Nothing
choose_lambda <-
function(diff_expr,diff_coex,lambda,subnet_size,num_random_sampling,best_score){
my.fun<-function(vector){
return(diff_coex[vector[1],vector[2]])
}
subset_score<-function(vector){
selected_subset<-which(vector==1)
n<-length(selected_subset)
if(n<2){return(0)}
else{
node_score<-sum(diff_expr[selected_subset])/sqrt(n)
edges<-combn(selected_subset,2)
edge_score<-sum(apply(edges,2,my.fun))/sqrt(choose(n,2))
total_score<- lam*edge_score + (1-lam)*node_score
return (-total_score)
}
}
monitor <- function(obj) {
minEval = min(obj$evaluations);
filter = obj$evaluations == minEval;
bestObjectCount = sum(rep(1, obj$popSize)[filter]);
if (bestObjectCount > 1) {
bestSolution = obj$population[filter,][1,];
} else {
bestSolution = obj$population[filter,];
}
outputBest = paste(obj$iter, " #selected=", sum(bestSolution),
" Best (Score=", -minEval, "):\n", sep="");
print(outputBest)
print(which(bestSolution==1))
}
#Random sampling
gene_num<-length(diff_expr)
random_score<-matrix(0,nrow=5,ncol=num_random_sampling)
for(i in 1:5){
for(j in 1:num_random_sampling){
lam<-lambda[i]
random_net<-rep(0,gene_num)
a<-sample(1:gene_num,subnet_size[i])
random_net[a]<-1
random_score[i,j]<- -subset_score(random_net)
print(j)
}
}
mean<-apply(random_score,1,mean)
sd<-apply(random_score,1,sd)
adjusted_score<-(best_score-mean)/sd
best_lambda <- lambda[which.max(adjusted_score)]
return (list(Adj_score=adjusted_score, Best_lam=best_lambda, Random_Score=random_score))
}
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