scripts/bivariate_Gaussian/bayesian_fusion/dissimilar_means_replicates.R
In rchan26/hierarchicalFusion: Divide-and-Conquer Monte Carlo Fusion
library(DCFusion)
seed <- 1994
set.seed(seed)
nsamples <- 10000
C <- 2
means <- list(rep(-0.25, 2), rep(0.25, 2))
corr <- 0.9
beta <- 1
a_mesh_vanilla <- seq(0, 0.01, length.out = 6)
a_mesh_gen <- seq(0, 2, length.out = 6)
diffusion_estimator <- 'NB'
resampling_method <- 'resid'
number_of_replicates <- 10
ESS_threshold <- 0.5
CESS_0_threshold <- 0.5
CESS_j_threshold <- 0.5
vanilla_b <- 1
k1 <- NULL
k2 <- NULL
k3 <- -log(CESS_j_threshold)/2
k4 <- -log(CESS_j_threshold)/2
data_sizes <- c(250, 500, 1000, 1500, 2000, 2500)
vanilla_guide <- list()
gen_guide <- list()
vanilla_guide_SH <- list()
gen_guide_SH <- list()
a_results <- list('vanilla' = list(), 'generalised' = list())
b_results <- list('vanilla' = list(), 'generalised' = list())
c_results <- list('vanilla' = list(), 'generalised' = list())
d1_results <- list('vanilla' = list(), 'generalised' = list())
d2_results <- list('vanilla' = list(), 'generalised' = list())
SH_adaptive_results <- list('vanilla' = list(), 'generalised' = list())
collect_results <- function(results, seed) {
print(paste('n:', length(results$CESS)-1))
print(paste('time:', results$time))
print(paste('log(time):', log(results$time)))
return(list('CESS_0' = results$CESS[1],
'CESS_j' = results$CESS[2:length(results$CESS)],
'CESS_j_avg' = mean(results$CESS[2:length(results$CESS)]),
'n' = length(results$CESS)-1,
'time_mesh' = results$particles$time_mesh,
'time' = results$time,
'elapsed_time' = results$elapsed_time,
'resampled' = results$resampled,
'ESS' = results$ESS,
'E_nu_j' = results$E_nu_j,
'chosen' = results$chosen,
'mesh_terms' = results$mesh_terms,
'k4_choice' = results$k4_choice,
'IAD' = integrated_abs_distance_biGaussian(fusion_post = resample_particle_y_samples(
particle_set = results$particles,
multivariate = TRUE,
resampling_method = resampling_method,
seed = seed)$y_samples,
marg_means = c(0,0),
marg_sds = sqrt(rep(1, 2)/data_sizes[i]),
bw = opt_bw)))
}
for (i in 1:length(data_sizes)) {
print(paste('i:', i))
print(paste('data_size:', data_sizes[i]))
vanilla_guide[[i]] <- list()
gen_guide[[i]] <- list()
vanilla_guide_SH[[i]] <- list()
gen_guide_SH[[i]] <- list()
a_results$vanilla[[i]] <- list()
a_results$generalised[[i]] <- list()
b_results$vanilla[[i]] <- list()
b_results$generalised[[i]] <- list()
c_results$vanilla[[i]] <- list()
c_results$generalised[[i]] <- list()
d1_results$vanilla[[i]] <- list()
d1_results$generalised[[i]] <- list()
d2_results$vanilla[[i]] <- list()
d2_results$generalised[[i]] <- list()
SH_adaptive_results$vanilla[[i]] <- list()
SH_adaptive_results$generalised[[i]] <- list()
for (rep in 1:number_of_replicates) {
print(paste('rep:', rep))
set.seed(seed*rep*i)
sd <- sqrt(rep(C, 2)/data_sizes[i])
cov_mat <- matrix(c(sd[1]^2, sd[1]*sd[2]*corr, sd[1]*sd[2]*corr, sd[2]^2),
nrow = 2, ncol = 2, byrow = T)
opt_bw <- ((4*sd^5)/(3*nsamples))^(1/5)
input_samples <- lapply(1:C, function(sub) mvrnormArma(N = nsamples, mu = means[[sub]], Sigma = cov_mat))
##### Fixed user-specified parameters #####
print('### performing standard Bayesian Fusion (with standard mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(a_mesh_vanilla))
a_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = a_mesh_vanilla,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### performing Bayesian Fusion with a preconditioning matrix (with standard mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(a_mesh_gen))
a_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = a_mesh_gen,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
a_results$vanilla[[i]][[rep]] <- collect_results(a_BF_standard, seed*rep*i)
a_results$generalised[[i]][[rep]] <- collect_results(a_BF_generalised, seed*rep*i)
##### Recommended scaling of T, fixed n #####
print('### performing standard Bayesian Fusion (with recommended T, fixed n)')
vanilla_guide[[i]][[rep]] <- BF_guidance(condition = 'SSH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
b = vanilla_b,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
k1 = k1,
k2 = k2,
vanilla = TRUE)
print(paste('vanilla recommened regular mesh n:', vanilla_guide[[i]][[rep]]$n))
b_mesh_vanilla <- seq(0, vanilla_guide[[i]][[rep]]$min_T, length.out = 6)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(b_mesh_vanilla))
b_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = b_mesh_vanilla,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### performing Bayesian Fusion with a preconditioning matrix (with recommended T, fixed n)')
gen_guide[[i]][[rep]] <- BF_guidance(condition = 'SSH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
precondition_matrices = lapply(input_samples, cov),
inv_precondition_matrices = lapply(input_samples, function(sub) solve(cov(sub))),
Lambda = inverse_sum_matrices(lapply(input_samples, function(sub) solve(cov(sub)))),
