scripts/robust_regression/power_plant/PP_plots.R
In rchan26/hierarchicalFusion: Divide-and-Conquer Monte Carlo Fusion
library(DCFusion)
load('PP4.RData')
load('PP8.RData')
load('PP16.RData')
load('PP32.RData')
load('PP64.RData')
load('PP128.RData')
# GBF <- list('reg' = c(integrated_abs_distance(full_posterior,
# GBF_4$reg$particles$y_samples),
# integrated_abs_distance(full_posterior,
# GBF_8$reg$particles$y_samples)),
# 'adaptive' = c(integrated_abs_distance(full_posterior,
# GBF_4$adaptive$particles$y_samples),
# integrated_abs_distance(full_posterior,
# GBF_8$adaptive$particles$y_samples)))
balanced <- list('reg' = c(integrated_abs_distance(full_posterior,
balanced_C4$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$reg$particles$y_samples)),
'adaptive' = c(integrated_abs_distance(full_posterior,
balanced_C4$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$adaptive$particles$y_samples)))
NB_fusion <- c(integrated_abs_distance(full_posterior,
NB_hc_4$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_8$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_16$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_32$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_64$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_128$particles$y_samples))
consensus <- c(integrated_abs_distance(full_posterior,
consensus_mat_4$samples),
integrated_abs_distance(full_posterior,
consensus_mat_8$samples),
integrated_abs_distance(full_posterior,
consensus_mat_16$samples),
integrated_abs_distance(full_posterior,
consensus_mat_32$samples),
integrated_abs_distance(full_posterior,
consensus_mat_64$samples),
integrated_abs_distance(full_posterior,
consensus_mat_128$samples))
neiswanger <- c(integrated_abs_distance(full_posterior,
neiswanger_false_4$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_8$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_16$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_32$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_64$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_128$samples))
weierstrass <- c(integrated_abs_distance(full_posterior,
weierstrass_rejection_4$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_8$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_16$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_32$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_64$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_128$samples))
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.5),
xlab = '',
ylab = '',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=rep("", 11), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$reg,
lty = 3, lwd = 3, type = 'b', pch = 5)
# lines(x = log(c(4, 8), 2), y = GBF$adaptive,
# lty = 1, lwd = 3, type = 'b', pch = 1)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = NB_fusion,
lty = 1, lwd = 3, type = 'b', pch = 20)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 0.5,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'D&C-GMCF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,1,4,5,6),
pch = c(5,4,20,3,2,1),
col = c(rep('black', 3), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
##### PAPER #####
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.5),
xlab = '',
ylab = '',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=rep("", 11), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$reg,
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 0.5,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
######################### TIME
# GBF_time <- list('reg' = c(GBF_4$reg$time[[1]],
# GBF_8$reg$time[[1]]),
# 'adaptive' = c(GBF_4$adaptive$time[[1]],
# GBF_8$adaptive$time[[1]]))
balanced_time <- list('reg' = c(sum(unlist(balanced_C4$reg$time)),
sum(unlist(balanced_C8$reg$time)),
sum(unlist(balanced_C16$reg$time)),
sum(unlist(balanced_C32$reg$time)),
sum(unlist(balanced_C64$reg$time)),
sum(unlist(balanced_C128$reg$time))),
'adaptive' = c(sum(unlist(balanced_C4$adaptive$time)),
sum(unlist(balanced_C8$adaptive$time)),
sum(unlist(balanced_C16$adaptive$time)),
sum(unlist(balanced_C32$adaptive$time)),
sum(unlist(balanced_C64$adaptive$time)),
sum(unlist(balanced_C128$adaptive$time))))
NB_fusion_time <- c(sum(unlist(NB_hc_4$time)),
sum(unlist(NB_hc_8$time)),
sum(unlist(NB_hc_16$time)),
sum(unlist(NB_hc_32$time)),
sum(unlist(NB_hc_64$time)),
sum(unlist(NB_hc_128$time)))
consensus_time <- c(consensus_mat_4$time,
consensus_mat_8$time,
consensus_mat_16$time,
consensus_mat_32$time,
consensus_mat_64$time,
consensus_mat_128$time)
neiswanger_time <- c(neiswanger_false_4$time,
neiswanger_false_8$time,
neiswanger_false_16$time,
neiswanger_false_32$time,
neiswanger_false_64$time,
neiswanger_false_128$time)
weierstrass_time <- c(weierstrass_rejection_4$time,
weierstrass_rejection_8$time,
weierstrass_rejection_16$time,
