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)
> 
> library(RobustGaSP)
#########
##
## Robust Gaussian Stochastic Process, RobustGaSP Package
## Copyright (C) 2016-2024 Mengyang Gu, Jesus Palomo and James O. Berger
#########

Attaching package: 'RobustGaSP'

The following object is masked from 'package:rlibkriging':

    simulate

The following object is masked from 'package:stats':

    simulate

> 
> context("RobustGaSP / Fit: 1D")
> 
> 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 = RobustGaSP::rgasp(design=X,response=y)
The upper bounds of the range parameters are 184.9743 
The initial values of range parameters are 3.699485 
Start of the optimization  1  : 
The number of iterations is  10 
 The value of the  marginal posterior  function is  2.497978 
 Optimized range parameters are 0.1921691 
 Optimized nugget parameter is 0 
 Convergence:  TRUE 
The initial values of range parameters are 0.05223118 
Start of the optimization  2  : 
The number of iterations is  6 
 The value of the  marginal posterior  function is  2.497978 
 Optimized range parameters are 0.1921691 
 Optimized nugget parameter is 0 
 Convergence:  TRUE 
> #library(rlibkriging)
> r <- Kriging(y, X,
+   kernel="matern5_2",
+   regmodel = "constant", normalize = FALSE,
+   optim = "BFGS",
+   objective = "LMP")
> # m = as.list(r)
> 
> # Check lmp function
> 
> lmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1);
+                   # print(dim(X));
+                   apply(X,1,
+                     function(x) {
+                       #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost
+                       y=RobustGaSP:::neg_log_marginal_post_approx_ref(param=(x),nugget=0, nugget.est=model@nugget.est, 
+                                         R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, 
+                                         CL=model@CL, 
+                                         a=0.2,
+                                         b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]),
+                                         kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha
+                                         )
+                       y})}
> lmp_rgasp(1)
[1] -1.901254
> 
> plot(lmp_rgasp,xlim=c(0.01,6))
> abline(v=(log(k@beta_hat)))
> 
> lmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1);
+                   # print(dim(X));
+                   apply(X,1,
+                     function(x) {
+                       y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1))$logMargPost
+                       y})}
> lmp_lk(1)
[1] -1.901254
> 
> lines(seq(0.1,6,,5),lmp_lk(seq(0.1,6,,5)),col='red')
> abline(v=(log(1/as.list(r)$theta)),col='red')
> 
> precision <- 1e-3
> test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp is the same that lk/lmp one"),
+           expect_equal(lmp_rgasp(1),lmp_lk(1),tol = precision))
Test passed 🥳
> test_that(desc=paste0("RobustGaSP / Fit: 1D / fitted theta is the same that RobustGaSP one"),
+           expect_equal(as.list(r)$theta[1],1/k@beta_hat,tol = precision))
Test passed 🥳
> 
> 
> 
> dlmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1);
+                   # print(dim(X));
+                   apply(X,1,
+                     function(x) {
+ 
+ #    print(RobustGaSP:::log_marginal_lik_deriv(param=(x),nugget=0,nugget_est=model@nugget.est, 
+ #                                        R0=model@R0,X=model@X, zero_mean=model@zero_mean,
+ #                                        output=model@output, 
+ #                                        kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha))
+ #
+ #    print(RobustGaSP:::log_approx_ref_prior_deriv(param=(x),nugget=0, nugget_est=model@nugget.est, 
+ #                                        CL=model@CL, 
+ #                                        a=0.2,
+ #                                        b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2])))
+ 
+ 
+                       #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost
+                       y=RobustGaSP:::neg_log_marginal_post_approx_ref_deriv(param=(x),nugget=0, nugget.est=model@nugget.est, 
+                                         R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, 
+                                         CL=model@CL, 
+                                         a=0.2,
+                                         b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]),
+                                         kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha
+                                         )
+                       y})}
> dlmp_rgasp(1)
[1] -1.703845
> 
> dlmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1);
+                   apply(X,1,
+                     function(x) {
+                       y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1),TRUE)$logMargPostGrad
+                       y})}
> -exp(-1)*dlmp_lk(1)
[1] -1.703845
> 
> precision <- 1e-3
> test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp deriv is the same that lk/lmp deriv"),
+           expect_equal(dlmp_rgasp(1),-exp(-1)*dlmp_lk(1),tol = precision))
Test passed 🎉
> 
> 
> # Check predict
> 
> ntest <- 10
> Xtest <- seq(0,1,,ntest)
> Ytest_rgasp <- predict(k,matrix(Xtest,ncol=1))
> Ytest_libK <- predict(r,Xtest)
> 
> plot(f)
> points(X,y)
> lines(Xtest,Ytest_rgasp$mean,col='blue')
> polygon(c(Xtest,rev(Xtest)),
+         c(Ytest_rgasp$mean+2*Ytest_rgasp$sd,rev(Ytest_rgasp$mean-2*Ytest_rgasp$sd)),
+         col=rgb(0,0,1,0.1), border=NA)
> 
> lines(Xtest,Ytest_libK$mean,col='red')
> polygon(c(Xtest,rev(Xtest)),
+         c(Ytest_libK$mean+2*Ytest_libK$stdev,rev(Ytest_libK$mean-2*Ytest_libK$stdev)),
+         col=rgb(1,0,0,0.1), border=NA)
> 
> precision <- 1e-3
> test_that(desc=paste0("pred mean is the same that RobustGaSP one"),
+           expect_equal(predict(r,0.7)$mean[1],predict(k,matrix(0.7))$mean,tol = precision))
Test passed 😸
> test_that(desc=paste0("pred sd is the same that RobustGaSP one"),
+           expect_equal(predict(r,0.7)$stdev[1],predict(k,matrix(0.7))$sd,tol = precision))
Test passed 🥇
> 
> 
> ## RobustGaSP examples
> 
>   #---------------------------------------
>   # a 1 dimensional example 
>   #---------------------------------------
> context("RobustGaSP / 1 dimensional example")
> 
> 
>   input=10*seq(0,1,1/14)
>   output<-higdon.1.data(input)
>   #the following code fit a GaSP with zero mean by setting zero.mean="Yes"
>   model<- rgasp(design = input, response = output, zero.mean="No")
The upper bounds of the range parameters are 670.0756 
The initial values of range parameters are 13.40151 
Start of the optimization  1  : 
The number of iterations is  11 
 The value of the  marginal posterior  function is  -2.570526 
 Optimized range parameters are 1.725711 
 Optimized nugget parameter is 0 
 Convergence:  TRUE 
The initial values of range parameters are 0.08888889 
Start of the optimization  2  : 
The number of iterations is  10 
 The value of the  marginal posterior  function is  -2.570526 
 Optimized range parameters are 1.725711 
 Optimized nugget parameter is 0 
 Convergence:  TRUE 
>   model

