R Under development (unstable) (2024-09-21 r87186 ucrt) -- "Unsuffered Consequences"
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> 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,,5)
> 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 <- 10
+   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,,5)
+   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))$stdev),
+         col=rgb(0,0,0,.1),border=NA)
+   #
+   #for (i in 1:10)
+   #    lines(x,simulate(r,x=x,seed=i),col='grey')
+   
+   .x = seq(0.1,0.9,,5)
+   p_allx = predict(r,.x,return_stdev=TRUE, return_cov=FALSE, return_deriv=TRUE)
+ 
+   # plot(.x,p_allx$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$stdev_deriv),tol = precision))
+               
+   }
+ }
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> 
> 
> #### dim > 1
> 
> for (kernel in c("gauss","exp","matern3_2","matern5_2")) {
+   context(paste0("Check predict 1D for kernel ",kernel))
+ 
+   f <- function(X) apply(X, 1, function(x)
+     prod(sin(2*pi*( x * (seq(0,1,l=1+length(x))[-1])^2 )))
+   )
+ 
+   .x=seq(0,1,,31); contour(.x,.x,matrix(f(expand.grid(.x,.x)),nrow=length(.x)))
+   n <- 20
+   set.seed(123)
+   X <- matrix(runif(2*n),ncol=2)
+   y = f(X)
+   points(X)
+   k = DiceKriging::km(design=X,response=y,covtype = kernel,control = list(trace=F))
+   #library(rlibkriging)
+   r <- Kriging(y,X,kernel,"constant",FALSE,"none","LL",
+                parameters=list(sigma2=k@covariance@sd2,has_sigma2=TRUE,
+                theta=matrix(k@covariance@range.val),has_theta=TRUE))
+   # m = as.list(r)
+ 
+   f_predict = function(X)
+     predict(r,data.matrix(X))
+     #DiceKriging::predict(k,X,type="UK",cov.compute = TRUE,checkNames=F)
+   contour(.x,.x,matrix(f(expand.grid(.x,.x)),nrow=length(.x)))
+   contour(.x,.x,matrix(f_predict(expand.grid(.x,.x))$mean,nrow=length(.x)),add=TRUE, col='blue')
+   points(X,col='blue')
+ 
+   ntest <- 10
+   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$cov) ,tol = precision))
+   
+   .x = seq(0.1,0.9,,5)
+   p_allx = predict(r,expand.grid(.x,.x), return_stdev=TRUE, return_cov=FALSE, return_deriv=TRUE)
+   for (i in 1:length(.x)) { j=i; {
+     # ref from DiceOptim::EI.grad
+     newdata = matrix(c(.x[i],.x[j]),ncol=2) # just check diagonal points
+     model = k
+     T <- model@T
+     X <- model@X
+     z <- model@z
+     u <- model@M
+     covStruct <- model@covariance
+     predx <- predict(object=model, newdata=newdata, type="UK", checkNames = FALSE,se.compute=TRUE,cov.compute=FALSE)
+     kriging.mean <- predx$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))
+               
+   }}
+ }
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> 
> proc.time()
   user  system elapsed 
  17.48    1.57   19.09