https://github.com/GPflow/GPflow
Revision db383233eaf595a04b4eafc9872845e66ba54634 authored by Mark van der Wilk on 23 November 2016, 18:31:25 UTC, committed by James Hensman on 23 November 2016, 18:31:25 UTC
This squash-commit contains a large volume of work by @markvdw and @alexisboukouvalas. I'm keeping all of the commit history here for posterity. Interested viewers can see some discussion on github, under pull request #195. * Initial code for new kernel expectations. - RBF done. - Added another transformation in etransforms.py. * first step to merge gplvm and kernexp approaches * Added kernel expectations of linear, and eKxz for RBF. - NB: Linear still need to be tested better. - Todo: exKxz for linear. * Added multidimensional quadrature in `Kern` base class for kernel expectations. - Replaced monte carlo tests with more reliable quadrature tests. - Added exKxz for `Linear` kernel * testing new ekernels code, starting on active dimensions, further testing needed as well as modifying Bayesian GPLVM code * Linear and Polynomial kernels did not respect active_dims properly in Kdiag. - Added slice call to Linear.Kdiag - Fixed TestSlice to test more kernels. - Fxied TestSlice to have the correct inputdim. * Begin work on sum kernel, smoothing out active_dims for ekernels. * Fixed `input_dim` in `test_kerns.py`. Added assertion. * kernexp quadrature now works with `active_dims`. - exKzx which doesn't work now raises an error from TensorFlow. - Various other assertions. * Better deduction of `input_dim` for `kernels.Combination`. * Small fix of test. * Fixed issue of KzxKxz in Add kernel. Solution checks for diagonal q(X) and performs quadrature on the covariance of KzxKxz if not diagonal. * GPLVM now works with new kernel expectation code. - Added a new DiagMatrix transform. - Removed legacy code. - Modified GPLVM to accept full covariance matrices. * Fixing a small error in DiagMatrix transform. * Added warnings. * Removed etransforms code. BlockTriDiagonalTransform is now only used in tests. * Prevent `TridiagonalBlockRep` from being tested as a `Transform`. * improvements to DiagonalMatrix transform * improved testing of kernel slice * update test to use kernels * `ekernels.RBF` ARD bug fixed. * Increased test coverage. * Increased test coverage. * docstring for gplvm, removed unused variable in ekernels * testing composite kernels in gplvm * Added Prod to `ekernels.py`. * Fixed usage of `tf.gather_nd`, which does not have a gradient in `_slice_cov()`. * add GPLVM notebook, increase testing to include Prod kernels, add documentation stub, fix bibliography * improve GPLVM notebook with working example * Fixed bug that `X_var` in `BayesianGPLVM` gets slightly different values. * Quadrature can be switched off, plus the appropriate checks. * Kernel expectations now accept 2D variances. * Fix to test. * Reworked quadrature code in Add in anticipation of adding exact expectations for certain pairs of kernels. * Added Linear + Add cross terms for overlapping active_dims. * Initial try for extra test for `ekernels.Add` cross terms. * `_slice_cov` now again compatible with numpy arrays. * Requested code reviews.
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Tip revision: db383233eaf595a04b4eafc9872845e66ba54634 authored by Mark van der Wilk on 23 November 2016, 18:31:25 UTC
Kernel expectations (#195)
Kernel expectations (#195)
Tip revision: db38323
docs_require.txt
ipykernel==4.3.1
ipython==4.2.0
ipython-genutils==0.1.0
jupyter==1.0.0
jupyter-client==4.3.0
jupyter-console==4.1.1
jupyter-contrib-core==0.3.0
jupyter-core==4.1.0
jupyter-nbextensions-configurator==0.2.1
nbsphinx==0.2.8
numpydoc==0.6.0
Pygments==2.1.3
scipy==0.18.0
pandas==0.18.1
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.11.0rc1-cp35-cp35m-linux_x86_64.whl
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