classification with four classes by matlab - matlab

Question

I have a classification problem with four classes of input vector.The four classes are
A = [1 , 1; 1 ,2];
B = [2,2; -1,0];
C = [-1,-2;2,1];
D = [-1,-2; -1,-2];
I wan Implement this problem by Matlab, I Use this code :
C = [-1,-2;2,1];
A = [1 , 1; 1 ,2];
B = [2,2; -1,0];
D = [-1,-2; -1,-2];
hold on
grid on
plot(A(1,:),A(2,:),'bs')
plot(B(1,:),B(2,:),'r+')
plot(C(1,:),C(2,:),'go')
plot(D(1,:),D(2,:),'m*')
a = [0 1]';
b = [1 1]';
c = [1 0]';
d = [0 0]';
P = [A B C D];
T = [repmat(a,1,length(A)) repmat(b,1,length(B)) repmat(c,1,length(C)) repmat(d,1,length(D)) ];
net = perceptron;
E = 1;
net.adaptParam.passes = 1;
linehandle = plotpc(net.IW{1},net.b{1});
n = 0;
while (sse(E))
n = n+1;
[net,Y,E] = adapt(net,P,T);
linehandle = plotpc(net.IW{1},net.b{1},linehandle);
drawnow;
end
but My Code does'nt work I have No idea why, please Help me....

Answer 1

As has been suggested by thewaywewalk, the trouble is your while-loop and the fact that you do not provide an adequate check for the statement you wish to evaluate.
Replace your while-statement with these two lines:
ACCEPTABLE_ERROR = 3.0;
while (sse(E)>ACCEPTABLE_ERROR)
And you should see your script terminate after three iterations. You can play with the ACCEPTABLE_ERROR variable to check which solution works best for you. If you set it too small, your while loop will not exit, because the statement will not be false.
An explanation to your original while-statement:
All you ever evaluated if sse(e) returned a results - which it did in each case. That's why it never stopped.
To the question of sse requires more than one input argument:
That depends on what input arguments you provide.
The documentation says:
perf = sse(net,t,y,ew) takes these input arguments and optional function parameters,
net: Neural network
t: Matrix or cell array of target vectors
y: Matrix or cell array of output vectors
ew: Error weights (default = {1})
and returns the sum squared error.
However, it is not necessary, to provide the error weights, ew, as the source code reveals:
Only the first three arguments are required. The default error weight is {1}, which weights the importance of all targets equally.
In your case you should, based on the documentation, call sse like this:
sse(net,T,Y)
Without being mentioned in the documentation (or I haven't found it), this is equivalent to what you have done, by providing simply the network errors, E as provided from adapt:
sse(E)
Both give the same results.