R Under development (unstable) (2024-09-28 r87201 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)
> 
> context ("no noise/nugget")
> 
> f <- function(x) {
+     1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
+ }
> plot(f)
> n <- 5
> X_o <- seq(from = 0, to = 1, length.out = n)
> noise = 0
> sigma2 = 0.01
> set.seed(1234)
> y_o <- f(X_o) + rnorm(n, sd = sqrt(noise))
> points(X_o, y_o,pch=16)
> 
> lk_no <- NoiseKriging(y = matrix(y_o, ncol = 1),
+               noise = matrix(rep(noise, n), ncol = 1),
+               X = matrix(X_o, ncol = 1),
+               kernel = "gauss",
+               regmodel = "linear",
+               optim = "none",
+               normalize = FALSE,
+               parameters = list(theta = matrix(0.1), sigma2 = sigma2))
> 
> lk_nu <- NuggetKriging(y = matrix(y_o, ncol = 1),
+               X = matrix(X_o, ncol = 1),
+               kernel = "gauss",
+               regmodel = "linear",
+               optim = "none",
+               normalize = FALSE,
+               parameters = list(theta = matrix(0.1), nugget=noise, sigma2=sigma2))
> 
> 
> lk = Kriging(y = matrix(y_o, ncol = 1),
+               X = matrix(X_o, ncol = 1),
+               kernel = "gauss",
+               regmodel = "linear",
+               optim = "none",
+               normalize = FALSE,
+               parameters = list(theta = matrix(0.1), sigma2 = sigma2))
> 
> test_that("Consistency between Noise(0)Kriging and Kriging", {
+     expect_equal(lk$T(), lk_no$T()/sqrt(sigma2))
+     expect_equal(lk$M(), lk_no$M()*sqrt(sigma2))
+     expect_equal(lk$beta(), lk_no$beta())
+     expect_equal(lk$z(), lk_no$z()*sqrt(sigma2))
+ })
Test passed 🌈
> 
> test_that("Consistency between Nugget(0)Kriging and Kriging", {
+     expect_equal(lk$T(), lk_nu$T())
+     expect_equal(lk$M(), lk_nu$M())
+     expect_equal(lk$beta(), lk_nu$beta())
+     expect_equal(lk$z(), lk_nu$z())
+ })
Test passed 🥳
> 
> ## Simulate
> 
> X_n = seq(0,1,,5)
> 
> ls = lk$simulate(100, 123, X_n, will_update=FALSE)
> #y_u = rs[i_u,1] # force matching 1st sim
> 
> ls_no = lk_no$simulate(100, 123, X_n, with_noise=NULL, will_update=FALSE)
> 
> ls_nu = lk_nu$simulate(100, 123, X_n, with_nugget=FALSE, will_update=FALSE)
> 
> plot(f)
> points(X_o,y_o,pch=16)
> for (i in 1:length(X_o)) {
+     lines(c(X_o[i],X_o[i]),c(y_o[i]+2*sqrt(noise),y_o[i]-2*sqrt(noise)),col='black',lwd=4)
+ }
> for (i in 1:min(10,ncol(ls))) {
+     lines(X_n,ls[,i],col=rgb(0,0,0,.51),lwd=2)
+     lines(X_n,ls_nu[,i],col=rgb(1,0.5,0,.51),lwd=4,lty=2)
+     lines(X_n,ls_no[,i],col=rgb(1,0,0.5,.51),lwd=6,lty=2)
+ }
> 
> for (i in 1:1:length(X_n)) {
+     ds=density(ls[i,])
+     ds_nu=density(ls_nu[i,])
+     ds_no=density(ls_no[i,])
+     polygon(
+         X_n[i] + ds$y/20,
+         ds$x,
+         col=rgb(0,0,0,0.2),border=NA)
