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    "info": {
        "author": "Luca Cappelletti",
        "author_email": "cappelletti.luca94@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Information Technology",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Information Analysis"
        ],
        "description": ".. role:: py(code)\n   :language: python\n\nDictances\n==========\n\n|travis| |coveralls| |sonar_quality| |sonar_maintainability| |code_climate_maintainability| |code_climate_test_coverage| |pip|\n\nDistances and divergences between dictionaries implemented in python 3.6.\n\nIn the complexity notations, :py:`n` is :py:`len(a)` and :py:`m` is :py:`len(b)`.\n\nThe samples are dictionaries generated by the `test utilities here`_.\n\nHow do I get it?\n----------------\nJust type into your terminal:\n\n.. code:: shell\n\n   pip install dictances\n\n\nBasic example\n--------------\nFor each metric, an example is present in `the folder examples`_. Here's a basic example for those too lazy to click links (like me).\n\n.. code:: python\n\n    import random\n    from dictances import cosine, euclidean, canberra\n    random.seed(42)  # for reproducibility\n\n    # Simple function to generate the example dictionaries\n\n\n    def generate_example_dict(n=1000):\n        return {random.randint(0, 1000): random.uniform(0, 1000) for i in range(n)}\n\n\n    a, b = generate_example_dict(), generate_example_dict()\n\n    print(cosine(a, b))\n    # >>> 0.52336690346601\n\n    print(euclidean(a, b))\n    # >>> 15119.400349404095\n\n    print(canberra(a, b))\n    # >>> 624.9088876554047\n\n\n\nMetrics table\n--------------\n\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| Metric name                    | Usage example                 | Average time on sample      | Complexity                           |\n+================================+===============================+=============================+======================================+\n| `Euclidean distance`_          | `euclidean`_                  | 90.4 \u00b5s \u00b1 2.5 \u00b5s            | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| Squared variation              | `squared_variation`_          | 90.8 \u00b5s \u00b1 1.43              | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Total variation`_             | `total_variation`_            | 92.3 \u00b5s \u00b1 1.28 \u00b5s           | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| Nth variation                  | `nth_variation`_              | 91.1 \u00b5s \u00b1 1.2 \u00b5s            | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Manhattan distance`_          | `manhattan`_                  | 92.7 \u00b5s \u00b1 1.43 \u00b5s           | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Mean absolute error`_         | `mae`_                        | 92.3 \u00b5s \u00b1 1.28 \u00b5s           | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Mean squared error`_          | `mse`_                        | 91.1 \u00b5s \u00b1 1.2 \u00b5s            | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Chebyshev distance`_          | `chebyshev`_                  | 101 \u00b5s \u00b1 2.14 \u00b5s            | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Minkowski distance`_          | `minkowsky`_                  | 91.1 \u00b5s \u00b1 2.05 \u00b5s           | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Canberra distance`_           | `canberra`_                   | 71.8 \u00b5s \u00b1 1.95 \u00b5s           | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Cosine distance`_             | `cosine`_                     | 61.3 \u00b5s \u00b1 835 ns            | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Pearson distance`_            | `pearson`_                    | 46.9 \u00b5s \u00b1 1.23 \u00b5s           | |On+m|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Hamming distance`_            | `hamming`_                    | 28.7 \u00b5s \u00b1 784 ns            | |Omin|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| Normalized Total Variation     | `normal_total_variation`_     | 34.6 \u00b5s \u00b1 543 ns            | |Omin|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Kullback Leibler divergence`_ | `kullback_leibler`_           | 24 \u00b5s \u00b1 587 ns              | |Omin|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Jensen Shannon divergence`_   | `jensen_shannon`_             | 38.2 \u00b5s \u00b1 1.18 \u00b5s           | |Omin|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Bhattacharyya distance`_      | `bhattacharyya`_              | 32.7 \u00b5s \u00b1 655 ns            | |Omin|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n| `Hellinger distance`_          | `hellinger`_                  | 42 \u00b5s \u00b1 467 ns              | |Omin|                               |\n+--------------------------------+-------------------------------+-----------------------------+--------------------------------------+\n\nTest computer