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Machine learning with polynomials

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![EFFECTIVE-QUADRATURES](https://static.wixstatic.com/media/dad873_3938470ea83849db8b53716c94dd20e8~mv2.png/v1/fill/w_269,h_66,al_c,usm_0.66_1.00_0.01/dad873_3938470ea83849db8b53716c94dd20e8~mv2.png)

# Effective Quadratures Version 7.5

What is Effective Quadratures? Effective Quadratures is a suite of tools for constructing polynomials for approximation, uncertainty quantification (UQ), optimization, dimension reduction and sensitivity analysis.

For further details, notebooks and papers see: <br> www.effective-quadratures.org <br>

# Installation For installation on Mac and Linux systems, simply type the following into the terminal. `bash > sudo python setup.py install ` For installation on Windows, you will need [Anaconda](https://www.continuum.io/downloads#windows); select the Python 2.7 option. Upon successful installation, open the Sypder IDE and go into the Effective-Quadratures-master directory and type the following in the command window `bash > python setup.py install ` This should build the code. Just make sure you include the location of effective_quadratures folder to your python file and you should be good to go. To run this code you will require python 2.7, numpy, scipy and matplotlib.

# Documentation We use Sphinx for code documentation; [click here for documentation](http://effective-quadratures.github.io/Effective-Quadratures/).

# Community guidelines If you have contributions, questions, or feedback use either the Github repository, or contact:<br> <br> The Effective Quadratures Team <br> contact -at- effective-quadratures.org <br>

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