www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/multialgorithms_commands.m
function multialgorithms_commands(command)
%This function processes events from the multi-algorithm GUI screen
switch(command)
case {'MoveLeft','MoveRight'}
%Move algorithms between the list boxes
if strcmp(command,'MoveLeft'),
hFrom = findobj('Tag','lstAllAlgorithms');
hTo = findobj('Tag','lstChosenAlgorithms');
else
hTo = findobj('Tag','lstAllAlgorithms');
hFrom = findobj('Tag','lstChosenAlgorithms');
end
%Find the selected algorithm and remove it from the 'From list'
val = get(hFrom,'Value');
algorithms = get(hFrom,'String');
algorithm = algorithms(val,:);
if (isempty(deblank(algorithm)))
break
end
newlist = 1:size(algorithms,1);
newlist(val) = 0;
newlist = newlist(find(newlist ~=0));
set(hFrom, 'Value', 1);
if ~isempty(newlist),
set(hFrom, 'String', algorithms(newlist,:));
else
set(hFrom, 'String', ' ');
end
%Put the new algorithm in the 'To list'
algorithms = get(hTo,'String');
L = max(size(algorithms,2),size(algorithm,2));
if ((isempty(deblank(algorithms(1,:)))) & (size(algorithms,1) == 1))
newalgorithms = algorithm;
else
newalgorithms = zeros(size(algorithms,1)+1,L);
newalgorithms(1:size(algorithms,1),:) = algorithms;
newalgorithms(size(algorithms,1)+1,:) = algorithm;
end
set(hTo, 'String', char(newalgorithms))
case {'Compare', 'Predict'}
Npoints = 100;
hFigure = gcf;
hm = findobj('Tag', 'Messages');
set(hm,'String','');
%Do some error checking
if evalin('base', '~exist(''targets'')')
set(hm,'String','No targets on workspace. Please load targets.')
break
end
if evalin('base', '~exist(''features'')')
set(hm,'String','No features on workspace. Please load features.')
break
end
features = evalin('base','features');
targets = evalin('base','targets');
if (evalin('base', 'exist(''distribution_parameters'')')),
distribution_parameters = evalin('base', 'distribution_parameters');
end
%Find the region for the grid
[region,x,y] = calculate_region(features, [zeros(1,4) Npoints]);
%Find which algorithms will be used
hAlgorithms = findobj('Tag','lstChosenAlgorithms');
algorithms = get(hAlgorithms,'String');
if ((isempty(deblank(algorithms(1,:)))) & (size(algorithms,1) == 1))
set(hm,'String','Please select at least one algorithm.')
break
end
Nalgorithms = size(algorithms,1);
All_algorithms = read_algorithms('Classification.txt');
for i = 1:Nalgorithms,
index = strmatch(deblank(algorithms(i,:)),char(All_algorithms(:).Name),'exact');
if ~isempty(index),
Chosen_algorithms(i).Name = deblank(algorithms(i,:));
if isempty(strmatch('N',All_algorithms(index).Field)),
Chosen_algorithms(i).Parameter = char(inputdlg(['Enter ' All_algorithms(index).Caption], All_algorithms(index).Name, 1, cellstr(All_algorithms(index).Default)));
else
Chosen_algorithms(i).Parameter = '';
end
end
end
if strcmp(command, 'Compare'),
%Comapre the algorithms
error_method_val = get(findobj('Tag', 'popErrorEstimation'),'Value');
error_method_str = get(findobj('Tag', 'popErrorEstimation'),'String');
error_method = char(error_method_str(error_method_val));
h = findobj('Tag', 'txtRedraws');
redraws = str2num(get(h, 'String'));
if isempty(redraws),
set(hm,'String','Please select how many redraws are needed.')
break
else
if strcmp(error_method, 'Cross-Validation'),
if (redraws < 2),
set(hm, 'String', 'Number of redraws must be larger than 1.')
break
end
else
if (redraws < 1),
set(hm,'String','Number of redraws must be larger than 0.')
