diff --git a/Comparaison_of_methods.rmd b/Comparaison_of_methods.rmd index 8780d2d..2b9a27e 100644 --- a/Comparaison_of_methods.rmd +++ b/Comparaison_of_methods.rmd @@ -273,6 +273,7 @@ ScoreDistribEmpiric <- function(lambda0, lambda1, n_sample, T){ ``` ```{r} + lambda0=5 lambda1=7 distrib_mc=ScoreDistribEmpiric(lambda0,lambda1,10000,T) @@ -289,14 +290,17 @@ print(E) barplot(distrib_mc[,2]) ``` + ```{r} ScoreDistribTheo <- function(lambda0, lambda1, T){ E = ComputeE(lambda0, lambda1) score_max = floor(E*log(lambda1/lambda0)) + ## score_min compute score_min_c = floor(E*log(lambda1/lambda0)+E*(lambda0-lambda1)*T) + l = seq(score_min_c,score_max,1) borne_inf = (l-E*log(lambda1/lambda0))/(E*(lambda0-lambda1)) @@ -311,6 +315,16 @@ ScoreDistribTheo <- function(lambda0, lambda1, T){ ``` ```{r} + +T=10 +distrib_score_mc=ScoreDistribEmpiric(2,3,10000,T) + +distrib_score_theo=ScoreDistribElisa(2,3,T) + +distrib_score_mc +distrib_score_theo + + distrib_score_mc = ScoreDistribEmpiric(2,3,10000,T) distrib_score_theo = ScoreDistribTheo(2,3,T) @@ -320,6 +334,7 @@ plot_graph_distrib_score <- function(distrib_score_theo, distrib_score_mc){ #diff_distrib_score=abs(distrib_score_mc[,2]-distrib_score_theo[,2]) + #par(mfrow = c(1,2)) barplot(distrib_score_mc[,2],col="blue",axes=F) mtext("Distribution of scores via Monte Carlo",side=1,line=2.5,col="blue") @@ -329,10 +344,10 @@ plot_graph_distrib_score <- function(distrib_score_theo, distrib_score_mc){ mtext("Distribution of scores using the theoretical method",side=1,line=4,col="red") } + plot_graph_distrib_score(distrib_score_theo, distrib_score_mc) ``` - ### 3.2. Local score calculation ```{r} LocalScoreMC <- function(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0){ diff --git a/Dataset_study.rmd b/Dataset_study.rmd index b16529d..cf52f85 100644 --- a/Dataset_study.rmd +++ b/Dataset_study.rmd @@ -505,6 +505,7 @@ x.verif=seq(range(x)[1],range(x)[2],1) #dist.theo.scores=lambda0*exp(-lambda0*(A*x.verif-B)) #dist.theo.scores dist.emp.scores +barplot(dist.emp.scores) ```