* multiplied RBF kernels can now be used with gradient observations
* standard periodic kernels can now be used with gradient observations
* predictive gradients (derivatives of posterior means and variances) can now be calculated when using gradient observations
* simplified and commented RBF & StdP kernel derivatives
* updated kernel slicing and commented prod kernel derivatives
* removed caching from stdp kern, as it breaks optimization for some reason
* fixed hyperparameter optimization for prod kernel
* improved code readability
* added unit tests for gradient observing MultioutputGP models
* added predictions check to unit tests
* bugfix for multioutput_kern
* improved testing coverage
* reduced size of some tests; led to an issue in an unrelated test
* updated testing
* added gradient MultioutputGP prod kernel example
* added keywords and plotting to example
This PR adds two main things to GPy:
- to- and from-dict functions for the kernels listed belop
- a fix for the appveyor CI
Please see the squashed commit messages listed below.
Authors: @gehbiszumeis @ppk42 respectively
Reviewer: @ekalosak
---
* new: added to_dict() method to Coregionalize kernel class
* new: added to_dict() method to MixedNoise likelihood class
* fix: made Y_metadata dict content serializable
* fix: typo
* added additional needed parameters to to_dict() method for Coregionalize kernel + added _build_from_input dict method
* new: added possibility to build MixedNoise likelihood from input_dict
* Y_metadata conversion from serializable to np.array when loading from dict
* fix: rework Y_metadata part for compatibility with unittests !minor
* conda cleanup in appveyors pipeline
* conda clean up after conda update
* conda clean before conda update
* try pinning packages for conda
* revert all conda changes
* conda clean all (not only packages)
* use conda update anaconda
* pin conda package
* pin conda package
* try installing charset-normalizer beforehand
* try to get from conda-forge
* revert all conda changes
* Try to fix the conda update challange.
See: https://community.intel.com/t5/Intel-Distribution-for-Python/Conda-update-Conda-fails/td-p/1126174
It is just a try for a different context/(conda version).
* Still fixing build error on appveyor
I also use a newer miniconda version for greater python versions.
* Update appveyor.yml
Thinking it over it decided to use miniconda38 for all python versions unless python 3.5.
* revert miniconda versioning changes
* adjust GPy version in appveyor.yml
* 1st attempt bring the appveyor build to life again
* #955 fixing ci build on appveyor
After bringing the miniconda env to work again, the wrong matplotlib version was used. This commit should fix that.
* #955 Fix CI build
Freezing numpy and scipy was a bad idea.
I freeze matplotlib dependend on the python version only.
* add: built_from_dict method for White Kernel
Co-authored-by: Peter Paul Kiefer <ppk42@users.noreply.github.com>
Co-authored-by: Peter Paul Kiefer <dafisppk@gmail.com>
This commit fixes issues observed in Windows where some
cython modules are successfully imported, and some are not.
This causes the global config cython.working to be inconsistent,
which causes import errors when unavailable cython modules
are tried to be imported (example
https://github.com/SheffieldML/GPy/issues/266). This commit uses
a separate flag for each module to fix the issue.
