##########################
# these following functions allow user to perform sequential fusion for target exp(-x^4/2)
# sequential fusion means that we perform fusion in steps to try recover samples for our target
##########################
######################################## sequential_fusion ########################################
#' Standard Sequential Monte Carlo Fusion
#'
#' Sequential Monte Carlo Fusion with base level of the form exp(-((x^4)*beta)/2)
#'
#' @param N_schedule vector of length (L-1), where N_schedule[l] is the number of samples per node at level l
#' @param mean mean value
#' @param time_schedule vector of legnth(L-1), where time_schedule[l] is the time chosen for Fusion at level l
#' @param start_beta beta for the base level
#' @param base_samples list of length (1/start_beta), where samples_to_fuse[c] containg the samples for the c-th node in the level
#' @param seed seed number - default is NULL, meaning there is no seed
#'
#' @return samples: samples from Sequential fusion
#' @return time: vector of length (L-1), where time[l] is the run time for level l
#' @return rho_acc: vector of length (L-1), where rho_acc[l] is the acceptance rate for first fusion step for level l
#' @return Q_acc: vector of length (L-1), where Q_acc[l] is the acceptance rate for second fusion step for level l
#' @return input_betas: list of length (L), where input_betas[[l]] is the input betas for level l
#' @return output_beta: vector of length(L-1), where output_beta[l] is the beta for level l
#' @return diffusion_times: vector of length (L-1), where diffusion_times[l] are the times for fusion in level l (= time_schedule)
#'
#' @examples
#' input_samples <- base_rejection_sampler_exp_4(beta = 1/4, nsamples = 100000, proposal_mean = 0, proposal_sd = 1.5, dominating_M = 1.4)
#' test <- parallel_sequential_fusion_exp_4_rcpp(N_schedule = rep(10000, 3), time_schedule = rep(1, 3),
#' start_beta = 1/4, base_samples = input_samples)
#'
#' # plot results
#' plot_levels_seq_exp_4(test, from = -3, to = 3, plot_rows = 2, plot_columns = 2)
#'
#' @export
parallel_sequential_fusion_exp_4_rcpp <- function(N_schedule, mean = 0, time_schedule, start_beta, base_samples, seed = NULL) {
# base samples is a list of length 1/beta containing samples for each node
if (is.list(base_samples) && length(base_samples)!=(1/start_beta)) {
stop('parallel_sequential_fusion_exp_4_rcpp: length of base_samples must be equal to 1/start_beta')
}
# N_schedule is how many samples you want in each node along the seq-tree
if (is.vector(N_schedule) && length(N_schedule)!=(1/start_beta)-1) {
stop('parallel_sequential_fusion_exp_4_rcpp: length of N_schedule must be equal to 1/start_beta - 1')
}
# time_schedule is the designated value for T for each node along the seq-tree
if (is.vector(time_schedule) && length(time_schedule)!=(1/start_beta)-1) {
stop('parallel_sequential_fusion_exp_4_rcpp: length of time_schedule must be equal to 1/start_beta - 1')
}
# initialising study results
seq_samples <- list()
seq_samples[[(1/start_beta)]] <- base_samples # base level
time_taken_per_level <- rep(0, (1/start_beta)-1)
input_betas <- list()
input_betas[[(1/start_beta)]] <- NA
output_beta <- c(1:((1/start_beta)-1), start_beta)
overall_rho_iters <- 0
overall_Q_iters <- 0
overall_N <- 0
# make some vectors for acceptance rates of the level
rho_acc <- rep(0, (1/start_beta)-1)
Q_acc <- rep(0, (1/start_beta)-1)
rhoQ_acc <- rep(0, (1/start_beta)-1)
# creating parallel cluster
n_cores <- parallel::detectCores()
cl <- parallel::makeCluster(n_cores, outfile = 'parallel_seq_fusion_exp_4.txt')
# creating variable and functions list to pass into cluster using clusterExport
varlist <- list("phi_exp_4", "bound_phi_exp_4", "diffusion_probability_exp_4", "fusion_diff_exp_4_rcpp",
"N_schedule", "mean", "time_schedule", "start_beta", "base_samples")
parallel::clusterExport(cl, envir = environment(), varlist = varlist)
# exporting functions from layeredBB package to simulate layered Brownian bridges
parallel::clusterExport(cl, varlist = ls("package:layeredBB"))
if (!is.null(seed)) {
# setting seed for the cores in the cluster
parallel::clusterSetRNGStream(cl, iseed = seed)
}
# add to output file that starting sequential fusion
cat('Starting sequential fusion \n', file = 'parallel_seq_fusion_exp_4.txt')
