php-ml/docs/index.md
Yuji Uchiyama 4562f1dfc9 Add a SvmDataset class for SVM-Light (or LibSVM) format files (#237)
* Add data loader for svm format

* Add tests for error cases

* Set proper exception messages

* Add documents

* Add error checking code for invalid column format

* Add missing documents
2018-02-24 11:17:35 +01:00

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5.0 KiB
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# PHP-ML - Machine Learning library for PHP
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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](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);
$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 developing, 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 Lerning
* [Apriori](machine-learning/association/apriori.md)
* Classification
* [SVC](machine-learning/classification/svc.md)
* [k-Nearest Neighbors](machine-learning/classification/k-nearest-neighbors.md)
* [Naive Bayes](machine-learning/classification/naive-bayes.md)
* Regression
* [Least Squares](machine-learning/regression/least-squares.md)
* [SVR](machine-learning/regression/svr.md)
* Clustering
* [k-Means](machine-learning/clustering/k-means.md)
* [DBSCAN](machine-learning/clustering/dbscan.md)
* Metric
* [Accuracy](machine-learning/metric/accuracy.md)
* [Confusion Matrix](machine-learning/metric/confusion-matrix.md)
* [Classification Report](machine-learning/metric/classification-report.md)
* Workflow
* [Pipeline](machine-learning/workflow/pipeline)
* Neural Network
* [Multilayer Perceptron Classifier](machine-learning/neural-network/multilayer-perceptron-classifier.md)
* Cross Validation
* [Random Split](machine-learning/cross-validation/random-split.md)
* [Stratified Random Split](machine-learning/cross-validation/stratified-random-split.md)
* Feature Selection
* [Variance Threshold](machine-learning/feature-selection/variance-threshold.md)
* [SelectKBest](machine-learning/feature-selection/selectkbest.md)
* Preprocessing
* [Normalization](machine-learning/preprocessing/normalization.md)
* [Imputation missing values](machine-learning/preprocessing/imputation-missing-values.md)
* Feature Extraction
* [Token Count Vectorizer](machine-learning/feature-extraction/token-count-vectorizer.md)
* [Tf-idf Transformer](machine-learning/feature-extraction/tf-idf-transformer.md)
* Datasets
* [Array](machine-learning/datasets/array-dataset.md)
* [CSV](machine-learning/datasets/csv-dataset.md)
* [Files](machine-learning/datasets/files-dataset.md)
* [SVM](machine-learning/datasets/svm-dataset.md)
* Ready to use:
* [Iris](machine-learning/datasets/demo/iris.md)
* [Wine](machine-learning/datasets/demo/wine.md)
* [Glass](machine-learning/datasets/demo/glass.md)
* Models management
* [Persistency](machine-learning/model-manager/persistency.md)
* Math
* [Distance](math/distance.md)
* [Matrix](math/matrix.md)
* [Set](math/set.md)
* [Statistic](math/statistic.md)
## Contribute
- Issue Tracker: [github.com/php-ai/php-ml/issues](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)