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, 131 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/update_ess.m b/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/update_ess.m
new file mode 100644
index 00000000..353a0b5c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@mlp_CPD/update_ess.m
@@ -0,0 +1,131 @@
+function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv)
+% UPDATE_ESS Update the Expected Sufficient Statistics of a CPD (MLP)
+% CPD = update_ess(CPD, family_marginal, evidence, node_sizes, 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) [SCG weights -> CPD.eso_weights]
+%
+% Hidden_bitv is ignored
+
+% Written by Pierpaolo Brutti
+
+if ~adjustable_CPD(CPD), return; end
+
+dom = fmarginal.domain;                              
+cdom = myintersect(dom, cnodes);                     
+assert(~any(isemptycell(evidence(cdom))));           
+ns(cdom)=1;
+
+self = dom(end);                                  
+ps=dom(1:end-1);                                     
+dpdom=mysetdiff(ps,cdom);                            
+
+dnodes = mysetdiff(1:length(ns), cnodes);            
+
+ddom = myintersect(ps, dnodes);                      %
+if isempty(evidence{self}),                          % if self is hidden in what follow we must 
+    ddom = myintersect(dom, dnodes);                 % consider its dimension
+end                                                  % 
+
+odom = dom(~isemptycell(evidence(dom)));    
+hdom = dom(isemptycell(evidence(dom)));              % hidden parents in domain
+ 
+dobs = myintersect(ddom, odom);             
+dvals = cat(1, evidence{dobs});             
+ens = ns;                                            % effective node sizes              
+ens(dobs) = 1;                              
+                                            
+dpsz=prod(ns(dpdom));
+S=prod(ens(ddom));
+subs = ind2subv(ens(ddom), 1:S);
+mask = find_equiv_posns(dobs, ddom);
+for i=1:length(mask),
+    subs(:,mask(i)) = dvals(i);
+end
+supportedQs = subv2ind(ns(ddom), subs);
+
+Qarity = prod(ns(ddom));
+if isempty(ddom),                      
+  Qarity = 1;                         
+end                                
+fullm.T = zeros(Qarity, 1);
+fullm.T(supportedQs) = fmarginal.T(:);
+
+% For dynamic (recurrent) net-------------------------------------------------------------
+% ----------------------------------------------------------------------------------------
+high=size(evidence,1);                                  % slice height
+ss_ns=ns(1:high);                                       % single slice nodes sizes
+pos=self;                                               %
+slice_num=0;                                            %
+while pos>high,                                         % 
+    slice_num=slice_num+1;                              % find active slice
+    pos=pos-high;                                       % pos=self posistion into a single slice
+end                                                     %
+
+last_dim=pos-1;                                         % 
+if isempty(evidence{self}),                             % 
+    last_dim=pos;                                       %
+end                                                     % last_dim=last reshaping dimension      
+reg=dom-slice_num*high;
+dex=myintersect(reg(find(reg>=0)), [1:last_dim]);       %           
+rs_dim=ss_ns(dex);                                      % reshaping dimensions
+
+if slice_num>0,
+    act_slice=[]; past_ancest=[];                       %
+    act_slice=slice_num*high+[1:high];                  % recover the active slice nodes
+    % past_ancest=mysetdiff(ddom, act_slice);
+    past_ancest=mysetdiff(ps, act_slice);               % recover ancestors contained into past slices
+    app=ns(past_ancest);
+    rs_dim=[app(:)' rs_dim(:)'];                        %
+end                                                     %
+if length(rs_dim)==1, rs_dim=[1 rs_dim]; end            %
+if size(rs_dim,1)~=1, rs_dim=rs_dim';    end            %
+
+fullm.T=reshape(fullm.T, rs_dim);                       % reshaping the marginal
+
+% ----------------------------------------------------------------------------------------
+% ----------------------------------------------------------------------------------------
+
+% X = cts parent, R = discrete self
+
+% 1) observations vector -> CPD.parents_vals -------------------------------------------------
+x = cat(1, evidence{cdom});
+
+% 2) weights vector -> CPD.eso_weights -------------------------------------------------------
+if isempty(evidence{self}) % R is hidden
+    sum_over=length(rs_dim);
+    app=sum(fullm.T, sum_over);    
+    pesi=reshape(app,[dpsz,1]);
+    clear app;
+else
+    pesi=reshape(fullm.T,[dpsz,1]);
+end
+
+assert(approxeq(sum(pesi),1));
+
+% 3) estimate (if R is hidden) or recover (if R is obs) self'value----------------------------
+if isempty(evidence{self})              % R is hidden    
+    app=mk_stochastic(fullm.T);         % P(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak)
+    app=reshape(app,[dpsz ns(self)]);   % matrix size: prod{j,ns(Paj)} x ns(self)      
+    r=app;
+    clear app;
+else
+    r = zeros(dpsz,ns(self));
+    for i=1:dpsz
+        if pesi(i)~=0, r(i,evidence{self}) = 1; end
+    end
+end
+for i=1:dpsz
+    if pesi(i) ~=0, assert(approxeq(sum(r(i,:)),1)); end
+end
+
+CPD.nsamples = CPD.nsamples + 1;            
+CPD.parent_vals(CPD.nsamples,:) = x(:)';
+for i=1:dpsz
+    CPD.eso_weights(CPD.nsamples,:,i)=pesi(i);
+    CPD.self_vals(CPD.nsamples,:,i) = r(i,:); 
+end