Metadata-Version: 2.1
Name: tslearn
Version: 0.2.2
Summary: A machine learning toolkit dedicated to time-series data
Home-page: http://tslearn.readthedocs.io/
Author: Romain Tavenard
Author-email: romain.tavenard@univ-rennes2.fr
License: UNKNOWN
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        `tslearn` is a Python package that provides machine learning tools for the analysis of time series.
        This package builds on `scikit-learn`, `numpy` and `scipy` libraries.
        
        If you would like to contribute to `tslearn`, please have a look at [our contribution guidelines](CONTRIBUTING.md).
        
        # Dependencies
        
        ```
        Cython
        numpy
        numba
        scipy
        scikit-learn
        joblib
        numba
        ```
        
        If you plan to use the `shapelets` module, `keras` and `tensorflow` should also be installed.
        
        # Installation
        
        ## Pre-requisites 
        
        C++ build tools should be available to perform installation.
        
        ## Using conda
        
        The easiest way to install `tslearn` is probably via `conda`:
        ```bash
        conda install -c conda-forge tslearn
        ```
        
        ## Using PyPI
        
        Using `pip` should also work fine:
        ```bash
        pip install tslearn
        ```
        
        ## Using latest github-hosted version
        
        If you want to get `tslearn`'s latest version, you can refer to the repository hosted at github:
        ```bash
        pip install git+https://github.com/rtavenar/tslearn.git
        ```
        
        ## Troubleshooting
        
        It seems on some platforms `Cython` dependency does not install properly.
        If you experiment such an issue, try installing it with the following command:
        
        ```bash
        pip install cython
        ```
        
        or (depending on your preferred python package manager):
        ```bash
        conda install -c anaconda cython
        ```
        
        before you start installing `tslearn`.
        
        # Documentation and API reference
        
        The documentation, including a gallery of examples, is hosted at [readthedocs](http://tslearn.readthedocs.io/en/latest/index.html).
        
        # Already available
        
        * A `generators` module provides Random Walks generators
        * A `datasets` module provides access to the famous UCR/UEA datasets through the `UCR_UEA_datasets` class
        * A `preprocessing` module provides standard time series scalers
        * A `metrics` module provides:
          * Dynamic Time Warping (DTW) (with Sakoe-Chiba band and Itakura parallelogram variants)
          * LB_Keogh
          * Global Alignment Kernel
          * Soft-DTW from Cuturi and Blondel
        * A `neighbors` module includes nearest neighbor algorithms to be used with time series
        * An `svm` module includes Support Vector Machine algorithms with:
          * Standard kernels offered in `sklearn` (with adequate array reshaping done for you)
          * Global Alignment Kernel
        * A `clustering` module includes the following time series clustering algorithms:
          * Standard Euclidean k-means (with adequate array reshaping done for you)
            * Based on `tslearn.barycenters`
          * DBA k-means from Petitjean _et al._
            * Based on `tslearn.barycenters` that offers DBA facility that could be used for other applications than just
            k-means
          * Global Alignment kernel k-means
          * KShape clustering from Paparizzos and Gravano
          * Soft-DTW k-means from Cuturi and Blondel
            * Based on `tslearn.barycenters` that offers Soft-DTW barycenter computation
          * It also provides a way to compute the silhouette coefficient for given clustering and metric
        * A `shapelets` module includes an efficient implementation of the Learning Time-Series method from Grabocka _et al._
          * **Warning:** to use the `shapelets` module, two extra dependencies are required: `keras` and `tensorflow`
        * A `piecewise` module includes standard time series transformations, as well as the corresponding distances:
          * Piecewise Aggregate Approximation (PAA)
          * Symbolic Aggregate approXimation (SAX)
          * 1d-Symbolic Aggregate approXimation (1d-SAX)
        
        # TODO list
        
        Have a look [there](https://github.com/rtavenar/tslearn/issues?utf8=✓&q=is%3Aissue%20is%3Aopen%20label%3A%22new%20feature%22%20) for a list of suggested features.
        **If you want other ML methods for time series to be added to this TODO list, do not hesitate to open an issue!** See [our contribution guidelines](CONTRIBUTING.md) for more information about how to proceed.
        
        # Acknowledgments
        
        Authors would like to thank Mathieu Blondel for providing code for
        [Kernel k-means](https://gist.github.com/mblondel/6230787) and [Soft-DTW](https://github.com/mblondel/soft-dtw) (both
        distributed under BSD license) that are used in the `clustering` and `metrics` modules of this library.
        
        # Referencing `tslearn`
        
        If you use `tslearn` in a scientific publication, we would appreciate citations:
        
        ```bibtex
        @misc{tslearn,
         title={tslearn: A machine learning toolkit dedicated to time-series data},
         author={Tavenard, Romain and Faouzi, Johann and Vandewiele, Gilles},
         year={2017},
         note={\url{https://github.com/rtavenar/tslearn}}
        }
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: tests
