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, 51 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/graph/minimum_spanning_tree.m b/sourcecodes/bnt-master/graph/minimum_spanning_tree.m
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+++ b/sourcecodes/bnt-master/graph/minimum_spanning_tree.m
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+function A = minimum_spanning_tree(C1, C2)
+%
+% Find the minimum spanning tree using Prim's algorithm.
+% C1(i,j) is the primary cost of connecting i to j.
+% C2(i,j) is the (optional) secondary cost of connecting i to j, used to break ties.
+% We assume that absent edges have 0 cost.
+% To find the maximum spanning tree, used -1*C.
+% See Aho, Hopcroft & Ullman 1983, "Data structures and algorithms", p 237.
+
+% Prim's is O(V^2). Kruskal's algorithm is O(E log E) and hence is more efficient
+% for sparse graphs, but is implemented in terms of a priority queue.
+
+% We partition the nodes into those in U and those not in U.
+% closest(i) is the vertex in U that is closest to i in V-U.
+% lowcost(i) is the cost of the edge (i, closest(i)), or infinity is i has been used.
+% In Aho, they say C(i,j) should be "some appropriate large value" if the edge is missing.
+% We set it to infinity.
+% However, since lowcost is initialized from C, we must distinguish absent edges from used nodes.
+
+n = length(C1);
+if nargin==1, C2 = zeros(n); end
+A = zeros(n);
+
+closest = ones(1,n);
+used = zeros(1,n); % contains the members of U
+used(1) = 1; % start with node 1
+C1(find(C1==0))=inf;
+C2(find(C2==0))=inf;
+lowcost1 = C1(1,:);
+lowcost2 = C2(1,:);
+
+for i=2:n
+  ks = find(lowcost1==min(lowcost1));
+  k = ks(argmin(lowcost2(ks)));
+  A(k, closest(k)) = 1;
+  A(closest(k), k) = 1;
+  lowcost1(k) = inf;
+  lowcost2(k) = inf;
+  used(k) = 1;
+  NU = find(used==0);
+  for ji=1:length(NU)
+    for j=NU(ji)
+      if C1(k,j) < lowcost1(j)
+	lowcost1(j) = C1(k,j);
+	lowcost2(j) = C2(k,j);
+	closest(j) = k;
+      end
+    end
+  end
+end
+