Displaying 1 to 18 from 18 results

goml - On-line Machine Learning in Go (and so much more)

  •    Go

While models include traditional, batch learning interfaces, goml includes many models which let you learn in an online, reactive manner by passing data to streams held on channels.The library includes comprehensive tests, extensive documentation, and clean, expressive, modular source code. Community contribution is heavily encouraged.

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

  •    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.

golearn - Machine Learning for Go

  •    Go

GoLearn is a 'batteries included' machine learning library for Go. Simplicity, paired with customisability, is the goal. We are in active development, and would love comments from users out in the wild. Drop us a line on Twitter.See here for installation instructions.

bayesian - Naive Bayesian Classification for Golang.

  •    Go

Perform naive Bayesian classification into an arbitrary number of classes on sets of strings. bayesian also supports term frequency-inverse document frequency calculations (TF-IDF).Copyright (c) 2011-2017. Jake Brukhman. (jbrukh@gmail.com). All rights reserved. See the LICENSE file for BSD-style license.




CloudForest - Ensembles of decision trees in go/golang.

  •    Go

Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go (golang).It can achieve quicker training times then many other popular implementations on some datasets. This is the result of cpu cache friendly memory utilization well suited to modern processors and separate, optimized paths to learn splits from binary, numerical and categorical data.

gago - An extensible toolkit for conceiving and running genetic algorithms

  •    Go

There is a lot of intellectual fog around the concept of genetic algorithms (GAs). It's important to appreciate the fact that GAs are composed of many nuts and bolts. There isn't a single definition of genetic algorithms. gago is intended to be a toolkit where one may run many kinds of genetic algorithms, with different evolution models and various genetic operators.

golinear - liblinear bindings for Go

  •    Go

golinear is a package for training and using linear classifiers in the Go programming language (golang).Ubuntu and Debian provide packages for liblinear. However, at the time of writing (July 2, 2014), these were serverly outdated. This package requires version 1.9 or later.

libsvm - libsvm go version

  •    Go

libsvm golang version derived work based on LIBSVM 3.14.this lib based on LIBSVM java version, just translate java to go.


godist - Probability distributions and associated methods in Go

  •    Go

godist provides some Go implementations of useful continuous and discrete probability distributions, as well as some handy methods for working with them.In practice, distributions may also provide other useful methods, where appropriate.

shield - Bayesian text classifier with flexible tokenizers and storage backends for Go

  •    Go

Bayesian text classifier with flexible tokenizers and storage backends for Go

gobrain - Neural Networks written in go

  •    Go

After running this code the network will be trained and ready to be used.Where the first values are the inputs, the values after the arrow -> are the output values from the network and the values after : are the expected outputs.

neat - NEAT (NeuroEvolution of Augmenting Topologies) implemented in Go

  •    Go

CURRENTLY NOT WORKING! There will be a further notice when it's updated.NEAT (NeuroEvolution of Augmenting Topologies) is a neuroevolution algorithm by Dr. Kenneth O. Stanley which evolves not only neural networks' weights but also their topologies. This method starts the evolution process with genomes with minimal structure, then complexifies the structure of each genome as it progresses. You can read the original paper from here.

neural-go - A multilayer perceptron network implemented in Go, with training via backpropagation.

  •    Go

I'm in the process of making significant changes to this package, particularly, to make it more modular, and to base it around an actual linear algebra library for Go.If you want the original version of this library, check out the v0.1 tag.

goRecommend - Collaborative Filtering (CF) Algorithms in Go!

  •    Go

Collaborative Filtering (CF) is oftentimes used for item recommendations for users, and many libraries exist for other languages (popular implementations include Mahout, Prediction.IO, Apache MLLib ALS etc..). As there are very few machine learning packages out there for Go, I decided to put together some model based CF algorithms that I thought were interesting.

goga - Golang Genetic Algorithm

  •    Go

Golang implementation of a genetic algorithm. See ./examples for info on how to use the library.Goga is a genetic algorithm solution written in Golang. It is used and configured by injecting different behaviours into the main genetic algorithm object. The main injectable components are the simulator, selector and mater.

go-fann - Go bindings for FANN, library for artificial neural networks

  •    Go

Go bindings for FANN, library for artificial neural networks