learning_svm2.m

一些自已编写的SVM应用程序,对初学者很有启发意义,

function  Learning_SVM2

X = [2 7; 3 6; 2 2; 8 1; 6 4; 4 8; 9 5; 9 9; 9 4; 6 9; 7 4];
Y = [ +1;  +1;  +1;  +1;  +1;  -1;  -1;  -1;  -1;  -1;  -1];
% define a simple artificial data set

x1ran = [0 10];
x2ran = [0 10];

plotdata(X, Y, x1ran, x2ran);

%net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
net = svm(size(X, 2), 'linear', [], 10);
%net = svmtrain(net, X, Y, alpha0, dodisplay)
net = svmtrain(net, X, Y);

f2 = figure;
plotboundary(net, x1ran, x2ran);
plotdata(X, Y, x1ran, x2ran);
plotsv(net, X, Y);
X = [X; [4 4]];
Y = [Y; -1];
net = svm(size(X, 2), 'linear', [], 10);
net = svmtrain(net, X, Y);

f3 = figure;
plotboundary(net, x1ran, x2ran);
plotdata(X, Y, x1ran, x2ran);
plotsv(net, X, Y);
net = svm(size(X, 2), 'rbf', [8], 100);
net = svmtrain(net, X, Y);

f4 = figure;
plotboundary(net, x1ran, x2ran);
plotdata(X, Y, x1ran, x2ran);
plotsv(net, X, Y);

f6 = figure;
delete(f1);
delete(f2);
delete(f3);
delete(f4);
delete(f5);
delete(f6);

function plotdata(X, Y, x1ran, x2ran)
% PLOTDATA - Plot 2D data set
% 

hold on;
ind = find(Y>0);
plot(X(ind,1), X(ind,2), 'ks');
ind = find(Y<0);
plot(X(ind,1), X(ind,2), 'kx');
text(X(:,1)+.2,X(:,2), int2str([1:length(Y)]'));
axis([x1ran x2ran]);
axis xy;

function plotsv(net, X, Y)
% PLOTSV - Plot Support Vectors
% 

hold on;
ind = find(Y(net.svind)>0);
plot(X(net.svind(ind),1),X(net.svind(ind),2),'rs');
ind = find(Y(net.svind)<0);
plot(X(net.svind(ind),1),X(net.svind(ind),2),'rx');

function [x11, x22, x1x2out] = plotboundary(net, x1ran, x2ran)
% PLOTBOUNDARY - Plot SVM decision boundary on range X1RAN and X2RAN
% 

hold on;
nbpoints = 100;
x1 = x1ran(1):(x1ran(2)-x1ran(1))/nbpoints:x1ran(2);
x2 = x2ran(1):(x2ran(2)-x2ran(1))/nbpoints:x2ran(2);
[x11, x22] = meshgrid(x1, x2);
[dummy, x1x2out] = svmfwd(net, [x11(:),x22(:)]);
x1x2out = reshape(x1x2out, [length(x1) length(x2)]);
contour(x11, x22, x1x2out, [-0.99 -0.99], 'b-');
contour(x11, x22, x1x2out, [0 0], 'k-');
contour(x11, x22, x1x2out, [0.99 0.99], 'g-');