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safwank/Numerix

A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning
Source code at GitHub https://github.com/safwank/Numerix
safwank/Numerix.json
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  "createdAt": "2016-04-27T22:39:13Z",
  "defaultBranch": "master",
  "description": "A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning",
  "fullName": "safwank/Numerix",
  "homepage": "",
  "language": "Elixir",
  "name": "Numerix",
  "pushedAt": "2021-05-30T22:44:10Z",
  "stargazersCount": 191,
  "topics": [
    "linear-algebra",
    "machine-learning",
    "math",
    "statistics"
  ],
  "updatedAt": "2025-09-22T07:22:24Z",
  "url": "https://github.com/safwank/Numerix"
}

Build Status

Numerix

Section titled “Numerix”

A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning.

Installation

Section titled “Installation”

Add numerix to your list of dependencies in mix.exs:

  def deps do
    [{:numerix, "~> 0.6"}]
  end

Ensure numerix and its dependencies are started before your application:

  def application do
    [applications: [:numerix, :gen_stage, :flow]]
  end

Examples

Section titled “Examples”

Check out the tests for examples.

Documentation

Section titled “Documentation”

Check out the API reference for the latest documentation.

Features

Section titled “Features”

Tensor API

Section titled “Tensor API”

Numerix now includes a Tensor API that lets you implement complex math functions with little code, similar to what you get from numpy. And since Numerix is written in Elixir, it uses Flow to run independent pieces of computation in parallel to speed things up. Depending on the type of calculations you’re doing, the bigger the data and the more cores you have, the faster it gets.

NOTE: Parallelization can only get you so far. In terms of raw speed, a pure Elixir solution will always be much slower compared to one that leverages low-level routines like BLAS or similar.

Statistics

Section titled “Statistics”
  • Mean
  • Weighted mean
  • Median
  • Mode
  • Range
  • Variance
  • Population variance
  • Standard deviation
  • Population standard deviation
  • Moment
  • Kurtosis
  • Skewness
  • Covariance
  • Weighted covariance
  • Population covariance
  • Quantile
  • Percentile

Correlation functions

Section titled “Correlation functions”
  • Pearson
  • Weighted Pearson

Distance functions

Section titled “Distance functions”
  • Mean squared error (MSE)
  • Root mean square error (RMSE)
  • Pearson
  • Minkowski
  • Euclidean
  • Manhattan
  • Jaccard

General math functions

Section titled “General math functions”
  • nth root

Special functions

Section titled “Special functions”
  • Logit
  • Logistic

Window functions

Section titled “Window functions”
  • Gaussian

Linear algebra

Section titled “Linear algebra”
  • Dot product
  • L1-norm
  • L2-norm
  • p-norm
  • Vector subtraction and multiplication

Linear regression

Section titled “Linear regression”
  • Least squares best fit
  • Prediction
  • R-squared

Kernel functions

Section titled “Kernel functions”
  • RBF

Optimization

Section titled “Optimization”
  • Genetic algorithms

Neural network activation functions

Section titled “Neural network activation functions”
  • softmax
  • softplus
  • softsign
  • sigmoid
  • ReLU, leaky ReLU, ELU and SELU
  • tanh