : options.initialCapacity;\n    if (initialCapacity < 0) {\n      throw new RangeError(\n        `initial capacity must not be less than zero: ${initialCapacity}`\n      );\n    }\n\n    const minLoadFactor =\n      options.minLoadFactor === undefined\n        ? defaultMinLoadFactor\n        : options.minLoadFactor;\n    const maxLoadFactor =\n      options.maxLoadFactor === undefined\n        ? defaultMaxLoadFactor\n        : options.maxLoadFactor;\n    if (minLoadFactor < 0 || minLoadFactor >= 1) {\n      throw new RangeError(`invalid minLoadFactor: ${minLoadFactor}`);\n    }\n    if (maxLoadFactor <= 0 || maxLoadFactor >= 1) {\n      throw new RangeError(`invalid maxLoadFactor: ${maxLoadFactor}`);\n    }\n    if (minLoadFactor >= maxLoadFactor) {\n      throw new RangeError(\n        `minLoadFactor (${minLoadFactor}) must be smaller than maxLoadFactor (${maxLoadFactor})`\n      );\n    }\n\n    let capacity = initialCapacity;\n    // User wants to put at least capacity elements. We need to choose the size based on the maxLoadFactor to\n    // avoid the need to rehash before this capacity is reached.\n    // actualCapacity * maxLoadFactor >= capacity\n    capacity = (capacity / maxLoadFactor) | 0;\n    capacity = nextPrime(capacity);\n    if (capacity === 0) capacity = 1;\n\n    this.table = newArray(capacity);\n    this.values = newArray(capacity);\n    this.state = newArray(capacity);\n\n    this.minLoadFactor = minLoadFactor;\n    if (capacity === largestPrime) {\n      this.maxLoadFactor = 1;\n    } else {\n      this.maxLoadFactor = maxLoadFactor;\n    }\n\n    this.distinct = 0;\n    this.freeEntries = capacity;\n\n    this.lowWaterMark = 0;\n    this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);\n  }\n\n  clone() {\n    return new HashTable(this);\n  }\n\n  get size() {\n    return this.distinct;\n  }\n\n  get(key) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return 0;\n    return this.values[i];\n  }\n\n  set(key, value) {\n    let i = this.indexOfInsertion(key);\n    if (i < 0) {\n      i = -i - 1;\n      this.values[i] = value;\n      return false;\n    }\n\n    if (this.distinct > this.highWaterMark) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n      return this.set(key, value);\n    }\n\n    this.table[i] = key;\n    this.values[i] = value;\n    if (this.state[i] === FREE) this.freeEntries--;\n    this.state[i] = FULL;\n    this.distinct++;\n\n    if (this.freeEntries < 1) {\n      const newCapacity = chooseGrowCapacity(\n        this.distinct + 1,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n\n    return true;\n  }\n\n  remove(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = REMOVED;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  delete(key, noRehash) {\n    const i = this.indexOfKey(key);\n    if (i < 0) return false;\n\n    this.state[i] = FREE;\n    this.distinct--;\n\n    if (!noRehash) this.maybeShrinkCapacity();\n\n    return true;\n  }\n\n  maybeShrinkCapacity() {\n    if (this.distinct < this.lowWaterMark) {\n      const newCapacity = chooseShrinkCapacity(\n        this.distinct,\n        this.minLoadFactor,\n        this.maxLoadFactor\n      );\n      this.rehash(newCapacity);\n    }\n  }\n\n  containsKey(key) {\n    return this.indexOfKey(key) >= 0;\n  }\n\n  indexOfKey(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = this.table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === FREE) return -1;\n    return i;\n  }\n\n  containsValue(value) {\n    return this.indexOfValue(value) >= 0;\n  }\n\n  indexOfValue(value) {\n    const values = this.values;\n    const state = this.state;\n\n    for (var i = 0; i < state.length; i++) {\n      if (state[i] === FULL && values[i] === value) {\n        return i;\n      }\n    }\n\n    return -1;\n  }\n\n  indexOfInsertion(key) {\n    const table = this.table;\n    const state = this.state;\n    const length = table.length;\n\n    const hash = key & 0x7fffffff;\n    let i = hash % length;\n    let decrement = hash % (length - 2);\n    if (decrement === 0) decrement = 1;\n\n    while (state[i] === FULL && table[i] !== key) {\n      i -= decrement;\n      if (i < 0) i += length;\n    }\n\n    if (state[i] === REMOVED) {\n      const j = i;\n      while (state[i] !== FREE && (state[i] === REMOVED || table[i] !== key)) {\n        i -= decrement;\n        if (i < 0) i += length;\n      }\n      if (state[i] === FREE) i = j;\n    }\n\n    if (state[i] === FULL) {\n      return -i - 1;\n    }\n\n    return i;\n  }\n\n  ensureCapacity(minCapacity) {\n    if (this.table.length < minCapacity) {\n      const newCapacity = nextPrime(minCapacity);\n      this.rehash(newCapacity);\n    }\n  }\n\n  rehash(newCapacity) {\n    const oldCapacity = this.table.length;\n\n    if (newCapacity <= this.distinct) throw new Error('Unexpected');\n\n    const oldTable = this.table;\n    const oldValues = this.values;\n    const oldState = this.state;\n\n    const newTable = newArray(newCapacity);\n    const newValues = newArray(newCapacity);\n    const newState = newArray(newCapacity);\n\n    this.lowWaterMark = chooseLowWaterMark(newCapacity, this.minLoadFactor);\n    this.highWaterMark = chooseHighWaterMark(newCapacity, this.maxLoadFactor);\n\n    this.table = newTable;\n    this.values = newValues;\n    this.state = newState;\n    this.freeEntries = newCapacity - this.distinct;\n\n    for (var i = 0; i < oldCapacity; i++) {\n      if (oldState[i] === FULL) {\n        var element = oldTable[i];\n        var index = this.indexOfInsertion(element);\n        newTable[index] = element;\n        newValues[index] = oldValues[i];\n        newState[index] = FULL;\n      }\n    }\n  }\n\n  forEachKey(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachValue(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n\n  forEachPair(callback) {\n    for (var i = 0; i < this.state.length; i++) {\n      if (this.state[i] === FULL) {\n        if (!callback(this.table[i], this.values[i])) return false;\n      }\n    }\n    return true;\n  }\n}\n\nfunction chooseLowWaterMark(capacity, minLoad) {\n  return (capacity * minLoad) | 0;\n}\n\nfunction chooseHighWaterMark(capacity, maxLoad) {\n  return Math.min(capacity - 2, (capacity * maxLoad) | 0);\n}\n\nfunction chooseGrowCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (3 * minLoad + maxLoad)) | 0)\n  );\n}\n\nfunction chooseShrinkCapacity(size, minLoad, maxLoad) {\n  return nextPrime(\n    Math.max(size + 1, ((4 * size) / (minLoad + 3 * maxLoad)) | 0)\n  );\n}\n\nfunction newArray(size) {\n  return Array(size).fill(0);\n}\n","import HashTable from 'ml-hash-table';\n\nexport class SparseMatrix {\n  constructor(rows, columns, options = {}) {\n    if (rows instanceof SparseMatrix) {\n      // clone\n      const other = rows;\n      this._init(\n        other.rows,\n        other.columns,\n        other.elements.clone(),\n        other.threshold\n      );\n      return;\n    }\n\n    if (Array.isArray(rows)) {\n      const matrix = rows;\n      rows = matrix.length;\n      options = columns || {};\n      columns = matrix[0].length;\n      this._init(rows, columns, new HashTable(options), options.threshold);\n      for (var i = 0; i < rows; i++) {\n        for (var j = 0; j < columns; j++) {\n          var value = matrix[i][j];\n          if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n          if (value !== 0) {\n            this.elements.set(i * columns + j, matrix[i][j]);\n          }\n        }\n      }\n    } else {\n      this._init(rows, columns, new HashTable(options), options.threshold);\n    }\n  }\n\n  _init(rows, columns, elements, threshold) {\n    this.rows = rows;\n    this.columns = columns;\n    this.elements = elements;\n    this.threshold = threshold || 0;\n  }\n\n  static eye(rows = 1, columns = rows) {\n    const min = Math.min(rows, columns);\n    const matrix = new SparseMatrix(rows, columns, { initialCapacity: min });\n    for (var i = 0; i < min; i++) {\n      matrix.set(i, i, 1);\n    }\n    return matrix;\n  }\n\n  clone() {\n    return new SparseMatrix(this);\n  }\n\n  to2DArray() {\n    const copy = new Array(this.rows);\n    for (var i = 0; i < this.rows; i++) {\n      copy[i] = new Array(this.columns);\n      for (var j = 0; j < this.columns; j++) {\n        copy[i][j] = this.get(i, j);\n      }\n    }\n    return copy;\n  }\n\n  isSquare() {\n    return this.rows === this.columns;\n  }\n\n  isSymmetric() {\n    if (!this.isSquare()) return false;\n\n    var symmetric = true;\n    this.forEachNonZero((i, j, v) => {\n      if (this.get(j, i) !== v) {\n        symmetric = false;\n        return false;\n      }\n      return v;\n    });\n    return symmetric;\n  }\n\n  /**\n   * Search for the wither band in the main diagonals\n   * @return {number}\n   */\n  bandWidth() {\n    let min = this.columns;\n    let max = -1;\n    this.forEachNonZero((i, j, v) => {\n      let diff = i - j;\n      min = Math.min(min, diff);\n      max = Math.max(max, diff);\n      return v;\n    });\n    return max - min;\n  }\n\n  /**\n   * Test if a matrix is consider banded using a threshold\n   * @param {number} width\n   * @return {boolean}\n   */\n  isBanded(width) {\n    let bandWidth = this.bandWidth();\n    return bandWidth <= width;\n  }\n\n  get cardinality() {\n    return this.elements.size;\n  }\n\n  get size() {\n    return this.rows * this.columns;\n  }\n\n  get(row, column) {\n    return this.elements.get(row * this.columns + column);\n  }\n\n  set(row, column, value) {\n    if (this.threshold && Math.abs(value) < this.threshold) value = 0;\n    if (value === 0) {\n      this.elements.remove(row * this.columns + column);\n    } else {\n      this.elements.set(row * this.columns + column, value);\n    }\n    return this;\n  }\n\n  mmul(other) {\n    if (this.columns !== other.rows) {\n      // eslint-disable-next-line no-console\n      console.warn(\n        'Number of columns of left matrix are not equal to number of rows of right matrix.'\n      );\n    }\n\n    const m = this.rows;\n    const p = other.columns;\n\n    const result = new SparseMatrix(m, p);\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        if (j === k) {\n          result.set(i, l, result.get(i, l) + v1 * v2);\n        }\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  kroneckerProduct(other) {\n    const m = this.rows;\n    const n = this.columns;\n    const p = other.rows;\n    const q = other.columns;\n\n    const result = new SparseMatrix(m * p, n * q, {\n      initialCapacity: this.cardinality * other.cardinality\n    });\n    this.forEachNonZero((i, j, v1) => {\n      other.forEachNonZero((k, l, v2) => {\n        result.set(p * i + k, q * j + l, v1 * v2);\n        return v2;\n      });\n      return v1;\n    });\n    return result;\n  }\n\n  forEachNonZero(callback) {\n    this.elements.forEachPair((key, value) => {\n      const i = (key / this.columns) | 0;\n      const j = key % this.columns;\n      let r = callback(i, j, value);\n      if (r === false) return false; // stop iteration\n      if (this.threshold && Math.abs(r) < this.threshold) r = 0;\n      if (r !== value) {\n        if (r === 0) {\n          this.elements.remove(key, true);\n        } else {\n          this.elements.set(key, r);\n        }\n      }\n      return true;\n    });\n    this.elements.maybeShrinkCapacity();\n    return this;\n  }\n\n  getNonZeros() {\n    const cardinality = this.cardinality;\n    const rows = new Array(cardinality);\n    const columns = new Array(cardinality);\n    const values = new Array(cardinality);\n    var idx = 0;\n    this.forEachNonZero((i, j, value) => {\n      rows[idx] = i;\n      columns[idx] = j;\n      values[idx] = value;\n      idx++;\n      return value;\n    });\n    return { rows, columns, values };\n  }\n\n  setThreshold(newThreshold) {\n    if (newThreshold !== 0 && newThreshold !== this.threshold) {\n      this.threshold = newThreshold;\n      this.forEachNonZero((i, j, v) => v);\n    }\n    return this;\n  }\n\n  /**\n   * @return {SparseMatrix} - New transposed sparse matrix\n   */\n  transpose() {\n    let trans = new SparseMatrix(this.columns, this.rows, {\n      initialCapacity: this.cardinality\n    });\n    this.forEachNonZero((i, j, value) => {\n      trans.set(j, i, value);\n      return value;\n    });\n    return trans;\n  }\n}\n\nSparseMatrix.prototype.klass = 'Matrix';\n\nSparseMatrix.identity = SparseMatrix.eye;\nSparseMatrix.prototype.tensorProduct = SparseMatrix.prototype.kroneckerProduct;\n\n/*\n Add dynamically instance and static methods for mathematical operations\n */\n\nvar inplaceOperator = `\n(function %name%(value) {\n    if (typeof value === 'number') return this.%name%S(value);\n    return this.%name%M(value);\n})\n`;\n\nvar inplaceOperatorScalar = `\n(function %name%S(value) {\n    this.forEachNonZero((i, j, v) => v %op% value);\n    return this;\n})\n`;\n\nvar inplaceOperatorMatrix = `\n(function %name%M(matrix) {\n    matrix.forEachNonZero((i, j, v) => {\n        this.set(i, j, this.get(i, j) %op% v);\n        return v;\n    });\n    return this;\n})\n`;\n\nvar staticOperator = `\n(function %name%(matrix, value) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%(value);\n})\n`;\n\nvar inplaceMethod = `\n(function %name%() {\n    this.forEachNonZero((i, j, v) => %method%(v));\n    return this;\n})\n`;\n\nvar staticMethod = `\n(function %name%(matrix) {\n    var newMatrix = new SparseMatrix(matrix);\n    return newMatrix.