Notebook exemplifying HeteroskedasticTFPDistribution usage #1462
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st--
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May 12, 2020
gustavocmv
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* HeteroskedasticLikelihood base class draft * fixup * cleanup * cleanup heteroskedastic * multioutput likelihood WIP * Notebook exemplifying HeteroskedasticTFPDistribution usage (#1462) * fixes * typo fix; reshaping fix * notebook showing how to use HeteroskedasticTFPDistribution likelihood * converting to .pct.py format * removed .ipynb * better descriptions * black auto-formatting Co-authored-by: Gustavo Carvalho <gustavo.carvalho@delfosim.com> * note and bugfix * add comment * Adding heteroskedastic tests (#1508) These tests ensure that heteroskedastic likelihood with a constant variance, will give the same results as a Gaussian likelihood with the same variance. * testing * added QuadratureLikelihood to base, refactored ScalarLikelihood to use it * fix * using the first dimension to hold the quadrature summation * adapting ndiagquad wrapper * merged with gustavocmv/quadrature-change-shape * removed unecessary tf.init_scope * removed print and tf.print * removed print and tf.print * Type annotations Co-authored-by: Vincent Dutordoir <dutordoirv@gmail.com> * Work * Fix test * Remove multioutput from PR * Fix notebook * Add student t test * More tests * Copyright * Removed NDiagGHQuadratureLikelihood class in favor of non-abstract QuadratureLikelihood * _set_latent_and_observation_dimension_eagerly * n_gh ---> num_gauss_hermite_points * removed NDiagGHQuadratureLikelihood from test * black * bugfix * removing NDiagGHQuadratureLikelihood from test * fixed bad commenting * black * refactoring scalar likelihood * adding dtype casts to quadrature * black * small merging fixes * DONE: swap n_gh for num_gauss_hermite_points * black Co-authored-by: ST John <st@prowler.io> Co-authored-by: gustavocmv <47801305+gustavocmv@users.noreply.github.com> Co-authored-by: Gustavo Carvalho <gustavo.carvalho@delfosim.com> Co-authored-by: st-- <st--@users.noreply.github.com> Co-authored-by: joshuacoales-pio <47976939+joshuacoales-pio@users.noreply.github.com>
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PR content:
Title
Notebook exemplifying HeteroskedasticTFPDistribution usage
Description
A minimal notebook showing how gpf.likelihoods.heterosckedastic.HeteroskedasticTFPDistribution can be used to model heteroskedastic data with two latent GPs learning both the location and scale of a Gaussian likelihood.
Small fixes to heterosckedastic.py file:
sMinimal working example
The notebook in /doc/source/notebooks/advanced/heteroskedastic.pct.py