wolffd@0: function T = convert_to_table(CPD, domain, evidence)
wolffd@0: % CONVERT_TO_TABLE Convert a mlp CPD to a table, incorporating any evidence 
wolffd@0: % T = convert_to_table(CPD, domain, evidence)
wolffd@0: 
wolffd@0: self = domain(end);                    
wolffd@0: ps = domain(1:end-1);                               % self' parents                                       
wolffd@0: %cps = myintersect(ps, cnodes);                      % self' continous parents      
wolffd@0: cnodes     = domain(CPD.cpndx);
wolffd@0: cps        = myintersect(ps, cnodes);
wolffd@0: odom = domain(~isemptycell(evidence(domain)));      % obs nodes in the net
wolffd@0: assert(myismember(cps, odom));                      % !ALL the CTS parents must be observed!
wolffd@0: ns(cps)=1;
wolffd@0: dps = mysetdiff(ps, cps);                           % self' discrete parents                                                    
wolffd@0: dobs = myintersect(dps, odom);                      % discrete obs parents
wolffd@0: 
wolffd@0: % Extract the params compatible with the observations (if any) on the discrete parents (if any)
wolffd@0: 
wolffd@0: if ~isempty(dobs),
wolffd@0:     dvals = cat(1, evidence{dobs});             
wolffd@0:     ns_eff= CPD.sizes;                               % effective node sizes              
wolffd@0:     ens=ns_eff;
wolffd@0:     ens(dobs) = 1;                              
wolffd@0:     S=prod(ens(dps));
wolffd@0:     subs = ind2subv(ens(dps), 1:S);
wolffd@0:     mask = find_equiv_posns(dobs, dps);        
wolffd@0:     for i=1:length(mask),
wolffd@0:         subs(:,mask(i)) = dvals(i);
wolffd@0:     end     
wolffd@0:     support = subv2ind(ns_eff(dps), subs)';
wolffd@0: else 
wolffd@0:     ns_eff= CPD.sizes;
wolffd@0:     support=[1:prod(ns_eff(dps))];
wolffd@0: end
wolffd@0: 
wolffd@0: W1=[]; b1=[]; W2=[]; b2=[];
wolffd@0: 
wolffd@0: W1 = CPD.W1(:,:,support);
wolffd@0: b1= CPD.b1(support,:);
wolffd@0: W2 = CPD.W2(:,:,support);
wolffd@0: b2= CPD.b2(support,:);
wolffd@0: ns(odom) = 1;
wolffd@0: dpsize = prod(ns(dps));                             % overall size of the self' discrete parents  
wolffd@0: 
wolffd@0: x = cat(1, evidence{cps});    
wolffd@0: ndata=size(x,2);
wolffd@0: 
wolffd@0: if ~isempty(evidence{self})                         %
wolffd@0:     app=struct(CPD);                                %
wolffd@0:     ns(self)=app.mlp{1}.nout;                       % pump up self to the original dimension if observed
wolffd@0:     clear app;                                      %
wolffd@0: end                                                 %
wolffd@0: 
wolffd@0: T =zeros(dpsize, ns(self));                         %
wolffd@0: for i=1:dpsize                                      %                 
wolffd@0:     W1app = W1(:,:,i);                              % 
wolffd@0:     b1app = b1(i,:);                                % 
wolffd@0:     W2app = W2(:,:,i);                              % 
wolffd@0:     b2app = b2(i,:);                                % for each of the dpsize combinations of self'parents values 
wolffd@0:     z = tanh(x(:)'*W1app + ones(ndata, 1)*b1app);   % we tabulate the corrisponding glm model
wolffd@0:     a = z*W2app + ones(ndata, 1)*b2app;             % (element of the cell array CPD.glim)
wolffd@0:     appoggio = normalise(exp(a));                   %
wolffd@0:     T(i,:)=appoggio;                                %
wolffd@0:     W1app=[]; W2app=[]; b1app=[]; b2app=[];         %
wolffd@0:     z=[]; a=[]; appoggio=[];                        %
wolffd@0: end                                                 %                
wolffd@0: 
wolffd@0: if ~isempty(evidence{self})
wolffd@0:     appoggio=[];                            %
wolffd@0:     appoggio=zeros(1,ns(self));             %
wolffd@0:     r = evidence{self};                     %...if self is observed => in output there's only the probability of the 'true' class
wolffd@0:     for i=1:dpsize                          % 
wolffd@0:           appoggio(i)=T(i,r);               % 
wolffd@0:     end
wolffd@0:     T=zeros(dpsize,1);
wolffd@0:     for i=1:dpsize
wolffd@0:         T(i,1)=appoggio(i);                        
wolffd@0:     end
wolffd@0:     clear appoggio;
wolffd@0:     ns(self) = 1;
wolffd@0: end