classifier_commands.m

最新的模式识别分类工具箱,希望对朋友们有用!

% This is the "core" of the 
%
%
function classifier_commands(command)

persistent Preprocessing_methods;
persistent Algorithms;
persistent Frameworks;
if (isempty(Preprocessing_methods) | isempty(Algorithms) | isempty(Frameworks))
   Algorithms = read_algorithms('Algorithms.txt');
   Preprocessing_methods = read_algorithms('Preprocessing.txt');
   load FrameworkNames;
end

%This function processes most events from the main GUI screen
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);

    h				= findobj('Tag', 'frameworkToBeUsed');
    set(h,'String',strvcat(Frameworks(:)));
    chosen = strmatch('Framework_for_Class',char(Frameworks(:)));
    set(h,'Value',chosen);

    
case 'Changed Preprocessing'
  fprintf('Changed Preprocessing\n');
   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'
  fprintf('Changed Algorithm\n');

   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);

       % Special Cases, where there is a difference between
       % the original framework and the 
       %       disp(Algorithms(chosen))

       if (strcmp(Algorithms(chosen).Name,'SVM'))
	 Algorithms(chosen) = DifferentSetup(Algorithms(chosen));
       end

       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 'FileNameInput'
   fprintf('FileNameInput\n');

   evalin('base','hold off')
   evalin('base','clear m0 m1 s0 s1 w0 w1 p0 features targets')
   evalin('base','h =  findobj(''Tag'', ''BayesError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''TestSetError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''TrainSetError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','region = [0 0 0 0 500];')
   evalin('base','filename = get(gcbo, ''String'');')
   evalin('base','pathname = cd;')
   evalin('base','[features, targets] = load_file([pathname ''\'' filename], ''points'', region);')
   
 case 'SearchForFile'
   fprintf('SearchForFile\n');
 
   evalin('base','region = [0 0 0 0 500];')
   evalin('base','[filename, pathname] = uigetfile(''*.mat'', ''Open file ...'');')
   if (evalin('base','filename ~= 0')),
	   evalin('base','clear m0 m1 s0 s1 w0 w1 p0 features targets')
       evalin('base','h =  findobj(''Tag'', ''BayesError'');')
       evalin('base','set(h, ''String'', '''');')
       evalin('base','h =  findobj(''Tag'', ''TestSetError'');')
       evalin('base','set(h, ''String'', '''');')
       evalin('base','h =  findobj(''Tag'', ''TrainSetError'');')
       evalin('base','set(h, ''String'', '''');')
       evalin('base','h = findobj(''Tag'', ''FileNameInput'');')
	   evalin('base','set(h, ''String'', [pathname filename]);')
       evalin('base','[features, targets] = load_file([pathname filename], ''points'', region);')
       evalin('base','region = calculate_region(features, [zeros(1,4) 100]);')
   end
   
 case 'EnterManualDist'
   fprintf('EnterManualDist\n');

   classifier_commands('ClearWorkspace');
   evalin('base','ttl	= ''Manual definition of distribution'';')
   evalin('base','n0		= str2num(char(inputdlg(''Enter the number of Gaussians in class 0:'',ttl,1,cellstr(num2str(1)))));')
   evalin('base','n1		= str2num(char(inputdlg(''Enter the number of Gaussians in class 1:'',ttl,1,cellstr(num2str(1)))));')
   evalin('base','p0		= str2num(char(inputdlg(''Enter the probability of class 0:'',ttl,1,cellstr(num2str(0.5)))));')
   evalin('base','m0		= zeros(n0,2);')
   evalin('base','m1 	= zeros(n1,2);')
   evalin('base','s0  	= zeros(n0,2,2);')
   evalin('base','s1  	= zeros(n1,2,2);')
   evalin('base','for i = 1:n0, s0(i,:,:) = eye(2); end')
   evalin('base','for i = 1:n1, s1(i,:,:) = eye(2); end')
   evalin('base','w0 	= ones(n0,1)/n0;')
   evalin('base','w1 	= ones(n1,1)/n1;')
   evalin('base','save synthetic m0 m1 s0 s1 w0 w1 p0')
   evalin('base','uiwait(enter_distributions);')
   evalin('base','region = [0 0 0 0 500];')
   evalin('base','[m0, m1, s0, s1, w0, w1, p0] = load_file(''synthetic'', ''dist'', region);')
   
