Skip to main content
Join the official 2020 Python Developers SurveyStart the survey!

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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cvxfit, version 0.1.9
Filename, size File type Python version Upload date Hashes
Filename, size cvxfit-0.1.9.tar.gz (7.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page