Metadata-Version: 2.1
Name: rank-similarity
Version: 0.0.1
Summary: Rank Similarity is a set of non-linear classification and transform tools for large datasets. 
Home-page: https://github.com/KatharineShapcott/rank-similarity
Maintainer: Katharine Shapcott
Maintainer-email: katharine.shapcott@esi-frankfurt.de
License: new BSD
Download-URL: https://github.com/KatharineShapcott/rank-similarity
Description: # Rank Similarity
        
        Rank Similarity is a set of non-linear classification and transform tools for large multi-dimensional datasets that use the scikit-learn API. 
        
        ## Installation
        ### Dependencies
        rank-similarity requires:
        
        - Scikit-learn (>= 0.23)
        - Python (>= 3.7)
        - NumPy (>= 1.14.6)
        - SciPy (>= 1.1.0)
        
        Optionally for plotting examples:
        - matplotlib (>= 2.2.2)
        
        ### Install via pip
        
        ```
        pip install rank-similarity
        ```
        
        ### Development version
        
        To get the latest development version, clone the GitHub repository:
        
        ```
        git clone https://github.com/KatharineShapcott/rank-similarity
        ```
        
        ## Usage
        
        ``` python
        from ranksim import RankSimilarityClassifier
        X = [[0, 1], [1, 0]]
        y = [0, 1]
        clf = RankSimilarityClassifier()
        clf.fit(X, y)
        pred = clf.predict(X)
        ```
        
        ## More Information
        
        ### Documentation
        More details and background information is available in the
        [online documentation](https://katharineshapcott.github.io/rank-similarity/).
        
        ### License
        The package is new BSD licensed.
        
        ### Citation
        Please cite the following publication (in preparation) [[1]](#1).
        
        <a id="1">[1]</a> 
        Shapcott, Bird, & Singer. Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data. In preperation. (2021)
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
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 :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
