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27 November 2021, 02:00:58 UTC
Revision 5c89c1e5ab1b9a91b6df60f7ce8e75cac810f14b authored by st-- on 08 April 2020, 09:48:25 UTC, committed by GitHub on 08 April 2020, 09:48:25 UTC 
Improve structure of likelihoods subdirectory (#1416)
 
Addresses #1405 

* New structure underneath gpflow/likelihoods/:
  * base.py: all base classes (Likelihood, MonteCarloLikelihood, ScalarLikelihood) and SwitchedLikelihood
  * multiclass.py: multi-class classification (Softmax, MultiClass + RobustMax)
  * scalar_continuous.py: continuous-Y subclasses of ScalarLikelihood (Gaussian, StudentT, Exponential, Beta, Gamma)
  * scalar_discrete.py: discrete-Y subclasses of ScalarLikelihood (Bernoulli, Poisson, Ordinal)
  * utils.py: the `inv_probit` link function used by Bernoulli and Beta likelihoods
  * misc.py: GaussianMC - used for demonstration/tests only.
  (Note that usage, i.e. accessing gpflow.likelihoods.<LikelihoodClass>, has not changed.)

* Tests for multi-class classification likelihoods moved out into their own test module (including stubs for the missing MultiClass quadrature tests of #1091)

* Re-activates the quadrature tests for ScalarLikelihood subclasses with analytic variational_expectations/predict_log_density/predict_mean_and_var that inadvertently got disabled by #1334 

* Fixes a bug in Bernoulli._predict_log_density that was uncovered by these tests

* Fixes random seed for mock data generation in test_natural_gradient to make svgp_vs_gpr test pass again
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Tip revision: 5c89c1e5ab1b9a91b6df60f7ce8e75cac810f14b authored by st-- on 08 April 2020, 09:48:25 UTC
Improve structure of likelihoods subdirectory (#1416)
Tip revision: 5c89c1e
GLOSSARY.md 
## Glossary

GPflow does not always follow standard Python naming conventions,
and instead tries to apply the notation in the relevant GP papers.\
The following is the convention we aim to use in the code.

---

<dl>
  <dt>GPR</dt>
  <dd>Gaussian process regression</dd>

  <dt>SVGP</dt>
  <dd>stochastic variational inference for Gaussian process models</dd>

  <dt>Shape constructions [..., A, B]</dt>
  <dd>the way of describing tensor shapes in docstrings and comments. Example: <i>[..., N, D, D]</i>, this is a tensor with an arbitrary number of leading dimensions indicated using the ellipsis sign, and the last two dimensions are equal</dd>

  <dt>X</dt>
  <dd>(and variations like Xnew) refers to input points; always of rank 2, e.g. shape <i>[N, D]</i>, even when <i>D=1</i></dd>

  <dt>Y</dt>
  <dd>(and variations like Ynew) refers to observed output values, potentially with multiple output dimensions; always of rank 2, e.g. shape <i>[N, P]</i>, even when <i>P=1</i></dd>

  <dt>Z</dt>
  <dd>refers to inducing points</dd>

  <dt>M</dt>
  <dd>stands for the number of inducing features (e.g. length of Z)</dd>

  <dt>N</dt>
  <dd>stands for the number of data or minibatch size in docstrings and shape constructions</dd>

  <dt>P</dt>
  <dd>stands for the number of output dimensions in docstrings and shape constructions</dd>

  <dt>D</dt>
  <dd>stands for the number of input dimensions in docstrings and shape constructions</dd>
</dl>
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