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108 lines
4.2 KiB
Markdown
108 lines
4.2 KiB
Markdown
# PHP-ML - Machine Learning library for PHP
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[![Minimum PHP Version](https://img.shields.io/badge/php-%3E%3D%207.0-8892BF.svg)](https://php.net/)
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[![Latest Stable Version](https://img.shields.io/packagist/v/php-ai/php-ml.svg)](https://packagist.org/packages/php-ai/php-ml)
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[![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)
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[![Documentation Status](https://readthedocs.org/projects/php-ml/badge/?version=develop)](http://php-ml.readthedocs.org/en/develop/?badge=develop)
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[![Total Downloads](https://poser.pugx.org/php-ai/php-ml/downloads.svg)](https://packagist.org/packages/php-ai/php-ml)
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[![License](https://poser.pugx.org/php-ai/php-ml/license.svg)](https://packagist.org/packages/php-ai/php-ml)
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[![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)
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![PHP-ML - Machine Learning library for PHP](assets/php-ml-logo.png)
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Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Preprocessing, Feature Extraction and much more in one library.
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PHP-ML requires PHP >= 7.0.
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Simple example of classification:
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```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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## 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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Currently this library is in the process of developing, but You can install it 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 Lerning
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* [Apriori](machine-learning/association/apriori/)
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* Classification
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* [SVC](machine-learning/classification/svc/)
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* [k-Nearest Neighbors](machine-learning/classification/k-nearest-neighbors/)
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* [Naive Bayes](machine-learning/classification/naive-bayes/)
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* Regression
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* [Least Squares](machine-learning/regression/least-squares/)
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* [SVR](machine-learning/regression/svr/)
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* Clustering
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* [k-Means](machine-learning/clustering/k-means/)
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* [DBSCAN](machine-learning/clustering/dbscan/)
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* Metric
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* [Accuracy](machine-learning/metric/accuracy/)
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* [Confusion Matrix](machine-learning/metric/confusion-matrix/)
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* [Classification Report](machine-learning/metric/classification-report/)
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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](machine-learning/neural-network/multilayer-perceptron/)
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* [Backpropagation training](machine-learning/neural-network/backpropagation/)
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* Cross Validation
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* [Random Split](machine-learning/cross-validation/random-split/)
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* [Stratified Random Split](machine-learning/cross-validation/stratified-random-split/)
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* Preprocessing
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* [Normalization](machine-learning/preprocessing/normalization/)
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* [Imputation missing values](machine-learning/preprocessing/imputation-missing-values/)
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* Feature Extraction
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* [Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer/)
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* [Tf-idf Transformer](machine-learning/feature-extraction/tf-idf-transformer/)
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* Datasets
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* [Array](machine-learning/datasets/array-dataset/)
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* [CSV](machine-learning/datasets/csv-dataset/)
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* [Files](machine-learning/datasets/files-dataset/)
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* Ready to use:
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* [Iris](machine-learning/datasets/demo/iris/)
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* [Wine](machine-learning/datasets/demo/wine/)
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* [Glass](machine-learning/datasets/demo/glass/)
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* Math
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* [Distance](math/distance/)
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* [Matrix](math/matrix/)
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* [Set](math/set/)
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* [Statistic](math/statistic/)
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## Contribute
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- Issue Tracker: github.com/php-ai/php-ml/issues
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- Source Code: github.com/php-ai/php-ml
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You can find more about contributing in [CONTRIBUTING.md](CONTRIBUTING.md).
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## License
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PHP-ML is released under the MIT Licence. See the bundled LICENSE file for details.
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## Author
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Arkadiusz Kondas (@ArkadiuszKondas)
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