# setting index for which base_sample to fuse the current node with
# starting with 3 since that's the first one we fuse with the second level
# the first level is manually indexed as 1 and 2
index <- 3
for (k in ((1/start_beta)-1):1) {
# splitting the number of samples to be obtained by each core
# samples_per_core[i]=N_schedule[k]/n_cores for i=1,...,n_cores
samples_per_core <- rep(floor(N_schedule[k]/n_cores), n_cores)
# N_schedule[k]/n_cores may not be an integer
# some cores will need to obtain one more sample so the total number of samples obtained is N_schedule[k]
if (sum(samples_per_core) != N_schedule[k]) {
remainder <- N_schedule[k] %% n_cores
samples_per_core[1:remainder] <- samples_per_core[1:remainder] + 1
}
# performing Fusion for this level
if (k == (1/start_beta)-1) {
# printing out some stuff to log file to track the progress
cat('########################\n', file = 'parallel_seq_fusion_exp_4.txt', append = T)
cat('Starting to fuse', 2, 'densities for level', k, 'which is using', n_cores, 'cores\n',
file = 'parallel_seq_fusion_exp_4.txt', append = T)
cat('Fusing samples for beta =', 1, '/', (1/start_beta), 'and beta =', 1, '/', (1/start_beta), 'to get', N_schedule[k],
'samples for beta =', 2, '/', (1/start_beta), '\n',
file = 'parallel_seq_fusion_exp_4.txt', append = T)
cat('########################\n', file = 'parallel_seq_fusion_exp_4.txt', append = T)
# starting fusion
pcm <- proc.time()
fused <- parallel::parLapply(cl, X = 1:n_cores, fun = function(i) {
fusion_diff_exp_4_rcpp(N = samples_per_core[i],
mean = mean,
time = time_schedule[k],
C = 2,
samples_to_fuse = list(base_samples[[1]], base_samples[[2]]),
betas = c(start_beta, start_beta))})
final <- proc.time() - pcm
# providing information of what was the input samples and what was the output
input_betas[[k]] <- c(start_beta, start_beta)
output_beta[k] <- 2*start_beta
} else {
# printing out some stuff to log file to track the progress
cat('########################\n', file = 'parallel_seq_fusion_exp_4.txt', append = T)
cat('Starting to fuse', 2, 'densities for level', k, 'which is using', n_cores, 'cores\n',
file = 'parallel_seq_fusion_exp_4.txt', append = T)
cat('Fusing samples for beta =', (index-1), '/', (1/start_beta), 'and beta =', 1, '/', (1/start_beta),
'with time', time_schedule[k], 'to get', N_schedule[k], 'samples for beta =', index, '/', (1/start_beta),
'\n', file = 'parallel_seq_fusion_exp_4.txt', append = T)
cat('########################\n', file = 'parallel_seq_fusion_exp_4.txt', append = T)
# starting fusion
pcm <- proc.time()
fused <- parallel::parLapply(cl, X = 1:n_cores, fun = function(i) {
fusion_diff_exp_4_rcpp(N = samples_per_core[i],
mean = mean,
time = time_schedule[k],
C = 2,
samples_to_fuse = list(seq_samples[[k+1]], base_samples[[index]]),
betas = c((index-1)*start_beta, start_beta))})
final <- proc.time() - pcm
# providing information of what was the input samples and what was the output
input_betas[[k]] <- c((index-1)*start_beta, start_beta)
output_beta[k] <- index*start_beta
# increasing index for which base_sample to fuse with for next node
index <- index + 1
}
# fused is a list of length n_cores containing N/n_cores samples
# need to combine the correct samples
seq_samples[[k]] <- unlist(lapply(1:n_cores, function(i) fused[[i]]$samples))
# calculating the acceptance rates for all nodes in the current level
# summing all the iterations for each core
rho_iterations <- sum(sapply(1:length(fused), function(i) fused[[i]]$iters_rho))
Q_iterations <- sum(sapply(1:length(fused), function(i) fused[[i]]$iters_Q))
rho_acc[k] <- Q_iterations / rho_iterations
Q_acc[k] <- N_schedule[k] / Q_iterations
rhoQ_acc[k] <- N_schedule[k] / rho_iterations
time_taken_per_level[k] <- final['elapsed']
overall_rho_iters <- overall_rho_iters + rho_iterations
overall_Q_iters <- overall_Q_iters + Q_iterations
overall_N <- overall_N + N_schedule[k]
}
# stopping cluster
parallel::stopCluster(cl)
# print completion
cat('Completed seq fusion\n', file = 'parallel_seq_fusion_exp_4.txt', append = T)
return(list('samples' = seq_samples, 'time' = time_taken_per_level,
'rho_acc' = rho_acc, 'Q_acc' = Q_acc, 'rhoQ_acc' = rhoQ_acc,
'input_betas' = input_betas, 'output_beta' = output_beta, 'diffusion_times' = time_schedule,
'overall_rho' = overall_Q_iters/overall_rho_iters, 'overall_Q' = overall_N/overall_Q_iters,
'overall_rhoQ' = overall_N/overall_rho_iters))
