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        "description": "[![PyPI version](https://badge.fury.io/py/tslearn.svg)](https://badge.fury.io/py/tslearn)\n[![Documentation Status](https://readthedocs.org/projects/tslearn/badge/?version=latest)](http://tslearn.readthedocs.io/en/latest/?badge=latest)\n[![Build Status](https://travis-ci.org/rtavenar/tslearn.svg?branch=master)](https://travis-ci.org/rtavenar/tslearn)\n[![Codecov](https://codecov.io/gh/rtavenar/tslearn/branch/master/graph/badge.svg)](https://codecov.io/gh/rtavenar/tslearn)\n[![Downloads](https://pepy.tech/badge/tslearn)](https://pepy.tech/project/tslearn)\n\n`tslearn` is a Python package that provides machine learning tools for the analysis of time series.\nThis package builds on `scikit-learn`, `numpy` and `scipy` libraries.\n\nIf you would like to contribute to `tslearn`, please have a look at [our contribution guidelines](CONTRIBUTING.md).\n\n# Dependencies\n\n```\nCython\nnumpy\nnumba\nscipy\nscikit-learn\njoblib\nnumba\n```\n\nIf you plan to use the `shapelets` module, `keras` and `tensorflow` should also be installed.\n\n# Installation\n\n## Pre-requisites \n\nC++ build tools should be available to perform installation.\n\n## Using conda\n\nThe easiest way to install `tslearn` is probably via `conda`:\n```bash\nconda install -c conda-forge tslearn\n```\n\n## Using PyPI\n\nUsing `pip` should also work fine:\n```bash\npip install tslearn\n```\n\nIn this case, you should have `numpy`, `cython` and C++ build tools available\nat build time.\n\n## Using latest github-hosted version\n\nIf you want to get `tslearn`'s latest version, you can refer to the repository \nhosted at github:\n```bash\npip install git+https://github.com/rtavenar/tslearn.git\n```\n\nIn this case, you should have `numpy`, `cython` and C++ build tools available\nat build time.\n\n# Documentation and API reference\n\nThe documentation, including a gallery of examples, is hosted at \n[readthedocs](http://tslearn.readthedocs.io/en/latest/index.html).\n\n# Already available\n\n* A `generators` module provides Random Walks generators\n* A `datasets` module provides access to the famous UCR/UEA datasets through the `UCR_UEA_datasets` class\n* A `preprocessing` module provides standard time series scalers\n* A `metrics` module provides:\n  * Dynamic Time Warping (DTW) (with Sakoe-Chiba band and Itakura parallelogram variants)\n  * LB_Keogh\n  * Global Alignment Kernel\n  * Soft-DTW from Cuturi and Blondel\n* A `neighbors` module includes nearest neighbor algorithms to be used with time series\n* An `svm` module includes Support Vector Machine algorithms with:\n  * Standard kernels offered in `sklearn` (with adequate array reshaping done for you)\n  * Global Alignment Kernel\n* A `clustering` module includes the following time series clustering algorithms:\n  * Standard Euclidean k-means (with adequate array reshaping done for you)\n    * Based on `tslearn.barycenters`\n  * DBA k-means from Petitjean _et al._\n    * Based on `tslearn.barycenters` that offers DBA facility that could be used for other applications than just\n    k-means\n  * Global Alignment kernel k-means\n  * KShape clustering from Paparizzos and Gravano\n  * Soft-DTW k-means from Cuturi and Blondel\n    * Based on `tslearn.barycenters` that offers Soft-DTW barycenter computation\n  * It also provides a way to compute the silhouette coefficient for given clustering and metric\n* A `shapelets` module includes an efficient implementation of the Learning Time-Series method from Grabocka _et al._\n  * **Warning:** to use the `shapelets` module, two extra dependencies are required: `keras` and `tensorflow`\n* A `piecewise` module includes standard time series transformations, as well as the corresponding distances:\n  * Piecewise Aggregate Approximation (PAA)\n  * Symbolic Aggregate approXimation (SAX)\n  * 1d-Symbolic Aggregate approXimation (1d-SAX)\n\n# TODO list\n\nHave a look [there](https://github.com/rtavenar/tslearn/issues?utf8=\u2713&q=is%3Aissue%20is%3Aopen%20label%3A%22new%20feature%22%20) for a list of suggested features.\n**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.\n\n# Acknowledgments\n\nAuthors would like to thank Mathieu Blondel for providing code for\n[Kernel k-means](https://gist.github.com/mblondel/6230787) and [Soft-DTW](https://github.com/mblondel/soft-dtw) (both\ndistributed under BSD license) that are used in the `clustering` and `metrics` modules of this library.\n\n# Referencing `tslearn`\n\nIf you use `tslearn` in a scientific publication, we would appreciate citations:\n\n```bibtex\n@misc{tslearn,\n title={tslearn: A machine learning toolkit dedicated to time-series data},\n author={Tavenard, Romain and Faouzi, Johann and Vandewiele, Gilles},\n year={2017},\n note={\\url{https://github.com/rtavenar/tslearn}}\n}\n```",
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