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, 82 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tree_CPD/evaluate_tree_performance.m b/sourcecodes/bnt-master/BNT/CPDs/@tree_CPD/evaluate_tree_performance.m
new file mode 100644
index 00000000..2f72a5b9
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tree_CPD/evaluate_tree_performance.m
@@ -0,0 +1,82 @@
+function [score,outputs] = evaluate(CPD, fam, data, ns, cnodes)
+% Evaluate evaluate the performance of the classification/regression tree on given complete data
+% score = evaluate(CPD, fam, data, ns, cnodes)
+%
+% fam(i) is the node id of the i-th node in the family of nodes, self node is the last one
+% data(i,m) is the value of node i in case m (can be cell array).
+% ns(i) is the node size for the i-th node in the whold bnet
+% cnodes(i) is the node id for the i-th continuous node in the whole bnet
+%  
+% Output
+% score is the classification accuracy (for classification) 
+%          or mean square deviation (for regression)
+%            here for every case we use the mean value at the tree leaf node as its predicted value
+% outputs(i) is the predicted output value for case i
+%
+% Author: yimin.zhang@intel.com
+% Last updated: Jan. 19, 2002
+
+
+if iscell(data)
+  local_data = cell2num(data(fam,:));
+else
+  local_data = data(fam, :);
+end
+
+%get local node sizes and node types
+node_sizes = ns(fam);
+node_types = zeros(1,size(ns,2)); %all nodes are disrete
+node_types(cnodes)=1;
+node_types=node_types(fam);
+
+fam_size=size(fam,2);
+output_type = node_types(fam_size);
+
+num_cases=size(local_data,2);
+total_error=0;
+
+outputs=zeros(1,num_cases);
+for i=1:num_cases
+  %class one case using the tree
+  cur_node=CPD.tree.root;  % at the root node of the tree
+  while (1)
+    if (CPD.tree.nodes(cur_node).is_leaf==1)
+      if (output_type==0) %output is discrete
+        %use the class with max probability as the output  
+        [maxvalue,class_id]=max(CPD.tree.nodes(cur_node).probs);
+        outputs(i)=class_id;
+        if (class_id~=local_data(fam_size,i))
+          total_error=total_error+1;
+        end
+      else   %output is continuous
+        %use the mean as the value
+        outputs(i)=CPD.tree.nodes(cur_node).mean;
+        cur_deviation = CPD.tree.nodes(cur_node).mean-local_data(fam_size,i);
+        total_error=total_error+cur_deviation*cur_deviation;
+      end
+      break;
+    end
+    cur_attr = CPD.tree.nodes(cur_node).split_id; 
+    attr_val = local_data(cur_attr,i);
+    if (node_types(cur_attr)==0)  %discrete attribute
+        % goto the attr_val -th child
+        cur_node = CPD.tree.nodes(cur_node).children(attr_val);
+    else
+        if (attr_val <= CPD.tree.nodes(cur_node).split_threshhold)
+          cur_node = CPD.tree.nodes(cur_node).children(1);
+        else
+          cur_node = CPD.tree.nodes(cur_node).children(2);  
+        end
+    end
+    if (cur_node > CPD.tree.num_node)
+      fprintf('Fatal error: Tree structure corrupted.\n');
+      return;
+    end
+  end
+  %update the classification error number
+end
+if (output_type==0)
+  score=1-total_error/num_cases;
+else
+  score=total_error/num_cases;
+end