www.gusucode.com > 超全的模式识别matlab源码程序 > code/classifier_commands.m
function classifier_commands(command)
%This function processes events from the main (single-algorithm) GUI screen
persistent Preprocessing_methods;
persistent Algorithms;
if (isempty(Preprocessing_methods) | isempty(Algorithms)),
Algorithms = read_algorithms('Classification.txt');
Preprocessing_methods = read_algorithms('Preprocessing.txt');
end
switch(command)
case 'Init'
%Init of the classifier GUI
h = findobj('Tag', 'Preprocessing');
set(h,'String',strvcat(Preprocessing_methods(:).Name));
chosen = strmatch('None',char(Preprocessing_methods(:).Name));
set(h,'Value',chosen);
h = findobj('Tag', 'Algorithm');
set(h,'String',strvcat(Algorithms(:).Name));
chosen = strmatch('LS',char(Algorithms(:).Name));
set(h,'Value',chosen);
case 'Changed Preprocessing'
h = findobj(gcbf, 'Tag', 'Preprocessing');
chosen = get(h, 'Value');
hLabel = findobj(gcbf, 'Tag', 'lblPreprocessingParameters');
hBox = findobj(gcbf, 'Tag', 'txtPreprocessingParameters');
set(hBox,'Visible','on');
if ~isempty(chosen),
set(hLabel,'String',Preprocessing_methods(chosen).Caption);
set(hBox,'String',Preprocessing_methods(chosen).Default);
if strcmp(char(Preprocessing_methods(chosen).Field),'N')
set(hLabel,'String','');
set(hBox,'Visible','off');
end
else
set(hLabel,'String','');
set(hBox,'Visible','off');
end
case 'Changed Algorithm'
h = findobj(gcbf, 'Tag', 'Algorithm');
chosen = get(h, 'Value');
hLabel = findobj(gcbf, 'Tag', 'lblAlgorithmParameters');
hBox = findobj(gcbf, 'Tag', 'txtAlgorithmParameters');
hLongBox = findobj(gcbf, 'Tag', 'txtAlgorithmParametersLong');
set(hBox,'Visible','on');
if ~isempty(chosen),
set(hLabel,'String',Algorithms(chosen).Caption);
switch Algorithms(chosen).Field
case 'S'
set(hBox,'String',Algorithms(chosen).Default);
set(hBox,'Visible','on');
set(hLongBox,'Visible','off');
case 'L'
set(hLongBox,'String',Algorithms(chosen).Default);
set(hBox,'Visible','off');
set(hLongBox,'Visible','on');
case 'N'
set(hLabel,'String','');
set(hBox,'Visible','off');
set(hLongBox,'Visible','off');
end
else
set(hLabel,'String','');
set(hBox,'Visible','off');
set(hLongBox,'Visible','off');
end
case 'Start'
%Start the classification process
Npoints = 100; %Number of points on each axis of the decision region
%Read data from the workspace
hm = findobj('Tag', 'Messages');
hParent = get(hm,'Parent'); %Get calling window tag
set(hm,'String','');
h = findobj('Tag', 'TestSetError');
set(h, 'String', '');
h = findobj('Tag', 'TrainSetError');
set(h, 'String', '');
%Do some error checking
if evalin('base', '~exist(''targets'')')
set(hm,'String','No targets on workspace. Please load targets.')
return
end
if evalin('base', '~exist(''patterns'')')
set(hm,'String','No patterns on workspace. Please load patterns.')
return
end
patterns = evalin('base','patterns');
targets = evalin('base','targets');
if (evalin('base', 'exist(''distribution_parameters'')')),
distribution_parameters = evalin('base', 'distribution_parameters');
end
error_method_val = get(findobj('Tag', 'popErrorEstimationMethod'),'Value');
error_method_str = get(findobj('Tag', 'popErrorEstimationMethod'),'String');
error_method = char(error_method_str(error_method_val(1)));
h = findobj('Tag', 'Redraws');
redraws = str2num(get(h, 'String'));
if isempty(redraws),
set(hm,'String','Please select how many redraws are needed.')
return
else
if (redraws < 1),
set(hm,'String','Number of redraws must be larger than 0.')
return
else
if (strcmp(error_method, 'Cross-Validation') & (redraws == 1)),
set(hm,'String','Number of redraws must be larger than 1.')
