Chaospy is a numerical tool for performing uncertainty quantification using polynomial chaos expansions and advanced Monte Carlo methods.
A article in Elsevier Journal of Computational Science has been published introducing the paper: (http://dx.doi.org/10.1016/j.jocs.2015.08.008)
python numpy scipy networkx
For regression analysis:
scikit-learn
To install the prerequisite on a Debian/Ubuntu machine:
sudo apt-get install python-scipy python-networkx cython gcc
For scikit-learn:
sudo apt-get install build-essential python-dev \
python-setuptools libatlas-dev libatlas3gf-base
To install in the site-packages directory and make it importable
from anywhere.
Automated download and installation can be done by running the following as super user:
pip install -e git+https://github.com/hplgit/chaospy.git#egg=chaospy
Alternative, download the Github folder and run the following command as super user in the root folder:
python setup.py install
For scikit-learn:
pip install --user --install-option="--prefix=" -U scikit-learn
The core code base is licensed under BSD terms. Files with deviating license have their own license written on top of the file.