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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 3.1 godist VS GoLearn

Machine Learning for Go
gorse

9.2 9.5 godist VS gorse

An open source recommender system service written in Go

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Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.

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Gorgonia

9.2 5.6 godist VS Gorgonia

Gorgonia is a library that helps facilitate machine learning in Go.
m2cgen

8.4 8.5 godist VS m2cgen

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
tfgo

8.3 5.0 godist VS tfgo

Tensorflow + Go, the gopher way
gosseract

8.2 5.6 godist VS gosseract

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

7.9 1.0 godist VS goml

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

7.4 0.0 godist VS bayesian

Naive Bayesian Classification for Golang.
gago

7.3 2.6 godist VS gago

:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
CloudForest

7.3 0.0 godist VS CloudForest

Ensembles of decision trees in go/golang.
ocrserver

6.8 3.2 godist VS ocrserver

A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well
gobrain

6.8 0.8 godist VS gobrain

Neural Networks written in go
go-deep

6.2 0.0 godist VS go-deep

Artificial Neural Network
onnx-go

6.2 0.0 godist VS onnx-go

onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
sklearn

6.0 0.0 godist VS sklearn

bits of sklearn ported to Go #golang
regommend

6.0 0.0 godist VS regommend

Recommendation engine for Go
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

Collaborative Filtering (CF) Algorithms in Go!
shield

5.2 0.0 godist VS shield

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

5.2 7.4 godist VS Goptuna

A hyperparameter optimization framework, inspired by Optuna.
go-fann

4.7 0.0 godist VS go-fann

Go bindings for FANN, library for artificial neural networks
goga

4.6 0.0 godist VS goga

Golang Genetic Algorithm
goscore

4.3 0.0 godist VS goscore

Go Scoring API for PMML
libsvm

4.2 0.0 godist VS libsvm

libsvm go version
neat

4.2 0.0 godist VS neat

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

3.9 0.0 godist VS neural-go

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

3.9 0.0 godist VS go-pr

Pattern recognition package in Go lang.
NEUGO

3.8 0.0 godist VS NEUGO

NEUGO: Neural Networks in Go
fonet

3.6 4.3 godist VS fonet

fonet is a deep neural network package for Go.
golinear

3.5 0.0 godist VS golinear

liblinear bindings for Go
go-featureprocessing

3.4 6.4 godist VS go-featureprocessing

Fast, simple sklearn-like feature processing for Go
Varis

3.3 0.0 godist VS Varis

Golang Neural Network
go-cluster

3.1 0.0 godist VS go-cluster

k-modes and k-prototypes clustering algorithms implementation in Go
EAGO

2.9 0.0 godist VS EAGO

EAGO: Evolutionary Algorithms in Go
evoli

2.6 1.7 godist VS evoli

Genetic Algorithm and Particle Swarm Optimization
randomforest

2.1 4.4 godist VS randomforest

Random Forest implementation in golang
probab

2.0 0.0 godist VS probab

Automatically exported from code.google.com/p/probab
GoMind

2.0 0.0 godist VS GoMind

A simplistic Neural Network Library in Go
ddt

1.6 3.9 godist VS ddt

Golang Dynamic Decision Tree
mlgo

0.9 0.0 godist VS mlgo

Automatically exported from code.google.com/p/mlgo

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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

Probability distributions and associated methods in Go

godist alternatives and similar packages

Popular Comparisons

README

godist

Current Distributions