Skip to main content

A module for calculation of PCA with the NIPALS algorithm

Project description

A module for calculation of PCA and PLS with the NIPALS algorithm. Based on the R packages nipals and pcaMethods as well as the statistical appendixes to “Introduction to Multi- and Megavariate Data Analysis using Projection Methods (PCA & PLS)” by Eriksson et. al. Tested to give same results as the above packages and Simca, with some rounding errors.

  • Free software: MIT license

Installation

pip install nipals

Documentation

See https://github.com/fredrikw/python-nipals/blob/master/docs/nipals_demo_iris.ipynb for an example and the tests at https://github.com/fredrikw/python-nipals/tree/master/tests.

Development

To run the all tests run:

tox

Note, to combine the coverage data from all the tox environments run:

Windows

set PYTEST_ADDOPTS=--cov-append
tox

Other

PYTEST_ADDOPTS=--cov-append tox

Changelog

0.5.6 (2023-10-20)

  • Updated supported Python versions

0.5.5 (2022-09-28)

  • Added check for X-matrix with row full of NAs

0.5.4 (2021-05-07)

  • Fixed Packaging error (0.5.3 was never released)

0.5.3 (2021-05-06)

  • Fixed error on numpy version >= 1.19

  • Updated supported versions

  • Moved CI to Github Action (pt 1)

0.5.2 (2019-06-04)

  • Added compatibility with Nipals objects saved from pre-0.5 versions

0.5.1 (2019-05-23)

  • Added checks for, and optional removal of, zero variance in variables

  • Added support for Python 3.7

  • (0.5.0 was never released due to failing CI tests)

0.4.3 (2018-04-24)

  • Fixed test that failed after last bug fix

0.4.2 (2018-04-24)

  • Fixed bug with selection of starting column for cross validation of PCA

0.4.1 (2018-04-09)

  • Fixed bug with cross validation of PCA

0.4.0 (2018-04-09)

  • Added cross validations

  • Added calculation of distance to model with plots

  • Added model overview plots

0.3.0 (2018-04-05)

  • Added R2X and R2Y to the PLS class

  • Made plot color selectable also for scoreplots without classes

0.2.0 (2018-03-29)

  • Added a PLS class

  • Improved plotting

  • Fixed some problems with missing/infinite values

0.1.0 (2018-03-14)

  • First release on PyPI.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nipals-0.5.7.tar.gz (294.7 kB view details)

Uploaded Source

Built Distribution

nipals-0.5.7-py2.py3-none-any.whl (10.7 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file nipals-0.5.7.tar.gz.

File metadata

  • Download URL: nipals-0.5.7.tar.gz
  • Upload date:
  • Size: 294.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for nipals-0.5.7.tar.gz
Algorithm Hash digest
SHA256 a02b2dab14589880326f478baed85a1b116f5657bfcfce2bae93b36c31538835
MD5 5ba0d7b2216933e572d4ad0a47af2f11
BLAKE2b-256 c0a3ab718f2eb9b5a6598949c1dcf5dfabf4de428ce0e5cf1b32192997e16d36

See more details on using hashes here.

File details

Details for the file nipals-0.5.7-py2.py3-none-any.whl.

File metadata

  • Download URL: nipals-0.5.7-py2.py3-none-any.whl
  • Upload date:
  • Size: 10.7 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for nipals-0.5.7-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 872633e2ef96d935c3e109f3ce79b1a46aab942d22149f0471c3a36ddbdb9186
MD5 ad40430931151e5eaeefc8ce87659809
BLAKE2b-256 b472e95c13a3b47eb6a27d3b053c314218ffbf672b4687e00f2e76294f4909d7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page