%input:  a column vector of weights w and a bias b of a fixed line
%in R^2, a numpoints by 2 matrix of data points where each row
%correspons to an point in two dimensions and a scaling constant c.

%output: the weights and bias of the linear separator computed using the
%SVM algorithm on the data x (with labels dictated by w^Tx + b) with 
%scaling c.  The code also plots the ground truth and the learned classifier.
function r = plotsvm(w,b,x,c)
[numpoints, numcols] = size(x);

%find the max-margin classifier
y = sign(x*w + b);
A = [x(:,1).*y x(:,2).*y y];
r = quadprog(diag([1;1;0]),zeros(3,1),-A,-c*ones(numpoints,1));

%make nice plots
figure;
scatter(x(:, 1),x(:, 2),30*ones(numpoints,1),[(y+1)/2 zeros(numpoints,1) (y+1)/2], 'fill');
hold;
plot(0:.1:1, (-w(1)*[0:.1:1; 0:.1:1]-b)/w(2), '--r','LineWidth',2);
msvm = r(1)/-r(2);
bsvm = r(3)/-r(2);
plot(0:.1:1, msvm*(0:.1:1)+bsvm, '--b','LineWidth',2);
plot(0:.1:1, msvm*(0:.1:1)+bsvm-c/r(2), 'b','LineWidth',2);
plot(0:.1:1, msvm*(0:.1:1)+bsvm+c/r(2), 'b','LineWidth',2);
axis manual;
axis([0 1 0 1]);