case 'LoadDistFile'
  fprintf('LoadDistFile\n');

   evalin('base','region = [0 0 0 0 500];')
   evalin('base','[filename, pathname] = uigetfile(''*.mat'', ''Open file ...'');')
   if (evalin('base','filename ~= 0')),
      evalin('base','[m0, m1, s0, s1, w0, w1, p0] = load_file([pathname filename], ''dist'', region);')
   end
   
 case 'ManualGraphData'
   fprintf('ManualGraphData\n');

   %Graphically enter a data set
   evalin('base','if ~(exist(''region'')), region = [-1 1 -1 1 100];end');
   evalin('base','if (sum(region(1:4)) == 0), region = [-1 1 -1 1 100];end');
   evalin('base','if ~(exist(''features'')), features = []; end');
   evalin('base','if ~(exist(''targets'')), targets = []; end');
   evalin('base','if ~(exist(''m0'')), m0 = []; m1 = []; s0 = []; s1 = []; w0 = []; w1 = []; end');
   evalin('base','[new_features, new_targets, new_m0, new_m1, new_s0, new_s1, new_w0, new_w1, p0, region] = click_points(region);');   
   evalin('base','features  = [features, new_features];');
   evalin('base','targets   = [targets, new_targets];');
   evalin('base','m0        = [m0; new_m0];');
   evalin('base','m1        = [m1; new_m1];');
   evalin('base','s0        = [s0; new_s0];');
   evalin('base','s1        = [s1; new_s1];');
   evalin('base','w0        = [w0; new_w0];w0=w0/sum(w0);');
   evalin('base','w1        = [w1; new_w1];w1=w1/sum(w1);');
   evalin('base','p0        = length(w0)/(length(w0)+length(w1));');
   evalin('base','region = calculate_region(features, [zeros(1,4) 100]);')
    
 case 'SaveFeatures'
   fprintf('SaveFeatures\n');

   evalin('base','[filename, pathname] = uiputfile(''*.mat'', ''Save file ...'');')
   if (evalin('base','filename ~= 0')),
      evalin('base','save([pathname filename], ''features'', ''targets'');')
   end
   
case 'SaveParameters'
  fprintf('SaveParameters\n');

   evalin('base','[filename, pathname] = uiputfile(''*.mat'', ''Save file ...'');')
   if (evalin('base','filename ~= 0')),
      evalin('base','save([pathname filename], ''m0'', ''m1'', ''s0'', ''s1'', ''w0'', ''w1'', ''p0'');')
   end
   
 case 'GenerateDistData'
   fprintf('GenerateDistData\n');

   evalin('base','region = [0 0 0 0 100];')
   evalin('base','[features, targets] = generate_data_set  (m0, m1, s0, s1, w0, w1, p0, region);')
   evalin('base','region = calculate_region(features, [zeros(1,4) 100]);')
   
case 'Params'
  fprintf('Params\n');

   %Print to mean, cov, main directions to workspace 
   if (evalin('base','~isempty(whos(''features''))'))
      %There are features on workspace
      evalin('base','train_one  = find(targets == 1);')
		evalin('base','train_zero = find(targets == 0);')

		%Estimate mean and covariance for class 0
      evalin('base','disp([''Mean for class 0 is: '' num2str(mean(features(:,train_zero)''))]);')
      evalin('base','disp([''Covariance matrix for class 0 is:''])')
      evalin('base','disp(num2str(cov(features(:,train_zero)'')));')
      evalin('base','disp([''Probability of class 0 is: '' num2str(length(train_zero)/length(targets))]);')

