Merge pull request #4 from php-ai/develop

New algorithms, math functions and documentation
2024-11-11 08:10:56 +00:00 · 2016-05-02 14:13:19 +02:00 · 2016-05-02 14:13:19 +02:00 · 096db0e9dd
commit 096db0e9dd
parent 662627c052 3fd5abf566
72 changed files with 3458 additions and 197 deletions
--- a/.travis.yml
+++ b/.travis.yml
@ -0,0 +1,5 @@
 language: php
 php:
  - '7.0'
 before_script: composer install
 script: bin/phpunit
--- a/README.md
+++ b/README.md
@ -6,11 +6,25 @@
 [![License](https://poser.pugx.org/php-ai/php-ml/license.svg)](https://packagist.org/packages/php-ai/php-ml)
 [![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/php-ai/php-ml/badges/quality-score.png?b=develop)](https://scrutinizer-ci.com/g/php-ai/php-ml/?branch=develop)
-Fresh approach to machine learning in PHP. Note that at the moment PHP is not the best choice for machine learning but maybe this will change ...
+Fresh approach to Machine Learning in PHP. Note that at the moment PHP is not the best choice for machine learning but maybe this will change ...
 Simple example of classification:
 ```php
 use Phpml\Classification\KNearestNeighbors;
 $samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
 $labels = ['a', 'a', 'a', 'b', 'b', 'b'];
 $classifier = new KNearestNeighbors();
 $classifier->train($samples, $labels);
 $classifier->predict([3, 2]); 
 // return 'b'
 ```
 ## Documentation
-To find out how to use PHP-ML follow [Documentation](php-ml.readthedocs.org).
+To find out how to use PHP-ML follow [Documentation](http://php-ml.readthedocs.org/).
 ## Installation
@ -20,14 +34,33 @@ Currently this library is in the process of developing, but You can install it w
 composer require php-ai/php-ml
 ```
-## To-Do
+## Features
-* implements more algorithms
+* Classification
-* integration with Lavacharts for data visualization
+    * [k-Nearest Neighbors](http://php-ml.readthedocs.io/en/latest/machine-learning/classification/k-nearest-neighbors/)
    * [Naive Bayes](http://php-ml.readthedocs.io/en/latest/machine-learning/classification/naive-bayes/)
 * Regression
    * [Least Squares](http://php-ml.readthedocs.io/en/latest/machine-learning/regression/least-squares/)
 * Clustering
    * [k-Means](http://php-ml.readthedocs.io/en/latest/machine-learning/clustering/k-means)
    * [DBSCAN](http://php-ml.readthedocs.io/en/latest/machine-learning/clustering/dbscan)
 * Cross Validation
    * [Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/random-split)
 * Datasets
    * [CSV](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/csv-dataset)
    * Ready to use:
        * [Iris](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/iris/)
 * Math
    * [Distance](http://php-ml.readthedocs.io/en/latest/math/distance/)
    * [Matrix](http://php-ml.readthedocs.io/en/latest/math/matrix/)
 ## Testing
-After installation, you can launch the test suite in project root directory (you will need to install dev requirements with composer)
+## Contribute
 - Issue Tracker: github.com/php-ai/php-ml/issues
 - Source Code: github.com/php-ai/php-ml
 After installation, you can launch the test suite in project root directory (you will need to install dev requirements with Composer)
 ```
 bin/phpunit
--- a/data/glass.csv
+++ b/data/glass.csv
@ -0,0 +1,215 @@
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 1.51839,12.85,3.67,1.24,72.57,0.62,8.68,0.00,0.35,building_windows_non_float_processed
 1.51769,13.65,3.66,1.11,72.77,0.11,8.60,0.00,0.00,vehicle_windows_float_processed
 1.51610,13.33,3.53,1.34,72.67,0.56,8.33,0.00,0.00,vehicle_windows_float_processed
 1.51670,13.24,3.57,1.38,72.70,0.56,8.44,0.00,0.10,vehicle_windows_float_processed
 1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0.00,0.00,vehicle_windows_float_processed
 1.51665,13.14,3.45,1.76,72.48,0.60,8.38,0.00,0.17,vehicle_windows_float_processed
 1.52127,14.32,3.90,0.83,71.50,0.00,9.49,0.00,0.00,vehicle_windows_float_processed
 1.51779,13.64,3.65,0.65,73.00,0.06,8.93,0.00,0.00,vehicle_windows_float_processed
 1.51610,13.42,3.40,1.22,72.69,0.59,8.32,0.00,0.00,vehicle_windows_float_processed
 1.51694,12.86,3.58,1.31,72.61,0.61,8.79,0.00,0.00,vehicle_windows_float_processed
 1.51646,13.04,3.40,1.26,73.01,0.52,8.58,0.00,0.00,vehicle_windows_float_processed
 1.51655,13.41,3.39,1.28,72.64,0.52,8.65,0.00,0.00,vehicle_windows_float_processed
 1.52121,14.03,3.76,0.58,71.79,0.11,9.65,0.00,0.00,vehicle_windows_float_processed
 1.51776,13.53,3.41,1.52,72.04,0.58,8.79,0.00,0.00,vehicle_windows_float_processed
 1.51796,13.50,3.36,1.63,71.94,0.57,8.81,0.00,0.09,vehicle_windows_float_processed
 1.51832,13.33,3.34,1.54,72.14,0.56,8.99,0.00,0.00,vehicle_windows_float_processed
 1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,vehicle_windows_float_processed
 1.52211,14.19,3.78,0.91,71.36,0.23,9.14,0.00,0.37,vehicle_windows_float_processed
 1.51514,14.01,2.68,3.50,69.89,1.68,5.87,2.20,0.00,containers
 1.51915,12.73,1.85,1.86,72.69,0.60,10.09,0.00,0.00,containers
 1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0.00,0.00,containers
 1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0.00,0.00,containers
 1.51969,12.64,0.00,1.65,73.75,0.38,11.53,0.00,0.00,containers
 1.51666,12.86,0.00,1.83,73.88,0.97,10.17,0.00,0.00,containers
 1.51994,13.27,0.00,1.76,73.03,0.47,11.32,0.00,0.00,containers
 1.52369,13.44,0.00,1.58,72.22,0.32,12.24,0.00,0.00,containers
 1.51316,13.02,0.00,3.04,70.48,6.21,6.96,0.00,0.00,containers
 1.51321,13.00,0.00,3.02,70.70,6.21,6.93,0.00,0.00,containers
 1.52043,13.38,0.00,1.40,72.25,0.33,12.50,0.00,0.00,containers
 1.52058,12.85,1.61,2.17,72.18,0.76,9.70,0.24,0.51,containers
 1.52119,12.97,0.33,1.51,73.39,0.13,11.27,0.00,0.28,containers
 1.51905,14.00,2.39,1.56,72.37,0.00,9.57,0.00,0.00,tableware
 1.51937,13.79,2.41,1.19,72.76,0.00,9.77,0.00,0.00,tableware
 1.51829,14.46,2.24,1.62,72.38,0.00,9.26,0.00,0.00,tableware
 1.51852,14.09,2.19,1.66,72.67,0.00,9.32,0.00,0.00,tableware
 1.51299,14.40,1.74,1.54,74.55,0.00,7.59,0.00,0.00,tableware
 1.51888,14.99,0.78,1.74,72.50,0.00,9.95,0.00,0.00,tableware
 1.51916,14.15,0.00,2.09,72.74,0.00,10.88,0.00,0.00,tableware
 1.51969,14.56,0.00,0.56,73.48,0.00,11.22,0.00,0.00,tableware
 1.51115,17.38,0.00,0.34,75.41,0.00,6.65,0.00,0.00,tableware
 1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,headlamps
 1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,headlamps
 1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,headlamps
 1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,headlamps
 1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,headlamps
 1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,headlamps
 1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,headlamps
 1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,headlamps
 1.51719,14.75,0.00,2.00,73.02,0.00,8.53,1.59,0.08,headlamps
 1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,headlamps
 1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,headlamps
 1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,headlamps
 1.51727,14.70,0.00,2.34,73.28,0.00,8.95,0.66,0.00,headlamps
 1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,headlamps
 1.51609,15.01,0.00,2.51,73.05,0.05,8.83,0.53,0.00,headlamps
 1.51508,15.15,0.00,2.25,73.50,0.00,8.34,0.63,0.00,headlamps
 1.51653,11.95,0.00,1.19,75.18,2.70,8.93,0.00,0.00,headlamps
 1.51514,14.85,0.00,2.42,73.72,0.00,8.39,0.56,0.00,headlamps
 1.51658,14.80,0.00,1.99,73.11,0.00,8.28,1.71,0.00,headlamps
 1.51617,14.95,0.00,2.27,73.30,0.00,8.71,0.67,0.00,headlamps
 1.51732,14.95,0.00,1.80,72.99,0.00,8.61,1.55,0.00,headlamps
 1.51645,14.94,0.00,1.87,73.11,0.00,8.67,1.38,0.00,headlamps
 1.51831,14.39,0.00,1.82,72.86,1.41,6.47,2.88,0.00,headlamps
 1.51640,14.37,0.00,2.74,72.85,0.00,9.45,0.54,0.00,headlamps
 1.51623,14.14,0.00,2.88,72.61,0.08,9.18,1.06,0.00,headlamps
 1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,headlamps
 1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,headlamps
 1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,headlamps
 1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,headlamps
--- a/data/wine.csv
+++ b/data/wine.csv
@ -0,0 +1,179 @@
 alcohol,malic acid,ash,alcalinity of ash,magnesium,total phenols,flavanoids,nonflavanoid phenols,proanthocyanins,color intensity,hue,OD280/OD315 of diluted wines,proline,class
 14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065,1
 13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050,1
 13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185,1
 14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480,1
 13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735,1
 14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450,1
 14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290,1
 14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295,1
 14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045,1
 13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045,1
 14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510,1
 14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280,1
 13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320,1
 14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150,1
 14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547,1
 13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310,1
 14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280,1
 13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130,1
 14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680,1
 13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845,1
 14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780,1
 12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770,1
 13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035,1
 12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015,1
 13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845,1
 13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830,1
 13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195,1
 13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285,1
 13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915,1
 14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035,1
 13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285,1
 13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515,1
 13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990,1
 13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235,1
 13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095,1
 13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920,1
 13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880,1
 13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105,1
 13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020,1
 14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760,1
 13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795,1
 13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035,1
 13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095,1
 13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680,1
 13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885,1
 14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080,1
 14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065,1
 13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985,1
 14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060,1
 13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260,1
 13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150,1
 13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265,1
 13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190,1
 13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375,1
 13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060,1
 13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120,1
 14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970,1
 13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270,1
 13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285,1
 12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520,2
 12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680,2
 12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450,2
 13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630,2
 12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420,2
 12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355,2
 12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678,2
 13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502,2
 12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510,2
 13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750,2
 12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718,2
 12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870,2
 13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410,2
 13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472,2
 12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985,2
 11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886,2
 11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428,2
 13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392,2
 11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500,2
 12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750,2
 12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463,2
 12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278,2
 12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714,2
 12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630,2
 13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515,2
 11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520,2
 12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450,2
 12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495,2
 11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562,2
 11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680,2
 12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625,2
 12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480,2
 12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450,2
 12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495,2
 12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290,2
 11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345,2
 12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937,2
 11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625,2
 12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428,2
 12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660,2
 12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406,2
 12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710,2
 12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562,2
 12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438,2
 11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415,2
 12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672,2
 12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315,2
 12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510,2
 12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488,2
 12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312,2
 11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680,2
 11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562,2
 12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325,2
 11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607,2
 11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434,2
 12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385,2
 11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407,2
 11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495,2
 12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345,2
 12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372,2
 12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564,2
 11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625,2
 11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465,2
 12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365,2
 13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380,2
 11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380,2
 12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378,2
 12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352,2
 11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466,2
 12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342,2
 12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580,2
 12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630,3
 12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530,3
 12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560,3
 12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600,3
 12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650,3
 12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695,3
 12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720,3
 12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515,3
 13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580,3
 12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590,3
 12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600,3
 13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780,3
 13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520,3
 13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550,3
 12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855,3
 13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830,3
 13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415,3
 12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625,3
 13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650,3
 13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550,3
 13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500,3
 12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480,3
 13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425,3
 13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675,3
 12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640,3
 13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725,3
 13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480,3
 12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880,3
 14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660,3
 13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620,3
 12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520,3
 13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680,3
 12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570,3
 12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675,3
 13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615,3
 13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520,3
 13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695,3
 12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685,3
 13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750,3
 13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630,3
 12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510,3
 12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470,3
 14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660,3
 13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740,3
 13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750,3
 13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835,3
 13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840,3
 14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560,3
--- a/docs/index.md
+++ b/docs/index.md
@ -1,11 +1,30 @@
-# PHP Machine Learning (PHP-ML)
+# PHP Machine Learning library
 [![Build Status](https://scrutinizer-ci.com/g/php-ai/php-ml/badges/build.png?b=develop)](https://scrutinizer-ci.com/g/php-ai/php-ml/build-status/develop)
 [![Documentation Status](https://readthedocs.org/projects/php-ml/badge/?version=develop)](http://php-ml.readthedocs.org/en/develop/?badge=develop)
 [![Total Downloads](https://poser.pugx.org/php-ai/php-ml/downloads.svg)](https://packagist.org/packages/php-ai/php-ml)
 [![License](https://poser.pugx.org/php-ai/php-ml/license.svg)](https://packagist.org/packages/php-ai/php-ml)
 [![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/php-ai/php-ml/badges/quality-score.png?b=develop)](https://scrutinizer-ci.com/g/php-ai/php-ml/?branch=develop)
-Fresh approach to machine learning in PHP. Note that at the moment PHP is not the best choice for machine learning but maybe this will change ...
