# The core of the Gibbs sampling.
#
# Perform GEODE without missing data.
#
rgeode_root<- function(Y, d, burn, its, tol, atau, asigma,
bsigma, starttime, stoptime, fast, c0, c1)
{
#*************************************************************************
#*** Author: L. Rimella <lorenzo.rimella@hotmail.it> ***
#*** ***
#*** Supervisors: M. Beccuti ***
#*** A. Canale ***
#*** ***
#*************************************************************************
# Fit GEODE on dataset with no missing data
##########################################################################
# First step:
# Computation of the mean and the principal axes and creation
# of the sufficient statistics A,Z
##########################################################################
#Mean and Principal axes
N= dim(Y)[1]
D= dim(Y)[2]
mu = matrix(apply(Y,2,mean), D, 1)
Y_c= t(apply(Y,1,function(x)return(x-mu)))
#Perform a fast rank-d SVD or a simple SVD
if(fast)
{
W= randSVD(t(Y_c), k= d )$u
}
if(!fast)
{
W= svd(t(Y_c), nu= d, nv= d)$u
}
#Sufficient Statistics
A = matrix(apply(Y_c, 1, function(x)return(sum(x^2))), N, 1)
Z = Y_c %*% W
##########################################################################
# Second step:
# Preparation for the Gibbs sampler:
# nc=its nb=burn tol=tol a=atau a_sigma = asigma; b_sigma= bsigma
# step=step stoptime = ; starttime = ; T= Time
##########################################################################
Time = burn + its
InD= rep(list(c(1:d)), stoptime)
u= matrix(0.5, d, Time)
tau= matrix(1, d, Time)
tau[,1]= rexptr(d, atau, range= c(1,Inf))
sigmaS= matrix(1, Time, 1)
sigmaS[1]= 1/rgamma(1,shape= asigma, rate= bsigma)
##########################################################################
# Third step:
# Gibbs sampler:
##########################################################################
InDtmp = seq(1,d)
#summ=0
for(iter in 2:Time)
{
#Update u
#u[,iter] = generateU_root(Z[,InDtmp], sigmaS[iter-1], N,
# tau[InDtmp,iter-1], u[,iter-1])
u[,iter]= u[,iter-1]
u[InDtmp,iter] = CgenerateU_root(tau[InDtmp,iter-1], N, sigmaS[iter-1],
Z[,InDtmp])
#tic(func.tic = NULL)
#tau[,iter] = generateTau_root(u[,iter], atau, InDtmp, tau[,iter-1])
tau[,iter]= tau[,iter-1]
tau[InDtmp,iter]= CgenerateTau_root(u[InDtmp,iter],tau[InDtmp,iter],
atau, max(InDtmp))
#c=toc(func.toc = NULL)
#cc= c$toc- c$tic
#summ=summ+cc
sigmaS[iter] = generateSigmaS_root(A, Z, u[,iter], N, asigma, bsigma,
D, InDtmp)
# Adaptively prune the intrinsic dimension
# Wang version:
#if (iter <= stoptime & iter > starttime)
#{
# u_accum = u_accum + ( u[,iter]==1 )
# if(any(adptpos == iter))
# {
# adapt[iter] = 1
# ind = InD[[iter-1]]
# tmp = u_accum[ind]
# vec = (1/u[InD[[iter-1]],iter]-1)*sigmaS[iter]
# d1 = ind
# if ( sum(tmp > (stoptime-starttime)*tol) + sum(vec/max(vec) < tol) )>0
# {
# ind = ind[ ind %in% ind[ d1 >= min(d1( (tmp > (stoptime-starttime)*tol) |
# (vec/max(vec) < tol) )) ] = [];
# }
# InD[[iter]] = ind
# else InD[[iter]]= InD[[iter-1]]
#
# }
# InDtmp = InD[[iter]]
#}
if(iter <= stoptime & iter > starttime)
{
ind= InD[[iter-1]]
#find our alphas
vec_alpha= (1/u[ind,iter]-1)*sigmaS[iter]
max_alpha= max(vec_alpha)
if(iter== stoptime)
{
if( sum(ind[vec_alpha/max_alpha < tol])>0 )
{
ind= ind[ ind < min( ind[vec_alpha/max_alpha < tol] ) ]
}
}
else if( runif(1, 0, 1)< p(iter, c0, c1) )
{
if( sum(ind[vec_alpha/max_alpha < tol])>0 )
{
ind= ind[ ind < min( ind[vec_alpha/max_alpha < tol] ) ]
}
else
{
if(ind[length(ind)]+1<d)
{
ind= c(ind, ind[length(ind)]+1)
}
}
}
InD[[iter]]= ind
InDtmp = InD[[iter]]
}
}
#print(summ)
return( list(InD, u, tau, sigmaS, W, mu) )
}
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