return
end
end
end
h = findobj('Tag', 'PercentTraining');
percent = str2num(get(h, 'String'));
if strcmp(error_method, 'Holdout'),
if isempty(percent),
set(hm,'String','Please select the percentage of training vectors.')
return
else
if (floor(percent/100*length(targets)) < 1),
set(hm,'String','Number of training vectors must be larger than 0.')
return
end
if (percent >= 100),
set(hm,'String','Number of training vectors must be smaller than 100%.')
return
end
if (floor((1-percent/100)*length(targets)) < 1),
set(hm,'String','Number of test vectors must be larger than 0.')
return
end
end
end
%Find the region for the grid
[region,x,y] = calculate_region(patterns, [zeros(1,4) Npoints]);
h = findobj('Tag', 'Algorithm');
val = get(h,'Value');
algorithm = get(h,'String');
algorithm = deblank(char(algorithm(val,:)));
h = findobj('Tag', 'Preprocessing');
val = get(h,'Value');
preprocessing = get(h,'String');
preprocessing = deblank(char(preprocessing(val,:)));
%Get algorithm paramters. If they don't contain a string, turn them into a number
if strcmp(get(findobj('Tag', 'txtAlgorithmParameters'),'Visible'),'on'),
AlgorithmParameters = char(get(findobj('Tag', 'txtAlgorithmParameters'),'String'));
else
AlgorithmParameters = char(get(findobj('Tag', 'txtAlgorithmParametersLong'),'String'));
end
if (~isempty(str2num(AlgorithmParameters))),
AlgorithmParameters = str2num(AlgorithmParameters);
end
if strcmp(get(findobj('Tag', 'txtPreprocessingParameters'),'Visible'),'on'),
PreprocessingParameters = char(get(findobj('Tag', 'txtPreprocessingParameters'),'String'));
else
PreprocessingParameters = char(get(findobj('Tag', 'txtPreprocessingParameters'),'String'));
end
if (~isempty(str2num(PreprocessingParameters))),
PreprocessingParameters = str2num(PreprocessingParameters);
end
plot_on = strcmp(get(findobj(gcbf,'Label','&Show center of partitions during training'),'Checked'),'on');
plot_on = plot_on + 2*strcmp(get(findobj(gcbf,'Label','Show &Voronoi regions during training'),'Checked'),'on');
SepratePreprocessing = strcmp(get(findobj(hParent, 'Tag', '&Options&SeparatePreprocessing'),'Checked'),'on');
%Now that the data is OK, start working
set(gcf,'pointer','watch');
hold off
plot_scatter(patterns, targets, hParent)
axis(region(1:4))
hold on
drawnow
%Call the main classification function
[D, test_err, train_err, train_patterns, train_targets, reduced_patterns, reduced_targets] = ...
start_classify(patterns, targets, error_method, redraws, percent, preprocessing, PreprocessingParameters, ...
algorithm, AlgorithmParameters, region, hm, SepratePreprocessing, plot_on);
%Put results on the workspace as well
assignin('base', 'D', D);
assignin('base', 'test_err', test_err);
assignin('base', 'train_err', train_err);
assignin('base', 'region', region);
if (~isempty(reduced_patterns))
assignin('base', 'reduced_patterns', reduced_patterns);
assignin('base', 'reduced_targets', reduced_targets);
else
evalin('base', 'clear reduced_patterns reduced_targets');
end
%Display error
Nclasses = length(unique(targets));
h = findobj('Tag', 'TestSetError');
s = 'Test set errors: ';
for j = 1:Nclasses,
s = [s 'Class ' num2str(j) ': ' num2str(mean(test_err(j,:)),2) '. '];
end
s = [s 'Total: ' num2str(mean(test_err(Nclasses+1,:)),2)];
set(h, 'String', s);
h = findobj('Tag', 'TrainSetError');
h = findobj('Tag', 'TrainSetError');
s = 'Train set errors: ';
for j = 1:Nclasses,
s = [s 'Class ' num2str(j) ': ' num2str(mean(train_err(j,:)),2) '. '];
end
s = [s 'Total: ' num2str(mean(train_err(Nclasses+1,:)),2)];
set(h, 'String', s);
%Show decision region
if strcmp(get(findobj(gcbf,'Label','Shade &Decision Regions'),'Checked'),'on'),
[s,h]=contourf(x,y,D+eps,1);colormap([198 198 255; 240 255 240]/255);
else
[s,h]=contour(x,y,D+eps,1);
end
