Machine Learning for OpenCV

Implementing your first perceptron

Perceptrons are easy enough to be implemented from scratch. We can mimic the typical OpenCV or scikit-learn implementation of a classifier by creating a Perceptron object. This will allow us to initialize new perceptron objects that can learn from data via a fit method and make predictions via a separate predict method.

When we initialize a new perceptron object, we want to pass a learning rate (lr, or η in the previous section) and the number of iterations after which the algorithm should terminate (n_iter):

In [1]: import numpy as npIn [2]: class Perceptron(object):...     def __init__(self, lr=0.01, n_iter=10):...     self.lr = lr...     self.n_iter = n_iter... 

The fit method is where most of the work is done. ...