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, 260 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Entries b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Entries
new file mode 100644
index 00000000..33ee0f34
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Entries
@@ -0,0 +1,4 @@
+/enter_evidence.m/1.1.1.1/Mon Jun 17 20:30:02 2002//
+/find_mpe.m/1.1.1.1/Mon Jun 17 20:29:40 2002//
+/jtree_mnet_inf_engine.m/1.1.1.1/Sat Jan 18 22:13:32 2003//
+D
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Repository b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Repository
new file mode 100644
index 00000000..2deff959
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/inference/static/@jtree_mnet_inf_engine
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Root b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/enter_evidence.m b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/enter_evidence.m
new file mode 100644
index 00000000..97546f4b
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/enter_evidence.m
@@ -0,0 +1,82 @@
+function [engine, loglik] = enter_evidence(engine, evidence, varargin)
+% ENTER_EVIDENCE Add the specified evidence to the network (jtree)
+% [engine, loglik] = enter_evidence(engine, evidence, ...)
+%
+% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default value in brackets]
+%
+% soft    - a cell array of soft/virtual evidence;
+%           soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ]
+%
+% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev)
+
+bnet = bnet_from_engine(engine);
+ns = bnet.node_sizes(:);
+N = length(bnet.dag);
+
+engine.evidence = evidence; % store this for marginal_nodes with add_ev option
+engine.maximize = 0;
+
+% set default params
+exclude = [];
+soft_evidence = cell(1,N);
+
+% parse optional params
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'soft',    soft_evidence = args{i+1}; 
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end
+
+onodes = find(~isemptycell(evidence));
+hnodes = find(isemptycell(evidence));
+pot_type = determine_pot_type(bnet, onodes);
+ if strcmp(pot_type, 'cg')
+  check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag);
+end
+
+% Evaluate CPDs with evidence, and convert to potentials  
+pot = cell(1, N);
+for n=1:N
+  fam = family(bnet.dag, n);
+  e = bnet.equiv_class(n);
+  if isempty(bnet.CPD{e})
+    error(['must define CPD ' num2str(e)])
+  else
+    pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence);
+  end
+end
+clqs = engine.clq_ass_to_node(1:N);
+
+% soft evidence
+soft_nodes = find(~isemptycell(soft_evidence));
+S = length(soft_nodes);
+if S > 0
+  assert(pot_type == 'd');
+  assert(mysubset(soft_nodes, bnet.dnodes));
+end
+for i=1:S
+  n = soft_nodes(i);
+  pot{end+1} = dpot(n, ns(n), soft_evidence{n});
+end
+clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; 
+
+
+[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes);
+[clpot, seppot] = collect_evidence(engine, clpot, seppot);
+[clpot, seppot] = distribute_evidence(engine, clpot, seppot);
+
+C = length(clpot);
+ll = zeros(1, C);
+for i=1:C
+  [clpot{i}, ll(i)] = normalize_pot(clpot{i});
+end
+loglik = ll(1); % we can extract the likelihood from any clique
+
+engine.clpot = clpot;
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/find_mpe.m b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/find_mpe.m
new file mode 100644
index 00000000..f5c04ba8
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/find_mpe.m
@@ -0,0 +1,71 @@
+function mpe = find_mpe(engine, evidence, varargin)
+% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes)
+% function mpe = find_mpe(engine, evidence,...)
