#Load the code for running NBDA from https://lalandlab.st-andrews.ac.uk/freeware/



#function for calculating estimated proportion of events occurring by social learning
oadaPropSolveByST<-function(par,oadata,innovations=1,additive=T,asocialVar=NULL){	
	if(is.character(oadata)) oadata<- combineOaCoxData(oadata);
	s<-par[1]
	beta<-par[-1]
	if(is.null(asocialVar)|!additive){
		data2<-oadata@coxdata[oadata@coxdata$status==1,6]
		number<-sum((s * data2)/(1+ s * data2))
		total<-length(data2)-innovations


	}else{
		data2<-oadata@coxdata[oadata@coxdata$status==1,c(6,6+asocialVar)]
		number<-sum((s * data2[,1])/(exp(as.matrix(data2[,-1])%*%beta)+ s * data2[,1]))	
		total<-dim(data2)[1]-innovations
	}
	return(number/total)	
}


#Set number of simulations
noSims<-1000
#Set number of individuals
noInd<-100
#Set s values to be considered
sVect<-c(0,0.1,0.2,0.3,0.4,0.5,1,1.5,2,3,4)
#Set bias to be considered
BiasVect<-c(-0.25,-0.1,-0.05,0,0.05,0.1,0.25)
#asocial learning set to exp(asocialLP) = 1
asocialLP<-rep(0,noInd)
asoc<-cbind(rep(0,noInd))
#Set base rate of asocial learning
baseRate<-1/100

#set up records to record outputs of models
sRecord<-aicRecord<-pRecord<-sRecordW<-aicRecordW<-pRecordW<-TsRecord<-TaicRecord<-TpRecord<-TsRecordW<-TaicRecordW<-TpRecordW<-profLikForS <-profLikForSW <-TprofLikForS <-TprofLikForSW <-estPropSolvesST<-truePropSolvesST<-TestPropSolvesST<-array(NA,dim=c(noSims,length(sVect),length(BiasVect)))


for(k in 1:length(sVect)){
	s<-sVect[k]
	for(j in 1:noSims){

		#generate a social network
		socialNet<-matrix(runif(noInd*noInd,0,0.1), ncol=noInd, nrow=noInd)
		for(i in 0:9){socialNet[i*10+(1:10),i*10+(1:10)]<-runif(100,0.5,1)}
		
		#simulate diffusion	
		z<-rep(0,noInd)
		orderAcq<-timeAcq <-rep(NA,noInd)
		runningTime<-0

		for(i in 1:noInd){
			rate<-baseRate*(exp(asocialLP)+s*z%*%t(socialNet))*(1-z)
			times<-rexp(noInd,rate)
			times[is.nan(times)]<-Inf
			orderAcq [i]<-which(times==min(times))[1]
			runningTime<-runningTime+min(times)
			timeAcq[i]<-runningTime
			z[which(times==min(times))[1]]<-1
		}

for(l in 1:length(BiasVect)){
	Bias<-BiasVect[l]

		#set recorded social net according to level of bias required
		RsocialNet<-socialNet+(socialNet-0.5)*Bias
		RsocialNet[RsocialNet<0]<-0
		RsocialNet[RsocialNet>1]<-1
		
		#Fit OADA and TADA
		oaDataObject<-oaData(assMatrix= RsocialNet, taskid="1", groupid="1", asoc=asoc, orderAcq= orderAcq)
		taDataObject<-taData(timeAcq,max(timeAcq)+1,oadata= oaDataObject)
		model<-addFit(oaDataObject,interval=c(0,9999))
		Tmodel<-tadaFit(taDataObject)

		#Record outputs from OADA and TADA (see "Simulations association net noise.R" for explanation)
		sRecord[j,k,l]<-model@optimisation$minimum
		aicRecord[j,k,l]<-model@aicc
		pRecord[j,k,l]<-model@ LRTsocTransPV
		profLikForS[j,k,l]<-profileLikelihoodAddOADA(s,1, model, oaDataObject)-model@optimisation$objective
		TsRecord[j,k,l]<-Tmodel@optimisation$par[1]
		TaicRecord[j,k,l]<-Tmodel@aicc
		TpRecord[j,k,l]<-Tmodel@ LRTsocTransPV
		TprofLikForS[j,k,l]<-profileLikelihoodTADA(s,1, Tmodel, taDataObject)-Tmodel@optimisation$objective
		estPropSolvesST[j,k,l]<-oadaPropSolveByST(par=model@optimisation$minimum,oadata= oaDataObject,innovations=1,additive=T,asocialVar=NULL)
		truePropSolvesST[j,k,l]<-oadaPropSolveByST(par=s,oadata= oaDataObject,innovations=1,additive=T,asocialVar=NULL)		
		TsRecord[j,k,l]<-Tmodel@optimisation$par[1]
		TaicRecord[j,k,l]<-Tmodel@aicc
		TpRecord[j,k,l]<-Tmodel@ LRTsocTransPV
		TprofLikForS[j,k,l]<-profileLikelihoodTADA(s,1, Tmodel, taDataObject)-Tmodel@optimisation$objective
		TestPropSolvesST[j,k,l]<-oadaPropSolveByST(par=Tmodel@optimisation$par[1],oadata= oaDataObject,innovations=1,additive=T,asocialVar=NULL)

		#Save it all in an object
				save(sRecord,aicRecord,pRecord,sRecordW,aicRecordW,pRecordW,TsRecord,TaicRecord,TpRecord,TsRecordW, TaicRecordW,TpRecordW,profLikForS ,profLikForSW ,TprofLikForS, estPropSolvesST , TestPropSolvesST , truePropSolvesST , file="NBDANoiseinNetBiasss100.dat")

	}
}
}