+     polygon(
+         X_n[i] + ds_nu$y/20,
+         ds_nu$x,
+         col=rgb(0,0,1,0.2),border=NA)
+     polygon(
+         X_n[i] + ds_no$y/20,
+         ds_no$x,
+         col=rgb(1,0,0,0.2),border=NA)
+     #test_that(desc="updated,simulated sample follows simulated,updated distribution",
+     #    expect_true(ks.test(lus[i,],lsu[i,])$p.value  > 0.01))
+ }
> 
> for (i in 1:length(X_n)) {
+     plot(density(ls[i,]),xlim=range(ls[i,], c(ls_no[i,],ls_nu[i,])))
+     lines(density(ls_no[i,]),col='orange')
+     lines(density(ls_nu[i,]),col='red')
+     if (sd(ls_no[i,])>1e-3 && sd(ls_nu[i,])>1e-3) # otherwise means that density is ~ dirac, so don't test
+     test_that(desc=paste0("updated,simulated sample follows simulated,updated distribution ",
+     mean(ls_no[i,]),",",sd(ls_no[i,])," != ",mean(ls_nu[i,]),",",sd(ls_nu[i,])),
+         expect_gt(suppressWarnings(ks.test(ls_no[i,],ls_nu[i,])$p.value), 0.01)) # just check that it is not clearly wrong
+ }
> 
> 
> ## Update simulate
> 
> #i_u = c(9,13)
> #X_u = X_n[i_u]# c(.4,.6)
> X_u = c(.4,.6)
> y_u = f(X_u) + rnorm(length(X_u), sd = sqrt(noise))
> noise_u = rep(noise, length(X_u))
> 
> ls = lk$simulate(100, 123, X_n, will_update=TRUE)
> #y_u = rs[i_u,1] # force matching 1st sim
> lus=NULL
> lus = lk$update_simulate(y_u, X_u)
> 
> ls_no = lk_no$simulate(100, 123, X_n, will_update=TRUE)
> lus_no=NULL
> lus_no = lk_no$update_simulate(y_u, noise_u, X_u)
> 
> ls_nu = lk_nu$simulate(100, 123, X_n, will_update=TRUE)
> lus_nu=NULL
> lus_nu = lk_nu$update_simulate(y_u, X_u)
> 
> lu = copy(lk)
> lu$update(y_u, matrix(X_u,ncol=1), refit=FALSE)
> lsu=NULL
> lsu = lu$simulate(100, 123, X_n)
> 
> plot(f)
> points(X_o,y_o,pch=16)
> for (i in 1:length(X_o)) {
+     lines(c(X_o[i],X_o[i]),c(y_o[i]+2*sqrt(noise),y_o[i]-2*sqrt(noise)),col='black',lwd=4)
+ }
> points(X_u,y_u,col='red',pch=16)
> for (i in 1:length(X_u)) {
+     lines(c(X_u[i],X_u[i]),c(y_u[i]+2*sqrt(noise),y_u[i]-2*sqrt(noise)),col='red',lwd=4)
+ }
> for (i in 1:min(10,ncol(lus))) {
+     lines(X_n,ls[,i],col=rgb(0,0,0,.1),lwd=2)
+     lines(X_n,lus_nu[,i],col=rgb(1,0.5,0,.51),lwd=4,lty=2)
+     lines(X_n,lus_no[,i],col=rgb(1,0,0.5,.51),lwd=6,lty=2)
+     lines(X_n,lus[,i],col=rgb(1,0,0,.51),lwd=2)
+     lines(X_n,lsu[,i],col=rgb(0,0,1,.51),lwd=2)
+ }
> 
> 
> for (i in 1:length(X_n)) {
+     plot(density(lus[i,]),xlim=range(lus[i,], c(lus_no[i,],lus_nu[i,])))
+     lines(density(lus_no[i,]),col='orange')
+     lines(density(lus_nu[i,]),col='red')
+     if (sd(lus_no[i,])>1e-3 && sd(lus_nu[i,])>1e-3) # otherwise means that density is ~ dirac, so don't test
+     test_that(desc=paste0("updated,simulated sample follows simulated,updated distribution ",
+     mean(lus_no[i,]),",",sd(lus_no[i,])," != ",mean(lus_nu[i,]),",",sd(lus_nu[i,])),
