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)
> #kernel="gauss"
> 
> for (kernel in c("gauss","exp","matern3_2","matern5_2")) {
+   context(paste0("Check predict 1D for kernel ",kernel))
+ 
+ ##library(testthat)
+ ##library(rlibkriging, lib.loc="bindings/R/Rlibs")
+ #rlibkriging:::optim_log(3)
+ #kernel="gauss"
+ 
+   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 = "gauss",control = list(trace=F), nugget=0.01,nugget.estim = FALSE)
+   ##library(rlibkriging)
+   r <- NuggetKriging(y,X,"gauss","constant",FALSE,"none","LL",
+                parameters=list(sigma2=k@covariance@sd2,has_sigma2=TRUE, is_sigma2_estim=FALSE,
+                theta=matrix(k@covariance@range.val),has_theta=TRUE, is_theta_estim=FALSE,
+                nugget=k@covariance@nugget,has_nugget=TRUE, is_nugget_estim=FALSE))
+   # m = as.list(r)
+   
+   ntest <- 10
+   Xtest <- as.matrix(c(X,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,xlim=c(X[1]-0.05,X[1]+0.05), ylim=c(Ytest$mean[1]-0.5105,Ytest$mean[1]+0.5105))  
+ #  points(X,y)
+ #  x=sort(c(seq(0,1,,5),X))
+ #  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))$stdev),
+ #        col=rgb(0,0,0,.1),border=NA)
+ #  
+ #  s_wn = matrix(NA,length(x),100)
+ #  s_won = matrix(NA,length(x),ncol(s_wn))
+ #  for (i in 1:ncol(s_wn)) {
+ #    s_wn[,i] = simulate(r,x=x,seed=i, with_nugget=TRUE)
+ #    s_won[,i] = simulate(r,x=x,seed=i, with_nugget=FALSE)
+ #      lines(x,s_wn[,i],col=rgb(1,0,0,0.2))
+ #      lines(x,s_won[,i],col=rgb(0,0,1,0.2))
+ #  }
+ #
+ #  j = which(x==X[1])-3
+ #  abline(v=x[j])
+ #
+ #  .x = seq(0.1,0.9,,5)
+ #  plot(.x,
+ #    dnorm(.x,
+ #      mean=predict(r,x[j],TRUE, return_cov=FALSE)$mean,
+ #      sd=predict(r,x[j],TRUE, return_cov=FALSE)$stdev),type='l')
+ #  lines(density(s_wn[j,]),col='red')
+ #  lines(density(s_won[j,]),col='blue')
+ 
+   .x = seq(0.1,0.9,,11)
+   p_allx = predict(r,.x,TRUE, return_cov=FALSE, return_deriv=TRUE)
+   for (i in 1:length(.x)) {
+     # ref from DiceOptim::EI.grad
+     newdata = .x[i]
+     model = k
+     T <- model@T
+     X <- model@X
+     z <- model@z
+     u <- model@M
+     covStruct <- model@covariance
+     predx <- predict(object=model, newdata=newdata, type="UK", checkNames = FALSE,se.compute=TRUE,cov.compute=FALSE)
+     kriging.mean <- predx$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],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$stdev_deriv),tol = precision))
+               
+   }
+ }
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> 
> test_that(desc="pred cov is the same that DiceKriging one", 
+           expect_equal(cktest,c(Ytest$cov) ,tol = precision))
Test passed 🎉
> 
> proc.time()
   user  system elapsed 
   6.92    0.62    7.54