- nnetsauce’s new version
- Installing nnetsauce for Python
- About nnetsauce for R
nnetsauce’s new version
A new version of nnetsauce, v0.12.0, is available on PyPI and for conda. It’s been mostly tested on Linux and macOS platforms. For Windows users: you can use the Windows Subsystem for Linux in case it doesn’t work directly on your computer.
As a reminder, nnetsauce does Statistical/Machine Learning (regression, classification, and time series forecasting for now) using randomized and quasi-randomized neural networks layers. More precisely, every 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 quasi-random sequences.
- b is an optional bias parameter.
- g is an activation function such as ReLU or the hyperbolic tangent, that makes the combination of explanatory variables – through W – nonlinear.
v0.12.0 is an important release, because it’s totally written in Python (using numpy, scipy, jax, and scikit-learn), and doesn’t use C++ nor Cython anymore. Because of this, nnetsauce is faster to install, and easier to maintain.
If you like using nnetsauce, do not hesitate to star the repo or submit a pull request!
Installing nnetsauce for Python
- 1st method: by using
pipat the command line for the stable version
pip install nnetsauce
- 2nd method: using
conda(Linux and macOS only for now)
conda install -c conda-forge nnetsauce
- 3rd method: from Github, for the development version
pip install git+https://github.com/Techtonique/nnetsauce.git
or in a virtual environment:
git clone https://github.com/Techtonique/nnetsauce.git cd nnetsauce make install
About nnetsauce for R
The R version is discontinued. Well, ‘discontinued’ until I finally wrap
my head around it… If you’re interested in solving this issue, and therefore, using nnetsauce for R,
everything happens in this R script.
You can submit a pull request (and star the repo 😉 )!