MATLAB画图

画图代码

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clear % http://www.peteryu.ca/tutorials/matlab/visualize_decision_boundaries   % load RankData % NumTrain =200;   load RankData2   % X = [X, -ones(size(X,1),1)];   lambda = 20; rho = 2; c1 =10; c2 =10; epsilon = 0.2; result=[]; ker = 'linear';  ker = 'rbf'; sigma = 1/1000; method=5 contour_level1 = [-epsilon,0, epsilon]; contour_level2 = [-epsilon,0, epsilon]; xrange = [-5 5]; yrange = [-5 5]; % step size for how finely you want to visualize the decision boundary. inc = 0.01; % generate grid coordinates. this will be the basis of the decision % boundary visualization. [x1, x2] = meshgrid(xrange(1):inc:xrange(2), yrange(1):inc:yrange(2)); % size of the (x, y) image, which will also be the size of the % decision boundary image that is used as the plot background. image_size = size(x1)   xy = [x1(:) x2(:)]; % make (x,y) pairs as a bunch of row vectors. %xy = [reshape(x, image_size(1)*image_size(2),1) reshape(y, image_size(1)*image_size(2),1)]   % loop through each class and calculate distance measure for each (x,y) % from the class prototype.   % calculate the city block distance between every (x,y) pair and % the sample mean of the class. % the sum is over the columns to produce a distance for each (x,y) % pair.   switch method     case 1         par = ParNonLinearDualSVORIM(X, y, c1, c2, epsilon, rho, ker, sigma);          f = TestPrecisionNonLinear(par,X, y,X, y, ker,epsilon,sigma);         % set up the domain over which you want to visualize the decision         % boundary         d = [];         for k=1:max(y)             d(:,k) =  decisionfun(xy, par, X,y,k,epsilon, ker,sigma)';         end        [~,idx] = min(abs(d)/par.normw{k},[],2);        contour_level=contour_level1;     case 2         par = ParNonLinearDualBoundSVORIM(X, y, c1, c2, epsilon, rho, ker, sigma);         f = TestPrecisionNonLinear(par,X, y,X, y, ker,epsilon,sigma);         % set up the domain over which you want to visualize the decision         % boundary         d = [];         for k=1:max(y)             d(:,k) =  decisionfun(xy, par, X,y,k,epsilon, ker,sigma)';         end        [~,idx] = min(abs(d)/par.normw{k},[],2);        contour_level=contour_level1;     case  3   %       par = NewSVORIM(X, y, c1, c2, epsilon, rho);        par = LinearDualSVORIM(X,y, c1, c2, epsilon, rho); % ADMM for linear dual model         d = [];         for k=1:max(y)             w= par.w(:,k)';              d(:,k) = w*xy'-par.b(k);         end          [~,idx] = min(abs(d)/norm(par.w),[],2);          contour_level=contour_level1;     case 4         path='C:\Users\hd\Desktop\svorim\svorim\';         name='RankData2';         k=0;         fname1 = strcat(path, name,'_train.', num2str(k));          fname2 = strcat(path, name,'_targets.', num2str(k));          fname2 = strcat(path, name,'_test.', num2str(k));         Data=[X y];         save(fname1,'Data','-ascii');         save(fname2,'y','-ascii');         save(fname2,'X','-ascii');         command= strcat(path,'svorim -F 1 -Z 0 -Co 10 -p 0 -Ko 1/10 C:\Users\hd\Desktop\svorim\svorim\', name, '_train.', num2str(k)); %        command= 'C:\Users\hd\Desktop\svorim\svorim\svorim -F 1 -Z 0 -Co 10 C:\Users\hd\Desktop\svorim\svorim\RankData2_train.0'; %        command='C:\Users\hd\Desktop\svorim\svorim\svorim -F 1 -Z 0 -Co 10 G:\datasets-orreview\discretized-regression\5bins\X4058\matlab\mytask_train.0'         dos(command);         fname2 = strcat(fname1, '.svm.alpha');         alpha_bais = textread(fname2);         r=length(unique(y));         model.alpha=alpha_bais(1:end-r+1);         model.b=alpha_bais(end-r+2:end);         xnew=xy;         nT=size(xnew,1);         for k=1:r-1 %             d(:,k)=model.alpha'*Kernel(ker,X',xy',sigma)- model.b(k);             if nT >1000                 for j=1:nT/1000                     xnewk=xnew(1000*(j-1)+1:1000*j,:);                     f(1000*(j-1)+1:1000*j) = model.alpha'*Kernel(ker,X',xnewk',sigma)- model.b(k);                 end                 xnewk=xnew(1000*j+1:nT,:);                 f(1000*j+1:nT)=model.alpha'*Kernel(ker,X',xnewk',sigma)- model.b(k);             else                 f =model.alpha'*Kernel(ker,X',xnew',sigma)- model.b(k);             end              d(:,k)=f;         end            pretarget=[];idx=[];         for i=1:size(xy,1)             idx(i) = min([find(d(i,:)<0,1,'first'),length(model.b)+1]);         end         contour_level=contour_level2;     case 5         train.patterns =X;         train.targets = y;         test.patterns =xy;         test.targets = ones(size(xy,1),1);         switch ker             case 'linear'                 parameters=[c1];                Algorithm = SVOREXLin();                 Title='SVORLin(c=10)';             case 'rbf'                 parameters=[c1 sigma];                  Algorithm = SVORIM();                  Title='SVORIM(\gamma=1/1000)';         end         [model_information] =  Algorithm.runAlgorithm(train, test, parameters);           %         r=length(unique(y)); %         model.alpha=model_information.model.projection'; %         model.b=model_information.model.thresholds; %         xnew=xy; %         nT=size(xnew,1); %         for k=1:r-1 % %             d(:,k)=model.alpha'*Kernel(ker,X',xy',sigma)- model.b(k); %             if nT >1000 %                 for j=1:nT/1000 %                     xnewk=xnew(1000*(j-1)+1:1000*j,:); %                     f(1000*(j-1)+1:1000*j) = model.alpha'*Kernel(ker,X',xnewk',sigma)- model.b(k); %                 end %                 xnewk=xnew(1000*j+1:nT,:); %                 f(1000*j+1:nT)=model.alpha'*Kernel(ker,X',xnewk',sigma)- model.b(k); %             else %                 f =model.alpha'*Kernel(ker,X',xnew',sigma)- model.b(k); %             end %              d(:,k)=f; %         end % %          pretarget=[];idx=[]; %         for i=1:size(xy,1) %             idx(i) = min([find(d(i,:)<0,1,'first'),length(model.b)+1]); %         end         idx=model_information.predictedTest;         contour_level=contour_level2;         MZE =1- mean(model_information.predictedTrain==y);         MAE= mean(abs(model_information.predictedTrain-y));         Title = [Title 'MZE=' num2str(MZE) 'MAE=' num2str(MAE)];             case 6         train.patterns =X;         train.targets = y;         test.patterns =xy;         test.targets = ones(size(xy,1),1);         switch ker             case 'linear'                 parameters=[c1];                  Algorithm = SVORLin();                 Title='REDSVMLin(c=10)';             case 'rbf'                 parameters=[c1 sigma];                  Algorithm = REDSVM();                  Title='REDSVM(\gamma=1/1000)';         end         [model_information] =  Algorithm.runAlgorithm(train, test, parameters);                idx=model_information.predictedTest;         contour_level=contour_level2;                MZE =1- mean(model_information.predictedTrain==y);         MAE= mean(abs(model_information.predictedTrain-y));         Title = [Title 'MZE=' num2str(MZE) 'MAE=' num2str(MAE)]; end        % reshape the idx (which contains the class label) into an image. decisionmap = reshape(idx, image_size);   % figure(7); %  % %show the image % imagesc(xrange,yrange,decisionmap); % hold on; % set(gca,'ydir','normal'); %  % % colormap for the classes: % % class 1 = light red, 2 = light green, 3 = light blue % cmap = [1 0.8 0.8; 0.95 1 0.95; 0.9 0.9 1]; % colormap cool; % % imagesc(xrange,yrange,decisionmap);   % plot the class training data.   color = {'r.','go','b*','r.','go','b*'};   for i=1:max(y)     plot(X(y==i,1),X(y==i,2), color{i});     hold on end % include legend % legend('Class 1', 'Class 2', 'Class 3','Location','NorthOutside', ... %     'Orientation', 'horizontal'); legend('Class 1', 'Class 2', 'Class 3'); set(gca,'ydir','normal'); hold on for k = 1:max(y)-1 %       decisionmapk = reshape(d(:,k), image_size); %       contour(x1,x2, decisionmapk, [contour_level(1) contour_level(1) ], color{k},'Fill','off'); %      contour(x1,x2, decisionmapk, [contour_level(2) contour_level(2) ], color{k},'Fill','off','LineWidth',2); %       contour(x1,x2, decisionmapk, [contour_level(3) contour_level(3) ], color{k},'Fill','off');         if k<max(y)            contour(x1,x2, decisionmap, [k+1 k+1], color{k},'Fill','off','LineWidth',2);       end end   hold off %  % label the axes. xlabel('x1'); ylabel('x2'); title(Title)