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Update Dataset_study.rmd

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Update_Automatic_Save_FileProba
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elisaduz 2022-03-22 11:16:18 +01:00
parent fd3ab9ac2e
commit aef2dfce41
1 changed files with 34 additions and 7 deletions
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Dataset_study.rmd
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@ -337,9 +337,19 @@ p
library(stringr)
E=10
lambda0=1
lambda1=2
# Effacement des objets de l'environnement de travail
rm(list=ls())
# Chgt du nbr de chiffres après la virgule
options("digits" = 15)
# Chemin du dossier à adapter
workpath = "."
E=1
lambda0=0.2
lambda1=0.5
max=0
while (exp(-lambda0*max) > 10^(-9)){
@ -356,7 +366,7 @@ head(x)
range(x)
s=seq(-15,6,0.1)
s=seq(min(x),max(x),0.1)
##tronquer la queue des x (x négatifs)
A=1/(lambda0-lambda1)
B=A*log(lambda1/lambda0)
@ -365,12 +375,29 @@ proba.s = proba.s/sum(proba.s)
barplot(proba.s)
# Troncage à un score minimal
minXk = as.numeric(x[max(which(proba.s<(probaseuilmin)))]) # On définit la classe pour proba < probaseuilmin
names(proba.s) = as.character(x)
minXk = as.numeric(s[max(which(proba.s<(probaseuilmin)))]) # On définit la classe pour proba < probaseuilmin
names(proba.s) = as.character(s)
proba.s[as.character(minXk)] = sum(proba.s[which(proba.s<probaseuilmin)]) # probabilité de la classe SCORE < minXk
proba.s = proba.s[which(x>=minXk)] # On ne garde que les scores supérieurs à minXk
score = s
score=score[which(score>=minXk)]
proba.s = proba.s[which(s>=minXk)] # On ne garde que les scores supérieurs à minXk
subtitle = paste("lambda0=",lambda0,";lambda1=",lambda1,";E=",E,sep="")
barplot(proba.s, col="steelblue",xlab="Score",ylab="Probabilité", main = paste("Probabilité d'apparition de chaque score\nLoi géométrique : ",subtitle,sep=""))
print(score)
min(score)
max(score)
mean(score)
# Enregistrement du fichier
fichier_proba = paste(workpath,"/LLR_Theorique_lambda0",gsub('\\.','',as.character(lambda0)),"_lambda1",gsub('\\.','',as.character(lambda1)),"_E",E,"_CL.txt",sep="")
write.table(proba.s, file = fichier_proba)
# Lecture fichier
#prob.X = read.table(fichier_proba)
```
```{r}
barplot(proba.s)
##trouver 1/lambda en fonction intervalle seq