godist Alternatives - Go Machine Learning

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Programming language: Go

License: MIT License

Tags: Machine Learning

godist alternatives and similar packages

Based on the "Machine Learning" category.
Alternatively, view godist alternatives based on common mentions on social networks and blogs.

GoLearn

9.7 5.9 godist VS GoLearn

General Machine Learning library for Go.
Gorgonia

9.2 8.2 godist VS Gorgonia

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

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tfgo

8.3 5.6 godist VS tfgo

Easy to use Tensorflow bindings: simplifies the usage of the official Tensorflow Go bindings. Define computational graphs in Go, load and execute models trained in Python.
gosseract

8.1 5.3 godist VS gosseract

Go package for OCR (Optical Character Recognition), by using Tesseract C++ library.
goml

7.9 0.0 godist VS goml

On-line Machine Learning in Go
gorse

7.8 4.9 godist VS gorse

A High Performance Recommender System Package based on Collaborative Filtering for Go.
CloudForest

7.4 0.0 godist VS CloudForest

Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
bayesian

7.4 0.0 godist VS bayesian

Naive Bayesian Classification for Golang.
gago

7.3 3.7 godist VS gago

Multi-population, flexible, parallel genetic algorithm.
gobrain

6.8 2.1 godist VS gobrain

Neural Networks written in go
ocrserver

6.6 0.3 godist VS ocrserver

A simple OCR API server, seriously easy to be deployed by Docker and Heroku.
onnx-go

6.1 1.3 godist VS onnx-go

Go Interface to Open Neural Network Exchange (ONNX).
go-deep

6.1 0.0 godist VS go-deep

A feature-rich neural network library in Go.
regommend

6.0 0.0 godist VS regommend

Recommendation & collaborative filtering engine
sklearn

6.0 0.8 godist VS sklearn

partial port of scikit_learn to golang
go-galib

5.7 0.0 godist VS go-galib

Genetic Algorithms library written in Go / golang
goRecommend

5.3 0.0 godist VS goRecommend

Recommendation Algorithms library written in Go.
shield

5.2 0.0 godist VS shield

Bayesian text classifier with flexible tokenizers and storage backends for Go
Goptuna

5.1 8.9 godist VS Goptuna

Bayesian optimization framework for black-box functions written in Go. Everything will be optimized.
go-fann

4.8 0.0 godist VS go-fann

Go bindings for Fast Artificial Neural Networks(FANN) library.
goga

4.5 0.0 godist VS goga

Genetic algorithm library for Go.
neat

4.2 0.0 godist VS neat

Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT).
goscore

4.2 0.0 godist VS goscore

Go Scoring API for PMML.
libsvm

4.2 0.0 godist VS libsvm

libsvm golang version derived work based on LIBSVM 3.14.
go-pr

3.9 0.0 godist VS go-pr

Pattern recognition package in Go lang.
neural-go

3.9 0.0 godist VS neural-go

A multilayer perceptron network implemented in Go, with training via backpropagation.
NEUGO

3.8 0.0 godist VS NEUGO

NEUGO: Neural Networks in Go
golinear

3.5 0.0 godist VS golinear

liblinear bindings for Go
fonet

3.3 0.0 godist VS fonet

A Deep Neural Network library written in Go.
Varis

3.1 0.0 godist VS Varis

Golang Neural Network.
go-featureprocessing

3.1 5.2 godist VS go-featureprocessing

Fast and convenient feature processing for low latency machine leraning in Go.
go-cluster

2.9 0.0 godist VS go-cluster

Go implementation of the k-modes and k-prototypes clustering algorithms.
EAGO

2.9 0.0 godist VS EAGO

EAGO: Evolutionary Algorithms in Go
evoli

2.3 2.4 godist VS evoli

Genetic Algorithm and Particle Swarm Optimization library.
probab

2.0 0.0 godist VS probab

Probability distribution functions. Bayesian inference. Written in pure Go.
GoMind

1.5 0.0 godist VS GoMind

A simplistic Neural Network Library in Go.
randomforest

1.4 0.0 godist VS randomforest

Easy to use Random Forest library for Go.
mlgo

1.0 0.0 godist VS mlgo

This project aims to provide minimalistic machine learning algorithms in Go.
ddt

0.7 5.8 godist VS ddt

Dynamic decision tree, create trees defining customizable rules.

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README

godist

godist provides some Go implementations of useful continuous and discrete probability distributions, as well as some handy methods for working with them.

The general idea is that I will add to these over time, but that each distribution will implement the following interface:

type Distribution interface{
    // distribution mean
    Mean() (float64, error)

    // distribution median
    Median() (float64, error)

    // distribution mode
    Mode() (float64, error)

    // distribution variance
    Variance() (float64, error)

    // generate a random value according to the probability distribution
    Float64() (float64, error)
}

In practice, distributions may also provide other useful methods, where appropriate.

The intentions of godist is not to provide the fastest, most efficient implementations, but instead to provide idiomatic Go implementations that can be easily understood and extended. Having said that, where there are useful and well-understood numerical tricks and tools to improve performance, these have been utilised and documented.

Contributions welcome!

Current Distributions

Beta Distribution
Empirical Distribution

godist

Various probability distributions, and associated methods.

godist alternatives and similar packages

Popular Comparisons

README

godist

Current Distributions