Merge pull request #49 from gyrdym/ml_linalg-5.6.0

ML prefix removed, equality operator for vector overridden, uniqueRows for matrix added

.travis.yml

@@ -1,6 +1,6 @@
 language: dart
 dart:
-- "2.1.0"
+- "2.2.0"
 dart_task:
 - test: --platform vm
 - dartanalyzer: true

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Changelog
 
+## 6.0.0
+- prefix `ML` removed from entities' names
+- Float32x4Vector: equality operator override added
+- Matrix: `uniqueRows` method added
+
 ## 5.5.1
 - MLMatrix: fixed bug in `setColumn` method when rows cache was not cleared 
 

README.md

@@ -56,12 +56,13 @@ computation architecture. Actually, the main purpose of the library - connect su
 the pure math. So, the library contains a high performance SIMD vector class, based on 
 [Float32x4](https://api.dartlang.org/stable/2.1.0/dart-typed_data/Float32x4-class.html) - 
 [Float32x4Vector](https://github.com/gyrdym/linalg/blob/master/lib/src/vector/float32x4/float32x4_vector.dart). 
-Most of operations in the vector are performed in four "threads". This kind of concurrency is reached by special 
+Most of operations in the vector class are performed in four "threads". This kind of concurrency is reached by special 
 128-bit processor registers, which are used directly by program code.  For better understanding of the topic please read 
 the [article](https://www.dartlang.org/articles/dart-vm/simd). It is also possible to implement [Float64x2](https://api.dartlang.org/stable/2.1.0/dart-typed_data/Float64x2-class.html)-based
-version of the vector using existing codebase, but so far there is no need to do so.
-    The class [Float32x4Vector] is hidden from the library's user. You can create an [Float32x4Vector] instance
-via [MLVector] factory (see examples below).
+version of vector using existing codebase, but so far there is no need to do so.
+    The class [Float32x4Vector](https://github.com/gyrdym/linalg/blob/master/lib/src/vector/float32x4/float32x4_vector.dart) 
+is hidden from the library's users. You can create a [Float32x4Vector](https://github.com/gyrdym/linalg/blob/master/lib/src/vector/float32x4/float32x4_vector.dart) 
+instance via [Vector](https://github.com/gyrdym/ml_linalg/blob/master/lib/vector.dart) factory (see examples below).
 
