creat docs files

docs/index.md

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+# 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)

docs/machine-learning/classification/knearestneighbors.md

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+# 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']
+```

docs/machine-learning/cross-validation/randomsplit.md

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+# 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();
+```

docs/machine-learning/datasets/array-dataset.md

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+# 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();
+```

docs/machine-learning/datasets/csv-dataset.md

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+# 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.

docs/machine-learning/datasets/demo/iris.md

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+# 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
+```

docs/machine-learning/metric/accuracy.md

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+# 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
+```

docs/machine-learning/metric/distance.md

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+# 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
+```

mkdocs.yml

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+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

tests/Phpml/CrossValidation/RandomSplitTest.php

@@ -32,10 +32,10 @@ public function testDatasetRandomSplitWithoutSeed()
             $labels = ['a', 'a', 'b', 'b']
         );
 
-        $randomSplit1 = new RandomSplit($dataset, 0.5);
+        $randomSplit = new RandomSplit($dataset, 0.5);
 
-        $this->assertEquals(2, count($randomSplit1->getTestSamples()));
-        $this->assertEquals(2, count($randomSplit1->getTrainSamples()));
+        $this->assertEquals(2, count($randomSplit->getTestSamples()));
+        $this->assertEquals(2, count($randomSplit->getTrainSamples()));
 
         $randomSplit2 = new RandomSplit($dataset, 0.25);