Call:
rgasp(design = input, response = output, zero.mean = "No")
Mean parameters:  2.613275e-16 
Variance parameter:  0.6613543 
Range parameters:  1.725711 
Noise parameter:  0 
>   
>   testing_input = as.matrix(seq(0,10,1/100))
>   model.predict<-predict(model,testing_input)
>   names(model.predict)
[1] "mean"    "lower95" "upper95" "sd"     
>   
>   #########plot predictive distribution
>   testing_output=higdon.1.data(testing_input)
>   plot(testing_input,model.predict$mean,type='l',col='blue',
+        xlab='input',ylab='output')
>   polygon( c(testing_input,rev(testing_input)),c(model.predict$lower95,
+         rev(model.predict$upper95)),col =  "grey80", border = FALSE)
>   lines(testing_input, testing_output)
>   lines(testing_input,model.predict$mean,type='l',col='blue')
>   lines(input, output,type='p')
>   
>   ## mean square erros
>   mean((model.predict$mean-testing_output)^2)
[1] 4.319999e-05
> 
> model_libK = Kriging(matrix(output,ncol=1), matrix(input,ncol=1), 
+   kernel="matern5_2", 
+   regmodel = "constant", normalize = FALSE, 
+   optim = "BFGS", 
+   objective = "LMP", parameters = NULL)
> 
>     lines(testing_input,predict(model_libK,testing_input)$mean,type='l',col='red')
>     polygon( 
+       c(testing_input,rev(testing_input)),
+       c(
+         predict(model_libK,testing_input)$mean+2*predict(model_libK,testing_input)$stdev,
+         rev(predict(model_libK,testing_input)$mean-2*predict(model_libK,testing_input)$stdev)),
+       col = rgb(1,0,0,0.1), border = FALSE)
> 
> precision <- 1e-3
> test_that(desc=paste0("RobustGaSP / 1 dimensional example / pred mean is the same that RobustGaSP one"),
+           expect_equal(predict(model_libK,0.7)$mean[1],predict(model,matrix(0.7))$mean,tol = precision))
Test passed 🌈
> test_that(desc=paste0("RobustGaSP / 1 dimensional example / pred sd is the same that RobustGaSP one"),
+           expect_equal(predict(model_libK,0.7)$stdev[1],predict(model,matrix(0.7))$sd,tol = precision))
Test passed 🎉
> 
> 
> 
> 
> 
> context("RobustGaSP / 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)
> model = NULL
> r = NULL
> library(RobustGaSP)
> k = rgasp(design=X,response=y)
The upper bounds of the range parameters are 58.61045 56.09173 
The initial values of range parameters are 1.172209 1.121835 
Start of the optimization  1  : 
The number of iterations is  16 
 The value of the  marginal posterior  function is  -61.1843 
 Optimized range parameters are 0.797434 2.544434 
 Optimized nugget parameter is 0 
 Convergence:  TRUE 
The initial values of range parameters are 0.2437167 0.2332433 
Start of the optimization  2  : 
The number of iterations is  37 
 The value of the  marginal posterior  function is  -61.1843 
 Optimized range parameters are 0.7974235 2.544395 
 Optimized nugget parameter is 0 
 Convergence:  FALSE 
> #library(rlibkriging)
> r <- Kriging(y, X, "matern5_2", objective="LMP", optim="BFGS10")
> 
> lmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+                   # print(dim(X));
+                   apply(X,1,
+                     function(x) {
+                       #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost
+     
+ #    print(RobustGaSP:::log_marginal_lik(param=(x),nugget=0,nugget_est=model@nugget.est, 
+ #                                        R0=model@R0,X=model@X, zero_mean=model@zero_mean,
+ #                                        output=model@output, 
+ #                                        kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha))
+ #
