Merge branch 'main' of https://github.com/Paul-Corbalan/Scan-Statistics-Project-4Y-INSA
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@ -273,6 +273,7 @@ ScoreDistribEmpiric <- function(lambda0, lambda1, n_sample, T){
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```
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```{r}
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lambda0=5
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lambda1=7
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distrib_mc=ScoreDistribEmpiric(lambda0,lambda1,10000,T)
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@ -289,15 +290,18 @@ print(E)
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barplot(distrib_mc[,2])
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```
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```{r}
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ScoreDistribTheo <- function(lambda0, lambda1, T){
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E = ComputeE(lambda0, lambda1)
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score_max = floor(E*log(lambda1/lambda0))
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## score_min compute
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score_min_c = floor(E*log(lambda1/lambda0)+E*(lambda0-lambda1)*T)
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l = seq(score_min_c,score_max,1)
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borne_inf = (l-E*log(lambda1/lambda0))/(E*(lambda0-lambda1))
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borne_sup = (l+1-E*log(lambda1/lambda0))/(E*(lambda0-lambda1))
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@ -311,6 +315,16 @@ ScoreDistribTheo <- function(lambda0, lambda1, T){
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```
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```{r}
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T=10
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distrib_score_mc=ScoreDistribEmpiric(2,3,10000,T)
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distrib_score_theo=ScoreDistribElisa(2,3,T)
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distrib_score_mc
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distrib_score_theo
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distrib_score_mc = ScoreDistribEmpiric(2,3,10000,T)
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distrib_score_theo = ScoreDistribTheo(2,3,T)
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@ -320,6 +334,7 @@ plot_graph_distrib_score <- function(distrib_score_theo, distrib_score_mc){
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#diff_distrib_score=abs(distrib_score_mc[,2]-distrib_score_theo[,2])
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#par(mfrow = c(1,2))
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barplot(distrib_score_mc[,2],col="blue",axes=F)
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mtext("Distribution of scores via Monte Carlo",side=1,line=2.5,col="blue")
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@ -329,10 +344,10 @@ plot_graph_distrib_score <- function(distrib_score_theo, distrib_score_mc){
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mtext("Distribution of scores using the theoretical method",side=1,line=4,col="red")
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}
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plot_graph_distrib_score(distrib_score_theo, distrib_score_mc)
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```
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### 3.2. Local score calculation
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```{r}
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LocalScoreMC <- function(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0){
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@ -505,6 +505,7 @@ x.verif=seq(range(x)[1],range(x)[2],1)
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#dist.theo.scores=lambda0*exp(-lambda0*(A*x.verif-B))
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#dist.theo.scores
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dist.emp.scores
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barplot(dist.emp.scores)
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```
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