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MCMC.gibbs.speedup.R
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MCMC.gibbs.speedup.R
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###### We want to use a MCMC version instead of the ICM version, the MCMC version will get the posterior distribution of gene's state Z by sampling ######
###### JUN WANG, PEIKING UNIVERSITY, 20151022 ##################################################################################################
###### The Vertion 2: using weight (non-equal weight),20151202
MCMC.gibbs.speedup.V2=function(pram,step.mcmc,permutate.order,node.state,sherlock.logLR){
#node.state.track=matrix(100,nrow=length(node.state),ncol=step.mcmc)
node.state.tmp=node.state
row.sum=matrix(0,nrow=length(node.state.tmp),ncol=1)
#node.neighbor.info=matrix(100000,nrow=length(node.state),ncol=2)
pram.tmp=pram
h.tmp=pram.tmp[1]
tao.one.tmp=pram.tmp[2]
tao.minus.one.tmp=pram.tmp[3]
iteration.times=0
if(permutate.order==0){
while(iteration.times<step.mcmc){
#print(iteration.times)
#node.state.previous=node.state.tmp
#log.odds.tmp=matrix(0,nrow=length(node.state.tmp),ncol=1)
for (node.index in 1:length(node.state.tmp)){
index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
betaij.one=tao.one.tmp*sum(weight.gene[index.state.one]+weight.gene[node.index])
betaij.minus.one=tao.minus.one.tmp*sum(weight.gene[index.state.minus.one]+weight.gene[node.index])
#betaij.one=tao.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#betaij.minus.one=tao.minus.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#Log.Odds.i=h.tmp+betaij.one+betaij.minus.one+sherlock.logLR[node.index]
log.odds.node.i=h.tmp+betaij.one+betaij.minus.one+sherlock.logLR[node.index]
#log.odds.node.i=Log.Odds(node.index,node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp,sherlock.logLR)
#index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#node.neighbor.info[node.index,1]=length(index.state.one)
#index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#node.neighbor.info[node.index,2]=length(index.state.minus.one)
#log.odds.tmp[node.index,1]=log.odds.node.i
#print(paste(paste("The log.odds of node",node.index),log.odds.node.i))
#if(log.odds.node.i>0){
# node.state.tmp[node.index]=1
#}else{
# node.state.tmp[node.index]=-1
#}
prob.1=1-1/(1+exp(log.odds.node.i))
node.state.tmp[node.index]=rbinom(1,1,prob.1)
}
#p.change=Log.Odds.only.prior.speedup(node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp)
#str(p.change)
#rand.num=runif(length(node.state.tmp),0,1)
#index.1=which(p.change>rand.num)
#node.state.tmp[index.1]=1
#node.state.tmp[-index.1]=-1
#tmp.error=sum(abs(node.state.previous-node.state.tmp))/length(node.state.tmp)/2
iteration.times=iteration.times+1
row.sum=row.sum+node.state.tmp
#print(paste("The iteration times is ",iteration.times))
#print(paste("The temporary error now is ", tmp.error))
#print(paste("The number of state 1 gene is ",sum(node.state.tmp==1)))
#node.state.track[,iteration.times]=node.state.tmp
#node.state.track[,4*iteration.times-2]=log.odds.tmp[,1]
#node.state.track[,c(4*iteration.times-1,4*iteration.times)]=node.neighbor.info
#node.info=cbind(node.state.tmp,log.odds.tmp)
#print(paste("the pseodo.likelyhood is",get.posterior.prob(node.info)))
}
}
if(permutate.order==1){
while(iteration.times<step.mcmc){
permutation.order=sample(length(node.state),length(node.state))
#node.state.tmp.previous=node.state.tmp
#log.odds.tmp=matrix(0,nrow=length(node.state.tmp),ncol=1)
for (index.pmt in 1:length(node.state.tmp)){
node.index=permutation.order[index.pmt]
index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
betaij.one=tao.one.tmp*sum(weight.gene[index.state.one]+weight.gene[node.index])
