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1 function [score,outputs] = evaluate(CPD, fam, data, ns, cnodes)
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2 % Evaluate evaluate the performance of the classification/regression tree on given complete data
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3 % score = evaluate(CPD, fam, data, ns, cnodes)
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4 %
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5 % fam(i) is the node id of the i-th node in the family of nodes, self node is the last one
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6 % data(i,m) is the value of node i in case m (can be cell array).
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7 % ns(i) is the node size for the i-th node in the whold bnet
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8 % cnodes(i) is the node id for the i-th continuous node in the whole bnet
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9 %
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10 % Output
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11 % score is the classification accuracy (for classification)
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12 % or mean square deviation (for regression)
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13 % here for every case we use the mean value at the tree leaf node as its predicted value
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14 % outputs(i) is the predicted output value for case i
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15 %
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16 % Author: yimin.zhang@intel.com
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17 % Last updated: Jan. 19, 2002
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18
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19
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20 if iscell(data)
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21 local_data = cell2num(data(fam,:));
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22 else
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23 local_data = data(fam, :);
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24 end
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25
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26 %get local node sizes and node types
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27 node_sizes = ns(fam);
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28 node_types = zeros(1,size(ns,2)); %all nodes are disrete
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29 node_types(cnodes)=1;
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30 node_types=node_types(fam);
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31
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32 fam_size=size(fam,2);
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33 output_type = node_types(fam_size);
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34
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35 num_cases=size(local_data,2);
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36 total_error=0;
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37
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38 outputs=zeros(1,num_cases);
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39 for i=1:num_cases
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40 %class one case using the tree
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41 cur_node=CPD.tree.root; % at the root node of the tree
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42 while (1)
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43 if (CPD.tree.nodes(cur_node).is_leaf==1)
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44 if (output_type==0) %output is discrete
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45 %use the class with max probability as the output
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46 [maxvalue,class_id]=max(CPD.tree.nodes(cur_node).probs);
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47 outputs(i)=class_id;
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48 if (class_id~=local_data(fam_size,i))
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49 total_error=total_error+1;
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50 end
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51 else %output is continuous
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52 %use the mean as the value
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53 outputs(i)=CPD.tree.nodes(cur_node).mean;
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54 cur_deviation = CPD.tree.nodes(cur_node).mean-local_data(fam_size,i);
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55 total_error=total_error+cur_deviation*cur_deviation;
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56 end
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57 break;
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58 end
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59 cur_attr = CPD.tree.nodes(cur_node).split_id;
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60 attr_val = local_data(cur_attr,i);
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61 if (node_types(cur_attr)==0) %discrete attribute
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62 % goto the attr_val -th child
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63 cur_node = CPD.tree.nodes(cur_node).children(attr_val);
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64 else
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65 if (attr_val <= CPD.tree.nodes(cur_node).split_threshhold)
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66 cur_node = CPD.tree.nodes(cur_node).children(1);
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67 else
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68 cur_node = CPD.tree.nodes(cur_node).children(2);
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69 end
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70 end
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71 if (cur_node > CPD.tree.num_node)
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72 fprintf('Fatal error: Tree structure corrupted.\n');
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73 return;
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74 end
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75 end
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76 %update the classification error number
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77 end
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78 if (output_type==0)
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79 score=1-total_error/num_cases;
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80 else
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81 score=total_error/num_cases;
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82 end
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