k1 = k1,
k2 = k2,
vanilla = FALSE)
print(paste('generalised recommened regular mesh n:', gen_guide[[i]][[rep]]$n))
b_mesh_gen <- seq(0, gen_guide[[i]][[rep]]$min_T, length.out = 6)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(b_mesh_gen))
b_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = b_mesh_gen,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
b_results$vanilla[[i]][[rep]] <- collect_results(b_BF_standard, seed*rep*i)
b_results$generalised[[i]][[rep]] <- collect_results(b_BF_generalised, seed*rep*i)
##### Recommended scaling of T, regular mesh #####
print('### performing standard Bayesian Fusion (with recommended T, regular mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide[[i]][[rep]]$mesh))
c_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### performing Bayesian Fusion with a preconditioning matrix (with recommended T, regular mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide[[i]][[rep]]$mesh))
c_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
c_results$vanilla[[i]][[rep]] <- collect_results(c_BF_standard, seed*rep*i)
c_results$generalised[[i]][[rep]] <- collect_results(c_BF_generalised, seed*rep*i)
##### Recommended scaling of T, adaptive mesh (equal k3,k4) #####
print('### performing standard Bayesian Fusion (with recommended T, adaptive mesh with equal k3,k4)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide[[i]][[rep]]$mesh))
d1_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'b' = vanilla_b,
'k3' = k3,
'k4' = k4,
'vanilla' = TRUE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
print('### performing Generalised Bayesian Fusion (with recommended T, adaptive mesh with equal k3,k4)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide[[i]][[rep]]$mesh))
d1_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'k3' = k3,
'k4' = k4,
'vanilla' = FALSE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
# save results
d1_results$vanilla[[i]][[rep]] <- collect_results(d1_BF_standard, seed*rep*i)
d1_results$generalised[[i]][[rep]] <- collect_results(d1_BF_generalised, seed*rep*i)
##### Recommended scaling of T, adaptive mesh (un-equal k3,k4) #####
print('### performing standard Bayesian Fusion (with recommended T, adaptive mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide[[i]][[rep]]$mesh))
d2_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'b' = vanilla_b,
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = TRUE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
print('### performing Generalised Bayesian Fusion (with recommended T, adaptive mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide[[i]][[rep]]$mesh))
d2_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = FALSE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
# save results
d2_results$vanilla[[i]][[rep]] <- collect_results(d2_BF_standard, seed*rep*i)
d2_results$generalised[[i]][[rep]] <- collect_results(d2_BF_generalised, seed*rep*i)
##### SH: Recommended scaling of T, adaptive mesh #####
print('### SH: performing standard Bayesian Fusion (with recommended T, adaptive mesh)')
vanilla_guide_SH[[i]][[rep]] <- BF_guidance(condition = 'SH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
b = vanilla_b,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
k1 = k1,
vanilla = TRUE)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide_SH[[i]][[rep]]$mesh))
SH_adaptive_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide_SH[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'b' = vanilla_b,
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = TRUE),
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### SH: performing Bayesian Fusion with a preconditioning matrix (with recommended T, adaptive mesh)')
gen_guide_SH[[i]][[rep]] <- BF_guidance(condition = 'SH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
precondition_matrices = lapply(input_samples, cov),
inv_precondition_matrices = lapply(input_samples, function(sub) solve(cov(sub))),
Lambda = inverse_sum_matrices(lapply(input_samples, function(sub) solve(cov(sub)))),
k1 = k1,
vanilla = FALSE)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide_SH[[i]][[rep]]$mesh))
SH_adaptive_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide_SH[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = FALSE),
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
SH_adaptive_results$vanilla[[i]][[rep]] <- collect_results(SH_adaptive_standard, seed*rep*i)
SH_adaptive_results$generalised[[i]][[rep]] <- collect_results(SH_adaptive_generalised, seed*rep*i)
# save progress
print('saving progress')
save.image('bivG_dissimilar_means_replicates.RData')
}
}
##### IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d1_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 1)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 3, col = 'red')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d1_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, col = 'orange')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) SH_adaptive_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 6, col = 'purple')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### IAD (max) #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d1_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance (maximum)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(0,1.4), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 20, lwd = 1.5)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, lwd = 1.5, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, lwd = 1.5, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, lwd = 1.5, col = 'red')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.4,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: time #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$time)))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(2,11), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$time)))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$time)))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$time)))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 13, 1), labels = seq(0, 13, 1), font = 2, cex = 1.5)
mtext('log(Elapsed time in seconds)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 11,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
##### IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d1_results$generalised[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$generalised[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 1)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 3, col = 'red')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d1_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, col = 'orange')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) SH_adaptive_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 6, col = 'purple')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### IAD (max) #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d1_results$generalised[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$generalised[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance (maximum)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(0,1.4), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 20, lwd = 1.5)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, lwd = 1.5, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, lwd = 1.5, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, lwd = 1.5, col = 'red')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.4,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: time #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$time)))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(2,11), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$time)))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$time)))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$time)))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 13, 1), labels = seq(0, 13, 1), font = 2, cex = 1.5)
mtext('log(Elapsed time in seconds)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 11,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
rchan26/hierarchicalFusion documentation built on Sept. 11, 2022, 10:30 p.m.
R Package Documentation
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library(DCFusion)
seed <- 1994
set.seed(seed)
nsamples <- 10000
C <- 2
means <- list(rep(-0.25, 2), rep(0.25, 2))
corr <- 0.9
beta <- 1
a_mesh_vanilla <- seq(0, 0.01, length.out = 6)
a_mesh_gen <- seq(0, 2, length.out = 6)
diffusion_estimator <- 'NB'
resampling_method <- 'resid'
number_of_replicates <- 10
ESS_threshold <- 0.5
CESS_0_threshold <- 0.5
CESS_j_threshold <- 0.5
vanilla_b <- 1
k1 <- NULL
k2 <- NULL
k3 <- -log(CESS_j_threshold)/2
k4 <- -log(CESS_j_threshold)/2
data_sizes <- c(250, 500, 1000, 1500, 2000, 2500)
vanilla_guide <- list()
gen_guide <- list()
vanilla_guide_SH <- list()
gen_guide_SH <- list()
a_results <- list('vanilla' = list(), 'generalised' = list())
b_results <- list('vanilla' = list(), 'generalised' = list())
c_results <- list('vanilla' = list(), 'generalised' = list())
d1_results <- list('vanilla' = list(), 'generalised' = list())
d2_results <- list('vanilla' = list(), 'generalised' = list())
SH_adaptive_results <- list('vanilla' = list(), 'generalised' = list())
collect_results <- function(results, seed) {
print(paste('n:', length(results$CESS)-1))
print(paste('time:', results$time))
print(paste('log(time):', log(results$time)))
return(list('CESS_0' = results$CESS[1],
'CESS_j' = results$CESS[2:length(results$CESS)],
'CESS_j_avg' = mean(results$CESS[2:length(results$CESS)]),
'n' = length(results$CESS)-1,
'time_mesh' = results$particles$time_mesh,
'time' = results$time,
'elapsed_time' = results$elapsed_time,
