library(distbayesianmc)
# test one single run
if(F){
source("~/R_programming/distbayesianmc/params_simulation/params_logit.R")
stan_code <- readChar(fileName, file.info(fileName)$size)
mod <- stan_model(model_code = stan_code, auto_write = T)
dataset_loaded <- f_dataset_loader(dataset, nobs = nobs)
splitted_data <- f_pack_split_data(dataset_loaded$X, dataset_loaded$y, ssplits=ssplits, iseed=iter, typesplit=typesplit)
splitted_data <- f_prep_prior_logistic(splitted_data, scale = scale)
res_approx <- f_stan_sampling_splitted_data(mod, splitted_data, dataset = dataset, i_seed = iter, iter = iter, typesplit = typesplit, nchain = nchain, typeprior=typeprior)
res_exact <- f_function_model_split(ssplits = ssplits, iter = iter, iseed = iseed, n_steps = nchain, burnin = 200, dataset = dataset, typesplit = typesplit, returnres = T, scale=scale)
f_plot_grid_params_dens(res_approx)
}
if(F){
source("~/R_programming/distbayesianmc/params_simulation/params_probit.R")
stan_code <- readChar(fileName, file.info(fileName)$size)
mod <- stan_model(model_code = stan_code, auto_write = T)
dataset_loaded <- f_dataset_loader(dataset, nobs = nobs)
splitted_data <- f_pack_split_data(dataset_loaded$X, dataset_loaded$y, ssplits=ssplits, iseed=iter, typesplit=typesplit)
splitted_data <- f_prep_prior_logistic(splitted_data, scale = scale)
res_approx <- f_stan_sampling_splitted_data(mod, splitted_data, dataset = dataset, i_seed = iter, iter = iter, typesplit = typesplit, nchain = nchain, typeprior=typeprior)
f_plot_grid_params_dens(res_approx)
}
if(T){
setwd("~/R_programming/distbayesianmc")
source("~/R_programming/distbayesianmc/params_simulation/params_logit.R")
#source("~/R_programming/distbayesianmc/params_simulation/params_probit.R")
stan_code <- readChar(fileName, file.info(fileName)$size)
mod <- stan_model(model_code = stan_code, auto_write = T)
#setwd("./sim_results/logistic/")
#setwd("./sim_results/probit/")
setwd("/scratch/alexander/distbayesianmc_logit/")
library(doParallel)
registerDoParallel(cores=8)
for(dataset in vec_datasets){
for(ssplits in vec_splits){
foreach(iter = 1:iters) %dopar% {
#for(iter in 1:iters){
dataset_loaded <- f_dataset_loader(dataset)
splitted_data <- f_pack_split_data(dataset_loaded$X, dataset_loaded$y, ssplits=ssplits, iseed=iter, typesplit=typesplit)
splitted_data <- f_prep_prior_logistic(splitted_data, scale = scale)
f_stan_sampling_splitted_data(mod, splitted_data, dataset = dataset, i_seed = iter, iter = iter, typesplit = typesplit, nchain = nchain, typeprior=typeprior)
}
}
}
}
df <- f_combine_const_data_in_frame(vec_splits, vec_datasets, vec_types_splits, iters)
#f_plot_res_data_frame(df)
f_plot_res_data_frame(df, vec_datasets = vec_datasets)
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