Merge pull request #2 from php-ai/develop

Develop to master
2025-01-25 08:08:31 +00:00 · 2016-04-09 00:51:31 +02:00 · 2016-04-09 00:51:31 +02:00 · 662627c052
commit 662627c052
parent 9e4c006786 04763b16f0
36 changed files with 1217 additions and 91 deletions
--- a/README.md
+++ b/README.md
@ -1,18 +1,24 @@
-# PHP Machine learning library
+# 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 ...

-## Available Algorithms
+## Documentation

-### Classification
-
-Identifying to which category an object belongs to.
-
-*  **Naive Bayes** - algorithm based on applying Bayes’ theorem with the “naive” assumption of independence between every pair of features
+To find out how to use PHP-ML follow [Documentation](php-ml.readthedocs.org).

 ## Installation

-This repo will be published do packagist.org soon...
+Currently this library is in the process of developing, but You can install it with Composer:
+
+```
+composer require php-ai/php-ml
+```

 ## To-Do

@ -21,7 +27,7 @@ This repo will be published do packagist.org soon...

 ## Testing

-After installation, you can launch the test suite in project root directory (you will need to install dev requiremnts with composer)
+After installation, you can launch the test suite in project root directory (you will need to install dev requirements with composer)

 ```
 bin/phpunit
@ -33,3 +39,4 @@ PHP-ML is released under the MIT Licence. See the bundled LICENSE file for detai

 ## Author

+Arkadiusz Kondas (@ArkadiuszKondas)
--- a/composer.json
+++ b/composer.json
@ -1,7 +1,7 @@
 {
    "name": "php-ai/php-ml",
    "type": "library",
-    "description": "PHP Machine learning library",
+    "description": "PHP Machine Learning library",
    "license": "MIT",
    "keywords": ["machine learning","pattern recognition","computational learning theory","artificial intelligence"],
    "homepage": "https://github.com/php-ai/php-ml",
@ -11,11 +11,16 @@
            "email": "arkadiusz.kondas@gmail.com"
        }
    ],
+    "autoload": {
+        "psr-0": {
+            "Phpml": "src/"
+        }
+    },
    "config": {
        "bin-dir": "bin"
    },
    "require": {
-        "php": ">=5.5.0"
+        "php": ">=7.0.0"
    },
    "require-dev": {
        "phpunit/phpunit": "^5.2"
--- a/composer.lock
+++ b/composer.lock
@ -4,8 +4,8 @@
        "Read more about it at https://getcomposer.org/doc/01-basic-usage.md#composer-lock-the-lock-file",
        "This file is @generated automatically"
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    "packages-dev": [
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@ -155,22 +155,24 @@
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            "require": {
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+                "php": "^5.3|^7.0",
                "phpdocumentor/reflection-docblock": "~2.0",
-                "sebastian/comparator": "~1.1"
+                "sebastian/comparator": "~1.1",
+                "sebastian/recursion-context": "~1.0"
            },
            "require-dev": {
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@ -178,7 +180,7 @@
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+                    "dev-master": "1.5.x-dev"
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            "autoload": {
@ -211,27 +213,28 @@
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                "stub"
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-            "time": "2015-08-13 10:07:40"
+            "time": "2016-02-15 07:46:21"
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+            "version": "3.3.0",
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+                "php": "^5.6 || ^7.0",
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                "phpunit/php-text-template": "~1.2",
-                "phpunit/php-token-stream": "~1.3",
+                "phpunit/php-token-stream": "^1.4.2",
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@ -247,7 +250,7 @@
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@ -273,7 +276,7 @@
                "testing",
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            ],
-            "time": "2016-02-04 13:05:19"
+            "time": "2016-03-03 08:49:08"
        },
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            "name": "phpunit/php-file-iterator",
@ -455,16 +458,16 @@
        },
        {
            "name": "phpunit/phpunit",
-            "version": "5.2.3",
+            "version": "5.3.1",
            "source": {
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@ -474,18 +477,19 @@
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                "ext-spl": "*",
                "myclabs/deep-copy": "~1.3",
-                "php": ">=5.6",
+                "php": "^5.6 || ^7.0",
                "phpspec/prophecy": "^1.3.1",
-                "phpunit/php-code-coverage": "~3.0",
+                "phpunit/php-code-coverage": "^3.3.0",
                "phpunit/php-file-iterator": "~1.4",
                "phpunit/php-text-template": "~1.2",
-                "phpunit/php-timer": ">=1.0.6",
-                "phpunit/phpunit-mock-objects": ">=3.0.5",
+                "phpunit/php-timer": "^1.0.6",
+                "phpunit/phpunit-mock-objects": "^3.1",
                "sebastian/comparator": "~1.1",
                "sebastian/diff": "~1.2",
                "sebastian/environment": "~1.3",
                "sebastian/exporter": "~1.2",
                "sebastian/global-state": "~1.0",
+                "sebastian/object-enumerator": "~1.0",
                "sebastian/resource-operations": "~1.0",
                "sebastian/version": "~1.0|~2.0",
                "symfony/yaml": "~2.1|~3.0"
@ -499,7 +503,7 @@
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+                    "dev-master": "5.3.x-dev"
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@ -525,20 +529,20 @@
                "testing",
                "xunit"
