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, 52 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/convert_to_table.m b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/convert_to_table.m
new file mode 100644
index 00000000..f703d79b
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/convert_to_table.m
@@ -0,0 +1,52 @@
+function T = convert_to_table(CPD, domain, evidence)
+% CONVERT_TO_TABLE Convert a softmax CPD to a table, incorporating any evidence 
+% T = convert_to_table(CPD, domain, evidence)
+
+self       = domain(end);             
+ps         = domain(1:end-1);                            
+cnodes     = domain(CPD.cpndx);
+cps        = myintersect(ps, cnodes);
+dps        = domain(CPD.dpndx); 
+dps_as_cps = domain(CPD.dps_as_cps.ndx);
+all_dps    = union(dps,dps_as_cps);
+odom       = domain(~isemptycell(evidence(domain))); 
+if ~isempty(cps), assert(myismember(cps, odom)); end % all cts parents must be observed
+
+ns         = zeros(1, max(domain));
+ns(domain) = CPD.sizes;
+ens        = ns; % effective node sizes
+ens(odom)  = 1;
+
+% dpsize >= glimsz because the glm parameters are tied across the dps_as_cps parents
+dpsize       = prod(ens(all_dps)); % size of ALL self'discrete parents
+dpvals       = cat(1, evidence{myintersect(all_dps, odom)});
+cpvals       = cat(1, evidence{cps});
+if ~isempty(dps_as_cps),
+  separator          = CPD.dps_as_cps.separator;
+  dp_as_cpmap        = find_equiv_posns(dps_as_cps, all_dps);
+  dops_map           = find_equiv_posns(myintersect(all_dps, odom), all_dps);
+  puredp_map         = find_equiv_posns(dps, all_dps);
+  subs               = ind2subv(ens(all_dps), 1:prod(ens(all_dps)));
+  if ~isempty(dops_map), subs(:,dops_map) = subs(:,dops_map)+repmat(dpvals(:)',[size(subs,1) 1])-1; end
+end
+
+[w,b] = extract_params(CPD);
+T = zeros(dpsize, ns(self));                                       
+for i=1:dpsize,    
+  active_glm  = i;
+  dp_as_cpvals=zeros(1,sum(ns(dps_as_cps)));                                                                  
+  if ~isempty(dps_as_cps),                          
+    active_glm = max([1,subv2ind(ns(dps), subs(i,puredp_map))]);
+    % Extract the params compatible with the observations (if any) on the 'pure' discrete parents (if any)
+    where_one = separator + subs(i,dp_as_cpmap);
+    % and get in the dp_as_cp parents...
+    dp_as_cpvals(where_one)=1;                    
+  end                                               
+  T(i,:) = normalise(exp([dp_as_cpvals(:); cpvals(:)]'*w(:,:,active_glm) + b(:,active_glm)'));
+end
+if myismember(self, odom)
+  r = evidence{self};
+  T = T(:,r);
+end
+
+T = myreshape(T, ens(domain));