set(h,'LineWidth',2,'EdgeColor','k');
set(h,'UserData','DecisionRegion');
%Show Bayes decision region and error (if possible)
hBayes = findobj('Tag','chkBayes');
if ((get(hBayes, 'Value')) & (exist('distribution_parameters'))),
xx = linspace(region(1), region(2), Npoints);
yy = linspace(region(3), region(4), Npoints);
mx = ones(Npoints,1) * xx;
my = yy' * ones(1,Npoints);
flatxy = [mx(:), my(:)]';
Dbayes = reshape(classify_paramteric(distribution_parameters, flatxy), Npoints, Npoints);
[s,h] = contour(x,y,Dbayes+eps,1);set(h,'LineWidth',1,'EdgeColor','r');
[classify, Bayes_err] = classification_error(Dbayes, patterns, targets, region);
h = findobj('Tag', 'BayesError');
s = 'Bayes errors: ';
for j = 1:Nclasses,
s = [s 'Class ' num2str(j) ': ' num2str(1 - classify(j,j),2) '. '];
end
s = [s 'Total: ' num2str(mean(1 - diag(classify)),2)];
set(h, 'String', s);
assignin('base', 'Dbayes', Dbayes);
end
%Draw grid if neccessary
h = findobj('Label', '&Grid');
if strcmp('on',get(h, 'Checked')),
grid on
set(gca,'layer','top')
else
grid off
end
%Replot training points if necessary
if strcmp(get(findobj(gcbf,'Label','Show &Training points'),'Checked'),'on'),
plot_scatter(train_patterns, 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');
case 'Compare'
%Start the algorithm comparison screen
h = multialgorithms;
h1 = findobj(h, 'Tag', 'lstAllAlgorithms');
set(h1, 'String', strvcat(Algorithms(:).Name));
case 'FileNameInput'
hold off
clear distribution_parameters patterns targets
h = findobj('Tag', 'BayesError');
set(h, 'String', '');
h = findobj('Tag', 'TestSetError');
set(h, 'String', '');
h = findobj('Tag', 'TrainSetError');
set(h, 'String', '');
evalin('base','[patterns, targets, distribution_parameters] = load_file(get(gcbo, ''String''));')
if (evalin('base', 'isempty(distribution_parameters)')),
evalin('base', 'clear distribution_parameters');
end
if (evalin('base', '~isempty(patterns)'))
evalin('base','region = calculate_region(patterns, [zeros(1,4) 100]);')
end
case 'SearchForFile'
evalin('base','region = [0 0 0 0 500];')
evalin('base','[filename, pathname] = uigetfile(''*.mat'', ''Open file ...'');')
if (evalin('base','filename ~= 0')),
evalin('base','clear distribution_parameters patterns targets');
h = findobj('Tag', 'BayesError');
set(h, 'String', '');
h = findobj('Tag', 'TestSetError');
set(h, 'String', '');
h = findobj('Tag', 'TrainSetError');
set(h, 'String', '');
evalin('base','set(findobj(''Tag'', ''FileNameInput''), ''String'', [pathname filename]);')
evalin('base','[patterns, targets, distribution_parameters] = load_file([pathname, filename]);')
evalin('base','clear pathname filename');
if (evalin('base', 'isempty(distribution_parameters)')),
evalin('base', 'clear distribution_parameters');
end
if (evalin('base', '~isempty(patterns)'))
evalin('base','region = calculate_region(patterns, [zeros(1,4) 100]);')
classifier_commands('ClearBounds');
else
%Clean the axis
cla
end
end
case 'EnterManualDist'
disp('Clearing current data from workspace');
evalin('base', 'clear')
cla
ttl = 'Manual definition of distribution';
n0 = str2num(char(inputdlg('Enter the number of distribution objects in class 0:',ttl,1,cellstr(num2str(1)))));
n1 = str2num(char(inputdlg('Enter the number of distribution objects in class 1:',ttl,1,cellstr(num2str(1)))));
distribution_parameters(1).p = str2num(char(inputdlg('Enter the probability of class 0:',ttl,1,cellstr(num2str(0.5)))));
distribution_parameters(2).p = 1 - distribution_parameters(1).p;
distribution_parameters(1).mu = zeros(n0,2);
distribution_parameters(2).mu = zeros(n1,2);
distribution_parameters(1).sigma= zeros(n0,2,2);
distribution_parameters(2).sigma= zeros(n1,2,2);
for i = 1:n0,
distribution_parameters(1).sigma(i,:,:) = eye(2);
