nnetsauce | Star
Welcome to nnetsauce's website.
nnetsauce does Statistical/Machine Learning (ML) using advanced combinations of randomized and quasi-randomized neural networks layers. It contains models for regression, classification, and time series forecasting. Every ML model in nnetsauce is based on components g(XW + b), where:
- X is a matrix containing explanatory variables and optional clustering information. Clustering the inputs helps in taking into account data’s heterogeneity before model fitting.
- W creates new, additional explanatory variables from X. W can be drawn from various random and quasirandom sequences.
- b is an optional bias parameter.
- g is an activation function such as the hyperbolic tangent or the sigmoid function, that renders the combination of explanatory variables – through W – nonlinear.
nnetsauce’s source code is available on GitHub.
Looking for a specific function? You can also use the search function available in the navigation bar.
Installing (for Python and R)
- 1st method: by using
pipat the command line for the stable version
pip install nnetsauce
- 2nd method: from Github, for the development version
pip install git+https://github.com/Techtonique/nnetsauce.git
git clone https://github.com/Techtonique/nnetsauce.git cd nnetsauce make install
- 1st method: From Github, in R console:
library(devtools) devtools::install_github("Techtonique/nnetsauce/R-package") library(nnetsauce)
General rule for using the package in R: object accesses with
.'s are replaced by
$'s. See also Quick start.
Examples of use:
For time series
R examples can be found in these notebooks:
The documentation for each model can be found (work in progress) here:
Want to contribute to nnetsauce's development on Github, read this!