+         expect_true(ks.test(lus_no[i,],lus_nu[i,])$p.value > 0.001)) # just check that it is not clearly wrong
+ }
> 
> ########################################## noise/nugget >0 ##########################################
> 
> context ("noise = nugget >0")
> 
> f <- function(x) {
+     1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
+ }
> plot(f)
> n <- 5
> X_o <- seq(from = 0, to = 1, length.out = n)
> noise = 0.01
> sigma2 = 0.09
> set.seed(1234)
> y_o <- f(X_o) + rnorm(n, sd = sqrt(noise))
> points(X_o, y_o,pch=16)
> 
> lk_no <- NoiseKriging(y = matrix(y_o, ncol = 1),
+               noise = matrix(rep(noise, n), ncol = 1),
+               X = matrix(X_o, ncol = 1),
+               kernel = "gauss",
+               regmodel = "linear",
+               optim = "none",
+               #normalize = TRUE,
+               parameters = list(theta = matrix(0.1), sigma2 = sigma2))
> 
> lk_nu <- NuggetKriging(y = matrix(y_o, ncol = 1),
+               X = matrix(X_o, ncol = 1),
+               kernel = "gauss",
+               regmodel = "linear",
+               optim = "none",
+               #normalize = TRUE,
+               parameters = list(theta = matrix(0.1), nugget=noise, sigma2=sigma2))
> 
> 
> 
> test_that("Consistency between Noise(0)Kriging and Kriging", {
+     expect_equal(lk_nu$T(), lk_no$T()/sqrt(noise+sigma2))
+     expect_equal(lk_nu$M(), lk_no$M()*sqrt(noise+sigma2))
+     expect_equal(lk_nu$beta(), lk_no$beta())
+     expect_equal(lk_nu$z(), lk_no$z()*sqrt(noise+sigma2))
+ })
Test passed 🌈
> 
> 
> ## Simulate
> 
> X_n = seq(0,1,,11)
> 
> ls_no = NULL
> ls_no = lk_no$simulate(10, 123, X_n, with_noise = 0, will_update=FALSE)
> 
> ls_nu = NULL
> ls_nu = lk_nu$simulate(10, 123, X_n, with_nugget=FALSE, will_update=FALSE)
> 
> plot(f,xlim=c(-0.1,0.1))
> points(X_o,y_o,pch=16)
> for (i in 1:length(X_o)) {
+     lines(c(X_o[i],X_o[i]),c(y_o[i]+2*sqrt(noise),y_o[i]-2*sqrt(noise)),col='black',lwd=4)
+ }
> for (i in 1:min(100,ncol(ls_nu))) {
+     lines(X_n,ls_nu[,i],col='red',lwd=3,lty=2)
+     lines(X_n,ls_no[,i],col='blue',lty=2)
+ }
> 
> for (i in 1:1:length(X_n)) {
+     ds_nu=density(ls_nu[i,])
+     ds_no=density(ls_no[i,])
+     polygon(
+         X_n[i] + ds_nu$y/20,
+         ds_nu$x,
+         col=rgb(0,0,1,0.2),border=NA)
+     polygon(
+         X_n[i] + ds_no$y/20,
+         ds_no$x,
+         col=rgb(1,0,0,0.2),border=NA)
+     #test_that(desc="updated,simulated sample follows simulated,updated distribution",
+     #    expect_true(ks.test(lus[i,],lsu[i,])$p.value  > 0.01))
+ }
> 
> for (i in 1:length(X_n)) {
+     plot(density(ls_no[i,]),xlim=range(c(ls_no[i,],ls_nu[i,])))
+     lines(density(ls_no[i,]),col='orange')
+     lines(density(ls_nu[i,]),col='red')
+     if (sd(ls_no[i,])>1e-3 && sd(ls_nu[i,])>1e-3) # otherwise means that density is ~ dirac, so don't test
