exp2d.C: 2-d Exponential Hessian Data

In plgp: Particle Learning of Gaussian Processes

2-d Exponential Hessian Data

Description

Generates 2-d classification data with two or three class labels, based on the Hessian data from a 2-d real-valued response

Usage

exp2d.C(X, threed = TRUE)

Arguments

X	a matrix or data.frame describing the design at which the response categories are desired
threed	a scalar logical indicating if the two or three-class version of the class labels should be returned.

Details

The underlying real-valued response is governed by

Z(X) = X1 * exp(-X1^2-X2^2).

Two class labels are generated by inspecting the sign of the sum of the eigenvalues of the Hessian (Broderick & Gramacy, 2010). This generates the first (-) and second (+) classes in a three-class function. A third class label (the default) may created from the first one where X[,1] > 0 (Gramacy & Polson, 2011)

Value

A vector of class labels of length nrow(X) is returned

Author(s)

Robert B. Gramacy, [e-mail]

References

Broderick, T. and Gramacy, R. (2010). “Classification and categorical inputs with treed Gaussian process models.” Tech. rep., University of Cambridge. ArXiv:0904.4891.

Gramacy, R. and Polson, N. (2011). “Particle learning of Gaussian process models for sequential design and optimization.” Journal of Computational and Graphical Statistics, 20(1), pp. 102-118; arXiv:0909.5262

Gramacy, R. (2020). “Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences”. Chapman Hall/CRC; https://bobby.gramacy.com/surrogates/

https://bobby.gramacy.com/r_packages/plgp/

Examples

## The following demos use this data
## Not run: 
## Illustrates classification GPs on a simple 2-d exponential
## data generating mechanism
demo("plcgp_exp", ask=FALSE)

## Illustrates active learning via entropy with classification
## GPs on a simple 2-d exponential data generating mechanism
demo("plcgp_exp_entropy", ask=FALSE)

## End(Not run)

plgp documentation built on Oct. 19, 2022, 5:20 p.m.