php-ml/README.md
Arkadiusz Kondas 3ba35918a3
Implement VarianceThreshold - simple baseline approach to feature selection. (#228)
* Add sum of squares deviations

* Calculate population variance

* Add VarianceThreshold - feature selection transformer

* Add docs about VarianceThreshold

* Add missing code for pipeline usage
2018-02-10 18:07:09 +01:00

133 lines
6.4 KiB
Markdown

# PHP-ML - Machine Learning library for PHP
[![Minimum PHP Version](https://img.shields.io/badge/php-%3E%3D%207.1-8892BF.svg)](https://php.net/)
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[![Documentation Status](https://readthedocs.org/projects/php-ml/badge/?version=master)](http://php-ml.readthedocs.org/)
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<a href="http://www.yegor256.com/2016/10/23/award-2017.html">
<img src="http://www.yegor256.com/images/award/2017/winner-itcraftsmanpl.png"
style="width:203px;height:45px;"/></a>
![PHP-ML - Machine Learning library for PHP](docs/assets/php-ml-logo.png)
Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.
PHP-ML requires PHP >= 7.1.
Simple example of classification:
```php
require_once __DIR__ . '/vendor/autoload.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);
echo $classifier->predict([3, 2]);
// return 'b'
```
## Documentation
To find out how to use PHP-ML follow [Documentation](http://php-ml.readthedocs.org/).
## Installation
Currently this library is in the process of being developed, but You can install it with Composer:
```
composer require php-ai/php-ml
```
## Examples
Example scripts are available in a separate repository [php-ai/php-ml-examples](https://github.com/php-ai/php-ml-examples).
## Features
* Association rule learning
* [Apriori](http://php-ml.readthedocs.io/en/latest/machine-learning/association/apriori/)
* Classification
* [SVC](http://php-ml.readthedocs.io/en/latest/machine-learning/classification/svc/)
* [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/)
* Decision Tree (CART)
* Ensemble Algorithms
* Bagging (Bootstrap Aggregating)
* Random Forest
* AdaBoost
* Linear
* Adaline
* Decision Stump
* Perceptron
* LogisticRegression
* Regression
* [Least Squares](http://php-ml.readthedocs.io/en/latest/machine-learning/regression/least-squares/)
* [SVR](http://php-ml.readthedocs.io/en/latest/machine-learning/regression/svr/)
* 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/)
* Fuzzy C-Means
* Metric
* [Accuracy](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/accuracy/)
* [Confusion Matrix](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/confusion-matrix/)
* [Classification Report](http://php-ml.readthedocs.io/en/latest/machine-learning/metric/classification-report/)
* Workflow
* [Pipeline](http://php-ml.readthedocs.io/en/latest/machine-learning/workflow/pipeline)
* Neural Network
* [Multilayer Perceptron Classifier](http://php-ml.readthedocs.io/en/latest/machine-learning/neural-network/multilayer-perceptron-classifier/)
* Cross Validation
* [Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/random-split/)
* [Stratified Random Split](http://php-ml.readthedocs.io/en/latest/machine-learning/cross-validation/stratified-random-split/)
* Feature Selection
* [Variance Threshold](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-selection/variance-threshold/)
* Preprocessing
* [Normalization](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/normalization/)
* [Imputation missing values](http://php-ml.readthedocs.io/en/latest/machine-learning/preprocessing/imputation-missing-values/)
* Feature Extraction
* [Token Count Vectorizer](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-extraction/token-count-vectorizer/)
* [Tf-idf Transformer](http://php-ml.readthedocs.io/en/latest/machine-learning/feature-extraction/tf-idf-transformer/)
* Dimensionality Reduction
* PCA (Principal Component Analysis)
* Kernel PCA
* LDA (Linear Discriminant Analysis)
* Datasets
* [Array](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/array-dataset/)
* [CSV](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/csv-dataset/)
* [Files](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/files-dataset/)
* Ready to use:
* [Iris](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/iris/)
* [Wine](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/wine/)
* [Glass](http://php-ml.readthedocs.io/en/latest/machine-learning/datasets/demo/glass/)
* Models management
* [Persistency](http://php-ml.readthedocs.io/en/latest/machine-learning/model-manager/persistency/)
* Math
* [Distance](http://php-ml.readthedocs.io/en/latest/math/distance/)
* [Matrix](http://php-ml.readthedocs.io/en/latest/math/matrix/)
* [Set](http://php-ml.readthedocs.io/en/latest/math/set/)
* [Statistic](http://php-ml.readthedocs.io/en/latest/math/statistic/)
* Linear Algebra
## Contribute
- [Issue Tracker: github.com/php-ai/php-ml](https://github.com/php-ai/php-ml/issues)
- [Source Code: github.com/php-ai/php-ml](https://github.com/php-ai/php-ml)
You can find more about contributing in [CONTRIBUTING.md](CONTRIBUTING.md).
## License
PHP-ML is released under the MIT Licence. See the bundled LICENSE file for details.
## Author
Arkadiusz Kondas (@ArkadiuszKondas)