Generates 2-d classification data with two or three class labels, based on the Hessian data from a 2-d real-valued response
exp2d.C(X, threed = TRUE)
A vector of class labels of length nrow(X)
is returned
a matrix
or data.frame
describing the design at which
the response categories are desired
a scalar logical
indicating if the two or three-class version
of the class labels should be returned.
Robert B. Gramacy, rbg@vt.edu
The underlying real-valued response is governed by
X[,1] > 0
(Gramacy & Polson, 2011)
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/
## The following demos use this data
if (FALSE) {
## 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)
}
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