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A package for fitting convex functions to data.

Project description

CvxFit is a package which provides classes for fitting convex functions to given data. Typical usage looks like this:

#!/usr/bin/env python

from cvxfit import CvxFit
import scipy as sp

#Generate data
N = 1000
n = 3

def f_actual(x):
    return sp.sum(x*x)

X = sp.randn(N, n)
Y = sp.array([f_actual(pt) for pt in X])

#Initialize object with 10 affine functions
#with regularization 0.001, and maximum
#number of iterations 20
fit_object = CvxFit(X=X, Y=Y, type='pwl', extra_param=[10, 0.001, 20])

#Perform fit

#See training error; repeat fit if high
print 'Training error: ' + str(fit_object.mean_training_error)

#Compare quality of fit at a random point
pt = sp.randn(1, n)
print 'Actual value: ' + str(f_actual(pt))
print 'Approximate value: ' + str(fit_object.evaluate(pt)[0])


This package was written and tested by Mainak Chowdhury, Alon Kipnis and Milind Rao.


This package came out of a course project for EE364b at Stanford University, Spring 2013-14, taught by Prof. Stephen Boyd. We would like to thank all members of the awesome teaching staff for their useful feedback and constructive suggestions.

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