%name%();\n})\n`;\n\nconst operators = [\n  // Arithmetic operators\n  ['+', 'add'],\n  ['-', 'sub', 'subtract'],\n  ['*', 'mul', 'multiply'],\n  ['/', 'div', 'divide'],\n  ['%', 'mod', 'modulus'],\n  // Bitwise operators\n  ['&', 'and'],\n  ['|', 'or'],\n  ['^', 'xor'],\n  ['<<', 'leftShift'],\n  ['>>', 'signPropagatingRightShift'],\n  ['>>>', 'rightShift', 'zeroFillRightShift']\n];\n\nfor (const operator of operators) {\n  for (let i = 1; i < operator.length; i++) {\n    SparseMatrix.prototype[operator[i]] = eval(\n      fillTemplateFunction(inplaceOperator, {\n        name: operator[i],\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}S`] = eval(\n      fillTemplateFunction(inplaceOperatorScalar, {\n        name: `${operator[i]}S`,\n        op: operator[0]\n      })\n    );\n    SparseMatrix.prototype[`${operator[i]}M`] = eval(\n      fillTemplateFunction(inplaceOperatorMatrix, {\n        name: `${operator[i]}M`,\n        op: operator[0]\n      })\n    );\n\n    SparseMatrix[operator[i]] = eval(\n      fillTemplateFunction(staticOperator, { name: operator[i] })\n    );\n  }\n}\n\nvar methods = [['~', 'not']];\n\n[\n  'abs',\n  'acos',\n  'acosh',\n  'asin',\n  'asinh',\n  'atan',\n  'atanh',\n  'cbrt',\n  'ceil',\n  'clz32',\n  'cos',\n  'cosh',\n  'exp',\n  'expm1',\n  'floor',\n  'fround',\n  'log',\n  'log1p',\n  'log10',\n  'log2',\n  'round',\n  'sign',\n  'sin',\n  'sinh',\n  'sqrt',\n  'tan',\n  'tanh',\n  'trunc'\n].forEach(function (mathMethod) {\n  methods.push([`Math.${mathMethod}`, mathMethod]);\n});\n\nfor (const method of methods) {\n  for (let i = 1; i < method.length; i++) {\n    SparseMatrix.prototype[method[i]] = eval(\n      fillTemplateFunction(inplaceMethod, {\n        name: method[i],\n        method: method[0]\n      })\n    );\n    SparseMatrix[method[i]] = eval(\n      fillTemplateFunction(staticMethod, { name: method[i] })\n    );\n  }\n}\n\nfunction fillTemplateFunction(template, values) {\n  for (const i in values) {\n    template = template.replace(new RegExp(`%${i}%`, 'g'), values[i]);\n  }\n  return template;\n}\n","export default function additiveSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i]) * (a[i] + b[i])) / (a[i] * b[i]);\n  }\n  return 2 * d;\n}\n","export default function avg(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var ans = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    ans += aux;\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return (max + ans) / 2;\n}\n","export default function bhattacharyya(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return -Math.log(ans);\n}\n","export default function canberra(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]) / (a[i] + b[i]);\n  }\n  return ans;\n}\n","export default function chebyshev(a, b) {\n  var ii = a.length;\n  var max = 0;\n  var aux = 0;\n  for (var i = 0; i < ii; i++) {\n    aux = Math.abs(a[i] - b[i]);\n    if (max < aux) {\n      max = aux;\n    }\n  }\n  return max;\n}\n","export default function clark(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.sqrt(\n      ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]))\n    );\n  }\n  return 2 * d;\n}\n","export default function czekanowskiSimilarity(a, b) {\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < a.length; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return (2 * up) / down;\n}\n","import czekanowskiSimilarity from '../similarities/czekanowski';\n\nexport default function czekanowskiDistance(a, b) {\n  return 1 - czekanowskiSimilarity(a, b);\n}\n","export default function dice(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p + q1);\n}\n","export default function divergence(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / ((a[i] + b[i]) * (a[i] + b[i]));\n  }\n  return 2 * d;\n}\n","export default function fidelity(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return ans;\n}\n","export default function gower(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.abs(a[i] - b[i]);\n  }\n  return ans / ii;\n}\n","export default function harmonicMean(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] * b[i]) / (a[i] + b[i]);\n  }\n  return 2 * ans;\n}\n","export default function hellinger(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return 2 * Math.sqrt(1 - ans);\n}\n","export default function innerProduct(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * b[i];\n  }\n  return ans;\n}\n","export default function intersection(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.min(a[i], b[i]);\n  }\n  return 1 - ans;\n}\n","export default function jaccard(a, b) {\n  var ii = a.length;\n  var p1 = 0;\n  var p2 = 0;\n  var q1 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p1 += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q1 += b[i] * b[i];\n    q2 += (a[i] - b[i]) * (a[i] - b[i]);\n  }\n  return q2 / (p2 + q1 - p1);\n}\n","export default function jeffreys(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += (a[i] - b[i]) * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function jensenDifference(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (a[i] * Math.log(a[i]) + b[i] * Math.log(b[i])) / 2 -\n      ((a[i] + b[i]) / 2) * Math.log((a[i] + b[i]) / 2);\n  }\n  return ans;\n}\n","export default function jensenShannon(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var q = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n    q += b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return (p + q) / 2;\n}\n","export default function kdivergence(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log((2 * a[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function kulczynski(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.min(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function kullbackLeibler(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += a[i] * Math.log(a[i] / b[i]);\n  }\n  return ans;\n}\n","export default function kumarHassebrook(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (p2 + q2 - p);\n}\n","export default function kumarJohnson(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      Math.pow(a[i] * a[i] - b[i] * b[i], 2) / (2 * Math.pow(a[i] * b[i], 1.5));\n  }\n  return ans;\n}\n","export default function lorentzian(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.log(Math.abs(a[i] - b[i]) + 1);\n  }\n  return ans;\n}\n","export default function manhattan(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.abs(a[i] - b[i]);\n  }\n  return d;\n}\n","export default function matusita(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += Math.sqrt(a[i] * b[i]);\n  }\n  return Math.sqrt(2 - 2 * ans);\n}\n","export default function minkowski(a, b, p) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += Math.pow(Math.abs(a[i] - b[i]), p);\n  }\n  return Math.pow(d, 1 / p);\n}\n","export default function motyka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += a[i] + b[i];\n  }\n  return 1 - up / down;\n}\n","export default function neyman(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / a[i];\n  }\n  return d;\n}\n","export default function pearson(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / b[i];\n  }\n  return d;\n}\n","export default function probabilisticSymmetric(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return 2 * d;\n}\n","export default function ruzicka(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.min(a[i], b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function soergel(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += Math.max(a[i], b[i]);\n  }\n  return up / down;\n}\n","export default function sorensen(a, b) {\n  var ii = a.length;\n  var up = 0;\n  var down = 0;\n  for (var i = 0; i < ii; i++) {\n    up += Math.abs(a[i] - b[i]);\n    down += a[i] + b[i];\n  }\n  return up / down;\n}\n","export default function squared(a, b) {\n  var i = 0;\n  var ii = a.length;\n  var d = 0;\n  for (; i < ii; i++) {\n    d += ((a[i] - b[i]) * (a[i] - b[i])) / (a[i] + b[i]);\n  }\n  return d;\n}\n","export default function squaredChord(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      (Math.sqrt(a[i]) - Math.sqrt(b[i])) * (Math.sqrt(a[i]) - Math.sqrt(b[i]));\n  }\n  return ans;\n}\n","export default function taneja(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      ((a[i] + b[i]) / 2) *\n      Math.log((a[i] + b[i]) / (2 * Math.sqrt(a[i] * b[i])));\n  }\n  return ans;\n}\n","export default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    var inter = 0;\n    var union = 0;\n    for (var j = 0; j < a.length; j++) {\n      inter += a[j] && b[j];\n      union += a[j] || b[j];\n    }\n    if (union === 0) {\n      return 1;\n    }\n    return inter / union;\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return 1 - (p + q - 2 * m) / (p + q - m);\n  }\n}\n","import tanimotoS from '../similarities/tanimoto';\n\nexport default function tanimoto(a, b, bitvector) {\n  if (bitvector) {\n    return 1 - tanimotoS(a, b, bitvector);\n  } else {\n    var ii = a.length;\n    var p = 0;\n    var q = 0;\n    var m = 0;\n    for (var i = 0; i < ii; i++) {\n      p += a[i];\n      q += b[i];\n      m += Math.min(a[i], b[i]);\n    }\n    return (p + q - 2 * m) / (p + q - m);\n  }\n}\n","export default function topsoe(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans +=\n      a[i] * Math.log((2 * a[i]) / (a[i] + b[i])) +\n      b[i] * Math.log((2 * b[i]) / (a[i] + b[i]));\n  }\n  return ans;\n}\n","export default function waveHedges(a, b) {\n  var ii = a.length;\n  var ans = 0;\n  for (var i = 0; i < ii; i++) {\n    ans += 1 - Math.min(a[i], b[i]) / Math.max(a[i], b[i]);\n  }\n  return ans;\n}\n","import binarySearch from 'binary-search';\nimport { ascending } from 'num-sort';\n\n/**\n * Function that creates the tree\n * @param {Array<Array<number>>} spectrum\n * @param {object} [options]\n * @return {Tree|null}\n * left and right have the same structure than the parent,\n * or are null if they are leaves\n */\nexport function createTree(spectrum, options = {}) {\n  var X = spectrum[0];\n  const {\n    minWindow = 0.16,\n    threshold = 0.01,\n    from = X[0],\n    to = X[X.length - 1]\n  } = options;\n\n  return mainCreateTree(\n    spectrum[0],\n    spectrum[1],\n    from,\n    to,\n    minWindow,\n    threshold\n  );\n}\n\nfunction mainCreateTree(X, Y, from, to, minWindow, threshold) {\n  if (to - from < minWindow) {\n    return null;\n  }\n\n  // search first point\n  var start = binarySearch(X, from, ascending);\n  if (start < 0) {\n    start = ~start;\n  }\n\n  // stop at last point\n  var sum = 0;\n  var center = 0;\n  for (var i = start; i < X.length; i++) {\n    if (X[i] >= to) {\n      break;\n    }\n    sum += Y[i];\n    center += X[i] * Y[i];\n  }\n\n  if (sum < threshold) {\n    return null;\n  }\n\n  center /= sum;\n  if (center - from < 1e-6 || to - center < 1e-6) {\n    return null;\n  }\n  if (center - from < minWindow / 4) {\n    return mainCreateTree(X, Y, center, to, minWindow, threshold);\n  } else {\n    if (to - center < minWindow / 4) {\n      return mainCreateTree(X, Y, from, center, minWindow, threshold);\n    } else {\n      return new Tree(\n        sum,\n        center,\n        mainCreateTree(X, Y, from, center, minWindow, threshold),\n        mainCreateTree(X, Y, center, to, minWindow, threshold)\n      );\n    }\n  }\n}\n\nclass Tree {\n  constructor(sum, center, left, right) {\n    this.sum = sum;\n    this.center = center;\n    this.left = left;\n    this.right = right;\n  }\n}\n","import { createTree } from './createTree';\n\n/**\n * Similarity between two nodes\n * @param {Tree|Array<Array<number>>} a - tree A node\n * @param {Tree|Array<Array<number>>} b - tree B node\n * @param {object} [options]\n * @return {number} similarity measure between tree nodes\n */\nexport function getSimilarity(a, b, options = {}) {\n  const { alpha = 0.1, beta = 0.33, gamma = 0.001 } = options;\n\n  if (a === null || b === null) {\n    return 0;\n  }\n  if (Array.isArray(a)) {\n    a = createTree(a);\n  }\n  if (Array.isArray(b)) {\n    b = createTree(b);\n  }\n\n  var C =\n    (alpha * Math.min(a.sum, b.sum)) / Math.max(a.sum, b.sum) +\n    (1 - alpha) * Math.exp(-gamma * Math.abs(a.center - b.center));\n\n  return (\n    beta * C +\n    ((1 - beta) *\n      (getSimilarity(a.left, b.left, options) +\n        getSimilarity(a.right, b.right, options))) /\n      2\n  );\n}\n","import { getSimilarity } from './getSimilarity';\n\nexport { createTree } from './createTree';\n\nexport function treeSimilarity(A, B, options = {}) {\n  return getSimilarity(A, B, options);\n}\n\nexport function getFunction(options = {}) {\n  return (A, B) => getSimilarity(A, B, options);\n}\n","export default function cosine(a, b) {\n  var ii = a.length;\n  var p = 0;\n  var p2 = 0;\n  var q2 = 0;\n  for (var i = 0; i < ii; i++) {\n    p += a[i] * b[i];\n    p2 += a[i] * a[i];\n    q2 += b[i] * b[i];\n  }\n  return p / (Math.sqrt(p2) * Math.sqrt(q2));\n}\n","import diceD from '../distances/dice';\n\nexport default function dice(a, b) {\n  return 1 - diceD(a, b);\n}\n","import intersectionD from '../distances/intersection';\n\nexport default function intersection(a, b) {\n  return 1 - intersectionD(a, b);\n}\n","import jaccardD from '../distances/jaccard';\n\nexport default function jaccard(a, b) {\n  return 1 - jaccardD(a, b);\n}\n","import kulczynskiD from '../distances/kulczynski';\n\nexport default function kulczynski(a, b) {\n  return 1 / kulczynskiD(a, b);\n}\n","import motykaD from '../distances/motyka';\n\nexport default function motyka(a, b) {\n  return 1 - motykaD(a, b);\n}\n","import mean from 'ml-array-mean';\n\nimport cosine from './cosine';\n\nexport default function pearson(a, b) {\n  var avgA = mean(a);\n  var avgB = mean(b);\n\n  var newA = new Array(a.length);\n  var newB = new Array(b.length);\n  for (var i = 0; i < newA.length; i++) {\n    newA[i] = a[i] - avgA;\n    newB[i] = b[i] - avgB;\n  }\n\n  return cosine(newA, newB);\n}\n","import squaredChordD from '../distances/squaredChord';\n\nexport default function squaredChord(a, b) {\n  return 1 - squaredChordD(a, b);\n}\n","'use strict';\n\n// Accuracy\nexports.acc = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.tn[i] + pred.tp[i]) / (l - 1);\n    }\n    return result;\n};\n\n// Error rate\nexports.err = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.fp[i] / (l - 1));\n    }\n    return result;\n};\n\n// False positive rate\nexports.fpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fp[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// True positive rate\nexports.tpr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tp[i] / pred.nPos;\n    }\n    return result;\n};\n\n// False negative rate\nexports.fnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.fn[i] / pred.nPos;\n    }\n    return result;\n};\n\n// True negative rate\nexports.tnr = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.tn[i] / pred.nNeg;\n    }\n    return result;\n};\n\n// Positive predictive value\nexports.ppv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 0;\n    }\n    return result;\n};\n\n// Negative predictive value\nexports.npv = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 0;\n    }\n    return result;\n};\n\n// Prediction conditioned fallout\nexports.pcfall = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fp[i] + pred.tp[i] !