'resampled' = results$resampled,
'ESS' = results$ESS,
'E_nu_j' = results$E_nu_j,
'chosen' = results$chosen,
'mesh_terms' = results$mesh_terms,
'k4_choice' = results$k4_choice,
'IAD' = integrated_abs_distance_biGaussian(fusion_post = resample_particle_y_samples(
particle_set = results$particles,
multivariate = TRUE,
resampling_method = resampling_method,
seed = seed)$y_samples,
marg_means = c(0,0),
marg_sds = sqrt(rep(1, 2)/data_sizes[i]),
bw = opt_bw)))
}
for (i in 1:length(data_sizes)) {
print(paste('i:', i))
print(paste('data_size:', data_sizes[i]))
vanilla_guide[[i]] <- list()
gen_guide[[i]] <- list()
vanilla_guide_SH[[i]] <- list()
gen_guide_SH[[i]] <- list()
a_results$vanilla[[i]] <- list()
a_results$generalised[[i]] <- list()
b_results$vanilla[[i]] <- list()
b_results$generalised[[i]] <- list()
c_results$vanilla[[i]] <- list()
c_results$generalised[[i]] <- list()
d1_results$vanilla[[i]] <- list()
d1_results$generalised[[i]] <- list()
d2_results$vanilla[[i]] <- list()
d2_results$generalised[[i]] <- list()
SH_adaptive_results$vanilla[[i]] <- list()
SH_adaptive_results$generalised[[i]] <- list()
for (rep in 1:number_of_replicates) {
print(paste('rep:', rep))
set.seed(seed*rep*i)
sd <- sqrt(rep(C, 2)/data_sizes[i])
cov_mat <- matrix(c(sd[1]^2, sd[1]*sd[2]*corr, sd[1]*sd[2]*corr, sd[2]^2),
nrow = 2, ncol = 2, byrow = T)
opt_bw <- ((4*sd^5)/(3*nsamples))^(1/5)
input_samples <- lapply(1:C, function(sub) mvrnormArma(N = nsamples, mu = means[[sub]], Sigma = cov_mat))
##### Fixed user-specified parameters #####
print('### performing standard Bayesian Fusion (with standard mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(a_mesh_vanilla))
a_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = a_mesh_vanilla,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### performing Bayesian Fusion with a preconditioning matrix (with standard mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(a_mesh_gen))
a_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = a_mesh_gen,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
a_results$vanilla[[i]][[rep]] <- collect_results(a_BF_standard, seed*rep*i)
a_results$generalised[[i]][[rep]] <- collect_results(a_BF_generalised, seed*rep*i)
##### Recommended scaling of T, fixed n #####
print('### performing standard Bayesian Fusion (with recommended T, fixed n)')
vanilla_guide[[i]][[rep]] <- BF_guidance(condition = 'SSH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
b = vanilla_b,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
k1 = k1,
k2 = k2,
vanilla = TRUE)
print(paste('vanilla recommened regular mesh n:', vanilla_guide[[i]][[rep]]$n))
b_mesh_vanilla <- seq(0, vanilla_guide[[i]][[rep]]$min_T, length.out = 6)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(b_mesh_vanilla))
b_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = b_mesh_vanilla,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### performing Bayesian Fusion with a preconditioning matrix (with recommended T, fixed n)')
gen_guide[[i]][[rep]] <- BF_guidance(condition = 'SSH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
precondition_matrices = lapply(input_samples, cov),
inv_precondition_matrices = lapply(input_samples, function(sub) solve(cov(sub))),
Lambda = inverse_sum_matrices(lapply(input_samples, function(sub) solve(cov(sub)))),
k1 = k1,
k2 = k2,
vanilla = FALSE)
print(paste('generalised recommened regular mesh n:', gen_guide[[i]][[rep]]$n))
b_mesh_gen <- seq(0, gen_guide[[i]][[rep]]$min_T, length.out = 6)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(b_mesh_gen))
b_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = b_mesh_gen,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
b_results$vanilla[[i]][[rep]] <- collect_results(b_BF_standard, seed*rep*i)
b_results$generalised[[i]][[rep]] <- collect_results(b_BF_generalised, seed*rep*i)
##### Recommended scaling of T, regular mesh #####
print('### performing standard Bayesian Fusion (with recommended T, regular mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide[[i]][[rep]]$mesh))
c_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### performing Bayesian Fusion with a preconditioning matrix (with recommended T, regular mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide[[i]][[rep]]$mesh))
c_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
c_results$vanilla[[i]][[rep]] <- collect_results(c_BF_standard, seed*rep*i)
c_results$generalised[[i]][[rep]] <- collect_results(c_BF_generalised, seed*rep*i)
##### Recommended scaling of T, adaptive mesh (equal k3,k4) #####
print('### performing standard Bayesian Fusion (with recommended T, adaptive mesh with equal k3,k4)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide[[i]][[rep]]$mesh))
d1_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'b' = vanilla_b,
'k3' = k3,
'k4' = k4,
'vanilla' = TRUE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
