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function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
% UPDATE_ESS Update the Expected Sufficient Statistics of a CPD (MLP)
% CPD = update_ess(CPD, family_marginal, evidence, node_sizes, cnodes, hidden_bitv)
%
% fmarginal = overall posterior distribution of self and its parents
% fmarginal(i1,i2...,ik,s)=prob(Pa1=i1,...,Pak=ik, self=s| X)
%
% => 1) prob(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) with prob(Pa1,...,Pak)=sum{s,fmarginal}
% [self estimation -> CPD.self_vals]
% 2) prob(Pa1,...,Pak) [SCG weights -> CPD.eso_weights]
%
% Hidden_bitv is ignored
% Written by Pierpaolo Brutti
if ~adjustable_CPD(CPD), return; end
dom = fmarginal.domain;
cdom = myintersect(dom, cnodes);
assert(~any(isemptycell(evidence(cdom))));
ns(cdom)=1;
self = dom(end);
ps=dom(1:end-1);
dpdom=mysetdiff(ps,cdom);
dnodes = mysetdiff(1:length(ns), cnodes);
ddom = myintersect(ps, dnodes); %
if isempty(evidence{self}), % if self is hidden in what follow we must
ddom = myintersect(dom, dnodes); % consider its dimension
end %
odom = dom(~isemptycell(evidence(dom)));
hdom = dom(isemptycell(evidence(dom))); % hidden parents in domain
dobs = myintersect(ddom, odom);
dvals = cat(1, evidence{dobs});
ens = ns; % effective node sizes
ens(dobs) = 1;
dpsz=prod(ns(dpdom));
S=prod(ens(ddom));
subs = ind2subv(ens(ddom), 1:S);
mask = find_equiv_posns(dobs, ddom);
for i=1:length(mask),
subs(:,mask(i)) = dvals(i);
end
supportedQs = subv2ind(ns(ddom), subs);
Qarity = prod(ns(ddom));
if isempty(ddom),
Qarity = 1;
end
fullm.T = zeros(Qarity, 1);
fullm.T(supportedQs) = fmarginal.T(:);
% For dynamic (recurrent) net-------------------------------------------------------------
% ----------------------------------------------------------------------------------------
high=size(evidence,1); % slice height
ss_ns=ns(1:high); % single slice nodes sizes
pos=self; %
slice_num=0; %
while pos>high, %
slice_num=slice_num+1; % find active slice
pos=pos-high; % pos=self posistion into a single slice
end %
last_dim=pos-1; %
if isempty(evidence{self}), %
last_dim=pos; %
end % last_dim=last reshaping dimension
reg=dom-slice_num*high;
dex=myintersect(reg(find(reg>=0)), [1:last_dim]); %
rs_dim=ss_ns(dex); % reshaping dimensions
if slice_num>0,
act_slice=[]; past_ancest=[]; %
act_slice=slice_num*high+[1:high]; % recover the active slice nodes
% past_ancest=mysetdiff(ddom, act_slice);
past_ancest=mysetdiff(ps, act_slice); % recover ancestors contained into past slices
app=ns(past_ancest);
rs_dim=[app(:)' rs_dim(:)']; %
end %
if length(rs_dim)==1, rs_dim=[1 rs_dim]; end %
if size(rs_dim,1)~=1, rs_dim=rs_dim'; end %
fullm.T=reshape(fullm.T, rs_dim); % reshaping the marginal
% ----------------------------------------------------------------------------------------
% ----------------------------------------------------------------------------------------
% X = cts parent, R = discrete self
% 1) observations vector -> CPD.parents_vals -------------------------------------------------
x = cat(1, evidence{cdom});
% 2) weights vector -> CPD.eso_weights -------------------------------------------------------
if isempty(evidence{self}) % R is hidden
sum_over=length(rs_dim);
app=sum(fullm.T, sum_over);
pesi=reshape(app,[dpsz,1]);
clear app;
else
pesi=reshape(fullm.T,[dpsz,1]);
end
assert(approxeq(sum(pesi),1));
% 3) estimate (if R is hidden) or recover (if R is obs) self'value----------------------------
if isempty(evidence{self}) % R is hidden
app=mk_stochastic(fullm.T); % P(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak)
app=reshape(app,[dpsz ns(self)]); % matrix size: prod{j,ns(Paj)} x ns(self)
r=app;
clear app;
else
r = zeros(dpsz,ns(self));
for i=1:dpsz
if pesi(i)~=0, r(i,evidence{self}) = 1; end
end
end
for i=1:dpsz
if pesi(i) ~=0, assert(approxeq(sum(r(i,:)),1)); end
end
CPD.nsamples = CPD.nsamples + 1;
CPD.parent_vals(CPD.nsamples,:) = x(:)';
for i=1:dpsz
CPD.eso_weights(CPD.nsamples,:,i)=pesi(i);
CPD.self_vals(CPD.nsamples,:,i) = r(i,:);
end
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