======= pydist2

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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