SVMshow

SVMshow

 

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85

% http://www.peteryu.ca/tutorials/matlab/visualize_decision_boundaries   % load RankData % NumTrain =200;   load RankData2   lambda = 20; rho = 1; c1 =10; c2 =10; epsilon = 0.2; result=[];       ker = 'lin'; sigma = 1/50; par = NonLinearDualSVORIM(X, y, c1, c2, epsilon, rho, ker, sigma);     % set up the domain over which you want to visualize the decision % boundary xrange = [-5 5]; yrange = [-5 5]; % step size for how finely you want to visualize the decision boundary. inc = 0.1; % 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.     d = [];     for k=1:max(y)         d(:,k) =  decisionfun(xy, par, X,y,k,epsilon, ker,sigma)';     end     [~,idx] = min(abs(d),[],2)    % reshape the idx (which contains the class label) into an image. decisionmap = reshape(idx, image_size);   figure;    %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(cmap);      % label the axes. xlabel('x1'); ylabel('x2');   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');   hold on; set(gca,'ydir','normal');