+Fresh approach to Machine Learning in PHP. Note that at the moment PHP is not the best choice for machine learning but maybe this will change ...
 Simple example of classification:
 ```php
 use Phpml\Classification\KNearestNeighbors;
 $samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
 $labels = ['a', 'a', 'a', 'b', 'b', 'b'];
 $classifier = new KNearestNeighbors();
 $classifier->train($samples, $labels);
 $classifier->predict([3, 2]); 
 // return 'b'
 ```
 ## Documentation
 To find out how to use PHP-ML follow [Documentation](http://php-ml.readthedocs.org/).
 ## Installation
@ -15,14 +34,33 @@ Currently this library is in the process of developing, but You can install it w
 composer require php-ai/php-ml
 ```
-## To-Do
+## Features
-* implements more algorithms
+* Classification
-* integration with Lavacharts for data visualization
+    * [k-Nearest Neighbors](http://php-ml.readthedocs.io/en/latest/machine-learning/classification/k-nearest-neighbors/)
    * [Naive Bayes](http://php-ml.readthedocs.io/en/latest/machine-learning/classification/naive-bayes/)
 * Regression
    * [Least Squares](http://php-ml.readthedocs.io/en/latest/machine-learning/regression/least-squares/)
 * Clustering
    * [k-Means](http://php-ml.readthedocs.io/en/latest/machine-learning/clustering/k-means)
    * [DBSCAN](http://php-ml.readthedocs.io/en/latest/machine-learning/clustering/dbscan)
 * Cross Validation
    * [Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/random-split)
 * Datasets
    * [CSV](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/csv-dataset)
    * Ready to use:
        * [Iris](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/iris/)
 * Math
    * [Distance](http://php-ml.readthedocs.io/en/latest/math/distance/)
    * [Matrix](http://php-ml.readthedocs.io/en/latest/math/matrix/)
 ## Testing
-After installation, you can launch the test suite in project root directory (you will need to install dev requirements with composer)
+## Contribute
 - Issue Tracker: github.com/php-ai/php-ml/issues
 - Source Code: github.com/php-ai/php-ml
 After installation, you can launch the test suite in project root directory (you will need to install dev requirements with Composer)
 ```
 bin/phpunit
--- a/docs/machine-learning/classification/k-nearest-neighbors.md
+++ b/docs/machine-learning/classification/k-nearest-neighbors.md
@ -5,14 +5,16 @@ Classifier implementing the k-nearest neighbors algorithm.
 ### Constructor Parameters
 * $k - number of nearest neighbors to scan (default: 3)
 * $distanceMetric - Distance object, default Euclidean (see [distance documentation](math/distance/))
 ```
 $classifier = new KNearestNeighbors($k=4);
 $classifier = new KNearestNeighbors($k=3, new Minkowski($lambda=4));
 ```
 ### Train
-To train a classifier simply provide train samples and labels (as `array`):
+To train a classifier simply provide train samples and labels (as `array`). Example:
 ```
 $samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
@ -24,7 +26,7 @@ $classifier->train($samples, $labels);
 ### Predict
-To predict sample class use `predict` method. You can provide one sample or array of samples:
+To predict sample label use `predict` method. You can provide one sample or array of samples:
 ```
 $classifier->predict([3, 2]);
--- a/docs/machine-learning/classification/naive-bayes.md
+++ b/docs/machine-learning/classification/naive-bayes.md
@ -0,0 +1,27 @@
 # NaiveBayes Classifier
 Classifier based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
 ### Train
 To train a classifier simply provide train samples and labels (as `array`). Example:
 ```
 $samples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
 $labels = ['a', 'b', 'c'];
 $classifier = new NaiveBayes();
 $classifier->train($samples, $labels);
 ```
 ### Predict
 To predict sample label use `predict` method. You can provide one sample or array of samples:
 ```
 $classifier->predict([3, 1, 1]);
 // return 'a'
 $classifier->predict([[3, 1, 1], [1, 4, 1]);
 // return ['a', 'b']
 ```
--- a/docs/machine-learning/clustering/dbscan.md
+++ b/docs/machine-learning/clustering/dbscan.md
@ -0,0 +1,27 @@
 # DBSCAN clustering
 It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.
 *(source: wikipedia)*
 ### Constructor Parameters
 * $epsilon - epsilon, maximum distance between two samples for them to be considered as in the same neighborhood
 * $minSamples - number of samples in a neighborhood for a point to be considered as a core point (this includes the point itself)
 * $distanceMetric - Distance object, default Euclidean (see [distance documentation](math/distance/))
 ```
 $dbscan = new DBSCAN($epsilon = 2, $minSamples = 3);
 $dbscan = new DBSCAN($epsilon = 2, $minSamples = 3, new Minkowski($lambda=4));
 ```
 ### Clustering
 To divide the samples into clusters simply use `cluster` method. It's return the `array` of clusters with samples inside.
 ```
 $samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];
 $dbscan = new DBSCAN($epsilon = 2, $minSamples = 3);
 $dbscan->cluster($samples);
 // return [0=>[[1, 1], ...], 1=>[[8, 7], ...]] 
 ```
--- a/docs/machine-learning/clustering/k-means.md
+++ b/docs/machine-learning/clustering/k-means.md
@ -0,0 +1,37 @@
 # K-means clustering
 The K-Means algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. 
 This algorithm requires the number of clusters to be specified.
 ### Constructor Parameters
 * $clustersNumber - number of clusters to find
 * $initialization - initialization method, default kmeans++ (see below)
 ```
 $kmeans = new KMeans(2);
 $kmeans = new KMeans(4, KMeans::INIT_RANDOM);
 ```
 ### Clustering
 To divide the samples into clusters simply use `cluster` method. It's return the `array` of clusters with samples inside.
 ```
 $samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];
 $kmeans = new KMeans(2);
 $kmeans->cluster($samples);
 // return [0=>[[1, 1], ...], 1=>[[8, 7], ...]] 
 ```
 ### Initialization methods
 #### kmeans++ (default)
 K-means++ method selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.
 It use the DASV seeding method consists of finding good initial centroids for the clusters.
 #### random
 Random initialization method chooses completely random centroid. It get the space boundaries to avoid placing clusters centroid too far from samples data.
--- a/docs/machine-learning/cross-validation/random-split.md
+++ b/docs/machine-learning/cross-validation/random-split.md
--- a/docs/machine-learning/datasets/csv-dataset.md
+++ b/docs/machine-learning/datasets/csv-dataset.md
@ -12,4 +12,4 @@ Helper class that loads data from CSV file. It extends the `ArrayDataset`.
 $dataset = new CsvDataset('dataset.csv', 2, true);
 ```
-See Array Dataset for more information.
+See [ArrayDataset](machine-learning/datasets/array-dataset/) for more information.
--- a/docs/machine-learning/datasets/demo/iris.md
+++ b/docs/machine-learning/datasets/demo/iris.md
@ -17,7 +17,7 @@ To load Iris dataset simple use:
 $dataset = new Iris();
 ```
-### Several samples
+### Several samples example
 ```
 sepal length,sepal width,petal length,petal width,class
--- a/docs/machine-learning/metric/accuracy.md
+++ b/docs/machine-learning/metric/accuracy.md
@ -4,7 +4,7 @@ Class for calculate classifier accuracy.
 ### Score
-To calculate classifier accuracy score use `score` static method. Parametrs:
+To calculate classifier accuracy score use `score` static method. Parameters:
 * $actualLabels - (array) true sample labels
 * $predictedLabels - (array) predicted labels (e.x. from test group)
--- a/docs/machine-learning/metric/distance.md
+++ b/docs/machine-learning/metric/distance.md
@ -1,17 +0,0 @@
 # Distance
 Special class for calculation of different types of distance.
 ### Euclidean
 ![euclidean](https://upload.wikimedia.org/math/8/4/9/849f040fd10bb86f7c85eb0bbe3566a4.png "Euclidean Distance")
 To calculate euclidean distance:
 ```
 $a = [4, 6];
 $b = [2, 5];
 Distance::euclidean($a, $b);
 // return 2.2360679774998
 ```
--- a/docs/machine-learning/regression/least-squares.md
+++ b/docs/machine-learning/regression/least-squares.md
@ -0,0 +1,51 @@
 # LeastSquares Linear Regression
 Linear model that use least squares method to approximate solution. 
 ### Train
 To train a model simply provide train samples and targets values (as `array`). Example:
 ```
 $samples = [[60], [61], [62], [63], [65]];
 $targets = [3.1, 3.6, 3.8, 4, 4.1];
 $regression = new LeastSquares();
 $regression->train($samples, $targets);
 ```
 ### Predict
 To predict sample target value use `predict` method with sample to check (as `array`). Example:
 ```
 $regression->predict([64]);
 // return 4.06
 ```
 ### Multiple Linear Regression
 The term multiple attached to linear regression means that there are two or more sample parameters used to predict target. 
 For example you can use: mileage and production year to predict price of a car.  
 ```
 $samples = [[73676, 1996], [77006, 1998], [10565, 2000], [146088, 1995], [15000, 2001], [65940, 2000], [9300, 2000], [93739, 1996], [153260, 1994], [17764, 2002], [57000, 1998], [15000, 2000]];
 $targets = [2000, 2750, 15500, 960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];
 $regression = new LeastSquares();
 $regression->train($samples, $targets);
 $regression->predict([60000, 1996])
 // return 4094.82
 ```
 ### Intercept and Coefficients
 After you train your model you can get the intercept and coefficients array.
 ```
 $regression->getIntercept();
 // return -7.9635135135131
 $regression->getCoefficients();
 // return [array(1) {[0]=>float(0.18783783783783)}]
 ```
--- a/docs/math/distance.md
+++ b/docs/math/distance.md
@ -0,0 +1,109 @@
 # Distance
 Selected algorithms require the use of a function for calculating the distance.
 ### Euclidean
 Class for calculation Euclidean distance.
 ![euclidean](https://upload.wikimedia.org/math/8/4/9/849f040fd10bb86f7c85eb0bbe3566a4.png "Euclidean Distance")
 To calculate Euclidean distance:
 ```
 $a = [4, 6];
 $b = [2, 5];
 $euclidean = new Euclidean();
 $euclidean->distance($a, $b);
 // return 2.2360679774998
 ```
 ### Manhattan
 Class for calculation Manhattan distance.
 ![manhattan](https://upload.wikimedia.org/math/4/c/5/4c568bd1d76a6b15e19cb2ac3ad75350.png "Manhattan Distance")
 To calculate Manhattan distance:
 ```
 $a = [4, 6];
 $b = [2, 5];
 $manhattan = new Manhattan();
 $manhattan->distance($a, $b);
 // return 3
 ```
 ### Chebyshev
 Class for calculation Chebyshev distance.
 ![chebyshev](https://upload.wikimedia.org/math/7/1/2/71200f7dbb43b3bcfbcbdb9e02ab0a0c.png "Chebyshev Distance")
 To calculate Chebyshev distance:
 ```
 $a = [4, 6];
 $b = [2, 5];
 $chebyshev = new Chebyshev();
 $chebyshev->distance($a, $b);
 // return 2
 ```
 ### Minkowski
 Class for calculation Minkowski distance.
