godist alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view godist alternatives based on common mentions on social networks and blogs.
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Gorgonia
Gorgonia is a library that helps facilitate machine learning in Go. -
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 -
gosseract
Go package for OCR (Optical Character Recognition), by using Tesseract C++ library -
gago
:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution) -
ocrserver
A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well -
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onnx-go
onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library. -
shield
Bayesian text classifier with flexible tokenizers and storage backends for Go -
neat
Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT). -
neural-go
A multilayer perceptron network implemented in Go, with training via backpropagation. -
go-featureprocessing
🔥 Fast, simple sklearn-like feature processing for Go -
go-cluster
k-modes and k-prototypes clustering algorithms implementation in Go
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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