R Under development (unstable) (2024-09-21 r87186 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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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

> 
> # 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)
> 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 <- Kriging(y, X,"gauss",parameters = list(theta=matrix(runif(40),ncol=2)))
> 
> # ll = function(X) {
> #   logLikelihoodFun(r,X,return_grad=F)$logLikelihood
> # }
> # contour(x,x,matrix(ll(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
> # gll = function(X) {
> #   logLikelihoodFun(r,X,return_grad=T)$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("print")
> 
> p = capture.output(print(r))
> 
> 
> context("logLikelihood")
> 
> ll = function(Theta){apply(Theta,1,function(theta) logLikelihoodFun(r,theta)$logLikelihood)}
> t=seq(0.01,2,,5)
> contour(t,t,matrix(ll(as.matrix(expand.grid(t,t))),nrow=length(t)),nlevels = 30)
> points(as.list(r)$theta[1],as.list(r)$theta[2],pch=20)
> # logLikelihoodFun(r,t(as.list(r)$theta))
> 
> test_that("logLikelihoodFun returned",
+           expect_equal(names(logLikelihoodFun(r,runif(d))),c("logLikelihood")))
Test passed 😸
> test_that("logLikelihoodFun logLikelihoodGrad returned",
+           expect_equal(names(logLikelihoodFun(r,runif(d),return_grad=T)),c("logLikelihood","logLikelihoodGrad")))
Test passed 🎉
> test_that("logLikelihoodFun logLikelihoodGrad logLikelihoodHess returned",
+           expect_equal(names(logLikelihoodFun(r,runif(d),return_grad=T,return_hess=T)),c("logLikelihood","logLikelihoodGrad","logLikelihoodHess")))
Test passed 😀
> 
> test_that("logLikelihoodFun dim",
+           expect_equal(dim(logLikelihoodFun(r,rbind(runif(d),runif(d)))$logLikelihood),c(2,1)))
Test passed 🌈
> test_that("logLikelihoodGrad dim",
+           expect_equal(dim(logLikelihoodFun(r,rbind(runif(d),runif(d)),return_grad=T)$logLikelihoodGrad),c(2,d)))
Test passed 😸
> test_that("logLikelihoodHess dim",
+           expect_equal(dim(logLikelihoodFun(r,rbind(runif(d),runif(d)),return_grad=T,return_hess=T)$logLikelihoodHess),c(2,d,d)))
Test passed 🎉
> 
> 
> context("leaveOneOut")
> 
> loo = function(Theta){apply(Theta,1,function(theta) leaveOneOutFun(r,theta)$leaveOneOut)}
> t=seq(0.01,2,,5)
> contour(t,t,matrix(loo(as.matrix(expand.grid(t,t))),nrow=length(t)),nlevels = 30)
> points(as.list(r)$theta[1],as.list(r)$theta[2])
> # leaveOneOutFun(r,t(as.list(r)$theta))
> 
> test_that("leaveOneOut returned",
+           expect_equal(names(leaveOneOutFun(r,runif(d))),c("leaveOneOut")))
Test passed 😸
> test_that("leaveOneOut leaveOneOutGrad returned",
+           expect_equal(names(leaveOneOutFun(r,runif(d),return_grad=T)),c("leaveOneOut","leaveOneOutGrad")))
Test passed 🎉
> 
> test_that("leaveOneOut dim",
+           expect_equal(dim(leaveOneOutFun(r,rbind(runif(d),runif(d)))$leaveOneOut),c(2,1)))
Test passed 🎉
> test_that("leaveOneOutGrad dim",
+           expect_equal(dim(leaveOneOutFun(r,rbind(runif(d),runif(d)),return_grad=T)$leaveOneOutGrad),c(2,d)))
Test passed 🎊
> 
> 
> context("predict")
> 
> test_that("predict mean stdev returned",
+           expect_equal(names(predict(r,runif(d))),c("mean","stdev")))
Test passed 🎉
> test_that("predict mean returned",
+           expect_equal(names(predict(r,runif(d),return_stdev=F)),c("mean")))
Test passed 🥇
> test_that("predict mean stdev cov returned",
+           expect_equal(names(predict(r,runif(d),return_cov=T)),c("mean","stdev","cov")))
Test passed 😸
> 
> test_that("predict mean dim",
+           expect_equal(dim(predict(r,rbind(runif(d),runif(d)))$mean),c(2,1)))
Test passed 🥳
> test_that("predict stdev dim",
+           expect_equal(dim(predict(r,rbind(runif(d),runif(d)))$stdev),c(2,1)))
Test passed 🌈
> test_that("predict cov dim",
+           expect_equal(dim(predict(r,rbind(runif(d),runif(d)),return_cov=T)$cov),c(2,2)))
Test passed 🎉
> 
> 
> context("simulate")
> 
> test_that("simulate dim",
+           expect_equal(dim(simulate(r,x=rbind(runif(d),runif(d)))),c(2,1)))
Test passed 🎉
> test_that("simulate nsim dim",
+           expect_equal(dim(simulate(r,x=rbind(runif(d),runif(d)),nsim=10)),c(2,10)))
Test passed 😸
> 
> test_that("simulate mean X",
+           expect_equal(mean(simulate(r,nsim = 100, x=X[1,]+0.00005)),y[1],tolerance = 0.01))
Test passed 🎊
> set.seed(12345)
> x = runif(d)
> test_that("simulate mean",
+           expect_equal(mean(simulate(r,nsim = 100, x=x)),predict(r,x)$mean[1],tolerance = 0.01))
Test passed 🥳
> test_that("simulate sd",
+           expect_equal(sd(simulate(r,nsim = 100, x=x)),predict(r,x)$stdev[1],tolerance = 0.01))
Test passed 🌈
> 
> 
> context("update")
> 
> set.seed(1234)
> X2 = matrix(runif(d*10),ncol=d)
> y2 = f(X2)
> 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 <- Kriging(c(y,y2), rbind(X,X2),"gauss")
> #ll = function(X) {
> #  logLikelihoodFun(r20,X,return_grad=F)$logLikelihood
> #}
> #contour(x,x,matrix(ll(as.matrix(expand.grid(x,x))),nrow=length(x)),nlevels = 30)
> #gll = function(X) {
> #  logLikelihoodFun(r20,X,return_grad=T)$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)
> #}
> #}
> 
> 
> 
> 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])
> 
> r2 <- Kriging(c(y,y2), rbind(X,X2),"gauss", parameters = list(theta=matrix(as.list(r)$theta,ncol=2)))
> ll2 = function(Theta){apply(Theta,1,function(theta) logLikelihoodFun(r2,theta)$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')
> 
> p2 = capture.output(print(r2))
> 
> rc = r$copy()
> pc = capture.output(print(rc))
> 
> update(object=r,y2,X2)
> llu = function(Theta){apply(Theta,1,function(theta) logLikelihoodFun(r,theta)$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')
> 
> pu = capture.output(print(r))
> 
> test_that("update",
+           expect_false(all(p == pu)))
Test passed 🎉
> test_that("update almost converge",
+           expect_true(all(pu == p2)))
Test passed 🎉
> 
> test_that("copy is well done",
+           expect_true(all(pc == p)))
Test passed 🎉
> test_that("copy is detached",
+           expect_false(all(pc == pu)))
Test passed 🌈
> 
> proc.time()
   user  system elapsed 
   4.01    0.56    4.53