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)
> 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)
> 
> k <- Kriging(y, X,"gauss",parameters = list(theta=matrix(runif(40),ncol=2)))
> print(k)
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.921711, 0.286891
  * fit:
    * objective: LL
    * optim: BFGS
> 
> unlink("k.json")
> rlibkriging::save(k, filename="k.json")
> 
> k2 <- rlibkriging::load(filename="k.json")
> print(k2)
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.921711, 0.286891
  * fit:
    * objective: LL
    * optim: BFGS
> 
> test_that("Save/Load NuggetKriging", expect_true( print(k) == print(k2)))
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.921711, 0.286891
  * fit:
    * objective: LL
    * optim: BFGS
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.921711, 0.286891
  * fit:
    * objective: LL
    * optim: BFGS
Test passed 🎉
> 
> nuk <- NuggetKriging(y, X,"gauss",parameters = list(theta=matrix(runif(40),ncol=2)))
> print(nuk)
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.92171, 0.286891
  * nugget (est.): 0
  * fit:
    * objective: LL
    * optim: BFGS
> 
> unlink("nuk.json")
> rlibkriging::save(nuk, filename="nuk.json")
> 
> nuk2 <- rlibkriging::load(filename="nuk.json")
> print(nuk2)
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.92171, 0.286891
  * nugget (est.): 0
  * fit:
    * objective: LL
    * optim: BFGS
> 
> test_that("Save/Load NuggetKriging", expect_true( print(nuk) == print(nuk2)))
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.92171, 0.286891
  * nugget (est.): 0
  * fit:
    * objective: LL
    * optim: BFGS
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
* trend constant (est.): 0.254277
* variance (est.): 0.480222
* covariance:
  * kernel: gauss
  * range (est.): 0.92171, 0.286891
  * nugget (est.): 0
  * fit:
    * objective: LL
    * optim: BFGS
Test passed 🎊
> 
> nok <- NoiseKriging(y, rep(0.1^2,nrow(X)), X,"gauss",parameters = list(theta=matrix(runif(40),ncol=2)))
> print(nok)
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
  * noise: 20x[0.01,0.01]
* trend constant (est.): 0.146854
* variance (est.): 0.305097
* covariance:
  * kernel: gauss
  * range (est.): 0.749121, 0.241537
  * fit:
    * objective: LL
    * optim: BFGS
> 
> unlink("nok.json")
> rlibkriging::save(nok, filename="nok.json")
> 
> nok2 <- rlibkriging::load(filename="nok.json")
> print(nok2)
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
  * noise: 20x[0.01,0.01]
* trend constant (est.): 0.146854
* variance (est.): 0.305097
* covariance:
  * kernel: gauss
  * range (est.): 0.749121, 0.241537
  * fit:
    * objective: LL
    * optim: BFGS
> 
> test_that("Save/Load NoiseKriging", expect_true( print(nok) == print(nok2)))
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
  * noise: 20x[0.01,0.01]
* trend constant (est.): 0.146854
* variance (est.): 0.305097
* covariance:
  * kernel: gauss
  * range (est.): 0.749121, 0.241537
  * fit:
    * objective: LL
    * optim: BFGS
* data: 20x[0.0420595,0.956833],[0.0246137,0.99427] -> 20x[-0.884835,0.990808]
  * noise: 20x[0.01,0.01]
* trend constant (est.): 0.146854
* variance (est.): 0.305097
* covariance:
  * kernel: gauss
  * range (est.): 0.749121, 0.241537
  * fit:
    * objective: LL
    * optim: BFGS
Test passed 🎉
> 
> proc.time()
   user  system elapsed 
   0.98    0.21    1.18