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CSDCD.logistic=function(num.var,num.sample,x.mat,y.vec,d.Star,it.num){
save.count=0
p=num.var
n=num.sample
delta.lam=0
design.Mat=x.mat
y.sim=y.vec
lambda=d.Star
thru.data=it.num
tot.it=thru.data*n
alpha=matrix(NA,tot.it,n)
nu.mat=matrix(NA,tot.it,p)
################ INITIAL VALUES ############################
alpha[1,]=rep(-.0001,n)
initial.x.i=matrix(NA,n,p)
#initial.x.i=t(design.Mat)%*%(-y.sim)
#nu.mat[1,]=(sum(alpha[1,]*initial.x.i)*(1/lambda))/n
phi.star=function(b){b*log(b)+(1-b)*log(1-b)}
for(i in 1:n){
initial.x.i[i,]=design.Mat[i,]*y.sim[i]
}
nu.mat[1,]=t(((alpha[1,]%*%initial.x.i)*(1/lambda))/n)
for(t in 2:tot.it){
random.seq=sample(1:n, 1)
if(is.nan(delta.lam) | is.na(alpha[t-1,random.seq])){
t=t-1
}else{
x.i=-y.sim[random.seq]*design.Mat[random.seq,]
p.rand=t(x.i)%*%nu.mat[t-1,]
if(alpha[t-1,random.seq]>0 | is.nan(alpha[t-1,random.seq]) | alpha[t-1,random.seq]<(-1) ){
#print(alpha[t-1,random.seq])
alpha[t-1,random.seq]=-.0001
##print("alpha fixed")
}
q=-1/(1+exp(-p.rand))-alpha[t-1,random.seq]
min.val=(log(1+exp(p.rand))+phi.star(-alpha[t-1,random.seq])+p.rand*alpha[t-1,random.seq]+2*q^2)/(q^2*((4+(sum(x.i^2*(1/lambda))/n))))
s=min(1,min.val)
# if(is.nan(s)==TRUE){
# delta.lam=0
#save.count=save.count+1
# cat("saved ",save.count," times"," q = ",q,"\n",sep="")
# alpha[t-1,random.seq]=-.0001
# #t=t-1
# }else{
delta.lam=s*q
#}
#cat("delta = ",delta.lam,", s = ",s,", q = ",q,"\n",sep="")
alpha[t,-random.seq]=alpha[t-1,-random.seq]
alpha[t,random.seq]=alpha[t-1,random.seq]+delta.lam
nu.mat[t,]=nu.mat[t-1,]+((delta.lam*x.i)/(lambda*n))
}
}
colMeans(nu.mat[(tot.it/2):tot.it,])
#nu.mat[tot.it,]
}
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