xlim(1.78, max(H)) +
labs(title=expression(italic(k)==6),
x="Entropy",
y="Estimated densities") +
theme_pander()
ggplot(HxC.kn.melt, aes(x=variable, y=H, col=n)) +
# geom_histogram(aes(y=..density..),
#                bins=nclass.FD(Hkn$H),
#                col=2) +
geom_density(aes(col=n, fill=n), kernel="epanechnikov", alpha=.3) +
xlim(1.78, max(H)) +
labs(title=expression(italic(k)==6),
x="Entropy",
y="Estimated densities") +
theme_pander()
ggplot(HxC.kn.melt, aes(x=variable, y=value, col=n)) +
# geom_histogram(aes(y=..density..),
#                bins=nclass.FD(Hkn$H),
#                col=2) +
geom_density(aes(col=n, fill=n), kernel="epanechnikov", alpha=.3) +
xlim(1.78, max(H)) +
labs(title=expression(italic(k)==6),
x="Entropy",
y="Estimated densities") +
theme_pander()
ggplot(HxC.kn.melt, aes(x=value, col=variable)) +
geom_histogram()
ggplot(HxC.kn.melt, aes(x=value, col=n)) +
geom_histogram()
ggplot(HxC.kn.melt, aes(x=value, col=n)) +
geom_histogram() +
facet_grid(~variable)
ggplot(HxC.kn.melt, aes(y=value, col=n)) +
geom_histogram() +
facet_grid(~variable)
ggplot(HxC.kn.melt, aes(y=value, col=n)) +
geom_histogram() +
facet_grid(~variable)
ggplot(HxC.kn.melt, aes(x=value, col=n)) +
geom_histogram() +
facet_grid(~variable)
ggplot(HxC.kn.melt, aes(x=value, col=n)) +
geom_density() +
facet_grid(~variable)
set.seed(1234567890, kind="Mersenne-Twister")
R <- 100 # number of replications
K <- c(6, 24)
N <- c(600, 1000, 5000, 10000)
# Data generation and preparation
total <- R*length(K)*length(N)
HxC.kn <- data.frame(matrix(ncol=4, nrow=total))
colnames(HxC.kn) <- c("H", "C", "k", "n")
i <- 0
u <- rep(1/k, k)
for(k in K){
for(n in N){
for(r in 1:R){
i <- i+1
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- sum(log10(p/u)*(p-u))
HxC.kn[i, ] <- c(H, C, k, n)
progress(i, max.value = total, progress.bar=FALSE, gui=TRUE)
}
}
}
R <- 1000 # number of replications
K <- c(6, 24)
N <- c(600, 1000, 5000, 10000)
# Data generation and preparation
total <- R*length(K)*length(N)
HxC.kn <- data.frame(matrix(ncol=4, nrow=total))
colnames(HxC.kn) <- c("H", "C", "k", "n")
i <- 0
u <- rep(1/k, k)
for(k in K){
for(n in N){
for(r in 1:R){
i <- i+1
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- sum(log10(p/u)*(p-u))
HxC.kn[i, ] <- c(H, C, k, n)
progress(i, max.value = total)
}
}
}
for(k in K){
for(n in N){
for(r in 1:R){
i <- i+1
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- sum(log10(p/u)*(p-u))
HxC.kn[i, ] <- c(H, C, k, n)
}
}
progress(i, max.value = total)
}
for(k in K){
for(n in N){
for(r in 1:R){
i <- i+1
progress(i, max.value = total)
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- sum(log10(p/u)*(p-u))
HxC.kn[i, ] <- c(H, C, k, n)
}
}
}
HxC.kn <- data.frame(HxC.kn)
i <- 0
u <- rep(1/k, k)
for(k in K){
for(n in N){
for(r in 1:R){
i <- i+1
progress(i, max.value = total)
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- sum(log10(p/u)*(p-u))
HxC.kn[i, ] <- c(H, C, k, n)
}
}
}
i <- 0
u <- rep(1/k, k)
for(k in K){
for(n in N){
for(r in 1:R){
i <- i+1
