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

6 files changed, 122 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Entries b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Entries
new file mode 100644
index 00000000..a79c7562
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Entries
@@ -0,0 +1,4 @@
+/cond_gauss_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002//
+/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002//
+/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Repository b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Repository
new file mode 100644
index 00000000..41961f94
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/inference/static/@cond_gauss_inf_engine
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Root b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/cond_gauss_inf_engine.m b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/cond_gauss_inf_engine.m
new file mode 100644
index 00000000..166ed4cd
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/cond_gauss_inf_engine.m
@@ -0,0 +1,23 @@
+function engine = cond_gauss_inf_engine(bnet)
+% COND_GAUSS_INF_ENGINE Conditional Gaussian inference engine
+% engine = cond_gauss_inf_engine(bnet)
+%
+% Enumerates all the discrete roots, and runs jtree on the remaining Gaussian nodes.
+
+dnodes = mysetdiff(1:length(bnet.dag), bnet.cnodes);
+
+%onodes = dnodes; % all the discrete ndoes will be observed
+%engine.sub_engine = jtree_inf_engine(bnet, onodes);
+bnet2 = bnet;
+bnet2.observed = dnodes;
+engine.sub_engine = jtree_inf_engine(bnet2);
+
+% This is where we will store the results between enter_evidence and marginal_nodes
+engine.T = [];
+engine.mu = [];
+engine.Sigma = [];
+engine.joint_dmarginal = [];
+engine.onodes = []; % needed for marginal_nodes
+engine.evidence = []; % needed for marginal_nodes add_ev
+
+engine = class(engine, 'cond_gauss_inf_engine', inf_engine(bnet));
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/enter_evidence.m b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/enter_evidence.m
new file mode 100644
index 00000000..db5019b1
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/enter_evidence.m
@@ -0,0 +1,57 @@
+function [engine, loglik] = enter_evidence(engine, evidence, varargin)
+% ENTER_EVIDENCE Add the specified evidence to the network (cond_gauss)
+% [engine, loglik] = enter_evidence(engine, evidence, ...)
+%
+% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector)
+
+bnet = bnet_from_engine(engine);
+ns = bnet.node_sizes(:);
+observed = ~isemptycell(evidence);
+onodes = find(observed);
+hnodes = find(isemptycell(evidence));
+engine.evidence = evidence;
+
+% check there are no C->D links where C is hidden
+pot_type = determine_pot_type(bnet, onodes);
+
+dhid = myintersect(hnodes, bnet.dnodes);
+S = prod(ns(dhid));
+T = zeros(S,1);
+
+N = length(bnet.dag);
+mu = cell(1,N);
+Sigma = cell(1,N); 
+cobs = myintersect(bnet.cnodes, onodes);
+chid = myintersect(bnet.cnodes, hnodes);
+ens = ns;
+ens(cobs) = 0;
+for j=chid(:)'
+  mu{j} = zeros(ens(j), S);
+  Sigma{j} = zeros(ens(j), ens(j), S);
+end
+ 
+for i=1:S
+  dvals = ind2subv(ns(dhid), i);
+  evidence(dhid) = num2cell(dvals);
+  [sub_engine, loglik] = enter_evidence(engine.sub_engine, evidence);
+  for j=chid(:)'
+    m = marginal_nodes(sub_engine, j);
+    mu{j}(:,i) = m.mu;
+    Sigma{j}(:,:,i) = m.Sigma;
+  end
+  T(i) = exp(loglik);
+end
+
+[T, lik] = normalise(T);
+loglik = log(lik);
+
+engine.T = T;
+engine.mu = mu;
+engine.Sigma = Sigma;
+
+dnodes = bnet.dnodes;
+dobs = myintersect(dnodes, onodes);
+ens(dobs) = 1;
+engine.joint_dmarginal = dpot(dnodes, ens(dnodes), myreshape(engine.T, ens(dnodes)));
+
+engine.onodes = onodes;
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/marginal_nodes.m b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/marginal_nodes.m
new file mode 100644
index 00000000..9c5d60a7
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@cond_gauss_inf_engine/marginal_nodes.m
@@ -0,0 +1,36 @@
+function marginal = marginal_nodes(engine, query, add_ev)
+% MARGINAL_NODES Compute the marginal on the specified query nodes (cond_gauss)
+% marginal = marginal_nodes(engine, query, add_ev)
+%
+% 'query' must be a singleton set
+% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes
+% to their original size, adding 0s to the positions which contradict the evidence
+
+if nargin < 3, add_ev = 0; end
+
+if length(query) ~= 1
+  error('cond_gauss_inf_engine can only handle marginal queries on single nodes')
+end
+j = query;
+bnet = bnet_from_engine(engine);
+
+if myismember(j, bnet.cnodes)
+  if ~myismember(j, engine.onodes)
+    [m, C] = collapse_mog(engine.mu{j}, engine.Sigma{j}, engine.T);    
+    marginal.mu = m;
+    marginal.Sigma = C;
+    marginal.T = 1.0; % single mixture component
+  else
+    marginal.mu = engine.evidence{j};
+    k = bnet.node_sizes(j);
+    marginal.Sigma = zeros(k,k);
+    marginal.T = 1.0; % since P(E|E)=1
+  end
+else
+  marginal = pot_to_marginal(marginalize_pot(engine.joint_dmarginal, j));
+  if add_ev
+    marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes);
+  end
+end
+
+marginal.domain = query;