specifications\n----------------------------\n\nThe computer on which the metrics where timed had the following specifications:\n\n+---------------------------------------+\n| Computer specifications               |\n+=======================+===============+\n| Model Name            | MacBook Pro   |\n+-----------------------+---------------+\n| Processor Name        | Intel Core i7 |\n+-----------------------+---------------+\n| Processor Speed       | 2.3 GHz       |\n+-----------------------+---------------+\n| Number of Processors  | 1             |\n+-----------------------+---------------+\n| Total Number of Cores | 4             |\n+-----------------------+---------------+\n| L2 Cache (per Core)   | 256 KB        |\n+-----------------------+---------------+\n| L3 Cache              | 6 MB          |\n+-----------------------+---------------+\n| Memory                | 16 GB         |\n+-----------------------+---------------+\n\n.. |travis| image:: https://travis-ci.org/LucaCappelletti94/dictances.png\n   :target: https://travis-ci.org/LucaCappelletti94/dictances\n\n.. |coveralls| image:: https://coveralls.io/repos/github/LucaCappelletti94/distances/badge.svg?branch=master\n    :target: https://coveralls.io/github/LucaCappelletti94/distances\n\n.. |sonar_quality| image:: https://sonarcloud.io/api/project_badges/measure?project=dictances.lucacappelletti&metric=alert_status\n    :target: https://sonarcloud.io/dashboard/index/dictances.lucacappelletti\n\n.. |sonar_maintainability| image:: https://sonarcloud.io/api/project_badges/measure?project=dictances.lucacappelletti&metric=sqale_rating\n    :target: https://sonarcloud.io/dashboard/index/dictances.lucacappelletti\n\n.. |code_climate_maintainability| image:: https://api.codeclimate.com/v1/badges/25fb7c6119e188dbd12c/maintainability\n   :target: https://codeclimate.com/github/LucaCappelletti94/dictances/maintainability\n   :alt: Maintainability\n\n.. |code_climate_test_coverage| image:: https://api.codeclimate.com/v1/badges/25fb7c6119e188dbd12c/test_coverage\n   :target: https://codeclimate.com/github/LucaCappelletti94/dictances/test_coverage\n   :alt: Test Coverage\n\n.. |pip| image:: https://badge.fury.io/py/dictances.svg\n    :target: https://badge.fury.io/py/dictances\n\n.. _Euclidean distance: https://en.wikipedia.org/wiki/Euclidean_distance\n.. _Manhattan distance: https://en.wikipedia.org/wiki/Taxicab_geometry\n.. _Jensen Shannon divergence: https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence\n.. _Bhattacharyya distance: https://en.wikipedia.org/wiki/Bhattacharyya_distance\n.. _Total variation: https://en.wikipedia.org/wiki/Total_variation\n.. _Hellinger distance: https://en.wikipedia.org/wiki/Hellinger_distance\n.. _Kullback Leibler divergence: https://en.wikipedia.org/wiki/Hellinger_distance\n.. _Mean absolute error: https://en.wikipedia.org/wiki/Mean_absolute_error\n.. _Mean squared error: https://en.wikipedia.org/wiki/Mean_squared_error\n.. _Chebyshev distance: https://en.wikipedia.org/wiki/Chebyshev_distance\n.. _Minkowski distance: https://en.wikipedia.org/wiki/Minkowski_distance\n.. _Canberra distance: https://en.wikipedia.org/wiki/Canberra_distance\n.. _Cosine distance: https://en.wikipedia.org/wiki/Cosine_similarity\n.. _Pearson distance: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\n.. _Hamming distance: https://en.wikipedia.org/wiki/Hamming_distance\n\n.. _euclidean: https://github.com/LucaCappelletti94/distances/blob/master/examples/euclidean.py\n.. _jensen_shannon: https://github.com/LucaCappelletti94/distances/blob/master/examples/jensen_shannon.py\n.. _bhattacharyya: https://github.com/LucaCappelletti94/distances/blob/master/examples/bhattacharyya.py\n.. _total_variation: https://github.com/LucaCappelletti94/distances/blob/master/examples/total_variation.py\n.. _squared_variation: https://github.com/LucaCappelletti94/distances/blob/master/examples/squared_variation.py\n.. _nth_variation: https://github.com/LucaCappelletti94/distances/blob/master/examples/nth_variation.py\n.. _hellinger: https://github.com/LucaCappelletti94/distances/blob/master/examples/hellinger.py\n.. _kullback_leibler: https://github.com/LucaCappelletti94/distances/blob/master/examples/kullback_leibler.py\n.. _manhattan: https://github.com/LucaCappelletti94/distances/blob/master/examples/manhattan.py\n.. _mae: https://github.com/LucaCappelletti94/distances/blob/master/examples/mae.py\n.. _mse: https://github.com/LucaCappelletti94/distances/blob/master/examples/mse.py\n.. _chebyshev: https://github.com/LucaCappelletti94/distances/blob/master/examples/chebyshev.py\n.. _minkowsky: https://github.com/LucaCappelletti94/distances/blob/master/examples/minkowski.py\n.. _canberra: https://github.com/LucaCappelletti94/distances/blob/master/examples/canberra.py\n.. _cosine: https://github.com/LucaCappelletti94/distances/blob/master/examples/cosine.py\n.. _pearson: https://github.com/LucaCappelletti94/distances/blob/master/examples/pearson.py\n.. _hamming: 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