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- sum(log10(p/u)*(p-u))
HxC.kn[i, ] <- c(H, C, k, n)
progress(i, max.value = total)
}
}
}
rm(ls=list())
rm(list=ls())
# Setup
require(ggplot2)
require(ggthemes)
require(reshape2)
set.seed(1234567890, kind="Mersenne-Twister")
R <- 50000 # number of replications
K <- c(6, 24)
N <- c(600, 1000, 5000, 10000)
# Data generation and preparation
total <- R*length(K)*length(N)
HxC.kn <- data.frame(matrix(ncol=4, nrow=total))
colnames(HxC.kn) <- c("H", "C", "k", "n")
i <- 0
for(k in K){
u <- rep(1/k, k)
for(n in N){
for(r in 1:R){
i <- i+1
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- sum(log10(p/u)*(p-u))
HxC.kn[i, ] <- c(H, C, k, n)
progress(i, max.value = total)
}
}
}
total
total - i
i
set.seed(1234567890, kind="Mersenne-Twister")
R <- 50000 # number of replications
K <- c(6, 24)
N <- c(600, 1000, 5000, 10000)
rm(list=ls())
# Setup
require(ggplot2)
require(ggthemes)
require(reshape2)
require(svMisc)
set.seed(1234567890, kind="Mersenne-Twister")
R <- 20000 # number of replications
K <- c(6, 24)
N <- c(600, 1000, 5000, 10000)
# Data generation and preparation
total <- R*length(K)*length(N)
HxC.kn <- data.frame(matrix(ncol=4, nrow=total))
colnames(HxC.kn) <- c("H", "C", "k", "n")
i <- 0
for(k in K){
u <- rep(1/k, k)
maxQprime <- 2*(((k+1)/k)*log10(k+1)-2*log10(2*k)+log10(k))
for(n in N){
for(r in 1:R){
i <- i+1
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))
C <- H*(sum(log10(p/u)*(p-u))/maxQprime)
HxC.kn[i, ] <- c(H, C, k, n)
progress(i, max.value = total)
}
}
}
k
2*(((k+1)/k)*log10(k+1)-2*log10(2*k)+log10(k))
2*(((k+1)/k)*log(k+1)-2*log(2*k)+log(k))
2*(((k+1)/k)*log10(k+1)-2*log10(2*k)+log10(k))
p
-sum(p*log10(p))
-sum(p*log(p))
-sum(p*log10(p))
-sum(p*log10(p))/log(k)
log10k <- log10(k)
-sum(p*log10(p))/log10k
H*(sum(log10(p/u)*(p-u))/maxQprime)
2*(((k+1)/k)*log10(k+1)-2*log10(2*k)+log10k)
-2*(((k+1)/k)*log10(k+1)-2*log10(2*k)+log10k)
maxQprime <- -2*(((k+1)/k)*log10(k+1)-2*log10(2*k)+log10k)
H*(sum(log10(p/u)*(p-u))/maxQprime)
rm(list=ls())
# Setup
require(ggplot2)
require(ggthemes)
require(reshape2)
require(svMisc)
set.seed(1234567890, kind="Mersenne-Twister")
R <- 30000 # number of replications
K <- c(6, 24)
N <- c(600, 1000, 5000, 10000)
# Data generation and preparation
total <- R*length(K)*length(N)
HxC.kn <- data.frame(matrix(ncol=4, nrow=total))
colnames(HxC.kn) <- c("H", "C", "k", "n")
i <- 0
for(k in K){
u <- rep(1/k, k)
log10k <- log10(k)
maxQprime <- -2*(((k+1)/k)*log10(k+1)-2*log10(2*k)+log10k)
for(n in N){
for(r in 1:R){
i <- i+1
N <- t(rmultinom(1, size=n, prob=u))
p <- N/n
H <- -sum(p*log10(p))/log10k
C <- H*(sum(log10(p/u)*(p-u))/maxQprime)
HxC.kn[i, ] <- c(H, C, k, n)
progress(i, max.value = total)
}
}
}
HxC.kn$k <- as.factor(HxC.kn$k)
HxC.kn$n <- as.factor(HxC.kn$n)
levels(HxC.kn$n)
q()
p <- seq(.01, .99, length.out=500)
plot(p, p*(1-p)(log10(p)+1)^2, type = "l")
plot(p, p*(1-p)*(log10(p)+1)^2, type = "l")
knitr::opts_chunk$set(echo = TRUE)
def.chunk.hook  <- knitr::knit_hooks$get("chunk")
