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

first hg version after svn

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