%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SVM tutorials
% Z. Li, 2009.02.08
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
 x = [
    2.5240    2.2263
    2.9271    2.9319
    2.7735    3.2238
    3.8484    3.8321
    4.4242    4.7080
    3.7524    4.6350
    4.5585    3.7105
    5.2879    5.3163
    5.0384    4.6107
    5.9597    5.8759
    ];

y =[
    0.8925    4.3431
    1.1996    4.8297
    1.0653    5.2920
    1.5259    5.4380
    1.5259    6.2409
    1.2188    6.8491
    1.7179    7.0438
    1.9290    6.2895
    2.0058    7.7737
    1.7370    8.2847
    ];

if (1)
    figure; hold on; axis([1 100 1 100]);
    nx = 24; ny = 24; 
    fprintf('\n input x points...');
    for k=1:nx
        [x1, x2]=ginput(1);
        plot(x1, x2, '+r'); 
        x(k, :)=[x1, x2];
    end
    fprintf('\n input y points...');

    for k=1:ny
        [x1, x2]=ginput(1);
        plot(x1, x2, '.k'); 
        y(k, :)=[x1, x2];
    end
    
    save svm_test_data.mat x y; 
else
    %load svm_test_data_linear.mat;
    load svm_test_data.mat;
    hold on; grid on; axis([1 100 1 100]);
    plot(x(:,1), x(:,2), '+r');
    plot(y(:,1), y(:,2), '.k');
    
end

[nx, d]=size(x); [ny,d]=size(y);

data(1:nx,:)=x;  labels(1:nx, 1) = 'x';
data(nx+1:nx+ny,:)=y; labels(nx+1:nx+ny, 1) = 'y';

if (1)
  svmStruct = svmtrain(data, labels, 'showplot',true);
else
  svmStruct = svmtrain(data, labels, 'kernel_function', 'quadratic','showplot',true);
end

% test svm
nTest = 3;
for k=1:nTest
    [x1, x2] = ginput(1); hold on; grid on;
    plot(x1, x2, '*m');
    test(k, :) = [x1, x2];
end
classes = svmclassify(svmStruct,  test,'showplot',true);

 
return;