A library for fitting a continuous piecewise linear function f(x) to data. Just specify the number of line segments you desire and your data set.

Check out the examples!

Read the blog post.

For a specified number of line segments, you can determine (and predict from) the optimal continuous piecewise linear function f(x). See this example.

You can fit and predict a continuous piecewise linear function f(x) if you know the specific x locations where the line segments terminate. See this example.

If you want to pass different keywords for the SciPy differential evolution algorithm see this example.

You can use a different optimization algorithm to find the optimal location for line segments by using the objective function that minimizes the sum of square of residuals. See this example.

This is based on a formulation of a piecewise linear least squares fit, where the user must specify the location of break points. A simple derivation of this fit has been done by Golovchenko (2004). The routine for fitting the piecewise linear function is based on Golovchenko's MATLAB code (which I can't seem to find on the internet), which I ported to Python. Alternatively you can view this code.

Global optimization is used to find the best location for the user defined number of line segments. I specifically use the differential evolution algorithm in SciPy. I default the differential evolution algorithm to be aggressive, and it is probably overkill for your problem. So feel free to pass your own differential evolution keywords to the library. See this example.

All other methods require the user to specify the specific location of break points, but in most cases the best location for these break points is unknown. It makes more sense to rather have the user specify the desired number of line segments, and then to quantitatively choose the best location for the ends of these line segments.

Python 2.7+ (Only Python 2.7 has been tested)

NumPy (Tested on version 1.11.3)

SciPy (Tested on version 0.19.0)

MIT License