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CHANGELOG.rst

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Changelog

This document records all notable changes to pyMCR.

This project adheres to PEP 440 -- Version Identification and Dependency Specification.

0.5.1 (21-10-25)

  • Fixed numpy deprecated dtype specifications

0.5.0 (21-10-25)

  • Providing sklearn-like features
    • Added fit_transform method that acts like the sklearn NMF fit_transform method.
      • Returns C
    • Added components attribute, which is synonymous with ST
    • Added fit_kwargs parameter to McrAR that will pass forward to the fit and fit_transform methods
    • One can, e.g., set the ST or C guess from instantiation instead of calling fit or fit_transform

0.4.0 (21-10-22)

  • Moved to Github actions for CD
  • Logging setup by default upon importing the library.
  • Updated Jupyter Notebooks to reflect the change to the logging setup
  • Minor bug fixes

0.3.3 (21-10-22)

  • Moved to Github actions for CI

0.3.2 (19-06-25)

  • Jupyter Notebook in Examples from JRes NIST publication.
  • Minor tweeks and fixes
  • Added Conda-Forge badge

0.3.1 (19-05-17)

  • Implemented logging and removed print() statements
  • Removed Jupyter Notebook from forthcoming pub -- will return in the future with better examples
  • Minor fixes to CI

0.3.0 (19-04-22)

  • Documentation: https://pages.nist.gov/pyMCR or build locally via Sphinx
  • Added Jupyter Notebook that generates images from forthcoming publication.
  • Perform semi-learning: assigning some input ST or C components to be static in fit method.
  • Main class pymcr.mcr.McrAls renamed to pymcr.mcr.McrAR
  • Constraints
    • Non-negative cumulative summation
    • Zero end-points
    • Zero (approx) cumulative summation end-points (can specify nodes as well)
    • Compress or cut values above or below a threshold value
    • Replace sum-across-features samples (e.g., 0 concentration) with prescribed target
    • Enforce a plane ("planarize"). E.g., a concentration image is a plane.

0.2.1 (18-05-16)

  • Improved Demo Notebook documentation

0.2.0 (18-05-02)

  • Total re-write that is incompatible with earlier version
  • Built-in solvers: non-negative least squares (scipy.optimize.nnls), ordinary least squares (scipy.linalg.lstsq)
  • Native support for scikit-learn estimators as least squares solvers / regressor
  • Can now explicitly list and order constraints.

0.1.1a0 (17-12-18)

  • Concentration and spectral mean relative distance tracked across iterations

0.1.0 (17-12-15)

  • Initial version