Nothing
context("covTP, gradient and derivatives")
PRINT <- FALSE
## ============================================================================
## Checks relative to the 'covMat' method for the class "TP". These
## concern: the gradient, the derivative (w.r.t. 'x').
## The code can be used to get more tests by changing the seed, 'd', 'n', ...
## and the 'k1Fun1' function
## ============================================================================
## require (numDeriv) ## now in 'Depends'
precision <- 1e-6
set.seed(12345)
n <- 10
d <- 4
## ============================================================================
## Define a covTP with the chosen one-dimensional kernel
## ============================================================================
## choose a design and parameter values
X <- array(runif(n * d), dim = c(n, d),
dimnames = list(NULL, paste("x", 1:d, sep = "")))
kerns <- c("Cos", "Exp", "Gauss", "Matern3_2", "Matern5_2")
k1Fun1s <- paste("k1Fun1", kerns, sep = "")
## ============================================================================
## Replicate the test for each target one-dimensional kernel if wanted
## ============================================================================
for (i in 1:1) {
for (iFun in 1:length(k1Fun1s)) {
k1Fun1 <- match.fun(k1Fun1s[iFun])
if (PRINT) {
cat(sprintf("Kernel function : %s\n", k1Fun1s[iFun]))
}
myCov <- covTP( k1Fun1 = k1Fun1, d = d, cov = "homo")
theta <- as.vector(simulPar(object = myCov, n = 1L))
## use small ranges to allow small kernel values
## theta[1:d] <- theta[1:d] / 20
coef(myCov) <- theta
## =====================================================================
## check the gradient w.r.t. parameters
## =====================================================================
res <- covAsVec(theta, myCov, X)
grad.check <- jacobian(covAsVec, theta, object = myCov, X = X)
errGrad <- grad.check - attr(res, "gradient")
if (PRINT) {
cat(sprintf(" gradient: %e\n", max(abs(errGrad))))
} else {
test_that(desc = sprintf("gradient, symmetric case %s",
kerns[iFun]),
code = expect_true(max(abs(errGrad)) < precision))
}
## ====================================================================
## check the derivative w.r.t. 'x'
## ====================================================================
nNew <- 1
XNew <- array(runif(nNew * d), dim = c(nNew, d),
dimnames = list(NULL, inputNames(myCov)))
res <- covAsVec2(XNew, myCov, X)
der.check <- jacobian(covAsVec2, XNew, object = myCov, X = X)
errDer <- der.check - attr(res, "der")
if (PRINT) {
cat(sprintf(" derivative: %e\n", max(abs(errDer))))
} else {
test_that(desc = sprintf("derivative, non-symmetric case",
kerns[iFun]),
code = expect_true(max(abs(errDer)) < precision))
}
}
}
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