Metadata-Version: 2.1
Name: giotto-learn
Version: 0.1.3a0
Summary: Toolbox for Machine Learning using Topological Data Analysis.
Home-page: https://github.com/giotto-learn/giotto-learn
Maintainer: Guillaume Tauzin
Maintainer-email: maintainers@giotto.ai
License: Apache 2.0
Download-URL: https://github.com/giotto-learn/giotto-learn/tarball/v0.1.3a
Description: .. image:: https://www.giotto.ai/static/vector/logo.svg
           :width: 850
        
        |Azure|_ |Azure-cov|_ |Azure-test|_ |binder|_
        
        .. |Azure| image:: https://dev.azure.com/maintainers/Giotto/_apis/build/status/giotto-ai.giotto-learn?branchName=master
        .. _Azure: https://dev.azure.com/maintainers/Giotto/_build/latest?definitionId=2&branchName=master
        
        .. |Azure-cov| image:: https://img.shields.io/badge/Coverage-93%25-passed
        .. _Azure-cov: https://dev.azure.com/maintainers/Giotto/_build/results?buildId=6&view=codecoverage-tab
        
        .. |Azure-test| image:: https://img.shields.io/badge/Testing-Passed-brightgreen
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        giotto-learn
        ============
        
        
        giotto-learn is a high performance topological machine learning toolbox in Python built on top of
        scikit-learn and is distributed under the Apache 2.0 license. It is part of the Giotto open-source project.
        
        Website: https://giotto.ai
        
        
        Installation
        ------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        giotto-learn requires:
        
        - Python (>= 3.5)
        - scikit-learn (>= 0.21.3)
        - NumPy (>= 1.11.0)
        - SciPy (>= 0.17.0)
        - joblib (>= 0.11)
        
        For running the examples jupyter, matplotlib and plotly are required.
        
        User installation
        ~~~~~~~~~~~~~~~~~
        
        If you already have a working installation of numpy and scipy,
        the easiest way to install giotto-learn is using ``pip``   ::
        
            pip install -U giotto-learn
        
        Documentation
        -------------
        
        - HTML documentation (stable release): https://docs.giotto.ai
        
        Contributing
        ------------
        
        We welcome new contributors of all experience levels. The Giotto
        community goals are to be helpful, welcoming, and effective. To learn more about
        making a contribution to giotto-learn, please see the `CONTRIBUTING.rst
        <https://github.com/giotto-ai/giotto-learn/blob/master/CONTRIBUTING.rst>`_ file.
        
        Developer installation
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        C++ dependencies:
        '''''''''''''''''
        
        -  C++14 compatible compiler
        -  CMake >= 3.9
        -  Boost >= 1.56
        
        Source code
        '''''''''''
        
        You can check the latest sources with the command::
        
            git clone https://github.com/giotto-ai/giotto-learn.git
        
        
        To install:
        '''''''''''
        
        .. code-block:: bash
        
           cd giotto-learn
           pip install -e .
        
        From there any change in the library files will be immediately available on your machine.
        
        Testing
        ~~~~~~~
        
        After installation, you can launch the test suite from outside the
        source directory::
        
            pytest giotto
        
        
        Changelog
        ---------
        
        See the `RELEASE.rst <https://github.com/giotto-ai/giotto-learn/blob/master/RELEASE.rst>`__ file
        for a history of notable changes to giotto-learn.
        
        Important links
        ~~~~~~~~~~~~~~~
        
        - Official source code repo: https://github.com/giotto-ai/giotto-learn
        - Download releases: https://pypi.org/project/giotto-learn/
        - Issue tracker: https://github.com/giotto-ai/giotto-learn/issues
        
        
        Contacts:
        ---------
        
        maintainers@giotto.ai
        
Keywords: machine learning topological data analysis persistent homology,persistence diagrams
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
Provides-Extra: tests
Provides-Extra: doc
Provides-Extra: examples