}
######################################## time_adapting sequential_fusion ########################################
#' Time-adapting Sequential Monte Carlo Fusion
#'
#' Sequential Time-adapting Monte Carlo Fusion with base level of the form exp(-((x^4)*beta)/2)
#'
#' @param N_schedule vector of length (L-1), where N_schedule[l] is the number of samples per node at level l
#' @param mean mean value
#' @param global_T time T for time-adapting fusion algorithm
#' @param start_beta beta for the base level
#' @param base_samples list of length (1/start_beta), where samples_to_fuse[c] containg the samples for the c-th node in the level
#' @param seed seed number - default is NULL, meaning there is no seed
#'
#' @return samples: samples from Sequential fusion
#' @return time: vector of length (L-1), where time[l] is the run time for level l
#' @return rho_acc: vector of length (L-1), where rho_acc[l] is the acceptance rate for first fusion step for level l
#' @return Q_acc: vector of length (L-1), where Q_acc[l] is the acceptance rate for second fusion step for level l
#' @return input_betas: list of length (L), where input_betas[[l]] is the input betas for level l
#' @return output_beta: vector of length(L-1), where output_beta[l] is the beta for level l
#' @return diffusion_times: list of length (L-1), where diffusion_times[[l]] are the scaled/adapted times for fusion in level l
#'
#' @examples
#' input_samples <- base_rejection_sampler_exp_4(beta = 1/4, nsamples = 100000, proposal_mean = 0, proposal_sd = 1.5, dominating_M = 1.4)
#' test <- parallel_sequential_fusion_TA_exp_4_rcpp(N_schedule = rep(10000, 3), global_T = 0.5,
#' start_beta = 1/4, base_samples = input_samples)
#'
#' # plot results
#' plot_levels_seq_exp_4(test, from = -3, to = 3, plot_rows = 2, plot_columns = 2)
#'
#' @export
parallel_sequential_fusion_TA_exp_4_rcpp <- function(N_schedule, mean = 0, global_T, start_beta, base_samples, seed = NULL) {
# base samples is a list of length 1/beta containing samples for each node
if (is.list(base_samples) && length(base_samples)!=(1/start_beta)) {
stop('parallel_sequential_fusion_TA_exp_4_rcpp: length of base_samples must be equal to 1/start_beta')
}
# N_schedule is how many samples you want in each node along the seq-tree
if (is.vector(N_schedule) && length(N_schedule)!=(1/start_beta)-1) {
stop('parallel_sequential_fusion_TA_exp_4_rcpp: length of N_schedule must be equal to 1/start_beta - 1')
}
# change global_T by multiplying it by sqrt(start_beta)
global_T <- global_T*sqrt(start_beta)
# initialising study results
seq_samples <- list()
seq_samples[[(1/start_beta)]] <- base_samples # base level
time_taken_per_level <- rep(0, (1/start_beta)-1)
input_betas <- list()
input_betas[[(1/start_beta)]] <- NA
output_beta <- c(1:((1/start_beta)-1), start_beta)
diffusion_times <- list()
overall_rho_iters <- 0
overall_Q_iters <- 0
overall_N <- 0
# make some vectors for acceptance rates of the level
rho_acc <- rep(0, (1/start_beta)-1)
Q_acc <- rep(0, (1/start_beta)-1)
rhoQ_acc <- rep(0, (1/start_beta)-1)
# creating parallel cluster
n_cores <- parallel::detectCores()
cl <- parallel::makeCluster(n_cores, outfile = 'parallel_seq_TA_fusion_exp_4.txt')
# creating variable and functions list to pass into cluster using clusterExport
varlist <- list("phi_exp_4", "bound_phi_exp_4", "diffusion_probability_exp_4", "fusion_TA_exp_4_rcpp",
"N_schedule", "mean", "global_T", "start_beta", "base_samples")
parallel::clusterExport(cl, envir = environment(), varlist = varlist)
# exporting functions from layeredBB package to simulate layered Brownian bridges
parallel::clusterExport(cl, varlist = ls("package:layeredBB"))
if (!is.null(seed)) {
# setting seed for the cores in the cluster
parallel::clusterSetRNGStream(cl, iseed = seed)
}
# add to output file that starting sequential fusion
cat('Starting sequential fusion \n', file = 'parallel_seq_TA_fusion_exp_4.txt')
# setting index for which base_sample to fuse the current node with
# starting with 3 since that's the first one we fuse with the second level
# the first level is manually indexed as 1 and 2
index <- 3
for (k in ((1/start_beta)-1):1) {
# splitting the number of samples to be obtained by each core
# samples_per_core[i]=N_schedule[k]/n_cores for i=1,...,n_cores
samples_per_core <- rep(floor(N_schedule[k]/n_cores), n_cores)
