Merge pull request #4 from php-ai/develop

New algorithms, math functions and documentation
2024-11-05 13:07:52 +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
-* integration with Lavacharts for data visualization
+* Classification
+    * [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
+## Contribute

-After installation, you can launch the test suite in project root directory (you will need to install dev requirements with composer)
+- 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
@ -39,4 +72,4 @@ PHP-ML is released under the MIT Licence. See the bundled LICENSE file for detai

 ## Author

-Arkadiusz Kondas (@ArkadiuszKondas)
+Arkadiusz Kondas (@ArkadiuszKondas)
--- a/data/glass.csv
+++ b/data/glass.csv
@ -0,0 +1,215 @@
+RI: refractive index,Na: Sodium,Mg: Magnesium,Al: Aluminum,Si: Silicon,K: Potassium,Ca: Calcium,Ba: Barium,Fe: Iron,type of glass
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--- 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
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+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
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+13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835,3
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+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
-* integration with Lavacharts for data visualization
+* Classification
+    * [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
+## Contribute

-After installation, you can launch the test suite in project root directory (you will need to install dev requirements with composer)
+- 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
@ -34,4 +72,4 @@ PHP-ML is released under the MIT Licence. See the bundled LICENSE file for detai

 ## Author

-Arkadiusz Kondas (@ArkadiuszKondas)
+Arkadiusz Kondas (@ArkadiuszKondas)
--- 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
-theme: readthedocs
+  - 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
+     * @param int           $k
+     * @param Distance|null $distanceMetric (if null then Euclidean distance as default)
     */
-    private $labels;
-
-    /**
-     * @param int $k
-     */
-    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 = [];
-    }
-
-    /**
-     * @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;
+        $this->distanceMetric = $distanceMetric;
    }

    /**
@ -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));
        }

-        $row = 0;
-        if (($handle = fopen($filepath, 'r')) !== false) {
-            while (($data = fgetcsv($handle, 1000, ',')) !== false) {
-                ++$row;
-                if ($headingRow && $row == 1) {
-                    continue;
-                }
-                $this->samples[] = array_slice($data, 0, $features);
-                $this->labels[] = $data[$features];
-            }
-            fclose($handle);
-        } else {
+        if (false === $handle = fopen($filepath, 'r')) {
            throw DatasetException::cantOpenFile(basename($filepath));
        }
+
+        if ($headingRow) {
+            fgets($handle);
+        }
+
+        while (($data = fgetcsv($handle, 1000, ',')) !== false) {
+            $this->samples[] = array_slice($data, 0, $features);
+            $this->labels[] = $data[$features];
+        }
+        fclose($handle);
    }
 }
--- 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($b)) {
-            throw InvalidArgumentException::sizeNotMatch();
+        if (count($a) !== count($b)) {
+            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\CrossValidation\RandomSplit;
-use Phpml\Dataset\Demo\Iris;
-use Phpml\Metric\Accuracy;
+use Phpml\Classification\KNearestNeighbors;
+use Phpml\Math\Distance\Chebyshev;

 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);
-        $classifier = new KNearestNeighbors($k = 4);
-        $classifier->train($dataset->getTrainSamples(), $dataset->getTrainLabels());
-        $predicted = $classifier->predict($dataset->getTestSamples());
-        $score = Accuracy::score($dataset->getTestLabels(), $predicted);
+        $trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
+        $trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];

-        $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);
+    }
+}