Building a simple classifier

Let's see how to build a simple classifier using some training data.

How to do it…

1. We will use the simple_classifier.py file that is already provided to you as reference. Assuming that you imported the numpy and matplotlib.pyplot packages like we did in the last chapter, let's create some sample data:

   X = np.array([[3,1], [2,5], [1,8], [6,4], [5,2], [3,5], [4,7], [4,-1]])

2. Let's assign some labels to these points:

   y = [0, 1, 1, 0, 0, 1, 1, 0]

3. As we have only two classes, the y list contains 0s and 1s. In general, if you have N classes, then the values in y will range from 0 to N-1. Let's separate the data into classes based on the labels:

   class_0 = np.array([X[i] for i in range(len(X)) if y[i]==0])
   class_1 = np.array([X[i] for i in range(len(X)) if y[i]==1])

4. To get an idea about our data, let's plot it, as follows:

   plt.figure()
   plt.scatter(class_0[:,0], class_0[:,1], color='black', marker='s')
   plt.scatter(class_1[:,0], class_1[:,1], color='black', marker='x')

   This is a...