Nothing
Mstep.hh.MSAR <-
function(data,theta,FB,sigma.diag=FALSE,sigma.equal=FALSE) {
T=dim(data)[1]
N.samples = dim(as.array(data))[2]
d = dim(as.array(data))[3]
if(is.null(d)|is.na(d)) {d = 1}
M <- attributes(theta)$NbRegimes
p <- attributes(theta)$order
order <- max(p,1)
data = array(data,c(T,N.samples,d))
data2 = array(0,c(order*d,T-order+1,N.samples))
cpt=1
for (o in order:1) {
for (kd in 1:d) {
data2[cpt,,] = data[o:(T-order+o),,kd]
cpt =cpt+1
}
}
T = length(o:(dim(data)[1]-order+o))
exp_num_trans = 0
exp_num_visit = 0
exp_num_visits1 = 0
postmix = 0
m = matrix(0,d*order,M) ; m_1 = m ; c = matrix(0,M,1) ; s=c ;
op = array(0,c(d*order,d*order,M) ); op_1 = op ; op_2 = op_1 ;
for (ex in 1:N.samples) {
xit = array(FB$probSS[,,,ex],c(M,M,T-2))
gamma = matrix(FB$probS[ex,(1:(T-1)),],T-1,M)
exp_num_trans = exp_num_trans+apply(xit,c(1,2),sum)
exp_num_visits1 = exp_num_visits1+gamma[1,]
postmix = postmix+apply(gamma,2,sum)
obs = array(data2[,,ex],c(d*order,T))
obs = t(obs)
for (j in 1:M) {
w = matrix(gamma[,j], 1,T-1)
wobs = obs[1:(T-1),] * repmat(t(w),1,d*order)
wobs_1= obs[2:T,] * repmat(t(w),1,d*order)
if (is.na(sum(obs))) {
wobs[is.na(wobs)] = 0
wobs_1[is.na(wobs_1)] = 0
obs[is.na(obs)] = 0
}
m[,j] = m[,j] + apply(wobs,2,sum,na.rm=TRUE)
m_1[,j] = m_1[,j] + apply(wobs_1, 2,sum,na.rm=TRUE)
op[,,j] = op[,,j] + t(wobs) %*% obs[1:(T-1),]
op_1[,,j] = op_1[,,j] + t(wobs) %*% obs[2:T,]
op_2[,,j] = op_2[,,j] + t(wobs_1) %*% obs[2:T,]
}
}
# Markov chain
prior = normalise(exp_num_visits1)
prior=matrix(prior,M,1)
transmat = mk_stochastic(exp_num_trans)
if (min(postmix)<1e-6) {stop("error : smoothing probabilities are to small, in one regime at least. You should revise initialisation.")}
moy <- array(0,c(M,d))
Sigma <- array(0,c(d,d,M))
A2 <-array(0,c(M,order,d^2))
if (p>0) {
for (j in 1:M) {
Cxx = postmix[j]*op[,,j] - m[,j]%*%t(m[,j])
Cxy = postmix[j]*op_1[,,j] - m[,j]%*%t(m_1[,j])
#if (d>1){if det(Cxx)<=0) {Cxx = Cxx+1e-4*diag(1,d)}}
A2tmp = t(Cxy)%*%solve(Cxx)
tmp = (m_1[,j]-(A2tmp)%*%m[,j])/postmix[j]
moy[j,1:d] = tmp[1:d]
op_1[,,j] = t(as.matrix(op_1[,,j]))
tmp2 = (op_2[, , j] + (A2tmp) %*% op[, , j] %*% t(A2tmp) -
((A2tmp) %*% t(op_1[, , j]) + t((A2tmp) %*% t(op_1[, , j]))))/postmix[j] - tmp %*% t(tmp)
if (!sigma.diag){
Sigma[1:d,1:d,j]=tmp2[1:d,1:d] #+(det(tmp2[1:d,1:d])==0)*1e-4*diag(1,4)
} else {
Sigma[1:d,1:d,j]=diag(diag(tmp2[1:d,1:d]),d)
}
for(kp in 1:order){
A2[j,kp,] = A2tmp[1:d,((kp-1)*d+1):(kp*d)]
}
}
if (sigma.equal & M>1){
pi_prob = normalise(postmix)
S=pi_prob[1]*Sigma[1:d,1:d,1]
for (j in 2:M){S = S+pi_prob[j]*Sigma[1:d,1:d,j]}
for (j in 1:M){Sigma[1:d,1:d,j] = S}
}
} else {
for (j in 1:M) {
tmp = (m[,j])/postmix[j]
moy[j,1:d] = tmp[1:d]
tmp2 = op[, , j]/postmix[j] - tmp %*% t(tmp)
if (!sigma.diag){ Sigma[1:d,1:d,j]=tmp2[1:d,1:d]
} else {
Sigma[1:d,1:d,j]=diag(diag(tmp2[1:d,1:d]),d)
}
}
if (sigma.equal & M>1){
pi_prob = normalise(postmix)
S=pi_prob[1]*Sigma[1:d,1:d,1]
for (j in 2:M){S = S+pi_prob[j]*Sigma[1:d,1:d,j]}
for (j in 1:M){Sigma[1:d,1:d,j] = S}
}
}
if(d>1){
Atmp=list()
sigma=list()
for(j in 1:M){
Atmp[[j]]=list()
for(kp in 1:order){
Atmp[[j]][[kp]]=matrix(A2[j,kp,],d,d)
}
sigma[[j]]=Sigma[,,j]
}
}
else{
Atmp=matrix(A2,M,order)
sigma=matrix(Sigma,byrow=TRUE,nrow=M)
}
if (p>0) {
list(A=Atmp,A0=moy,sigma=sigma,prior=prior,transmat=transmat)
} else {
list(sigma=sigma,A0=moy,prior=prior,transmat=transmat)
}
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.