distribution_parameters(1).type(i) = {'Gaussian'};
end
for i = 1:n1,
distribution_parameters(2).sigma(i,:,:) = eye(2);
distribution_parameters(2).type(i) = {'Gaussian'};
end
distribution_parameters(1).w = ones(n0,1)/n0;
distribution_parameters(2).w = ones(n1,1)/n1;
save synthetic distribution_parameters
uiwait(enter_distributions);
evalin('base','distribution_parameters = load_file(''synthetic'');')
case 'ManualGraphData'
%Graphically enter a data set
evalin('base','if ~(exist(''region'')), region = [-1 1 -1 1 100];end');
evalin('base','if (sum(abs(region(1:4))) == 0), region = [-1 1 -1 1 100];end');
evalin('base','[new_patterns, new_targets, new_params, region] = click_points(region);');
new_patterns = evalin('base','new_patterns');
new_targets = evalin('base','new_targets');
new_params = evalin('base','new_params');
s = questdlg('Do you want to permute the data (Recommended)?','Graphic input','Yes','No','Yes');
if strcmp(s,'Yes'),
i = randperm(length(new_targets));
new_targets = new_targets(i);
new_patterns = new_patterns(:,i);
end
if (evalin('base','exist(''patterns'')')),
patterns = evalin('base','patterns');
targets = evalin('base','targets');
else
patterns = [];
targets = [];
end
if (evalin('base','exist(''distribution_parameters'')')),
distribution_parameters = evalin('base','distribution_parameters');
else
distribution_parameters = [];
end
if isempty(distribution_parameters),
%If now patterns exist on the workspace, this is a new distribution, so take it
%(Otherwise, we may be saving only pars of the distribution)
if isempty(patterns)
distribution_parameters = new_params;
end
else
distribution_parameters(1).mu = [distribution_parameters(1).mu; new_params(1).mu];
distribution_parameters(2).mu = [distribution_parameters(2).mu; new_params(2).mu];
distribution_parameters(1).sigma= [distribution_parameters(1).sigma; new_params(1).sigma];
distribution_parameters(2).sigma= [distribution_parameters(2).sigma; new_params(2).sigma];
distribution_parameters(1).w = [distribution_parameters(1).w*sum(~targets); new_params(1).w*sum(~new_targets)];
distribution_parameters(2).w = [distribution_parameters(2).w*sum( targets); new_params(2).w*sum( new_targets)];
distribution_parameters(1).w = distribution_parameters(1).w / sum(distribution_parameters(1).w);
distribution_parameters(2).w = distribution_parameters(2).w / sum(distribution_parameters(2).w);
distribution_parameters(1).p = length(distribution_parameters(1).w) / (length(distribution_parameters(1).w)+length(distribution_parameters(2).w));
distribution_parameters(2).p = length(distribution_parameters(2).w) / (length(distribution_parameters(1).w)+length(distribution_parameters(2).w));
distribution_parameters(1).type = [distribution_parameters(1).type; new_params(1).type];
distribution_parameters(2).type = [distribution_parameters(2).type; new_params(2).type];
end
patterns = [patterns, new_patterns];
targets = [targets, new_targets];
assignin('base', 'patterns', patterns);
assignin('base', 'targets', targets);
assignin('base', 'distribution_parameters', distribution_parameters);
evalin('base','region = calculate_region(patterns, [zeros(1,4) 100]);')
classifier_commands('ClearBounds');
case 'Save'
evalin('base','[filename, pathname] = uiputfile(''*.mat'', ''Save file ...'');')
if (evalin('base','filename ~= 0')),
if (evalin('base', 'exist(''patterns'')')),
if (evalin('base', 'exist(''distribution_parameters'')')),
evalin('base','save([pathname filename], ''patterns'', ''targets'', ''distribution_parameters'');')
else
evalin('base','save([pathname filename], ''patterns'', ''targets'');')
end
else
if (evalin('base', 'exist(''distribution_parameters'')')),
evalin('base','save([pathname filename], ''distribution_parameters'');')
else
error('No data to save!')
