JSAT - Java Statistical Analysis Tool, a Java library for Machine Learning

  •        74

JSAT is a library for quickly getting started with Machine Learning problems. It is developed in my free time, and made available for use under the GPL 3. Part of the library is for self education, as such - all code is self contained. JSAT has no external dependencies, and is pure Java. I also aim to make the library suitably fast for small to medium size problems. As such, much of the code supports parallel execution.If you want to use the bleeding edge, but don't want to bother building yourself, I recomend you look at jitpack.io. It can build a POM repo for you for any specific commit version. Click on "Commits" in the link and then click "get it" for the commit version you want.

https://github.com/EdwardRaff/JSAT

Tags
Implementation
License
Platform

   




Related Projects

Machine Learning Framework


Machine Learning Framework (MLF) is a library based on .NET Framework for machine learning implementation. This library consists of collection of machine learning algorithms such as Bayesian, Neural Network, SOM, Genetic Algorithm, SVM, and etc.

talon - Mailgun library to extract message quotations and signatures


Mailgun library to extract message quotations and signatures.For machine learning talon currently uses the scikit-learn library to build SVM classifiers. The core of machine learning algorithm lays in talon.signature.learning package. It defines a set of features to apply to a message (featurespace.py), how data sets are built (dataset.py), classifier’s interface (classifier.py).

rb-libsvm - Ruby language bindings for LIBSVM


This package provides a Ruby bindings to the LIBSVM library. SVM is a machine learning and classification algorithm, and LIBSVM is a popular free implementation of it, written by Chih-Chung Chang and Chih-Jen Lin, of National Taiwan University, Taipei. See the book "Programming Collective Intelligence," among others, for a usage example. There is a JRuby implementation of this gem named jrb-libsvm by Andreas Eger.

limdu - Machine-learning for Node.js


Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

Java Machine Learning Library


Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...


Smile - Statistical Machine Intelligence & Learning Engine


Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

ABAGAIL - The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms


The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves. See Issues page.

machine-learning-with-ruby - Curated list: Resources for machine learning in Ruby.


Machine Learning is a field of Computational Science - often nested under AI research - with many practical applications due to the ability of resulting algorithms to systematically implement a specific solution without explicit programmer's instructions. Obviously many algorithms need a definition of features to look at or a biggish training set of data to derive the solution from. This curated list comprises awesome libraries, data sources, tutorials and presentations about Machine Learning utilizing the Ruby programming language.

photon-ml - A scalable machine learning library on Apache Spark


New: check out our hands-on tutorial.Photon Machine Learning (Photon ML) is a machine learning library based upon Apache Spark originally developed by the LinkedIn Machine Learning Algorithms team.

H2O - Fast Scalable Machine Learning API For Smarter Applications


H2O is for data scientists and application developers who need fast, in-memory scalable machine learning for smarter applications. H2O is an open source parallel processing engine for machine learning. Unlike traditional analytics tools, H2O provides a combination of extraordinary math, a high performance parallel architecture, and unrivaled ease of use.

Dclib - Portable C++ library


dlib is a library for developing portable applications dealing with networking, threads, graphical interfaces, data structures, linear algebra, machine learning, XML and text parsing, numerical optimization, Bayesian nets, data compression routines, linked lists, binary search trees, linear algebra and matrix utilities, machine learning algorithms, and many other general utilities.

Jubatus - Framework and Library for Distributed Online Machine Learning


Jubatus is a distributed processing framework and streaming machine learning library. Jubatus includes these functionalities: Online Machine Learning Library: Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering, Feature Vector Converter (fv_converter): Data Preprocess and Feature Extraction, Framework for Distributed Online Machine Learning with Fault Tolerance.

Sensorbee - Lightweight stream processing engine for IoT


Sensorbee is designed for low-latency processing of streaming data at the edge of the network. IoT devices frequently generate large volumes of unstructured streaming data, such as video and audio streams. Even if the data streams are structured, they may be meaningless if their temporal characteristics are not considered. Cloud-based services are generally not good at processing these kinds of data. Preprocessing data streams before they are sent to the cloud makes large scale data processing in the cloud more efficient and reduces the usage of network bandwidth.

Learning-Library-for-PHP - The rudimentary workings of a machine learning library in PHP.


Some machine learning/artificial intelligence/natural language processing algorithms implemented in PHP. Note that in almost all cases, PHP as it stands today is the wrong tool for most machine learning jobs. This library provides a pedagogical introduction to these tools more than it is a recommendation that it is used for day-to-day development. Copyright (C) 2011-2015 Giuseppe Burtini joe@truephp.com and contributors as appropriate.

PRMLT - Matlab code for machine learning algorithms in book PRML


This package is a Matlab implementation of the algorithms described in the classical machine learning textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML). Note: this package requires Matlab R2016b or latter, since it utilizes a new syntax of Matlab called Implicit expansion (a.k.a. broadcasting in Python).

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET


The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

ml-agents - Unity Machine Learning Agents


Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.

gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.


Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.

encog-dotnet-core


Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported. Most Encog training algorithms are multi-threaded and scale well to multicore hardware. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008.