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, 78 insertions, 0 deletions

diff --git a/sourcecodes/parameter_learning/checkStructure.m b/sourcecodes/parameter_learning/checkStructure.m
new file mode 100644
index 00000000..5931c187
--- /dev/null
+++ b/sourcecodes/parameter_learning/checkStructure.m
@@ -0,0 +1,78 @@
+function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, cases, dag, node_sizes)
+    %checkStructure Check to see if nodes are sorted correctly.  They must be
+    %   in topological order (i.e., parents before children) before parameter
+    %   learning can take place.
+    %
+    %Input and output have the same meaning.  The output has just been
+    %topologically ordered.
+    %   labels = cell array with the names of the nodes.
+    %   cases = cell array with the data.
+    %   dag = matrix with the strucutre of the network.
+    %   node_sizes = vector with the size of each node.
+
+%make connections array
+%count how big you need the connections array to be
+nnodes = size(dag,1);
+narcs = 0;
+for i = 1:nnodes
+    for j = 1:nnodes
+        if dag(i,j) == 1
+            narcs = narcs + 1;
+        end
+    end
+end
+%fill connections array with label names
+connections = cell(narcs,2);
+ncount = 0;
+for i = 1:nnodes
+    for j = 1:nnodes
+        if dag(i,j) == 1
+            ncount = ncount + 1;
+            connections{ncount,1} = labels{i};
+            connections{ncount,2} = labels{j};
+        end
+    end
+end
+
+%get topologically sorted dag and labels
+[new_dag, new_labels] = mk_adj_mat(connections, labels, 1);
+
+%check to see if order changed
+ord_flag = 0;
+for i = 1:nnodes
+    if ~strcmp(new_labels{i},labels{i})
+        ord_flag = 1;
+    end
+end
+
+if ord_flag
+    %get new ordering of nodes
+    order = cell(1,nnodes);
+    for i = 1:nnodes
+        for j = 1:nnodes
+            if strcmp(new_labels{j},labels{i})
+                order{i} = j;
+            end
+        end
+    end
+
+    %reorder cases and node_sizes
+    new_cases = cell(size(cases));
+    for i = 1:nnodes
+        new_cases(order{i},:) = cases(i,:);
+    end
+    new_node_sizes = zeros(1,nnodes);
+    for i = 1:nnodes
+        new_node_sizes(order{i}) = node_sizes(i);
+    end
+
+
+    dag = new_dag;
+    cases = new_cases;
+    node_sizes = new_node_sizes;
+    labels = new_labels;    
+end
+
+end
+%end checkStructure.m
+