R Under development (unstable) (2024-09-15 r87152 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)
> 
> for (kernel in c("exp","matern3_2","matern5_2","gauss")) {
+   context(paste0("Check LogLikelihood for kernel ",kernel))
+   
+ 
+ #rlibkriging:::optim_log(3)
+ #kernel="exp"
+ 
+   f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)
+   n <- 5
+   set.seed(123)
+   X <- as.matrix(runif(n))
+   y = f(X) + 0.1*rnorm(nrow(X))
+ 
+ tmin=0.01
+ tmax=1
+ 
+   k = DiceKriging::km(design=X,response=y,noise.var=rep(0.1^2,nrow(X)),covtype = kernel,control = list(trace=F))
+   ll_k = function(theta_sigma2) apply(theta_sigma2,1,function(...)DiceKriging::logLikFun(...,k))
+   x=seq(tmin,tmax,,5)
+   contour(x,x,matrix(ll_k(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
+   for (x1 in seq(tmin,tmax,,11)){
+   for (x2 in seq(tmin,tmax,,11)){
+     envx = new.env()
+     llx = DiceKriging::logLikFun(c(x1,x2),k,envx)
+     gllx = DiceKriging::logLikGrad(c(x1,x2),k,envx)
+     arrows(x1,x2,x1+0.01*gllx[1],x2+0.01*gllx[2])
+   }}
+   
+   ##library(rlibkriging)
+   r <- NoiseKriging(y,noise=rep(0.1^2,nrow(X)), X, kernel)
+   ll_r = function(theta_sigma2) logLikelihoodFun(r,theta_sigma2)$logLikelihood
+   x=seq(tmin,tmax,,5)
+   contour(x,x,matrix(ll_r(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
+   for (x1 in seq(tmin,tmax,,11)){
+   for (x2 in seq(tmin,tmax,,11)){
+     envx = new.env()
+     llx = logLikelihoodFun(r,c(x1,x2))$logLikelihood
+     gllx = logLikelihoodFun(r,c(x1,x2),return_grad = T)$logLikelihoodGrad
+     arrows(x1,x2,x1+.01*gllx[1],x2+.01*gllx[2])
+   }}
+   
+ 
+   precision <- 1e-8  # the following tests should work with it, since the computations are analytical
+   x=c(.5,.5)
+   xenv=new.env()
+   test_that(desc="logLik is the same that DiceKriging one", 
+             expect_equal(logLikelihoodFun(r,x)$logLikelihood,DiceKriging::logLikFun(x,k,xenv),tolerance = precision))
+   
+   test_that(desc="logLik Grad is the same that DiceKriging one", 
+             expect_equal(logLikelihoodFun(r,x,return_grad=T)$logLikelihoodGrad,t(DiceKriging::logLikGrad(x,k,xenv)),tolerance= precision))
+ }
Test passed 🎉
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Test passed 😀
> 
> proc.time()
   user  system elapsed 
   1.76    0.17    1.93