 ![minkowski](https://upload.wikimedia.org/math/a/a/0/aa0c62083c12390cb15ac3217de88e66.png "Minkowski Distance")
 To calculate Minkowski distance:
 ```
 $a = [4, 6];
 $b = [2, 5];
 $minkowski = new Minkowski();
 $minkowski->distance($a, $b);
 // return 2.080
 ```
 You can provide the `lambda` parameter:
 ```
 $a = [6, 10, 3];
 $b = [2, 5, 5];
 $minkowski = new Minkowski($lambda = 5);
 $minkowski->distance($a, $b);
 // return 5.300
 ```
 ### Custom distance
 To apply your own function of distance use `Distance` interface. Example
 ```
 class CustomDistance implements Distance
 {
    /**
     * @param array $a
     * @param array $b
     *
     * @return float
     */
    public function distance(array $a, array $b): float
    {
        $distance = [];
        $count = count($a);
        for ($i = 0; $i < $count; ++$i) {
            $distance[] = $a[$i] * $b[$i];
        }
        return min($distance);    
    }
 }
 ```
--- a/docs/math/matrix.md
+++ b/docs/math/matrix.md
@ -0,0 +1,129 @@
 # Matrix
 Class that wraps PHP arrays to mathematical matrix.
 ### Creation
 To create Matrix use simple arrays:
 ```
 $matrix = new Matrix([
    [3, 3, 3],
    [4, 2, 1],
    [5, 6, 7],
 ]);
 ```
 You can also create Matrix (one dimension) from flat array:
 ```
 $flatArray = [1, 2, 3, 4];
 $matrix = Matrix::fromFlatArray($flatArray);
 ```
 ### Matrix data
 Methods for reading data from Matrix:
 ```
 $matrix->toArray(); // cast matrix to PHP array
 $matrix->getRows(); // rows count
 $matrix->getColumns(); // columns count
 $matrix->getColumnValues($column=4); // get values from given column
 ```
 ### Determinant
 Read more about [matrix determinant](https://en.wikipedia.org/wiki/Determinant).
 ```
 $matrix = new Matrix([
    [3, 3, 3],
    [4, 2, 1],
    [5, 6, 7],
 ]);
 $matrix->getDeterminant();
 // return -3
 ```
 ### Transpose
 Read more about [matrix transpose](https://en.wikipedia.org/wiki/Transpose).
 ```
 $matrix->transpose();
 // return new Matrix 
 ```
 ### Multiply
 Multiply Matrix by another Matrix.
 ```
 $matrix1 = new Matrix([
    [1, 2, 3],
    [4, 5, 6],
 ]);
 $matrix2 = new Matrix([
    [7, 8],
    [9, 10],
    [11, 12],
 ]);
 $matrix1->multiply($matrix2);
 // result $product = [
 //  [58, 64],
 //  [139, 154],
 //];
 ```
 ### Divide by scalar
 You can divide Matrix by scalar value.
 ```
 $matrix->divideByScalar(2);
 ```
 ### Inverse
 Read more about [invertible matrix](https://en.wikipedia.org/wiki/Invertible_matrix)
 ```
 $matrix = new Matrix([
    [3, 4, 2],
    [4, 5, 5],
    [1, 1, 1],
 ]);
 $matrix->inverse();
 // result $inverseMatrix = [
 //    [0, -1, 5],
 //    [1 / 2, 1 / 2, -7 / 2],
 //    [-1 / 2, 1 / 2, -1 / 2],
 //];
 ```
 ### Cross out
 Cross out given row and column from Matrix.
 ```
 $matrix = new Matrix([
    [3, 4, 2],
    [4, 5, 5],
    [1, 1, 1],
 ]);
 $matrix->crossOut(1, 1)
 // result $crossOuted = [
 //    [3, 2],
 //    [1, 1],
 //];
 ```
--- a/mkdocs.yml
+++ b/mkdocs.yml
@ -3,15 +3,23 @@ pages:
  - Home: index.md
  - Machine Learning:
    - Classification:
-      - KNearestNeighbors: machine-learning/classification/knearestneighbors.md
+      - KNearestNeighbors: machine-learning/classification/k-nearest-neighbors.md
      - NaiveBayes: machine-learning/classification/naive-bayes.md
    - Regression:
      - LeastSquares: machine-learning/regression/least-squares.md
    - Clustering:
      - KMeans: machine-learning/clustering/k-means.md
      - DBSCAN: machine-learning/clustering/dbscan.md
    - Cross Validation:
-      - RandomSplit: machine-learning/cross-validation/randomsplit.md
+      - RandomSplit: machine-learning/cross-validation/random-split.md
    - Datasets:
      - Array Dataset: machine-learning/datasets/array-dataset.md
      - CSV Dataset: machine-learning/datasets/csv-dataset.md
-      - Demo:
+      - Ready to use datasets:
        - Iris: machine-learning/datasets/demo/iris.md
    - Metric:
      - Accuracy: machine-learning/metric/accuracy.md
-      - Distance: machine-learning/metric/distance.md
+  - Math:
    - Distance: math/distance.md
    - Matrix: math/matrix.md
 theme: readthedocs
--- a/src/Phpml/Classification/Classifier.php
+++ b/src/Phpml/Classification/Classifier.php
@ -2,7 +2,7 @@
 declare (strict_types = 1);
-namespace Phpml\Classifier;
+namespace Phpml\Classification;
 interface Classifier
 {
--- a/src/Phpml/Classification/KNearestNeighbors.php
+++ b/src/Phpml/Classification/KNearestNeighbors.php
@ -2,64 +2,41 @@
 declare (strict_types = 1);
-namespace Phpml\Classifier;
+namespace Phpml\Classification;
-use Phpml\Metric\Distance;
+use Phpml\Classification\Traits\Predictable;
 use Phpml\Classification\Traits\Trainable;
 use Phpml\Math\Distance;
 use Phpml\Math\Distance\Euclidean;
 class KNearestNeighbors implements Classifier
 {
    use Trainable, Predictable;
    /**
     * @var int
     */
    private $k;
    /**
-     * @var array
+     * @var Distance
     */
-    private $samples;
+    private $distanceMetric;
    /**
     * @var array
     */
    private $labels;
    /**
     * @param int           $k
     * @param Distance|null $distanceMetric (if null then Euclidean distance as default)
     */
-    public function __construct(int $k = 3)
+    public function __construct(int $k = 3, Distance $distanceMetric = null)
    {
        if (null === $distanceMetric) {
            $distanceMetric = new Euclidean();
        }
        $this->k = $k;
        $this->samples = [];
        $this->labels = [];
-    }
+        $this->distanceMetric = $distanceMetric;
    /**
     * @param array $samples
     * @param array $labels
     */
    public function train(array $samples, array $labels)
    {
        $this->samples = $samples;
        $this->labels = $labels;
    }
    /**
     * @param array $samples
     *
     * @return mixed
     */
    public function predict(array $samples)
    {
        if (!is_array($samples[0])) {
            $predicted = $this->predictSample($samples);
        } else {
            $predicted = [];
            foreach ($samples as $index => $sample) {
                $predicted[$index] = $this->predictSample($sample);
            }
        }
        return $predicted;
    }
    /**
@ -67,7 +44,7 @@ class KNearestNeighbors implements Classifier
     *
     * @return mixed
     */
-    private function predictSample(array $sample)
+    protected function predictSample(array $sample)
    {
        $distances = $this->kNeighborsDistances($sample);
@ -95,7 +72,7 @@ class KNearestNeighbors implements Classifier
        $distances = [];
        foreach ($this->samples as $index => $neighbor) {
-            $distances[$index] = Distance::euclidean($sample, $neighbor);
+            $distances[$index] = $this->distanceMetric->distance($sample, $neighbor);
        }
        asort($distances);
--- a/src/Phpml/Classification/NaiveBayes.php
+++ b/src/Phpml/Classification/NaiveBayes.php
@ -0,0 +1,36 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Classification;
 use Phpml\Classification\Traits\Predictable;
 use Phpml\Classification\Traits\Trainable;
 class NaiveBayes implements Classifier
 {
    use Trainable, Predictable;
    /**
     * @param array $sample
     *
     * @return mixed
     */
    protected function predictSample(array $sample)
    {
        $predictions = [];
        foreach ($this->labels as $index => $label) {
            $predictions[$label] = 0;
            foreach ($sample as $token => $count) {
                if (array_key_exists($token, $this->samples[$index])) {
                    $predictions[$label] += $count * $this->samples[$index][$token];
                }
            }
        }
        arsort($predictions, SORT_NUMERIC);
        reset($predictions);
        return key($predictions);
    }
 }
--- a/src/Phpml/Classification/SupportVectorMachine.php
+++ b/src/Phpml/Classification/SupportVectorMachine.php
@ -0,0 +1,61 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Classification;
 use Phpml\Classification\Traits\Predictable;
 use Phpml\Classification\Traits\Trainable;
 use Phpml\Math\Kernel;
 class SupportVectorMachine implements Classifier
 {
    use Trainable, Predictable;
    /**
     * @var Kernel
     */
    private $kernel;
    /**
     * @var float
     */
    private $C;
    /**
     * @var float
     */
    private $tolerance;
    /**
     * @var int
     */
    private $upperBound;
    /**
     * @param Kernel $kernel
     * @param float  $C
     * @param float  $tolerance
     * @param int    $upperBound
     */
    public function __construct(Kernel $kernel = null, float $C = 1.0, float $tolerance = .001, int $upperBound = 100)
    {
        if (null === $kernel) {
            $kernel = new Kernel\RBF($gamma = .001);
        }
        $this->kernel = $kernel;
        $this->C = $C;
        $this->tolerance = $tolerance;
        $this->upperBound = $upperBound;
    }
    /**
     * @param array $sample
     *
     * @return mixed
     */
    protected function predictSample(array $sample)
    {
    }
 }
--- a/src/Phpml/Classification/Traits/Predictable.php
+++ b/src/Phpml/Classification/Traits/Predictable.php
@ -0,0 +1,34 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Classification\Traits;
 trait Predictable
 {
    /**
     * @param array $samples
     *
     * @return mixed
     */
    public function predict(array $samples)
    {
        if (!is_array($samples[0])) {
            $predicted = $this->predictSample($samples);
        } else {
            $predicted = [];
            foreach ($samples as $index => $sample) {
                $predicted[$index] = $this->predictSample($sample);
            }
        }
        return $predicted;
    }
    /**
     * @param array $sample
     *
     * @return mixed
     */
    abstract protected function predictSample(array $sample);
 }
--- a/src/Phpml/Classification/Traits/Trainable.php
+++ b/src/Phpml/Classification/Traits/Trainable.php
@ -0,0 +1,28 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Classification\Traits;
 trait Trainable
 {
    /**
     * @var array
     */
    private $samples;
    /**
     * @var array
     */
    private $labels;
    /**
     * @param array $samples
     * @param array $labels
     */
    public function train(array $samples, array $labels)
    {
        $this->samples = $samples;
        $this->labels = $labels;
    }
 }
--- a/src/Phpml/Classifier/NaiveBayes.php
+++ b/src/Phpml/Classifier/NaiveBayes.php
@ -1,25 +0,0 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Classifier;
 class NaiveBayes implements Classifier
 {
    /**
     * @param array $samples
     * @param array $labels
     */
    public function train(array $samples, array $labels)
    {
    }
    /**
     * @param array $samples
     *
     * @return mixed
     */
    public function predict(array $samples)
    {
    }
 }
--- a/src/Phpml/Clustering/Clusterer.php
+++ b/src/Phpml/Clustering/Clusterer.php
@ -0,0 +1,15 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Clustering;
 interface Clusterer
 {
    /**
     * @param array $samples
     *
     * @return array
     */
    public function cluster(array $samples);
 }
--- a/src/Phpml/Clustering/DBSCAN.php
+++ b/src/Phpml/Clustering/DBSCAN.php
@ -0,0 +1,111 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Clustering;
 use Phpml\Math\Distance;
 use Phpml\Math\Distance\Euclidean;
 class DBSCAN implements Clusterer
 {
    /**
     * @var float
     */
    private $epsilon;
    /**
     * @var int
     */
    private $minSamples;
    /**
     * @var Distance
     */
    private $distanceMetric;
    /**
     * @param float    $epsilon
     * @param int      $minSamples
     * @param Distance $distanceMetric
     */
    public function __construct($epsilon = 0.5, $minSamples = 3, Distance $distanceMetric = null)
    {
        if (null === $distanceMetric) {
            $distanceMetric = new Euclidean();
        }
        $this->epsilon = $epsilon;
        $this->minSamples = $minSamples;
        $this->distanceMetric = $distanceMetric;
    }
    /**
     * @param array $samples
     *
     * @return array
     */
    public function cluster(array $samples)
    {
        $clusters = [];
        $visited = [];
        foreach ($samples as $index => $sample) {
            if (isset($visited[$index])) {
                continue;
            }
            $visited[$index] = true;
            $regionSamples = $this->getSamplesInRegion($sample, $samples);
            if (count($regionSamples) >= $this->minSamples) {
                $clusters[] = $this->expandCluster($regionSamples, $visited);
            }
        }
        return $clusters;
    }
    /**
     * @param array $localSample
     * @param array $samples
     *
     * @return array
     */
    private function getSamplesInRegion($localSample, $samples)
    {
        $region = [];
        foreach ($samples as $index => $sample) {
            if ($this->distanceMetric->distance($localSample, $sample) < $this->epsilon) {
                $region[$index] = $sample;
            }
        }
        return $region;
    }
    /**
     * @param array $samples
     * @param array $visited
     *
     * @return array
     */
    private function expandCluster($samples, &$visited)
    {
        $cluster = [];
        foreach ($samples as $index => $sample) {
            if (!isset($visited[$index])) {
                $visited[$index] = true;
                $regionSamples = $this->getSamplesInRegion($sample, $samples);
                if (count($regionSamples) > $this->minSamples) {
                    $cluster = array_merge($regionSamples, $cluster);
                }
            }
            $cluster[] = $sample;
        }
        return $cluster;
    }
 }
--- a/src/Phpml/Clustering/KMeans.php
+++ b/src/Phpml/Clustering/KMeans.php
@ -0,0 +1,60 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Clustering;
 use Phpml\Clustering\KMeans\Space;
 use Phpml\Exception\InvalidArgumentException;
 class KMeans implements Clusterer
 {
    const INIT_RANDOM = 1;
    const INIT_KMEANS_PLUS_PLUS = 2;
    /**
     * @var int
     */
    private $clustersNumber;
    /**
     * @var int
     */
    private $initialization;
    /**
     * @param int $clustersNumber
     * @param int $initialization