            ],
-            "time": "2016-02-08 12:15:53"
+            "time": "2016-04-07 07:04:34"
        },
        {
            "name": "phpunit/phpunit-mock-objects",
-            "version": "3.0.6",
+            "version": "3.1.2",
            "source": {
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                "url": "https://github.com/sebastianbergmann/phpunit-mock-objects.git",
-                "reference": "49bc700750196c04dd6bc2c4c99cb632b893836b"
+                "reference": "7c34c9bdde4131b824086457a3145e27dba10ca1"
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@ -556,7 +560,7 @@
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@ -581,7 +585,52 @@
                "mock",
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-            "time": "2015-12-08 08:47:06"
+            "time": "2016-03-24 05:58:25"
+        },
+        {
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+            "version": "1.0.0",
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+            "require": {
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+            "require-dev": {
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+            },
+            "type": "library",
+            "extra": {
+                "branch-alias": {
+                    "dev-master": "1.0.x-dev"
+                }
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+            "autoload": {
+                "classmap": [
+                    "src/"
+                ]
+            },
+            "notification-url": "https://packagist.org/downloads/",
+            "license": [
+                "BSD-3-Clause"
+            ],
+            "authors": [
+                {
+                    "name": "Sebastian Bergmann",
+                    "email": "sebastian@phpunit.de"
+                }
+            ],
+            "description": "Looks up which function or method a line of code belongs to",
+            "homepage": "https://github.com/sebastianbergmann/code-unit-reverse-lookup/",
+            "time": "2016-02-13 06:45:14"
        },
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            "name": "sebastian/comparator",
@ -701,16 +750,16 @@
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        {
            "name": "sebastian/environment",
-            "version": "1.3.3",
+            "version": "1.3.5",
            "source": {
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                "url": "https://github.com/sebastianbergmann/environment.git",
-                "reference": "6e7133793a8e5a5714a551a8324337374be209df"
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@ -747,7 +796,7 @@
                "environment",
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-            "time": "2015-12-02 08:37:27"
+            "time": "2016-02-26 18:40:46"
        },
        {
            "name": "sebastian/exporter",
@ -866,6 +915,52 @@
            ],
            "time": "2015-10-12 03:26:01"
        },
+        {
+            "name": "sebastian/object-enumerator",
+            "version": "1.0.0",
+            "source": {
+                "type": "git",
+                "url": "https://github.com/sebastianbergmann/object-enumerator.git",
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+            },
+            "require": {
+                "php": ">=5.6",
+                "sebastian/recursion-context": "~1.0"
+            },
+            "require-dev": {
+                "phpunit/phpunit": "~5"
+            },
+            "type": "library",
+            "extra": {
+                "branch-alias": {
+                    "dev-master": "1.0.x-dev"
+                }
+            },
+            "autoload": {
+                "classmap": [
+                    "src/"
+                ]
+            },
+            "notification-url": "https://packagist.org/downloads/",
+            "license": [
+                "BSD-3-Clause"
+            ],
+            "authors": [
+                {
+                    "name": "Sebastian Bergmann",
+                    "email": "sebastian@phpunit.de"
+                }
+            ],
+            "description": "Traverses array structures and object graphs to enumerate all referenced objects",
+            "homepage": "https://github.com/sebastianbergmann/object-enumerator/",
+            "time": "2016-01-28 13:25:10"
+        },
        {
            "name": "sebastian/recursion-context",
            "version": "1.0.2",
@ -1006,16 +1101,16 @@
        },
        {
            "name": "symfony/yaml",
-            "version": "v3.0.2",
+            "version": "v3.0.4",
            "source": {
                "type": "git",
                "url": "https://github.com/symfony/yaml.git",
-                "reference": "3cf0709d7fe936e97bee9e954382e449003f1d9a"
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-                "reference": "3cf0709d7fe936e97bee9e954382e449003f1d9a",
+                "url": "https://api.github.com/repos/symfony/yaml/zipball/0047c8366744a16de7516622c5b7355336afae96",
+                "reference": "0047c8366744a16de7516622c5b7355336afae96",
                "shasum": ""
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            "require": {
@ -1051,7 +1146,7 @@
            ],
            "description": "Symfony Yaml Component",
            "homepage": "https://symfony.com",
-            "time": "2016-02-02 13:44:19"
+            "time": "2016-03-04 07:55:57"
        }
    ],
    "aliases": [],
@ -1059,6 +1154,8 @@
    "stability-flags": [],
    "prefer-stable": false,
    "prefer-lowest": false,
-    "platform": [],
+    "platform": {
+        "php": ">=7.0.0"
+    },
    "platform-dev": []
 }
--- a/docs/index.md
+++ b/docs/index.md
@ -0,0 +1,37 @@
+# PHP Machine Learning (PHP-ML)
+
+[![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)
+[![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 ...
