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Programming language: Go
Tags: Machine Learning    

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README

godist

Build Status

GoDoc

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