# Benchmark the communication overhead in calling the algorithm
# multiple times for each iteration.
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
library(gmo)
data <- list(J = 8,
K = 2,
y = c(28, 8, -3, 7, -1, 1, 18, 12),
sigma = c(15, 10, 16, 11, 9, 11, 10, 18))
local_file <- "models/8schools_local.stan"
M <- 1
draws <- 10
phi <- c(2,5)
seed <- 42
# TODO
# start with same alpha's
# Check conditional approximation.
g_alpha <- optimizing(stan_model(local_file),
data=c(data, list(phi=phi)),
seed=seed, #init=list(alpha=...), # TODO
draws=M*draws, constrained=FALSE)
#iter=5L) # TODO
for (t in 1:10) {
g_alpha <- optimizing(stan_model(local_file),
data=c(data, list(phi=phi)),
seed=seed, #init=list(alpha=...), # TODO
draws=M*draws, constrained=FALSE)
iter=1L) # TODO
# TODO
# get alpha from previous iteration
}
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