 #### Vector operations examples
 At the present moment most common vector operations are implemented:
@@ -70,8 +71,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1 + vector2;
   print(result.toList()); // [3.0, 5.0, 7.0, 9.0, 11.0]
 ````
@@ -80,8 +81,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([4.0, 5.0, 6.0, 7.0, 8.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 2.0, 3.0, 2.0]);
+  final vector1 = Vector.from([4.0, 5.0, 6.0, 7.0, 8.0]);
+  final vector2 = Vector.from([2.0, 3.0, 2.0, 3.0, 2.0]);
   final result = vector1 - vector2;
   print(result.toList()); // [2.0, 2.0, 4.0, 4.0, 6.0]
 ````
@@ -90,8 +91,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1 * vector2;
   print(result.toList()); // [2.0, 6.0, 12.0, 20.0, 30.0]
 ````
@@ -100,8 +101,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([6.0, 12.0, 24.0, 48.0, 96.0]);
-  final vector2 = MLVector.from([3.0, 4.0, 6.0, 8.0, 12.0]);
+  final vector1 = Vector.from([6.0, 12.0, 24.0, 48.0, 96.0]);
+  final vector2 = Vector.from([3.0, 4.0, 6.0, 8.0, 12.0]);
   final result = vector1 / vector2;
   print(result.toList()); // [2.0, 3.0, 4.0, 6.0, 8.0]
 ````
@@ -110,7 +111,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.norm();
   print(result); // sqrt(2^2 + 3^2 + 4^2 + 5^2 + 6^2) = sqrt(90) ~~ 9.48
 ````
@@ -119,7 +120,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.norm(Norm.manhattan);
   print(result); // 2 + 3 + 4 + 5 + 6 = 20.0
 ````
@@ -128,7 +129,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.mean();
   print(result); // (2 + 3 + 4 + 5 + 6) / 5 = 4.0
 ````
@@ -137,7 +138,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.sum();
   print(result); // 2 + 3 + 4 + 5 + 6 = 20.0 (equivalent to Manhattan norm)
 ````
@@ -146,8 +147,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.dot(vector2);
   print(result); // 1.0 * 2.0 + 2.0 * 3.0 + 3.0 * 4.0 + 4.0 * 5.0 + 5.0 * 6.0 = 70.0
 ````
@@ -156,7 +157,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final scalar = 5.0;
   final result = vector1 + scalar;
   print(result.toList()); // [6.0, 7.0, 8.0, 9.0, 10.0]
@@ -166,7 +167,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final scalar = 5.0;
   final result = vector1 - scalar;
   print(result.toList()); // [-4.0, -3.0, -2.0, -1.0, 0.0]
@@ -176,7 +177,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final scalar = 5.0;
   final result = vector1 * scalar;
   print(result.toList()); // [5.0, 10.0, 15.0, 20.0, 25.0]
@@ -186,7 +187,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([25.0, 50.0, 75.0, 100.0, 125.0]);
+  final vector1 = Vector.from([25.0, 50.0, 75.0, 100.0, 125.0]);
   final scalar = 5.0;
   final result = vector1.scalarDiv(scalar);
   print(result.toList()); // [5.0, 10.0, 15.0, 20.0, 25.0]
@@ -196,8 +197,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.distanceTo(vector2);
   print(result); // ~~2.23
 ````
@@ -206,8 +207,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.distanceTo(vector2, Norm.manhattan);
   print(result); // 5.0
 ````
@@ -219,7 +220,7 @@ Performs mapping from one vector to another in efficient way (using simd computa
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final result = vector.fastMap<Float32x4>((Float32x4 element, int offsetStart, int offsetEnd) {
     // offsetStart - start index for the current vectorized element, e.g. if `element` is second in the inner collection,
     // the offsetStart will be 4 (because Float32x4 contains 4 elements)
@@ -240,12 +241,12 @@ Also, a class for matrix is available. It is based on Float32x4 and Float32x4Vec
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix1 = MLMatrix.from([
+final matrix1 = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
 ]);
-final matrix2 = MLMatrix.from([
+final matrix2 = Matrix.from([
   [10.0, 20.0, 30.0, 40.0],
   [-5.0, 16.0, 2.0, 18.0],
   [2.0, -1.0, -2.0, -7.0],
@@ -262,7 +263,7 @@ print(matrix1 + matrix2);
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix = MLMatrix.from([
+final matrix = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
@@ -279,12 +280,12 @@ print(matrix + 7);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
     [9.0, .0, -2.0, -3.0],
   ]);
-  final vector = MLVector.from([2.0, 3.0, 4.0, 5.0]);
+  final vector = Vector.from([2.0, 3.0, 4.0, 5.0]);
   final result = matrix * vector;
   print(result); 
   // a vector-column [
@@ -298,12 +299,12 @@ print(matrix + 7);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix1 = MLMatrix.from([
+  final matrix1 = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
     [9.0, .0, -2.0, -3.0],
   ]);
-  final matrix2 = MLMatrix.from([
+  final matrix2 = Matrix.from([
     [1.0, 2.0],
     [5.0, 6.0],
     [9.0, .0],
@@ -322,7 +323,7 @@ print(matrix + 7);
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix = MLMatrix.from([
+final matrix = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
@@ -339,12 +340,12 @@ print(matrix * 3);
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix1 = MLMatrix.from([
+final matrix1 = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
 ]);
-final matrix2 = MLMatrix.from([
+final matrix2 = Matrix.from([
   [10.0, 20.0, 30.0, 40.0],
   [-5.0, 16.0, 2.0, 18.0],
   [2.0, -1.0, -2.0, -7.0],
@@ -361,7 +362,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
   
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
     [9.0, .0, -2.0, -3.0],
@@ -380,7 +381,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
   ]); 
@@ -392,7 +393,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 17.0, 18.0],
     [21.0, 22.0, 23.0, 24.0],
@@ -405,11 +406,11 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
   ]); 
-  final modifier = MLVector.filled(4, 2.0);
+  final modifier = Vector.filled(4, 2.0);
   final newMatrix = matrix.rowsMap((row) => row + modifier);
   print(newMatrix); 
   // [
@@ -422,11 +423,11 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
   ]); 
-  final modifier = MLVector.filled(2, 2.0);
+  final modifier = Vector.filled(2, 2.0);
   final newMatrix = matrix.columnsMap((column) => column + modifier);
   print(newMatrix); 
   // [
@@ -439,7 +440,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 17.0, 18.0],
     [21.0, 22.0, 23.0, 24.0],
@@ -457,7 +458,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 17.0, 18.0],
     [21.0, 22.0, 23.0, 24.0],
@@ -472,7 +473,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 0.0, 18.0],
     [21.0, 22.0, -23.0, 24.0],
@@ -490,7 +491,7 @@ Performs mapping from one matrix to another in efficient way (using simd computa
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 0.0, 18.0],
     [21.0, 22.0, -23.0, 24.0],
@@ -536,7 +537,7 @@ print(result);
 ````dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix = MLMatrix.from([
+final matrix = Matrix.from([
   [11.0, 12.0, 13.0, 14.0],
   [15.0, 16.0, 0.0, 18.0],
   [21.0, 22.0, -23.0, 24.0],