betaij.minus.one=tao.minus.one.tmp*sum(weight.gene[index.state.minus.one]+weight.gene[node.index])
#betaij.one=tao.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#betaij.minus.one=tao.minus.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
log.odds.node.i=h.tmp+betaij.one+betaij.minus.one+sherlock.logLR[node.index]
#log.odds.node.i=Log.Odds(node.index,node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp,sherlock.logLR)
#index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#node.neighbor.info[node.index,1]=length(index.state.one)
#index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#node.neighbor.info[node.index,2]=length(index.state.minus.one)
#log.odds.tmp[node.index,1]=log.odds.node.i
#if(log.odds.node.i>0){
# node.state.tmp[node.index]=1
#}else{
# node.state.tmp[node.index]=-1
#}
prob.1=1-1/(1+exp(log.odds.node.i))
node.state.tmp[node.index]=rbinom(1,1,prob.1)
}
#p.change=Log.Odds.only.prior.speedup(node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp)
#str(p.change)
#rand.num=runif(length(node.state.tmp),0,1)
#index.1=which(p.change>rand.num)
#node.state.tmp[index.1]=1
#node.state.tmp[-index.1]=-1
#tmp.error=sum(abs(node.state.tmp.previous-node.state.tmp))/length(node.state.tmp)/2
iteration.times=iteration.times+1
row.sum=row.sum+node.state.tmp
#print(paste("The iteration times is ",iteration.times))
#print(paste("The error now is ", tmp.error))
#print(paste("The number of state 1 gene is ",sum(node.state.tmp==1)))
#node.state.track[,iteration.times]=node.state.tmp
#node.state.track[,4*iteration.times-2]=log.odds.tmp[,1]
#node.state.track[,c(4*iteration.times-1,4*iteration.times)]=node.neighbor.info
#node.info=cbind(node.state.tmp,log.odds.tmp)
#print(paste("the pseodo.likelyhood is",get.posterior.prob(node.info)))
}
}
#node.info=cbind(node.state.tmp,log.odds.tmp)
#print(paste("the pseodo.likelyhood is",get.posterior.prob(node.info)))
#return(cbind(node.info,node.neighbor.info))
#return(node.info)
#row.ave=row.sum/step.mcmc
return(row.sum/step.mcmc)
}
##########################################################################################################################################
###### The Vertion 1: using equal weight, 20151022
MCMC.gibbs.speedup.V1=function(pram,step.mcmc,permutate.order,node.state,sherlock.logLR){
#node.state.track=matrix(100,nrow=length(node.state),ncol=step.mcmc)
node.state.tmp=node.state
row.sum=matrix(0,nrow=length(node.state.tmp),ncol=1)
#node.neighbor.info=matrix(100000,nrow=length(node.state),ncol=2)
pram.tmp=pram
h.tmp=pram.tmp[1]
tao.one.tmp=pram.tmp[2]
tao.minus.one.tmp=pram.tmp[3]
iteration.times=0
if(permutate.order==0){
while(iteration.times<step.mcmc){
#print(iteration.times)
#node.state.previous=node.state.tmp
#log.odds.tmp=matrix(0,nrow=length(node.state.tmp),ncol=1)
for (node.index in 1:length(node.state.tmp)){
#index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#betaij.one=tao.one.tmp*sum(weight.gene[index.state.one]+weight.gene[node.index])/2
#betaij.minus.one=tao.minus.one.tmp*sum(weight.gene[index.state.minus.one]+weight.gene[node.index])/2
betaij.one=tao.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
betaij.minus.one=tao.minus.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#Log.Odds.i=h.tmp+betaij.one+betaij.minus.one+sherlock.logLR[node.index]
log.odds.node.i=h.tmp+betaij.one+betaij.minus.one+sherlock.logLR[node.index]
#log.odds.node.i=Log.Odds(node.index,node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp,sherlock.logLR)
#index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#node.neighbor.info[node.index,1]=length(index.state.one)
#index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#node.neighbor.info[node.index,2]=length(index.state.minus.one)
