Linear Regression with numpy only.
The numpy-linreg
git repo is available as PyPi package
pip install numpy-linreg
pip install git+ssh://git@github.com/ulf1/numpy-linreg.git
Ridge Regression
import numpy_linreg.ridge as ridge
import numpy_linreg.metrics as metrics
beta = ridge.lu(y, X)
rmse = metrics.rmse(y, X, beta)
OLS Regression
import numpy_linreg.ols as ols
beta = ols.lu(y, X)
beta = ols.pinv(y, X)
beta = ols.qr(y, X)
beta = ols.svd(y, X)
Check the examples folder for notebooks.
Install a virtual environment
python3.6 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt
pip3 install -r requirements-dev.txt
pip3 install -r requirements-demo.txt
(If your git repo is stored in a folder with whitespaces, then don't use the subfolder .venv
. Use an absolute path without whitespaces.)
- Jupyter for the examples:
jupyter lab
- Check syntax:
flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')
Publish
pandoc README.md --from markdown --to rst -s -o README.rst
python setup.py sdist
twine upload -r pypi dist/*
find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venv
Please open an issue for support.
Please contribute using Github Flow. Create a branch, add commits, and open a pull request.