SVMshow

SVMshow

% 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');