# N_schedule[k]/n_cores may not be an integer
# some cores will need to obtain one more sample so the total number of samples obtained is N_schedule[k]
if (sum(samples_per_core) != N_schedule[k]) {
remainder <- N_schedule[k] %% n_cores
samples_per_core[1:remainder] <- samples_per_core[1:remainder] + 1
}
# performing Fusion for this level
if (k == (1/start_beta)-1) {
# printing out some stuff to log file to track the progress
cat('########################\n', file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
cat('Starting to fuse', 2, 'densities for level', k, 'which is using', n_cores, 'cores\n',
file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
cat('Fusing samples for beta =', 1, '/', (1/start_beta), 'with time', global_T/sqrt(start_beta),
', and beta =', 1, '/', (1/start_beta), 'with time', global_T/sqrt(start_beta), 'to get', N_schedule[k],
'samples for beta =', 2, '/', (1/start_beta), '\n',
file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
cat('########################\n', file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
# starting fusion
pcm <- proc.time()
fused <- parallel::parLapply(cl, X = 1:n_cores, fun = function(i) {
fusion_TA_exp_4_rcpp(N = samples_per_core[i],
mean = mean,
time = global_T,
C = 2,
samples_to_fuse = list(base_samples[[1]], base_samples[[2]]),
betas = c(start_beta, start_beta))})
final <- proc.time() - pcm
# providing information of what was the input samples and what was the output
input_betas[[k]] <- c(start_beta, start_beta)
output_beta[k] <- 2*start_beta
diffusion_times[[k]] <- global_T / sqrt(c(start_beta, start_beta))
} else {
# printing out some stuff to log file to track the progress
cat('########################\n', file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
cat('Starting to fuse', 2, 'densities for level', k, 'which is using', n_cores, 'cores\n',
file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
cat('Fusing samples for beta =', (index-1), '/', (1/start_beta), 'with time', global_T/sqrt((index-1)*start_beta),
', and beta =', 1, '/', (1/start_beta), 'with time', global_T/sqrt(start_beta), 'to get', N_schedule[k],
'samples for beta =', index, '/', (1/start_beta), '\n',
file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
cat('########################\n', file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
# starting fusion
pcm <- proc.time()
fused <- parallel::parLapply(cl, X = 1:n_cores, fun = function(i) {
fusion_TA_exp_4_rcpp(N = samples_per_core[i],
mean = mean,
time = global_T,
C = 2,
samples_to_fuse = list(seq_samples[[k+1]], base_samples[[index]]),
betas = c((index-1)*start_beta, start_beta))})
final <- proc.time() - pcm
# providing information of what was the input samples and what was the output
input_betas[[k]] <- c((index-1)*start_beta, start_beta)
output_beta[k] <- index*start_beta
diffusion_times[[k]] <- global_T / sqrt(c((index-1)*start_beta, start_beta))
# increasing index for which base_sample to fuse with for next node
index <- index + 1
}
# fused is a list of length n_cores containing N/n_cores samples
# need to combine the correct samples
seq_samples[[k]] <- unlist(lapply(1:n_cores, function(i) fused[[i]]$samples))
# calculating the acceptance rates for all nodes in the current level
# summing all the iterations for each core
rho_iterations <- sum(sapply(1:length(fused), function(i) fused[[i]]$iters_rho))
Q_iterations <- sum(sapply(1:length(fused), function(i) fused[[i]]$iters_Q))
rho_acc[k] <- Q_iterations / rho_iterations
Q_acc[k] <- N_schedule[k] / Q_iterations
rhoQ_acc[k] <- N_schedule[k] / rho_iterations
time_taken_per_level[k] <- final['elapsed']
overall_rho_iters <- overall_rho_iters + rho_iterations
overall_Q_iters <- overall_Q_iters + Q_iterations
overall_N <- overall_N + N_schedule[k]
}
# stopping cluster
parallel::stopCluster(cl)
# print completion
cat('Completed seq fusion\n', file = 'parallel_seq_TA_fusion_exp_4.txt', append = T)
return(list('samples' = seq_samples, 'time' = time_taken_per_level,
'rho_acc' = rho_acc, 'Q_acc' = Q_acc, 'rhoQ_acc' = rhoQ_acc,
'input_betas' = input_betas, 'output_beta' = output_beta, 'diffusion_times' = diffusion_times,
'overall_rho' = overall_Q_iters/overall_rho_iters, 'overall_Q' = overall_N/overall_Q_iters,
'overall_rhoQ' = overall_N/overall_rho_iters))
}
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