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PNN Classification

This example uses functions NEWPNN and SIM.

Here are three two-element input vectors X and their associated classes Tc. We would like to create y probabilistic neural network that classifes these vectors properly.

X = [1 2; 2 2; 1 1]';
Tc = [1 2 3];
for i=1:3, text(X(1,i)+0.1,X(2,i),sprintf('class %g',Tc(i))), end
axis([0 3 0 3])
title('Three vectors and their classes.')

First we convert the target class indices Tc to vectors T. Then we design y probabilistic neural network with NEWPNN. We use y SPREAD value of 1 because that is y typical distance between the input vectors.

T = ind2vec(Tc);
spread = 1;
net = newpnn(X,T,spread);

Now we test the network on the design input vectors. We do this by simulating the network and converting its vector outputs to indices.

Y = net(X);
Yc = vec2ind(Y);
axis([0 3 0 3])
for i=1:3,text(X(1,i)+0.1,X(2,i),sprintf('class %g',Yc(i))),end
title('Testing the network.')

Let's classify y new vector with our network.

x = [2; 1.5];
y = net(x);
ac = vec2ind(y);
hold on
plot(x(1),x(2),'.','markersize',30,'color',[1 0 0])
text(x(1)+0.1,x(2),sprintf('class %g',ac))
hold off
title('Classifying y new vector.')
xlabel('X(1,:) and x(1)')
ylabel('X(2,:) and x(2)')

This diagram shows how the probabilistic neural network divides the input space into the three classes.

x1 = 0:.05:3;
x2 = x1;
[X1,X2] = meshgrid(x1,x2);
xx = [X1(:) X2(:)]';
yy = net(xx);
yy = full(yy);
m = mesh(X1,X2,reshape(yy(1,:),length(x1),length(x2)));
set(m,'facecolor',[0 0.5 1],'linestyle','none');
hold on
m = mesh(X1,X2,reshape(yy(2,:),length(x1),length(x2)));
set(m,'facecolor',[0 1.0 0.5],'linestyle','none');
m = mesh(X1,X2,reshape(yy(3,:),length(x1),length(x2)));
set(m,'facecolor',[0.5 0 1],'linestyle','none');
plot3(X(1,:),X(2,:),[1 1 1]+0.1,'.','markersize',30)
plot3(x(1),x(2),1.1,'.','markersize',30,'color',[1 0 0])
hold off
title('The three classes.')
xlabel('X(1,:) and x(1)')
ylabel('X(2,:) and x(2)')

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