#library(verification,lib.loc="/home/paulb/R/x86_64-redhat-linux-gnu-library/2.15");
source("/Volumes/shared_data/paulb/R/Functions/verify_mod.R") #I modified the function "verify" of the package to add the computation of ACC

#Parameters of the program you can change
########################################################################
XXX<-"MultiModel" # WCT, MAC, AM, ABP or multiModel. Select the model to be tested
ens<-"ensemble" # choose "single" or "ensemble"
cor<-"corrected" # choose "corrected" or "uncorrected"
method<-"SMC"

if (cor=="uncorrected"){correct<-cor}
if (cor=="corrected"){correct<-paste(cor,"_",method,sep="")}

Directory_Communication<-"/Volumes/shared_data/paulb/Results/Communication/Verification/"
Directory_Output<-"/Volumes/shared_data/paulb/Results/Results/Skills/" #Directory where you want your output to be stocked. 

###################
# Lauch on scilab :
# exec('~/Scilab/infectedVerification/infected_XXX') and
# exec('~/Scilab/infectedVerification/Climatology.sci')
###################
# Computation of the scores for the monthly mean values
Names<-c("Scores","MAE","ME","MSE","MSE baseline","MSE persistence","SS baseline","ACC","bias")
#Names<-c("Scores","MAE","ME","MSE","bias")
Summary<-NULL;
for (k in 1:12) {
	SummaryMonth<-NULL;
	for (l in 0:2) {
		predic<- as.vector(as.matrix(read.csv(paste(Directory_Communication,XXX,"_",ens,"_",correct,"_MonthlyMean_Start_Month_",k,"_Pred_Month_",k+l,"_SimulatedCases.csv",sep=""),sep=",",header=FALSE)))
		pred<-NULL;
		for (y in 1:24){
			pred[y]=mean(predic[y:(y+9-1)]); # Take the mean of the model outputs
		}
		obs<-as.vector(as.matrix(read.csv(paste(Directory_Communication,XXX,"_",ens,"_",correct,"_MonthlyMean_Start_Month_",k,"_Pred_Month_",k+l,"_PositiveCases.csv",sep=""),sep=",",header=FALSE)))
		baseline <- as.vector(as.matrix(read.csv(paste(Directory_Communication,XXX,"_",ens,"_",correct,"_MonthlyMean_Start_Month_",k,"_Pred_Month_",k+l,"_BaselineCases.csv",sep=""),sep=",",header=FALSE)))
		
		A<- verify(obs, pred, baseline = baseline,  frcst.type = "cont", obs.type = "cont")

		Skills<-c(A$MAE,A$ME,A$MSE,A$MSE.baseline,A$MSE.pers,A$SS.baseline,A$ACC,A$bias)
#		Skills<-c(A$MAE,A$ME,A$MSE,A$bias)
		SummaryMonth<-cbind(SummaryMonth,Skills)
	}
	SummaryMonth<-rbind(c(k,k+1,k+2),SummaryMonth)
	SummaryMonth<-cbind(Names,SummaryMonth)
	Summary<-rbind(Summary,SummaryMonth)
}
		write.table(Summary,file=paste(Directory_Output,XXX,"_",ens,"_",cor,"_",method,"_MonthlyMean_Continuous_Skill_Scores_3_Month_Predictions.csv",sep=""),quote=FALSE,sep=",")
		
# #Computation of the scores for the anomalies
# Names_A<-c("Scores","MAE","ME","MSE","MSE baseline","SS baseline","bias")
# Summary_A<-NULL;
# for (k in 1:12) {
	# SummaryMonth_A<-NULL;
	# for (l in 0:2) {
		# pred_A<- as.vector(as.matrix(read.csv(paste(Directory_Communication,XXX,"_",ens,"_",cor,"_mean_Anomalies_Start_Month_",k,"_Pred_Month_",k+l,"_SimulatedCases.csv",sep=""),sep=",",header=FALSE)))
		# obs_A<-as.vector(as.matrix(read.csv(paste(Directory_Communication,XXX,"_",ens,"_",cor,"_mean_Anomalies_Start_Month_",k,"_Pred_Month_",k+l,"_PositiveCases.csv",sep=""),sep=",",header=FALSE)))
		# baseline_A<-as.vector(as.matrix(read.csv(paste(Directory_Communication,XXX,"_",ens,"_",cor,"_mean_Anomalies_Start_Month_",k,"_Pred_Month_",k+l,"_BaselineCases.csv",sep=""),sep=",",header=FALSE)))
		
		# A<- verify(obs_A, pred_A, baseline=baseline_A, frcst.type = "cont", obs.type = "cont")

		# Skills_A<-c(A$MAE,A$ME,A$MSE,A$MSE.baseline,A$SS.baseline,A$bias)
		# SummaryMonth_A<-cbind(SummaryMonth_A,Skills_A)
	# }
	# SummaryMonth_A<-rbind(c(k,k+1,k+2),SummaryMonth_A)
	# SummaryMonth_A<-cbind(Names_A,SummaryMonth_A)
	# Summary_A<-rbind(Summary_A,SummaryMonth_A)
# }

# write.table(Summary_A,file=paste(Directory_Output,XXX,"_",ens,"_",cor,"_mean_Anomalies_Continuous_Skill_Scores_3_Month_Predictions.csv",sep=""),quote=FALSE,sep=",")