end
end
end
case 'GenerateDistData'
if (evalin('base','~exist(''distribution_parameters'')')),
error('The parameters needed to generate the data are missing on the workspace!')
end
evalin('base','region = [0 0 0 0 100];')
evalin('base','[patterns, targets] = generate_data_set (distribution_parameters);')
evalin('base','region = calculate_region(patterns, [zeros(1,4) 100]);')
evalin('base','plot_scatter(patterns, targets, gcbf)')
classifier_commands('ClearBounds');
case 'Params'
%Print to mean, cov, main directions to workspace
if (evalin('base','~isempty(whos(''patterns''))'))
%There are patterns on workspace
patterns = evalin('base','patterns');
targets = evalin('base','targets');
train_one = find(targets == 1);
train_zero = find(targets == 0);
%Estimate mean and covariance for class 0
m0 = mean(patterns(:,train_zero)'');
c0 = cov(patterns(:,train_zero)'',1);
p0 = length(train_zero)/length(targets);
%Estimate mean and covariance for class 1
m1 = mean(patterns(:,train_one)'');
c1 = cov(patterns(:,train_one)'',1);
vChernoff = Chernoff(mean(patterns(:,train_zero)'), cov(patterns(:,train_zero)',1), mean(patterns(:,train_one)'), cov(patterns(:,train_one)',1),length(train_one)/length(targets));
vBhattacharyya = Bhattacharyya(mean(patterns(:,train_zero)'), cov(patterns(:,train_zero)',1), mean(patterns(:,train_one)'), cov(patterns(:,train_one)',1),length(train_one)/length(targets));
h0 = GaussianParameters;
set(findobj(h0,'Tag','txtMean00'),'String',num2str(m0(1),3))
set(findobj(h0,'Tag','txtMean01'),'String',num2str(m0(2),3))
set(findobj(h0,'Tag','txtMean10'),'String',num2str(m1(1),3))
set(findobj(h0,'Tag','txtMean11'),'String',num2str(m1(2),3))
set(findobj(h0,'Tag','txtCov000'),'String',num2str(c0(1,1),3))
set(findobj(h0,'Tag','txtCov001'),'String',num2str(c0(1,2),3))
set(findobj(h0,'Tag','txtCov010'),'String',num2str(c0(2,1),3))
set(findobj(h0,'Tag','txtCov011'),'String',num2str(c0(2,2),3))
set(findobj(h0,'Tag','txtCov100'),'String',num2str(c1(1,1),3))
set(findobj(h0,'Tag','txtCov101'),'String',num2str(c1(1,2),3))
set(findobj(h0,'Tag','txtCov110'),'String',num2str(c1(2,1),3))
set(findobj(h0,'Tag','txtCov111'),'String',num2str(c1(2,2),3))
set(findobj(h0,'Tag','txtBound1'),'String',['The Bhattacharyya bound value is: ' num2str(vBhattacharyya,3)])
set(findobj(h0,'Tag','txtBound2'),'String',['The Chernoff bound value is: ' num2str(vChernoff,3)])
waitfor(h0)
end
case 'MenuZoom'
umtoggle(findobj(gcbf,'Label','&Zoom'));
h = findobj(gcbf, 'Label', '&Zoom');
v = strcmp('on',get(h, 'Checked'));
if v,
zoom on
else
zoom off
end
case 'MenuGrid'
umtoggle(findobj(gcbf,'Label','&Grid'));
h = findobj(gcbf, 'Label', '&Grid');
v = strcmp('on',get(h, 'Checked'));
if v,
grid on
set(gca,'layer','top')
else
grid off
end
case 'Copy'
map = colormap;
h = figure;
copyobj(findobj(gcbf, 'Type','axes'),h);
set(gca,'Position',[0.1300 0.1100 0.7750 0.8150]);
set(h, 'colormap', map);
if ispc
print -dmeta
else
warndlg('Copy only works on Windows operating systems. Try the PRINT option.')
