Metadata-Version: 2.1
Name: pwlf
Version: 2.0.0
Summary: fit piecewise linear functions to data
Home-page: https://github.com/cjekel/piecewise_linear_fit_py
Author: Charles Jekel
Author-email: cjekel@gmail.com
License: MIT License
Description: # About
        A library for fitting continuous piecewise linear functions to data. Just specify the number of line segments you desire and provide the data.
        
        ![Downloads a month](https://img.shields.io/pypi/dm/pwlf.svg) [![Build Status](https://travis-ci.org/cjekel/piecewise_linear_fit_py.svg?branch=master)](https://travis-ci.org/cjekel/piecewise_linear_fit_py)  [![Coverage Status](https://coveralls.io/repos/github/cjekel/piecewise_linear_fit_py/badge.svg?branch=master)](https://coveralls.io/github/cjekel/piecewise_linear_fit_py?branch=master)![PyPI version](https://img.shields.io/pypi/v/pwlf)
        
        Check out the [documentation](https://jekel.me/piecewise_linear_fit_py)!
        
        Read the [blog post](http://jekel.me/2017/Fit-a-piecewise-linear-function-to-data/).
        
        ![Example of a continuous piecewise linear fit to data.](https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/examplePiecewiseFit.png)
        
        ![Example of a continuous piecewise linear fit to a sine wave.](https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/sinWaveFit.png)
        
        Now you can perform segmented constant fitting and piecewise polynomials!
        ![Example of multiple degree fits to a sine wave.](https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/figs/multi_degree.png)
        
        # Features
        For a specified number of line segments, you can determine (and predict from) the optimal continuous piecewise linear function f(x). See [this example](https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitForSpecifiedNumberOfLineSegments.py).
        
        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](https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitWithKnownLineSegmentLocations.py).
        
        If you want to pass different keywords for the SciPy differential evolution algorithm see [this example](https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitForSpecifiedNumberOfLineSegments_passDiffEvoKeywords.py).
        
        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](https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/useCustomOptimizationRoutine.py).
        
        Instead of using differential evolution, you can now use a multi-start gradient optimization with fitfast() function. You can specify the number of starting points to use. The default is 2. This means that a latin hyper cube sampling (space filling DOE) of 2 is used to run 2 L-BFGS-B optimizations. See [this example](https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/sineWave_time_compare.py) which runs fit() function, then runs the fitfast() to compare the runtime differences!
        
        # Installation
        
        You can now install with pip.
        ```
        [sudo] pip install pwlf
        ```
        
        Or clone the repo
        ```
        git clone https://github.com/cjekel/piecewise_linear_fit_py.git
        ```
        
        then install with pip
        ```
        [sudo] pip install ./piecewise_linear_fit_py
        ```
        
        # How it works
        This [paper](https://github.com/cjekel/piecewise_linear_fit_py/raw/master/paper/pwlf_Jekel_Venter_v2.pdf) explains how this library works in detail.
        
        This is based on a formulation of a piecewise linear least squares fit, where the user must specify the location of break points. See [this post](http://jekel.me/2018/Continous-piecewise-linear-regression/) which goes through the derivation of a least squares regression problem if the break point locations are known. Alternatively check out [Golovchenko (2004)](http://golovchenko.org/docs/ContinuousPiecewiseLinearFit.pdf).
        
        Global optimization is used to find the best location for the user defined number of line segments. I specifically use the [differential evolution](https://docs.scipy.org/doc/scipy-0.17.0/reference/generated/scipy.optimize.differential_evolution.html) 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](https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitForSpecifiedNumberOfLineSegments_passDiffEvoKeywords.py).
        
        # Changelog
        All changes now stored in [CHANGELOG.md](https://github.com/cjekel/piecewise_linear_fit_py/blob/master/CHANGELOG.md)
        
        New ```weights=``` keyword allows you to perform weighted pwlf fits! Removed TensorFlow code which can now be found [here](https://github.com/cjekel/piecewise_linear_fit_py_tf). 
        
        # Requirements
        Python 2.7+
        
        NumPy >= 1.14.0
        
        SciPy >= 1.2.0
        
        pyDOE >= 0.3.8
        
        setuptools >= 38.6.0
        
        # License
        MIT License
        
        # Citation
        
        ```bibtex
        @Manual{pwlf,
        	author = {Jekel, Charles F. and Venter, Gerhard},
        	title = {{pwlf:} A Python Library for Fitting 1D Continuous Piecewise Linear Functions},
        	year = {2019},
        	url = {https://github.com/cjekel/piecewise_linear_fit_py}
        }
        ```
        
Platform: any
Description-Content-Type: text/markdown
