A Python implementation of the perceptron learning algorithm. Classifies data points based on given training and testing datasets (see output images below).
Initial weights: [ 0.5146966 0.41554422 0.54194774]
Final decision boundary: 0.51x + 0.42y + 0.54 = 0
Number of iterations made over training set: 50
Number of weight vector updates: 2500
Final misclassification error for training data: 26.0%
Final misclassification error for test data: 0.2%
If you choose to run this, please use Python 2