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)
> #kernel="gauss"
> 
> for (kernel in c("exp","matern3_2","matern5_2","gauss")) {
+   context(paste0("Check LogLikelihood 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),nugget=0, nugget.estim=TRUE)
+   alpha0 = k@covariance@sd2/(k@covariance@sd2+k@covariance@nugget)
+   ll_theta = function(theta) DiceKriging::logLikFun(c(theta,alpha0),k)
+   plot(Vectorize(ll_theta),ylab="LL",xlab="theta",xlim=c(0.01,1))
+   for (x in seq(0.01,1,,5)){
+     envx = new.env()
+     llx = DiceKriging::logLikFun(c(x,alpha0),k,envx)
+     gllx = DiceKriging::logLikGrad(c(x,alpha0),k,envx)[1,]
+     arrows(x,llx,x+.1,llx+.1*gllx)
+   }
+   
+   ##library(rlibkriging)
+   r <- NuggetKriging(y, X, kernel, parameters=list(nugget=0,is_nugget_estim=TRUE))
+   ll2_theta = function(theta) logLikelihoodFun(r,c(theta,alpha0))$logLikelihood
+   # second arg is alpha=1 for nugget=0
+   # plot(Vectorize(ll2),col='red'), add=T) 
+   for (x in seq(0.01,1,,5)){
+     envx = new.env()
+     ll2x = logLikelihoodFun(r,c(x,alpha0))$logLikelihood
+     gll2x = logLikelihoodFun(r,c(x,alpha0),return_grad = T)$logLikelihoodGrad[,1]
+     arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
+   }
+   
+   theta0 = k@covariance@range.val
+   ll_alpha = function(alpha) DiceKriging::logLikFun(c(theta0,alpha),k)
+   plot(Vectorize(ll_alpha),ylab="LL",xlab="alpha",xlim=c(0.01,1))
+   for (x in seq(0.01,1,,5)){
+     envx = new.env()
+     llx = DiceKriging::logLikFun(c(theta0,x),k,envx)
+     gllx = DiceKriging::logLikGrad(c(theta0,x),k,envx)[2,]
+     arrows(x,llx,x+.1,llx+.1*gllx)
+   }
+   ll2_alpha = function(alpha) logLikelihoodFun(r,c(theta0,alpha))$logLikelihood
+   #plot(Vectorize(ll2_alpha),col='red',add=T)
+   for (x in seq(0.01,1,,5)){
+     envx = new.env()
+     ll2x = logLikelihoodFun(r,c(theta0,x))$logLikelihood
+     gll2x = logLikelihoodFun(r,c(theta0,x),return_grad = T)$logLikelihoodGrad[,2]
+     arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
+   }
+ 
+   precision <- 1e-8  # the following tests should work with it, since the computations are analytical
+   x=.25
+   xenv=new.env()
+   test_that(desc="logLik is the same that DiceKriging one /alpha", 
+             expect_equal(
+               logLikelihoodFun(r,c(theta0,x))$logLikelihood[1]
+               ,
+               DiceKriging::logLikFun(c(theta0,x),k,xenv)
+               ,tolerance = precision))
+   
+   test_that(desc="logLik Grad is just good /alpha", 
+             expect_equal(
+               logLikelihoodFun(r,c(theta0,x),return_grad=T)$logLikelihoodGrad[,2]
+               ,
+               -(logLikelihoodFun(r,c(theta0,x-1e-5))$logLikelihood[1]-logLikelihoodFun(r,c(theta0,x))$logLikelihood[1])/1e-5,
+               ,tolerance= 1e-5))
+               
+   test_that(desc="logLik Grad is the same that DiceKriging one /alpha", 
+             expect_equal(
+               logLikelihoodFun(r,c(theta0,x),return_grad=T)$logLikelihoodGrad[,2]
+               ,
+               DiceKriging::logLikGrad(c(theta0,x),k,xenv)[2,]
+               ,tolerance= precision))
+               
+   xenv=new.env()
+   test_that(desc="logLik is the same that DiceKriging one /theta", 
+             expect_equal(
+               logLikelihoodFun(r,c(x,alpha0))$logLikelihood[1]
+               ,
+               DiceKriging::logLikFun(c(x,alpha0),k,xenv)
+               ,tolerance = precision))
+ 
+   test_that(desc="logLik Grad is just good /theta", 
+             expect_equal(
+               logLikelihoodFun(r,c(x,alpha0),return_grad=T)$logLikelihoodGrad[,1]
+               ,
+               (logLikelihoodFun(r,c(x+1e-5,alpha0))$logLikelihood[1]-logLikelihoodFun(r,c(x,alpha0))$logLikelihood[1])/1e-5
+               ,tolerance= 1e-3))
+ 
+   test_that(desc="logLik Grad is the same that DiceKriging one /theta", 
+             expect_equal(
+               logLikelihoodFun(r,c(x,alpha0),return_grad=T)$logLikelihoodGrad[,1]
+               ,
+               DiceKriging::logLikGrad(c(x,alpha0),k,xenv)[1,]
+               ,tolerance= precision))
+ 
+ }
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> 
> 
> ########################## 2D
> 
> 
> 
> for (kernel in c("matern3_2","matern5_2","gauss","exp")) {
+   context(paste0("Check LogLikelihood for kernel ",kernel))
+   
+   f <- function(X) apply(X, 1, function(x) prod(sin((x-.5)^2)))
+   n <- 10
+   set.seed(123)
+   X <- cbind(runif(n),runif(n),runif(n))
+   y <- f(X)
+ 
+   k = DiceKriging::km(design=X,response=y,covtype = kernel,control = list(trace=F),nugget=0, nugget.estim=TRUE)
+   
+   ##library(rlibkriging)
+   r <- NuggetKriging(y, X, kernel, parameters=list(nugget=0,is_nugget_estim=TRUE))
+   
+   precision <- 1e-8  # the following tests should work with it, since the computations are analytical
+   #x = c(.2,.5,.7,0.01)
+   #x=c(.5,.5,.5,.9999995)
+   x = c(r$theta(),r$sigma2()/(r$sigma2()+r$nugget()))
+   #x = c(k@covariance@range.val,k@covariance@sd2/(k@covariance@sd2+k@covariance@nugget))
+   xenv=new.env()
+   test_that(desc="logLik is the same that DiceKriging one", 
+             expect_equal(
+               logLikelihoodFun(r,x)$logLikelihood[1]
+               ,
+               DiceKriging::logLikFun(x,k,xenv)
+               ,tolerance = precision))
+   
+   test_that(desc="logLik Grad is the same that DiceKriging one", 
+             expect_equal(
+               logLikelihoodFun(r,x,return_grad=T)$logLikelihoodGrad[1,]
+               ,
+               t(DiceKriging::logLikGrad(x,k,xenv))[1,]
+               ,tolerance= precision))
+ 
+   eps=0.000001
+   test_that(desc="logLik Grad is just good", 
+             expect_equal(
+               -logLikelihoodFun(r,x,return_grad=T)$logLikelihoodGrad[1,]
+               ,
+               c( # finite-diff grad
+                 (logLikelihoodFun(r,x-c(eps,0,0,0))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps,
+                 (logLikelihoodFun(r,x-c(0,eps,0,0))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps,
+                 (logLikelihoodFun(r,x-c(0,0,eps,0))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps,
+                 (logLikelihoodFun(r,x-c(0,0,0,eps))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps
+               )
+               ,tolerance= 1e-2))
+ }
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> 
> proc.time()
   user  system elapsed 
   6.07    0.60    6.62