From 2e89ba20b5c9e067ef8eaf80e6071c1f7906510d Mon Sep 17 00:00:00 2001
From: Paul-Corbalan <58653590+Paul-Corbalan@users.noreply.github.com>
Date: Tue, 5 Apr 2022 10:50:23 +0200
Subject: [PATCH] Local score work

---
 Comparaison_of_methods.rmd | 37 +++++++++++++++++++++----------------
 1 file changed, 21 insertions(+), 16 deletions(-)

diff --git a/Comparaison_of_methods.rmd b/Comparaison_of_methods.rmd
index 86f8c92..bd428c7 100644
--- a/Comparaison_of_methods.rmd
+++ b/Comparaison_of_methods.rmd
@@ -123,12 +123,12 @@ PValue <- function(Emp,ppH1, T, tau){
 ### 2.2. Simulation under $\mathcal{H}_0$ and computation of p-values
 On simule des séquences sous $\mathcal{H}_0$, que l'on stocke. On calcule la valeur de la scan stat et de la p-value, que l'on stocke aussi. On a une séquence de p-valeur des scans et une séquence de score local.
 ```{r}
-NbSeqH0=5000
+NbSeqH0=1000
 NbSeqH1=NbSeqH0
 DataH0=vector("list")
 DataH1=vector("list")
 lambda0=3
-lambda1=4
+lambda1=5
 T=10
 tau=1
 
@@ -164,7 +164,7 @@ We compute the p-value associated to all 5 sequences, and stock them in a vector
 ```{r}
 #We start by computing the empirical distribution for lambda0
 Emp=EmpDistrib(lambda0,n_sample,T,tau)
-scan=c()
+
 pvalue=c()
 index_scan=c()
 
@@ -220,15 +220,23 @@ ppH0 = PoissonProcess(lambda0,10^4)
 n1 = length(ppH0)
 tbe0 = ppH0[2:n1]-ppH0[1:n1-1]
 print(ks.test(tbe0,'exp'))
-x = floor(E*(log(lambda1/lambda0)+(lambda0-lambda1)*tbe0)) # ne pas mettre le floor ni le E (certes égale à 1)
+xp = floor(E*(log(lambda1/lambda0)+(lambda0-lambda1)*tbe0)) # ne pas mettre le floor ni le E (certes égale à 1)
 #hist(x)
 #print(summary(x))
 
-P_X = table(factor(x, levels = min(x):max(x)))
-P_X = P_X/sum(table(x))
+min_X = min(xp)
+max_X = max(xp)
+
+vect.score = min_X:max_X
+
+P_X = table(factor(xp, levels = min(xp):max(xp)))
+P_X = P_X/sum(table(xp))
+
+Mean_xp = sum(vect.score*P_X)
+print(Mean_xp)
 
 #print(dist.theo.scores) # Mettre à jour avec Elisa
-print(P_X)
+#print(P_X)
 ```
 
 ### Calcul du local score
@@ -237,19 +245,16 @@ library("localScore")
 library(Rcpp)
 E = 10
 pvalue=c()
+X=c()
 
 for (i in 1:NbSeqH0){
-    X = floor(E*log(dexp(tbe0[[i]], rate = lambda1)/dexp(tbe0[[i]], rate = lambda0)))
+    x = floor(E*log(dexp(tbe0[[i]], rate = lambda1)/dexp(tbe0[[i]], rate = lambda0)))
+    X=c(X,x)
+    LS=localScoreC(x)$localScore[1]
     
-    max_X = max(X)
-    min_X = min(X)
-    P_X = table(factor(X, levels = min_X:max_X))/length(X) # supprimer pour les séquences de MC
+    result = daudin(localScore = LS, score_probabilities = P_X, sequence_length = length(x), sequence_min = min_X, sequence_max = max_X)
     
-    LS=localScoreC(X)$localScore[1]
-    
-    result = daudin(localScore = LS, score_probabilities = P_X, sequence_length = length(X), sequence_min = min_X, sequence_max =max_X)
-    
-    pvalue=c(pvalue,result)
+    pvalue = c(pvalue,result)
 }
 LS_H0=data.frame(num=1:NbSeqH0, pvalue_scan=pvalue, class=(pvalue<0.05))
 LS_H0