torchpwl 0.2.0

Piecewise Linear Functions (PWLs) can be used to approximate any 1D function. PWLs are built with a configurable number of line segments - the more segments the more accurate the approximation. This package implements PWLs in PyTorch and as such they can be fit to the data using standard gradient descent. For example:

import torchpwl

# Create a PWL consisting of 3 segments for 5 features - each feature will have its own PWL function. pwl = torchpwl.PWL(num_features=5, num_breakpoints=3) x = torch.Tensor(11, 5).normal_() y = pwl(x)

Monotonicity is also supported via MonoPWL. See the class documentations for more details.