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https://github.com/safwank/Numerix
A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning
https://github.com/safwank/Numerix
linear-algebra machine-learning math statistics
Last synced: 4 days ago
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A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning
- Host: GitHub
- URL: https://github.com/safwank/Numerix
- Owner: safwank
- License: mit
- Created: 2016-04-27T22:39:13.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2021-05-30T22:44:10.000Z (over 3 years ago)
- Last Synced: 2024-09-26T21:17:08.681Z (about 1 month ago)
- Topics: linear-algebra, machine-learning, math, statistics
- Language: Elixir
- Homepage:
- Size: 141 KB
- Stars: 186
- Watchers: 8
- Forks: 17
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- freaking_awesome_elixir - Elixir - A collection of useful mathematical functions with a slant towards statistics, linear algebra and machine learning. (Statistics)
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- awesome-elixir - numerix - A collection of useful mathematical functions with a slant towards statistics, linear algebra and machine learning. (Statistics)
README
[](https://travis-ci.org/safwank/Numerix)
# Numerix
A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning.
## Installation
Add `numerix` to your list of dependencies in `mix.exs`:
```elixir
def deps do
[{:numerix, "~> 0.6"}]
end
```
Ensure `numerix` and its dependencies are started before your application:
```elixir
def application do
[applications: [:numerix, :gen_stage, :flow]]
end
```
## Examples
Check out the [tests](https://github.com/safwank/Numerix/tree/master/test) for examples.
## Documentation
Check out the [API reference](https://hexdocs.pm/numerix/api-reference.html) for the latest documentation.
## Features
### Tensor API
Numerix now includes a [Tensor API](https://hexdocs.pm/numerix/Numerix.Tensor.html) 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
* 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
* Pearson
* Weighted Pearson
### Distance functions
* Mean squared error (MSE)
* Root mean square error (RMSE)
* Pearson
* Minkowski
* Euclidean
* Manhattan
* Jaccard
### General math functions
* nth root
### Special functions
* Logit
* Logistic
### Window functions
* Gaussian
### Linear algebra
* Dot product
* L1-norm
* L2-norm
* p-norm
* Vector subtraction and multiplication
### Linear regression
* Least squares best fit
* Prediction
* R-squared
### Kernel functions
* RBF
### Optimization
* Genetic algorithms
### Neural network activation functions
* softmax
* softplus
* softsign
* sigmoid
* ReLU, leaky ReLU, ELU and SELU
* tanh