godist alternatives and similar packages
Based on the "Machine Learning" category
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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. -
CloudForest
Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go. -
Goptuna
Bayesian optimization framework for black-box functions written in Go. Everything will be optimized.
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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