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

> 
> context("test_copied_kriging_returns_same_result")
>     X = matrix(c(0.0, 0.2, 0.5, 0.8, 1.0))
>     f = function(x) (1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x ^ 5 + 0.7))
>     y = f(X)
> 
>     rl1 = Kriging(y, X, "gauss", parameters=list(sigma2=1, is_theta_estim=TRUE))
>     print(rl1)
* data: 5x[0,1] -> 5x[0.030241,0.772403]
* trend constant (est.): 0.453087
* variance (est.): 0.0653536
* covariance:
  * kernel: gauss
  * range (est.): 0.02
  * fit:
    * objective: LL
    * optim: BFGS
> 
>     rl2 = rl1$copy()  # true copy not reference copy
>     print(rl2)
* data: 5x[0,1] -> 5x[0.030241,0.772403]
* trend constant (est.): 0.453087
* variance (est.): 0.0653536
* covariance:
  * kernel: gauss
  * range (est.): 0.02
  * fit:
    * objective: LL
    * optim: BFGS
> 
>     test_that(desc="not same object reference",
+           expect_false(identical(rl1,rl2)))  # not same object reference                  
Test passed 🎊
> 
>     x = seq(0, 1, 1 / 99)
> 
>     p1 = rl1$predict(x, TRUE, TRUE, TRUE)
>     p1 = list(mean=p1[1], stdev=p1[2], cov=p1[3], mean_deriv=p1[4], stdev_deriv=na.omit(p1[5]))
> 
>     p2 = rl2$predict(x, TRUE, TRUE, TRUE)
>     p2 =  list(mean=p2[1], stdev=p2[2], cov=p2[3], mean_deriv=p2[4], stdev_deriv=na.omit(p2[5]))
> 
>     test_that(desc="mean",expect_equal(p1["mean"], p2["mean"]))
Test passed 😸
>     test_that(desc="stdev",expect_equal(p1["stdev"], p2["stdev"]))
Test passed 🎊
>     test_that(desc="cov",expect_equal(p1["cov"], p2["cov"]))
Test passed 😸
>     test_that(desc="mean_deriv",expect_equal(p1["mean_deriv"], p2["mean_deriv"]))
Test passed 🎊
>     test_that(desc="stdev_deriv",expect_equal(p1["stdev_deriv"], p2["stdev_deriv"]))
Test passed 🥇
> 
> 
> context("test_copied_and_changed_kriging_returns_different_result")
>     X = matrix(c(0.0, 0.2, 0.5, 0.8, 1.0))
>     f = function(x) (1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x ^ 5 + 0.7))
>     y = f(X)
> 
>     rl1 = Kriging(y, X, "gauss", parameters=list(sigma2=1, is_theta_estim=TRUE))
>     print(rl1)
* data: 5x[0,1] -> 5x[0.030241,0.772403]
* trend constant (est.): 0.453087
* variance (est.): 0.0653536
* covariance:
  * kernel: gauss
  * range (est.): 0.02
  * fit:
    * objective: LL
    * optim: BFGS
> 
>     rl2 = rl1$copy()  # true copy not reference copy
>     print(rl2)
* data: 5x[0,1] -> 5x[0.030241,0.772403]
* trend constant (est.): 0.453087
* variance (est.): 0.0653536
* covariance:
  * kernel: gauss
  * range (est.): 0.02
  * fit:
    * objective: LL
    * optim: BFGS
> 
>     test_that(desc="not same object reference",
+           expect_false(identical(rl1,rl2)))  # not same object reference                  
Test passed 🎊
> 
>     x = seq(0, 1, 1 / 99)
> 
>     p1 = rl1$predict(x, TRUE, FALSE, FALSE)
>     p1 = list(mean=p1[1], stdev=p1[2], cov=p1[3], mean_deriv=p1[4], stdev_deriv=p1[5])
> 
>     rl2$update(f(0.6), 0.6, TRUE)
>     p2 = rl2$predict(x, TRUE, FALSE, FALSE)
>     p2 =  list(mean=p2[1], stdev=p2[2], cov=p2[3], mean_deriv=p2[4], stdev_deriv=p2[5])
> 
>     test_that(desc="mean",expect_false(identical(p1["mean"], p2["mean"])))
Test passed 😸
>     test_that(desc="stdev",expect_false(identical(p1["stdev"], p2["stdev"])))
Test passed 🥇
> 
> proc.time()
   user  system elapsed 
   1.42    0.17    1.59