== 0) ? 1 - (pred.tp[i] / (pred.fp[i] + pred.tp[i])) : 1;\n    }\n    return result;\n};\n\n// Prediction conditioned miss\nexports.pcmiss = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.fn[i] + pred.tn[i] !== 0) ? 1 - (pred.tn[i] / (pred.fn[i] + pred.tn[i])) : 1;\n    }\n    return result;\n};\n\n// Lift value\nexports.lift = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = (pred.nPosPred[i] !== 0) ? ((pred.tp[i] / pred.nPos) / (pred.nPosPred[i] / pred.nSamples)) : 0;\n    }\n    return result;\n};\n\n// Rate of positive predictions\nexports.rpp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nPosPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Rate of negative predictions\nexports.rnp = pred => {\n    const l = pred.cutoffs.length;\n    const result = new Array(l);\n    for (var i = 0; i < l; i++) {\n        result[i] = pred.nNegPred[i] / pred.nSamples;\n    }\n    return result;\n};\n\n// Threshold\nexports.threshold = pred => {\n    const clone = pred.cutoffs.slice();\n    clone[0] = clone[1]; // Remove the infinite value\n    return clone;\n};\n","'use strict';\n\nconst measures = require('./measures');\n\nclass Performance {\n    /**\n     *\n     * @param prediction - The prediction matrix\n     * @param target - The target matrix (values: truthy for same class, falsy for different class)\n     * @param options\n     *\n     * @option    all    True if the entire matrix must be used. False to ignore the diagonal and lower part (default is false, for similarity/distance matrices)\n     * @option    max    True if the max value corresponds to a perfect match (like in similarity matrices), false if it is the min value (default is false, like in distance matrices. All values will be multiplied by -1)\n     */\n    constructor(prediction, target, options) {\n        options = options || {};\n        if (prediction.length !== target.length || prediction[0].length !== target[0].length) {\n            throw new Error('dimensions of prediction and target do not match');\n        }\n        const rows = prediction.length;\n        const columns = prediction[0].length;\n        const isDistance = !options.max;\n\n        const predP = [];\n\n        if (options.all) {\n            for (var i = 0; i < rows; i++) {\n                for (var j = 0; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        } else {\n            if (rows < 3 || rows !== columns) {\n                throw new Error('When \"all\" option is false, the prediction matrix must be square and have at least 3 columns');\n            }\n            for (var i = 0; i < rows - 1; i++) {\n                for (var j = i + 1; j < columns; j++) {\n                    predP.push({\n                        pred: prediction[i][j],\n                        targ: target[i][j]\n                    });\n                }\n            }\n        }\n\n        if (isDistance) {\n            predP.sort((a, b) => a.pred - b.pred);\n        } else {\n            predP.sort((a, b) => b.pred - a.pred);\n        }\n        \n        const cutoffs = this.cutoffs = [isDistance ? Number.MIN_VALUE : Number.MAX_VALUE];\n        const fp = this.fp = [0];\n        const tp = this.tp = [0];\n\n        var nPos = 0;\n        var nNeg = 0;\n\n        var currentPred = predP[0].pred;\n        var nTp = 0;\n        var nFp = 0;\n        for (var i = 0; i < predP.length; i++) {\n            if (predP[i].pred !== currentPred) {\n                cutoffs.push(currentPred);\n                fp.push(nFp);\n                tp.push(nTp);\n                currentPred = predP[i].pred;\n            }\n            if (predP[i].targ) {\n                nPos++;\n                nTp++;\n            } else {\n                nNeg++;\n                nFp++;\n            }\n        }\n        cutoffs.push(currentPred);\n        fp.push(nFp);\n        tp.push(nTp);\n\n        const l = cutoffs.length;\n        const fn = this.fn = new Array(l);\n        const tn = this.tn = new Array(l);\n        const nPosPred = this.nPosPred = new Array(l);\n        const nNegPred = this.nNegPred = new Array(l);\n\n        for (var i = 0; i < l; i++) {\n            fn[i] = nPos - tp[i];\n            tn[i] = nNeg - fp[i];\n\n            nPosPred[i] = tp[i] + fp[i];\n            nNegPred[i] = tn[i] + fn[i];\n        }\n\n        this.nPos = nPos;\n        this.nNeg = nNeg;\n        this.nSamples = nPos + nNeg;\n    }\n\n    /**\n     * Computes a measure from the prediction object.\n     *\n     * Many measures are available and can be combined :\n     * To create a ROC curve, you need fpr and tpr\n     * To create a DET curve, you need fnr and fpr\n     * To create a Lift chart, you need rpp and lift\n     *\n     * Possible measures are : threshold (Threshold), acc (Accuracy), err (Error rate),\n     * fpr (False positive rate), tpr (True positive rate), fnr (False negative rate), tnr (True negative rate), ppv (Positive predictive value),\n     * npv (Negative predictive value), pcfall (Prediction-conditioned fallout), pcmiss (Prediction-conditioned miss), lift (Lift value), rpp (Rate of positive predictions), rnp (Rate of negative predictions)\n     *\n     * @param measure - The short name of the measure\n     *\n     * @return [number]\n     */\n    getMeasure(measure) {\n        if (typeof measure !== 'string') {\n            throw new Error('No measure specified');\n        }\n        if (!measures[measure]) {\n            throw new Error(`The specified measure (${measure}) does not exist`);\n        }\n        return measures[measure](this);\n    }\n\n    /**\n     * Returns the area under the ROC curve\n     */\n    getAURC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fp[i] / this.nNeg;\n            y[i] = this.tp[i] / this.nPos;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] - x[i - 1]) * (y[i] + y[i - 1]);\n        }\n        return auc;\n    }\n\n    /**\n     * Returns the area under the DET curve\n     */\n    getAUDC() {\n        const l = this.cutoffs.length;\n        const x = new Array(l);\n        const y = new Array(l);\n        for (var i = 0; i < l; i++) {\n            x[i] = this.fn[i] / this.nPos;\n            y[i] = this.fp[i] / this.nNeg;\n        }\n        var auc = 0;\n        for (i = 1; i < l; i++) {\n            auc += 0.5 * (x[i] + x[i - 1]) * (y[i] - y[i - 1]);\n        }\n        return auc;\n    }\n\n    getDistribution(options) {\n        options = options || {};\n        var cutLength = this.cutoffs.length;\n        var cutLow = options.xMin || Math.floor(this.cutoffs[cutLength - 1] * 100) / 100;\n        var cutHigh = options.xMax || Math.ceil(this.cutoffs[1] * 100) / 100;\n        var interval = options.interval || Math.floor(((cutHigh - cutLow) / 20 * 10000000) - 1) / 10000000; // Trick to avoid the precision problem of float numbers\n\n        var xLabels = [];\n        var interValues = [];\n        var intraValues = [];\n        var interCumPercent = [];\n        var intraCumPercent = [];\n\n        var nTP = this.tp[cutLength - 1], currentTP = 0;\n        var nFP = this.fp[cutLength - 1], currentFP = 0;\n\n        for (var i = cutLow, j = (cutLength - 1); i <= cutHigh; i += interval) {\n            while (this.cutoffs[j] < i)\n                j--;\n\n            xLabels.push(i);\n\n            var thisTP = nTP - currentTP - this.tp[j];\n            var thisFP = nFP - currentFP - this.fp[j];\n\n            currentTP += thisTP;\n            currentFP += thisFP;\n\n            interValues.push(thisFP);\n            intraValues.push(thisTP);\n\n            interCumPercent.push(100 - (nFP - this.fp[j]) / nFP * 100);\n            intraCumPercent.push(100 - (nTP - this.tp[j]) / nTP * 100);\n        }\n\n        return {\n            xLabels: xLabels,\n            interValues: interValues,\n            intraValues: intraValues,\n            interCumPercent: interCumPercent,\n            intraCumPercent: intraCumPercent\n        };\n    }\n}\n\nPerformance.names = {\n    acc: 'Accuracy',\n    err: 'Error rate',\n    fpr: 'False positive rate',\n    tpr: 'True positive rate',\n    fnr: 'False negative rate',\n    tnr: 'True negative rate',\n    ppv: 'Positive predictive value',\n    npv: 'Negative predictive value',\n    pcfall: 'Prediction-conditioned fallout',\n    pcmiss: 'Prediction-conditioned miss',\n    lift: 'Lift value',\n    rpp: 'Rate of positive predictions',\n    rnp: 'Rate of negative predictions',\n    threshold: 'Threshold'\n};\n\nmodule.exports = Performance;\n","'use strict';\n\nvar defaultOptions = {\n  size: 1,\n  value: 0\n};\n\n/**\n * Case when the entry is an array\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction arrayCase(data, options) {\n  var len = data.length;\n  if (typeof options.size === 'number') {\n    options.size = [options.size, options.size];\n  }\n\n  var cond = len + options.size[0] + options.size[1];\n\n  var output;\n  if (options.output) {\n    if (options.output.length !== cond) {\n      throw new RangeError('Wrong output size');\n    }\n    output = options.output;\n  } else {\n    output = new Array(cond);\n  }\n\n  var i;\n  if (options.value === 'circular') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) {\n        output[i] = data[(len - (options.size[0] % len) + i) % len];\n      } else if (i < options.size[0] + len) {\n        output[i] = data[i - options.size[0]];\n      } else {\n        output[i] = data[(i - options.size[0]) % len];\n      }\n    }\n  } else if (options.value === 'replicate') {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[0];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[len - 1];\n    }\n  } else if (options.value === 'symmetric') {\n    if (options.size[0] > len || options.size[1] > len) {\n      throw new RangeError(\n        'expanded value should not be bigger than the data length'\n      );\n    }\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = data[options.size[0] - 1 - i];\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = data[2 * len + options.size[0] - i - 1];\n    }\n  } else {\n    for (i = 0; i < cond; i++) {\n      if (i < options.size[0]) output[i] = options.value;\n      else if (i < options.size[0] + len) output[i] = data[i - options.size[0]];\n      else output[i] = options.value;\n    }\n  }\n\n  return output;\n}\n\n/**\n * Case when the entry is a matrix\n * @param data\n * @param options\n * @returns {Array}\n */\nfunction matrixCase(data, options) {\n  // var row = data.length;\n  // var col = data[0].length;\n  if (options.size[0] === undefined) {\n    options.size = [options.size, options.size, options.size, options.size];\n  }\n  throw new Error('matrix not supported yet, sorry');\n}\n\n/**\n * Pads and array\n * @param {Array <number>} data\n * @param {object} options\n */\nfunction padArray(data, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (Array.isArray(data)) {\n    if (Array.isArray(data[0])) return matrixCase(data, options);\n    else return arrayCase(data, options);\n  } else {\n    throw new TypeError('data should be an array');\n  }\n}\n\nmodule.exports = padArray;\n","import { Matrix, MatrixTransposeView, inverse } from 'ml-matrix';\nimport padArray from 'ml-pad-array';\n\n/**\n * Factorial of a number\n * @ignore\n * @param n\n * @return {number}\n */\nfunction factorial(n) {\n  let r = 1;\n  while (n > 0) r *= n--;\n  return r;\n}\n\nconst defaultOptions = {\n  windowSize: 5,\n  derivative: 1,\n  polynomial: 2,\n  pad: 'none',\n  padValue: 'replicate',\n};\n\n/**\n * Savitzky-Golay filter\n * @param {Array <number>} data\n * @param {number} h\n * @param {Object} options\n * @returns {Array}\n */\nexport default function savitzkyGolay(data, h, options) {\n  options = Object.assign({}, defaultOptions, options);\n  if (\n    options.windowSize % 2 === 0 ||\n    options.windowSize < 5 ||\n    !Number.isInteger(options.windowSize)\n  ) {\n    throw new RangeError(\n      'Invalid window size (should be odd and at least 5 integer number)',\n    );\n  }\n  if (options.derivative < 0 || !Number.isInteger(options.derivative)) {\n    throw new RangeError('Derivative should be a positive integer');\n  }\n  if (options.polynomial < 1 || !Number.isInteger(options.polynomial)) {\n    throw new RangeError('Polynomial should be a positive integer');\n  }\n\n  let C, norm;\n  let step = Math.floor(options.windowSize / 2);\n\n  if (options.pad === 'pre') {\n    data = padArray(data, { size: step, value: options.padValue });\n  }\n\n  let ans = new Array(data.length - 2 * step);\n\n  if (\n    options.windowSize === 5 &&\n    options.polynomial === 2 &&\n    (options.derivative === 1 || options.derivative === 2)\n  ) {\n    if (options.derivative === 1) {\n      C = [-2, -1, 0, 1, 2];\n      norm = 10;\n    } else {\n      C = [2, -1, -2, -1, 2];\n      norm = 7;\n    }\n  } else {\n    let J = Matrix.ones(options.windowSize, options.polynomial + 1);\n    let inic = -(options.windowSize - 1) / 2;\n    for (let i = 0; i < J.rows; i++) {\n      for (let j = 0; j < J.columns; j++) {\n        if (inic + 1 !== 0 || j !