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Type 'q()' to quit R. > library(testthat) > Sys.setenv('OMP_THREAD_LIMIT'=2) > library(rlibkriging) Attaching package: 'rlibkriging' The following objects are masked from 'package:base': load, save > > ##library(rlibkriging, lib.loc="bindings/R/Rlibs") > ##library(testthat) > > context("Check predict args (T,FALSE) are consistent") > > f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7) > plot(f) > n <- 5 > set.seed(123) > X <- as.matrix(runif(n)) > y = f(X) > points(X,y) > > r <- Kriging(y,X,"matern3_2","constant",FALSE,"none","LL", + parameters=list(sigma2=0.2,theta=matrix(0.2))) > > x =seq(0,1,,101) > pred_def_mean = r$predict(x)$mean > pred_def_sd = r$predict(x)$stdev > lines(x,pred_def_mean,col='blue') > > pred_TFF_mean = r$predict(x,TRUE,FALSE,FALSE)$mean > pred_TFF_sd = r$predict(x,TRUE,FALSE,FALSE)$stdev > lines(x,pred_TFF_mean,col='red') > test_that(desc="predict(.,TRUE,FALSE,FALSE) is is the same that default one", + expect_equal(pred_TFF_mean,pred_def_mean)) Test passed 🥳 > > pred_TTF_mean = r$predict(x,TRUE,TRUE,FALSE)$mean > pred_TTF_sd = r$predict(x,TRUE,TRUE,FALSE)$stdev > lines(x,pred_TTF_mean,col='red') > test_that(desc="predict(.,TRUE,TRUE,FALSE) is is the same that default one", + expect_equal(pred_TTF_mean,pred_def_mean)) Test passed 🥳 > > pred_TTT_mean = r$predict(x,TRUE,TRUE,TRUE)$mean > pred_TTT_sd = r$predict(x,TRUE,TRUE,TRUE)$stdev > lines(x,pred_TTT_mean,col='red') > test_that(desc="predict(.,TRUE,TRUE,TRUE) is is the same that default one", + expect_equal(pred_TTT_mean,pred_def_mean)) Test passed 🎉 > > > for (kernel in c("gauss","exp","matern3_2","matern5_2")) { + context(paste0("Check predict 1D for kernel ",kernel)) + + f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7) + #plot(f) + n <- 5 + set.seed(123) + X <- as.matrix(runif(n)) + y = f(X) + #points(X,y) + k = DiceKriging::km(design=X,response=y,covtype = kernel,control = list(trace=F)) + #library(rlibkriging) + r <- Kriging(y,X,kernel,"constant",FALSE,"none","LL", + parameters=list(sigma2=k@covariance@sd2,has_sigma2=TRUE, + theta=matrix(k@covariance@range.val),has_theta=TRUE)) + # m = as.list(r) + + ntest <- 100 + Xtest <- as.matrix(runif(ntest)) + ptest <- DiceKriging::predict(k,Xtest,type="UK",cov.compute = TRUE,checkNames=F) + Yktest <- ptest$mean + sktest <- ptest$sd + cktest <- c(ptest$cov) + Ytest <- predict(r,Xtest,TRUE,TRUE) + + plot(f) + points(X,y) + points(Xtest,Yktest,col='blue') + points(Xtest,Ytest$mean,col='red') + + precision <- 1e-5 + test_that(desc=paste0("pred mean is the same that DiceKriging one:\n ",paste0(collapse=",",Yktest),"\n ",paste0(collapse=",",Ytest$mean)), + expect_equal(array(Yktest),array(Ytest$mean),tol = precision)) + + test_that(desc="pred sd is the same that DiceKriging one", + expect_equal(array(sktest),array(Ytest$stdev) ,tol = precision)) + + test_that(desc="pred cov is the same that DiceKriging one", + expect_equal(cktest,c(Ytest$cov) ,tol = precision)) + + plot(f) + points(X,y) + x=seq(0,1,,101) + lines(x,predict(r,x)$mean,lty=2) + polygon( + c(x,rev(x)), + c(predict(r,x)$mean-predict(r,x)$stdev, + predict(r,rev(x))$mean+predict(r,rev(x))$stdev), + col=rgb(0,0,0,.1),border=NA) + # + #for (i in 1:10) + # lines(x,simulate(r,x=x,seed=i),col='grey') + + .x = seq(0.1,0.9,,101) + p_allx = predict(r,.x,return_stdev=TRUE, return_cov=FALSE, return_deriv=TRUE) + + # plot(.x,p_allx$mean) + # for (i in 1:length(.x)) + # arrows(.x[i],p_allx$mean[i], .x[i]+0.1, p_allx$mean[i]+0.1*p_allx$mean_deriv[i]) + # + # plot(.x,p_allx$stdev) + # for (i in 1:length(.x)) + # arrows(.x[i], p_allx$stdev[i], + # .x[i]+0.1, p_allx$stdev[i]+0.1*p_allx$stdev_deriv[i]) + # + + for (i in 1:length(.x)) { + # ref from DiceOptim::EI.grad + newdata = .x[i] + model = k + T <- model@T + X <- model@X + z <- model@z + u <- model@M + covStruct <- model@covariance + predx <- predict(object=model, newdata=newdata, type="UK", checkNames = FALSE,se.compute=TRUE,cov.compute=FALSE) + kriging.mean <- predx$mean + kriging.sd <- predx$sd + v <- predx$Tinv.c + c <- predx$c + dc <- DiceKriging::covVector.dx(x=newdata, X=X, object=covStruct, c=c) + #d = model@d + #h=sqrt(.Machine$double.eps) + #A <- matrix(newdata, nrow=d, ncol=d, byrow=TRUE) + #Apos <- A+h*diag(d) + #Aneg <- A-h*diag(d) + #newpoints <- data.frame(rbind(Apos, Aneg)) + #f.newdata <- model.matrix(model@trend.formula, data = newpoints) + #f.deltax <- (f.newdata[1:d,]-f.newdata[(d+1):(2*d),])/(2*h) + f.deltax <- DiceKriging::trend.deltax(x=newdata, model=model) + W <- backsolve(t(T), dc, upper.tri=FALSE) + kriging.mean.grad <- t(W)%*%z + t(model@trend.coef%*%f.deltax) + tuuinv <- solve(t(u)%*%u) + F.newdata <- model.matrix(model@trend.formula, data=as.data.frame(newdata)) + kriging.sd2.grad <- t( -2*t(v)%*%W + + 2*(F.newdata - t(v)%*%u )%*% tuuinv %*% + (f.deltax - t(t(W)%*%u) )) + kriging.sd.grad <- kriging.sd2.grad / (2*kriging.sd) + + p = predict(r,.x[i],return_stdev=TRUE, return_cov=FALSE, return_deriv=TRUE) + + test_that(desc=paste0("vect pred mean deriv is ok:\n ",paste0(collapse=",",p_allx$mean_deriv[i]),"\n ",paste0(collapse=",",p$mean_deriv)), + expect_equal(array(p_allx$mean_deriv[i]),array(p$mean_deriv),tol = precision)) + test_that(desc=paste0("vect pred sd deriv is ok:\n ",paste0(collapse=",",p_allx$stdev_deriv[i]),"\n ",paste0(collapse=",",p$stdev_deriv)), + expect_equal(array(p_allx$stdev_deriv[i]),array(p$stdev_deriv),tol = precision)) + + arrows(.x[i],p$mean, .x[i]+0.1, p$mean+0.1*p$mean_deriv) + arrows(.x[i],p$mean+p$stdev, .x[i]+0.1, p$mean+p$stdev+0.1*p$mean_deriv+0.1*p$stdev_deriv, col='darkgrey') + + test_that(desc=paste0("pred mean deriv is the same that DiceKriging one:\n ",paste0(collapse=",",kriging.mean.grad),"\n ",paste0(collapse=",",p$mean_deriv)), + expect_equal(array(kriging.mean.grad),array(p$mean_deriv),tol = precision)) + + test_that(desc=paste0("pred sd deriv is the same that DiceKriging one:\n ",paste0(collapse=",",kriging.sd.grad),"\n ",paste0(collapse=",",p$stdev_deriv)), + expect_equal(array(kriging.sd.grad),array(p$stdev_deriv),tol = precision)) + + } + } Test passed 🎉 Test passed 🥳 Test passed 🎊 Test passed 🥇 Test passed 🥇 Test passed 😸 Test passed 🥳 Test passed 🥳 Test passed 🥳 Test passed 🌈 Test passed 🥳 Test passed 🥳 Test passed 🎉 Test passed 🎊 Test passed 🌈 Test passed 🌈 Test passed 🎉 Test passed 😀 Test passed 🎉 Test passed 🎉 Test passed 🎉 Test passed 😸 Test passed 🎉 Test passed 😸 Test passed 🎉 Test passed 🎊 Test passed 😀 Test passed 🎊 Test passed 😸 Test passed 🎊 Test passed 🥇 Test passed 🎊 Test passed 🎊 Test passed 🥳 Test passed 🎉 Test passed 🎉 Test passed 🎊 Test passed 🎊 Test passed 😸 Test passed 🥳 Test passed 🥇 Test passed 🎉 Test passed 🥇 Test passed 🎊 Test passed 😸 Test passed 🌈 Test passed 🌈 Test passed 🎉 Test passed 🌈 Test passed 🥇 Test passed 😸 Test passed 😀 Test passed 🎊 Test passed 🎉 Test passed 🥳 Test passed 😀 Test passed 🎊 Test passed 🥳 Test passed 🥇 Test passed 😸 Test passed 🌈 Test passed 🎊 Test passed 🎊 Test passed 🌈 Test passed 😀 Test 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<- function(X) apply(X, 1, function(x) + prod(sin(2*pi*( x * (seq(0,1,l=1+length(x))[-1])^2 ))) + ) + + .x=seq(0,1,,31); contour(.x,.x,matrix(f(expand.grid(.x,.x)),nrow=length(.x))) + n <- 20 + set.seed(123) + X <- matrix(runif(2*n),ncol=2) + y = f(X) + points(X) + k = DiceKriging::km(design=X,response=y,covtype = kernel,control = list(trace=F)) + #library(rlibkriging) + r <- Kriging(y,X,kernel,"constant",FALSE,"none","LL", + parameters=list(sigma2=k@covariance@sd2,has_sigma2=TRUE, + theta=matrix(k@covariance@range.val),has_theta=TRUE)) + # m = as.list(r) + + f_predict = function(X) + predict(r,data.matrix(X)) + #DiceKriging::predict(k,X,type="UK",cov.compute = TRUE,checkNames=F) + contour(.x,.x,matrix(f(expand.grid(.x,.x)),nrow=length(.x))) + contour(.x,.x,matrix(f_predict(expand.grid(.x,.x))$mean,nrow=length(.x)),add=TRUE, col='blue') + points(X,col='blue') + + ntest <- 100 + Xtest <- matrix(runif(2*ntest),ncol=2) + ptest <- DiceKriging::predict(k,Xtest,type="UK",cov.compute = TRUE,checkNames=F) + Yktest <- ptest$mean + sktest <- ptest$sd + cktest <- c(ptest$cov) + Ytest <- predict(r,Xtest,TRUE,TRUE) + + precision <- 1e-5 + test_that(desc=paste0("pred mean is the same that DiceKriging one:\n ",paste0(collapse=",",Yktest),"\n ",paste0(collapse=",",Ytest$mean)), + expect_equal(array(Yktest),array(Ytest$mean),tol = precision)) + + test_that(desc="pred sd is the same that DiceKriging one", + expect_equal(array(sktest),array(Ytest$stdev) ,tol = precision)) + + test_that(desc="pred cov is the same that DiceKriging one", + expect_equal(cktest,c(Ytest$cov) ,tol = precision)) + + .x = seq(0.1,0.9,,5) + p_allx = predict(r,expand.grid(.x,.x), return_stdev=TRUE, return_cov=FALSE, return_deriv=TRUE) + for (i in 1:length(.x)) { for (j in 1:length(.x)) { + # ref from DiceOptim::EI.grad + newdata = matrix(c(.x[i],.x[j]),ncol=2) # just check diagonal points + model = k + T <- model@T + X <- model@X + z <- model@z + u <- model@M + covStruct <- model@covariance + predx <- predict(object=model, newdata=newdata, type="UK", checkNames = FALSE,se.compute=TRUE,cov.compute=FALSE) + kriging.mean <- predx$mean + kriging.sd <- predx$sd + v <- predx$Tinv.c + c <- predx$c + dc <- DiceKriging::covVector.dx(x=newdata, X=X, object=covStruct, c=c) + #d = model@d + #h=sqrt(.Machine$double.eps) + #A <- matrix(newdata, nrow=d, ncol=d, byrow=TRUE) + #Apos <- A+h*diag(d) + #Aneg <- A-h*diag(d) + #newpoints <- data.frame(rbind(Apos, Aneg)) + #f.newdata <- model.matrix(model@trend.formula, data = newpoints) + #f.deltax <- (f.newdata[1:d,]-f.newdata[(d+1):(2*d),])/(2*h) + f.deltax <- DiceKriging::trend.deltax(x=newdata, model=model) + W <- backsolve(t(T), dc, upper.tri=FALSE) + kriging.mean.grad <- t(W)%*%z + t(model@trend.coef%*%f.deltax) + tuuinv <- solve(t(u)%*%u) + F.newdata <- model.matrix(model@trend.formula, data=as.data.frame(newdata)) + kriging.sd2.grad <- t( -2*t(v)%*%W + + 2*(F.newdata - t(v)%*%u )%*% tuuinv %*% + (f.deltax - t(t(W)%*%u) )) + kriging.sd.grad <- kriging.sd2.grad / (2*kriging.sd) + + p = predict(r,c(.x[i],.x[j]),TRUE, return_cov=FALSE, return_deriv=TRUE) + + test_that(desc=paste0("vect pred mean deriv is ok:\n ",paste0(collapse=",",p_allx$mean_deriv[(j-1)*length(.x)+i,]),"\n ",paste0(collapse=",",p$mean_deriv)), + expect_equal(array(p_allx$mean_deriv[(j-1)*length(.x)+i,]),array(p$mean_deriv),tol = precision)) + test_that(desc=paste0("vect pred sd deriv is ok:\n ",paste0(collapse=",",p_allx$stdev_deriv[(j-1)*length(.x)+i,]),"\n ",paste0(collapse=",",p$stdev_deriv)), + expect_equal(array(p_allx$stdev_deriv[(j-1)*length(.x)+i,]),array(p$stdev_deriv),tol = precision)) + + arrows(.x[i],.x[j],.x[i]+0.1*p$mean_deriv[1],.x[j]+0.1*p$mean_deriv[2], length = 0.1, col='blue') + + test_that(desc=paste0("pred mean deriv is the same that DiceKriging one:\n ",paste0(collapse=",",kriging.mean.grad),"\n ",paste0(collapse=",",p$mean_deriv)), + expect_equal(array(kriging.mean.grad),array(p$mean_deriv),tol = precision)) + + test_that(desc=paste0("pred sd deriv is the same that DiceKriging one:\n ",paste0(collapse=",",kriging.sd.grad),"\n ",paste0(collapse=",",p$stdev_deriv)), + expect_equal(array(kriging.sd.grad),array(p$stdev_deriv),tol = precision)) + + }} + } Test passed 🌈 Test passed 🥳 Test passed 🎊 Test passed 😀 Test passed 🎊 Test passed 🥳 Test passed 🌈 Test passed 🥇 Test passed 😀 Test passed 😀 Test passed 😀 Test passed 🥇 Test passed 🎊 Test passed 🎉 Test passed 🎉 Test passed 🎉 Test passed 😸 Test passed 😀 Test passed 🥳 Test passed 🥳 Test passed 😀 Test passed 🥳 Test passed 😀 Test passed 🥇 Test passed 🥳 Test passed 😸 Test passed 🥇 Test passed 🎊 Test passed 🌈 Test passed 🥇 Test passed 🥳 Test passed 😀 Test passed 🌈 Test passed 🎉 Test passed 🥳 Test passed 🎉 Test passed 🎊 Test passed 🥇 Test passed 😀 Test passed 🥇 Test passed 😀 Test passed 🌈 Test passed 😸 Test passed 😀 Test passed 🎉 Test passed 😀 Test passed 🥳 Test passed 🌈 Test passed 🥳 Test passed 😀 Test passed 🥇 Test passed 🥇 Test passed 😀 Test passed 🎊 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passed 😸 > > proc.time() user system elapsed 188.68 21.31 210.59