NuggetKriging::save & NuggetKriging::load
Description
Save/Load a NuggetKriging Model
Usage
Python
# k = NuggetKriging(...) k.save("k.json") k2 = load("k.json")
R
# k = NuggetKriging(...) k$save("k.json") k2 = load("k.json")
Matlab/Octave
% k = NuggetKriging(...) k.save("k.json") k2 = load("k.json")
Value
The loaded object.
Examples
f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
set.seed(123)
X <- as.matrix(runif(10))
y <- f(X) + 0.1 * rnorm(nrow(X))
k <- NuggetKriging(y, X, kernel = "matern3_2")
k
k$save("nuk.json")
print(load("nuk.json"))
Results
* data: 10x[0.0455565,0.940467] -> 10x[0.149491,0.940566]
* trend constant (est.): 0.488156
* variance (est.): 0.078856
* covariance:
* kernel: matern3_2
* range (est.): 0.274956
* nugget (est.): 0.00347513
* fit:
* objective: LL
* optim: BFGS
* data: 10x[0.0455565,0.940467] -> 10x[0.149491,0.940566]
* trend constant (est.): 0.488156
* variance (est.): 0.078856
* covariance:
* kernel: matern3_2
* range (est.): 0.274956
* nugget (est.): 0.00347513
* fit:
* objective: LL
* optim: BFGS
{
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},
"M": {
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"R": {
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},
"X": {
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"beta": {
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"centerX": {
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"circ": {
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"n_cols": 1,
"n_rows": 2,
"type": "mat"
},
"content": "NuggetKriging",
"covType": "matern3_2",
"dX": {
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},
"est_beta": true,
"est_nugget": true,
"est_sigma2": true,
"est_theta": true,
"maxdX": {
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"size": 1,
"type": "colvec"
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"normalize": false,
"nugget": 0.0034751316130604633,
"objective": "LL",
"optim": "BFGS",
"regmodel": "constant",
"scaleX": {
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"scaleY": 1.0,
"sigma2": 0.07885598208622975,
"star": {
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"theta": {
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"version": 2,
"y": {
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"z": {
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}
}