== 0) J.set(i, j, Math.pow(inic + i, j));\n      }\n    }\n    let Jtranspose = new MatrixTransposeView(J);\n    let Jinv = inverse(Jtranspose.mmul(J));\n    C = Jinv.mmul(Jtranspose);\n    C = C.getRow(options.derivative);\n    norm = 1 / factorial(options.derivative);\n  }\n  let det = norm * Math.pow(h, options.derivative);\n  for (let k = step; k < data.length - step; k++) {\n    let d = 0;\n    for (let l = 0; l < C.length; l++) d += (C[l] * data[l + k - step]) / det;\n    ans[k - step] = d;\n  }\n\n  if (options.pad === 'post') {\n    ans = padArray(ans, { size: step, value: options.padValue });\n  }\n\n  return ans;\n}\n","// auxiliary file to create the 256 look at table elements\n\nvar ans = new Array(256);\nfor (var i = 0; i < 256; i++) {\n    var num = i;\n    var c = 0;\n    while (num) {\n        num = num & (num - 1);\n        c++;\n    }\n    ans[i] = c;\n}\n\nmodule.exports = ans;","'use strict';\n\nvar eightBits = require('./creator');\n\n/**\n * Count the number of true values in an array\n * @param {Array} arr\n * @return {number}\n */\nfunction count(arr) {\n    var c = 0;\n    for (var i = 0; i < arr.length; i++) {\n        c += eightBits[arr[i] & 0xff] + eightBits[(arr[i] >> 8) & 0xff] + eightBits[(arr[i] >> 16) & 0xff] + eightBits[(arr[i] >> 24) & 0xff];\n    }\n    return c;\n}\n\n/**\n * Logical AND operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction and(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] & arr2[i];\n    return ans;\n}\n\n/**\n * Logical OR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction or(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] | arr2[i];\n    return ans;\n}\n\n/**\n * Logical XOR operation\n * @param {Array} arr1\n * @param {Array} arr2\n * @return {Array}\n */\nfunction xor(arr1, arr2) {\n    var ans = new Array(arr1.length);\n    for (var i = 0; i < arr1.length; i++)\n        ans[i] = arr1[i] ^ arr2[i];\n    return ans;\n}\n\n/**\n * Logical NOT operation\n * @param {Array} arr\n * @return {Array}\n */\nfunction not(arr) {\n    var ans = new Array(arr.length);\n    for (var i = 0; i < ans.length; i++)\n        ans[i] = ~arr[i];\n    return ans;\n}\n\n/**\n * Gets the n value of array arr\n * @param {Array} arr\n * @param {number} n\n * @return {boolean}\n */\nfunction getBit(arr, n) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    return Boolean(arr[index] & mask);\n}\n\n/**\n * Sets the n value of array arr to the value val\n * @param {Array} arr\n * @param {number} n\n * @param {boolean} val\n * @return {Array}\n */\nfunction setBit(arr, n, val) {\n    var index = n >> 5; // Same as Math.floor(n/32)\n    var mask = 1 << (31 - n % 32);\n    if (val)\n        arr[index] = mask | arr[index];\n    else\n        arr[index] = ~mask & arr[index];\n    return arr;\n}\n\n/**\n * Translates an array of numbers to a string of bits\n * @param {Array} arr\n * @returns {string}\n */\nfunction toBinaryString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(2);\n        str += '00000000000000000000000000000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a string of bits\n * @param {string} str\n * @returns {Array}\n */\nfunction parseBinaryString(str) {\n    var len = str.length / 32;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*32, 32), 2) | 0;\n    }\n    return ans;\n}\n\n/**\n * Translates an array of numbers to a hex string\n * @param {Array} arr\n * @returns {string}\n */\nfunction toHexString(arr) {\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        var obj = (arr[i] >>> 0).toString(16);\n        str += '00000000'.substr(obj.length) + obj;\n    }\n    return str;\n}\n\n/**\n * Creates an array of numbers based on a hex string\n * @param {string} str\n * @returns {Array}\n */\nfunction parseHexString(str) {\n    var len = str.length / 8;\n    var ans = new Array(len);\n    for (var i = 0; i < len; i++) {\n        ans[i] = parseInt(str.substr(i*8, 8), 16) | 0;\n    }\n    return ans;\n}\n\n/**\n * Creates a human readable string of the array\n * @param {Array} arr\n * @returns {string}\n */\nfunction toDebug(arr) {\n    var binary = toBinaryString(arr);\n    var str = '';\n    for (var i = 0; i < arr.length; i++) {\n        str += '0000'.substr((i * 32).toString(16).length) + (i * 32).toString(16) + ':';\n        for (var j = 0; j < 32; j += 4) {\n            str += ' ' + binary.substr(i * 32 + j, 4);\n        }\n        if (i < arr.length - 1) str += '\\n';\n    }\n    return str\n}\n\nmodule.exports = {\n    count: count,\n    and: and,\n    or: or,\n    xor: xor,\n    not: not,\n    getBit: getBit,\n    setBit: setBit,\n    toBinaryString: toBinaryString,\n    parseBinaryString: parseBinaryString,\n    toHexString: toHexString,\n    parseHexString: parseHexString,\n    toDebug: toDebug\n};\n","export default function SavitzkyGolay(data, h, options = {}) {\n  let { windowSize = 9, derivative = 0, polynomial = 3 } = options;\n\n  if (windowSize % 2 === 0 || windowSize < 5 || !Number.isInteger(windowSize)) {\n    throw new RangeError(\n      'Invalid window size (should be odd and at least 5 integer number)',\n    );\n  }\n  if (windowSize > data.length) {\n    throw new RangeError(\n      `Window size is higher than the data length ${windowSize}>${data.length}`,\n    );\n  }\n  if (derivative < 0 || !Number.isInteger(derivative)) {\n    throw new RangeError('Derivative should be a positive integer');\n  }\n  if (polynomial < 1 || !Number.isInteger(polynomial)) {\n    throw new RangeError('Polynomial should be a positive integer');\n  }\n  if (polynomial >= 6) {\n    // eslint-disable-next-line no-console\n    console.warn(\n      'You should not use polynomial grade higher than 5 if you are' +\n        ' not sure that your data arises from such a model. Possible polynomial oscillation problems',\n    );\n  }\n\n  let half = Math.floor(windowSize / 2);\n  let np = data.length;\n  let ans = new Array(np);\n  let weights = fullWeights(windowSize, polynomial, derivative);\n  let hs = 0;\n  let constantH = true;\n  if (Array.isArray(h)) {\n    constantH = false;\n  } else {\n    hs = Math.pow(h, derivative);\n  }\n\n  //For the borders\n  for (let i = 0; i < half; i++) {\n    let wg1 = weights[half - i - 1];\n    let wg2 = weights[half + i + 1];\n    let d1 = 0;\n    let d2 = 0;\n    for (let l = 0; l < windowSize; l++) {\n      d1 += wg1[l] * data[l];\n      d2 += wg2[l] * data[np - windowSize + l];\n    }\n    if (constantH) {\n      ans[half - i - 1] = d1 / hs;\n      ans[np - half + i] = d2 / hs;\n    } else {\n      hs = getHs(h, half - i - 1, half, derivative);\n      ans[half - i - 1] = d1 / hs;\n      hs = getHs(h, np - half + i, half, derivative);\n      ans[np - half + i] = d2 / hs;\n    }\n  }\n\n  //For the internal points\n  let wg = weights[half];\n  for (let i = windowSize; i <= np; i++) {\n    let d = 0;\n    for (let l = 0; l < windowSize; l++) d += wg[l] * data[l + i - windowSize];\n    if (!constantH) hs = getHs(h, i - half - 1, half, derivative);\n    ans[i - half - 1] = d / hs;\n  }\n  return ans;\n}\n\nfunction getHs(h, center, half, derivative) {\n  let hs = 0;\n  let count = 0;\n  for (let i = center - half; i < center + half; i++) {\n    if (i >= 0 && i < h.length - 1) {\n      hs += h[i + 1] - h[i];\n      count++;\n    }\n  }\n  return Math.pow(hs / count, derivative);\n}\n\nfunction GramPoly(i, m, k, s) {\n  let Grampoly = 0;\n  if (k > 0) {\n    Grampoly =\n      ((4 * k - 2) / (k * (2 * m - k + 1))) *\n        (i * GramPoly(i, m, k - 1, s) + s * GramPoly(i, m, k - 1, s - 1)) -\n      (((k - 1) * (2 * m + k)) / (k * (2 * m - k + 1))) *\n        GramPoly(i, m, k - 2, s);\n  } else {\n    if (k === 0 && s === 0) {\n      Grampoly = 1;\n    } else {\n      Grampoly = 0;\n    }\n  }\n  return Grampoly;\n}\n\nfunction GenFact(a, b) {\n  let gf = 1;\n  if (a >= b) {\n    for (let j = a - b + 1; j <= a; j++) {\n      gf *= j;\n    }\n  }\n  return gf;\n}\n\nfunction Weight(i, t, m, n, s) {\n  let sum = 0;\n  for (let k = 0; k <= n; k++) {\n    //console.log(k);\n    sum +=\n      (2 * k + 1) *\n      (GenFact(2 * m, k) / GenFact(2 * m + k + 1, k + 1)) *\n      GramPoly(i, m, k, 0) *\n      GramPoly(t, m, k, s);\n  }\n  return sum;\n}\n\n/**\n *\n * @param m  Number of points\n * @param n  Polynomial grade\n * @param s  Derivative\n */\nfunction fullWeights(m, n, s) {\n  let weights = new Array(m);\n  let np = Math.floor(m / 2);\n  for (let t = -np; t <= np; t++) {\n    weights[t + np] = new Array(m);\n    for (let j = -np; j <= np; j++) {\n      weights[t + np][j + np] = Weight(j, t, np, n, s);\n    }\n  }\n  return weights;\n}\n\n/*function entropy(data,h,options){\n    var trend = SavitzkyGolay(data,h,trendOptions);\n    var copy = new Array(data.length);\n    var sum = 0;\n    var max = 0;\n    for(var i=0;i<data.length;i++){\n        copy[i] = data[i]-trend[i];\n    }\n\n    sum/=data.length;\n    console.log(sum+\" \"+max);\n    console.log(stat.array.standardDeviation(copy));\n    console.log(Math.abs(stat.array.mean(copy))/stat.array.standardDeviation(copy));\n    return sum;\n\n}\n\n\n\nfunction guessWindowSize(data, h){\n    console.log(\"entropy \"+entropy(data,h,trendOptions));\n    return 5;\n}\n*/\n","import SG from 'ml-savitzky-golay-generalized';\n\n/**\n * Global spectra deconvolution\n * @param {Array<number>} x - Independent variable\n * @param {Array<number>} yIn - Dependent variable\n * @param {object} [options] - Options object\n * @param {object} [options.sgOptions] - Options object for Savitzky-Golay filter. See https://github.com/mljs/savitzky-golay-generalized#options\n * @param {number} [options.sgOptions.windowSize = 9] - points to use in the approximations\n * @param {number} [options.sgOptions.polynomial = 3] - degree of the polynomial to use in the approximations\n * @param {number} [options.minMaxRatio = 0.00025] - Threshold to determine if a given peak should be considered as a noise\n * @param {number} [options.broadRatio = 0.00] - If `broadRatio` is higher than 0, then all the peaks which second derivative\n * smaller than `broadRatio * maxAbsSecondDerivative` will be marked with the soft mask equal to true.\n * @param {number} [options.noiseLevel = 0] - Noise threshold in spectrum units\n * @param {boolean} [options.maxCriteria = true] - Peaks are local maximum(true) or minimum(false)\n * @param {boolean} [options.smoothY = true] - Select the peak intensities from a smoothed version of the independent variables\n * @param {boolean} [options.realTopDetection = false] - Use a quadratic optimizations with the peak and its 3 closest neighbors\n * to determine the true x,y values of the peak?\n * @param {number} [options.heightFactor = 0] - Factor to multiply the calculated height (usually 2)\n * @param {number} [options.derivativeThreshold = -1] - Filters based on the amplitude of the first derivative\n * @return {Array<object>}\n */\nexport function gsd(x, yIn, options = {}) {\n  let {\n    noiseLevel,\n    sgOptions = {\n      windowSize: 9,\n      polynomial: 3,\n    },\n    smoothY = true,\n    heightFactor = 0,\n    broadRatio = 0.0,\n    maxCriteria = true,\n    minMaxRatio = 0.00025,\n    derivativeThreshold = -1,\n    realTopDetection = false,\n  } = options;\n\n  const y = yIn.slice();\n  let equalSpaced = isEqualSpaced(x);\n\n  if (noiseLevel === undefined) {\n    noiseLevel = equalSpaced ? getNoiseLevel(y) : 0;\n  }\n\n  const yCorrection = { m: 1, b: noiseLevel };\n\n  if (!maxCriteria) {\n    yCorrection.m = -1;\n    yCorrection.b *= -1;\n  }\n\n  for (let i = 0; i < y.length; i++) {\n    y[i] = yCorrection.m * y[i] - yCorrection.b;\n  }\n\n  for (let i = 0; i < y.length; i++) {\n    if (y[i] < 0) {\n      y[i] = 0;\n    }\n  }\n  // If the max difference between delta x is less than 5%, then,\n  // we can assume it to be equally spaced variable\n  let yData = y;\n  let dY, ddY;\n  const { windowSize, polynomial } = sgOptions;\n\n  if (equalSpaced) {\n    if (smoothY) {\n      yData = SG(y, x[1] - x[0], {\n        windowSize,\n        polynomial,\n        derivative: 0,\n      });\n    }\n    dY = SG(y, x[1] - x[0], {\n      windowSize,\n      polynomial,\n      derivative: 1,\n    });\n    ddY = SG(y, x[1] - x[0], {\n      windowSize,\n      polynomial,\n      derivative: 2,\n    });\n  } else {\n    if (smoothY) {\n      yData = SG(y, x, {\n        windowSize,\n        polynomial,\n        derivative: 0,\n      });\n    }\n    dY = SG(y, x, {\n      windowSize,\n      polynomial,\n      derivative: 1,\n    });\n    ddY = SG(y, x, {\n      windowSize,\n      polynomial,\n      derivative: 2,\n    });\n  }\n\n  const xData = x;\n  const dX = x[1] - x[0];\n  let maxDdy = 0;\n  let maxY = 0;\n  for (let i = 0; i < yData.length; i++) {\n    if (Math.abs(ddY[i]) > maxDdy) {\n      maxDdy = Math.abs(ddY[i]);\n    }\n    if (Math.abs(yData[i]) > maxY) {\n      maxY = Math.abs(yData[i]);\n    }\n  }\n\n  let lastMax = null;\n  let lastMin = null;\n  let minddY = new Array(yData.length - 2);\n  let intervalL = new Array(yData.length);\n  let intervalR = new Array(yData.length);\n  let broadMask = new Array(yData.length - 2);\n  let minddYLen = 0;\n  let intervalLLen = 0;\n  let intervalRLen = 0;\n  let broadMaskLen = 0;\n  // By the intermediate value theorem We cannot find 2 consecutive maximum or minimum\n  for (let i = 1; i < yData.length - 1; ++i) {\n    // filter based on derivativeThreshold\n    // console.log('pasa', y[i], dY[i], ddY[i]);\n    if (Math.abs(dY[i]) > derivativeThreshold) {\n      // Minimum in first derivative\n      if (\n        (dY[i] < dY[i - 1] && dY[i] <= dY[i + 1]) ||\n        (dY[i] <= dY[i - 1] && dY[i] < dY[i + 1])\n      ) {\n        lastMin = {\n          x: xData[i],\n          index: i,\n        };\n        if (dX > 0 && lastMax !== null) {\n          intervalL[intervalLLen++] = lastMax;\n          intervalR[intervalRLen++] = lastMin;\n        }\n      }\n\n      // Maximum in first derivative\n      if (\n        (dY[i] >= dY[i - 1] && dY[i] > dY[i + 1]) ||\n        (dY[i] > dY[i - 1] && dY[i] >= dY[i + 1])\n      ) {\n        lastMax = {\n          x: xData[i],\n          index: i,\n        };\n        if (dX < 0 && lastMin !== null) {\n          intervalL[intervalLLen++] = lastMax;\n          intervalR[intervalRLen++] = lastMin;\n        }\n      }\n    }\n\n    // Minimum in second derivative\n    if (ddY[i] < ddY[i - 1] && ddY[i] < ddY[i + 1]) {\n      // TODO should we change this to have 3 arrays ? Huge overhead creating arrays\n      minddY[minddYLen++] = i; // ( [xData[i], yData[i], i] );\n      broadMask[broadMaskLen++] = Math.abs(ddY[i]) <= broadRatio * maxDdy;\n    }\n  }\n  minddY.length = minddYLen;\n  intervalL.length = intervalLLen;\n  intervalR.length = intervalRLen;\n  broadMask.length = broadMaskLen;\n\n  let signals = new Array(minddY.length);\n  let signalsLen = 0;\n  let lastK = -1;\n  let possible, frequency, distanceJ, minDistance, gettingCloser;\n  for (let j = 0; j < minddY.length; ++j) {\n    frequency = xData[minddY[j]];\n    possible = -1;\n    let k = lastK + 1;\n    minDistance = Number.MAX_VALUE;\n    distanceJ = 0;\n    gettingCloser = true;\n    while (possible === -1 && k < intervalL.length && gettingCloser) {\n      distanceJ = Math.abs(frequency - (intervalL[k].x + intervalR[k].x) / 2);\n\n      // Still getting closer?\n      if (distanceJ < minDistance) {\n        minDistance = distanceJ;\n      } else {\n        gettingCloser = false;\n      }\n      if (distanceJ < Math.abs(intervalL[k].x - intervalR[k].x) / 2) {\n        possible = k;\n        lastK = k;\n      }\n      ++k;\n    }\n\n    if (possible !== -1) {\n      if (Math.abs(yData[minddY[j]]) > minMaxRatio * maxY) {\n        signals[signalsLen++] = {\n          index: minddY[j],\n          x: frequency,\n          y: (yData[minddY[j]] + yCorrection.b) / yCorrection.m,\n          width: Math.abs(intervalR[possible].x - intervalL[possible].x), // widthCorrection\n          soft: broadMask[j],\n        };\n\n        signals[signalsLen - 1].left = intervalL[possible];\n        signals[signalsLen - 1].right = intervalR[possible];\n\n        if (heightFactor) {\n          let yLeft = yData[intervalL[possible].index];\n          let yRight = yData[intervalR[possible].index];\n          signals[signalsLen - 1].height =\n            heightFactor * (signals[signalsLen - 1].y - (yLeft + yRight) / 2);\n        }\n      }\n    }\n  }\n  signals.length = signalsLen;\n\n  if (realTopDetection) {\n    determineRealTop(signals, xData, yData);\n  }\n\n  // Correct the values to fit the original spectra data\n  for (let j = 0; j < signals.length; j++) {\n    signals[j].base = noiseLevel;\n  }\n\n  signals.sort(function (a, b) {\n    return a.x - b.x;\n  });\n\n  return signals;\n}\n\nconst isEqualSpaced = (x) => {\n  let tmp;\n  let maxDx = 0;\n  let minDx = Number.MAX_SAFE_INTEGER;\n  for (let i = 0; i < x.length - 1; ++i) {\n    tmp = Math.abs(x[i + 1] - x[i]);\n    if (tmp < minDx) {\n      minDx = tmp;\n    }\n    if (tmp > maxDx) {\n      maxDx = tmp;\n    }\n  }\n  return (maxDx - minDx) / maxDx < 0.05;\n};\n\nconst getNoiseLevel = (y) => {\n  let mean = 0;\n\n  let stddev = 0;\n  let length = y.length;\n  for (let i = 0; i < length; ++i) {\n    mean += y[i];\n  }\n  mean /= length;\n  let averageDeviations = new Array(length);\n  for (let i = 0; i < length; ++i) {\n    averageDeviations[i] = Math.abs(y[i] - mean);\n  }\n  averageDeviations.sort((a, b) => a - b);\n  if (length % 2 === 1) {\n    stddev = averageDeviations[(length - 1) / 2] / 0.6745;\n  } else {\n    stddev =\n      (0.5 *\n        (averageDeviations[length / 2] + averageDeviations[length / 2 - 1])) /\n      0.6745;\n  }\n\n  return stddev;\n};\n\nconst determineRealTop = (peakList, x, y) => {\n  let alpha, beta, gamma, p, currentPoint;\n  for (let j = 0; j < peakList.length; j++) {\n    currentPoint = peakList[j].index; // peakList[j][2];\n    // The detected peak could be moved 1 or 2 units to left or right.\n    if (\n      y[currentPoint - 1] >= y[currentPoint - 2] &&\n      y[currentPoint - 1] >= y[currentPoint]\n    ) {\n      currentPoint--;\n    } else {\n      if (\n        y[currentPoint + 1] >= y[currentPoint] &&\n        y[currentPoint + 1] >= y[currentPoint + 2]\n      ) {\n        currentPoint++;\n      } else {\n        if (\n          y[currentPoint - 2] >= y[currentPoint - 3] &&\n          y[currentPoint - 2] >= y[currentPoint - 1]\n        ) {\n          currentPoint -= 2;\n        } else {\n          if (\n            y[currentPoint + 2] >= y[currentPoint + 1] &&\n            y[currentPoint + 2] >= y[currentPoint + 3]\n          ) {\n            currentPoint += 2;\n          }\n        }\n      }\n    }\n    // interpolation to a sin() function\n    if (\n      y[currentPoint - 1] > 0 &&\n      y[currentPoint + 1] > 0 &&\n      y[currentPoint] >= y[currentPoint - 1] &&\n      y[currentPoint] >= y[currentPoint + 1] &&\n      (y[currentPoint] !== y[currentPoint - 1] ||\n        y[currentPoint] !== y[currentPoint + 1])\n    ) {\n      alpha = 20 * Math.log10(y[currentPoint - 1]);\n      beta = 20 * Math.log10(y[currentPoint]);\n      gamma = 20 * Math.log10(y[currentPoint + 1]);\n      p = (0.5 * (alpha - gamma)) / (alpha - 2 * beta + gamma);\n      // console.log(alpha, beta, gamma, `p: ${p}`);\n      // console.log(x[currentPoint]+\" \"+tmp+\" \"+currentPoint);\n      peakList[j].x =\n        x[currentPoint] + (x[currentPoint] - x[currentPoint - 1]) * p;\n      peakList[j].y =\n        y[currentPoint] -\n        0.25 * (y[currentPoint - 1] - y[currentPoint + 1]) * p;\n    }\n  }\n};\n","/**\n * This function calculates the spectrum as a sum of gaussian functions. The Gaussian\n * parameters are divided in 3 batches. 1st: centers; 2nd: height; 3th: std's;\n * @param t Ordinate values\n * @param p Gaussian parameters\n * @param c Constant parameters(Not used)\n * @returns {*}\n */\nexport function sumOfGaussians(p) {\n  return function (t) {\n    let nL = p.length / 3;\n    let factor;\n    let rows = t.length;\n    let result = rows === undefined ? 0 : new Float64Array(rows).fill(0);\n    for (let i = 0; i < nL; i++) {\n      factor = Math.pow(p[i + nL * 2], 2) * 2;\n      if (rows === undefined) {\n        result += p[i + nL] * Math.exp(-Math.pow(t - p[i], 2) / factor);\n      } else {\n        for (let j = 0; j < rows; j++) {\n          result[j] += p[i + nL] * Math.exp(-Math.pow(t[j] - p[i], 2) / factor);\n        }\n      }\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { sumOfGaussians } from './sumOfGaussians';\n\n/**\n *\n * @param xy A two column matrix containing the x and y data to be fitted\n * @param group A set of initial lorentzian parameters to be optimized [center, heigth, half_width_at_half_height]\n * @returns {Array} A set of final lorentzian parameters [center, heigth, hwhh*2]\n */\nexport function optimizeGaussianSum(xy, group, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n  let nL = group.length;\n  let pInit = new Float64Array(nL * 3);\n  let pMin = new Float64Array(nL * 3);\n  let pMax = new Float64Array(nL * 3);\n  let dt = Math.abs(t[0] - t[1]);\n\n  for (let i = 0; i < nL; i++) {\n    pInit[i] = group[i].x;\n    pInit[i + nL] = group[i].y / maxY;\n    pInit[i + 2 * nL] = group[i].width;\n\n    pMin[i] = group[i].x - dt;\n    pMin[i + nL] = 0;\n    pMin[i + 2 * nL] = group[i].width / 4;\n\n    pMax[i] = group[i].x + dt;\n    pMax[i + nL] = (group[i].y * 1.2) / maxY;\n    pMax[i + 2 * nL] = group[i].width * 4;\n  }\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n  let result = new Array(nL);\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n\n  opts = Object.assign({}, lmOptions, opts);\n\n  let pFit = LM(data, sumOfGaussians, opts);\n  for (let i = 0; i < nL; i++) {\n    result[i] = {\n      parameters: [\n        pFit.parameterValues[i],\n        pFit.parameterValues[i + nL] * maxY,\n        pFit.parameterValues[i + nL * 2],\n      ],\n      error: pFit.parameterError,\n    };\n  }\n  return result;\n}\n","/**\n * Single 3 parameter gaussian function\n * @param t Ordinate values\n * @param p Gaussian parameters [mean, height, std]\n * @param c Constant parameters(Not used)\n * @returns {*}\n */\n\nexport function singleGaussian(p) {\n  return function (t) {\n    let factor2 = (p[2] * p[2]) / 2;\n    let rows = t.length;\n    if (!rows) return p[1] * Math.exp((-(t - p[0]) * (t - p[0])) / factor2);\n    let result = new Float64Array(t.length);\n    for (let i = 0; i < t.length; i++) {\n      result[i] = p[1] * Math.exp((-(t[i] - p[0]) * (t[i] - p[0])) / factor2);\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { singleGaussian } from './singleGaussian';\n\n/**\n * Fits a set of points to a gaussian bell. Returns the mean of the peak, the std and the height of the signal.\n * @param data,[y]\n * @returns {*[]}\n */\nexport function optimizeSingleGaussian(xy, peak, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n  let dt = Math.abs(t[0] - t[1]);\n  let pInit = new Float64Array([peak.x, 1, peak.width]);\n  let pMin = new Float64Array([peak.x - dt, 0, peak.width / 4]);\n  let pMax = new Float64Array([peak.x + dt, 1.25, peak.width * 4]);\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n\n  opts = Object.assign({}, lmOptions, opts);\n  let pFit = LM(data, singleGaussian, opts);\n  return {\n    parameters: [\n      pFit.parameterValues[0],\n      pFit.parameterValues[1] * maxY,\n      pFit.parameterValues[2],\n    ],\n    error: pFit.parameterError,\n  };\n}\n","/**\n * This function calculates the spectrum as a sum of lorentzian functions. The Lorentzian\n * parameters are divided in 3 batches. 1st: centers; 2nd: heights; 3th: widths;\n * @param t Ordinate values\n * @param p Lorentzian parameters\n * @returns {*}\n */\n\nexport function sumOfLorentzians(p) {\n  return function (t) {\n    let nL = p.length / 3;\n    let factor;\n    let p2;\n    let rows = t.length;\n    let result = rows === undefined ? 0 : new Float64Array(rows).fill(0);\n    for (let i = 0; i < nL; i++) {\n      p2 = Math.pow(p[i + nL * 2] / 2, 2);\n      factor = p[i + nL] * p2;\n      if (rows === undefined) {\n        result += factor / (Math.pow(t - p[i], 2) + p2);\n      } else {\n        for (let j = 0; j < rows; j++) {\n          result[j] += factor / (Math.pow(t[j] - p[i], 2) + p2);\n        }\n      }\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { sumOfLorentzians } from './sumOfLorentzians';\n\n/**\n *\n * @param xy A two column matrix containing the x and y data to be fitted\n * @param group A set of initial lorentzian parameters to be optimized [center, heigth, half_width_at_half_height]\n * @returns {Array} A set of final lorentzian parameters [center, heigth, hwhh*2]\n */\nexport function optimizeLorentzianSum(xy, group, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n\n  let nL = group.length;\n  let pInit = new Float64Array(nL * 3);\n  let pMin = new Float64Array(nL * 3);\n  let pMax = new Float64Array(nL * 3);\n  let dt = Math.abs(t[0] - t[1]);\n\n  for (let i = 0; i < nL; i++) {\n    pInit[i] = group[i].x;\n    pInit[i + nL] = 1;\n    pInit[i + 2 * nL] = group[i].width;\n\n    pMin[i] = group[i].x - dt;\n    pMin[i + nL] = 0;\n    pMin[i + 2 * nL] = group[i].width / 4;\n\n    pMax[i] = group[i].x + dt;\n    pMax[i + nL] = 1.5;\n    pMax[i + 2 * nL] = group[i].width * 4;\n  }\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n\n  let result = new Array(nL);\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n\n  opts = Object.assign({}, lmOptions, opts);\n\n  let pFit = LM(data, sumOfLorentzians, opts);\n  for (let i = 0; i < nL; i++) {\n    result[i] = {\n      parameters: [\n        pFit.parameterValues[i],\n        pFit.parameterValues[i + nL] * maxY,\n        pFit.parameterValues[i + nL * 2],\n      ],\n      error: pFit.parameterError,\n    };\n  }\n  return result;\n}\n","/**\n * Single 4 parameter lorentzian function\n * @param t Ordinate values\n * @param p Lorentzian parameters\n * @param c Constant parameters(Not used)\n * @returns {*}\n */\n\nexport function singleLorentzian(p) {\n  return function (t) {\n    let factor = p[1] * Math.pow(p[2] / 2, 2);\n    let rows = t.length;\n    if (!rows) return factor / (Math.pow(t - p[0], 2) + Math.pow(p[2] / 2, 2));\n    let result = new Float64Array(rows);\n    for (let i = 0; i < rows; i++) {\n      result[i] = factor / (Math.pow(t[i] - p[0], 2) + Math.pow(p[2] / 2, 2));\n    }\n    return result;\n  };\n}\n","import LM from 'ml-levenberg-marquardt';\n\nimport { singleLorentzian } from './singleLorentzian';\n\n/**\n * * Fits a set of points to a Lorentzian function. Returns the center of the peak, the width at half height, and the height of the signal.\n * @param data,[y]\n * @returns {*[]}\n */\nexport function optimizeSingleLorentzian(xy, peak, opts = {}) {\n  let t = xy[0];\n  let yData = xy[1];\n  let maxY = Math.max(...yData);\n  yData.forEach((x, i, arr) => (arr[i] /= maxY));\n  let dt = Math.abs(t[0] - t[1]);\n  let pInit = new Float64Array([peak.x, 1, peak.width]);\n  let pMin = new Float64Array([peak.x - dt, 0.75, peak.width / 4]);\n  let pMax = new Float64Array([peak.x + dt, 1.25, peak.width * 4]);\n\n  let data = {\n    x: t,\n    y: yData,\n  };\n\n  let lmOptions = {\n    damping: 1.5,\n    initialValues: pInit,\n    minValues: pMin,\n    maxValues: pMax,\n    gradientDifference: dt / 10000,\n    maxIterations: 100,\n    errorTolerance: 10e-5,\n  };\n  opts = Object.assign({}, lmOptions, opts);\n  let pFit = LM(data, singleLorentzian, opts);\n  return {\n    parameters: [\n      pFit.parameterValues[0],\n      pFit.parameterValues[1] * maxY,\n      pFit.parameterValues[2],\n    ],\n    error: pFit.parameterError,\n  };\n}\n","import {\n  optimizeGaussianSum,\n  optimizeLorentzianSum,\n  optimizeSingleGaussian,\n  optimizeSingleLorentzian,\n} from 'ml-optimize-lorentzian';\n\nexport function optimizePeaks(peakList, x, y, options = {}) {\n  const {\n    functionName = 'gaussian',\n    factorWidth = 4,\n    optimizationOptions = {\n      damping: 1.5,\n      maxIterations: 100,\n      errorTolerance: 10e-5,\n    },\n  } = options;\n\n  let lastIndex = [0];\n  let groups = groupPeaks(peakList, factorWidth);\n  let result = [];\n  let factor = 1;\n  if (functionName === 'gaussian') {\n    factor = 1.17741;\n  } // From https://en.wikipedia.org/wiki/Gaussian_function#Properties\n  let sampling;\n  for (let i = 0; i < groups.length; i++) {\n    let peaks = groups[i].group;\n    if (peaks.length > 1) {\n      // Multiple peaks\n      sampling = sampleFunction(\n        groups[i].limits[0] - groups[i].limits[1],\n        groups[i].limits[0] + groups[i].limits[1],\n        x,\n        y,\n        lastIndex,\n      );\n      if (sampling[0].length > 5) {\n        let optPeaks = [];\n        if (functionName === 'gaussian') {\n          optPeaks = optimizeGaussianSum(sampling, peaks, optimizationOptions);\n        } else {\n          if (functionName === 'lorentzian') {\n            optPeaks = optimizeLorentzianSum(\n              sampling,\n              peaks,\n              optimizationOptions,\n            );\n          }\n        }\n\n        for (let j = 0; j < optPeaks.length; j++) {\n          let { parameters } = optPeaks[j];\n          result.push({\n            x: parameters[0],\n            y: parameters[1],\n            width: parameters[2] * factor,\n            index: peaks[j].index,\n          });\n        }\n      }\n    } else {\n      // Single peak\n      peaks = peaks[0];\n      sampling = sampleFunction(\n        peaks.x - factorWidth * peaks.width,\n        peaks.x + factorWidth * peaks.width,\n        x,\n        y,\n        lastIndex,\n      );\n\n      if (sampling[0].length > 5) {\n        let fitResult = [];\n        if (functionName === 'gaussian') {\n          fitResult = optimizeSingleGaussian(\n            [sampling[0], sampling[1]],\n            peaks,\n            optimizationOptions,\n          );\n        } else {\n          if (functionName === 'lorentzian') {\n            fitResult = optimizeSingleLorentzian(\n              [sampling[0], sampling[1]],\n              peaks,\n              optimizationOptions,\n            );\n          }\n        }\n\n        let { parameters } = fitResult;\n        result.push({\n          x: parameters[0],\n          y: parameters[1],\n          width: parameters[2] * factor,\n          index: peaks.index,\n        }); // From https://en.wikipedia.org/wiki/Gaussian_function#Properties}\n      }\n    }\n  }\n  return result;\n}\n\nfunction sampleFunction(from, to, x, y, lastIndex) {\n  let nbPoints = x.length;\n  let sampleX = [];\n  let sampleY = [];\n  let direction = Math.sign(x[1] - x[0]); // Direction of the derivative\n  if (direction === -1) {\n    lastIndex[0] = x.length - 1;\n  }\n\n  let delta = Math.abs(to - from) / 2;\n  let mid = (from + to) / 2;\n  let stop = false;\n  let index = lastIndex[0];\n  while (!stop && index < nbPoints && index >= 0) {\n    if (Math.abs(x[index] - mid) <= delta) {\n      sampleX.push(x[index]);\n      sampleY.push(y[index]);\n      index += direction;\n    } else {\n      // It is outside the range.