+ #    print(RobustGaSP:::log_approx_ref_prior(param=(x),nugget=0, nugget_est=model@nugget.est, 
+ #                                        CL=model@CL, 
+ #                                        a=0.2,
+ #                                        b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@output))[2])))
+ 
+                       y=RobustGaSP:::neg_log_marginal_post_approx_ref(param=(x),nugget=0, nugget.est=model@nugget.est, 
+                                         R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, 
+                                         CL=model@CL, 
+                                         a=0.2,
+                                         b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]),
+                                         kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha
+                                         )
+                       y})}
> lmp_rgasp(c(1,1))
[1] 63.73664
> 
> x=seq(-3,3,,5)
> contour(x,x,matrix(lmp_rgasp(as.matrix(expand.grid(x,x))),nrow=length(x)),levels=seq(50,100,,5))
> points(log(k@beta_hat[1]),log(k@beta_hat[2]))
> 
> lmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+                   apply(X,1,
+                     function(x) {
+                       y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=2))$logMargPost
+                       y})}
> lmp_lk(c(1,1))
[1] 63.73664
> 
> #contour(x,x,matrix(lmp_lk(as.matrix(expand.grid(x,x))),nrow=length(x)),levels = seq(50,100,,5),col='red')
> points(log(1/as.list(r)$theta[1]),log(1/as.list(r)$theta[2]),col='red')
> 
> precision <- 1e-1
> test_that(desc=paste0("Fit: 2D (Branin) / rgasp/lmp is the same that lk/lmp one"),
+           expect_equal(lmp_rgasp(c(1,1)),lmp_lk(c(1,1)),tol = precision))
Test passed 😸
> test_that(desc=paste0("Fit: 2D (Branin) / fitted theta is the same that RobustGaSP one"),
+           expect_equal(lmp_rgasp(log(k@beta_hat)),lmp_rgasp(t(log(1/as.list(r)$theta))),tol = precision))
Test passed 🌈
> 
> 
> dlmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+                   # print(dim(X));
+                   apply(X,1,
+                     function(x) {
+                       #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost
+                       y=RobustGaSP:::neg_log_marginal_post_approx_ref_deriv(param=(x),nugget=0, nugget.est=model@nugget.est, 
+                                         R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, 
+                                         CL=model@CL, 
+                                         a=0.2,
+                                         b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]),
+                                         kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha
+                                         )
+                       y})}
> dlmp_rgasp(c(1,1))
           [,1]
[1,] -0.4587818
[2,]  2.4580082
> 
> dlmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=2);
+                   apply(X,1,
+                     function(x) {
+                       y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=2),TRUE)$logMargPostGrad
+                       y})}
> -exp(-1)*dlmp_lk(c(1,1))
           [,1]
[1,] -0.4587818
[2,]  2.4580082
> 
> precision <- 1e-1
> test_that(desc=paste0("Fit: 2D (Branin) / rgasp/lmp deriv is the same that lk/lmp deriv"),
+           expect_equal(dlmp_rgasp(c(1,1)),-exp(-1)*dlmp_lk(c(1,1)),tol = precision))
Test passed 🎊
> 
> proc.time()
   user  system elapsed 
   1.81    0.07    1.87