weierstrass_rejection_32$time,
weierstrass_rejection_64$time,
weierstrass_rejection_128$time)
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-2, 20),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 2), labels = seq(-4, 20, 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 1), labels=rep("", 25), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$reg, 2),
lty = 3, lwd = 3, type = 'b', pch = 5)
# lines(x = log(c(4, 8), 2), y = log(GBF_time$adaptive, 2),
# lty = 1, lwd = 3, type = 'b', pch = 1)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(NB_fusion_time, 2),
lty = 1, lwd = 3, type = 'b', pch = 20)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 20,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'D&C-GMCF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,1,4,5,6),
pch = c(5,4,20,3,2,1),
col = c(rep('black', 3), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
# legend(x = 4.5, y = 20,
# legend = c('CMC',
# 'KDEMC',
# 'WRS'),
# lwd = rep(3, 3),
# lty = c(4,5,6),
# pch = c(3,2,1),
# col = rep('red', 3),
# cex = 1.25,
# text.font = 2,
# bty = 'n')
##### PAPER #####
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-2, 16),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 2), labels = seq(-4, 20, 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 1), labels=rep("", 25), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$reg, 2),
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 16,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
rchan26/hierarchicalFusion documentation built on Sept. 11, 2022, 10:30 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(DCFusion)
load('PP4.RData')
load('PP8.RData')
load('PP16.RData')
load('PP32.RData')
load('PP64.RData')
load('PP128.RData')
# GBF <- list('reg' = c(integrated_abs_distance(full_posterior,
# GBF_4$reg$particles$y_samples),
# integrated_abs_distance(full_posterior,
# GBF_8$reg$particles$y_samples)),
# 'adaptive' = c(integrated_abs_distance(full_posterior,
# GBF_4$adaptive$particles$y_samples),
# integrated_abs_distance(full_posterior,
# GBF_8$adaptive$particles$y_samples)))
balanced <- list('reg' = c(integrated_abs_distance(full_posterior,
balanced_C4$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$reg$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$reg$particles$y_samples)),
'adaptive' = c(integrated_abs_distance(full_posterior,
balanced_C4$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C8$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C16$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C32$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C64$adaptive$particles$y_samples),
integrated_abs_distance(full_posterior,
balanced_C128$adaptive$particles$y_samples)))
NB_fusion <- c(integrated_abs_distance(full_posterior,
NB_hc_4$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_8$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_16$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_32$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_64$particles$y_samples),
integrated_abs_distance(full_posterior,
NB_hc_128$particles$y_samples))
consensus <- c(integrated_abs_distance(full_posterior,
consensus_mat_4$samples),
integrated_abs_distance(full_posterior,
consensus_mat_8$samples),
integrated_abs_distance(full_posterior,
consensus_mat_16$samples),
integrated_abs_distance(full_posterior,
consensus_mat_32$samples),
integrated_abs_distance(full_posterior,
consensus_mat_64$samples),
integrated_abs_distance(full_posterior,
consensus_mat_128$samples))
neiswanger <- c(integrated_abs_distance(full_posterior,
neiswanger_false_4$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_8$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_16$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_32$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_64$samples),
integrated_abs_distance(full_posterior,
neiswanger_false_128$samples))
weierstrass <- c(integrated_abs_distance(full_posterior,
weierstrass_rejection_4$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_8$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_16$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_32$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_64$samples),
integrated_abs_distance(full_posterior,
weierstrass_rejection_128$samples))
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.5),
xlab = '',
ylab = '',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=rep("", 11), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$reg,
lty = 3, lwd = 3, type = 'b', pch = 5)
# lines(x = log(c(4, 8), 2), y = GBF$adaptive,
# lty = 1, lwd = 3, type = 'b', pch = 1)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = NB_fusion,
lty = 1, lwd = 3, type = 'b', pch = 20)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 0.5,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'D&C-GMCF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,1,4,5,6),
