View source: R/max_vorob_parallel.R
max_vorob_parallel | R Documentation |
Minimization, based on the package rgenoud (or on exhaustive search on a discrete set), of the Vorob'ev criterion for a batch of candidate sampling points.
max_vorob_parallel(lower, upper, optimcontrol = NULL, batchsize, integration.param, T, model, new.noise.var = 0, penalisation = NULL, typeEx = ">")
lower |
Vector containing the lower bounds of the design space. |
upper |
Vector containing the upper bounds of the design space. |
optimcontrol |
Optional list of control parameters for the optimization of the sampling criterion. The field |
batchsize |
Number of points to sample simultaneously. The sampling criterion will return batchsize points at a time for sampling. |
integration.param |
Optional list of control parameter for the computation of integrals, containing the fields |
T |
Target value (scalar). The criterion CANNOT be used with multiple thresholds. |
model |
A Kriging model of |
new.noise.var |
Optional scalar value of the noise variance of the new observations. |
penalisation |
Optional penalization constant for type I errors. If equal to zero, computes the Type II criterion. |
typeEx |
A character (">" or "<") identifying the type of excursion |
A list with components:
par |
the best set of parameters found. |
value |
the value of the Vorob'ev criterion at par. |
allvalues |
If an optimization on a discrete set of points is chosen, the value of the criterion at all these points. |
Clement Chevalier (University of Neuchatel, Switzerland)
Dario Azzimonti (IDSIA, Switzerland)
Chevalier C., Ginsbouger D., Bect J., Molchanov I. (2013) Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with gaussian process models mODa 10, Advances in Model-Oriented Design and Analysis, Contributions to Statistics, pp 35-43
Chevalier C. (2013) Fast uncertainty reduction strategies relying on Gaussian process models Ph.D Thesis, University of Bern
Azzimonti, D., Ginsbourger, D., Chevalier, C., Bect, J., and Richet, Y. (2018). Adaptive design of experiments for conservative estimation of excursion sets. Under revision. Preprint at hal-01379642
EGIparallel
,max_sur_parallel
#max_vorob_parallel set.seed(9) N <- 20 #number of observations T <- 80 #threshold testfun <- branin lower <- c(0,0) upper <- c(1,1) #a 20 points initial design design <- data.frame( matrix(runif(2*N),ncol=2) ) response <- testfun(design) #km object with matern3_2 covariance #params estimated by ML from the observations model <- km(formula=~., design = design, response = response,covtype="matern3_2") optimcontrol <- list(method="genoud",pop.size=200,optim.option=2) integcontrol <- list(distrib="timse",n.points=400,init.distrib="MC") integration.param <- integration_design(integcontrol=integcontrol,d=2, lower=lower,upper=upper,model=model, T=T) batchsize <- 5 #number of new points ## Not run: obj <- max_vorob_parallel(lower=lower,upper=upper,optimcontrol=optimcontrol, batchsize=batchsize,T=T,model=model, integration.param=integration.param) #5 optims in dimension 2 ! obj$par;obj$value #optimum in 5 new points new.model <- update(object=model,newX=obj$par,newy=apply(obj$par,1,testfun), cov.reestim=TRUE) par(mfrow=c(1,2)) print_uncertainty(model=model,T=T,type="pn",lower=lower,upper=upper,vorobmean=TRUE, cex.points=2.5,main="probability of excursion") print_uncertainty(model=new.model,T=T,type="pn",lower=lower,upper=upper,vorobmean=TRUE, new.points=batchsize,col.points.end="red",cex.points=2.5, main="updated probability of excursion") ## End(Not run)
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