To download and install the code please use the python package index command:
pip install equadratures
or if you are using python3, then
pip3 install equadratures
Alternatively you can visit our GitHub page and click either on the Fork Code button or Clone. For issues with the code, please do raise an issue on our Github page; do make sure to add the relevant bits of code and specifics on package version numbers. We welcome contributions and suggestions from both users and folks interested in developing the code further. Our code is designed to require minimal dependencies; current package requirements include numpy, scipy and matplotlib.
The description below covers some of the key modules in Effective Quadratures; they are split into two parts. First we have the core building blocks to assist users in creating bespoke polynomial approximations for their data.
Then we have the polynomial exploiting utilities that leverage a polynomial approximation to facilitate specific user centric tasks. These include finding dimension reducing subspaces, optimisation with polynomial surrogates, polynomial based deep learning and generating correlated sample sets.
Below we provide some introductory tutorials to give you an idea of what Effective Quadratures can do. The tutorials are organized by complexity, so if you are new to the notion of supervised learning through polynomials, consider starting from the first tutorial.