end
case 'Print'
s = questdlg('How do you want to print the figure?','Print','To printer','EPS','TIFF','To printer');
map = colormap;
h = figure;
copyobj(findobj(gcbf, 'Type','axes'),h);
set(gca,'Position',[0.1300 0.1100 0.7750 0.8150]);
set(h, 'colormap', map);
%Make the points plot on top
fig_h = get(get(h,'Children'),'Children');
uistack(fig_h(end), 'top')
uistack(fig_h(end-1), 'top')
switch lower(s)
case 'to printer'
printpreview
case 'eps'
[filename, pathname] = uiputfile('*.*', 'Save figure as');
print(h, '-depsc', [pathname, filename]);
case 'tiff'
[filename, pathname] = uiputfile('*.*', 'Save figure as');
print(h, '-dtiff', [pathname, filename]);
end
case 'Exit'
h = findobj(gcbf, 'Tag', 'Messages');
set(h,'String','');
h = findobj('Tag', 'TestSetError');
set(h, 'String', '');
h = findobj('Tag', 'TrainSetError');
set(h, 'String', '');
h = findobj('Tag', 'FileNameInput');
set(h, 'String', '');
h = findobj('Tag', 'PercentTraining');
set(h, 'String', '');
h = findobj('Tag', 'Redraws');
set(h, 'String', '');
h = findobj('Tag', 'HowMany');
set(h, 'String', '');
set(gcf, 'pointer', 'arrow');
close
case 'SeparatePreprocessing'
umtoggle(findobj(gcbf,'Label','&Perform preprocessing separately for each class'));
case 'ShowPartitions'
umtoggle(findobj(gcbf,'Label','&Show center of partitions during training'));
case 'ShowVoronoiPartitions'
umtoggle(findobj(gcbf,'Label','Show &Voronoi regions during training'));
case 'ShowTrainingPoints'
umtoggle(findobj(gcbf,'Label','Show &Training points'));
case 'ShadeDecisionRegion'
umtoggle(findobj(gcbf,'Label','Shade &Decision Regions'));
case 'ClearBounds'
%Clear all bounds
evalin('base','hold off')
cla
if (evalin('base', 'exist(''patterns'') & exist(''targets'')'))
evalin('base','plot_scatter(patterns, targets, gcbf)')
end
if (evalin('base', 'exist(''region'')'))
evalin('base', 'axis(region(1:4))')
end
evalin('base', 'drawnow');
h = findobj('Tag', 'TestSetError');
set(h, 'String', '');
h = findobj('Tag', 'TrainSetError');
set(h, 'String', '');
h = findobj(gcbf, 'Label', '&Grid');
v = strcmp('on',get(h, 'Checked'));
if v,
grid on
set(gca,'layer','top')
else
grid off
end
h = findobj('Tag', 'Messages');
set(h,'String','');
case 'ClearWorkspace'
if strcmp(questdlg('Clear current data on the workspace?', 'Classification toolbox', 'Yes', 'No', 'Yes'), 'Yes')
%Clear all
h = findobj('Tag', 'TestSetError');
set(h, 'String', '');
h = findobj('Tag', 'TrainSetError');
set(h, 'String', '');
h = findobj('Tag', 'BayesError');
set(h, 'String', '');
h = findobj('Tag', 'FileNameInput');
set(h, 'String', '');
hold off
plot(0,0); grid on;
h = findobj('Tag', 'Messages');
set(h,'String','');
evalin('base','clear all')
end
case 'Mixpatterns'
%Mix (permute) the patterns
if (evalin('base','exist(''patterns'')') & evalin('base','exist(''targets'')')),
evalin('base','indexes = randperm(length(targets));')
evalin('base','patterns = patterns(:,indexes);')
evalin('base','targets = targets(indexes);')
h = findobj('Tag', 'Messages');
set(h,'String','patterns were permuted.');
else
error('No patterns or targets in workspace!');
end
case 'HelpPreprocessing'
h = findobj(gcbf, 'Tag', 'Preprocessing');
chosen = get(h, 'Value');
evalin('base', ['help ' Preprocessing_methods(chosen).Name ])
case 'HelpClassifier'
h = findobj(gcbf, 'Tag', 'Algorithm');
chosen = get(h, 'Value');
evalin('base', ['help ' Algorithms(chosen).Name ])
case 'About'
im = imread('about','bmp');
plot(0,0)
h = image(im);
end