print('### performing Generalised Bayesian Fusion (with recommended T, adaptive mesh with equal k3,k4)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide[[i]][[rep]]$mesh))
d1_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'k3' = k3,
'k4' = k4,
'vanilla' = FALSE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
# save results
d1_results$vanilla[[i]][[rep]] <- collect_results(d1_BF_standard, seed*rep*i)
d1_results$generalised[[i]][[rep]] <- collect_results(d1_BF_generalised, seed*rep*i)
##### Recommended scaling of T, adaptive mesh (un-equal k3,k4) #####
print('### performing standard Bayesian Fusion (with recommended T, adaptive mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide[[i]][[rep]]$mesh))
d2_BF_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'b' = vanilla_b,
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = TRUE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
print('### performing Generalised Bayesian Fusion (with recommended T, adaptive mesh)')
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide[[i]][[rep]]$mesh))
d2_BF_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = FALSE),
diffusion_estimator = diffusion_estimator,
seed = seed*i*rep)
# save results
d2_results$vanilla[[i]][[rep]] <- collect_results(d2_BF_standard, seed*rep*i)
d2_results$generalised[[i]][[rep]] <- collect_results(d2_BF_generalised, seed*rep*i)
##### SH: Recommended scaling of T, adaptive mesh #####
print('### SH: performing standard Bayesian Fusion (with recommended T, adaptive mesh)')
vanilla_guide_SH[[i]][[rep]] <- BF_guidance(condition = 'SH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
b = vanilla_b,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
k1 = k1,
vanilla = TRUE)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(vanilla_guide_SH[[i]][[rep]]$mesh))
SH_adaptive_standard <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = vanilla_guide_SH[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = rep(list(diag(1,2)), C),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'b' = vanilla_b,
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = TRUE),
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
print('### SH: performing Bayesian Fusion with a preconditioning matrix (with recommended T, adaptive mesh)')
gen_guide_SH[[i]][[rep]] <- BF_guidance(condition = 'SH',
CESS_0_threshold = CESS_0_threshold,
CESS_j_threshold = CESS_j_threshold,
sub_posterior_samples = input_samples,
C = C,
d = 2,
data_size = data_sizes[i],
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
precondition_matrices = lapply(input_samples, cov),
inv_precondition_matrices = lapply(input_samples, function(sub) solve(cov(sub))),
Lambda = inverse_sum_matrices(lapply(input_samples, function(sub) solve(cov(sub)))),
k1 = k1,
vanilla = FALSE)
input_particles <- initialise_particle_sets(samples_to_fuse = input_samples,
multivariate = TRUE,
number_of_steps = length(gen_guide_SH[[i]][[rep]]$mesh))
SH_adaptive_generalised <- parallel_GBF_biGaussian(particles_to_fuse = input_particles,
N = nsamples,
m = C,
time_mesh = gen_guide_SH[[i]][[rep]]$mesh,
mean_vecs = means,
sd_vecs = rep(list(sd), C),
corrs = rep(corr, C),
betas = rep(beta, C),
precondition_matrices = lapply(input_samples, cov),
resampling_method = resampling_method,
ESS_threshold = ESS_threshold,
sub_posterior_means = t(sapply(input_samples, function(sub) apply(sub, 2, mean))),
adaptive_mesh = TRUE,
adaptive_mesh_parameters = list('data_size' = data_sizes[i],
'CESS_j_threshold' = CESS_j_threshold,
'vanilla' = FALSE),
diffusion_estimator = diffusion_estimator,
seed = seed*rep*i)
# save results
SH_adaptive_results$vanilla[[i]][[rep]] <- collect_results(SH_adaptive_standard, seed*rep*i)
SH_adaptive_results$generalised[[i]][[rep]] <- collect_results(SH_adaptive_generalised, seed*rep*i)
# save progress
print('saving progress')
save.image('bivG_dissimilar_means_replicates.RData')
}
}
##### IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d1_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 1)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 3, col = 'red')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d1_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, col = 'orange')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) SH_adaptive_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 6, col = 'purple')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### IAD (max) #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d1_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance (maximum)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(0,1.4), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 20, lwd = 1.5)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, lwd = 1.5, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, lwd = 1.5, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, lwd = 1.5, col = 'red')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.4,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: time #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) a_results$vanilla[[i]][[rep]]$time)))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(2,11), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) b_results$vanilla[[i]][[rep]]$time)))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) c_results$vanilla[[i]][[rep]]$time)))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) d2_results$vanilla[[i]][[rep]]$time)))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 13, 1), labels = seq(0, 13, 1), font = 2, cex = 1.5)
mtext('log(Elapsed time in seconds)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 11,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
##### IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d1_results$generalised[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$generalised[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 1)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 3, col = 'red')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d1_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, col = 'orange')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) SH_adaptive_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 6, col = 'purple')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### IAD (max) #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD))
}),
type = 'b', pch = 1, lty = 1, lwd = 3, ylim = c(0,1.6), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD))
}),
pch = 2, lty = 2, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD))
}),
pch = 3, lty = 3, lwd = 3, type = 'b', col = 'red')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d1_results$generalised[[i]][[rep]]$IAD))
}),
pch = 4, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD))
}),
pch = 5, lty = 5, lwd = 3, type = 'b', col = 'orange')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
max(sapply(1:number_of_replicates, function(rep) SH_adaptive_results$generalised[[i]][[rep]]$IAD))
}),
pch = 6, lty = 6, lwd = 3, type = 'b', col = 'purple')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance (maximum)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.6,
legend = c('Fixed T, fixed n',
'SSH rec. T, fixed n',
'SSH rec. T, reg. mesh',
'SSH rec. T, adapt. mesh (k3=k4)',
'SSH rec. T, adapt. mesh',
'SH rec. T, adapt. mesh'),
col = c('black', 'blue', 'red', 'green', 'orange', 'purple'),
lty = 1:6,
pch = 1:6,
lwd = rep(3, 6),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: IAD #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(0,1.4), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 20, lwd = 1.5)
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 2, lwd = 1.5, col = 'green')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 5, lwd = 1.5, col = 'blue')
}
for (i in 1:length(data_sizes)) {
IAD <- sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$IAD)
points(x = rep(data_sizes[i], length(IAD)), y = IAD, cex = 0.5, pch = 4, lwd = 1.5, col = 'red')
}
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels = c("0.0", seq(0.1, 0.9, 0.1), "1.0", seq(1.1, 1.6, 0.1)),
font = 2, cex = 1.5)
axis(2, at = seq(0, 1.6, 0.1), labels=rep("", 17), lwd.ticks = 0.5,
font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 1.4,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
##### Paper: time #####
plot(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) a_results$generalised[[i]][[rep]]$time)))
}),
type = 'b', pch = 20, lty = 1, lwd = 3, ylim = c(2,11), xaxt = 'n', yaxt ='n', xlab = '', ylab = '')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) b_results$generalised[[i]][[rep]]$time)))
}),
pch = 2, lty = 4, lwd = 3, type = 'b', col = 'green')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) c_results$generalised[[i]][[rep]]$time)))
}),
pch = 5, lty = 3, lwd = 3, type = 'b', col = 'blue')
lines(x = data_sizes,
y = sapply(1:length(data_sizes), function(i) {
mean(log(sapply(1:number_of_replicates, function(rep) d2_results$generalised[[i]][[rep]]$time)))
}),
pch = 4, lty = 2, lwd = 3, type = 'b', col = 'red')
axis(1, at = seq(0, 2500, 500), labels = seq(0, 2500, 500), font = 2, cex = 1.5)
axis(1, at = seq(0, 2500, 250), labels = rep("", 11), lwd.ticks = 0.5, font = 2, cex = 1.5)
mtext('Data Sizes', 1, 2.75, font = 2, cex = 1.5)
axis(2, at = seq(0, 13, 1), labels = seq(0, 13, 1), font = 2, cex = 1.5)
mtext('log(Elapsed time in seconds)', 2, 2.75, font = 2, cex = 1.5)
legend(x = 250, y = 11,
legend = c('Fixed T, fixed n',
'Recommended T, fixed n',
'Recommneded T, regular mesh',
'Recommended T, adaptive mesh'),
col = c('black', 'green', 'blue', 'red'),
lty = c(1,4,3,2),
pch = c(20,2,5,4),
lwd = rep(3, 4),
cex = 1.25,
text.font = 2,
bty = 'n')
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