Implementing a perceptron learning algorithm in Python

In the previous section, we learned how Rosenblatt's perceptron rule works; let us now go ahead and implement it in Python and apply it to the Iris dataset that we introduced in Chapter 1, Giving Computers the Ability to Learn from Data. We will take an objected-oriented approach to define the perceptron interface as a Python Class, which allows us to initialize new perceptron objects that can learn from data via a fit method, and make predictions via a separate predict method. As a convention, we add an underscore to attributes that are not being created upon the initialization of the object but by calling the object's other methods—for example, self.w_.

Note

If you are not yet familiar with Python's scientific libraries or need a refresher, please see the following resources:

NumPy: http://wiki.scipy.org/Tentative_NumPy_Tutorial

Pandas: http://pandas.pydata.org/pandas-docs/stable/tutorials.html

Matplotlib: http...