pch = c(5,4,20,3,2,1),
col = c(rep('black', 3), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
##### PAPER #####
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$adaptive,
ylim = c(0, 0.5),
xlab = '',
ylab = '',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('Integrated Absolute Distance', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=c("0.0", seq(0.1, 0.9, 0.1), "1.0"), font = 2, cex = 1.5)
axis(2, at=seq(0, 1, 0.1), labels=rep("", 11), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = balanced$reg,
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = consensus,
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = neiswanger,
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = weierstrass,
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 0.5,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
######################### TIME
# GBF_time <- list('reg' = c(GBF_4$reg$time[[1]],
# GBF_8$reg$time[[1]]),
# 'adaptive' = c(GBF_4$adaptive$time[[1]],
# GBF_8$adaptive$time[[1]]))
balanced_time <- list('reg' = c(sum(unlist(balanced_C4$reg$time)),
sum(unlist(balanced_C8$reg$time)),
sum(unlist(balanced_C16$reg$time)),
sum(unlist(balanced_C32$reg$time)),
sum(unlist(balanced_C64$reg$time)),
sum(unlist(balanced_C128$reg$time))),
'adaptive' = c(sum(unlist(balanced_C4$adaptive$time)),
sum(unlist(balanced_C8$adaptive$time)),
sum(unlist(balanced_C16$adaptive$time)),
sum(unlist(balanced_C32$adaptive$time)),
sum(unlist(balanced_C64$adaptive$time)),
sum(unlist(balanced_C128$adaptive$time))))
NB_fusion_time <- c(sum(unlist(NB_hc_4$time)),
sum(unlist(NB_hc_8$time)),
sum(unlist(NB_hc_16$time)),
sum(unlist(NB_hc_32$time)),
sum(unlist(NB_hc_64$time)),
sum(unlist(NB_hc_128$time)))
consensus_time <- c(consensus_mat_4$time,
consensus_mat_8$time,
consensus_mat_16$time,
consensus_mat_32$time,
consensus_mat_64$time,
consensus_mat_128$time)
neiswanger_time <- c(neiswanger_false_4$time,
neiswanger_false_8$time,
neiswanger_false_16$time,
neiswanger_false_32$time,
neiswanger_false_64$time,
neiswanger_false_128$time)
weierstrass_time <- c(weierstrass_rejection_4$time,
weierstrass_rejection_8$time,
weierstrass_rejection_16$time,
weierstrass_rejection_32$time,
weierstrass_rejection_64$time,
weierstrass_rejection_128$time)
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-2, 20),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 2), labels = seq(-4, 20, 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 1), labels=rep("", 25), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$reg, 2),
lty = 3, lwd = 3, type = 'b', pch = 5)
# lines(x = log(c(4, 8), 2), y = log(GBF_time$adaptive, 2),
# lty = 1, lwd = 3, type = 'b', pch = 1)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(NB_fusion_time, 2),
lty = 1, lwd = 3, type = 'b', pch = 20)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 20,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'D&C-GMCF',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,1,4,5,6),
pch = c(5,4,20,3,2,1),
col = c(rep('black', 3), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
# legend(x = 4.5, y = 20,
# legend = c('CMC',
# 'KDEMC',
# 'WRS'),
# lwd = rep(3, 3),
# lty = c(4,5,6),
# pch = c(3,2,1),
# col = rep('red', 3),
# cex = 1.25,
# text.font = 2,
# bty = 'n')
##### PAPER #####
plot(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$adaptive, 2),
ylim = c(-2, 16),
xlab = '',
ylab = '',
yaxt = 'n',
xaxt = 'n', lty = 2, lwd = 3, pch = 4, type = 'b')
mtext('log(C, 2)', 1, 2.75, font = 2, cex = 1.5)
mtext('log(Time elapsed in seconds, 2)', 2, 2.75, font = 2, cex = 1.5)
axis(1, at=c(seq(0, 0.9, 0.1), 0.95), labels=c("0.0", c(seq(0.1, 0.9, 0.1), 0.95)), font = 2, cex = 1.5)
axis(1, at=log(c(4, 8, 16, 32, 64, 128), 2), labels = log(c(4, 8, 16, 32, 64, 128), 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 2), labels = seq(-4, 20, 2), font = 2, cex = 1.5)
axis(2, at=seq(-4, 20, 1), labels=rep("", 25), lwd.ticks = 0.5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(balanced_time$reg, 2),
lty = 3, lwd = 3, type = 'b', pch = 5)
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(consensus_time, 2),
lty = 4, lwd = 3, type = 'b', pch = 3, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(neiswanger_time, 2),
lty = 5, lwd = 3, type = 'b', pch = 2, col = 'red')
lines(x = log(c(4, 8, 16, 32, 64, 128), 2), y = log(weierstrass_time, 2),
lty = 6, lwd = 3, type = 'b', pch = 1, col = 'red')
legend(x = 2, y = 16,
legend = c('D&C-GBF (regular mesh)',
'D&C-GBF (adaptive mesh)',
'CMC',
'KDEMC',
'WRS'),
lwd = rep(3, 6),
lty = c(3,2,4,5,6),
pch = c(5,4,3,2,1),
col = c(rep('black', 2), rep('red', 3)),
cex = 1.25,
text.font = 2,
bty = 'n')
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.