R Under development (unstable) (2024-09-18 r87177 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

> 
> #kernel="matern3_2" #
> for (kernel in c("gauss","exp")){ # NOT YET WORKING: ,"matern3_2","matern5_2")) {
+   context(paste0("Check LogLikelihood for kernel ",kernel))
+ 
+ f <- function(X) apply(X, 1, function(x) prod(sin((x-.5)^2)))
+ 
+ logn <- seq(1.1, 2, by=.1)
+ 
+ #i=1 #
+ for (i in 1:length(logn)) {
+   n <- floor(10^logn[i])
+   d <- 1+floor(log(n)) #floor(2+i/3)
+   
+   # print(n)
+ 
+   set.seed(123)
+   X <- matrix(runif(n*d),ncol=d)
+   y <- f(X)
+   
+ 
+ k = DiceKriging::km(design=X,response=y,covtype = kernel,control = list(trace=F))
+ ll = function(theta) DiceKriging::logLikFun(theta,k)
+ gll = function(theta) {e = new.env(); ll=DiceKriging::logLikFun(theta,k,e); DiceKriging::logLikGrad(theta,k,e)}
+ hll = function(theta,eps=0.0001) {
+   d = length(theta)
+   h = matrix(NA,d,d)
+   g = gll(theta)
+   for (i in 1:d) {
+     eps_i = matrix(0, nrow=d,ncol=1);
+     eps_i[i] = eps;
+     h[i,] = (gll(as.numeric(theta + eps_i))-g)/eps
+   }
+   (h+t(h))/2
+ }
+ 
+ #library(rlibkriging)
+ r <- Kriging(y, X, kernel)
+ ll_C = function(theta) logLikelihoodFun(r,theta)$logLikelihood[1]
+ gll_C = function(theta) t(logLikelihoodFun(r,theta,return_grad=T)$logLikelihoodGrad)
+ hll_C = function(theta) logLikelihoodFun(r,theta,return_hess=T)$logLikelihoodHess[,,]
+ 
+ 
+ x=runif(d)
+ 
+ #print(ll(x))
+ #print(ll_C(x))
+ #print(gll(x))
+ #print(gll_C(x))
+ #print(hll(x))
+ #print(hll_C(x))
+ 
+ precision <- 0.1
+ test_that(desc="logLik is the same that DiceKriging one", 
+          expect_true(abs(ll(x)-ll_C(x)) < precision))
+ 
+ test_that(desc="logLik Grad is the same that DiceKriging one", 
+          expect_true(max(abs(gll(x)-gll_C(x))/abs(gll(x))) < precision))
+ 
+ test_that(desc="logLik Hess is the same that DiceKriging one", 
+          expect_true(max(abs(hll(x)-hll_C(x))/abs(hll(x))) < precision))
+ 
+ }
+          
+ }
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> 
> proc.time()
   user  system elapsed 
   9.73    1.46   11.14