+
+## Installation
+
+Currently this library is in the process of developing, but You can install it with Composer:
+
+```
+composer require php-ai/php-ml
+```
+
+## To-Do
+
+* implements more algorithms
+* integration with Lavacharts for data visualization
+
+## Testing
+
+After installation, you can launch the test suite in project root directory (you will need to install dev requirements with composer)
+
+```
+bin/phpunit
+```
+
+## License
+
+PHP-ML is released under the MIT Licence. See the bundled LICENSE file for details.
+
+## Author
+
+Arkadiusz Kondas (@ArkadiuszKondas)
--- a/docs/machine-learning/classification/knearestneighbors.md
+++ b/docs/machine-learning/classification/knearestneighbors.md
@ -0,0 +1,35 @@
+# KNearestNeighbors Classifier
+
+Classifier implementing the k-nearest neighbors algorithm.
+
+### Constructor Parameters
+
+* $k - number of nearest neighbors to scan (default: 3)
+
+```
+$classifier = new KNearestNeighbors($k=4);
+```
+
+### Train
+
+To train a classifier simply provide train samples and labels (as `array`):
+
+```
+$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, $labels);
+```
+
+### Predict
+
+To predict sample class use `predict` method. You can provide one sample or array of samples:
+
+```
+$classifier->predict([3, 2]);
+// return 'b'
+
+$classifier->predict([[3, 2], [1, 5]]);
+// return ['b', 'a']
+```
--- a/docs/machine-learning/cross-validation/randomsplit.md
+++ b/docs/machine-learning/cross-validation/randomsplit.md
@ -0,0 +1,29 @@
+# RandomSplit
+
+One of the simplest methods from Cross-validation is implemented as `RandomSpilt` class. Samples are split to two groups: train group and test group. You can adjust number of samples in each group.
+
+### Constructor Parameters
+
+* $dataset - object that implements `Dataset` interface
+* $testSize - a fraction of test split (float, from 0 to 1, default: 0.3)
+* $seed - seed for random generator (for tests)
+ 
+```
+$randomSplit = new RandomSplit($dataset, 0.2);
+```
+
+### Samples and labels groups
+
+To get samples or labels from test and train group you can use getters:
+
+```
+$dataset = new RandomSplit($dataset, 0.3, 1234);
+
+// train group
+$dataset->getTrainSamples();
+$dataset->getTrainLabels();
+
+// test group
+$dataset->getTestSamples();
+$dataset->getTestLabels();
+```
--- a/docs/machine-learning/datasets/array-dataset.md
+++ b/docs/machine-learning/datasets/array-dataset.md
@ -0,0 +1,21 @@
+# ArrayDataset
+
+Helper class that holds data as PHP `array` type. Implements the `Dataset` interface which is used heavily in other classes.
+
+### Constructors Parameters
+
+* $samples - (array) of samples
+* $labels - (array) of labels
+
+```
+$dataset = new ArrayDataset([[1, 1], [2, 1], [3, 2], [4, 1]], ['a', 'a', 'b', 'b']);
+```
+
+### Samples and labels
+
+To get samples or labels you can use getters:
+
+```
+$dataset->getSamples();
+$dataset->getLabels();
+```
--- a/docs/machine-learning/datasets/csv-dataset.md
+++ b/docs/machine-learning/datasets/csv-dataset.md
@ -0,0 +1,15 @@
+# CsvDataset
+
+Helper class that loads data from CSV file. It extends the `ArrayDataset`.
+
+### Constructors Parameters
+
+* $filepath - (string) path to `.csv` file
+* $features - (int) number of columns that are features (starts from first column), last column must be a label
+* $headingRow - (bool) define is file have a heading row (if `true` then first row will be ignored)
+
+```
+$dataset = new CsvDataset('dataset.csv', 2, true);
+```
+
+See Array Dataset for more information.
--- a/docs/machine-learning/datasets/demo/iris.md
+++ b/docs/machine-learning/datasets/demo/iris.md
@ -0,0 +1,34 @@
+# Iris Dataset
+
+Most popular and widely available dataset of iris flower measurement and class names. 
+
+### Specification
+
+| Classes               | 3     |
+| Samples per class     | 50    |
+| Samples total         | 150   |
+| Features per sample   | 4     |
+
+### Load
+
+To load Iris dataset simple use:
+
+```
+$dataset = new Iris();
+```
+
+### Several samples
+
+```
+sepal length,sepal width,petal length,petal width,class
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginicacs
+```
--- a/docs/machine-learning/metric/accuracy.md
+++ b/docs/machine-learning/metric/accuracy.md
@ -0,0 +1,24 @@
+# Accuracy
+
+Class for calculate classifier accuracy.
