Slowpca is a package to perform principal component analysis based on correlation or covariance. This Repository is a only an example of python packaging.
The method was published in:
Authors, ..., Title of publication goes here, in preparation
We kindly ask you to cite these articles if you use this software package for published works.
- performs PCA
- easy to install
- ...
So far the package is only published to PyPI. For installing it to within a python environment simple call:
python3 -m pip install slowpca
or for the latest dev version
# via ssh key
python3 -m pip install git+ssh://git@github.com/moldyn/python-packaging.git
# , or via password-based login
python3 -m pip install git+https://github.com/moldyn/python-packaging.git
In general one can call the module directly by its entry point $ slowpca
or by calling the module $ python -m slowpca
. The latter method is
preferred to ensure using the desired python environment.
For help simply call $ python -m slowpca --help
.
from slowpca import estimate_pca
# Load file
# X is np.ndarray of shape (n_samples, n_features)
proj, evecs = estimate_pca(X)
...