     *
     * @throws InvalidArgumentException
     */
    public function __construct(int $clustersNumber, int $initialization = self::INIT_KMEANS_PLUS_PLUS)
    {
        if ($clustersNumber <= 0) {
            throw InvalidArgumentException::invalidClustersNumber();
        }
        $this->clustersNumber = $clustersNumber;
        $this->initialization = $initialization;
    }
    /**
     * @param array $samples
     *
     * @return array
     */
    public function cluster(array $samples)
    {
        $space = new Space(count($samples[0]));
        foreach ($samples as $sample) {
            $space->addPoint($sample);
        }
        $clusters = [];
        foreach ($space->cluster($this->clustersNumber, $this->initialization) as $cluster) {
            $clusters[] = $cluster->getPoints();
        }
        return $clusters;
    }
 }
--- a/src/Phpml/Clustering/KMeans/Cluster.php
+++ b/src/Phpml/Clustering/KMeans/Cluster.php
@ -0,0 +1,137 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Clustering\KMeans;
 use IteratorAggregate;
 use Countable;
 use SplObjectStorage;
 use LogicException;
 class Cluster extends Point implements IteratorAggregate, Countable
 {
    /**
     * @var Space
     */
    protected $space;
    /**
     * @var SplObjectStorage|Point[]
     */
    protected $points;
    /**
     * @param Space $space
     * @param array $coordinates
     */
    public function __construct(Space $space, array $coordinates)
    {
        parent::__construct($coordinates);
        $this->space = $space;
        $this->points = new SplObjectStorage();
    }
    /**
     * @return array
     */
    public function getPoints()
    {
        $points = [];
        foreach ($this->points as $point) {
            $points[] = $point->toArray();
        }
        return $points;
    }
    /**
     * @return array
     */
    public function toArray()
    {
        return array(
            'centroid' => parent::toArray(),
            'points' => $this->getPoints(),
        );
    }
    /**
     * @param Point $point
     *
     * @return Point
     */
    public function attach(Point $point)
    {
        if ($point instanceof self) {
            throw new LogicException('cannot attach a cluster to another');
        }
        $this->points->attach($point);
        return $point;
    }
    /**
     * @param Point $point
     *
     * @return Point
     */
    public function detach(Point $point)
    {
        $this->points->detach($point);
        return $point;
    }
    /**
     * @param SplObjectStorage $points
     */
    public function attachAll(SplObjectStorage $points)
    {
        $this->points->addAll($points);
    }
    /**
     * @param SplObjectStorage $points
     */
    public function detachAll(SplObjectStorage $points)
    {
        $this->points->removeAll($points);
    }
    public function updateCentroid()
    {
        if (!$count = count($this->points)) {
            return;
        }
        $centroid = $this->space->newPoint(array_fill(0, $this->dimension, 0));
        foreach ($this->points as $point) {
            for ($n = 0; $n < $this->dimension; ++$n) {
                $centroid->coordinates[$n] += $point->coordinates[$n];
            }
        }
        for ($n = 0; $n < $this->dimension; ++$n) {
            $this->coordinates[$n] = $centroid->coordinates[$n] / $count;
        }
    }
    /**
     * @return Point[]|SplObjectStorage
     */
    public function getIterator()
    {
        return $this->points;
    }
    /**
     * @return mixed
     */
    public function count()
    {
        return count($this->points);
    }
 }
--- a/src/Phpml/Clustering/KMeans/Point.php
+++ b/src/Phpml/Clustering/KMeans/Point.php
@ -0,0 +1,124 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Clustering\KMeans;
 use ArrayAccess;
 class Point implements ArrayAccess
 {
    /**
     * @var int
     */
    protected $dimension;
    /**
     * @var array
     */
    protected $coordinates;
    /**
     * @param array $coordinates
     */
    public function __construct(array $coordinates)
    {
        $this->dimension = count($coordinates);
        $this->coordinates = $coordinates;
    }
    /**
     * @return array
     */
    public function toArray()
    {
        return $this->coordinates;
    }
    /**
     * @param Point $point
     * @param bool  $precise
     *
     * @return int|mixed
     */
    public function getDistanceWith(self $point, $precise = true)
    {
        $distance = 0;
        for ($n = 0; $n < $this->dimension; ++$n) {
            $difference = $this->coordinates[$n] - $point->coordinates[$n];
            $distance  += $difference * $difference;
        }
        return $precise ? sqrt($distance) : $distance;
    }
    /**
     * @param $points
     *
     * @return mixed
     */
    public function getClosest($points)
    {
        foreach ($points as $point) {
            $distance = $this->getDistanceWith($point, false);
            if (!isset($minDistance)) {
                $minDistance = $distance;
                $minPoint = $point;
                continue;
            }
            if ($distance < $minDistance) {
                $minDistance = $distance;
                $minPoint = $point;
            }
        }
        return $minPoint;
    }
    /**
     * @return array
     */
    public function getCoordinates()
    {
        return $this->coordinates;
    }
    /**
     * @param mixed $offset
     *
     * @return bool
     */
    public function offsetExists($offset)
    {
        return isset($this->coordinates[$offset]);
    }
    /**
     * @param mixed $offset
     *
     * @return mixed
     */
    public function offsetGet($offset)
    {
        return $this->coordinates[$offset];
    }
    /**
     * @param mixed $offset
     * @param mixed $value
     */
    public function offsetSet($offset, $value)
    {
        $this->coordinates[$offset] = $value;
    }
    /**
     * @param mixed $offset
     */
    public function offsetUnset($offset)
    {
        unset($this->coordinates[$offset]);
    }
 }
--- a/src/Phpml/Clustering/KMeans/Space.php
+++ b/src/Phpml/Clustering/KMeans/Space.php
@ -0,0 +1,233 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Clustering\KMeans;
 use Phpml\Clustering\KMeans;
 use SplObjectStorage;
 use LogicException;
 use InvalidArgumentException;
 class Space extends SplObjectStorage
 {
    /**
     * @var int
     */
    protected $dimension;
    /**
     * @param $dimension
     */
    public function __construct($dimension)
    {
        if ($dimension < 1) {
            throw new LogicException('a space dimension cannot be null or negative');
        }
        $this->dimension = $dimension;
    }
    /**
     * @return array
     */
    public function toArray()
    {
        $points = [];
        foreach ($this as $point) {
            $points[] = $point->toArray();
        }
        return ['points' => $points];
    }
    /**
     * @param array $coordinates
     *
     * @return Point
     */
    public function newPoint(array $coordinates)
    {
        if (count($coordinates) != $this->dimension) {
            throw new LogicException('('.implode(',', $coordinates).') is not a point of this space');
        }
        return new Point($coordinates);
    }
    /**
     * @param array $coordinates
     * @param null  $data
     */
    public function addPoint(array $coordinates, $data = null)
    {
        return $this->attach($this->newPoint($coordinates), $data);
    }
    /**
     * @param object $point
     * @param null   $data
     */
    public function attach($point, $data = null)
    {
        if (!$point instanceof Point) {
            throw new InvalidArgumentException('can only attach points to spaces');
        }
        return parent::attach($point, $data);
    }
    /**
     * @return int
     */
    public function getDimension()
    {
        return $this->dimension;
    }
    /**
     * @return array|bool
     */
    public function getBoundaries()
    {
        if (!count($this)) {
            return false;
        }
        $min = $this->newPoint(array_fill(0, $this->dimension, null));
        $max = $this->newPoint(array_fill(0, $this->dimension, null));
        foreach ($this as $point) {
            for ($n = 0; $n < $this->dimension; ++$n) {
                ($min[$n] > $point[$n] || $min[$n] === null) && $min[$n] = $point[$n];
                ($max[$n] < $point[$n] || $max[$n] === null) && $max[$n] = $point[$n];
            }
        }
        return array($min, $max);
    }
    /**
     * @param Point $min
     * @param Point $max
     *
     * @return Point
     */
    public function getRandomPoint(Point $min, Point $max)
    {
        $point = $this->newPoint(array_fill(0, $this->dimension, null));
        for ($n = 0; $n < $this->dimension; ++$n) {
            $point[$n] = rand($min[$n], $max[$n]);
        }
        return $point;
    }
    /**
     * @param int $clustersNumber
     * @param int $initMethod
     *
     * @return array|Cluster[]
     */
    public function cluster(int $clustersNumber, int $initMethod = KMeans::INIT_RANDOM)
    {
        $clusters = $this->initializeClusters($clustersNumber, $initMethod);
        do {
        } while (!$this->iterate($clusters));
        return $clusters;
    }
    /**
     * @param $clustersNumber
     * @param $initMethod
     *
     * @return array|Cluster[]
     */
    protected function initializeClusters(int $clustersNumber, int $initMethod)
    {
        switch ($initMethod) {
            case KMeans::INIT_RANDOM:
                list($min, $max) = $this->getBoundaries();
                for ($n = 0; $n < $clustersNumber; ++$n) {
                    $clusters[] = new Cluster($this, $this->getRandomPoint($min, $max)->getCoordinates());
                }
                break;
            case KMeans::INIT_KMEANS_PLUS_PLUS:
                $position = rand(1, count($this));
                for ($i = 1, $this->rewind(); $i < $position && $this->valid(); $i++, $this->next());
                $clusters[] = new Cluster($this, $this->current()->getCoordinates());
                $distances = new SplObjectStorage();
                for ($i = 1; $i < $clustersNumber; ++$i) {
                    $sum = 0;
                    foreach ($this as $point) {
                        $distance = $point->getDistanceWith($point->getClosest($clusters));
                        $sum += $distances[$point] = $distance;
                    }
                    $sum = rand(0, (int) $sum);
                    foreach ($this as $point) {
                        if (($sum -= $distances[$point]) > 0) {
                            continue;
                        }
                        $clusters[] = new Cluster($this, $point->getCoordinates());
                        break;
                    }
                }
                break;
        }
        $clusters[0]->attachAll($this);
        return $clusters;
    }
    /**
     * @param $clusters
     *
     * @return bool
     */
    protected function iterate($clusters)
    {
        $convergence = true;
        $attach = new SplObjectStorage();
        $detach = new SplObjectStorage();
        foreach ($clusters as $cluster) {
            foreach ($cluster as $point) {
                $closest = $point->getClosest($clusters);
                if ($closest !== $cluster) {
                    isset($attach[$closest]) || $attach[$closest] = new SplObjectStorage();
                    isset($detach[$cluster]) || $detach[$cluster] = new SplObjectStorage();
                    $attach[$closest]->attach($point);
                    $detach[$cluster]->attach($point);
                    $convergence = false;
                }
            }
        }
        foreach ($attach as $cluster) {
            $cluster->attachAll($attach[$cluster]);
        }
        foreach ($detach as $cluster) {
            $cluster->detachAll($detach[$cluster]);
        }
        foreach ($clusters as $cluster) {
            $cluster->updateCentroid();
        }
        return $convergence;
    }
 }
--- a/src/Phpml/Dataset/ArrayDataset.php
+++ b/src/Phpml/Dataset/ArrayDataset.php
@ -27,7 +27,7 @@ class ArrayDataset implements Dataset
    public function __construct(array $samples, array $labels)
    {
        if (count($samples) != count($labels)) {
-            throw InvalidArgumentException::sizeNotMatch();
+            throw InvalidArgumentException::arraySizeNotMatch();
        }
        $this->samples = $samples;
--- a/src/Phpml/Dataset/CsvDataset.php
+++ b/src/Phpml/Dataset/CsvDataset.php
@ -26,19 +26,18 @@ class CsvDataset extends ArrayDataset
            throw DatasetException::missingFile(basename($filepath));
        }
-        $row = 0;
+        if (false === $handle = fopen($filepath, 'r')) {
-        if (($handle = fopen($filepath, 'r')) !== false) {
+            throw DatasetException::cantOpenFile(basename($filepath));
            while (($data = fgetcsv($handle, 1000, ',')) !== false) {
                ++$row;
                if ($headingRow && $row == 1) {
                    continue;
        }
        if ($headingRow) {
            fgets($handle);
        }
        while (($data = fgetcsv($handle, 1000, ',')) !== false) {
            $this->samples[] = array_slice($data, 0, $features);
            $this->labels[] = $data[$features];
        }
        fclose($handle);
        } else {
            throw DatasetException::cantOpenFile(basename($filepath));
        }
    }
 }
--- a/src/Phpml/Dataset/Dataset.php
+++ b/src/Phpml/Dataset/Dataset.php
@ -6,6 +6,7 @@ namespace Phpml\Dataset;
 interface Dataset
 {
    const SOME = 'z';
    /**
     * @return array
     */
--- a/src/Phpml/Dataset/Demo/Glass.php
+++ b/src/Phpml/Dataset/Demo/Glass.php
@ -0,0 +1,28 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Dataset\Demo;
 use Phpml\Dataset\CsvDataset;
 /**
 * Classes: 6
 * Samples per class:
 *      70 float processed building windows
 *      17 float processed vehicle windows
 *      76 non-float processed building windows
 *      13 containers
 *      9 tableware
 *      29 headlamps
 * Samples total: 214
 * Features per sample: 9.
