www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/start_classify.m
function [D, test_err, train_err, train_features, train_targets, reduced_features, reduced_targets] = start_classify(features, targets, error_method, redraws, percent, Preprocessing_algorithm, PreprocessingParameters, Classification_algorithm, AlgorithmParameters, region, hm, SepratePreprocessing, plot_on)
% Main function for evaluating a single classifier
% Inputs:
% features - The examples of the data
% targets - The labels for the data
% error_method - Error estimation method (Cross-validation, Holdout or Resubstitution)
% redraws - Number of redraws needed
% percent - Percentage of training vectors
% Preprocessing_algorithm - A preprocessing algorithm
% PreprocessingParameters - ...and it's parameters
% Classification_algorithm - A classification algorithm
% AlgorithmParameters - ...and it's parameters
% region - Decision region vector: [-x x -y y number_of_points]
% hm - Handle to the message box on the GUI (Can be [])
% SepratePreprocessing - Perform separate preprocessing for each class
% plot_on - Plot during preprocessing
%
% Outputs:
% D - The last decision region
% test_err - The test errors
% train_err - The train errors
% train_features - The train features
% train_targets - ...and targets
% reduced_features - Features after preprocessing
% reduced_targets - ...and targets
%Some variable definitions
Nclasses = find_classes(targets); %Number of classes in targets
test_err = zeros(Nclasses+1,redraws);
train_err = zeros(Nclasses+1,redraws);
x = linspace(region(1), region(2), region(5));
y = linspace(region(3), region(4), region(5));
reduced_features= [];
reduced_targets = [];
if ~isempty(hm),
hParent = get(hm,'Parent'); %Get calling window tag
end
for i = 1: redraws,
if ~isempty(hm),
set(hm, 'String', ['Processing iteration ' num2str(i) ' of ' num2str(redraws) ' iterations...']);
end
%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')
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);
%Preprocess and then find decision region
switch Preprocessing_algorithm
case cellstr('None')
disp('Generating decision region')
D = feval(Classification_algorithm, train_features, train_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);
case cellstr('PCA')
disp('Performing preprocessing')
[reduced_features, reduced_targets, uw, m] = feval(Preprocessing_algorithm, train_features, train_targets, PreprocessingParameters, region);
reduced_features = uw*(train_features - m*ones(1,size(train_features,2)));
disp('Generating decision region')
[region, x, y] = calculate_region(uw*(features-m*ones(1,size(features,2))), region);
D = feval(Classification_algorithm, reduced_features, reduced_targets, AlgorithmParameters, region);
disp('Calculating the error')
[train_err(:,i), test_err(:,i)] = calculate_error (D, reduced_features, reduced_targets, uw*(test_features-m*ones(1,size(test_features,2))), test_targets, region, Nclasses);
case cellstr('FishersLinearDiscriminant')
disp('Performing preprocessing')
[reduced_features, reduced_targets, w] = feval(Preprocessing_algorithm, train_features, train_targets, [], region);
[region, x, y] = calculate_region(reduced_features, region);
disp('Generating decision region')
D = feval(Classification_algorithm, reduced_features, reduced_targets, AlgorithmParameters, region);
disp('Calculating the error')
[train_err(:,i), test_err(:,i)] = calculate_error (D, reduced_features, reduced_targets, [w'*test_features; zeros(1,length(test_targets))], test_targets, region, Nclasses);
%If possible, replot the data
if ~isempty(hParent),
hold off
plot_scatter([w'*features; zeros(1,length(targets))], targets, hParent)
hold on
end
otherwise
disp('Performing preprocessing')
if SepratePreprocessing,
disp('Perform seperate preprocessing for each class.')
in0 = find(train_targets == 0);
in1 = find(train_targets == 1);
[reduced_features0, reduced_targets0] = feval(Preprocessing_algorithm, train_features(:,in0), train_targets(in0), PreprocessingParameters, region, plot_on);
[reduced_features1, reduced_targets1] = feval(Preprocessing_algorithm, train_features(:,in1), train_targets(in1), PreprocessingParameters, region, plot_on);
reduced_features = [reduced_features0, reduced_features1];
reduced_targets = [reduced_targets0, reduced_targets1];
else
[reduced_features, reduced_targets] = feval(Preprocessing_algorithm, 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) & (~isempty(hParent)))
%Plot only during the last iteration
plot_scatter(reduced_features, reduced_targets, hParent, 1)
axis(region(1:4))
end
%Show Voronoi diagram
if ~isempty(findobj('Tag','Voronoi diagram')),
%Voronoi diagram figure exists
figure(findobj('Tag','Voronoi diagram'))
clf;
else
figure;
set(gcf,'Tag','Voronoi diagram');
end
hold on
contour(x,y,voronoi_regions(reduced_features, region),length(reduced_targets))
plot_scatter(reduced_features, reduced_targets)
hold off
axis(region(1:4));
grid on;
title('Voronoi regions')
if ~isempty(hParent),
figure(hParent)
end
if ((sum(reduced_targets) <= 1) & (sum(~reduced_targets) <= 1) & (~strcmp(Classification_algorithm,'None')))
error('Too few reduced points (This program needs at least two points of each class). Please restart.')
else
if strcmp(Classification_algorithm,'None'),
%No classification was asked for
D = zeros(region(5));
set(gcf,'pointer','arrow');
end
end
disp('Generating decision region')
D = feval(Classification_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