+
+### Score
+
+To calculate classifier accuracy score use `score` static method. Parametrs:
+
+* $actualLabels - (array) true sample labels
+* $predictedLabels - (array) predicted labels (e.x. from test group)
+* $normalize - (bool) normalize or not the result (default: true)
+
+### Example
+
+```
+$actualLabels = ['a', 'b', 'a', 'b'];
+$predictedLabels = ['a', 'a', 'a', 'b'];
+
+Accuracy::score($actualLabels, $predictedLabels);
+// return 0.75
+
+Accuracy::score($actualLabels, $predictedLabels, false);
+// return 3
+```
--- a/docs/machine-learning/metric/distance.md
+++ b/docs/machine-learning/metric/distance.md
@ -0,0 +1,17 @@
+# Distance
+
+Special class for calculation of different types of distance.
+
+### Euclidean
+
+![euclidean](https://upload.wikimedia.org/math/8/4/9/849f040fd10bb86f7c85eb0bbe3566a4.png "Euclidean Distance")
+
+To calculate euclidean distance:
+
+```
+$a = [4, 6];
+$b = [2, 5];
+        
+Distance::euclidean($a, $b);
+// return 2.2360679774998
+```
--- a/mkdocs.yml
+++ b/mkdocs.yml
@ -0,0 +1,17 @@
+site_name: PHP Machine Learning (PHP-ML)
+pages:
+  - Home: index.md
+  - Machine Learning:
+    - Classification:
+      - KNearestNeighbors: machine-learning/classification/knearestneighbors.md
+    - Cross Validation:
+      - RandomSplit: machine-learning/cross-validation/randomsplit.md
+    - Datasets:
+      - Array Dataset: machine-learning/datasets/array-dataset.md
+      - CSV Dataset: machine-learning/datasets/csv-dataset.md
+      - Demo:
+        - Iris: machine-learning/datasets/demo/iris.md
+    - Metric:
+      - Accuracy: machine-learning/metric/accuracy.md
+      - Distance: machine-learning/metric/distance.md
+theme: readthedocs
--- a/phpunit.xml
+++ b/phpunit.xml
@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<phpunit
+        colors="true"
+        beStrictAboutTestsThatDoNotTestAnything="true"
+        beStrictAboutOutputDuringTests="true"
+        beStrictAboutTestSize="true"
+        beStrictAboutChangesToGlobalState="true"
+>
+    <testsuites>
+        <testsuite name="PHP-ML Test Suite">
+            <directory>tests/*</directory>
+        </testsuite>
+    </testsuites>
+</phpunit>
--- a/src/Phpml/Classifier/Classifier.php
+++ b/src/Phpml/Classifier/Classifier.php
@ -1,20 +1,21 @@
 <?php

+declare (strict_types = 1);
+
 namespace Phpml\Classifier;

 interface Classifier
 {
-
    /**
-     * @param array $features
+     * @param array $samples
     * @param array $labels
     */
-    public function train($features, $labels);
+    public function train(array $samples, array $labels);

    /**
-     * @param mixed $feature
+     * @param array $samples
+     *
     * @return mixed
     */
-    public function predict($feature);
-
+    public function predict(array $samples);
 }
--- a/src/Phpml/Classifier/KNearestNeighbors.php
+++ b/src/Phpml/Classifier/KNearestNeighbors.php
@ -0,0 +1,105 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Classifier;
+
+use Phpml\Metric\Distance;
+
+class KNearestNeighbors implements Classifier
+{
+    /**
+     * @var int
+     */
+    private $k;
+
+    /**
+     * @var array
+     */
+    private $samples;
+
+    /**
+     * @var array
+     */
+    private $labels;
+
+    /**
+     * @param int $k
+     */
+    public function __construct(int $k = 3)
+    {
+        $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;
+    }
+
+    /**
+     * @param array $sample
+     *
+     * @return mixed
+     */
+    private function predictSample(array $sample)
+    {
+        $distances = $this->kNeighborsDistances($sample);
+
+        $predictions = array_combine(array_values($this->labels), array_fill(0, count($this->labels), 0));
+
+        foreach ($distances as $index => $distance) {
+            ++$predictions[$this->labels[$index]];
+        }
+
+        arsort($predictions);
+        reset($predictions);
+
+        return key($predictions);
+    }
+
+    /**
+     * @param array $sample
+     *
+     * @return array
+     *
+     * @throws \Phpml\Exception\InvalidArgumentException
+     */
+    private function kNeighborsDistances(array $sample): array
+    {
+        $distances = [];
+
+        foreach ($this->samples as $index => $neighbor) {
+            $distances[$index] = Distance::euclidean($sample, $neighbor);
+        }
+
+        asort($distances);
+
+        return array_slice($distances, 0, $this->k, true);
+    }
+}
--- a/src/Phpml/Classifier/NaiveBayes.php
+++ b/src/Phpml/Classifier/NaiveBayes.php
@ -1,26 +1,25 @@
 <?php

+declare (strict_types = 1);
+
 namespace Phpml\Classifier;

-abstract class NaiveBayes implements Classifier
+class NaiveBayes implements Classifier
 {
-
    /**
-     * @param array $features
+     * @param array $samples
     * @param array $labels
     */
-    public function train($features, $labels)
+    public function train(array $samples, array $labels)
    {
-
    }

    /**
-     * @param mixed $feature
+     * @param array $samples
+     *
     * @return mixed
     */
-    public function predict($feature)
+    public function predict(array $samples)
    {
-
    }