 */
 class Glass extends CsvDataset
 {
    public function __construct()
    {
        $filepath = dirname(__FILE__).'/../../../../data/glass.csv';
        parent::__construct($filepath, 9, true);
    }
 }
--- a/src/Phpml/Dataset/Demo/Wine.php
+++ b/src/Phpml/Dataset/Demo/Wine.php
@ -0,0 +1,22 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Dataset\Demo;
 use Phpml\Dataset\CsvDataset;
 /**
 * Classes: 3
 * Samples per class: class 1 59; class 2 71; class 3 48
 * Samples total: 178
 * Features per sample: 13.
 */
 class Wine extends CsvDataset
 {
    public function __construct()
    {
        $filepath = dirname(__FILE__).'/../../../../data/wine.csv';
        parent::__construct($filepath, 13, true);
    }
 }
--- a/src/Phpml/Exception/InvalidArgumentException.php
+++ b/src/Phpml/Exception/InvalidArgumentException.php
@ -9,9 +9,9 @@ class InvalidArgumentException extends \Exception
    /**
     * @return InvalidArgumentException
     */
-    public static function sizeNotMatch()
+    public static function arraySizeNotMatch()
    {
-        return new self('Size of given arguments not match');
+        return new self('Size of given arrays not match');
    }
    /**
@ -23,4 +23,46 @@ class InvalidArgumentException extends \Exception
    {
        return new self(sprintf('%s must be between 0.0 and 1.0', $name));
    }
    /**
     * @return InvalidArgumentException
     */
    public static function arrayCantBeEmpty()
    {
        return new self('The array has zero elements');
    }
    /**
     * @param int $minimumSize
     *
     * @return InvalidArgumentException
     */
    public static function arraySizeToSmall($minimumSize = 2)
    {
        return new self(sprintf('The array must have at least %s elements', $minimumSize));
    }
    /**
     * @return InvalidArgumentException
     */
    public static function matrixDimensionsDidNotMatch()
    {
        return new self('Matrix dimensions did not match');
    }
    /**
     * @return InvalidArgumentException
     */
    public static function inconsistentMatrixSupplied()
    {
        return new self('Inconsistent matrix aupplied');
    }
    /**
     * @return InvalidArgumentException
     */
    public static function invalidClustersNumber()
    {
        return new self('Invalid clusters number');
    }
 }
--- a/src/Phpml/Exception/MatrixException.php
+++ b/src/Phpml/Exception/MatrixException.php
@ -0,0 +1,24 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Exception;
 class MatrixException extends \Exception
 {
    /**
     * @return MatrixException
     */
    public static function notSquareMatrix()
    {
        return new self('Matrix is not square matrix');
    }
    /**
     * @return MatrixException
     */
    public static function columnOutOfRange()
    {
        return new self('Column out of range');
    }
 }
--- a/src/Phpml/Math/Distance.php
+++ b/src/Phpml/Math/Distance.php
@ -0,0 +1,16 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math;
 interface Distance
 {
    /**
     * @param array $a
     * @param array $b
     *
     * @return float
     */
    public function distance(array $a, array $b): float;
 }
--- a/src/Phpml/Math/Distance/Chebyshev.php
+++ b/src/Phpml/Math/Distance/Chebyshev.php
@ -0,0 +1,35 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math\Distance;
 use Phpml\Exception\InvalidArgumentException;
 use Phpml\Math\Distance;
 class Chebyshev implements Distance
 {
    /**
     * @param array $a
     * @param array $b
     *
     * @return float
     *
     * @throws InvalidArgumentException
     */
    public function distance(array $a, array $b): float
    {
        if (count($a) !== count($b)) {
            throw InvalidArgumentException::arraySizeNotMatch();
        }
        $differences = [];
        $count = count($a);
        for ($i = 0; $i < $count; ++$i) {
            $differences[] = abs($a[$i] - $b[$i]);
        }
        return max($differences);
    }
 }
--- a/src/Phpml/Math/Distance/Euclidean.php
+++ b/src/Phpml/Math/Distance/Euclidean.php
@ -2,11 +2,12 @@
 declare (strict_types = 1);
-namespace Phpml\Metric;
+namespace Phpml\Math\Distance;
 use Phpml\Exception\InvalidArgumentException;
 use Phpml\Math\Distance;
-class Distance
+class Euclidean implements Distance
 {
    /**
     * @param array $a
@ -16,10 +17,10 @@ class Distance
     *
     * @throws InvalidArgumentException
     */
-    public static function euclidean(array $a, array $b): float
+    public function distance(array $a, array $b): float
    {
-        if (count($a) != count($b)) {
+        if (count($a) !== count($b)) {
-            throw InvalidArgumentException::sizeNotMatch();
+            throw InvalidArgumentException::arraySizeNotMatch();
        }
        $distance = 0;
--- a/src/Phpml/Math/Distance/Manhattan.php
+++ b/src/Phpml/Math/Distance/Manhattan.php
@ -0,0 +1,35 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math\Distance;
 use Phpml\Exception\InvalidArgumentException;
 use Phpml\Math\Distance;
 class Manhattan implements Distance
 {
    /**
     * @param array $a
     * @param array $b
     *
     * @return float
     *
     * @throws InvalidArgumentException
     */
    public function distance(array $a, array $b): float
    {
        if (count($a) !== count($b)) {
            throw InvalidArgumentException::arraySizeNotMatch();
        }
        $distance = 0;
        $count = count($a);
        for ($i = 0; $i < $count; ++$i) {
            $distance += abs($a[$i] - $b[$i]);
        }
        return $distance;
    }
 }
--- a/src/Phpml/Math/Distance/Minkowski.php
+++ b/src/Phpml/Math/Distance/Minkowski.php
@ -0,0 +1,48 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math\Distance;
 use Phpml\Exception\InvalidArgumentException;
 use Phpml\Math\Distance;
 class Minkowski implements Distance
 {
    /**
     * @var float
     */
    private $lambda;
    /**
     * @param float $lambda
     */
    public function __construct(float $lambda = 3)
    {
        $this->lambda = $lambda;
    }
    /**
     * @param array $a
     * @param array $b
     *
     * @return float
     *
     * @throws InvalidArgumentException
     */
    public function distance(array $a, array $b): float
    {
        if (count($a) !== count($b)) {
            throw InvalidArgumentException::arraySizeNotMatch();
        }
        $distance = 0;
        $count = count($a);
        for ($i = 0; $i < $count; ++$i) {
            $distance += pow(abs($a[$i] - $b[$i]), $this->lambda);
        }
        return pow($distance, 1 / $this->lambda);
    }
 }
--- a/src/Phpml/Math/Kernel.php
+++ b/src/Phpml/Math/Kernel.php
@ -0,0 +1,16 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math;
 interface Kernel
 {
    /**
     * @param float $a
     * @param float $b
     *
     * @return float
     */
    public function compute($a, $b);
 }
--- a/src/Phpml/Math/Kernel/RBF.php
+++ b/src/Phpml/Math/Kernel/RBF.php
@ -0,0 +1,39 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math\Kernel;
 use Phpml\Math\Kernel;
 use Phpml\Math\Product;
 class RBF implements Kernel
 {
    /**
     * @var float
     */
    private $gamma;
    /**
     * @param float $gamma
     */
    public function __construct(float $gamma)
    {
        $this->gamma = $gamma;
    }
    /**
     * @param float $a
     * @param float $b
     *
     * @return float
     */
    public function compute($a, $b)
    {
        $score = 2 * Product::scalar($a, $b);
        $squares = Product::scalar($a, $a) + Product::scalar($b, $b);
        $result = exp(-$this->gamma * ($squares - $score));
        return $result;
    }
 }
--- a/src/Phpml/Math/Matrix.php
+++ b/src/Phpml/Math/Matrix.php
@ -0,0 +1,273 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math;
 use Phpml\Exception\InvalidArgumentException;
 use Phpml\Exception\MatrixException;
 class Matrix
 {
    /**
     * @var array
     */
    private $matrix;
    /**
     * @var int
     */
    private $rows;
    /**
     * @var int
     */
    private $columns;
    /**
     * @var float
     */
    private $determinant;
    /**
     * @param array $matrix
     * @param bool  $validate
     *
     * @throws InvalidArgumentException
     */
    public function __construct(array $matrix, bool $validate = true)
    {
        $this->rows = count($matrix);
        $this->columns = count($matrix[0]);
        if ($validate) {
            for ($i = 0; $i < $this->rows; ++$i) {
                if (count($matrix[$i]) !== $this->columns) {
                    throw InvalidArgumentException::matrixDimensionsDidNotMatch();
                }
            }
        }
        $this->matrix = $matrix;
    }
    /**
     * @param array $array
     *
     * @return Matrix
     */
    public static function fromFlatArray(array $array)
    {
        $matrix = [];
        foreach ($array as $value) {
            $matrix[] = [$value];
        }
        return new self($matrix);
    }
    /**
     * @return array
     */
    public function toArray()
    {
        return $this->matrix;
    }
    /**
     * @return int
     */
    public function getRows()
    {
        return $this->rows;
    }
    /**
     * @return int
     */
    public function getColumns()
    {
        return $this->columns;
    }
    /**
     * @param $column
     *
     * @return array
     *
     * @throws MatrixException
     */
    public function getColumnValues($column)
    {
        if ($column >= $this->columns) {
            throw MatrixException::columnOutOfRange();
        }
        $values = [];
        for ($i = 0; $i < $this->rows; ++$i) {
            $values[] = $this->matrix[$i][$column];
        }
        return $values;
    }
    /**
     * @return float|int
     * 
     * @throws MatrixException
     */
    public function getDeterminant()
    {
        if ($this->determinant) {
            return $this->determinant;
        }
        if (!$this->isSquare()) {
            throw MatrixException::notSquareMatrix();
        }
        return $this->determinant = $this->calculateDeterminant();
    }
    /**
     * @return float|int
     *
     * @throws MatrixException
     */
    private function calculateDeterminant()
    {
        $determinant = 0;
        if ($this->rows == 1 && $this->columns == 1) {
            $determinant = $this->matrix[0][0];
        } elseif ($this->rows == 2 && $this->columns == 2) {
            $determinant =
                $this->matrix[0][0] * $this->matrix[1][1] -
                $this->matrix[0][1] * $this->matrix[1][0];
        } else {
            for ($j = 0; $j < $this->columns; ++$j) {
                $subMatrix = $this->crossOut(0, $j);
                $minor = $this->matrix[0][$j] * $subMatrix->getDeterminant();
                $determinant += fmod($j, 2) == 0 ? $minor : -$minor;
            }
        }
        return $determinant;
    }
    /**
     * @return bool
     */
    public function isSquare()
    {
        return $this->columns === $this->rows;
    }
    /**
     * @return Matrix
     */
    public function transpose()
    {
        $newMatrix = [];
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($j = 0; $j < $this->columns; ++$j) {
                $newMatrix[$j][$i] = $this->matrix[$i][$j];
            }
        }
        return new self($newMatrix, false);
    }
    /**
     * @param Matrix $matrix
     *
     * @return Matrix
     *
     * @throws InvalidArgumentException
     */
    public function multiply(Matrix $matrix)
    {
