MuyGPyS (|version|) Reference
MuyGPyS is toolkit for training approximate Gaussian Process (GP) models using the MuyGPs (Muyskens, Goumiri, Priest, Schneider) algorithm.
If you use MuyGPyS in a research paper, please reference our article:
@article{muygps2021,
title={MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation},
author={Muyskens, Amanda and Priest, Benjamin W. and Goumiri, Im{\`e}ne and Schneider, Michael},
journal={arXiv preprint arXiv:2104.14581},
year={2021}
}
.. toctree:: :maxdepth: 2 :caption: Package Documentation: MuyGPyS/neighbors MuyGPyS/gp MuyGPyS/optimize MuyGPyS/examples MuyGPyS/torch
.. toctree:: :maxdepth: 2 :caption: Examples: examples/univariate_regression_tutorial.ipynb examples/neighborhood_illustration.ipynb examples/torch_tutorial.ipynb examples/fast_regression_tutorial.ipynb examples/anisotropic_tutorial.ipynb examples/loss_tutorial.ipynb
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: resources/references