\n      if (Math.sign(mid - x[index]) === 1) {\n        // We'll reach the mid going in the current direction\n        index += direction;\n      } else {\n        // There is not more peaks in the current range\n        stop = true;\n      }\n    }\n  }\n  lastIndex[0] = index;\n  return [sampleX, sampleY];\n}\n\nfunction groupPeaks(peakList, nL) {\n  let group = [];\n  let groups = [];\n  let limits = [peakList[0].x, nL * peakList[0].width];\n  let upperLimit, lowerLimit;\n  // Merge forward\n  for (let i = 0; i < peakList.length; i++) {\n    // If the 2 things overlaps\n    if (\n      Math.abs(peakList[i].x - limits[0]) <\n      nL * peakList[i].width + limits[1]\n    ) {\n      // Add the peak to the group\n      group.push(peakList[i]);\n      // Update the group limits\n      upperLimit = limits[0] + limits[1];\n      if (peakList[i].x + nL * peakList[i].width > upperLimit) {\n        upperLimit = peakList[i].x + nL * peakList[i].width;\n      }\n      lowerLimit = limits[0] - limits[1];\n      if (peakList[i].x - nL * peakList[i].width < lowerLimit) {\n        lowerLimit = peakList[i].x - nL * peakList[i].width;\n      }\n      limits = [\n        (upperLimit + lowerLimit) / 2,\n        Math.abs(upperLimit - lowerLimit) / 2,\n      ];\n    } else {\n      groups.push({ limits: limits, group: group });\n      // var optmimalPeak = fitSpectrum(group,limits,spectrum);\n      group = [peakList[i]];\n      limits = [peakList[i].x, nL * peakList[i].width];\n    }\n  }\n  groups.push({ limits: limits, group: group });\n  // Merge backward\n  for (let i = groups.length - 2; i >= 0; i--) {\n    // The groups overlaps\n    if (\n      Math.abs(groups[i].limits[0] - groups[i + 1].limits[0]) <\n      (groups[i].limits[1] + groups[i + 1].limits[1]) / 2\n    ) {\n      for (let j = 0; j < groups[i + 1].group.length; j++) {\n        groups[i].group.push(groups[i + 1].group[j]);\n      }\n      upperLimit = groups[i].limits[0] + groups[i].limits[1];\n      if (groups[i + 1].limits[0] + groups[i + 1].limits[1] > upperLimit) {\n        upperLimit = groups[i + 1].limits[0] + groups[i + 1].limits[1];\n      }\n      lowerLimit = groups[i].limits[0] - groups[i].limits[1];\n      if (groups[i + 1].limits[0] - groups[i + 1].limits[1] < lowerLimit) {\n        lowerLimit = groups[i + 1].limits[0] - groups[i + 1].limits[1];\n      }\n\n      groups[i].limits = [\n        (upperLimit + lowerLimit) / 2,\n        Math.abs(upperLimit - lowerLimit) / 2,\n      ];\n\n      groups.splice(i + 1, 1);\n    }\n  }\n  return groups;\n}\n","import { optimizeSingleLorentzian } from 'ml-optimize-lorentzian';\n\n/**\n * This function try to join the peaks that seems to belong to a broad signal in a single broad peak.\n * @param peakList\n * @param options\n */\nexport function joinBroadPeaks(peakList, options = {}) {\n  let width = options.width;\n  let broadLines = [];\n  // Optimize the possible broad lines\n  let max = 0;\n\n  let maxI = 0;\n\n  let count = 1;\n  for (let i = peakList.length - 1; i >= 0; i--) {\n    if (peakList[i].soft) {\n      broadLines.push(peakList.splice(i, 1)[0]);\n    }\n  }\n  // Push a feke peak\n  broadLines.push({ x: Number.MAX_VALUE });\n\n  let candidates = [[broadLines[0].x, broadLines[0].y]];\n  let indexes = [broadLines[0].index];\n\n  for (let i = 1; i < broadLines.length; i++) {\n    // console.log(broadLines[i-1].x+\" \"+broadLines[i].x);\n    if (Math.abs(broadLines[i - 1].x - broadLines[i].x) < width) {\n      candidates.push([broadLines[i].x, broadLines[i].y]);\n      if (broadLines[i].y > max) {\n        max = broadLines[i].y;\n        maxI = i;\n      }\n      indexes.push(broadLines[i].index);\n      count++;\n    } else {\n      if (count > 2) {\n        let fitted = optimizeSingleLorentzian(candidates, {\n          x: broadLines[maxI].x,\n          y: max,\n          width: Math.abs(\n            candidates[0][0] - candidates[candidates.length - 1][0],\n          ),\n        });\n        let { parameters } = fitted;\n        peakList.push({\n          x: parameters[0],\n          y: parameters[1],\n          width: parameters[2],\n          index: Math.floor(\n            indexes.reduce((a, b) => a + b, 0) / indexes.length,\n          ),\n          soft: false,\n        });\n      } else {\n        // Put back the candidates to the signals list\n        indexes.forEach((index) => {\n          peakList.push(broadLines[index]);\n        });\n      }\n      candidates = [[broadLines[i].x, broadLines[i].y]];\n      indexes = [i];\n      max = broadLines[i].y;\n      maxI = i;\n      count = 1;\n    }\n  }\n\n  peakList.sort(function (a, b) {\n    return a.x - b.x;\n  });\n\n  return peakList;\n}\n","/**\n * This method will allow to enlarge peaks and prevent overlap between peaks\n * Because peaks may not be symmetric after we add 2 properties, from and to.\n * @param {Array} peakList\n * @param {object} [options={}]\n * @param {number} [factor=2]\n * @param {boolean} [overlap=false] by default we don't allow overlap\n * @return {Array} peakList\n */\nexport function broadenPeaks(peakList, options = {}) {\n  const { factor = 2, overlap = false } = options;\n\n  for (let peak of peakList) {\n    if (!peak.right || !peak.left) {\n      peak.from = peak.x - (peak.width / 2) * factor;\n      peak.to = peak.x + (peak.width / 2) * factor;\n    } else {\n      peak.from = peak.x - (peak.x - peak.left.x) * factor;\n      peak.to = peak.x + (peak.right.x - peak.x) * factor;\n    }\n  }\n\n  if (!overlap) {\n    for (let i = 0; i < peakList.length - 1; i++) {\n      let peak = peakList[i];\n      let nextPeak = peakList[i + 1];\n      if (peak.to > nextPeak.from) {\n        peak.to = nextPeak.from = (peak.to + nextPeak.from) / 2;\n      }\n    }\n  }\n\n  for (let peak of peakList) {\n    peak.width = peak.to - peak.from;\n  }\n\n  return peakList;\n}\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction min(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var minValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] < minValue) minValue = input[i];\n  }\n\n  return minValue;\n}\n\nexport default min;\n","import isArray from 'is-any-array';\n\nfunction max(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var maxValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\n\nfunction mode(input) {\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var maxValue = 0;\n  var maxCount = 0;\n  var count = 0;\n  var counts = {};\n\n  for (var i = 0; i < input.length; ++i) {\n    var element = input[i];\n    count = counts[element];\n\n    if (count) {\n      counts[element]++;\n      count++;\n    } else {\n      counts[element] = count = 1;\n    }\n\n    if (count > maxCount) {\n      maxCount = count;\n      maxValue = input[i];\n    }\n  }\n\n  return maxValue;\n}\n\nexport default mode;\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\n\nfunction max(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  if (input.length === 0) {\n    throw new TypeError('input must not be empty');\n  }\n\n  var _options$fromIndex = options.fromIndex,\n      fromIndex = _options$fromIndex === void 0 ? 0 : _options$fromIndex,\n      _options$toIndex = options.toIndex,\n      toIndex = _options$toIndex === void 0 ? input.length : _options$toIndex;\n\n  if (fromIndex < 0 || fromIndex >= input.length || !Number.isInteger(fromIndex)) {\n    throw new Error('fromIndex must be a positive integer smaller than length');\n  }\n\n  if (toIndex <= fromIndex || toIndex > input.length || !Number.isInteger(toIndex)) {\n    throw new Error('toIndex must be an integer greater than fromIndex and at most equal to length');\n  }\n\n  var maxValue = input[fromIndex];\n\n  for (var i = fromIndex + 1; i < toIndex; i++) {\n    if (input[i] > maxValue) maxValue = input[i];\n  }\n\n  return maxValue;\n}\n\nexport default max;\n","import isArray from 'is-any-array';\nimport max from 'ml-array-max';\nimport sum from 'ml-array-sum';\n\nfunction norm(input) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  var _options$algorithm = options.algorithm,\n      algorithm = _options$algorithm === void 0 ? 'absolute' : _options$algorithm,\n      _options$sumValue = options.sumValue,\n      sumValue = _options$sumValue === void 0 ? 1 : _options$sumValue,\n      _options$maxValue = options.maxValue,\n      maxValue = _options$maxValue === void 0 ? 1 : _options$maxValue;\n\n  if (!isArray(input)) {\n    throw new Error('input must be an array');\n  }\n\n  var output;\n\n  if (options.output !== undefined) {\n    if (!isArray(options.output)) {\n      throw new TypeError('output option must be an array if specified');\n    }\n\n    output = options.output;\n  } else {\n    output = new Array(input.length);\n  }\n\n  if (input.length === 0) {\n    throw new Error('input must not be empty');\n  }\n\n  switch (algorithm.toLowerCase()) {\n    case 'absolute':\n      {\n        var absoluteSumValue = absoluteSum(input) / sumValue;\n        if (absoluteSumValue === 0) return input.slice(0);\n\n        for (var i = 0; i < input.length; i++) {\n          output[i] = input[i] / absoluteSumValue;\n        }\n\n        return output;\n      }\n\n    case 'max':\n      {\n        var currentMaxValue = max(input);\n        if (currentMaxValue === 0) return input.slice(0);\n        var factor = maxValue / currentMaxValue;\n\n        for (var _i = 0; _i < input.length; _i++) {\n          output[_i] = input[_i] * factor;\n        }\n\n        return output;\n      }\n\n    case 'sum':\n      {\n        var sumFactor = sum(input) / sumValue;\n        if (sumFactor === 0) return input.slice(0);\n\n        for (var _i2 = 0; _i2 < input.length; _i2++) {\n          output[_i2] = input[_i2] / sumFactor;\n        }\n\n        return output;\n      }\n\n    default:\n      throw new Error(\"norm: unknown algorithm: \".concat(algorithm));\n  }\n}\n\nfunction absoluteSum(input) {\n  var sumValue = 0;\n\n  for (var i = 0; i < input.length; i++) {\n    sumValue += Math.abs(input[i]);\n  }\n\n  return sumValue;\n}\n\nexport default norm;\n","import isArray from 'is-any-array';\n\nfunction _typeof(obj) {\n  \"@babel/helpers - typeof\";\n\n  if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") {\n    _typeof = function (obj) {\n      return typeof obj;\n    };\n  } else {\n    _typeof = function (obj) {\n      return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj;\n    };\n  }\n\n  return _typeof(obj);\n}\n\n/**\r\n * Fill an array with sequential numbers\r\n * @param {Array<number>} [input] - optional destination array (if not provided a new array will be created)\r\n * @param {object} [options={}]\r\n * @param {number} [options.from=0] - first value in the array\r\n * @param {number} [options.to=10] - last value in the array\r\n * @param {number} [options.size=input.length] - size of the array (if not provided calculated from step)\r\n * @param {number} [options.step] - if not provided calculated from size\r\n * @return {Array<number>}\r\n */\n\nfunction sequentialFill() {\n  var input = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (_typeof(input) === 'object' && !isArray(input)) {\n    options = input;\n    input = [];\n  }\n\n  if (!isArray(input)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options = options,\n      _options$from = _options.from,\n      from = _options$from === void 0 ? 0 : _options$from,\n      _options$to = _options.to,\n      to = _options$to === void 0 ? 10 : _options$to,\n      _options$size = _options.size,\n      size = _options$size === void 0 ? input.length : _options$size,\n      step = _options.step;\n\n  if (size !== 0 && step) {\n    throw new Error('step is defined by the array size');\n  }\n\n  if (!size) {\n    if (step) {\n      size = Math.floor((to - from) / step) + 1;\n    } else {\n      size = to - from + 1;\n    }\n  }\n\n  if (!step && size) {\n    step = (to - from) / (size - 1);\n  }\n\n  if (Array.isArray(input)) {\n    // only works with normal array\n    input.length = 0;\n\n    for (var i = 0; i < size; i++) {\n      input.push(from);\n      from += step;\n    }\n  } else {\n    if (input.length !