data.GP: Supply GP data to PL

In plgp: Particle Learning of Gaussian Processes

Supply GP data to PL

Description

Functions to supply data to PL for Gaussian process (GP) regression, classification, or combined unknown constraint models

Usage

data.GP(begin, end = NULL, X, Y)
data.GP.improv(begin, end = NULL, f, rect, prior,
               adapt = ei.adapt, cands = 40,
               save = TRUE, oracle = TRUE, verb = 2,
               interp = interp.loess)
data.CGP(begin, end = NULL, X, C)
data.CGP.adapt(begin, end = NULL, f, rect, prior,
               cands = 40, verb = 2, interp=interp.loess)
data.ConstGP(begin, end = NULL, X, Y, C)
data.ConstGP.improv(begin, end = NULL, f, rect, prior,
                    adapt = ieci.const.adapt , cands = 40, 
                    save = TRUE, oracle = TRUE, verb = 2,
                    interp = interp.loess)

Arguments

begin	positive integer starting time for data to be returned
end	positive integer (end >= begin) ending time for data being returned; may be NULL if only data at time begin is needed
X	data.frame with at least end rows containing covariates
Y	vector of length at least end containing real-valued responses
C	vector of length at least end containing class labels
f	function returning a responses when called as f(X) for matrix X; for data.GP.improv the responses must be real-valued returned as a vector; for data.CGP.adapt they must be class labels returned as a vector; for data.ConstGP.improv they must be pairs of real-valued and in {0,1} (1 indicates constraint violation), returned as a 2-column data.frame
rect	bounding rectangle for the inputs X to f(X) with two columns and rows equalling nrow(X)
prior	prior parameters passed from PL generated by one of the prior functions, e.g., prior.GP
adapt	function that evaluates a sequential design criterion on some candidate locations; the default ei.adapt EI about the minimum; ieci.adapt providing IECI is another possibility , which is hard coded into data.ConstGP.adapt
cands	number of Latin Hypercube candidate locations used to choose the next adaptively sampled input design point
save	scalar logical indicating if the improvment information for chosen candidate should be saved in the psave global variable
oracle	scalar logical indicating if the candidates should be augmented with the point found to maximize the predictive surface (with a search starting at the most recently chosen input)
verb	verbosity level for printing the progress of improv and other adaptive sampling calculations
interp	function for smoothing of 2-d image plots. The default comes from interp.loess, but what works best is interp which requires the interp or akima package

Details

These functions provide data to PL for Gaussian progress regression and classification methods in a variety of ways. The simplest, data.GP and data.CGP supply pre-recorded regression and classification data stored in data frames and vectors; data.ConstGP is a hybrid that does joint regression and classification. The other functions provide data by active learning/sequential design:

The data.GP.improv function uses expected improvement (EI); data.CGP.improv uses predictive entropy; data.ConstGP.improv uses integrated expected conditional improvement (IECI). In these cases, once the x-location(s) is/are chosen, the function f is used to provide the response(s)

Value

The output are vectors or data.frames.

Author(s)

Robert B. Gramacy, [e-mail]

References

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. and Lee, H. (2010). “Optimization under unknown constraints”. Bayesian Statistics 9, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (Eds.); Oxford University Press

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/

See Also

PL

Examples

## See the demos via demo(package="plgp") and the examples
## section of ?plgp

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