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, 97 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/update_ess.m b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/update_ess.m
new file mode 100644
index 00000000..143c567c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/update_ess.m
@@ -0,0 +1,97 @@
+function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+% UPDATE_ESS Update the Expected Sufficient Statistics of a softmax node
+% function CPD = update_ess(CPD, fmarginal, evidence, ns, 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) [WIRLS weights -> CPD.eso_weights]
+%
+% Hidden_bitv is ignored
+
+% Written by Pierpaolo Brutti
+
+if ~adjustable_CPD(CPD), return; end
+
+domain     = fmarginal.domain;                              
+self       = domain(end);          
+ps         = domain(1:end-1);                                     
+cnodes     = domain(CPD.cpndx);
+cps        = myintersect(domain, cnodes);                     
+dps        = mysetdiff(ps, cps);                            
+dn_use     = dps;
+if isempty(evidence{self}) dn_use = [dn_use self]; end % if self is hidden we must consider its dimension  
+dps_as_cps = domain(CPD.dps_as_cps.ndx);
+odom       = domain(~isemptycell(evidence(domain))); 
+
+ns = zeros(1, max(domain));
+ns(domain) = CPD.sizes;     % CPD.sizes = bnet.node_sizes([ps self]);
+ens = ns;                   % effective node sizes
+ens(odom) = 1;              
+dpsize = prod(ns(dps));
+
+% Extract the params compatible with the observations (if any) on the discrete parents (if any)
+dops = myintersect(dps, odom);
+dpvals = cat(1, evidence{dops});
+
+subs = ind2subv(ens(dn_use), 1:prod(ens(dn_use)));
+dpmap = find_equiv_posns(dops, dn_use);
+if ~isempty(dpmap), subs(:,dpmap) = subs(:,dpmap)+repmat(dpvals(:)',[size(subs,1) 1])-1; end
+supportedQs = subv2ind(ns(dn_use), subs); subs=subs(1:prod(ens(dps)),1:length(dps));
+Qarity = prod(ns(dn_use));
+if isempty(dn_use), Qarity = 1; end   
+
+fullm.T              = zeros(Qarity, 1);
+fullm.T(supportedQs) = fmarginal.T(:);
+rs_dim = CPD.sizes;    rs_dim(CPD.cpndx) = 1;           %
+if ~isempty(evidence{self}), rs_dim(end)=1; end         % reshaping the marginal
+fullm.T              = reshape(fullm.T, rs_dim);        %
+
+% --------------------------------------------------------------------------------UPDATE--
+
+CPD.nsamples = CPD.nsamples + 1;
+
+% 1) observations vector -> CPD.parents_vals ---------------------------------------------
+cpvals = cat(1, evidence{cps});
+
+if ~isempty(dps_as_cps),   % ...get in the dp_as_cp parents... 
+    separator          = CPD.dps_as_cps.separator;
+    dp_as_cpmap        = find_equiv_posns(dps_as_cps, dps);       
+    for i=1:dpsize,
+        dp_as_cpvals=zeros(1,sum(ns(dps_as_cps)));
+        possible_vals = ind2subv(ns(dps),i);
+        ll=find(ismember(subs(:,dp_as_cpmap), possible_vals(dp_as_cpmap), 'rows')==1);   
+        if ~isempty(ll),
+            where_one = separator + possible_vals(dp_as_cpmap);
+            dp_as_cpvals(where_one)=1;                            
+        end
+        CPD.parent_vals(CPD.nsamples,:,i) = [dp_as_cpvals(:); cpvals(:)]';
+    end
+else
+    CPD.parent_vals(CPD.nsamples,:) = cpvals(:)';
+end
+
+% 2) weights vector -> CPD.eso_weights ----------------------------------------------------
+if isempty(evidence{self}),             % self is hidden
+    pesi=reshape(sum(fullm.T, length(rs_dim)),[dpsize,1]);
+else
+    pesi=reshape(fullm.T,[dpsize,1]);
+end
+assert(approxeq(sum(pesi),1));          % check
+
+% 3) estimate (if R is hidden) or recover (if R is obs) self'value-------------------------
+if isempty(evidence{self})                                  % P(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak)
+    r=reshape(mk_stochastic(fullm.T), [dpsize ns(self)]);   % matrix size: prod{j,ns(Paj)} x ns(self)      
+else
+    r = zeros(dpsize,ns(self));
+    for i=1:dpsize, if pesi(i)~=0, r(i,evidence{self}) = 1; end; end
+end
+for i=1:dpsize, if pesi(i)~=0, assert(approxeq(sum(r(i,:)),1)); end; end     % check
+
+% 4) save the previous values --------------------------------------------------------------
+for i=1:dpsize
+    CPD.eso_weights(CPD.nsamples,:,i)=pesi(i);
+    CPD.self_vals(CPD.nsamples,:,i) = r(i,:); 
+end