== size) {\n      throw new Error('sequentialFill typed array must have the correct length');\n    }\n\n    for (var _i = 0; _i < size; _i++) {\n      input[_i] = from;\n      from += step;\n    }\n  }\n\n  return input;\n}\n\nexport default sequentialFill;\n","const toString = Object.prototype.toString;\n\nexport default function isAnyArray(object) {\n  return toString.call(object).endsWith('Array]');\n}\n","import isArray from 'is-any-array';\nimport arrayMean from 'ml-array-mean';\n\nfunction variance(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n  if (!isArray(values)) {\n    throw new TypeError('input must be an array');\n  }\n\n  var _options$unbiased = options.unbiased,\n      unbiased = _options$unbiased === void 0 ? true : _options$unbiased,\n      _options$mean = options.mean,\n      mean = _options$mean === void 0 ? arrayMean(values) : _options$mean;\n  var sqrError = 0;\n\n  for (var i = 0; i < values.length; i++) {\n    var x = values[i] - mean;\n    sqrError += x * x;\n  }\n\n  if (unbiased) {\n    return sqrError / (values.length - 1);\n  } else {\n    return sqrError / values.length;\n  }\n}\n\nexport default variance;\n","import variance from 'ml-array-variance';\n\nfunction standardDeviation(values) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n  return Math.sqrt(variance(values, options));\n}\n\nexport default standardDeviation;\n","/**\n * Merge abscissa values if the ordinate value is in a list of centroids\n * @param {object} originalPoints\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {Array<number>} centroids\n * @param {object} [options]\n * @param {number} [options.window = 0.01] - has to be a positive number\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function mergeByCentroids(\n  originalPoints,\n  centroids,\n  options = {}\n) {\n  const { window = 0.01 } = options;\n\n  var mergedPoints = {\n    x: centroids.slice(),\n    y: new Array(centroids.length).fill(0)\n  };\n\n  var originalIndex = 0;\n  var mergedIndex = 0;\n  while (\n    originalIndex < originalPoints.x.length &&\n    mergedIndex < centroids.length\n  ) {\n    var diff = originalPoints.x[originalIndex] - centroids[mergedIndex];\n    if (Math.abs(diff) < window) {\n      mergedPoints.y[mergedIndex] += originalPoints.y[originalIndex++];\n    } else if (diff < 0) {\n      originalIndex++;\n    } else {\n      mergedIndex++;\n    }\n  }\n\n  return mergedPoints;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} originalPoints.x\n * @param {Array<number>} originalPoints.y\n * @param {*} options\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function closestX(points, options) {\n  const { x, y } = points;\n  const { target = x[0], reverse = false } = options;\n\n  let index;\n  if (reverse) {\n    index = binarySearch(x, target, descending);\n  } else {\n    index = binarySearch(x, target, ascending);\n  }\n\n  if (index >= 0) {\n    return {\n      x: x[index],\n      y: y[index]\n    };\n  } else {\n    index = ~index;\n    if (\n      (index !== 0 && Math.abs(x[index] - target) > 0.5) ||\n      index === x.length\n    ) {\n      return {\n        x: x[index - 1],\n        y: y[index - 1]\n      };\n    } else {\n      return {\n        x: x[index],\n        y: y[index]\n      };\n    }\n  }\n}\n","import mean from 'ml-array-mean';\n\n/**\n *\n * @param {object} points\n * @param {Array<number>} points.x\n * @param {Array<number>} points.y\n * @param {object} [options]\n * @param {boolean} [options.unbiased = true] - if true, divide by (n-1); if false, divide by n.\n * @return {number}\n */\nexport default function covariance(points, options = {}) {\n  const { x, y } = points;\n  const { unbiased = true } = options;\n\n  const meanX = mean(x);\n  const meanY = mean(y);\n\n  var error = 0;\n\n  for (let i = 0; i < x.length; i++) {\n    error += (x[i] - meanX) * (y[i] - meanY);\n  }\n\n  if (unbiased) {\n    return error / (x.length - 1);\n  } else {\n    return error / x.length;\n  }\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function maxMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var maxAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      maxAbscissa.x.push(x[index]);\n      maxAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      if (y[index] > maxAbscissa.y[size - 1]) {\n        maxAbscissa.x[size - 1] = x[index];\n        maxAbscissa.y[size - 1] = y[index];\n      }\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  merged.x = maxAbscissa.x.slice();\n\n  return merged;\n}\n","import binarySearch from 'binary-search';\nimport { ascending, descending } from 'num-sort';\n\n/**\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {object} [options.from = {index: 0}]\n * @param {object} [options.to = {index: x.length-1}]\n * @param {boolean} [options.reverse = false]\n * @return {{index: number, value: number}}\n */\nexport default function maxY(points, options = {}) {\n  const { x, y } = points;\n  let {\n    from = { index: 0 },\n    to = { index: x.length },\n    reverse = false\n  } = options;\n\n  if (from.value !== undefined && from.index === undefined) {\n    from.index = calculateIndex(from.value, x, reverse);\n  }\n\n  if (to.value !== undefined && to.index === undefined) {\n    to.index = calculateIndex(to.value, x, reverse);\n  }\n\n  var currentMax = Number.MIN_VALUE;\n  var currentIndex;\n  for (var i = from.index; i < to.index; i++) {\n    if (currentMax < y[i]) {\n      currentMax = y[i];\n      currentIndex = i;\n    }\n  }\n\n  return {\n    index: currentIndex,\n    value: currentMax\n  };\n}\n\n/**\n * @param {number} value\n * @param {Array<number>} x\n * @param {boolean} reverse\n * @return {number} index of the value in the array\n */\nfunction calculateIndex(value, x, reverse) {\n  let index;\n  if (reverse) {\n    index = binarySearch(x, value, descending);\n  } else {\n    index = binarySearch(x, value, ascending);\n  }\n\n  if (index < 0) {\n    throw new Error(`the value ${value} doesn't belongs to the abscissa value`);\n  }\n\n  return index;\n}\n","export default function sortX(points, options = {}) {\n  const { x, y } = points;\n  const { reverse = false } = options;\n\n  var sortFunc;\n  if (!reverse) {\n    sortFunc = (a, b) => a.x - b.x;\n  } else {\n    sortFunc = (a, b) => b.x - a.x;\n  }\n\n  var grouped = x\n    .map((val, index) => ({\n      x: val,\n      y: y[index]\n    }))\n    .sort(sortFunc);\n\n  var response = { x: x.slice(), y: y.slice() };\n  for (var i = 0; i < x.length; i++) {\n    response.x[i] = grouped[i].x;\n    response.y[i] = grouped[i].y;\n  }\n\n  return response;\n}\n","\n/**\n * In place modification of the 2 arrays to make X unique and sum the Y if X has the same value\n * @param {object} [points={}] : Object of points contains property x (an array) and y (an array)\n * @return points\n */\n\nexport default function uniqueX(points = {}) {\n  const { x, y } = points;\n  if (x.length < 2) return;\n  if (x.length !== y.length) {\n    throw new Error('The X and Y arrays mush have the same length');\n  }\n\n  let current = x[0];\n  let counter = 0;\n\n  for (let i = 1; i < x.length; i++) {\n    if (current !== x[i]) {\n      counter++;\n      current = x[i];\n      x[counter] = x[i];\n      if (i !== counter) {\n        y[counter] = 0;\n      }\n    }\n    if (i !== counter) {\n      y[counter] += y[i];\n    }\n  }\n\n  x.length = counter + 1;\n  y.length = counter + 1;\n}\n","/**\n * Merge abscissas values on similar ordinates and weight the group of abscissas\n * @param {object} points\n * @param {Array<number>} points.x - sorted abscissas values\n * @param {Array<number>} points.y - ordinates values\n * @param {object} [options]\n * @param {number} [options.groupWidth = 0.001] - window for abscissas to merge\n * @return {{x: Array<number>, y: Array<number>}}\n */\nexport default function weightedMerge(points, options = {}) {\n  const { x, y } = points;\n  const { groupWidth = 0.001 } = options;\n\n  var merged = { x: [], y: [] };\n  var weightedAbscissa = { x: [], y: [] };\n  var size = 0;\n  var index = 0;\n\n  while (index < x.length) {\n    if (size === 0 || x[index] - merged.x[size - 1] > groupWidth) {\n      weightedAbscissa.x.push(x[index] * y[index]);\n      weightedAbscissa.y.push(y[index]);\n      merged.x.push(x[index]);\n      merged.y.push(y[index]);\n      index++;\n      size++;\n    } else {\n      weightedAbscissa.x[size - 1] += x[index] * y[index];\n      weightedAbscissa.y[size - 1] += y[index];\n      merged.x[size - 1] = x[index];\n      merged.y[size - 1] += y[index];\n      index++;\n    }\n  }\n\n  for (var i = 0; i < merged.x.length; i++) {\n    merged.x[i] = weightedAbscissa.x[i] / weightedAbscissa.y[i];\n  }\n\n  return merged;\n}\n","/**\n * Normalize an array of zones:\n * - ensure than from < to\n * - merge overlapping zones\n *\n * The method will always check if from if lower than to and will swap if required.\n * @param {Array} [zones=[]]\n * @param {object} [options={}]\n * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of a zone\n * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of a zone\n */\n\nexport function normalize(zones = [], options = {}) {\n  if (zones.length === 0) return [];\n  let {\n    from = Number.NEGATIVE_INFINITY,\n    to = Number.POSITIVE_INFINITY,\n  } = options;\n  if (from > to) [from, to] = [to, from];\n\n  zones = JSON.parse(JSON.stringify(zones)).map((zone) =>\n    zone.from > zone.to ? { from: zone.to, to: zone.from } : zone,\n  );\n  zones = zones.sort((a, b) => {\n    if (a.from !== b.from) return a.from - b.from;\n    return a.to - b.to;\n  });\n\n  zones.forEach((zone) => {\n    if (from > zone.from) zone.from = from;\n    if (to < zone.to) zone.to = to;\n  });\n\n  zones = zones.filter((zone) => zone.from <= zone.to);\n  if (zones.length === 0) return [];\n\n  let currentZone = zones[0];\n  let result = [currentZone];\n  for (let i = 1; i < zones.length; i++) {\n    let zone = zones[i];\n    if (zone.from <= currentZone.to) {\n      currentZone.to = zone.to;\n    } else {\n      currentZone = zone;\n      result.push(currentZone);\n    }\n  }\n  return result;\n}\n","import { normalize } from './normalize';\n\n/**\n * Convert an array of exclusions and keep only from / to\n *\n * The method will always check if from if lower than to and will swap if required.\n * @param {Array} [exclusions=[]]\n * @param {object} [options={}]\n * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of zones (after inversion)\n * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of zones (after inversion)\n */\n\nexport function invert(exclusions = [], options = {}) {\n  let {\n    from = Number.NEGATIVE_INFINITY,\n    to = Number.POSITIVE_INFINITY,\n  } = options;\n  if (from > to) [from, to] = [to, from];\n\n  exclusions = normalize(exclusions, { from, to });\n  if (exclusions.length === 0) return [{ from, to }];\n\n  let zones = [];\n  for (let i = 0; i < exclusions.length; i++) {\n    let exclusion = exclusions[i];\n    let nextExclusion = exclusions[i + 1];\n    if (i === 0) {\n      if (exclusion.from > from) {\n        zones.push({ from, to: exclusion.from });\n      }\n    }\n    if (i === exclusions.length - 1) {\n      if (exclusion.to < to) {\n        zones.push({ from: exclusion.to, to });\n      }\n    } else {\n      zones.push({ from: exclusion.to, to: nextExclusion.from });\n    }\n  }\n\n  return zones;\n}\n","import { normalize } from './normalize';\n\n/**\n * Add the number of points per zone to reach a specified total\n * @param {Array} [zones=[]]\n * @param {number} [numberOfPoints] Total number of points to distribute between zones\n * @param {object} [options={}]\n * @param {number} [options.from=Number.NEGATIVE_INFINITY] Specify min value of a zone\n * @param {number} [options.to=Number.POSITIVE_INFINITY] Specify max value of a zone\n */\n\nexport function zonesWithPoints(zones, numberOfPoints, options = {}) {\n  if (zones.length === 0) return zones;\n  zones = normalize(zones, options);\n\n  const totalSize = zones.reduce((previous, current) => {\n    return previous + (current.to - current.from);\n  }, 0);\n\n  let unitsPerPoint = totalSize / numberOfPoints;\n  let currentTotal = 0;\n  for (let i = 0; i < zones.length - 1; i++) {\n    let zone = zones[i];\n    zone.numberOfPoints = Math.min(\n      Math.round((zone.to - zone.from) / unitsPerPoint),\n      numberOfPoints - currentTotal,\n    );\n    currentTotal += zone.numberOfPoints;\n  }\n\n  zones[zones.length - 1].numberOfPoints = numberOfPoints - currentTotal;\n\n  return zones;\n}\n","/**\n * Function that calculates the integral of the line between two\n * x-coordinates, given the slope and intercept of the line.\n * @param {number} x0\n * @param {number} x1\n * @param {number} slope\n * @param {number} intercept\n * @return {number} integral value.