<commit_before>ParametricModeling
============
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ParametricModeling is a Python class for non-linear as well as linear parametric
model estimation implemented as an sklearn interface around scipy.optimize.least_squares. 
It is distributed under the 3-Clause BSD license.

least_squares can be used to solve a nonlinear least-squares problem with bounds 
on the variables.
The aim of this project is to enclose scipy.optimize.least_squares 
function in a sklearn type interface for easier usage and ensure compatibility 
with sklearn tools like grid_search.

Important links
===============

- Official source code repo: http://github.com/shubhankar90/ParametricModeling
- Sklearn Development Guide: http://scikit-learn.org/stable/developers/index.html
- Scipy least_squares documentation: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html

Dependencies
============
Created and tested to work under Python 3.4.4.1, scipy 0.17.0, sklearn 0.17.1, numpy 1.10.4.<commit_msg>Pushing changes<commit_after>ParametricModeling
============

ParametricModeling is a Python class for non-linear as well as linear parametric
model estimation implemented as an sklearn interface around scipy.optimize.least_squares. 
It is distributed under the 3-Clause BSD license.

least_squares can be used to solve a nonlinear least-squares problem with bounds 
on the variables.
The aim of this project is to enclose scipy.optimize.least_squares 
function in a sklearn type interface for easier usage and ensure compatibility 
with sklearn tools like grid_search.

Important links
===============

- Official source code repo: http://github.com/shubhankar90/ParametricModeling
- Sklearn Development Guide: http://scikit-learn.org/stable/developers/index.html
- Scipy least_squares documentation: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html

Dependencies
============
Created and tested to work under Python 3.4.4.1, scipy 0.17.0, sklearn 0.17.1, numpy 1.10.4.
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