new_start_classify.m

最新的模式识别分类工具箱,希望对朋友们有用!

% ------------------------------------------------
% This method has a much be
% 
%
disp('In new_start_classify');
myalgorithm = [algorithm '_VC'];
train_err = [];
test_err =  [];
[Nclasses, classes] = find_classes(targets);

for i = 1: redraws,  
  
  set(hm, 'String', ['Processing iteration ' num2str(i) ' of ' num2str(redraws) ' iterations...']);
  
  %  Make a draw according to the error method chosen
  L = length(targets);

  switch error_method

  case cellstr('Resubstitution')
    test_indices  = 1:L;
    train_indices = 1:L;
    
  case cellstr('Holdout')
    [test_indices, train_indices] = make_a_draw(floor(percent/100*L), L); ...

  case cellstr('Cross-Validation')
    fprintf('Cross-validation with %d redraws\n',redraws);
    chunk          = floor(L/redraws);
    test_indices   = 1 + (i-1)*chunk : i * chunk;
    train_indices  = [1:(i-1)*chunk, i * chunk + 1:L];
  end

  train_features = features(:, train_indices);    
  train_targets  = targets (:, train_indices);    
  test_features  = features(:, test_indices);     
  test_targets   = targets (:, test_indices);     

  train_err(:,i) = zeros(Nclasses+1,1);
  test_err(:,i)  = zeros(Nclasses+1,1);
  
%  fprintf('have %d training and %d test samples, fraction %f\n', length(train_targets), length(test_targets),percent);
  
  %  Preprocess and then find decision region

  switch preprocessing

  case cellstr('None')
    [train_err(:,i), test_err(:,i)] = feval(myalgorithm, train_features, ...
	train_targets, AlgorithmParameters, test_features, test_targets);
    
  case cellstr('PCA')
    % preprocessing with PCA
    disp('Performing preprocessing with PCA')

    [reduced_features, reduced_targets, UW] = ...
	feval(preprocessing, train_features, train_targets, PreprocessingParameters, region); 
    
    [train_err(:,i), test_err(:,i)] = feval(myalgorithm, reduced_features, ...
	train_targets, AlgorithmParameters, UW*test_features, test_targets);
    
    % ================================================================
  case cellstr('Fishers_linear_discriminant')

    disp('Performing preprocessing with Fisher''s linear discriminant ')
    [reducedFeatures, reducedTargets, w] = feval(preprocessing, train_features, train_targets, [], region); 

    % projects the test features
    reducedTestFeatures = [w' * test_features; zeros(1,length(test_targets))];

    [train_err(:,i), test_err(:,i)] = feval(myalgorithm, reducedFeatures, ...
	reducedTargets, AlgorithmParameters, reducedTestFeatures, reducedTestTargets);
    % Replot the data
    hold off
    plot_scatter([w'*features; zeros(1,length(targets))], targets, hParent)
    hold on
      
  otherwise

    fprintf('Performing preprocessing with %s\n',preprocessing);
    
    % performing separate preprocessing
    
    if strcmp(get(findobj(gcbf, 'Tag', ...
	                  '&Options&SeparatePreprocessing'),'Checked'),'on'),

      %
      % MUST TAKE CARE OF THIS

      disp('Perform separate preprocessing for each class.')
      in0 = find(train_targets == 0);
      in1 = find(train_targets == 1);
      [reduced_features0, reduced_targets0] = ...
	  feval(preprocessing, train_features(:,in0), ...
	        train_targets(in0), PreprocessingParameters, region, ...
		plot_on);  
	    
      [reduced_features1, reduced_targets1] = ...
	  feval(preprocessing, train_features(:,in1), ...
	         train_targets(in1), PreprocessingParameters, region, ...
		 plot_on); 
	     
      reduced_features = [reduced_features0, reduced_features1];
      reduced_targets  = [reduced_targets0,  reduced_targets1];
    else
      % or not
      [reduced_features, reduced_targets] = ...
	  feval(preprocessing, train_features, train_targets, ...
	  PreprocessingParameters, region, plot_on); 
    end

    pause(1);
    plot_process([]);
    indices = find(sum(isfinite(reduced_features)) > 0);
    reduced_features = reduced_features(:,indices);
    reduced_targets  = reduced_targets(:,indices);

    if (i == redraws)
      % Plot only during the last iteration
      plot_scatter(reduced_features, reduced_targets, hParent, 1)
      axis(region(1:4))
    end

    if ((sum(reduced_targets) <= 1) & (sum(~reduced_targets) <= 1) & (~strcmp(algorithm,'None')))
      error('Too few reduced points (This program needs at least two points of each class). Please restart.')
    else
      if strcmp(algorithm,'None'),
	%No classification was asked for
	set(gcf,'pointer','arrow');     
	return
      end
    end

    disp('Generating decision region')
    D = feval(algorithm, reduced_features, ...
	reduced_targets, AlgorithmParameters, region); 

    disp('Calculating the error')
    [train_err(:,i), test_err(:,i)] = ...
	calculate_error (D, train_features, train_targets, ...
	test_features, test_targets, region, Nclasses);
  end      
end      
  
%Display error
%% ====================
h =  findobj('Tag', 'TestSetError');
s = 'Test set errors: ';
fprintf('\n Test Set Error Rates\n');
for j = 1:Nclasses,
   s = [s 'Class ' num2str(j) ': ' num2str(mean(test_err(j,:)),2) '. '];
   fprintf('Class %d - Error Rate %f\n',j, mean(test_err(j,:)));
 end
m_m = mean(test_err(Nclasses+1,:));
fprintf('Overall Error Rate %f\n\n', m_m); 
s = [s 'Total: ' num2str(mean(test_err(Nclasses+1,:)),2)];
set(h, 'String', s);

%% ====================
h =  findobj('Tag', 'TrainSetError');
s = 'Train set errors: ';
fprintf('\n Training Set Error Rates\n');
for j = 1:Nclasses,
   s = [s 'Class ' num2str(j) ': ' num2str(mean(train_err(j,:)),2) '. '];
   fprintf('Class %d - Error Rate %f\n',j, mean(train_err(j,:)));
end
m_m = mean(train_err(Nclasses+1,:));
fprintf('Overall Error Rate %f\n', m_m);
s = [s 'Total: ' num2str(mean(train_err(Nclasses+1,:)),2)];
set(h, 'String', s);


%Show Bayes decision region and error (if possible)
% 
grid on

%Replot training points if necessary
if strcmp(get(findobj(gcbf,'Label','Show &Training points'),'Checked'),'on'),
   plot_scatter(train_features, train_targets, hParent, 2)
end

hold off

%That's all folks!
s = 'Finished!';
if (redraws > 1),
   s = [s ' (Note that only the last decision region is shown)'];
 end
set(hm, 'String', s);   
set(gcf,'pointer','arrow');
echo off;