Metadata-Version: 1.0
Name: pwlf
Version: 0.3.5
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.
        
        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/>`__.
        
        .. figure:: https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/examplePiecewiseFit.png
           :alt: Example of a continuous piecewise linear fit to data.
        
           Example of a continuous piecewise linear fit to data.
        
        .. figure:: https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/sinWaveFit.png
           :alt: Example of a continuous piecewise linear fit to a sine wave.
        
           Example of a continuous piecewise linear fit to a sine wave.
        
        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_v1.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 r\_squared() function, new p\_values() function, all docstrings
        follows numpydoc style...
        
        Requirements
        ============
        
        Python 2.7+
        
        NumPy (Tested on version >= 1.14.0)
        
        SciPy (Tested on version >= 0.19.0)
        
        pyDOE (Tested on version >= 0.3.8)
        
        License
        =======
        
        MIT License
        
        Citation
        ========
        
        .. code:: 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
