View source: R/max_futureVol_parallel.R
max_futureVol_parallel | R Documentation |
Maximizes the criterion vorobVol_optim_parallel
.
max_futureVol_parallel(lower, upper, optimcontrol = NULL, batchsize, integration.param, T, model, new.noise.var = 0, typeEx = ">")
lower |
lower bounds of the domain |
upper |
upper bounds of the domain |
optimcontrol |
optional list of control parameters for optimization aspects, see |
batchsize |
size of the batch of new points |
integration.param |
Optional list of control parameter for the computation of integrals, containing the fields |
T |
threshold |
model |
a km Model |
new.noise.var |
Optional scalar with the noise variance at the new observation |
typeEx |
a character (">" or "<") identifying the type of excursion |
A list containing par
, the best set of parameters found, value
the value of the criterion and alpha
, the Vorob'ev quantile corresponding to the conservative estimate.
Dario Azzimonti (IDSIA, Switzerland)
Azzimonti, D. and Ginsbourger, D. (2018). Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation. Journal of Computational and Graphical Statistics, 27(2), 255-267.
Azzimonti, D. (2016). Contributions to Bayesian set estimation relying on random field priors. PhD 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
Chevalier, C., Bect, J., Ginsbourger, D., Vazquez, E., Picheny, V., and Richet, Y. (2014). Fast kriging-based stepwise uncertainty reduction with application to the identification of an excursion set. Technometrics, 56(4):455-465.
EGIparallel
,max_vorob_parallel
#max_futureVol_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_futureVol_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) consLevel = 0.95; n_discrete_design=500*new.model@d CE_design=as.matrix (randtoolbox::sobol (n = n_discrete_design, dim = new.model@d)) colnames(CE_design) <- colnames(new.model@X) current.pred = predict.km(object = new.model, newdata = CE_design, type = "UK",cov.compute = TRUE) current.pred$cov <- current.pred$cov +1e-7*diag(nrow = nrow(current.pred$cov), ncol = ncol(current.pred$cov)) current.CE = anMC::conservativeEstimate(alpha = consLevel, pred=current.pred, design=CE_design, threshold=T, pn = NULL, type = ">", verb = 1, lightReturn = TRUE, algo = "GANMC") par(mfrow=c(1,2)) print_uncertainty(model=model,T=T,type="pn",lower=lower,upper=upper, cex.points=2.5,main="probability of excursion",consQuantile=obj$alpha) print_uncertainty(model=new.model,T=T,type="pn",lower=lower,upper=upper, new.points=batchsize,col.points.end="red",cex.points=2.5, main="updated probability of excursion",consQuantile=current.CE$lvs) ## End(Not run)
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