rubix/ml

A high-level machine learning and deep learning library for the PHP language.

2.5.2 2024-11-09 23:02 UTC

Requires

• php: >=7.4
• ext-json: *
• amphp/parallel: ^1.3
• andrewdalpino/okbloomer: ^1.0
• psr/log: ^1.1|^2.0|^3.0
• rubix/tensor: ^3.0
• symfony/polyfill-mbstring: ^1.0
• symfony/polyfill-php80: ^1.17
• symfony/polyfill-php82: ^1.27
• symfony/polyfill-php83: ^1.27
• wamania/php-stemmer: ^3.0

Requires (Dev)

• friendsofphp/php-cs-fixer: ^3.0
• phpbench/phpbench: ^1.0
• phpstan/extension-installer: ^1.0
• phpstan/phpstan: ^1.0
• phpstan/phpstan-phpunit: ^1.0
• phpunit/phpunit: ^9.0

Suggests

• ext-gd: For image support
• ext-mbstring: For fast multibyte string manipulation
• ext-svm: For Support Vector Machine engine (libsvm)
• ext-tensor: For fast Matrix/Vector computing

MIT

• Andrew DalPino
• Contributors

• php
• cart
• analytics
• classification
• Algorithm
• inference
• data mining
• dataset
• artificial intelligence
• recommendation
• Neural network
• classifier
• etl
• nlp
• machine learning
• prediction
• svm
• ml
• ranking
• ensemble
• natural language processing
• clustering
• regression
• ai
• image recognition
• k-means
• Rubix
• Linear regression
• kmeans
• data science
• Deep learning
• feature extraction
• knn
• nearest neighbors
• TF-IDF
• estimator
• naive bayes
• predictive modeling
• anomaly detection
• manifold learning
• dimensionality reduction
• cross validation
• k-nearest neighbors
• adaboost
• dbscan
• feature selection
• feature importance
• gaussian mixture
• gmm
• gradient boost
• grid search
• imputation
• isolation forest
• loda
• local outlier factor
• logistic regression
• mean shift
• multilayer perceptron
• php ml
• random forest
• regressor
• rubixml
• rubix ml
• support vector machine
• t-sne
• tsne
• outlier detection
• php machine learning
• text mining
• tf idf
• gbm
• softmax
• lof
• ridge
• supervised learning
• unsupervised learning
• mlp
• php ai

README

A high-level machine learning and deep learning library for the PHP language.

• Developer-friendly API is delightful to use
• 40+ supervised and unsupervised learning algorithms
• Support for ETL, preprocessing, and cross-validation
• Open source and free to use commercially

Installation

Install Rubix ML into your project using Composer:

$ composer require rubix/ml

Requirements

• PHP 7.4 or above

Recommended

• Tensor extension for fast Matrix/Vector computing

Optional

• GD extension for image support
• Mbstring extension for fast multibyte string manipulation
• SVM extension for Support Vector Machine engine (libsvm)
• PDO extension for relational database support
• GraphViz for graph visualization

Documentation

Read the latest docs here.

What is Rubix ML?

Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.

Getting Started

If you are new to machine learning, we recommend taking a look at the What is Machine Learning? section to get started. If you are already familiar with basic ML concepts, you can browse the basic introduction for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.

Tutorials & Example Projects

Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.

• CIFAR-10 Image Recognizer
• Color Clusterer
• Credit Default Risk Predictor
• Customer Churn Predictor
• Divorce Predictor
• DNA Taxonomer
• Dota 2 Game Outcome Predictor
• Human Activity Recognizer
• Housing Price Predictor
• Iris Flower Classifier
• MNIST Handwritten Digit Recognizer
• Text Sentiment Analyzer
• Titanic Survival Predictor

Interact With The Community

• Join Our Telegram Channel

Contributing

See CONTRIBUTING.md for guidelines.

License

The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.