-
 }
--- a/src/Phpml/Classifier/NaiveBayes/GaussianNaiveBayes.php
+++ b/src/Phpml/Classifier/NaiveBayes/GaussianNaiveBayes.php
@ -1,8 +0,0 @@
-<?php
-
-use Phpml\Classifier\NaiveBayes;
-
-class GaussianNaiveBayes extends NaiveBayes
-{
-
-}
--- a/src/Phpml/CrossValidation/RandomSplit.php
+++ b/src/Phpml/CrossValidation/RandomSplit.php
@ -0,0 +1,105 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\CrossValidation;
+
+use Phpml\Dataset\Dataset;
+use Phpml\Exception\InvalidArgumentException;
+
+class RandomSplit
+{
+    /**
+     * @var array
+     */
+    private $trainSamples = [];
+
+    /**
+     * @var array
+     */
+    private $testSamples = [];
+
+    /**
+     * @var array
+     */
+    private $trainLabels = [];
+
+    /**
+     * @var array
+     */
+    private $testLabels = [];
+
+    /**
+     * @param Dataset $dataset
+     * @param float   $testSize
+     * @param int     $seed
+     *
+     * @throws InvalidArgumentException
+     */
+    public function __construct(Dataset $dataset, float $testSize = 0.3, int $seed = null)
+    {
+        if (0 >= $testSize || 1 <= $testSize) {
+            throw InvalidArgumentException::percentNotInRange('testSize');
+        }
+        $this->seedGenerator($seed);
+
+        $samples = $dataset->getSamples();
+        $labels = $dataset->getLabels();
+        $datasetSize = count($samples);
+
+        for ($i = $datasetSize; $i > 0; --$i) {
+            $key = mt_rand(0, $datasetSize - 1);
+            $setName = count($this->testSamples) / $datasetSize >= $testSize ? 'train' : 'test';
+
+            $this->{$setName.'Samples'}[] = $samples[$key];
+            $this->{$setName.'Labels'}[] = $labels[$key];
+
+            $samples = array_values($samples);
+            $labels = array_values($labels);
+        }
+    }
+
+    /**
+     * @return array
+     */
+    public function getTrainSamples()
+    {
+        return $this->trainSamples;
+    }
+
+    /**
+     * @return array
+     */
+    public function getTestSamples()
+    {
+        return $this->testSamples;
+    }
+
+    /**
+     * @return array
+     */
+    public function getTrainLabels()
+    {
+        return $this->trainLabels;
+    }
+
+    /**
+     * @return array
+     */
+    public function getTestLabels()
+    {
+        return $this->testLabels;
+    }
+
+    /**
+     * @param int|null $seed
+     */
+    private function seedGenerator(int $seed = null)
+    {
+        if (null === $seed) {
+            mt_srand();
+        } else {
+            mt_srand($seed);
+        }
+    }
+}
--- a/src/Phpml/Dataset/ArrayDataset.php
+++ b/src/Phpml/Dataset/ArrayDataset.php
@ -0,0 +1,52 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Dataset;
+
+use Phpml\Exception\InvalidArgumentException;
+
+class ArrayDataset implements Dataset
+{
+    /**
+     * @var array
+     */
+    protected $samples = [];
+
+    /**
+     * @var array
+     */
+    protected $labels = [];
+
+    /**
+     * @param array $samples
+     * @param array $labels
+     *
+     * @throws InvalidArgumentException
+     */
+    public function __construct(array $samples, array $labels)
+    {
+        if (count($samples) != count($labels)) {
+            throw InvalidArgumentException::sizeNotMatch();
+        }
+
+        $this->samples = $samples;
+        $this->labels = $labels;
+    }
+
+    /**
+     * @return array
+     */
+    public function getSamples(): array
+    {
+        return $this->samples;
+    }
+
+    /**
+     * @return array
+     */
+    public function getLabels(): array
+    {
+        return $this->labels;
+    }
+}
--- a/src/Phpml/Dataset/CsvDataset.php
+++ b/src/Phpml/Dataset/CsvDataset.php
@ -0,0 +1,44 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Dataset;
+
+use Phpml\Exception\DatasetException;
+
+class CsvDataset extends ArrayDataset
+{
+    /**
+     * @var string
+     */
+    protected $filepath;
+
+    /**
+     * @param string $filepath
+     * @param int    $features
+     * @param bool   $headingRow
+     *
+     * @throws DatasetException
+     */
+    public function __construct(string $filepath, int $features, bool $headingRow = true)
+    {
+        if (!file_exists($filepath)) {
+            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($handle);
+        } else {
+            throw DatasetException::cantOpenFile(basename($filepath));
+        }
+    }
+}
--- a/src/Phpml/Dataset/Dataset.php
+++ b/src/Phpml/Dataset/Dataset.php
@ -1,10 +1,18 @@
 <?php

-abstract class Dataset
+declare (strict_types = 1);
+
+namespace Phpml\Dataset;
+
+interface Dataset
 {
    /**
-     * @var string
+     * @return array
     */
-    protected $filepath;
+    public function getSamples(): array;

+    /**
+     * @return array
+     */
+    public function getLabels(): array;
 }
--- a/src/Phpml/Dataset/Demo/Iris.php
+++ b/src/Phpml/Dataset/Demo/Iris.php
@ -0,0 +1,22 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Dataset\Demo;
+
+use Phpml\Dataset\CsvDataset;
+
+/**
+ * Classes: 3
+ * Samples per class: 50
+ * Samples total: 150
+ * Features per sample: 4.
