BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 80 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/convert_to_table.m b/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/convert_to_table.m
new file mode 100644
index 00000000..7e25d072
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/convert_to_table.m
@@ -0,0 +1,80 @@
+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