\n */\nexport default function integral(x0, x1, slope, intercept) {\n  return (\n    0.5 * slope * x1 * x1 +\n    intercept * x1 -\n    (0.5 * slope * x0 * x0 + intercept * x0)\n  );\n}\n","import integral from './integral';\n\n/**\n * function that retrieves the getEquallySpacedData with the variant \"smooth\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"smooth\"\n */\nexport default function equallySpacedSmooth(x, y, from, to, numberOfPoints) {\n  let xLength = x.length;\n\n  let step = (to - from) / (numberOfPoints - 1);\n  let halfStep = step / 2;\n\n  let output = new Array(numberOfPoints);\n\n  let initialOriginalStep = x[1] - x[0];\n  let lastOriginalStep = x[xLength - 1] - x[xLength - 2];\n\n  // Init main variables\n  let min = from - halfStep;\n  let max = from + halfStep;\n\n  let previousX = Number.MIN_VALUE;\n  let previousY = 0;\n  let nextX = x[0] - initialOriginalStep;\n  let nextY = 0;\n\n  let currentValue = 0;\n  let slope = 0;\n  let intercept = 0;\n  let sumAtMin = 0;\n  let sumAtMax = 0;\n\n  let i = 0; // index of input\n  let j = 0; // index of output\n\n  function getSlope(x0, y0, x1, y1) {\n    return (y1 - y0) / (x1 - x0);\n  }\n\n  let add = 0;\n  main: while (true) {\n    if (previousX <= min && min <= nextX) {\n      add = integral(0, min - previousX, slope, previousY);\n      sumAtMin = currentValue + add;\n    }\n\n    while (nextX - max >= 0) {\n      // no overlap with original point, just consume current value\n      add = integral(0, max - previousX, slope, previousY);\n      sumAtMax = currentValue + add;\n\n      output[j++] = (sumAtMax - sumAtMin) / step;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      sumAtMin = sumAtMax;\n    }\n\n    currentValue += integral(previousX, nextX, slope, intercept);\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else if (i === xLength) {\n      nextX += lastOriginalStep;\n      nextY = 0;\n    }\n\n    slope = getSlope(previousX, previousY, nextX, nextY);\n    intercept = -slope * previousX + previousY;\n  }\n\n  return output;\n}\n","/**\n * function that retrieves the getEquallySpacedData with the variant \"slot\"\n *\n * @param {Array<number>} x\n * @param {Array<number>} y\n * @param {number} from - Initial point\n * @param {number} to - Final point\n * @param {number} numberOfPoints\n * @return {Array} - Array of y's equally spaced with the variant \"slot\"\n */\nexport default function equallySpacedSlot(x, y, from, to, numberOfPoints) {\n  let xLength = x.length;\n\n  let step = (to - from) / (numberOfPoints - 1);\n  let halfStep = step / 2;\n  let lastStep = x[x.length - 1] - x[x.length - 2];\n\n  let start = from - halfStep;\n  let output = new Array(numberOfPoints);\n\n  // Init main variables\n  let min = start;\n  let max = start + step;\n\n  let previousX = -Number.MAX_VALUE;\n  let previousY = 0;\n  let nextX = x[0];\n  let nextY = y[0];\n  let frontOutsideSpectra = 0;\n  let backOutsideSpectra = true;\n\n  let currentValue = 0;\n\n  // for slot algorithm\n  let currentPoints = 0;\n\n  let i = 1; // index of input\n  let j = 0; // index of output\n\n  main: while (true) {\n    if (previousX >= nextX) throw new Error('x must be an increasing serie');\n    while (previousX - max > 0) {\n      // no overlap with original point, just consume current value\n      if (backOutsideSpectra) {\n        currentPoints++;\n        backOutsideSpectra = false;\n      }\n\n      output[j] = currentPoints <= 0 ? 0 : currentValue / currentPoints;\n      j++;\n\n      if (j === numberOfPoints) {\n        break main;\n      }\n\n      min = max;\n      max += step;\n      currentValue = 0;\n      currentPoints = 0;\n    }\n\n    if (previousX > min) {\n      currentValue += previousY;\n      currentPoints++;\n    }\n\n    if (previousX === -Number.MAX_VALUE || frontOutsideSpectra > 1) {\n      currentPoints--;\n    }\n\n    previousX = nextX;\n    previousY = nextY;\n\n    if (i < xLength) {\n      nextX = x[i];\n      nextY = y[i];\n      i++;\n    } else {\n      nextX += lastStep;\n      nextY = 0;\n      frontOutsideSpectra++;\n    }\n  }\n\n  return output;\n}\n","import sequentialFill from 'ml-array-sequential-fill';\nimport { zonesWithPoints, invert } from 'ml-zones';\n\nimport equallySpacedSmooth from './equallySpacedSmooth';\nimport equallySpacedSlot from './equallySpacedSlot';\n\n/**\n * Function that returns a Number array of equally spaced numberOfPoints\n * containing a representation of intensities of the spectra arguments x\n * and y.\n *\n * The options parameter contains an object in the following form:\n * from: starting point\n * to: last point\n * numberOfPoints: number of points between from and to\n * variant: \"slot\" or \"smooth\" - smooth is the default option\n *\n * The slot variant consist that each point in the new array is calculated\n * averaging the existing points between the slot that belongs to the current\n * value. The smooth variant is the same but takes the integral of the range\n * of the slot and divide by the step size between two points in the new array.\n *\n * If exclusions zone are present, zones are ignored !\n * @param {object} [arrayXY={}] - object containing 2 properties x and y (both an array)\n * @param {object} [options={}]\n * @param {number} [options.from=x[0]]\n * @param {number} [options.to=x[x.length-1]]\n * @param {string} [options.variant='smooth']\n * @param {number} [options.numberOfPoints=100]\n * @param {Array} [options.exclusions=[]] array of from / to that should be skipped for the generation of the points\n * @param {Array} [options.zones=[]] array of from / to that should be kept\n * @return {object<x: Array, y:Array>} new object with x / y array with the equally spaced data.\n */\n\nexport default function equallySpaced(arrayXY = {}, options = {}) {\n  let { x, y } = arrayXY;\n  let xLength = x.length;\n  let reverse = false;\n  if (x.length > 1 && x[0] > x[1]) {\n    x = x.slice().reverse();\n    y = y.slice().reverse();\n    reverse = true;\n  }\n\n  let {\n    from = x[0],\n    to = x[xLength - 1],\n    variant = 'smooth',\n    numberOfPoints = 100,\n    exclusions = [],\n    zones = [],\n  } = options;\n\n  if (xLength !== y.length) {\n    throw new RangeError(\"the x and y vector doesn't have the same size.\");\n  }\n\n  if (typeof from !== 'number' || isNaN(from)) {\n    throw new RangeError(\"'from' option must be a number\");\n  }\n\n  if (typeof to !== 'number' || isNaN(to)) {\n    throw new RangeError(\"'to' option must be a number\");\n  }\n\n  if (typeof numberOfPoints !== 'number' || isNaN(numberOfPoints)) {\n    throw new RangeError(\"'numberOfPoints' option must be a number\");\n  }\n\n  if (numberOfPoints < 2) {\n    throw new RangeError(\"'numberOfPoints' option must be greater than 1\");\n  }\n\n  if (zones.length === 0) {\n    zones = invert(exclusions, { from, to });\n  }\n\n  zones = zonesWithPoints(zones, numberOfPoints, { from, to });\n\n  let xResult = [];\n  let yResult = [];\n  for (let zone of zones) {\n    let zoneResult = processZone(\n      x,\n      y,\n      zone.from,\n      zone.to,\n      zone.numberOfPoints,\n      variant,\n      reverse,\n    );\n\n    xResult = xResult.concat(zoneResult.x);\n    yResult = yResult.concat(zoneResult.y);\n  }\n  if (reverse) {\n    if (from < to) {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    } else {\n      return { x: xResult, y: yResult };\n    }\n  } else {\n    if (from < to) {\n      return { x: xResult, y: yResult };\n    } else {\n      return { x: xResult.reverse(), y: yResult.reverse() };\n    }\n  }\n}\n\nfunction processZone(x, y, from, to, numberOfPoints, variant) {\n  if (numberOfPoints < 1) {\n    throw new RangeError('the number of points must be at least 1');\n  }\n\n  let output =\n    variant === 'slot'\n      ? equallySpacedSlot(x, y, from, to, numberOfPoints)\n      : equallySpacedSmooth(x, y, from, to, numberOfPoints);\n\n  return {\n    x: sequentialFill({\n      from,\n      to,\n      size: numberOfPoints,\n    }),\n    y: output,\n  };\n}\n","export default function getZones(from, to, exclusions = []) {\n  if (from > to) {\n    [from, to] = [to, from];\n  }\n\n  // in exclusions from and to have to be defined\n  exclusions = exclusions.filter(\n    (exclusion) => exclusion.from !== undefined && exclusion.to !== undefined\n  );\n\n  exclusions = JSON.parse(JSON.stringify(exclusions));\n  // we ensure that from before to\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from > exclusion.to) {\n      [exclusion.to, exclusion.from] = [exclusion.from, exclusion.to];\n    }\n  });\n\n  exclusions.sort((a, b) => a.from - b.from);\n\n  // we will rework the exclusions in order to remove overlap and outside range (from / to)\n  exclusions.forEach((exclusion) => {\n    if (exclusion.from < from) exclusion.from = from;\n    if (exclusion.to > to) exclusion.to = to;\n  });\n  for (let i = 0; i < exclusions.length - 1; i++) {\n    if (exclusions[i].to > exclusions[i + 1].from) {\n      exclusions[i].to = exclusions[i + 1].from;\n    }\n  }\n  exclusions = exclusions.filter((exclusion) => exclusion.from < exclusion.to);\n\n  if (!exclusions || exclusions.length === 0) {\n    return [{ from, to }];\n  }\n\n  let zones = [];\n  let currentFrom = from;\n  for (let exclusion of exclusions) {\n    if (currentFrom < exclusion.from) {\n      zones.push({\n        from: currentFrom,\n        to: exclusion.from\n      });\n    }\n\n    currentFrom = exclusion.to;\n  }\n  if (currentFrom < to) {\n    zones.push({\n      from: currentFrom,\n      to: to\n    });\n  }\n\n  return zones;\n}\n","import getZones from './getZones';\n\n/**\n * Filter an array x/y based on various criteria\n * x points are expected to be sorted\n *\n * @param {object} points\n * @param {object} [options={}]\n * @param {array} [options.from]\n * @param {array} [options.to]\n * @param {array} [options.exclusions=[]]\n * @return {{x: Array<number>, y: Array<number>}}\n */\n\nexport default function filterX(points, options = {}) {\n  const { x, y } = points;\n  const { from = x[0], to = x[x.length - 1], exclusions = [] } = options;\n\n  let zones = getZones(from, to, exclusions);\n\n\n  let currentZoneIndex = 0;\n  let newX = [];\n  let newY = [];\n  let position = 0;\n  while (position < x.length) {\n    if (\n      x[position] <= zones[currentZoneIndex].to &&\n      x[position] >= zones[currentZoneIndex].from\n    ) {\n      newX.push(x[position]);\n      newY.push(y[position]);\n    } else {\n      if (x[position] > zones[currentZoneIndex].to) {\n        currentZoneIndex++;\n        if (!zones[currentZoneIndex]) break;\n      }\n    }\n    position++;\n  }\n\n  return {\n    x: newX,\n    y: newY\n  };\n}\n","import { DecisionTreeClassifier, DecisionTreeRegression } from \"ml-cart\";\nimport {\n  RandomForestClassifier,\n  RandomForestRegression\n} from \"ml-random-forest\";\n\n// Try to keep this list in the same structure as the README.\n\n// Unsupervised learning\nexport { PCA } from \"ml-pca\";\nimport * as HClust from \"ml-hclust\";\nexport { HClust };\nexport { default as KMeans } from \"ml-kmeans\";\n\n// Supervised learning\nimport * as NaiveBayes from \"ml-naivebayes\";\nexport { NaiveBayes };\nexport { default as KNN } from \"ml-knn\";\nexport { PLS, KOPLS, OPLS, OPLSNipals } from \"ml-pls\";\nimport * as CrossValidation from \"ml-cross-validation\";\nexport { CrossValidation };\nexport { default as ConfusionMatrix } from \"ml-confusion-matrix\";\nexport { DecisionTreeClassifier };\nexport { RandomForestClassifier };\n\n// Artificial neural networks\nexport { default as FNN } from \"ml-fnn\";\nexport { default as SOM } from \"ml-som\";\n\n// Regression\nexport {\n  SimpleLinearRegression,\n  PolynomialRegression,\n  MultivariateLinearRegression,\n  PowerRegression,\n  ExponentialRegression,\n  TheilSenRegression,\n  RobustPolynomialRegression\n} from \"ml-regression\";\nexport { DecisionTreeRegression };\nexport { RandomForestRegression };\n\n// Optimization\nexport { default as levenbergMarquardt } from \"ml-levenberg-marquardt\";\nimport * as FCNNLS from \"ml-fcnnls\";\nexport { FCNNLS };\n\n// Math\nimport * as MatrixLib from \"ml-matrix\";\nconst {\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n} = MatrixLib;\nexport {\n  MatrixLib,\n  Matrix,\n  SVD,\n  EVD,\n  CholeskyDecomposition,\n  LuDecomposition,\n  QrDecomposition\n};\n\nexport { SparseMatrix } from \"ml-sparse-matrix\";\nexport { default as Kernel } from \"ml-kernel\";\nimport { distance, similarity } from \"ml-distance\";\nexport { distance as Distance, similarity as Similarity };\nexport { default as distanceMatrix } from \"ml-distance-matrix\";\nexport { default as XSadd } from \"ml-xsadd\";\n\n// Statistics\nexport { default as Performance } from \"ml-performance\";\n\n// Data preprocessing\nexport { default as savitzkyGolay } from \"ml-savitzky-golay\";\n\n// Utility\nexport { default as BitArray } from \"ml-bit-array\";\nexport { default as HashTable } from \"ml-hash-table\";\nexport { default as padArray } from \"ml-pad-array\";\nexport { default as binarySearch } from \"binary-search\";\nimport * as numSort from \"num-sort\";\nexport { numSort };\nexport { default as Random } from \"ml-random\";\nimport * as GSD from 'ml-gsd';\nexport { GSD };\n\nimport min from \"ml-array-min\";\nimport max from \"ml-array-max\";\nimport median from \"ml-array-median\";\nimport mean from \"ml-array-mean\";\nimport mode from \"ml-array-mode\";\nimport normed from \"ml-array-normed\";\nimport rescale from \"ml-array-rescale\";\nimport sequentialFill from \"ml-array-sequential-fill\";\nimport sum from \"ml-array-sum\";\nimport standardDeviation from \"ml-array-standard-deviation\";\nimport variance from \"ml-array-variance\";\nexport const Array = {\n  min,\n  max,\n  median,\n  mean,\n  mode,\n  normed,\n  rescale,\n  sequentialFill,\n  standardDeviation,\n  sum,\n  variance\n};\n\nimport centroidsMerge from \"ml-array-xy-centroids-merge\";\nimport closestX from \"ml-arrayxy-closestx\";\nimport covariance from \"ml-array-xy-covariance\";\nimport maxMerge from \"ml-array-xy-max-merge\";\nimport maxY from \"ml-array-xy-max-y\";\nimport sortX from \"ml-array-xy-sort-x\";\nimport uniqueX from \"ml-arrayxy-uniquex\";\nimport weightedMerge from \"ml-array-xy-weighted-merge\";\nimport equallySpaced from \"ml-array-xy-equally-spaced\";\nimport filterX from \"ml-array-xy-filter-x\";\nexport const ArrayXY = {\n  centroidsMerge,\n  closestX,\n  covariance,\n  maxMerge,\n  maxY,\n  sortX,\n  uniqueX,\n  weightedMerge,\n  equallySpaced,\n  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\ No newline at end of file
diff --git a/package.json b/package.json
index 477bdf5..dabfd6c 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "ml",
-  "version": "5.1.1",
+  "version": "5.2.0",
   "description": "Machine learning tools",
   "main": "src/index.js",
   "scripts": {
@@ -37,14 +37,14 @@
   "homepage": "https://github.com/mljs/ml",
   "dependencies": {
     "binary-search": "^1.3.6",
-    "ml-array-max": "^1.1.2",
+    "ml-array-max": "^1.2.0",
     "ml-array-mean": "^1.1.3",
     "ml-array-median": "^1.1.3",
-    "ml-array-min": "^1.1.2",
-    "ml-array-mode": "^1.1.2",
+    "ml-array-min": "^1.2.0",
+    "ml-array-mode": "^1.1.3",
     "ml-array-normed": "^1.3.1",
     "ml-array-rescale": "^1.3.1",
-    "ml-array-sequential-fill": "^1.1.2",
+    "ml-array-sequential-fill": "^1.1.3",
     "ml-array-standard-deviation": "^1.1.4",
     "ml-array-variance": "^1.1.4",
     "ml-array-xy-centroids-merge": "^1.0.1",