knitr::knit_hooks$set(chunk = function(x, options) {
x <- def.chunk.hook(x, options)
ifelse(options$size != "normalsize", paste0("\n \\", options$size,"\n\n", x, "\n\n \\normalsize"), x)
})
if(!require(ggplot2)){
install.packages("ggplot2")
library(ggplot2)
}
if(!require(ggthemes)){
install.packages("ggthemes")
library(ggthemes)
}
theme_set(theme_clean())
if(!require(uniformly)){
install.packages("uniformly")
library(uniformly)
}
if(!require(ggforce)){
install.packages("ggforce")
library(ggforce)
}
if(!require(ggpubr)){
install.packages("ggpubr")
library(ggpubr)
}
if(!require(reshape2)){
install.packages("reshape2")
library(reshape2)
}
if(!require(cowplot)){
install.packages("cowplot")
library(cowplot)
}
if(!require(rticles)){
install.packages("rticles")
library(rticles)
}
if(!require(svMisc)){
install.packages("svMisc")
library(svMisc)
}
if(!require(rstudioapi)){
install.packages("rstudioapi")
library(rstudioapi)
}
if(!require(moments)){
install.packages("moments")
library(moments)
}
options(digits=3)
set.seed(1234567890, kind="Mersenne-Twister")
names(HxC.kn$n) <- "T"
load(file="../../Data/R/HC/HxC.kn.Rdata")
names(HxC.kn$n) <- "T"
knitr::kable(TableH,
format = "latex",
booktabs = TRUE,
position = "hbt",
caption="Quantiles, skewness and excess kurtosis of $h$")
q()
factorial(9)
factorial(8)
q()
sum.int <- function(n, k, p) {
sum.int <- 0
for(ell in 1:k) {
sum.int <- sum.int +
p^(n-j+1) * (1-p)^j-1
}
return(sum.int)
}
ExactExpectedH <- function(n, p){
k <- length(p)
sum.out <- 0
for(j in 1:(n-2)) {
sum.out <- sum.out +
chose(n-1, n-j) * log(n-j+1, base = exp(1)) *
sum.int(n, k, p)
}
return(log(n, base=2) - sum.out))
}
sum.int <- function(n, k, p) {
sum.int <- 0
for(ell in 1:k) {
sum.int <- sum.int +
p^(n-j+1) * (1-p)^j-1
}
return(sum.int)
}
ExactExpectedH <- function(n, p){
k <- length(p)
sum.out <- 0
for(j in 1:(n-2)) {
sum.out <- sum.out +
chose(n-1, n-j) * log(n-j+1, base = exp(1)) *
sum.int(n, k, p)
}
return(log(n, base=2) - sum.out)
}
ExactExpectedH(100, rep(1/6, 6))
sum.int <- function(n, k, p) {
sum.int <- 0
for(ell in 1:k) {
sum.int <- sum.int +
p^(n-j+1) * (1-p)^j-1
}
return(sum.int)
}
ExactExpectedH <- function(n, p){
k <- length(p)
sum.out <- 0
for(j in 1:(n-2)) {
sum.out <- sum.out +
choose(n-1, n-j) * log(n-j+1, base = exp(1)) *
sum.int(n, k, p)
}
return(log(n, base=2) - sum.out)
}
ExactExpectedH(100, rep(1/6, 6))
sum.int <- function(n, j, k, p) {
sum.int <- 0
for(ell in 1:k) {
sum.int <- sum.int +
p^(n-j+1) * (1-p)^j-1
}
return(sum.int)
}
ExactExpectedH <- function(n, p){
k <- length(p)
sum.out <- 0
for(j in 1:(n-2)) {
sum.out <- sum.out +
choose(n-1, n-j) * log(n-j+1, base = exp(1)) *
sum.int(n, j, k, p)
}
return(log(n, base=2) - sum.out)
}
ExactExpectedH(100, rep(1/6, 6))
q()
start_time <- Sys.time()
end_time <- Sys.time()
end_time-start_time
cat(end_time-start_time)
cat("Elapsed time:", end_time-start_time)
cat("Elapsed time:", end_time-start_time, "seconds")
q()
source("~/Documents/GitHub/DespecklingEntropy/Code/Filter/DespecklingbyEntropiesMDPI.R", echo=TRUE)
