Release Notes Version 9 0

Beta Testing

• As of 09/17/2021 PLS_Toolbox and Solo are in pre-release.
• Users should contact helpdesk@eigenvector.com to report bugs.
• These notes are subject to change.

Version 9.0 of PLS_Toolbox and Solo is scheduled for released in October, 2021.

General Information

For general product information, see PLS_Toolbox Product Page. For information on Solo, see Solo Product Page.

(back to Release Notes PLS Toolbox and Solo)

New Features in Solo and PLS_Toolbox

• Solo is now built with version 2020b of Matlab.
• Python Integration
  • This release introduces several Python methods. In order to use these please follow the instructions to get started: Python Configuration. These steps are necessary to use the new methods. Once configured, try the following:
    • ANNDL - Artificial Neural Network Deep Learning.
    • ANNDLDA - Artificial Neural Network Deep Learning for classification.
    • UMAP - Uniform Manifold Approximation and Projection (Unsupervised).
    • TSNE - t-distributed Stochastic Neighbor Embedding.
    • For more info about PLS_Toolbox Python integration see the wiki Python.
  • Things to watch out for:
    • Any problems during Python configuration.
    • Building models without seeing errors that resemble 'Unable to resolve py.<anything_can_be_here>'.
      • Examples: 'Unable to resolve py.numpy.array', 'Unable to resolve py.sklearn.manifold.TSNE'.
      • Saving and loading models.
      • MATLAB crashing when model building.
• PLOTGUI - Create a Y-block from selected points a plot of X-block data via context (right-click) menu.
• KNN
  • Select Class Groups interface now available in the KNN Analysis window.
  • Add option to use compression.
• SIMCA
  • Sub models can now use independent preprocessing and included variables from the Analysis interface.
  • Building SIMCA model from command line can now pass cell array of individual PCA models (built from the same dataset).

Other Changes

File	Comment
analysis	When creating Y-block from column in X-block, column will be hard-deleted from X-block.
constrainfit	Add 'exponential' to type of constraints available.
experimentreadr	When doing Cal/Val split and keeping replicates, user can now select class set from X or Y block.