+     test_that(desc=paste0("nugget & noise simulated are consistent: ",
+     mean(ls_no[i,]),",",sd(ls_no[i,])," != ",mean(ls_nu[i,]),",",sd(ls_nu[i,])),
+         expect_true(ks.test(ls_no[i,],ls_nu[i,])$p.value > 0.001)) # just check that it is not clearly wrong
+ }
Test passed 🥳
Test passed 🎊
Test passed 🎉
Test passed 🥳
Test passed 🎉
Test passed 🎊
Test passed 🎉
Test passed 🌈
Test passed 🎉
Test passed 🎉
Test passed 🎉
> 
> 
> ## Update simulate
> 
> #i_u = c(9,13)
> #X_u = X_n[i_u]# c(.4,.6)
> X_u = c(.4,.6)
> y_u = f(X_u) + rnorm(length(X_u), sd = sqrt(noise))
> noise_u = rep(noise, length(X_u))
> 
> ls_no = lk_no$simulate(100, 123, X_n, will_update=TRUE)
> lus_no=NULL
> lus_no = lk_no$update_simulate(y_u, noise_u, X_u)
> 
> ls_nu = lk_nu$simulate(100, 123, X_n, with_nugget=FALSE, will_update=TRUE)
> lus_nu=NULL
> lus_nu = lk_nu$update_simulate(y_u, X_u)
> 
> plot(f,xlim=c(-0.1,0.1))
> points(X_o,y_o,pch=16)
> for (i in 1:length(X_o)) {
+     lines(c(X_o[i],X_o[i]),c(y_o[i]+2*sqrt(noise),y_o[i]-2*sqrt(noise)),col='black',lwd=4)
+ }
> points(X_u,y_u,col='red',pch=16)
> for (i in 1:length(X_u)) {
+     lines(c(X_u[i],X_u[i]),c(y_u[i]+2*sqrt(noise),y_u[i]-2*sqrt(noise)),col='red',lwd=4)
+ }
> for (i in 1:min(10,ncol(lus_nu))) {
+     lines(X_n,lus_nu[,i],col=rgb(1,0.5,0,.51),lwd=4,lty=2)
+     lines(X_n,lus_no[,i],col=rgb(1,0,0.5,.51),lwd=4,lty=2)
+ }
> 
> for (i in 1:1:length(X_n)) {
+     dus_nu=density(lus_nu[i,])
+     dus_no=density(lus_no[i,])
+     polygon(
+         X_n[i] + dus_nu$y/100,
+         dus_nu$x,
+         col=rgb(0,0,1,0.2),border=NA)
+     polygon(
+         X_n[i] + dus_no$y/100,
+         dus_no$x,
+         col=rgb(1,0,0,0.2),border=NA)
+     #test_that(desc="updated,simulated sample follows simulated,updated distribution",
+     #    expect_true(ks.test(lus[i,],lsu[i,])$p.value  > 0.01))
+ }
> 
> for (i in 1:length(X_n)) {
+     plot(density(ls_nu[i,]),xlim=range(c(ls_no[i,],ls_nu[i,],lus_no[i,],lus_nu[i,])))
+     lines(density(ls_no[i,]),col='blue')
+     lines(density(lus_no[i,]),col='orange')
+     lines(density(lus_nu[i,]),col='red')
+     #if (all(abs(X_n[i]-X_o)>0)) # means we are not on design points (where nuggetK is deterministic, while noiseK is not)
+     if (sd(lus_no[i,])>1e-3 && sd(lus_nu[i,])>1e-3) # otherwise means that density is ~ dirac, so don't test
+     test_that(desc=paste0("nugget & noise updated simulate are consistent at x=",X_n[i]," ",
+     mean(lus_no[i,]),",",sd(lus_no[i,])," != ",mean(lus_nu[i,]),",",sd(lus_nu[i,])),
+         expect_true(ks.test(lus_no[i,],lus_nu[i,])$p.value > 0.000001)) # just check that it is not clearly wrong
+ }
Test passed 🌈
Test passed 😸
Test passed 🌈
Test passed 🎊
Test passed 🎊
Test passed 🥇
Test passed 🥳
Test passed 🥇
Test passed 🥳
> 
> proc.time()
   user  system elapsed 
   4.40    0.45    4.84