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)
> 
> # f <- function(X) apply(X, 1, function(x) prod(sin((x-.5)^2)))
> f <- function(X) apply(X, 1, function(x)
+   prod(sin(2*pi*( x * (seq(0,1,l=1+length(x))[-1])^2 )))
+ )
> n <- 20
> set.seed(123)
> X <- cbind(runif(n),runif(n))
> y <- f(X) + rnorm(n,0,0.1)
> d = ncol(X)
> 
> x=seq(0,1,,5)
> contour(x,x,matrix(f(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
> points(X)
> 
> r <- NoiseKriging(y, noise=rep(0.1^2,nrow(X)), X,"gauss",parameters = list(theta=matrix(runif(40),ncol=2),sigma2=1))
> l= as.list(r)
> #ll = function(X) {
> #  logLikelihoodFun(r,X,return_grad=F)$logLikelihood
> #}
> #contour(x,x,matrix(ll(cbind(as.matrix(expand.grid(x,x)), r$sigma2())),nrow=length(x)),nlevels = 30)
> #gll = function(X) {
> #  logLikelihoodFun(r,X,return_grad=T)$logLikelihoodGrad
> #}
> #for (ix in 1:21) {
> #for (iy in 1:21) {
> #  xx = c(ix/21,iy/21)
> #  g = gll(xx)
> #  arrows(xx[1],xx[2],xx[1]+g[1]/1000,xx[2]+g[2]/1000,col='grey',length=0.05)
> #}
> #}
> 
> context("noise / print")
> 
> p = capture.output(print(r))
> 
> 
> context("noise / logLikelihood")
> 
> ll = function(Theta){apply(Theta,1,function(theta) logLikelihoodFun(r,c(theta,l$sigma2))$logLikelihood)}
> t=seq(0.01,2,,5)
> contour(t,t,matrix(ll(as.matrix(expand.grid(t,t))),nrow=length(t)),nlevels = 30)
> points(as.list(r)$theta[1],as.list(r)$theta[2])
> 
> for (.t1 in t) for (.t2 in t) {
+   llg = logLikelihoodFun(r,c(.t1,.t2,l$sigma2),return_grad=T)$logLikelihoodGrad
+   if (sum(llg[1:2]^2)<0.01) next
+   arrows(.t1,.t2, .t1+0.001*llg[1],.t2+0.001*llg[2],col='red') 
+ }
> 
> # logLikelihoodFun(r,t(as.list(r)$theta))
> 
> test_that("noise / logLikelihoodFun returned",
+           expect_equal(names(logLikelihoodFun(r,runif(d+1))),c("logLikelihood")))
Test passed 😸
> test_that("noise / logLikelihoodFun logLikelihoodGrad returned",
+           expect_equal(names(logLikelihoodFun(r,runif(d+1),return_grad=TRUE)),c("logLikelihood","logLikelihoodGrad")))
Test passed 🎊
> 
> test_that("noise / logLikelihoodFun dim",
+           expect_equal(dim(logLikelihoodFun(r,rbind(runif(d+1),runif(d+1)))$logLikelihood),c(2,1)))
Test passed 🥳
> test_that("noise / logLikelihoodGrad dim",
+           expect_equal(dim(logLikelihoodFun(r,rbind(runif(d+1),runif(d+1)),return_grad=TRUE)$logLikelihoodGrad),c(2,d+1)))
Test passed 🌈
> 
> 
> context("noise / predict")
> 
> test_that("noise / predict mean stdev returned",
+           expect_equal(names(predict(r,runif(d))),c("mean","stdev")))
Test passed 🎉
> test_that("noise / predict mean returned",
+           expect_equal(names(predict(r,runif(d),return_stdev=FALSE)),c("mean")))
Test passed 😸
> test_that("noise / predict mean stdev cov returned",
+           expect_equal(names(predict(r,runif(d),return_cov=TRUE)),c("mean","stdev","cov")))
Test passed 😸
> 
> test_that("noise / predict mean dim",
+           expect_equal(dim(predict(r,rbind(runif(d),runif(d)))$mean),c(2,1)))
Test passed 🎊
> test_that("noise / predict stdev dim",
+           expect_equal(dim(predict(r,rbind(runif(d),runif(d)))$stdev),c(2,1)))
Test passed 🎊
> test_that("noise / predict cov dim",
+           expect_equal(dim(predict(r,rbind(runif(d),runif(d)),return_cov=TRUE)$cov),c(2,2)))
Test passed 🎊
> 
> 
> context("noise / simulate")
> 
> test_that("noise / simulate dim",
+           expect_equal(dim(simulate(r,x=rbind(runif(d),runif(d)), with_noise=NULL)),c(2,1)))
Test passed 🥇
> test_that("noise / simulate nsim dim",
+           expect_equal(dim(simulate(r,x=rbind(runif(d),runif(d)),nsim=10, with_noise=NULL)),c(2,10)))
Test passed 🎉
> 
> expect_not_equal = function(x,y,...)
