comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/update_ess.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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