#log.odds.tmp[node.index,1]=log.odds.node.i
#print(paste(paste("The log.odds of node",node.index),log.odds.node.i))
#if(log.odds.node.i>0){
# node.state.tmp[node.index]=1
#}else{
# node.state.tmp[node.index]=-1
#}
prob.1=1-1/(1+exp(log.odds.node.i))
node.state.tmp[node.index]=rbinom(1,1,prob.1)
}
#p.change=Log.Odds.only.prior.speedup(node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp)
#str(p.change)
#rand.num=runif(length(node.state.tmp),0,1)
#index.1=which(p.change>rand.num)
#node.state.tmp[index.1]=1
#node.state.tmp[-index.1]=-1
#tmp.error=sum(abs(node.state.previous-node.state.tmp))/length(node.state.tmp)/2
iteration.times=iteration.times+1
row.sum=row.sum+node.state.tmp
#print(paste("The iteration times is ",iteration.times))
#print(paste("The temporary error now is ", tmp.error))
#print(paste("The number of state 1 gene is ",sum(node.state.tmp==1)))
#node.state.track[,iteration.times]=node.state.tmp
#node.state.track[,4*iteration.times-2]=log.odds.tmp[,1]
#node.state.track[,c(4*iteration.times-1,4*iteration.times)]=node.neighbor.info
#node.info=cbind(node.state.tmp,log.odds.tmp)
#print(paste("the pseodo.likelyhood is",get.posterior.prob(node.info)))
}
}
if(permutate.order==1){
while(iteration.times<step.mcmc){
permutation.order=sample(length(node.state),length(node.state))
#node.state.tmp.previous=node.state.tmp
#log.odds.tmp=matrix(0,nrow=length(node.state.tmp),ncol=1)
for (index.pmt in 1:length(node.state.tmp)){
node.index=permutation.order[index.pmt]
#index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#betaij.one=tao.one.tmp*sum(weight.gene[index.state.one]+weight.gene[node.index])/2
#betaij.minus.one=tao.minus.one.tmp*sum(weight.gene[index.state.minus.one]+weight.gene[node.index])/2
betaij.one=tao.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
betaij.minus.one=tao.minus.one.tmp*sum((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
log.odds.node.i=h.tmp+betaij.one+betaij.minus.one+sherlock.logLR[node.index]
#log.odds.node.i=Log.Odds(node.index,node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp,sherlock.logLR)
#index.state.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp==1))
#node.neighbor.info[node.index,1]=length(index.state.one)
#index.state.minus.one=which((ppi.matrix.noself[,node.index]!=0)&(node.state.tmp!=1))
#node.neighbor.info[node.index,2]=length(index.state.minus.one)
#log.odds.tmp[node.index,1]=log.odds.node.i
#if(log.odds.node.i>0){
# node.state.tmp[node.index]=1
#}else{
# node.state.tmp[node.index]=-1
#}
prob.1=1-1/(1+exp(log.odds.node.i))
node.state.tmp[node.index]=rbinom(1,1,prob.1)
}
#p.change=Log.Odds.only.prior.speedup(node.state.tmp,h.tmp,tao.one.tmp,tao.minus.one.tmp)
#str(p.change)
#rand.num=runif(length(node.state.tmp),0,1)
#index.1=which(p.change>rand.num)
#node.state.tmp[index.1]=1
#node.state.tmp[-index.1]=-1
#tmp.error=sum(abs(node.state.tmp.previous-node.state.tmp))/length(node.state.tmp)/2
iteration.times=iteration.times+1
row.sum=row.sum+node.state.tmp
#print(paste("The iteration times is ",iteration.times))
#print(paste("The error now is ", tmp.error))
#print(paste("The number of state 1 gene is ",sum(node.state.tmp==1)))
#node.state.track[,iteration.times]=node.state.tmp
#node.state.track[,4*iteration.times-2]=log.odds.tmp[,1]
#node.state.track[,c(4*iteration.times-1,4*iteration.times)]=node.neighbor.info
#node.info=cbind(node.state.tmp,log.odds.tmp)
#print(paste("the pseodo.likelyhood is",get.posterior.prob(node.info)))
}
}
#node.info=cbind(node.state.tmp,log.odds.tmp)
#print(paste("the pseodo.likelyhood is",get.posterior.prob(node.info)))
#return(cbind(node.info,node.neighbor.info))
#return(node.info)
#row.ave=row.sum/step.mcmc
return(row.sum/step.mcmc)
}