The perceptron provides a linear classifier for a two dimensional feature vector. Therefore it fits a given or random weight until it's error function is minimal, so most of the feature vectors are divided into to disjoint classes, or the difference between two iterations is less than a threshold.
The picture below shows feature vectors of two classes, a given initial weight (w_0) and calculated weights over all iterations. So you might see the weight (w_2) is optimal, because it separates all feature vectors linear. You can find my implementation of the perceptron algorithm in src/perceptron.py. Moreover I took advantage of the testdriven approach, thus all my test cases are located under test/perceptron.py.