setwd("~/Documents/GitHub/DespecklingEntropy/Code/Phantom")
load(file="../../Data/Rdata/Phantom.Rdata")
f.15.5.05 <- FiltroConElpvalor(15, 5, Z, .05)
load("/Users/acfrery/Documents/Images/Avik/out_full_2009/C3/Code/f.15.7.02.Rdata")
source("../../../../../Programas/R/imagematrix.R")
source("../../../../Programas/R/imagematrix.R")
plot(imagematrix(equalize(f.15.5.05)))
plot(imagematrix(equalize(f.15.7.05)))
plot(imagematrix(equalize(f.15.7.02)))
q()
setwd("~/Documents/GitHub/DespecklingEntropy/Code/Phantom")
require(png)
source("../../../../Programas/R/imagematrix.R")
Z <- Phantom4 <- readPNG("../../Figures/PNG/Phantom4.png")[,,1]
dim(Phantom4)
plot(imagematrix(Phantom4))
E <- function(a, g) {-g/(a+1)}
H <- function(a, g) {(a-1)/a - log(-a/g, base=exp(1))}
HE1 <- function(a) {(a-1)/a - log(a/(a+1), base=exp(1))}
HE1(-10)
H(-2,50)
H(-2,5)
E(-6, 10)
p.up.le <- Phantom4[1:250,1:250]
p.up.ri <- Phantom4[1:250,251:500]
p.bo.le <- Phantom4[251:500,1:250]
p.bo.ri <- Phantom4[251:500,251:500]
### Upper Left (means 1 and 1)
z.up.le.b <- rGI0(n=sum(1-p.up.le),
p_alpha=-2, p_gamma=1, p_Looks=1)
mean(z.up.le.b)
p.up.le <- Phantom4[1:250,1:250]
p.up.ri <- Phantom4[1:250,251:500]
p.bo.le <- Phantom4[251:500,1:250]
p.bo.ri <- Phantom4[251:500,251:500]
### Upper Left (means 1 and 1)
z.up.le.b <- rGI0(n=sum(1-p.up.le),
p_alpha=-2, p_gamma=1, p_Looks=1)
require(GI0)
### Upper Left (means 1 and 1)
z.up.le.b <- rGI0(n=sum(1-p.up.le),
p_alpha=-2, p_gamma=1, p_Looks=1)
mean(z.up.le.b)
z.up.le.w <- rGI0(n=sum(p.up.le),
p_alpha=-10, p_gamma=9, p_Looks=1)
mean(z.up.le.w)
z.up.le <- p.up.le
z.up.le[p.up.le==0] <- z.up.le.b
z.up.le[p.up.le==1] <- z.up.le.w
plot(imagematrix(equalize(z.up.le)))
### Upper Right (means 50 and 5)
z.up.ri.b <- rGI0(n=sum(1-p.up.ri),
p_alpha=-2, p_gamma=50, p_Looks=1)
mean(z.up.ri.b)
z.up.ri.w <- rGI0(n=sum(p.up.ri),
p_alpha=-2, p_gamma=5, p_Looks=1)
mean(z.up.ri.w)
z.up.ri <- p.up.ri
z.up.ri[p.up.ri==0] <- z.up.ri.b
z.up.ri[p.up.ri==1] <- z.up.ri.w
plot(imagematrix(equalize(z.up.ri)))
z.bo.ri.b <- rGI0(n=sum(1-p.bo.ri),
p_alpha=-6, p_gamma=250, p_Looks=1)
mean(z.bo.le.b)
z.bo.ri.w <- rGI0(n=sum(p.bo.ri),
p_alpha=-3, p_gamma=10, p_Looks=1)
mean(z.bo.le.w)
z.bo.ri <- p.bo.ri
z.bo.ri[p.bo.ri==0] <- z.bo.ri.b
z.bo.ri[p.bo.ri==1] <- z.bo.ri.w
plot(imagematrix(equalize(z.bo.ri)))
z.bo.le.b <- rGI0(n=sum(1-p.bo.le),
p_alpha=-3, p_gamma=20, p_Looks=1)
mean(z.bo.le.b)
z.bo.le.w <- rGI0(n=sum(p.bo.le),
p_alpha=-9, p_gamma=80, p_Looks=1)
mean(z.bo.le.w)
z.bo.le <- p.bo.le
z.bo.le[p.bo.le==0] <- z.bo.le.b
z.bo.le[p.bo.le==1] <- z.bo.le.w
plot(imagematrix(equalize(z.bo.le)))
Z[1:250,1:250] <- z.up.le
Z[1:250,251:500] <- z.up.ri
Z[251:500,1:250] <- z.bo.le
Z[251:500,251:500] <- z.bo.ri
plot(imagematrix(equalize(Z)))
imagematrixPNG(imagematrix(equalize(Z)), file="Phantom4Speckle.png")
imagematrixPNG("Phantom4Speckle.png", imagematrix(equalize(Z)))
View(imagematrixPNG)
imagematrixPNG
imagematrixPNG(imagematrix(equalize(Z)), "Phantom4Speckle.png")
getwd()
q()
require(GI0)
rGI0()
rGI0
q()
