Training a single neuron perceptron on Matlab - matlab

Question

I am required to train a single neuron perceptron which classifies two different classes.
I wrote this code on matlab.
function error = train_perc( PercepClassTraining,eta,weights )
%initialization:
xZero=1;
wZero=0.1;
t=1; % round number
x=[];
y=[];
error=[];
epoch_error=[];
c=1;
n=1;
x1=PercepClassTraining(n,1);
x2=PercepClassTraining(n,2);
y(n)=(xZero*wZero)+(x1*weights(1))+(x2*weights(2));
error(n)=PercepClassTraining(n,3)-y(n);
while(error(n)~=0)
weights(1)=weights(1)+(eta*error(n)*x1);
weights(2)=weights(2)+(eta*error(n)*x2);
if(n==2000)
epoch_error(c)=mean(error);
error=[];
c=c+1
n=1;
else
n=n+1;
end
y(n)=(xZero*wZero)+(x1*weights(1))+(x2*weights(2));
error(n)=PercepClassTraining(n,3)-y(n);
end
Where eta is learning rate, and PercepClassTraining is the training set (2000 inputs).
It converges at epoch #58 when I use 0.1 for learning rate and [0.01 0.01] for initial weights, and that doesn't use all the training set.
When I set eta to 0.01 and weights to 0.1, 0.1 it loops forever! Is there anything wrong in the code? How about the stopping criteria?