if (FALSE) {
# load the package and the Python env
library(dgpsi)
# model 1
f1 <- function(x) {
(sin(7.5*x)+1)/2
}
# model 2
f2 <- function(x) {
2/3*sin(2*(2*x - 1))+4/3*exp(-30*(2*(2*x-1))^2)-1/3
}
# linked model
f12 <- function(x) {
f2(f1(x))
}
# training data for Model 1
X1 <- seq(0, 1, length = 9)
Y1 <- sapply(X1, f1)
# training data for Model 2
X2 <- seq(0, 1, length = 13)
Y2 <- sapply(X2, f2)
# emulation of model 1
m1 <- gp(X1, Y1, name = "matern2.5", id = "emulator1")
# emulation of model 2
m2 <- dgp(X2, Y2, depth = 2, name = "matern2.5", id = "emulator2")
struc <- data.frame(From_Emulator = c("Global", "emulator1"),
To_Emulator = c("emulator1", "emulator2"),
From_Output = c(1, 1),
To_Input = c(1, 1))
emulators <- list(m1, m2)
# construct the linked emulator for visual inspection
m_link <- lgp(struc, emulators, activate = FALSE)
# visual inspection
summary(m_link)
# build the linked emulator for prediction
m_link <- lgp(struc, emulators, activate = TRUE)
test_x <- seq(0, 1, length = 300)
m_link <- predict(m_link, x = test_x)
# OOS validation
validate_x <- sample(test_x, 20)
validate_y <- sapply(validate_x, f12)
plot(m_link, validate_x, validate_y, style = 2)
# write and read the constructed linked emulator
write(m_link, 'linked_emulator')
m_link <- read('linked_emulator')
}
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