PHP-ML – Machine Learning library for PHP

PHP-ML is a library developed to handle Machine learning tasks using PHP, and this library includes ML algorithms as well as data processing APIs that can handle data cleanups and feature extractions. Machine learning focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

PHP-ML requires PHP >= 7.1.

Fresh approach to Machine Learning in PHP.Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.

• Applications that are not able to afford cost associated with complex hardware and software development platforms, and only require simple predictions and data analytics php-ml solves the purpose.
• It allows developers to perform complex machine learning tasks with a simple and intuitive API.
• This library helps web developers, who are beginners of AI and just wish to learn AI concepts, without overhead of learning new languages such as R, Python or other.

• Association rule learning
• Classification
  • SVC
  • k-Nearest Neighbors
  • Naive Bayes
  • Decision Tree (CART)
  • Ensemble Algorithms
    • Bagging (Bootstrap Aggregating)
    • Random Forest
    • AdaBoost
  • Linear
    • Adaline
    • Decision Stump
    • Perceptron
    • LogisticRegression
• Regression
• Clustering
• Metric
• Workflow
• Neural Network
• Cross Validation
• Feature Selection
• Preprocessing
• Feature Extraction
• Dimensionality Reduction
  • PCA (Principal Component Analysis)
  • Kernel PCA
  • LDA (Linear Discriminant Analysis)
• Datasets
• Models management
• Math

composer require php-ai/php-ml

require_once __DIR__ . '/vendor/autoload.php';
//it will return an instance of the Composer Autoloader

use PhpmlClassificationKNearestNeighbors;
// Import library

$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'

$classifier = new KNearestNeighbors($k=4);
$classifier = new KNearestNeighbors($k=3, new Minkowski($lambda=4));

$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'

$classifier->predict([[3, 2], [1, 5]]);
// return ['b', 'a']