View source: R/timse_optim_parallel.R
timse_optim_parallel | R Documentation |
Evaluation of the "timse"
criterion for some candidate points. To be used in optimization routines, like in max_timse_parallel
.
To avoid numerical instabilities, the new points are evaluated only if they are not too close to an existing observation, or if there is some observation noise.
The criterion is the integral of the posterior timse uncertainty.
timse_optim_parallel(x, integration.points, integration.weights = NULL, intpoints.oldmean = NULL, intpoints.oldsd = NULL, precalc.data, model, T, new.noise.var = 0, weight = NULL, batchsize, current.timse)
x |
Input vector of size d at which one wants to evaluate the criterion. |
integration.points |
p*d matrix of points for numerical integration in the X space. |
integration.weights |
Vector of size p corresponding to the weights of these integration points. |
intpoints.oldmean |
Vector of size p corresponding to the kriging mean at the integration points before adding |
intpoints.oldsd |
Vector of size p corresponding to the kriging standard deviation at the integration points before adding |
precalc.data |
List containing useful data to compute quickly the updated kriging variance. This list can be generated using the |
model |
Object of class |
T |
Array containing one or several thresholds. |
new.noise.var |
Optional scalar value of the noise variance of the new observations. |
weight |
Vector of weight function (length must be equal to the number of lines of the matrix integration.points). If nothing is set, the imse criterion is used instead of timse. It corresponds to equal weights. |
batchsize |
Number of points to sample simultaneously. The sampling criterion will return batchsize points at a time for sampling. |
current.timse |
Current value of the timse criterion (before adding new observations) |
Targeted imse value
Victor Picheny (INRA, Toulouse, France)
Clement Chevalier (University of Neuchatel, Switzerland)
Picheny V., Ginsbourger D., Roustant O., Haftka R.T., (2010) Adaptive designs of experiments for accurate approximation of a target region, J. Mech. Des. vol. 132(7)
Picheny V. (2009) Improving accuracy and compensating for uncertainty in surrogate modeling, Ph.D. thesis, University of Florida and Ecole Nationale Superieure des Mines de Saint-Etienne
Chevalier C., Bect J., Ginsbourger D., Vazquez E., Picheny V., Richet Y. (2014), Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set, Technometrics, vol. 56(4), pp 455-465
EGIparallel
, max_timse_parallel
#timse_optim_parallel set.seed(9) N <- 20 #number of observations T <- c(80,100) #thresholds testfun <- branin #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") ###we need to compute some additional arguments: #integration points, and current kriging means and variances at these points integcontrol <- list(n.points=1000,distrib="timse",init.distrib="MC") obj <- integration_design(integcontrol=integcontrol,lower=c(0,0), upper=c(1,1),model=model,T=T) integration.points <- obj$integration.points integration.weights <- obj$integration.weights pred <- predict_nobias_km(object=model,newdata=integration.points, type="UK",se.compute=TRUE) intpoints.oldmean <- pred$mean ; intpoints.oldsd<-pred$sd #another precomputation precalc.data <- precomputeUpdateData(model,integration.points) #we also need to compute weights. Otherwise the (more simple) #imse criterion will be evaluated weight0 <- 1/sqrt( 2*pi*(intpoints.oldsd^2) ) weight <- 0 for(i in 1:length(T)){ Ti <- T[i] weight <- weight + weight0 * exp(-0.5*((intpoints.oldmean-Ti)/sqrt(intpoints.oldsd^2))^2) } batchsize <- 4 x <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8) #one evaluation of the timse_optim_parallel criterion #we calculate the expectation of the future "timse" #uncertainty when 4 points are added to the doe #the 4 points are (0.1,0.2) , (0.3,0.4), (0.5,0.6), (0.7,0.8) timse_optim_parallel(x=x,integration.points=integration.points, integration.weights=integration.weights, intpoints.oldmean=intpoints.oldmean,intpoints.oldsd=intpoints.oldsd, precalc.data=precalc.data,T=T,model=model,weight=weight, batchsize=batchsize,current.timse=Inf) #the function max_timse_parallel will help to find the optimum: #ie: the batch of 4 minimizing the expectation of the future uncertainty
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.