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, 176 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/inference/dynamic/@cbk_inf_engine/junk b/sourcecodes/bnt-master/BNT/inference/dynamic/@cbk_inf_engine/junk
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+function engine = cbk_inf_engine(bnet, varargin)
+% Just the same as bk_inf_engine, but you can specify overlapping clusters.
+
+ss = length(bnet.intra);
+% set default params
+clusters = 'exact';
+
+if nargin >= 2
+  args = varargin;
+  nargs = length(args);
+  for i=1:2:nargs
+    switch args{i},
+     case 'clusters',  clusters = args{i+1};
+     otherwise, error(['unrecognized argument ' args{i}])
+    end
+  end
+end
+
+if strcmp(clusters, 'exact')
+  %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) };
+  clusters = { 1:ss };
+elseif strcmp(clusters, 'ff')
+  clusters = num2cell(1:ss);
+end
+
+
+% We need to insert the prior on the clusters in slice 1,
+% and extract the posterior on the clusters in slice 2.
+% We don't need to care about the separators, b/c they're subsets of the clusters.
+C = length(clusters);
+clusters2 = cell(1,2*C);
+clusters2(1:C) = clusters;
+for c=1:C
+  clusters2{c+C} = clusters{c} + ss;
+end
+
+onodes = bnet.observed;
+obs_nodes = [onodes(:) onodes(:)+ss];
+engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2);
+
+%FH >>>
+%Compute separators. 
+ns = bnet.node_sizes(:,1);
+ns(onodes) = 1;
+[clusters, separators] = build_jt(clusters, 1:length(ns), ns);
+S = length(separators);
+engine.separators = separators;
+
+%Compute size of clusters.
+cl_sizes = zeros(1,C);
+for c=1:C
+    cl_sizes(c) = prod(ns(clusters{c}));
+end
+
+%Assign separators to the smallest cluster subsuming them.
+engine.cluster_ass_to_separator = zeros(S, 1);
+for s=1:S
+    subsuming_clusters = [];
+    %find smaunk
+    
+    for c=1:C
+        if mysubset(separators{s}, clusters{c}) 
+            subsuming_clusters(end+1) = c;
+        end
+    end
+    c = argmin(cl_sizes(subsuming_clusters));
+    engine.cluster_ass_to_separator(s) = subsuming_clusters(c);
+end
+
+%<<< FH
+
+engine.clq_ass_to_cluster = zeros(C, 2);
+for c=1:C
+  engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c});
+  engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss);
+end
+engine.clusters = clusters;
+
+engine.clq_ass_to_node = zeros(ss, 2);
+for i=1:ss
+  engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i);
+  engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss);
+end
+
+
+
+% Also create an engine just for slice 1
+bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ...
+		'equiv_class', bnet.equiv_class(:,1), 'observed', onodes);
+for i=1:max(bnet1.equiv_class)
+  bnet1.CPD{i} = bnet.CPD{i};
+end
+
+engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters);
+
+engine.clq_ass_to_cluster1 = zeros(1,C);
+for c=1:C
+  engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c});
+end
+
+engine.clq_ass_to_node1 = zeros(1, ss);
+for i=1:ss
+  engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i);
+end
+
+engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes
+engine.filter = [];
+engine.maximize = [];
+engine.T = [];
+
+engine.bel = [];
+engine.bel_clpot = [];
+engine.slice1 = [];
+%engine.pot_type = 'cg';
+% hack for online inference so we can cope with hidden Gaussians and discrete
+% it will not affect the pot type used in enter_evidence
+engine.pot_type = determine_pot_type(bnet, onodes);
+
+engine = class(engine, 'cbk_inf_engine', inf_engine(bnet));
+
+
+
+
+function [cliques, seps, jt_size] = build_jt(cliques, vars, ns)
+% BUILD_JT connects the cliques into a jtree, computes the respective 
+% separators and the size of the resulting jtree.
+%
+% [cliques, seps, jt_size] = build_jt(cliques, vars, ns)
+% ns(i) has to hold the size of vars(i)
+% vars has to be a superset of the union of cliques.
+
+%======== Compute the jtree with tool from BNT. This wants the vars to be 1:N.
+%==== Map from nodes to their indices.
+%disp('Computing jtree for cliques with vars and ns:');
+%cliques
+%vars
+%ns'
+
+inv_nodes = sparse(1,max(vars));
+N = length(vars);
+for i=1:N
+    inv_nodes(vars(i)) = i;
+end
+
+tmp_cliques = cell(1,length(cliques));
+%==== Temporarily map clique vars to their indices.
+for i=1:length(cliques)
+    tmp_cliques{i} = inv_nodes(cliques{i});
+end
+
+%=== Compute the jtree, using BNT.
+[jtree, root, B, w] = cliques_to_jtree(tmp_cliques, ns);
+
+
+%======== Now, compute the separators between connected cliques and their weights.
+seps = {};
+s_w = [];
+[is,js] = find(jtree > 0);
+for k=1:length(is)
+  i = is(k); j = js(k);
+  sep = vars(find(B(i,:) & B(j,:))); % intersect(cliques{i}, cliques{j});
+  if i>j | length(sep) == 0, continue; end;
+  seps{end+1} = sep;
+  s_w(end+1) = prod(ns(inv_nodes(seps{end})));
+end
+
+cl_w = sum(w);
+sep_w = sum(s_w);
+assert(cl_w > sep_w, 'Weight of cliques must be bigger than weight of separators');
+
+jt_size = cl_w + sep_w;
+% jt.cliques = cliques;
+% jt.seps = seps;
+% jt.size = jt_size;
+% jt.ns = ns';
+% jt;