+ */
+class Iris extends CsvDataset
+{
+    public function __construct()
+    {
+        $filepath = dirname(__FILE__).'/../../../../data/iris.csv';
+        parent::__construct($filepath, 4, true);
+    }
+}
--- a/src/Phpml/Dataset/Iris.php
+++ b/src/Phpml/Dataset/Iris.php
@ -1,10 +0,0 @@
-<?php
-
-class Iris extends Dataset
-{
-    /**
-     * @var string
-     */
-    protected $filepath = 'iris.csv';
-
-}
--- a/src/Phpml/Exception/DatasetException.php
+++ b/src/Phpml/Exception/DatasetException.php
@ -0,0 +1,21 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Exception;
+
+class DatasetException extends \Exception
+{
+    /**
+     * @return DatasetException
+     */
+    public static function missingFile($filepath)
+    {
+        return new self(sprintf('Dataset file %s missing.', $filepath));
+    }
+
+    public static function cantOpenFile($filepath)
+    {
+        return new self(sprintf('Dataset file %s can\'t be open.', $filepath));
+    }
+}
--- a/src/Phpml/Exception/InvalidArgumentException.php
+++ b/src/Phpml/Exception/InvalidArgumentException.php
@ -0,0 +1,26 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Exception;
+
+class InvalidArgumentException extends \Exception
+{
+    /**
+     * @return InvalidArgumentException
+     */
+    public static function sizeNotMatch()
+    {
+        return new self('Size of given arguments not match');
+    }
+
+    /**
+     * @param $name
+     *
+     * @return InvalidArgumentException
+     */
+    public static function percentNotInRange($name)
+    {
+        return new self(sprintf('%s must be between 0.0 and 1.0', $name));
+    }
+}
--- a/src/Phpml/Metric/Accuracy.php
+++ b/src/Phpml/Metric/Accuracy.php
@ -0,0 +1,39 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Metric;
+
+use Phpml\Exception\InvalidArgumentException;
+
+class Accuracy
+{
+    /**
+     * @param array $actualLabels
+     * @param array $predictedLabels
+     * @param bool  $normalize
+     *
+     * @return float|int
+     *
+     * @throws InvalidArgumentException
+     */
+    public static function score(array $actualLabels, array $predictedLabels, bool $normalize = true)
+    {
+        if (count($actualLabels) != count($predictedLabels)) {
+            throw InvalidArgumentException::sizeNotMatch();
+        }
+
+        $score = 0;
+        foreach ($actualLabels as $index => $label) {
+            if ($label === $predictedLabels[$index]) {
+                ++$score;
+            }
+        }
+
+        if ($normalize) {
+            $score = $score / count($actualLabels);
+        }
+
+        return $score;
+    }
+}
--- a/src/Phpml/Metric/Distance.php
+++ b/src/Phpml/Metric/Distance.php
@ -0,0 +1,34 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace Phpml\Metric;
+
+use Phpml\Exception\InvalidArgumentException;
+
+class Distance
+{
+    /**
+     * @param array $a
+     * @param array $b
+     *
+     * @return float
+     *
+     * @throws InvalidArgumentException
+     */
+    public static function euclidean(array $a, array $b): float
+    {
+        if (count($a) != count($b)) {
+            throw InvalidArgumentException::sizeNotMatch();
+        }
+
+        $distance = 0;
+        $count = count($a);
+
+        for ($i = 0; $i < $count; ++$i) {
+            $distance += pow($a[$i] - $b[$i], 2);
+        }
+
+        return sqrt($distance);
+    }
+}
--- a/tests/Phpml/Classifier/KNearestNeighborsTest.php
+++ b/tests/Phpml/Classifier/KNearestNeighborsTest.php
@ -0,0 +1,58 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Classifier;
+
+use Phpml\Classifier\KNearestNeighbors;
+use Phpml\CrossValidation\RandomSplit;
+use Phpml\Dataset\Demo\Iris;
+use Phpml\Metric\Accuracy;
+
+class KNearestNeighborsTest extends \PHPUnit_Framework_TestCase
+{
+    public function testPredictSingleSampleWithDefaultK()
+    {
+        $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);
+
+        $this->assertEquals('b', $classifier->predict([3, 2]));
+        $this->assertEquals('b', $classifier->predict([5, 1]));
+        $this->assertEquals('b', $classifier->predict([4, 3]));
+        $this->assertEquals('b', $classifier->predict([4, -5]));
+
