cross_validate.m

一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

% Input pararmeter: 
% D: data array, including the feature data and output class

function run = cross_validate(D, classifier_wrapper_handle, classifier)

global preprocess;
[X, Y, num_data, num_feature] = preprocessing(D);
clear D;

% The statistics of dataset
num_folder = preprocess.NumCrossFolder;
num_class = length(preprocess.ClassSet);
class_set = preprocess.ClassSet;

run.Y_pred = zeros(num_data, 4);
run.Y_pred(:, 1) = (1:num_data)';
for i = 1:num_folder
  fprintf('Iteration %d ......\n', i);  
  % Generate the data indeces for the testing data
  testindex = floor((i-1) * num_data / num_folder)+1 : floor( i * num_data/num_folder);
  if (preprocess.ShotAvailable == 1) & (preprocess.ValidateByShot == 1)      
    num_shot = length(preprocess.ShotIDSet);
    ValidateTestShot = preprocess.ShotIDSet(floor((i-1) * num_shot / num_folder) + 1 : floor(i * num_shot / num_folder));
    testindex = []; for j = 1:length(ValidateTestShot), testindex = [testindex; find(preprocess.ShotInfo == ValidateTestShot(j))]; end;
  end;  
  trainindex = setdiff(1:num_data, testindex);
  
  %%% RemoveConstraints;
  
  % Classificaiton
  run_class(i) = feval(classifier_wrapper_handle, X, Y, trainindex, testindex, classifier); 
  run.Y_pred(testindex, 2) = run_class(i).Y_prob; 
  run.Y_pred(testindex, 3) = run_class(i).Y_compute; 
  run.Y_pred(testindex, 4) = run_class(i).Y_test;
end

if (isfield(run_class(1), 'Err')), run.Err = mean([run_class(:).Err]); end;
if (isfield(run_class(1), 'Prec')), run.Prec = mean([run_class(:).Prec]); end;
if (isfield(run_class(1), 'Rec')), run.Rec = mean([run_class(:).Rec]); end;
if (isfield(run_class(1), 'F1')), run.F1 = mean([run_class(:).F1]); end;
if (isfield(run_class(1), 'Micro_Prec')), run.Micro_Prec = mean([run_class(:).Micro_Prec]); end;
if (isfield(run_class(1), 'Micro_Rec')), run.Micro_Rec = mean([run_class(:).Micro_Rec]); end;
if (isfield(run_class(1), 'Micro_F1')), run.Micro_F1 = mean([run_class(:).Micro_F1]); end;
if (isfield(run_class(1), 'Macro_Prec')), run.Macro_Prec = mean([run_class(:).Macro_Prec]); end;
if (isfield(run_class(1), 'Macro_Rec')), run.Macro_Rec = mean([run_class(:).Macro_Rec]); end;
if (isfield(run_class(1), 'Macro_F1')), run.Macro_F1 = mean([run_class(:).Macro_F1]); end;
if (isfield(run_class(1), 'AvgPrec')), run.AvgPrec = mean([run_class(:).AvgPrec]); end;
if (isfield(run_class(1), 'BaseAvgPrec')), run.BaseAvgPrec = mean([run_class(:).BaseAvgPrec]); end;

function RemoveConstraints()

global preprocess;
if (preprocess.ConstraintAvailable == 1) & (preprocess.ShotAvailable == 1)
      for j = 1:size(preprocess.constraintMap, 1),
          ShotInfo = preprocess.ShotInfo;
          preprocess.constraintUsed(j) = (all(ShotInfo(trainindex) ~= preprocess.constraintMap(j,1)) && ...
              all(ShotInfo(trainindex) ~= preprocess.constraintMap(j,2)));
      end;
end;