        if ($this->columns != $matrix->getRows()) {
            throw InvalidArgumentException::inconsistentMatrixSupplied();
        }
        $product = [];
        $multiplier = $matrix->toArray();
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($j = 0; $j < $matrix->getColumns(); ++$j) {
                $product[$i][$j] = 0;
                for ($k = 0; $k < $this->columns; ++$k) {
                    $product[$i][$j] += $this->matrix[$i][$k] * $multiplier[$k][$j];
                }
            }
        }
        return new self($product, false);
    }
    /**
     * @param $value
     *
     * @return Matrix
     */
    public function divideByScalar($value)
    {
        $newMatrix = array();
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($j = 0; $j < $this->columns; ++$j) {
                $newMatrix[$i][$j] = $this->matrix[$i][$j] / $value;
            }
        }
        return new self($newMatrix, false);
    }
    /**
     * @return Matrix
     *
     * @throws MatrixException
     */
    public function inverse()
    {
        if (!$this->isSquare()) {
            throw MatrixException::notSquareMatrix();
        }
        $newMatrix = array();
        for ($i = 0; $i < $this->rows; ++$i) {
            for ($j = 0; $j < $this->columns; ++$j) {
                $minor = $this->crossOut($i, $j)->getDeterminant();
                $newMatrix[$i][$j] = fmod($i + $j, 2) == 0 ? $minor : -$minor;
            }
        }
        $cofactorMatrix = new self($newMatrix, false);
        return $cofactorMatrix->transpose()->divideByScalar($this->getDeterminant());
    }
    /**
     * @param int $row
     * @param int $column
     *
     * @return Matrix
     */
    public function crossOut(int $row, int $column)
    {
        $newMatrix = [];
        $r = 0;
        for ($i = 0; $i < $this->rows; ++$i) {
            $c = 0;
            if ($row != $i) {
                for ($j = 0; $j < $this->columns; ++$j) {
                    if ($column != $j) {
                        $newMatrix[$r][$c] = $this->matrix[$i][$j];
                        ++$c;
                    }
                }
                ++$r;
            }
        }
        return new self($newMatrix, false);
    }
 }
--- a/src/Phpml/Math/Product.php
+++ b/src/Phpml/Math/Product.php
@ -0,0 +1,24 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math;
 class Product
 {
    /**
     * @param array $a
     * @param array $b
     *
     * @return mixed
     */
    public static function scalar(array $a, array $b)
    {
        $product = 0;
        foreach ($a as $index => $value) {
            $product += $value * $b[$index];
        }
        return $product;
    }
 }
--- a/src/Phpml/Math/Statistic/Correlation.php
+++ b/src/Phpml/Math/Statistic/Correlation.php
@ -0,0 +1,45 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math\Statistic;
 use Phpml\Exception\InvalidArgumentException;
 class Correlation
 {
    /**
     * @param array|int[]|float[] $x
     * @param array|int[]|float[] $y
     *
     * @return float
     *
     * @throws InvalidArgumentException
     */
    public static function pearson(array $x, array $y)
    {
        if (count($x) !== count($y)) {
            throw InvalidArgumentException::arraySizeNotMatch();
        }
        $count = count($x);
        $meanX = Mean::arithmetic($x);
        $meanY = Mean::arithmetic($y);
        $axb = 0;
        $a2 = 0;
        $b2 = 0;
        for ($i = 0;$i < $count;++$i) {
            $a = $x[$i] - $meanX;
            $b = $y[$i] - $meanY;
            $axb = $axb + ($a * $b);
            $a2 = $a2 + pow($a, 2);
            $b2 = $b2 + pow($b, 2);
        }
        $corr = $axb / sqrt($a2 * $b2);
        return $corr;
    }
 }
--- a/src/Phpml/Math/Statistic/Mean.php
+++ b/src/Phpml/Math/Statistic/Mean.php
@ -0,0 +1,18 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math\Statistic;
 class Mean
 {
    /**
     * @param array $a
     *
     * @return float
     */
    public static function arithmetic(array $a)
    {
        return array_sum($a) / count($a);
    }
 }
--- a/src/Phpml/Math/Statistic/StandardDeviation.php
+++ b/src/Phpml/Math/Statistic/StandardDeviation.php
@ -0,0 +1,44 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Math\Statistic;
 use Phpml\Exception\InvalidArgumentException;
 class StandardDeviation
 {
    /**
     * @param array|float[] $a
     * @param bool          $sample
     *
     * @return float
     *
     * @throws InvalidArgumentException
     */
    public static function population(array $a, $sample = true)
    {
        if (empty($a)) {
            throw InvalidArgumentException::arrayCantBeEmpty();
        }
        $n = count($a);
        if ($sample && $n === 1) {
            throw InvalidArgumentException::arraySizeToSmall(2);
        }
        $mean = Mean::arithmetic($a);
        $carry = 0.0;
        foreach ($a as $val) {
            $d = $val - $mean;
            $carry += $d * $d;
        };
        if ($sample) {
            --$n;
        }
        return sqrt($carry / $n);
    }
 }
--- a/src/Phpml/Metric/Accuracy.php
+++ b/src/Phpml/Metric/Accuracy.php
@ -20,12 +20,12 @@ class Accuracy
    public static function score(array $actualLabels, array $predictedLabels, bool $normalize = true)
    {
        if (count($actualLabels) != count($predictedLabels)) {
-            throw InvalidArgumentException::sizeNotMatch();
+            throw InvalidArgumentException::arraySizeNotMatch();
        }
        $score = 0;
        foreach ($actualLabels as $index => $label) {
-            if ($label === $predictedLabels[$index]) {
+            if ($label == $predictedLabels[$index]) {
                ++$score;
            }
        }
--- a/src/Phpml/Regression/LeastSquares.php
+++ b/src/Phpml/Regression/LeastSquares.php
@ -0,0 +1,116 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Regression;
 use Phpml\Math\Matrix;
 class LeastSquares implements Regression
 {
    /**
     * @var array
     */
    private $samples;
    /**
     * @var array
     */
    private $targets;
    /**
     * @var float
     */
    private $intercept;
    /**
     * @var array
     */
    private $coefficients;
    /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets)
    {
        $this->samples = $samples;
        $this->targets = $targets;
        $this->computeCoefficients();
    }
    /**
     * @param array $sample
     *
     * @return mixed
     */
    public function predict($sample)
    {
        $result = $this->intercept;
        foreach ($this->coefficients as $index => $coefficient) {
            $result += $coefficient * $sample[$index];
        }
        return $result;
    }
    /**
     * @return array
     */
    public function getCoefficients()
    {
        return $this->coefficients;
    }
    /**
     * @return float
     */
    public function getIntercept()
    {
        return $this->intercept;
    }
    /**
     * coefficient(b) = (X'X)-1X'Y.
     */
    private function computeCoefficients()
    {
        $samplesMatrix = $this->getSamplesMatrix();
        $targetsMatrix = $this->getTargetsMatrix();
        $ts = $samplesMatrix->transpose()->multiply($samplesMatrix)->inverse();
        $tf = $samplesMatrix->transpose()->multiply($targetsMatrix);
        $this->coefficients = $ts->multiply($tf)->getColumnValues(0);
        $this->intercept = array_shift($this->coefficients);
    }
    /**
     * Add one dimension for intercept calculation.
     *
     * @return Matrix
     */
    private function getSamplesMatrix()
    {
        $samples = [];
        foreach ($this->samples as $sample) {
            array_unshift($sample, 1);
            $samples[] = $sample;
        }
        return new Matrix($samples);
    }
    /**
     * @return Matrix
     */
    private function getTargetsMatrix()
    {
        if (is_array($this->targets[0])) {
            return new Matrix($this->targets);
        }
        return Matrix::fromFlatArray($this->targets);
    }
 }
--- a/src/Phpml/Regression/Regression.php
+++ b/src/Phpml/Regression/Regression.php
@ -0,0 +1,21 @@
 <?php
 declare (strict_types = 1);
 namespace Phpml\Regression;
 interface Regression
 {
    /**
     * @param array $samples
     * @param array $targets
     */
    public function train(array $samples, array $targets);
    /**
     * @param float $sample
     *
     * @return mixed
     */
    public function predict($sample);
 }
--- a/tests/Phpml/Classification/KNearestNeighborsTest.php
+++ b/tests/Phpml/Classification/KNearestNeighborsTest.php
@ -2,12 +2,10 @@
 declare (strict_types = 1);
-namespace tests\Classifier;
+namespace tests\Classification;
-use Phpml\Classifier\KNearestNeighbors;
+use Phpml\Classification\KNearestNeighbors;
-use Phpml\CrossValidation\RandomSplit;
+use Phpml\Math\Distance\Chebyshev;
 use Phpml\Dataset\Demo\Iris;
 use Phpml\Metric\Accuracy;
 class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
 {
@ -45,14 +43,18 @@ class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
        $this->assertEquals($testLabels, $predicted);
    }
-    public function testAccuracyOnIrisDataset()
+    public function testPredictArrayOfSamplesUsingChebyshevDistanceMetric()
    {
-        $dataset = new RandomSplit(new Iris(), $testSize = 0.5, $seed = 123);
+        $trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
-        $classifier = new KNearestNeighbors($k = 4);
+        $trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
        $classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
        $predicted = $classifier->predict($dataset->getTestSamples());
        $score = Accuracy::score($dataset->getTestLabels(), $predicted);
-        $this->assertEquals(0.96, $score);
+        $testSamples = [[3, 2], [5, 1], [4, 3], [4, -5], [2, 3], [1, 2], [1, 5], [3, 10]];
        $testLabels = ['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a'];
        $classifier = new KNearestNeighbors(3, new Chebyshev());
        $classifier->train($trainSamples, $trainLabels);
        $predicted = $classifier->predict($testSamples);
        $this->assertEquals($testLabels, $predicted);
    }
 }
--- a/tests/Phpml/Classification/NaiveBayesTest.php
+++ b/tests/Phpml/Classification/NaiveBayesTest.php
@ -0,0 +1,38 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Classification;
 use Phpml\Classification\NaiveBayes;
 class NaiveBayesTest extends \PHPUnit_Framework_TestCase
 {
    public function testPredictSingleSample()
    {
        $samples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
        $labels = ['a', 'b', 'c'];
        $classifier = new NaiveBayes();
        $classifier->train($samples, $labels);
        $this->assertEquals('a', $classifier->predict([3, 1, 1]));
        $this->assertEquals('b', $classifier->predict([1, 4, 1]));
        $this->assertEquals('c', $classifier->predict([1, 1, 6]));
    }
    public function testPredictArrayOfSamples()
    {
        $trainSamples = [[5, 1, 1], [1, 5, 1], [1, 1, 5]];
        $trainLabels = ['a', 'b', 'c'];
        $testSamples = [[3, 1, 1], [5, 1, 1], [4, 3, 8], [1, 1, 2], [2, 3, 2], [1, 2, 1], [9, 5, 1], [3, 1, 2]];
        $testLabels = ['a', 'a', 'c', 'c', 'b', 'b', 'a', 'a'];
        $classifier = new NaiveBayes();
        $classifier->train($trainSamples, $trainLabels);
        $predicted = $classifier->predict($testSamples);
        $this->assertEquals($testLabels, $predicted);
    }
 }
--- a/tests/Phpml/Clustering/DBSCANTest.php