+        $this->assertEquals('a', $classifier->predict([2, 3]));
+        $this->assertEquals('a', $classifier->predict([1, 2]));
+        $this->assertEquals('a', $classifier->predict([1, 5]));
+        $this->assertEquals('a', $classifier->predict([3, 10]));
+    }
+
+    public function testPredictArrayOfSamples()
+    {
+        $trainSamples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
+        $trainLabels = ['a', 'a', 'a', 'b', 'b', 'b'];
+
+        $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();
+        $classifier->train($trainSamples, $trainLabels);
+        $predicted = $classifier->predict($testSamples);
+
+        $this->assertEquals($testLabels, $predicted);
+    }
+
+    public function testAccuracyOnIrisDataset()
+    {
+        $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);
+
+        $this->assertEquals(0.96, $score);
+    }
+}
--- a/tests/Phpml/CrossValidation/RandomSplitTest.php
+++ b/tests/Phpml/CrossValidation/RandomSplitTest.php
@ -0,0 +1,94 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\CrossValidation;
+
+use Phpml\CrossValidation\RandomSplit;
+use Phpml\Dataset\ArrayDataset;
+
+class RandomSplitTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @expectedException \Phpml\Exception\InvalidArgumentException
+     */
+    public function testThrowExceptionOnToSmallTestSize()
+    {
+        new RandomSplit(new ArrayDataset([], []), 0);
+    }
+
+    /**
+     * @expectedException \Phpml\Exception\InvalidArgumentException
+     */
+    public function testThrowExceptionOnToBigTestSize()
+    {
+        new RandomSplit(new ArrayDataset([], []), 1);
+    }
+
+    public function testDatasetRandomSplitWithoutSeed()
+    {
+        $dataset = new ArrayDataset(
+            $samples = [[1], [2], [3], [4]],
+            $labels = ['a', 'a', 'b', 'b']
+        );
+
+        $randomSplit = new RandomSplit($dataset, 0.5);
+
+        $this->assertEquals(2, count($randomSplit->getTestSamples()));
+        $this->assertEquals(2, count($randomSplit->getTrainSamples()));
+
+        $randomSplit2 = new RandomSplit($dataset, 0.25);
+
+        $this->assertEquals(1, count($randomSplit2->getTestSamples()));
+        $this->assertEquals(3, count($randomSplit2->getTrainSamples()));
+    }
+
+    public function testDatasetRandomSplitWithSameSeed()
+    {
+        $dataset = new ArrayDataset(
+            $samples = [[1], [2], [3], [4], [5], [6], [7], [8]],
+            $labels = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b']
+        );
+
+        $seed = 123;
+
+        $randomSplit1 = new RandomSplit($dataset, 0.5, $seed);
+        $randomSplit2 = new RandomSplit($dataset, 0.5, $seed);
+
+        $this->assertEquals($randomSplit1->getTestLabels(), $randomSplit2->getTestLabels());
+        $this->assertEquals($randomSplit1->getTestSamples(), $randomSplit2->getTestSamples());
+        $this->assertEquals($randomSplit1->getTrainLabels(), $randomSplit2->getTrainLabels());
+        $this->assertEquals($randomSplit1->getTrainSamples(), $randomSplit2->getTrainSamples());
+    }
+
+    public function testDatasetRandomSplitWithDifferentSeed()
+    {
+        $dataset = new ArrayDataset(
+            $samples = [[1], [2], [3], [4], [5], [6], [7], [8]],
+            $labels = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b']
+        );
+
+        $randomSplit1 = new RandomSplit($dataset, 0.5, 4321);
+        $randomSplit2 = new RandomSplit($dataset, 0.5, 1234);
+
+        $this->assertNotEquals($randomSplit1->getTestLabels(), $randomSplit2->getTestLabels());
+        $this->assertNotEquals($randomSplit1->getTestSamples(), $randomSplit2->getTestSamples());
+        $this->assertNotEquals($randomSplit1->getTrainLabels(), $randomSplit2->getTrainLabels());
+        $this->assertNotEquals($randomSplit1->getTrainSamples(), $randomSplit2->getTrainSamples());
+    }
+
+    public function testRandomSplitCorrectSampleAndLabelPosition()
+    {
+        $dataset = new ArrayDataset(
+            $samples = [[1], [2], [3], [4]],
+            $labels = [1, 2, 3, 4]
+        );
+
+        $randomSplit = new RandomSplit($dataset, 0.5);
+
+        $this->assertEquals($randomSplit->getTestSamples()[0][0], $randomSplit->getTestLabels()[0]);