		%Estimate mean and covariance for class 1
      evalin('base','disp([''Mean for class 1 is: '' num2str(mean(features(:,train_one)''))]);')
      evalin('base','disp([''Covariance matrix for class 1 is:''])')
      evalin('base','disp(num2str(cov(features(:,train_one)'')));')
      evalin('base','disp([''Probability of class 1 is: '' num2str(length(train_one)/length(targets))]);')
      
   end
   
case 'MenuZoom'
  fprintf('MenuZoom\n');

   evalin('base','umtoggle(findobj(gcbf,''Label'',''&Zoom''));')
   evalin('base','h = findobj(gcbf, ''Label'', ''&Zoom'');')
   evalin('base','v = strcmp(''on'',get(h, ''Checked''));')
   if evalin('base','v'),
      evalin('base','zoom on')
   else                      
      evalin('base','zoom off')
   end

case 'MenuGrid'
  fprintf('MenuGrid\n');

   evalin('base','umtoggle(findobj(gcbf,''Label'',''&Grid''));')
   evalin('base','h = findobj(gcbf, ''Label'', ''&Grid'');')
   evalin('base','v = strcmp(''on'',get(h, ''Checked''));')
   if evalin('base','v'),
      evalin('base','grid on')
   else
      evalin('base','grid off')
   end
   
case 'Copy'
  fprintf('Copy\n');

   evalin('base','h = figure;')
   evalin('base','c = copyobj(findobj(''Type'',''axes''),h);')
   evalin('base','set(gca,''Position'',[0.1300    0.1100    0.7750    0.8150])')
   %evalin('base','colormap([198 198 255; 240 255 240]/255)')
   evalin('base','print -dmeta')
   
case 'Print'
  fprintf('Print\n');

   evalin('base','h = figure;')
   evalin('base','c = copyobj(findobj(''Type'',''axes''),h);')
   evalin('base','set(gca,''Position'',[0.1300    0.1100    0.7750    0.8150])')
   evalin('base','colormap([198 198 255; 240 255 240]/255)')
   evalin('base','printpreview')
   
case 'Exit'
  fprintf('Print\n');

   evalin('base','h = findobj(''Tag'', ''Messages'');')
   evalin('base','set(h,''String'','''');')
   evalin('base','h =  findobj(''Tag'', ''TestSetError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''TrainSetError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''FileNameInput'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''PercentTraining'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''Redraws'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''HowMany'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','set(gcf, ''pointer'', ''arrow'');')
   
   evalin('base','close') 
   
case 'SeparatePreprocessing'
  fprintf('Print\n');

   evalin('base','umtoggle(findobj(gcbf,''Label'',''&Perform preprocessing separately for each class''));')
   
case 'ShowPartitions'
  fprintf('Print\n');

   evalin('base','umtoggle(findobj(gcbf,''Label'',''&Show center of partitions during training''));')
   
 case 'ShowTrainingPoints'
   fprintf('ShowTrainingPoints\n');

   evalin('base','umtoggle(findobj(gcbf,''Label'',''Show &Training points''));')
   
case 'ShadeDecisionRegion'
  fprintf('ShadeDecisionRegion\n');

   evalin('base','umtoggle(findobj(gcbf,''Label'',''Shade &Decision Regions''));')
   
case 'ClearBounds'
  fprintf('ClearBounds\n');

   %Clear all bounds
   evalin('base','hold off')
   evalin('base','plot_scatter(features, targets, gcbf)')
   evalin('base','drawnow')
   evalin('base','h =  findobj(''Tag'', ''TestSetError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''TrainSetError'');')
   evalin('base','set(h, ''String'', '''');')

case 'ClearWorkspace'
  fprintf('ClearWorkspace\n');

   %Clear all 
   evalin('base','h =  findobj(''Tag'', ''TestSetError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''TrainSetError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''BayesError'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','h =  findobj(''Tag'', ''FileNameInput'');')
   evalin('base','set(h, ''String'', '''');')
   evalin('base','hold off')
   evalin('base','plot(0,0); grid on;')
   evalin('base','clear all')

case 'MixFeatures'
  fprintf('MixFeatures\n');

   %Mix (permute) the features
   if (evalin('base','exist(''features'')') & evalin('base','exist(''targets'')')),
       evalin('base','indexes  = randperm(length(targets));')
       evalin('base','features = features(:,indexes);')
       evalin('base','targets  = targets(indexes);')
       evalin('base','h = findobj(''Tag'', ''Messages'');')
       evalin('base','set(h,''String'',''Features were permuted.'');')
   else
       error('No features or targets in workspace!');
   end

case 'About'
   im = imread('about','bmp');
   plot(0,0)
   h  = image(im);
end