Kriging::save & Kriging::load
Description
Save/Load a Kriging Model
Usage
Python
# k = Kriging(...) k.save("k.json") k2 = load("k.json")
R
# k = Kriging(...) k$save("k.json") k2 = load("k.json")
Matlab/Octave
% k = Kriging(...) k.save("k.json") k2 = load("k.json")
Julia
# k = Kriging(...) save(k, "k.json") k2 = load("k.json")
Arguments
Argument |
Description |
|---|---|
|
Path to the JSON file used for persistence. |
Details
save() serialises the fitted model to JSON, and load() reconstructs a compatible Kriging object from that file. This is useful for checkpointing fitted models or exchanging them across wrapper sessions.
Value
save() writes the file and returns invisibly; load() returns the restored object.
Examples
f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
set.seed(123)
X <- as.matrix(runif(10))
y <- f(X)
k <- Kriging(y, X, kernel = "matern3_2")
k
k$save("k.json")
print(load("k.json"))
Results
* data: 10x[0.0455565,0.940467] -> 10x[0.194057,1.00912]
* trend constant (est.): 0.433954
* variance (est.): 0.0873685
* covariance:
* kernel: matern3_2
* range (est.): 0.240585
* fit:
* objective: LL
* optim: BFGS
* data: 10x[0.0455565,0.940467] -> 10x[0.194057,1.00912]
* trend constant (est.): 0.433954
* variance (est.): 0.0873685
* covariance:
* kernel: matern3_2
* range (est.): 0.240585
* fit:
* objective: LL
* optim: BFGS
{
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},
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},
"R": {
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"circ": {
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"content": "Kriging",
"covType": "matern3_2",
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"est_beta": true,
"est_sigma2": true,
"est_theta": true,
"maxdX": {
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"size": 1,
"type": "colvec"
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"noise_model": "None",
"normalize": false,
"objective": "LL",
"optim": "BFGS",
"regmodel": "constant",
"scaleX": {
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"scaleY": 1.0,
"sigma2": 0.08736845168977267,
"star": {
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"theta": {
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"version": 2,
"y": {
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}