+        $this->assertEquals($randomSplit->getTestSamples()[1][0], $randomSplit->getTestLabels()[1]);
+        $this->assertEquals($randomSplit->getTrainSamples()[0][0], $randomSplit->getTrainLabels()[0]);
+        $this->assertEquals($randomSplit->getTrainSamples()[1][0], $randomSplit->getTrainLabels()[1]);
+    }
+}
--- a/tests/Phpml/Dataset/ArrayDatasetTest.php
+++ b/tests/Phpml/Dataset/ArrayDatasetTest.php
@ -0,0 +1,29 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\Dataset;
+
+use Phpml\Dataset\ArrayDataset;
+
+class ArrayDatasetTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @expectedException \Phpml\Exception\InvalidArgumentException
+     */
+    public function testThrowExceptionOnInvalidArgumentsSize()
+    {
+        new ArrayDataset([0, 1], [0]);
+    }
+
+    public function testArrayDataset()
+    {
+        $dataset = new ArrayDataset(
+            $samples = [[1], [2], [3], [4]],
+            $labels = ['a', 'a', 'b', 'b']
+        );
+
+        $this->assertEquals($samples, $dataset->getSamples());
+        $this->assertEquals($labels, $dataset->getLabels());
+    }
+}
--- a/tests/Phpml/Dataset/CsvDatasetTest.php
+++ b/tests/Phpml/Dataset/CsvDatasetTest.php
@ -0,0 +1,28 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\Dataset;
+
+use Phpml\Dataset\CsvDataset;
+
+class CsvDatasetTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @expectedException \Phpml\Exception\DatasetException
+     */
+    public function testThrowExceptionOnMissingFile()
+    {
+        new CsvDataset('missingFile', 3);
+    }
+
+    public function testSampleCsvDataset()
+    {
+        $filePath = dirname(__FILE__).'/Resources/dataset.csv';
+
+        $dataset = new CsvDataset($filePath, 2, true);
+
+        $this->assertEquals(10, count($dataset->getSamples()));
+        $this->assertEquals(10, count($dataset->getLabels()));
+    }
+}
--- a/tests/Phpml/Dataset/Demo/IrisTest.php
+++ b/tests/Phpml/Dataset/Demo/IrisTest.php
@ -0,0 +1,22 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\Dataset\Demo;
+
+use Phpml\Dataset\Demo\Iris;
+
+class IrisTest extends \PHPUnit_Framework_TestCase
+{
+    public function testLoadingIrisDataset()
+    {
+        $iris = new Iris();
+
+        // whole dataset
+        $this->assertEquals(150, count($iris->getSamples()));
+        $this->assertEquals(150, count($iris->getLabels()));
+
+        // one sample features count
+        $this->assertEquals(4, count($iris->getSamples()[0]));
+    }
+}
--- a/tests/Phpml/Dataset/Resources/dataset.csv
+++ b/tests/Phpml/Dataset/Resources/dataset.csv
@ -0,0 +1,11 @@
+feature1,feature2,label
+1,1,a
+2,1,b
+3,1,c
+4,5,a
+2,4,a
+1,5,a
+2,6,b
+3,7,c
+4,4,a
+2,0,a
--- a/tests/Phpml/Metric/AccuracyTest.php
+++ b/tests/Phpml/Metric/AccuracyTest.php
@ -0,0 +1,37 @@
+<?php
+
+declare (strict_types = 1);
+
+namespace tests\Phpml\Metric;
+
+use Phpml\Metric\Accuracy;
+
+class AccuracyTest extends \PHPUnit_Framework_TestCase
+{
+    /**
+     * @expectedException \Phpml\Exception\InvalidArgumentException
+     */
+    public function testThrowExceptionOnInvalidArguments()
+    {
+        $actualLabels = ['a', 'b', 'a', 'b'];
+        $predictedLabels = ['a', 'a'];
+
+        Accuracy::score($actualLabels, $predictedLabels);
+    }
+
+    public function testCalculateNormalizedScore()
+    {
+        $actualLabels = ['a', 'b', 'a', 'b'];
+        $predictedLabels = ['a', 'a', 'b', 'b'];
+
+        $this->assertEquals(0.5, Accuracy::score($actualLabels, $predictedLabels));
+    }
+
+    public function testCalculateNotNormalizedScore()
+    {
+        $actualLabels = ['a', 'b', 'a', 'b'];
+        $predictedLabels = ['a', 'b', 'b', 'b'];
+
+        $this->assertEquals(3, Accuracy::score($actualLabels, $predictedLabels, false));
+    }
+}
--- a/tests/Phpml/Metric/DistanceTest.php
+++ b/tests/Phpml/Metric/DistanceTest.php
@ -0,0 +1,51 @@
+<?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/tools/php-cs-fixer.sh
+++ b/tools/php-cs-fixer.sh
@ -0,0 +1,6 @@
+#!/bin/bash
+echo "Fixing src/ folder"
+php-cs-fixer fix src/ --level=symfony
+
+echo "Fixing tests/ folder"
+php-cs-fixer fix tests/ --level=symfony