Nothing
context('param')
source(file.path('helpers', 'helpers.R'))
# create some data
set.seed(1234)
n <- 30
nt <- 30
x <- runif(n)*6-3
xt <- runif(nt)*6-3
f <- 2*x - 2
trials <- sample(10, n, replace = T)
#
cfs <- list(
cf_const(),
cf_lin(),
cf_sexp(),
cf_matern32(),
cf_matern52(),
cf_nn(),
cf_periodic()
)
liks <- list(
lik_gaussian(),
lik_bernoulli('logit'),
lik_bernoulli('probit'),
lik_binomial('logit'),
lik_binomial('probit'),
lik_betabinom('logit'),
lik_betabinom('probit'),
lik_poisson()
)
# create some gps
k <- 1
gps <- list()
yval <- list()
# loop through the likelihoods
for (j in seq_along(liks)) {
# all covariance functions alone
for (i in seq_along(cfs)) {
gps[[k]] <- gp_init(cfs=cfs[[i]], lik=liks[[j]])
yval[[k]] <- generate_target(gps[[k]], f, trials=trials)
k <- k+1
}
# all pairs of covariance functions
cf_comb <- combn(cfs,2)
for (i in 1:NCOL(cf_comb)) {
gps[[k]] <- gp_init(cfs=cf_comb[,i], lik=liks[[j]])
yval[[k]] <- generate_target(gps[[k]], f, trials=trials)
k <- k+1
}
# add products of kernels
cf_comb <- combn(cfs,3)
for (i in 1:NCOL(cf_comb)) {
SWO(cf <- cf_comb[[1,i]] * cf_comb[[2,i]] * cf_comb[[3,i]])
gps[[k]] <- gp_init(cfs=cf, lik=liks[[j]])
k <- k+1
}
}
test_that("set_param: setting the parameters manually has the expected effect", {
for (k in seq_along(gps)) {
gp <- gps[[k]]
# set the parameters to new values and check that the result is correct
param0 <- get_param(gp)
param <- runif(length(param0))
names(param) <- names(param0)
expect_equal(get_param(set_param(gp, param)), param)
}
})
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