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

> 
> context("Fit: 1D")
> 
> 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)
> k = NULL
> r = NULL
> k = DiceKriging::km(design=X,response=y,covtype = "gauss",control = list(trace=F))
> r <- Kriging(y, X, "gauss")
> 
> ll = Vectorize(function(x) logLikelihoodFun(r,x)$logLikelihood)
> plot(ll,xlim=c(0.000001,10))
>   for (x in seq(0.000001,10,,11)){
+     envx = new.env()
+     ll2x = logLikelihoodFun(r,x)$logLikelihood
+     gll2x = logLikelihoodFun(r,x,return_grad = T)$logLikelihoodGrad
+     arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
+   }
> 
> theta_ref = optimize(ll,interval=c(0.001,2),maximum=T)$maximum
> abline(v=theta_ref,col='black')
> abline(v=as.list(r)$theta,col='red')
> abline(v=k@covariance@range.val,col='blue')
> 
> test_that(desc="Fit: 1D / fit of theta by DiceKriging is right",
+           expect_equal(theta_ref, k@covariance@range.val, tol= 1e-3))
Test passed 😸
> 
> test_that(desc="Fit: 1D / fit of theta by libKriging is right",
+           expect_equal(array(theta_ref), array(as.list(r)$theta), tol= 0.01))
Test passed 🥳
> 
> #############################################################
> 
> context("Fit: 2D (Branin)")
> 
> f = function(X) apply(X,1,DiceKriging::branin)
> n <- 15
> set.seed(1234)
> X <- cbind(runif(n),runif(n))
> y = f(X)
> k = NULL
> r = NULL
> k = DiceKriging::km(design=X,response=y,covtype = "gauss",control = list(trace=F),parinit = c(.2,.5))
> r <- Kriging(y, X, "gauss", parameters=list(theta=matrix(c(.2,.5),ncol=2)))
> 
> ll = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+                   # print(dim(X));
+                   apply(X,1,
+                     function(x) {
+                       # print(dim(x))
+                       #print(matrix(unlist(x),ncol=2));
+                       y=-logLikelihoodFun(r,matrix(unlist(x),ncol=2))$logLikelihood
+                       #print(y);
+                       y})}
> #DiceView::contourview(ll,xlim=c(0.01,2),ylim=c(0.01,2))
> x=seq(0.01,2,,5)
> contour(x,x,matrix(ll(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
> 
> theta_ref = optim(par=matrix(c(.2,.5),ncol=2),ll,lower=c(0.01,0.01),upper=c(2,2),method="L-BFGS-B")$par
> points(theta_ref,col='black')
> points(as.list(r)$theta[1],as.list(r)$theta[2],col='red')
> points(k@covariance@range.val[1],k@covariance@range.val[2],col='blue')
> 
> test_that(desc="Fit: 2D (Branin) / fit of theta 2D is _quite_ the same that DiceKriging one",
+           expect_equal(ll(array(as.list(r)$theta)), ll(k@covariance@range.val), tol=1e-1))
Test passed 😸
> 
> 
> 
> #############################################################
> 
> context("Fit: 2D (Branin) multistart")
> 
> f = function(X) apply(X,1,DiceKriging::branin)
> n <- 15
> set.seed(1234)
> X <- cbind(runif(n),runif(n))
> y = f(X)
> k = NULL
> r = NULL
> 
> parinit = matrix(runif(10*ncol(X)),ncol=ncol(X))
> k <- tryCatch( # needed to catch warning due to %dopar% usage when using multistart
+     withCallingHandlers(
+       {
+         error_text <- "No error."
