diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/evaluate_tree_performance.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/evaluate_tree_performance.m	Tue Feb 10 15:05:51 2015 +0000
@@ -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: [e-mail]
+% 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