Using the PLA on 100 points and counting the number of iterations required for convergence I observed some interesting behavior. I set the max number of iterations to 1000 and then ran 10,000 simulations. This histogram shows the results:

The data at 1000 can be interpreted as those simulations which would have taken over 1000 iterations to converge. When I removed the limit on the max number of iterations and ran the experiment I had one case take

iterations to converge. From this I conclude that the perceptron doesn't always converge rapidly.

In my experiment the mean number of iterations required for convergence was around 240, which is not particularly close to the possible answers of 100 and 500. The median, however, is certainly near 100. Do other people have similar results?