https://github.com/wiseaidev/pydist2

A python library for computing distances.
https://github.com/wiseaidev/pydist2

distance distance-calculation distance-matrix euclidean-distances hacktoberfest python3

Last synced: 5 months ago
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A python library for computing distances.

• Host: GitHub
• URL: https://github.com/wiseaidev/pydist2
• Owner: wiseaidev
• License: mit
• Created: 2021-03-04T20:05:15.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-10-02T11:07:08.000Z (about 3 years ago)
• Last Synced: 2025-05-01T09:16:39.742Z (5 months ago)
• Topics: distance, distance-calculation, distance-matrix, euclidean-distances, hacktoberfest, python3
• Language: Python
• Homepage: https://pydist2.readthedocs.io
• Size: 82 KB
• Stars: 22
• Watchers: 2
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Codeowners: .github/CODEOWNERS

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README

          
=======

pydist2

=======

.. image:: https://img.shields.io/pypi/v/pydist2.svg

        :target: https://pypi.python.org/pypi/pydist2

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        :alt: Documentation Status

.. image:: https://img.shields.io/pypi/status/pydist2.svg

        :target: https://pypi.python.org/pypi/pydist2/

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        :target: https://pypi.python.org/pypi/pydist2/

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        :target: https://github.com/wiseaidev/pydist2

pydist2 is a python library that provides a set of methods for calculating distances between observations.

There are two main classes:

* **pdist1** which calculates the pairwise distances between observations in one matrix and returns a distance matrix.

* **pdist2** computes the distances between observations in two matrices and also returns a distance matrix.

Usage

-----

.. code-block:: console

   pdist1(P, metric = "euclidean", matrix=False)

   pdist2(P, Q, metric = "minkowski", exp = 3)

**Arguments**: 

* two matrices P and Q.

* metric: The distance function to use.

* exp: The exponent of the Minkowski distance.

Installation

-------------

The pydist2 library is available on Pypi_. Thus, you can install the latest available version using *pip*::

   pip install pydist2

Supported Python versions

-------------------------

pydist2 has been tested with Python 3.7 and 3.8. 

For more information, please checkout the documentation which is available at readthedocs_.

This program and the accompanying materials are made available under the terms of the `MIT License`_.

Progress & Features

-------------------

- ☑ Commit the first code's version.

- ☑ Support the following `list of distances`_. 

- ☑ Display the distance in a matrix form(a combination for each pair of points)::

   >>> X = np.array([[100, 100],[0, 100],[100, 0], [500, 400], [300, 600]])

   >>> pdist1(X,matrix=True) # by default, metric = 'euclidean'

   array([[100.    , 100.    , 100.    ,   0.    , 100.    ],

          [100.    , 100.    , 100.    , 100.    ,   0.    ],

          [500.    , 100.    , 100.    , 500.    , 400.    ],

          [538.5165, 100.    , 100.    , 300.    , 600.    ],

          [141.4214,   0.    , 100.    , 100.    ,   0.    ],

          [583.0952,   0.    , 100.    , 500.    , 400.    ],

          [583.0952,   0.    , 100.    , 300.    , 600.    ],

          [565.6854, 100.    ,   0.    , 500.    , 400.    ],

          [632.4555, 100.    ,   0.    , 300.    , 600.    ],

          [282.8427, 500.    , 400.    , 300.    , 600.    ]])

where the first column represents the distance between each pair of observations. for instance, the euclidean distance between (100. , 100.) and ( 0. , 100.) is 100.

- ☑ Support numpy arrays of the same size only.

Todo list

---------

- ☐ Re-validate the correctness of the distances equations.

- ☐ Performance tests & vectorization.

- ☐ Adding new distances.

- ☐ Adding a squared form of the distance.

- ☐ Support tuples and list.

- ☐ Remove numpy from dependencies.

- ☐ Write more test cases.

- ☐ Handling Exceptions.

- ☐ Restructure the docs.

.. _MIT License: https://opensource.org/licenses/MIT

.. _Pypi: https://pypi.org/project/pydist2/

.. _readthedocs: https://pydist2.readthedocs.io

.. _list of distances: https://pydist2.readthedocs.io/en/latest/guide.html