+   expect_false(isTRUE(all.equal(x, y)))
> test_that("noise / simulate mean X",
+           expect_not_equal(mean(simulate(r,nsim = 100, x=X[1,]+0.00005, with_noise=NULL)),y[1],tolerance = 0.01))
Test passed 🌈
> set.seed(12345)
> x = runif(d)
> test_that("noise / simulate mean",
+           expect_equal(mean(simulate(r,nsim = 100, x=x, with_noise=NULL)),predict(r,x)$mean[1],tolerance = 0.01))
Test passed 🥳
> test_that("noise / simulate sd",
+           expect_equal(sd(simulate(r,nsim = 100, x=x, with_noise=NULL)),predict(r,x)$stdev[1],tolerance = 0.01))
Test passed 🌈
> 
> 
> context("noise / update")
> 
> set.seed(1234)
> X2 = matrix(runif(d*10),ncol=d)
> y2 = f(X2) + rnorm(nrow(X2),0,0.1)
> x=seq(0,1,,5)
> contour(x,x,matrix(f(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
> points(X)
> points(X2,col='red')
> 
> r20 <- NoiseKriging(c(y,y2), c(y-f(X),y2-f(X2))^2,rbind(X,X2),"gauss")
> ll = function(X) {
+   logLikelihoodFun(r20,X,return_grad=F)$logLikelihood
+ }
> contour(x,x,matrix(ll(cbind(as.matrix(expand.grid(x,x)),r$sigma2())),nrow=length(x)),nlevels = 30)
> gll = function(X) {
+   logLikelihoodFun(r20,X,return_grad=T)$logLikelihoodGrad
+ }
> xy = seq(0,1,,5)
> for (ix in 1:length(xy)) {
+ for (iy in 1:length(xy)) {
+   xx = c(ix/length(xy),iy/length(xy),r$sigma2())
+   g = gll(xx)
+   arrows(xx[1],xx[2],xx[1]+g[1]/1000,xx[2]+g[2]/1000,col='grey',length=0.05)
+ }
+ }
> 
> 
> 
> t=seq(0.01,2,,5)
> contour(t,t,matrix(ll(cbind(as.matrix(expand.grid(t,t)),r$sigma2())),nrow=length(t)),nlevels = 30)
> points(as.list(r)$theta[1],as.list(r)$theta[2],pch=20)
> 
> #cat("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!","\n")
> #cat(paste0(collapse="\n",p),"\n")
> #cat(r$logLikelihood(),"\n")
> #
> #rlibkriging:::optim_log(1)
> 
> set.seed(1234)
> r2 <- NoiseKriging(c(y,y2), noise=rep(0.1^2,nrow(X)+nrow(X2)),rbind(X,X2),"gauss", parameters = list(theta=matrix(as.list(r)$theta,ncol=2),sigma2=1))
> ll2 = function(Theta){apply(Theta,1,function(theta) logLikelihoodFun(r2,c(theta,as.list(r2)$sigma2))$logLikelihood)}
> contour(t,t,matrix(ll2(as.matrix(expand.grid(t,t))),nrow=length(t)),nlevels = 30,add=T,col='red')
> points(as.list(r2)$theta[1],as.list(r2)$theta[2],col='red',pch=20)
> 
> p2 = capture.output(print(r2))
> 
> update(object=r,y2,rep(0.1^2,nrow(X2)),X2)
> llu = function(Theta){apply(Theta,1,function(theta) logLikelihoodFun(r,  c(theta,as.list(r2)$sigma2) )$logLikelihood)}
> contour(t,t,matrix(llu(as.matrix(expand.grid(t,t))),nrow=length(t)),nlevels = 30,add=T,col='blue')
> points(as.list(r)$theta[1],as.list(r)$theta[2],col='blue',pch=20)
> 
> pu = capture.output(print(r))
> 
> test_that("noise / update",
+           expect_false(all(p == pu)))
Test passed 🌈
> 
> # cat(paste0(collapse="\n",p2),"\n")
> # cat(r2$logLikelihood(),"\n")
> # cat(paste0(collapse="\n",capture.output(print( r2$logLikelihoodFun(c(r2$theta(),r2$sigma2()/(r2$sigma2()+r2$noise())),TRUE) ))),"\n")
> # cat(paste0(collapse="\n",pu),"\n")
> # cat(r$logLikelihood(),"\n")
> # cat(paste0(collapse="\n",capture.output(print( r$logLikelihoodFun(c(r$theta(),r$sigma2()/(r$sigma2()+r$noise())),TRUE) ))),"\n")
> 
> test_that("noise / update almost converge",
+           expect_equal(as.list(r2)$theta, as.list(r)$theta, tolerance = 2E-2))
Test passed 🥳
> 
> proc.time()
   user  system elapsed 
   2.21    0.21    2.42