Metadata-Version: 1.0
Name: torchpwl
Version: 0.2.0
Summary: Implementation of Piecewise Linear Functions (PWL) in PyTorch.
Home-page: https://github.com/PiotrDabkowski/torchpwl
Author: Piotr Dabkowski
Author-email: piodrus@gmail.com
License: MIT
Description: 
        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.
        
Platform: UNKNOWN
