mirror of
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113 lines
4.6 KiB
Markdown
113 lines
4.6 KiB
Markdown
# PHP-ML - Machine Learning library for PHP
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[](https://php.net/)
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[](https://packagist.org/packages/php-ai/php-ml)
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[](https://travis-ci.org/php-ai/php-ml)
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[](http://php-ml.readthedocs.org/)
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[](https://packagist.org/packages/php-ai/php-ml)
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[](https://packagist.org/packages/php-ai/php-ml)
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[](https://coveralls.io/github/php-ai/php-ml?branch=master)
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[](https://scrutinizer-ci.com/g/php-ai/php-ml/?branch=master)
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<p align="center">
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<img src="https://github.com/php-ai/php-ml/raw/master/docs/assets/php-ml-logo.png" />
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</p>
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Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.
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PHP-ML requires PHP >= 7.2.
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Simple example of classification:
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```php
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require_once __DIR__ . '/vendor/autoload.php';
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use Phpml\Classification\KNearestNeighbors;
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$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$classifier = new KNearestNeighbors();
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$classifier->train($samples, $labels);
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$classifier->predict([3, 2]);
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// return 'b'
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```
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## Awards
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<a href="http://www.yegor256.com/2016/10/23/award-2017.html">
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<img src="http://www.yegor256.com/images/award/2017/winner-itcraftsmanpl.png" width="400"/></a>
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## Documentation
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To find out how to use PHP-ML follow [Documentation](http://php-ml.readthedocs.org/).
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## Installation
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This library is still in beta. However, it can be installed with Composer:
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```
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composer require php-ai/php-ml
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```
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## Examples
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Example scripts are available in a separate repository [php-ai/php-ml-examples](https://github.com/php-ai/php-ml-examples).
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## Features
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* Association rule Learning
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* [Apriori](machine-learning/association/apriori.md)
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* Classification
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* [SVC](machine-learning/classification/svc.md)
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* [k-Nearest Neighbors](machine-learning/classification/k-nearest-neighbors.md)
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* [Naive Bayes](machine-learning/classification/naive-bayes.md)
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* Regression
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* [Least Squares](machine-learning/regression/least-squares.md)
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* [SVR](machine-learning/regression/svr.md)
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* Clustering
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* [k-Means](machine-learning/clustering/k-means.md)
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* [DBSCAN](machine-learning/clustering/dbscan.md)
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* Metric
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* [Accuracy](machine-learning/metric/accuracy.md)
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* [Confusion Matrix](machine-learning/metric/confusion-matrix.md)
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* [Classification Report](machine-learning/metric/classification-report.md)
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* Workflow
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* [Pipeline](machine-learning/workflow/pipeline)
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* Neural Network
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* [Multilayer Perceptron Classifier](machine-learning/neural-network/multilayer-perceptron-classifier.md)
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* Cross Validation
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* [Random Split](machine-learning/cross-validation/random-split.md)
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* [Stratified Random Split](machine-learning/cross-validation/stratified-random-split.md)
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* Feature Selection
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* [Variance Threshold](machine-learning/feature-selection/variance-threshold.md)
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* [SelectKBest](machine-learning/feature-selection/selectkbest.md)
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* Preprocessing
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* [Normalization](machine-learning/preprocessing/normalization.md)
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* [Imputation missing values](machine-learning/preprocessing/imputation-missing-values.md)
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* LabelEncoder
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* Feature Extraction
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* [Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer.md)
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* [Tf-idf Transformer](machine-learning/feature-extraction/tf-idf-transformer.md)
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* Datasets
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* [Array](machine-learning/datasets/array-dataset.md)
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* [CSV](machine-learning/datasets/csv-dataset.md)
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* [Files](machine-learning/datasets/files-dataset.md)
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* [SVM](machine-learning/datasets/svm-dataset.md)
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* [MNIST](machine-learning/datasets/mnist-dataset.md)
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* Ready to use:
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* [Iris](machine-learning/datasets/demo/iris.md)
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* [Wine](machine-learning/datasets/demo/wine.md)
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* [Glass](machine-learning/datasets/demo/glass.md)
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* Models management
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* [Persistency](machine-learning/model-manager/persistency.md)
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* Math
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* [Distance](math/distance.md)
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* [Matrix](math/matrix.md)
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* [Set](math/set.md)
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* [Statistic](math/statistic.md)
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##
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Arkadiusz Kondas (@ArkadiuszKondas)
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