+%
+% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default value in brackets]
+%
+% soft    - a cell array of soft/virtual evidence;
+%           soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ]
+%
+
+bnet = bnet_from_engine(engine);
+ns = bnet.node_sizes(:);
+N = length(bnet.dag);
+
+engine.evidence = evidence;
+  
+% set default params
+exclude = [];
+soft_evidence = cell(1,N);
+
+% parse optional params
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'soft',    soft_evidence = args{i+1}; 
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end
+engine.maximize = 1;
+
+onodes = find(~isemptycell(evidence));
+hnodes = find(isemptycell(evidence));
+pot_type = determine_pot_type(bnet, onodes);
+ if strcmp(pot_type, 'cg')
+  check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag);
+end
+
+hard_nodes = 1:N;
+soft_nodes = find(~isemptycell(soft_evidence));
+S = length(soft_nodes);
+if S > 0
+  assert(pot_type == 'd');
+  assert(mysubset(soft_nodes, bnet.dnodes));
+end
+ 
+% Evaluate CPDs with evidence, and convert to potentials  
+pot = cell(1, N+S);
+for n=1:N
+  fam = family(bnet.dag, n);
+  e = bnet.equiv_class(n);
+  if isempty(bnet.CPD{e})
+    error(['must define CPD ' num2str(e)])
+  else
+    pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence);
+  end
+end
+
+for i=1:S
+  n = soft_nodes(i);
+  pot{N+i} = dpot(n, ns(n), soft_evidence{n});
+end
+clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); 
+
+[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes);
+[clpot, seppot] = collect_evidence(engine, clpot, seppot);
+mpe = find_max_config(engine, clpot, seppot);
diff --git a/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/jtree_mnet_inf_engine.m b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/jtree_mnet_inf_engine.m
new file mode 100644
index 00000000..ff21ae47
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/inference/static/@jtree_mnet_inf_engine/jtree_mnet_inf_engine.m
@@ -0,0 +1,101 @@
+function engine = jtree_mnet_inf_engine(model, varargin)
+% JTREE_MNET_INF_ENGINE Junction tree inference engine for Markov nets
+% engine = jtree_inf_engine(mnet, ...)
+%
+
+% set default params
+N = length(mnet.graph);
+root = N;
+
+engine = init_fields;
+engine = class(engine, 'jtree_mnet_inf_engine', inf_engine(bnet));
+
+onodes = bnet.observed;
+if is_mnet(bnet)
+  MG = bnet.graph;
+else
+  error('should be a mnet')
+end
+
+%[engine.jtree, dummy, engine.cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ...
+%    dag_to_jtree(bnet, onodes, stages, clusters);
+
+porder = determine_elim_constraints(bnet, onodes);
+strong = ~isempty(porder);
+ns = bnet.node_sizes(:);
+ns(onodes) = 1; % observed nodes have only 1 possible value
+[engine.jtree, root2, engine.cliques, B, w] = ...
+    graph_to_jtree(MG, ns, porder, stages, clusters);
+
+engine.cliques_bitv = B;
+engine.clique_weight = w;
+C = length(engine.cliques);
+engine.clpot = cell(1,C);
+
+% Compute the separators between connected cliques.
+[is,js] = find(engine.jtree > 0);
+engine.separator = cell(C,C);
+for k=1:length(is)
+  i = is(k); j = js(k);
+  engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j});
+end
+
+% A node can be a member of many cliques, but is assigned to exactly one, to avoid
+% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that
+% contains i's family, so it can accomodate its CPD.
+
+engine.clq_ass_to_node = zeros(1, N);
+for i=1:N
+  %c = clq_containing_nodes(engine, family(bnet.dag, i));
+  clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1
+  c = clqs_containing_family(argmin(w(clqs_containing_family)));  
+  engine.clq_ass_to_node(i) = c; 
+end
+
+% Make the jtree rooted, so there is a fixed message passing order.
+if strong
+  % the last clique is guaranteed to be a strong root
+  engine.root_clq = length(engine.cliques);
+else
+  % jtree_dbn_inf_engine requires the root to contain the interface.
+  % This may conflict with the strong root requirement! *********** BUG *************
+  engine.root_clq = clq_containing_nodes(engine, root);
+  if engine.root_clq <= 0
+    error(['no clique contains ' num2str(root)]);
+  end
+end  
+
+[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root_clq);
+
+% collect 
+engine.postorder_parents = cell(1,length(engine.postorder));
+for n=engine.postorder(:)'
+  engine.postorder_parents{n} = parents(engine.jtree, n);
+end
+% distribute
+engine.preorder_children = cell(1,length(engine.preorder));
+for n=engine.preorder(:)'
+  engine.preorder_children{n} = children(engine.jtree, n);
+end
+
+  
+
+%%%%%%%%
+
+function engine = init_fields()
+
+engine.jtree = [];
+engine.cliques = [];
+engine.separator = [];
+engine.cliques_bitv = [];
+engine.clique_weight = [];
+engine.clpot = [];
+engine.clq_ass_to_node = [];
+engine.root_clq = [];
+engine.preorder = [];
+engine.postorder = [];
+engine.preorder_children = [];
+engine.postorder_parents = [];
+engine.maximize = [];
+engine.evidence = [];
+