break
end
end
end
h = findobj('Tag', 'txtPrecentage');
percent = str2num(get(h, 'String'));
if strcmp(error_method, 'Holdout'),
if isempty(percent),
set(hm,'String','Please select the percentage of training vectors.')
break
else
if (floor(percent/100*length(targets)) < 1),
set(hm,'String','Number training vectors must be larger than 0.')
break
end
end
end
%Now that the data is OK, start working
set(gcf,'pointer','watch');
%Some variable definitions
Nclasses = find_classes(targets); %Number of classes in targets
test_err = zeros(Nalgorithms,redraws);
train_err = zeros(Nalgorithms,redraws);
for k = 1: Nalgorithms,
for i = 1: redraws,
set(hm, 'String', [Chosen_algorithms(k).Name ' algorithm: 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')
chunk = floor(L/redraws);
test_indices = 1 + (i-1)*chunk : i * chunk;
train_indices = [1:(i-1)*chunk, 1+i * chunk:L];
end
train_features = features(:, train_indices);
train_targets = targets (:, train_indices);
test_features = features(:, test_indices);
test_targets = targets (:, test_indices);
param = str2num(Chosen_algorithms(k).Parameter);
if isempty(param),
param = Chosen_algorithms(k).Parameter;
end
D = feval(Chosen_algorithms(k).Name, train_features, train_targets, param, region);
[classify, err] = classification_error(D, train_features, train_targets, region);
train_err(k,i) = err;
[classify, err] = classification_error(D, test_features, test_targets, region);
test_err(k,i) = err;
end
end
hDisp = findobj('Tag','popErrorDisplay');
sDisp = get(hDisp,'String');
switch char(sDisp(get(hDisp,'Value'))),
case 'Test error'
if (redraws > 1),
err = mean(test_err');
else
err = test_err;
end
case 'Train error'
if (redraws > 1),
err = mean(train_err');
else
err = train_err';
end
otherwise
if (redraws > 1),
err = mean(test_err')*length(test_targets)+mean(train_err')*length(train_targets);
err = err / (length(test_targets)+length(train_targets));
else
err = test_err*length(test_targets)+train_err*length(train_targets);
err = err / (length(test_targets)+length(train_targets));
end
end
hBayes = findobj('Tag','chkBayes');
if ((get(hBayes, 'Value')) & (exist('distribution_parameters'))),
if ~isempty(distribution_parameters)
Dbayes = decision_region(distribution_parameters, region);
[classify, Bayes_err] = classification_error(Dbayes, features, targets, region);
err(length(err)+1) = Bayes_err;
Nalgorithms = Nalgorithms + 1;
Chosen_algorithms(Nalgorithms).Name='Bayes err ';
end
end
%Plot the results
figure
bar(err)
title('Average classification errors')
for k=1:Nalgorithms,
str = deblank(Chosen_algorithms(k).Name);
str(findstr(str,'_')) = ' ';
h=text(k,err(k)+.02,str);
set(h,'HorizontalAlignment','Center')
set(h,'FontSize',12)
%set(h,'Color',[1 1 1])
end
ax = axis;ax(3)=0;ax(4)=max(1,max(err));
axis(ax)
s = 'Finished!';
set(hm, 'String', s);
set(hFigure,'pointer','arrow');
assignin('base','final_errors',err)
else
%Predict performance
a = zeros(1, Nalgorithms);
for k = 1: Nalgorithms,
set(hm, 'String', [Chosen_algorithms(k).Name ' algorithm']);
param = str2num(Chosen_algorithms(k).Parameter);
if isempty(param),
param = Chosen_algorithms(k).Parameter;
end
a(k) = predict_performance(Chosen_algorithms(k).Name, param, features, targets, region);
end
%Plot the results
figure
bar(a)
title('Prediction values')
for k=1:Nalgorithms,
str = deblank(Chosen_algorithms(k).Name);
str(findstr(str,'_')) = ' ';
h=text(k,a(k)+.02,str);
set(h,'HorizontalAlignment','Center')
set(h,'FontSize',12)
%set(h,'Color',[1 1 1])
end
assignin('base','final_predictions',a)
end
otherwise
error('Unknown commands')
end