RubixML/ML
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Last Updated: 2023-09-11 15:51:27
#machine-learning
#data-science
#neural-network
#analytics
#php
#classification
#clustering
#regression
#anomaly-detection
#deep-learning
#cross-validation
#machine-learning-library
#php-ml
#php-machine-learning
#prediction
#natural-language-processing
#algorithm
#php-ai
#artificial-intelligence
#ai
A high-level machine learning and deep learning library for the PHP language.
License: MIT License
Languages: PHP
Rubix ML
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/mlRequirements
- 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
Contributing
See CONTRIBUTING.md for guidelines.
License
The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.
OPEN ISSUES
See all- Predict market trend up or down, unlabeled? by @BasvanH
- Native Time Series and Forecasting Support (Sequence Learning) by @andrewdalpino
- Interoperability of persisted models by @marcelklehr
- What method is used to compute gini split index on the Classification and regression tree? by @nizariyah
- Accelerate t-SNE using Barnes-Hut approximation by @andrewdalpino
- YOLO or SSD implementation by @ghostjat
- How should we do something like More Like This (MLT) in Rubix ML? by @marclaporte
- Harmonize keywords / tags used in various informational entries about Rubix ML by @marclaporte
- SMAC - AutoML hyper-parameter optimizer by @andrewdalpino
- Recurent Neural Network & LSTM by @Pmulwahali
- Blog post Tag prediction/recommendation? by @elfeffe
- Simple spam filter (Naive Bayes) by @GeorgeGardiner
- News article category prediction by @oliverbj
- Adults image filtering by @sgoranov
- RandomForest example? by @zenichanin
- VAR Model Research by @andrewdalpino
- rubix/tensor uses same classes as scienide/tensor by @marcuus
- How to use this library to recognize images like Baidu and Bing Search Engines ??? by @chegmarco1989
- Memory & compute optimizations for sparse arrays by @aiaf
- Convert String on Sample Data Into Integer by @halimkun
- How to make future team match predictions? by @yunatamos
- How to adjust MLP output layer settings? by @bpersevere
- [QUESTION] How to perform basic cluster analysis with weighted factors by @LarryBarker
- Can wordcountvectorizer support custom dictionaries ? by @bpersevere
- Typed property Rubix\ML\NeuralNet\Layers\Multiclass::$softmax must not be accessed before initialization by @AdrienBayles
- How can I do a sequence learning with Rubix-ML? by @sr13579
- How can I retrain a model that already trained before? by @alitokmakci
- Complement Naive Bayes? by @DrDub
- Getting "undefined method Tensor\Matrix::maximum()" error using AdaMax optimiser by @MarkLuds
- Estimator unexpectedly predicting empty class labels by @MarkLuds
- Training with incompatible data types by @Snicser
- Does Rubix ML support Online learning? by @Snicser
- Weird issue with Truncated SVD and NumericStringConvertor by @MihailoJoksimovic
- How do LSTM recurrent neural networks work in PHP with this library ??? by @chegmarco1989
- Does Rubix ML support multi-dimensional output? by @epoxa
- Add "Model Explanation" section to the User Guide by @andrewdalpino
- Prune redundant Decision Tree leaf nodes by @andrewdalpino
- Fixed Array Memory Optimizations by @andrewdalpino
- Use new PHP 8.0 features in version 3.0 by @andrewdalpino
- Use pretrained models? by @nibra
- How to Train only One Class? by @takielias
- Not working in PHP 8.2 because of voku/portable-utf8/src/voku/helper/UTF8.php by @follestad
- Question, which model users for Fraud Prediction by @fhferreira
- Which alogorithm can be used for search result ranking ? by @chegmarco1989
- Is "Transformer Architecture Marchine Learning Model" supported on RubixML ??? by @chegmarco1989
- Multi Language Tokenization Support by @andrewdalpino
- TruncatedSVD() made PHP crash without any message by @ahmaruff
- Evaluation of the cluster quality with indicators by @cammarano
- Requirements not resolved to an installable set of packages by @programarivm
- Softmax Classifier & partial training by @ElGigi
RELEASES
See all- 0.0.2-alpha by @andrewdalpino
- 0.0.1-alpha by @andrewdalpino
- 0.0.3-alpha by @andrewdalpino
- 0.0.4-alpha by @andrewdalpino
- 0.0.5-alpha by @andrewdalpino
- 0.0.6-alpha by @andrewdalpino
- 0.0.7-alpha by @andrewdalpino
- 0.0.8-alpha by @andrewdalpino
- 0.0.9-beta by @andrewdalpino
- 0.0.10-beta by @andrewdalpino
- 0.0.11-beta by @andrewdalpino
- 0.0.12-beta by @andrewdalpino
- 0.0.13-beta by @andrewdalpino
- 0.0.14-beta by @andrewdalpino
- 0.0.15-beta by @andrewdalpino
- 0.0.16-beta by @andrewdalpino
- 0.0.17-beta by @andrewdalpino
- 0.0.18-beta by @andrewdalpino
- 0.0.19-beta by @andrewdalpino
- 0.1.0-rc1 by @andrewdalpino
- 0.1.0-rc2 by @andrewdalpino
- 0.1.0-rc3 by @andrewdalpino
- 0.1.0-rc4 by @andrewdalpino
- 0.1.0-rc5 by @andrewdalpino
- 0.1.0 by @andrewdalpino
- 0.1.1 by @andrewdalpino
- 0.1.2 by @andrewdalpino
- 0.1.3 by @andrewdalpino
- 0.1.4 by @andrewdalpino
- 0.1.5 by @andrewdalpino
- 0.1.6 by @andrewdalpino
- 0.2.0 by @andrewdalpino
- 0.2.1 by @andrewdalpino
- 0.2.2 by @andrewdalpino
- 0.2.3 by @andrewdalpino
- 0.2.4 by @andrewdalpino
- 0.3.0 by @andrewdalpino
- 0.3.1 by @andrewdalpino
- 0.3.2 by @andrewdalpino
- 0.4.0 by @andrewdalpino
- 0.4.1 by @andrewdalpino
- 1.0.0-beta1 by @andrewdalpino
- 1.0.0-beta2 by @andrewdalpino
- 1.0.0-rc1 by @andrewdalpino
- 1.0.0 by @andrewdalpino
- 1.0.1 by @andrewdalpino
- 1.0.2 by @andrewdalpino
- 1.0.3 by @andrewdalpino
- 1.1.0 by @andrewdalpino
- 1.1.1 by @andrewdalpino
- 1.1.2 by @andrewdalpino
- 1.1.3 by @andrewdalpino
- 1.2.0 by @andrewdalpino
- 1.2.1 by @andrewdalpino
- 1.2.2 by @andrewdalpino
- 1.2.3 by @andrewdalpino
- 1.3.0 by @andrewdalpino
- 1.3.1 by @andrewdalpino
- 0.4.2 by @andrewdalpino
- 1.3.2 by @andrewdalpino
- 2.0.0 by @andrewdalpino
- 2.0.1 by @andrewdalpino
- 1.3.4 by @andrewdalpino
- 2.0.2 by @andrewdalpino
- 2.1.0 by @andrewdalpino
- 2.2.1 by @andrewdalpino
- 0.4.3 by @andrewdalpino
- 2.2.0 by @andrewdalpino
- 2.1.1 by @andrewdalpino
- 2.2.2 by @andrewdalpino
- 1.3.5 by @andrewdalpino
- 2.4.0 by @andrewdalpino
- 2.3.2 by @andrewdalpino
- 2.3.1 by @andrewdalpino
- 2.3.0 by @andrewdalpino