A package for fitting convex functions to data.
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 fit_object.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))
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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