feat: implement exponential regression

.babelrc

@@ -0,0 +1,3 @@
+{
+  "plugins": ["transform-es2015-modules-commonjs"]
+}

.eslintrc.yml

@@ -0,0 +1,5 @@
+extends: 'eslint-config-cheminfo'
+parserOptions:
+  sourceType: module
+env:
+  jest: true

.gitignore

@@ -0,0 +1,39 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+
+# Runtime data
+pids
+*.pid
+*.seed
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+lib-cov
+
+# Coverage directory used by tools like istanbul
+coverage
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files)
+.grunt
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons (http://nodejs.org/api/addons.html)
+build/Release
+
+# Dependency directories
+node_modules
+jspm_packages
+
+# Optional npm cache directory
+.npm
+
+# Optional REPL history
+.node_repl_history
+
+lib

.travis.yml

@@ -0,0 +1,4 @@
+language: node_js
+node_js:
+  - node
+  - lts/boron

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 mljs
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,40 @@
+# regression-exponential
+
+  [![NPM version][npm-image]][npm-url]
+  [![build status][travis-image]][travis-url]
+  [![npm download][download-image]][download-url]
+
+Exponential Regression.
+
+## Installation
+
+`$ npm install --save ml-regression-exponential`
+
+## Usage
+
+This calculates parameters A and B for the equation `y = A * e^(B * x)`.
+
+```js
+import ExponentialRegression from 'ml-regression-exponential';
+
+const x = [0, 1, 2, 3, 4];
+const y = [1.5, 2.5, 3.5, 5.0, 7.5];
+
+const regression = new ExponentialRegression(x, y);
+
+regression.A // 0.391202
+regression.B // 1.579909
+regression.predict(2); // 3.454825
+regression.toString(3); // f(x) = 1.58 * exp(0.391 * x)
+```
+
+## License
+
+  [MIT](./LICENSE)
+
+[npm-image]: https://img.shields.io/npm/v/ml-regression-exponential.svg?style=flat-square
+[npm-url]: https://npmjs.org/package/ml-regression-exponential
+[travis-image]: https://img.shields.io/travis/mljs/regression-exponential/master.svg?style=flat-square
+[travis-url]: https://travis-ci.org/mljs/regression-exponential
+[download-image]: https://img.shields.io/npm/dm/ml-regression-exponential.svg?style=flat-square
+[download-url]: https://npmjs.org/package/ml-regression-exponential

package.json

@@ -0,0 +1,46 @@
+{
+  "name": "ml-regression-exponential",
+  "version": "0.0.0",
+  "description": "Exponential Regression",
+  "main": "lib/index.js",
+  "module": "src/index.js",
+  "files": [
+    "lib",
+    "src"
+  ],
+  "scripts": {
+    "eslint": "eslint src",
+    "eslint-fix": "npm run eslint -- --fix",
+    "prepublish": "rollup -c",
+    "test": "run-s testonly eslint",
+    "testonly": "jest"
+  },
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/mljs/regression-exponential.git"
+  },
+  "keywords": [],
+  "author": "Michaël Zasso",
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/mljs/regression-exponential/issues"
+  },
+  "homepage": "https://github.com/mljs/regression-exponential#readme",
+  "jest": {
+    "testEnvironment": "node"
+  },
+  "devDependencies": {
+    "babel-jest": "^19.0.0",
+    "babel-plugin-transform-es2015-modules-commonjs": "^6.24.1",
+    "eslint": "^3.19.0",
+    "eslint-config-cheminfo": "^1.7.0",
+    "eslint-plugin-no-only-tests": "^1.1.0",
+    "jest": "^19.0.2",
+    "npm-run-all": "^4.0.2",
+    "rollup": "^0.41.6"
+  },
+  "dependencies": {
+    "ml-regression-base": "^1.1.1",
+    "ml-regression-simple-linear": "^1.0.1"
+  }
+}

rollup.config.js

@@ -0,0 +1,5 @@
+export default {
+    entry: 'src/index.js',
+    format: 'cjs',
+    dest: 'lib/index.js'
+};

src/__tests__/test.js

@@ -0,0 +1,35 @@
+import ExponentialRegression from '..';
+
+describe('Exponential regression', () => {
+    it('basic test', () =>{
+        const x = [0, 1, 2, 3, 4];
+        const y = [1.5, 2.5, 3.5, 5.0, 7.5];
+        const result = new ExponentialRegression(x, y);
+        expect(result.A).toBeCloseTo(0.3912023, 10e-7);
+        expect(result.B).toBeCloseTo(1.579910, 10e-7);
+
+        const score = result.score(x, y);
+        expect(score.r2).toBeGreaterThan(0.8);
+        expect(score.chi2).toBeLessThan(0.1);
+        expect(score.rmsd).toBeLessThan(0.01);
+        expect(result.toString(4)).toEqual('f(x) = 1.580 * e^(0.3912 * x)');
+        expect(result.toLaTeX(4)).toEqual('f(x) = 1.580e^{0.3912x}');
+    });
+
+    it('toJSON / load model', function () {
+        const regression = ExponentialRegression.load({
+            name: 'exponentialRegression',
+            A: -1,
+            B: 1
+        });
+
+        expect(regression.predict(1)).toEqual(0.36787944117144233);
+
+        const model = regression.toJSON();
+        expect(model).toEqual({
+            name: 'exponentialRegression',
+            A: -1,
+            B: 1
+        });
+    });
+});

src/index.js

@@ -0,0 +1,62 @@
+import BaseRegression, {maybeToPrecision} from 'ml-regression-base';
+import SimpleLinearRegression from 'ml-regression-simple-linear';
+
+export default class ExponentialRegression extends BaseRegression {
+    constructor(x, y) {
+        super();
+        if (x === true) {
+            this.A = y.A;
+            this.B = y.B;
+        } else {
+            const n = x.length;
+            if (n !== y.length) {
+                throw new RangeError('input and output array have a different length');
+            }
+
+            regress(this, x, y, n);
+        }
+    }
+
+    _predict(input) {
+        return this.B * Math.exp(input * this.A);
+    }
+
+    toJSON() {
+        return {
+            name: 'exponentialRegression',
+            A: this.A,
+            B: this.B
+        };
+    }
+
+    toString(precision) {
+        return 'f(x) = ' + maybeToPrecision(this.B, precision) + ' * e^(' + maybeToPrecision(this.A, precision) + ' * x)';
+    }
+
+    toLaTeX(precision) {
+        if (this.A >= 0) {
+            return 'f(x) = ' + maybeToPrecision(this.B, precision) + 'e^{' + maybeToPrecision(this.A, precision) + 'x}';
+        } else {
+            return 'f(x) = \\frac{' + maybeToPrecision(this.B, precision) + '}{e^{' + maybeToPrecision(-this.A, precision) + 'x}}';
+        }
+
+    }
+
+    static load(json) {
+        if (json.name !== 'exponentialRegression') {
+            throw new TypeError('not a exponential regression model');
+        }
+        return new ExponentialRegression(true, json);
+    }
+}
+
+function regress(er, x, y, n) {
+    const yl = new Array(n);
+    for (let i = 0; i < n; i++) {
+        yl[i] = Math.log(y[i]);
+    }
+
+    const linear = new SimpleLinearRegression(x, yl);
+    er.A = linear.slope;
+    er.B = Math.exp(linear.intercept);
+}