+++ b/tests/Phpml/Clustering/DBSCANTest.php
@ -0,0 +1,33 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Clustering;
 use Phpml\Clustering\DBSCAN;
 class DBSCANTest extends \PHPUnit_Framework_TestCase
 {
    public function testDBSCANSamplesClustering()
    {
        $samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];
        $clustered = [
            [[1, 1], [1, 2], [2, 1]],
            [[8, 7], [7, 8], [8, 9]],
        ];
        $dbscan = new DBSCAN($epsilon = 2, $minSamples = 3);
        $this->assertEquals($clustered, $dbscan->cluster($samples));
        $samples = [[1, 1], [6, 6], [1, -1], [5, 6], [-1, -1], [7, 8], [-1, 1], [7, 7]];
        $clustered = [
            [[1, 1], [1, -1], [-1, -1], [-1, 1]],
            [[6, 6], [5, 6], [7, 8], [7, 7]],
        ];
        $dbscan = new DBSCAN($epsilon = 3, $minSamples = 4);
        $this->assertEquals($clustered, $dbscan->cluster($samples));
    }
 }
--- a/tests/Phpml/Clustering/KMeansTest.php
+++ b/tests/Phpml/Clustering/KMeansTest.php
@ -0,0 +1,51 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Clustering;
 use Phpml\Clustering\KMeans;
 class KMeansTest extends \PHPUnit_Framework_TestCase
 {
    public function testKMeansSamplesClustering()
    {
        $samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];
        $kmeans = new KMeans(2);
        $clusters = $kmeans->cluster($samples);
        $this->assertEquals(2, count($clusters));
        foreach ($samples as $index => $sample) {
            if (in_array($sample, $clusters[0]) || in_array($sample, $clusters[1])) {
                unset($samples[$index]);
            }
        }
        $this->assertEquals(0, count($samples));
    }
    public function testKMeansInitializationMethods()
    {
        $samples = [
            [180, 155], [186, 159], [119, 185], [141, 147], [157, 158],
            [176, 122], [194, 160], [113, 193], [190, 148], [152, 154],
            [162, 146], [188, 144], [185, 124], [163, 114], [151, 140],
            [175, 131], [186, 162], [181, 195], [147, 122], [143, 195],
            [171, 119], [117, 165], [169, 121], [159, 160], [159, 112],
            [115, 122], [149, 193], [156, 135], [118, 120], [139, 159],
            [150, 115], [181, 136], [167, 162], [132, 115], [175, 165],
            [110, 147], [175, 118], [113, 145], [130, 162], [195, 179],
            [164, 111], [192, 114], [194, 149], [139, 113], [160, 168],
            [162, 110], [174, 144], [137, 142], [197, 160], [147, 173],
        ];
        $kmeans = new KMeans(4, KMeans::INIT_KMEANS_PLUS_PLUS);
        $clusters = $kmeans->cluster($samples);
        $this->assertEquals(4, count($clusters));
        $kmeans = new KMeans(4, KMeans::INIT_RANDOM);
        $clusters = $kmeans->cluster($samples);
        $this->assertEquals(4, count($clusters));
    }
 }
--- a/tests/Phpml/Dataset/CsvDatasetTest.php
+++ b/tests/Phpml/Dataset/CsvDatasetTest.php
@ -16,7 +16,7 @@ class CsvDatasetTest extends \PHPUnit_Framework_TestCase
        new CsvDataset('missingFile', 3);
    }
-    public function testSampleCsvDataset()
+    public function testSampleCsvDatasetWithHeaderRow()
    {
        $filePath = dirname(__FILE__).'/Resources/dataset.csv';
@ -25,4 +25,14 @@ class CsvDatasetTest extends \PHPUnit_Framework_TestCase
        $this->assertEquals(10, count($dataset->getSamples()));
        $this->assertEquals(10, count($dataset->getLabels()));
    }
    public function testSampleCsvDatasetWithoutHeaderRow()
    {
        $filePath = dirname(__FILE__).'/Resources/dataset.csv';
        $dataset = new CsvDataset($filePath, 2, false);
        $this->assertEquals(11, count($dataset->getSamples()));
        $this->assertEquals(11, count($dataset->getLabels()));
    }
 }
--- a/tests/Phpml/Dataset/Demo/GlassTest.php
+++ b/tests/Phpml/Dataset/Demo/GlassTest.php
@ -0,0 +1,22 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Dataset\Demo;
 use Phpml\Dataset\Demo\Glass;
 class GlassTest extends \PHPUnit_Framework_TestCase
 {
    public function testLoadingWineDataset()
    {
        $glass = new Glass();
        // whole dataset
        $this->assertEquals(214, count($glass->getSamples()));
        $this->assertEquals(214, count($glass->getLabels()));
        // one sample features count
        $this->assertEquals(9, count($glass->getSamples()[0]));
    }
 }
--- a/tests/Phpml/Dataset/Demo/WineTest.php
+++ b/tests/Phpml/Dataset/Demo/WineTest.php
@ -0,0 +1,22 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Dataset\Demo;
 use Phpml\Dataset\Demo\Wine;
 class WineTest extends \PHPUnit_Framework_TestCase
 {
    public function testLoadingWineDataset()
    {
        $wine = new Wine();
        // whole dataset
        $this->assertEquals(178, count($wine->getSamples()));
        $this->assertEquals(178, count($wine->getLabels()));
        // one sample features count
        $this->assertEquals(13, count($wine->getSamples()[0]));
    }
 }
--- a/tests/Phpml/Math/Distance/ChebyshevTest.php
+++ b/tests/Phpml/Math/Distance/ChebyshevTest.php
@ -0,0 +1,64 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Metric;
 use Phpml\Math\Distance\Chebyshev;
 class ChebyshevTest extends \PHPUnit_Framework_TestCase
 {
    /**
     * @var Chebyshev
     */
    private $distanceMetric;
    public function setUp()
    {
        $this->distanceMetric = new Chebyshev();
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnInvalidArguments()
    {
        $a = [0, 1, 2];
        $b = [0, 2];
        $this->distanceMetric->distance($a, $b);
    }
    public function testCalculateDistanceForOneDimension()
    {
        $a = [4];
        $b = [2];
        $expectedDistance = 2;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateDistanceForTwoDimensions()
    {
        $a = [4, 6];
        $b = [2, 5];
        $expectedDistance = 2;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateDistanceForThreeDimensions()
    {
        $a = [6, 10, 3];
        $b = [2, 5, 5];
        $expectedDistance = 5;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
 }
--- a/tests/Phpml/Math/Distance/EuclideanTest.php
+++ b/tests/Phpml/Math/Distance/EuclideanTest.php
@ -0,0 +1,64 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Metric;
 use Phpml\Math\Distance\Euclidean;
 class EuclideanTest extends \PHPUnit_Framework_TestCase
 {
    /**
     * @var Euclidean
     */
    private $distanceMetric;
    public function setUp()
    {
        $this->distanceMetric = new Euclidean();
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnInvalidArguments()
    {
        $a = [0, 1, 2];
        $b = [0, 2];
        $this->distanceMetric->distance($a, $b);
    }
    public function testCalculateDistanceForOneDimension()
    {
        $a = [4];
        $b = [2];
        $expectedDistance = 2;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateDistanceForTwoDimensions()
    {
        $a = [4, 6];
        $b = [2, 5];
        $expectedDistance = 2.2360679774998;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateDistanceForThreeDimensions()
    {
        $a = [6, 10, 3];
        $b = [2, 5, 5];
        $expectedDistance = 6.7082039324993694;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
 }
--- a/tests/Phpml/Math/Distance/ManhattanTest.php
+++ b/tests/Phpml/Math/Distance/ManhattanTest.php
@ -0,0 +1,64 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Metric;
 use Phpml\Math\Distance\Manhattan;
 class ManhattanTest extends \PHPUnit_Framework_TestCase
 {
    /**
     * @var Manhattan
     */
    private $distanceMetric;
    public function setUp()
    {
        $this->distanceMetric = new Manhattan();
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnInvalidArguments()
    {
        $a = [0, 1, 2];
        $b = [0, 2];
        $this->distanceMetric->distance($a, $b);
    }
    public function testCalculateDistanceForOneDimension()
    {
        $a = [4];
        $b = [2];
        $expectedDistance = 2;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateDistanceForTwoDimensions()
    {
        $a = [4, 6];
        $b = [2, 5];
        $expectedDistance = 3;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateDistanceForThreeDimensions()
    {
        $a = [6, 10, 3];
        $b = [2, 5, 5];
        $expectedDistance = 11;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
 }
--- a/tests/Phpml/Math/Distance/MinkowskiTest.php
+++ b/tests/Phpml/Math/Distance/MinkowskiTest.php
@ -0,0 +1,77 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Metric;
 use Phpml\Math\Distance\Minkowski;
 class MinkowskiTest extends \PHPUnit_Framework_TestCase
 {
    /**
     * @var Minkowski
     */
    private $distanceMetric;
    public function setUp()
    {
        $this->distanceMetric = new Minkowski();
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnInvalidArguments()
    {
        $a = [0, 1, 2];
        $b = [0, 2];
        $this->distanceMetric->distance($a, $b);
    }
    public function testCalculateDistanceForOneDimension()
    {
        $a = [4];
        $b = [2];
        $expectedDistance = 2;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateDistanceForTwoDimensions()
    {
        $a = [4, 6];
        $b = [2, 5];
        $expectedDistance = 2.080;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance, '', $delta = 0.001);
    }
    public function testCalculateDistanceForThreeDimensions()
    {
        $a = [6, 10, 3];
        $b = [2, 5, 5];
        $expectedDistance = 5.819;
        $actualDistance = $this->distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance, '', $delta = 0.001);
    }
    public function testCalculateDistanceForThreeDimensionsWithDifferentLambda()
    {
        $distanceMetric = new Minkowski($lambda = 5);
        $a = [6, 10, 3];
        $b = [2, 5, 5];
        $expectedDistance = 5.300;
        $actualDistance = $distanceMetric->distance($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance, '', $delta = 0.001);
    }
 }
--- a/tests/Phpml/Math/Kernel/RBFTest.php
+++ b/tests/Phpml/Math/Kernel/RBFTest.php
@ -0,0 +1,25 @@
 <?php
 declare (strict_types = 1);
 namespace test\Phpml\Math\Kernel;
 use Phpml\Math\Kernel\RBF;
 class RBFTest extends \PHPUnit_Framework_TestCase
 {
    public function testComputeRBFKernelFunction()
    {
        $rbf = new RBF($gamma = 0.001);
        $this->assertEquals(1, $rbf->compute([1, 2], [1, 2]));
        $this->assertEquals(0.97336, $rbf->compute([1, 2, 3], [4, 5, 6]), '', $delta = 0.0001);
        $this->assertEquals(0.00011, $rbf->compute([4, 5], [1, 100]), '', $delta = 0.0001);
        $rbf = new RBF($gamma = 0.2);
        $this->assertEquals(1, $rbf->compute([1, 2], [1, 2]));
        $this->assertEquals(0.00451, $rbf->compute([1, 2, 3], [4, 5, 6]), '', $delta = 0.0001);
        $this->assertEquals(0, $rbf->compute([4, 5], [1, 100]));
    }
 }
--- a/tests/Phpml/Math/MatrixTest.php
+++ b/tests/Phpml/Math/MatrixTest.php
@ -0,0 +1,176 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Math;
 use Phpml\Math\Matrix;
 class MatrixTest extends \PHPUnit_Framework_TestCase
 {
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnInvalidMatrixSupplied()
    {
        new Matrix([[1, 2], [3]]);
    }
    public function testCreateMatrixFromFlatArray()
    {
        $flatArray = [1, 2, 3, 4];
        $matrix = Matrix::fromFlatArray($flatArray);
        $this->assertInstanceOf(Matrix::class, $matrix);
        $this->assertEquals([[1], [2], [3], [4]], $matrix->toArray());
        $this->assertEquals(4, $matrix->getRows());
