Release Notes Version 9 0: Difference between revisions

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==Changes and Bug Fixes in Version 9.0==
==Changes and Bug Fixes in Version 9.0==


==Beta Testing==
Version 9.0 of PLS_Toolbox and Solo is scheduled for released in October, 2021. See our [https://eigenvector.com/aiovg_videos/evri-thing-you-need-to-know-about-pls_toolbox-solo-90/ webinar] on new features.  
* 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 [http://www.eigenvector.com/software/pls_toolbox.htm PLS_Toolbox Product Page]. For information on Solo, see [http://www.eigenvector.com/software/solo.htm Solo Product Page].


(back to [[Release Notes PLS Toolbox and Solo]])
(back to [[Release Notes PLS Toolbox and Solo]])
==New Features in Solo and PLS_Toolbox==
==New Features in Solo and PLS_Toolbox==


* Solo is now built with version 2020b of Matlab.  
* '''Solo is now built with version 2020b of Matlab'''. This will add major improvements to graphics over previous versions of Solo. Expect to see better performance across many tools and better utilization of modern hardware. Matlab 2020b also enables Python (see below).  
* Python Integration
* 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:
** This release introduces several Python methods. In order to use these please follow the instructions to get started: [[python configuration|Python Configuration]]. These steps are necessary to use the new methods. Once configured, try the following:
*** [[ANNDL]] - Artificial Neural Network Deep Learning.
*** [[anndl|ANNDL]] - Artificial Neural Network Deep Learning.
*** [[ANNDLDA]] - Artificial Neural Network Deep Learning for classification.
*** [[anndlda|ANNDLDA]] - Artificial Neural Network Deep Learning for classification.
*** [[UMAP]] - Uniform Manifold Approximation and Projection (Unsupervised).
*** [[umap|UMAP]] - Uniform Manifold Approximation and Projection (Unsupervised).
*** [[TSNE]] - t-distributed Stochastic Neighbor Embedding.
*** [[tsne|TSNE]] - t-distributed Stochastic Neighbor Embedding.
*** For more info about PLS_Toolbox Python integration see the wiki [[Python]].
*** For more info about PLS_Toolbox Python integration see the wiki [[Python]].
** Things to watch out for:
* [[plotgui|PLOTGUI]] - Create an axisscale from selected points in a plot of X-block data via context (right-click) menu. This can be used to create a Y-block using create Y from X-block Axis Scale menu item.
*** 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|PLOTGUI]] - Create a Y-block from selected points a plot of X-block data via context (right-click) menu.
* [[knn|KNN]]
* [[knn|KNN]]
** Select Class Groups interface now available in the KNN Analysis window.  
** Select Class Groups interface now available in the KNN Analysis window.  
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|'''[[analysis]]'''
|'''[[analysis]]'''
|
|
* When creating Y-block from column in X-block, column will be hard-deleted from X-block.
* Can now create Y-block from X-block column, axisscale, or class set. And where appropriate, can choose to delete or exclude selection from X-block.
|----
|----


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|'''[[experimentreadr]]'''
|'''[[experimentreadr]]'''
|
|
* When doing Cal/Val split and keeping replicates, user can now select class set from X or Y block.
* When splitting data into Cal/Val can now keep replicates based on class set from X or Y block. Also can choose to use Mahalanobis distance or Euclidean distance.
|----
 
|----valign="top"
|'''[[hjyreadr]]'''
|
* Horiba Raman (.l6s, .l6m) file importer is now faster and can import larger files (up to a maximum of 2048x2048x1024 elements).
|----
 
|----valign="top"
|'''CLS-based residuals'''
|
* Residuals based on a Classical Least Squares model of the data for use in the development of GLSW and EPO filters.
|----
|----


|}
|}

Latest revision as of 13:25, 6 January 2023

Changes and Bug Fixes in Version 9.0

Version 9.0 of PLS_Toolbox and Solo is scheduled for released in October, 2021. See our webinar on new features.

(back to Release Notes PLS Toolbox and Solo)

New Features in Solo and PLS_Toolbox

  • Solo is now built with version 2020b of Matlab. This will add major improvements to graphics over previous versions of Solo. Expect to see better performance across many tools and better utilization of modern hardware. Matlab 2020b also enables Python (see below).
  • 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.
  • PLOTGUI - Create an axisscale from selected points in a plot of X-block data via context (right-click) menu. This can be used to create a Y-block using create Y from X-block Axis Scale menu item.
  • 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
  • Can now create Y-block from X-block column, axisscale, or class set. And where appropriate, can choose to delete or exclude selection from X-block.
constrainfit
  • Add 'exponential' to type of constraints available.
experimentreadr
  • When splitting data into Cal/Val can now keep replicates based on class set from X or Y block. Also can choose to use Mahalanobis distance or Euclidean distance.
hjyreadr
  • Horiba Raman (.l6s, .l6m) file importer is now faster and can import larger files (up to a maximum of 2048x2048x1024 elements).
CLS-based residuals
  • Residuals based on a Classical Least Squares model of the data for use in the development of GLSW and EPO filters.
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