From bdc6b7701fae8437844e24f7a4f02a75b13d9b33 Mon Sep 17 00:00:00 2001 From: "Min W. Priest (they/them)" Date: Wed, 25 Sep 2024 16:57:52 -0700 Subject: [PATCH] added notes on variable names to the docs index --- docs/index.rst | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/docs/index.rst b/docs/index.rst index d6daebad..f2667115 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -44,6 +44,22 @@ If you use MuyGPyS in a research paper, please reference our article:: examples/anisotropic_tutorial.ipynb examples/loss_tutorial.ipynb +Variable Name Conventions +========================= + +We make use of several canonical variable names that refer to tensor shape +dimensions. +Here is a partial list of the major names and their meanings. + +* `train_count` - the number of training observations. +* `test_count` - the number of test or prediction observations. +* `batch_count` - the number of elements to be predicted. Can coincide with `train_count` or `test_count` depending on usage. Sometimes also called `data_count`. +* `feature_count` - the number of features in the observations. Omitted for univariate feature spaces. +* `response_count` - the number of response variables. Omitted for univariate responses. +* `nn_count` - the number of nearest neighbors upon which predictions are conditioned. +* `out_shape` - a tuple referring to the shape associated with the output shape of the cross-covariance. For a univariate problem, `in_shape = (nn_count,)`. For a multivariate problem, `out_shape` most likely refers to `(nn_count, response_count)`. +* `in_shape` - a tuple referring to the shape associated with how the covariance is conditioned on observations. For a univariate problem, `in_shape == (nn_count,)`. For a multivariate problem, `in_shape` might refer to `(nn_count, response_count)`, but could instead have a different second element if the observations do not come from the same space as the predictions. + .. toctree:: :maxdepth: 2 :caption: Resources: