https://github.com/jakevdp/wpca

Weighted Principal Component Analysis (PCA) in Python
https://github.com/jakevdp/wpca

Last synced: 9 days ago
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Weighted Principal Component Analysis (PCA) in Python

• Host: GitHub
• URL: https://github.com/jakevdp/wpca
• Owner: jakevdp
• License: bsd-3-clause
• Created: 2016-01-15T15:59:04.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2018-01-19T00:35:48.000Z (almost 7 years ago)
• Last Synced: 2024-10-17T10:05:48.359Z (23 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 1.94 MB
• Stars: 146
• Watchers: 5
• Forks: 24
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGES.md
  • License: LICENSE

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README

        
# Weighted Principal Component Analysis in Python

*Author: Jake VanderPlas*

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This repository contains several implementations of Weighted Principal Component

Analysis, using a very similar interface to scikit-learn's

[``sklearn.decomposition.PCA``](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html):

- ``wpca.WPCA`` uses a direct decomposition of a weighted covariance matrix to

  compute principal vectors, and then a weighted least squares optimization

  to compute principal components. It is based on the algorithm presented

  in [Delchambre (2014)](http://arxiv.org/abs/1412.4533)

- ``wpca.EMPCA`` uses an iterative expectation-maximization approach to solve

  simultaneously for the principal vectors and principal components of

  weighted data. It is based on the algorithm presented in

  [Bailey (2012)](http://arxiv.org/abs/1208.4122).

- ``wpca.PCA`` is a standard non-weighted PCA implemented using the singular

  value decomposition. It is mainly included for the sake of testing.

## Examples and Documentation

For an example application of a weighted PCA approach, See

[WPCA-Example.ipynb](WPCA-Example.ipynb).

## Installation & Dependencies

This package has the following requirements:

- Python versions 2.7, or 3.4+

- [numpy](http://numpy.org/) (tested with version 1.10)

- [scipy](http://scipy.org/) (tested with version 0.16)

- [scikit-learn](http://scikit-learn.org/) (tested with version 0.17)

- [nose](http://nose.readthedocs.org/) (optional) to run unit tests.

With these requirements satisfied, you can install this package by running

```

$ pip install wpca

```

or to install from the source tree, run

```

$ python setup.py install

```

To run the suite of unit tests, make sure ``nose`` is installed and run

```

$ nosetests wpca

```