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Releases: dme65/pySOT

v0.3.3

23 Jun 18:50
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  • Exclude examples and tests from the package

v0.2.2

12 Feb 20:57
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  • Experimental designs can now map and round to domains
  • Support for generating multiple experimental designs and picking the best

v0.2.0

12 Feb 00:32
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  • Most of the pySOT codebase has been rewritten
  • We support asynchronous function evaluations
  • The strategy has been merged with the adaptive sampling
  • The penalty method strategy has been removed, but may be added back later
  • A CheckpointController has been added that enables resuming terminated runs
  • Python 2 support has been dropped, we now support Python 3.4 and later
  • Expected improvement (EI) and lower confidence bound (LCB) have been added

v0.1.35

27 Apr 15:24
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  • Added support for termination based on elapsed time
  • Added the Hartman6 test problem

v0.1.32

07 Dec 19:40
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  • Made several of the pySOT dependencies optional
  • Updated the tests to check for the dependencies
  • Made the GUI Python3 compatible
  • Updated documentation

v0.1.30

19 Nov 17:05
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  • Moved all of the official pySOT documentation over to Sphinx
  • Five pySOT tutorials were added to the documentation
  • The documentation is now hosted on Read the Docs (https://pysot.readthedocs.io)
  • Removed pyKriging in order to remove the matplotlib and inspyred dependencies. A new Kriging module will be added in the next version.
  • Added the MARS installation to the setup.py since it can now be installed via scikit-learn
  • Updated the Sphinx documentation to include all of the source files
  • The License, Changes, Contributors, and README files are not in .rst
  • Renamed sampling_methods.py to adaptive_sampling.py
  • Moved the kernels and tails to separate Python files
  • Added a Gitter for pySOT

v0.1.26

16 Oct 01:46
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  • Signficant changes in the RBFInterpolant. Users need to update their code
  • Added RBF regression surfaces
  • Added version information in the module. pySOT.version gives the version of the current pySOT installation
  • The Gutmann strategy has been temporarily removed due to the RBF redesign, but will be added back soon
  • Check out test_rbf.py to see how to use the new RBF

First release

30 Sep 14:18
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v0.1.25

Small changes + Travis-CI build test