www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/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.') 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 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.') break else if (redraws < 1), set(hm,'String','Number of redraws must be larger than 0.') break else if (strcmp(error_method, 'Cross-Validation') & (redraws == 1)), set(hm,'String','Number of redraws must be larger than 1.') break 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.') break else if (floor(percent/100*length(targets)) < 1), set(hm,'String','Number of training vectors must be larger than 0.') break end if (percent >= 100), set(hm,'String','Number of training vectors must be smaller than 100%.') break end if (floor((1-percent/100)*length(targets)) < 1), set(hm,'String','Number of test vectors must be larger than 0.') break end end end %Find the region for the grid [region,x,y] = calculate_region(features, [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'); 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(features, targets, hParent) axis(region(1:4)) hold on drawnow %Call the main classification function [D, test_err, train_err, train_features, train_targets, reduced_features, reduced_targets] = ... start_classify(features, 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_features)) assignin('base', 'reduced_features', reduced_features); assignin('base', 'reduced_targets', reduced_targets); else evalin('base', 'clear reduced_features 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,1);colormap([198 198 255; 240 255 240]/255); else [s,h]=contour(x,y,D,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'))), Dbayes = decision_region(distribution_parameters, region); [s,h] = contour(x,y,Dbayes,1);set(h,'LineWidth',1,'EdgeColor','r'); [classify, Bayes_err] = classification_error(Dbayes, features, 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_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'); 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 features targets h = findobj('Tag', 'BayesError'); set(h, 'String', ''); h = findobj('Tag', 'TestSetError'); set(h, 'String', ''); h = findobj('Tag', 'TrainSetError'); set(h, 'String', ''); evalin('base','region = [0 0 0 0 500];') evalin('base','[features, targets, distribution_parameters] = load_file(get(gcbo, ''String''), region);') if (evalin('base', 'isempty(distribution_parameters)')), evalin('base', 'clear distribution_parameters'); end if (evalin('base', '~isempty(features)')) evalin('base','region = calculate_region(features, [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')), clear distribution_parameters features 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','[features, targets, distribution_parameters] = load_file([pathname, filename], region);') evalin('base','clear pathname filename'); if (evalin('base', 'isempty(distribution_parameters)')), evalin('base', 'clear distribution_parameters'); end if (evalin('base', '~isempty(features)')) evalin('base','region = calculate_region(features, [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 Gaussians in class 0:',ttl,1,cellstr(num2str(1))))); n1 = str2num(char(inputdlg('Enter the number of Gaussians in class 1:',ttl,1,cellstr(num2str(1))))); distribution_parameters.p0 = str2num(char(inputdlg('Enter the probability of class 0:',ttl,1,cellstr(num2str(0.5))))); distribution_parameters.m0 = zeros(n0,2); distribution_parameters.m1 = zeros(n1,2); distribution_parameters.s0 = zeros(n0,2,2); distribution_parameters.s1 = zeros(n1,2,2); for i = 1:n0, distribution_parameters.s0(i,:,:) = eye(2); end for i = 1:n1, distribution_parameters.s1(i,:,:) = eye(2); end distribution_parameters.w0 = ones(n0,1)/n0; distribution_parameters.w1 = ones(n1,1)/n1; save synthetic distribution_parameters uiwait(enter_distributions); evalin('base','region = [0 0 0 0 500];') evalin('base','distribution_parameters = load_file(''synthetic'', region);') 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_features, new_targets, new_params, region] = click_points(region);'); new_features = evalin('base','new_features'); 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_features = new_features(:,i); end if (evalin('base','exist(''features'')')), features = evalin('base','features'); targets = evalin('base','targets'); else features = []; targets = []; end if (evalin('base','exist(''distribution_parameters'')')), distribution_parameters = evalin('base','distribution_parameters'); else distribution_parameters = []; end if isempty(distribution_parameters), %If now features exist on the workspace, this is a new distribution, so take it %(Otherwise, we may be saving only pars of the distribution) if isempty(features) distribution_parameters = new_params; end else distribution_parameters.m0=[distribution_parameters.m0; new_params.m0]; distribution_parameters.m1=[distribution_parameters.m1; new_params.m1]; distribution_parameters.s0=[distribution_parameters.s0; new_params.s0]; distribution_parameters.s1=[distribution_parameters.s1; new_params.s1]; distribution_parameters.w0=[distribution_parameters.w0*sum(~targets); new_params.w0*sum(~new_targets)]; distribution_parameters.w1=[distribution_parameters.w1*sum( targets); new_params.w1*sum( new_targets)]; distribution_parameters.w0=distribution_parameters.w0/sum(distribution_parameters.w0); distribution_parameters.w1=distribution_parameters.w1/sum(distribution_parameters.w1); distribution_parameters.p0=length(distribution_parameters.w0)/(length(distribution_parameters.w0)+length(distribution_parameters.w1)); end features = [features, new_features]; targets = [targets, new_targets]; assignin('base', 'features', features); assignin('base', 'targets', targets); assignin('base', 'distribution_parameters', distribution_parameters); evalin('base','region = calculate_region(features, [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(''features'')')), if (evalin('base', 'exist(''distribution_parameters'')')), evalin('base','save([pathname filename], ''features'', ''targets'', ''distribution_parameters'');') else evalin('base','save([pathname filename], ''features'', ''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','[features, targets] = generate_data_set (distribution_parameters, region);') evalin('base','region = calculate_region(features, [zeros(1,4) 100]);') classifier_commands('ClearBounds'); case 'Params' %Print to mean, cov, main directions to workspace if (evalin('base','~isempty(whos(''features''))')) %There are features on workspace features = evalin('base','features'); targets = evalin('base','targets'); train_one = find(targets == 1); train_zero = find(targets == 0); %Estimate mean and covariance for class 0 m0 = mean(features(:,train_zero)''); c0 = cov(features(:,train_zero)'',1); p0 = length(train_zero)/length(targets); %Estimate mean and covariance for class 1 m1 = mean(features(:,train_one)''); c1 = cov(features(:,train_one)'',1); Chernoff = Chernoff(mean(features(:,train_zero)'), cov(features(:,train_zero)',1), mean(features(:,train_one)'), cov(features(:,train_one)',1),length(train_one)/length(targets)); Bhattacharyya = Bhattacharyya(mean(features(:,train_zero)'), cov(features(:,train_zero)',1), mean(features(:,train_one)'), cov(features(:,train_one)',1),length(train_one)/length(targets)); Discriminability = Discriminability(mean(features(:,train_zero)'), cov(features(:,train_zero)',1), mean(features(:,train_one)'), cov(features(:,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(Bhattacharyya,3)]) set(findobj(h0,'Tag','txtBound2'),'String',['The Chernoff bound value is: ' num2str(Chernoff,3)]) set(findobj(h0,'Tag','txtDiscriminability'),'String',['The discriminability value is: ' num2str(Discriminability,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); print -dmeta case 'Print' 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); printpreview 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 '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(''features'') & exist(''targets'')')) evalin('base','plot_scatter(features, 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 'MixFeatures' %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);') h = findobj('Tag', 'Messages'); set(h,'String','Features were permuted.'); else error('No features 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