        $this->assertEquals(1, $matrix->getColumns());
        $this->assertEquals($flatArray, $matrix->getColumnValues(0));
    }
    /**
     * @expectedException \Phpml\Exception\MatrixException
     */
    public function testThrowExceptionOnInvalidColumnNumber()
    {
        $matrix = new Matrix([[1, 2, 3], [4, 5, 6]]);
        $matrix->getColumnValues(4);
    }
    /**
     * @expectedException \Phpml\Exception\MatrixException
     */
    public function testThrowExceptionOnGetDeterminantIfArrayIsNotSquare()
    {
        $matrix = new Matrix([[1, 2, 3], [4, 5, 6]]);
        $matrix->getDeterminant();
    }
    public function testGetMatrixDeterminant()
    {
        //http://matrix.reshish.com/determinant.php
        $matrix = new Matrix([
            [3, 3, 3],
            [4, 2, 1],
            [5, 6, 7],
        ]);
        $this->assertEquals(-3, $matrix->getDeterminant());
        $matrix = new Matrix([
            [1, 2, 3, 3, 2, 1],
            [1 / 2, 5, 6, 7, 1, 1],
            [3 / 2, 7 / 2, 2, 0, 6, 8],
            [1, 8, 10, 1, 2, 2],
            [1 / 4, 4, 1, 0, 2, 3 / 7],
            [1, 8, 7, 5, 4, 4 / 5],
        ]);
        $this->assertEquals(1116.5035, $matrix->getDeterminant(), '', $delta = 0.0001);
    }
    public function testMatrixTranspose()
    {
        $matrix = new Matrix([
            [3, 3, 3],
            [4, 2, 1],
            [5, 6, 7],
        ]);
        $transposedMatrix = [
            [3, 4, 5],
            [3, 2, 6],
            [3, 1, 7],
        ];
        $this->assertEquals($transposedMatrix, $matrix->transpose()->toArray());
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnMultiplyWhenInconsistentMatrixSupplied()
    {
        $matrix1 = new Matrix([[1, 2, 3], [4, 5, 6]]);
        $matrix2 = new Matrix([[3, 2, 1], [6, 5, 4]]);
        $matrix1->multiply($matrix2);
    }
    public function testMatrixMultiplyByMatrix()
    {
        $matrix1 = new Matrix([
            [1, 2, 3],
            [4, 5, 6],
        ]);
        $matrix2 = new Matrix([
            [7, 8],
            [9, 10],
            [11, 12],
        ]);
        $product = [
            [58, 64],
            [139, 154],
        ];
        $this->assertEquals($product, $matrix1->multiply($matrix2)->toArray());
    }
    public function testDivideByScalar()
    {
        $matrix = new Matrix([
            [4, 6, 8],
            [2, 10, 20],
        ]);
        $quotient = [
            [2, 3, 4],
            [1, 5, 10],
        ];
        $this->assertEquals($quotient, $matrix->divideByScalar(2)->toArray());
    }
    /**
     * @expectedException \Phpml\Exception\MatrixException
     */
    public function testThrowExceptionWhenInverseIfArrayIsNotSquare()
    {
        $matrix = new Matrix([[1, 2, 3], [4, 5, 6]]);
        $matrix->inverse();
    }
    public function testInverseMatrix()
    {
        //http://ncalculators.com/matrix/inverse-matrix.htm
        $matrix = new Matrix([
            [3, 4, 2],
            [4, 5, 5],
            [1, 1, 1],
        ]);
        $inverseMatrix = [
            [0, -1, 5],
            [1 / 2, 1 / 2, -7 / 2],
            [-1 / 2, 1 / 2, -1 / 2],
        ];
        $this->assertEquals($inverseMatrix, $matrix->inverse()->toArray(), '', $delta = 0.0001);
    }
    public function testCrossOutMatrix()
    {
        $matrix = new Matrix([
            [3, 4, 2],
            [4, 5, 5],
            [1, 1, 1],
        ]);
        $crossOuted = [
            [3, 2],
            [1, 1],
        ];
        $this->assertEquals($crossOuted, $matrix->crossOut(1, 1)->toArray());
    }
 }
--- a/tests/Phpml/Math/ProductTest.php
+++ b/tests/Phpml/Math/ProductTest.php
@ -0,0 +1,17 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Math;
 use Phpml\Math\Product;
 class ProductTest extends \PHPUnit_Framework_TestCase
 {
    public function testScalarProduct()
    {
        $this->assertEquals(10, Product::scalar([2, 3], [-1, 4]));
        $this->assertEquals(-0.1, Product::scalar([1, 4, 1], [-2, 0.5, -0.1]));
        $this->assertEquals(8, Product::scalar([2], [4]));
    }
 }
--- a/tests/Phpml/Math/Statistic/CorrelationTest.php
+++ b/tests/Phpml/Math/Statistic/CorrelationTest.php
@ -0,0 +1,38 @@
 <?php
 declare (strict_types = 1);
 namespace test\Phpml\Math\StandardDeviation;
 use Phpml\Math\Statistic\Correlation;
 class CorrelationTest extends \PHPUnit_Framework_TestCase
 {
    public function testPearsonCorrelation()
    {
        //http://www.stat.wmich.edu/s216/book/node126.html
        $delta = 0.001;
        $x = [9300,  10565,  15000,  15000,  17764,  57000,  65940,  73676,  77006,  93739, 146088, 153260];
        $y = [7100, 15500, 4400, 4400, 5900, 4600, 8800, 2000, 2750, 2550,  960, 1025];
        $this->assertEquals(-0.641, Correlation::pearson($x, $y), '', $delta);
        //http://www.statisticshowto.com/how-to-compute-pearsons-correlation-coefficients/
        $delta = 0.001;
        $x = [43, 21, 25, 42, 57, 59];
        $y = [99, 65, 79, 75, 87, 82];
        $this->assertEquals(0.549, Correlation::pearson($x, $y), '', $delta);
        $delta = 0.001;
        $x = [60, 61, 62, 63, 65];
        $y = [3.1, 3.6, 3.8, 4, 4.1];
        $this->assertEquals(0.911, Correlation::pearson($x, $y), '', $delta);
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnInvalidArgumentsForPearsonCorrelation()
    {
        Correlation::pearson([1, 2, 4], [3, 5]);
    }
 }
--- a/tests/Phpml/Math/Statistic/MeanTest.php
+++ b/tests/Phpml/Math/Statistic/MeanTest.php
@ -0,0 +1,18 @@
 <?php
 declare (strict_types = 1);
 namespace test\Phpml\Math\StandardDeviation;
 use Phpml\Math\Statistic\Mean;
 class MeanTest extends \PHPUnit_Framework_TestCase
 {
    public function testArithmeticMean()
    {
        $delta = 0.01;
        $this->assertEquals(3.5, Mean::arithmetic([2, 5]), '', $delta);
        $this->assertEquals(41.16, Mean::arithmetic([43, 21, 25, 42, 57, 59]), '', $delta);
        $this->assertEquals(1.7, Mean::arithmetic([0.5, 0.5, 1.5, 2.5, 3.5]), '', $delta);
    }
 }
--- a/tests/Phpml/Math/Statistic/StandardDeviationTest.php
+++ b/tests/Phpml/Math/Statistic/StandardDeviationTest.php
@ -0,0 +1,42 @@
 <?php
 declare (strict_types = 1);
 namespace test\Phpml\Math\StandardDeviation;
 use Phpml\Math\Statistic\StandardDeviation;
 class StandardDeviationTest extends \PHPUnit_Framework_TestCase
 {
    public function testStandardDeviationOfPopulationSample()
    {
        //https://pl.wikipedia.org/wiki/Odchylenie_standardowe
        $delta = 0.001;
        $population = [5, 6, 8, 9];
        $this->assertEquals(1.825, StandardDeviation::population($population), '', $delta);
        //http://www.stat.wmich.edu/s216/book/node126.html
        $delta = 0.5;
        $population = [7100, 15500, 4400, 4400, 5900, 4600, 8800, 2000, 2750, 2550,  960, 1025];
        $this->assertEquals(4079, StandardDeviation::population($population), '', $delta);
        $population = [9300,  10565,  15000,  15000,  17764,  57000,  65940,  73676,  77006,  93739, 146088, 153260];
        $this->assertEquals(50989, StandardDeviation::population($population), '', $delta);
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnEmptyArrayIfNotSample()
    {
        StandardDeviation::population([], false);
    }
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnToSmallArray()
    {
        StandardDeviation::population([1]);
    }
 }
--- a/tests/Phpml/Metric/DistanceTest.php
+++ b/tests/Phpml/Metric/DistanceTest.php
@ -1,51 +0,0 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Phpml\Metric;
 use Phpml\Metric\Distance;
 class DistanceTest extends \PHPUnit_Framework_TestCase
 {
    /**
     * @expectedException \Phpml\Exception\InvalidArgumentException
     */
    public function testThrowExceptionOnInvalidArgumentsInEuclidean()
    {
        $a = [0, 1, 2];
        $b = [0, 2];
        Distance::euclidean($a, $b);
    }
    public function testCalculateEuclideanDistanceForOneDimension()
    {
        $a = [4];
        $b = [2];
        $expectedDistance = 2;
        $actualDistance = Distance::euclidean($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
    public function testCalculateEuclideanDistanceForTwoAndMoreDimension()
    {
        $a = [4, 6];
        $b = [2, 5];
        $expectedDistance = 2.2360679774998;
        $actualDistance = Distance::euclidean($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
        $a = [6, 10, 3];
        $b = [2, 5, 5];
        $expectedDistance = 6.7082039324993694;
        $actualDistance = Distance::euclidean($a, $b);
        $this->assertEquals($expectedDistance, $actualDistance);
    }
 }
--- a/tests/Phpml/Regression/LeastSquaresTest.php
+++ b/tests/Phpml/Regression/LeastSquaresTest.php
@ -0,0 +1,68 @@
 <?php
 declare (strict_types = 1);
 namespace tests\Regression;
 use Phpml\Regression\LeastSquares;
 class LeastSquaresTest extends \PHPUnit_Framework_TestCase
 {
    public function testPredictSingleFeatureSamples()
    {
        $delta = 0.01;
        //https://www.easycalculation.com/analytical/learn-least-square-regression.php
        $samples = [[60], [61], [62], [63], [65]];
        $targets = [3.1, 3.6, 3.8, 4, 4.1];
        $regression = new LeastSquares();
        $regression->train($samples, $targets);
        $this->assertEquals(4.06, $regression->predict([64]), '', $delta);
        //http://www.stat.wmich.edu/s216/book/node127.html
        $samples = [[9300], [10565], [15000], [15000], [17764], [57000], [65940], [73676], [77006], [93739], [146088], [153260]];
        $targets = [7100, 15500, 4400, 4400, 5900, 4600, 8800, 2000, 2750, 2550,  960, 1025];
        $regression = new LeastSquares();
        $regression->train($samples, $targets);
        $this->assertEquals(7659.35, $regression->predict([9300]), '', $delta);
        $this->assertEquals(5213.81, $regression->predict([57000]), '', $delta);
        $this->assertEquals(4188.13, $regression->predict([77006]), '', $delta);
        $this->assertEquals(7659.35, $regression->predict([9300]), '', $delta);
        $this->assertEquals(278.66, $regression->predict([153260]), '', $delta);
    }
    public function testPredictSingleFeatureSamplesWithMatrixTargets()
    {
        $delta = 0.01;
        //https://www.easycalculation.com/analytical/learn-least-square-regression.php
        $samples = [[60], [61], [62], [63], [65]];
        $targets = [[3.1], [3.6], [3.8], [4], [4.1]];
        $regression = new LeastSquares();
        $regression->train($samples, $targets);
        $this->assertEquals(4.06, $regression->predict([64]), '', $delta);
    }
    public function testPredictMultiFeaturesSamples()
    {
        $delta = 0.01;
        //http://www.stat.wmich.edu/s216/book/node129.html
        $samples = [[73676, 1996], [77006, 1998], [10565, 2000], [146088, 1995], [15000, 2001], [65940, 2000], [9300, 2000], [93739, 1996], [153260, 1994], [17764, 2002], [57000, 1998], [15000, 2000]];
        $targets = [2000, 2750, 15500, 960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];
        $regression = new LeastSquares();
        $regression->train($samples, $targets);
        $this->assertEquals(-800614.957, $regression->getIntercept(), '', $delta);
        $this->assertEquals([-0.0327, 404.14], $regression->getCoefficients(), '', $delta);
        $this->assertEquals(4094.82, $regression->predict([60000, 1996]), '', $delta);
        $this->assertEquals(5711.40, $regression->predict([60000, 2000]), '', $delta);
    }
 }