Mauch MIREX 2010

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