+         DiceKriging::km(design=X,response=y,covtype = "gauss",multistart = 10, parinit=parinit,control = list(trace=F))
+       },
+       warning = function(e) {
+         error_text <<- trimws(paste0("WARNING: ", e))
+         invokeRestart("muffleWarning")
+       }
+     ),
+     error = function(e) {
+       return(list(value = NA, error_text = trimws(paste0("ERROR: ", e))))
+     },
+     finally = {
+     }
+   )
> r <- Kriging(y, X, "gauss", parameters=list(theta=parinit))
> l = as.list(r)
> 
> # save(list=ls(),file="fit-2d-multistart.Rdata")
> 
> ll = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+ # print(dim(X));
+ apply(X,1,
+       function(x) {
+         # print(dim(x))
+         #print(matrix(unlist(x),ncol=2));
+         y=-logLikelihoodFun(r,matrix(unlist(x),ncol=2))$logLikelihood
+         #print(y);
+         y})}
> #DiceView::contourview(ll,xlim=c(0.01,2),ylim=c(0.01,2))
> x=seq(0.01,2,,5)
> contour(x,x,matrix(ll(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
> 
> theta_ref = optim(par=matrix(c(.2,.5),ncol=2),ll,lower=c(0.01,0.01),upper=c(2,2),method="L-BFGS-B")$par
> points(theta_ref,col='black')
> points(as.list(r)$theta[1],as.list(r)$theta[2],col='red')
> points(k@covariance@range.val[1],k@covariance@range.val[2],col='blue')
> 
> test_that(desc="Fit: 2D (Branin) multistart / fit of theta 2D is _quite_ the same that DiceKriging one",
+           expect_equal(ll(array(as.list(r)$theta)), ll(k@covariance@range.val), tol= 1e-3))
Test passed 🌈
> 
> 
> 
> #############################################################
> 
> context("Fit: 2D")
> 
> f <- function(X) apply(X, 1,
+                        function(x)
+                          prod(
+                            sin(2*pi*
+                                  ( x * (seq(0,1,l=1+length(x))[-1])^2 )
+                            )))
> logn <- 1 #seq(1, 2.5, by=.1)
> n <- floor(10^logn)
> d <- 2
> set.seed(1234)
> X <- matrix(runif(n*d),ncol=d)
> y <- f(X)
> k = NULL
> r = NULL
> k = DiceKriging::km(design=X,response=y,covtype = "gauss",control = list(trace=F))
> 
> x=seq(0,2,,5)
> mll_fun <- function(x) -apply(x,1,
+                               function(theta)
+                                 DiceKriging::logLikFun(theta,k)
+ )
> contour(x,x,matrix(mll_fun(expand.grid(x,x)),nrow=length(x)),nlevels = 30)
> 
> # use same startup point for convergence
> r <- Kriging(y, X, "gauss","constant",FALSE,"BFGS","LL",
+              parameters=list(theta=matrix(k@parinit,ncol=2)))
> #mll2_fun <- function(x) -apply(x,1,
> #                              function(theta)
> #                                r$logLikelihoodFun(theta)$logLikelihood
> #)
> #contour(x,x,matrix(mll2_fun(expand.grid(x,x)),nrow=length(x)),nlevels = 30)
> 
> l = as.list(r)
> 
> # save(list=ls(),file="fit-2d.Rdata")
> 
> points(as.list(r)$theta[1],as.list(r)$theta[2],col='red')
> points(k@covariance@range.val[1],k@covariance@range.val[2],col='blue')
> 
> test_that(desc="Fit: 2D / fit of theta 2D is the same that DiceKriging one",
+           expect_equal(array(as.list(r)$theta),array(k@covariance@range.val),tol=  5e-2))
Test passed 🎉
> 
> ################################################################################
> 
> context("Fit: 2D _not_ in [0,1]^2")
> 
> # "unnormed" version of Branin: [0,1]x[0,15] -> ...
> branin_15 <- function (x) {
+   x1 <- x[1] * 15 - 5
+   x2 <- x[2] #* 15
+   (x2 - 5/(4 * pi^2) * (x1^2) + 5/pi * x1 - 6)^2 + 10 * (1 - 1/(8 * pi)) * cos(x1) + 10
+ }
> 
> f = function(X) apply(X,1,branin_15)
> n <- 15
> set.seed(1234)
> X <- cbind(runif(n,0,1),runif(n,0,15))
> y = f(X)
> k = NULL
> r = NULL
> k = DiceKriging::km(design=X,response=y,covtype = "gauss",control = list(trace=F),parinit = c(0.25,10))
> r <- Kriging(y, X, "gauss",parameters=list(theta=matrix(c(0.25,10),ncol=2)))
> l = as.list(r)
> 
> # save(list=ls(),file="fit-2d-not01.Rdata")
> 
> ll_r = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+ # print(dim(X));
+ apply(X,1,
+       function(x) {
+         # print(dim(x))
+         #print(matrix(unlist(x),ncol=2));
+         -logLikelihoodFun(r,matrix(unlist(x),ncol=2))$logLikelihood
+         #print(y);
+         })}
> #DiceView::contourview(ll,xlim=c(0.01,2),ylim=c(0.01,2))
> x1=seq(0.001,2,,5)
> x2=seq(0.001,30,,5)
> contour(x1,x2,matrix(ll_r(as.matrix(expand.grid(x1,x2))),nrow=length(x1)),nlevels = 30,col='red')
> points(as.list(r)$theta[1],as.list(r)$theta[2],col='red')
> ll_r(t(as.list(r)$theta))
[1] 66.31244
> 
> ll_k = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+ apply(X,1,function(x) {-DiceKriging::logLikFun(x,k)})}
> contour(x1,x2,matrix(ll_k(as.matrix(expand.grid(x1,x2))),nrow=length(x1)),nlevels = 30,add=T)
> points(k@covariance@range.val[1],k@covariance@range.val[2])
> ll_k(k@covariance@range.val)
[1] 66.31255
> 
> theta_ref = optim(par=matrix(c(.25,10),ncol=2),ll_r,lower=c(0.001,0.001),upper=c(2,30),method="L-BFGS-B")$par
> points(theta_ref,col='black')
> 
> test_that(desc="Fit: 2D _not_ in [0,1]^2 / fit of theta 2D is _quite_ the same that DiceKriging one",
+           expect_equal(ll_r(array(as.list(r)$theta)), ll_k(k@covariance@range.val), tol=1e-1))
Test passed 😸
> 
> proc.time()
   user  system elapsed 
   2.31    0.26    2.56