Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

How to do it...

1. Import the following packages:

from sklearn.naive_bayes import GaussianNBimport numpy as npimport matplotlib.pyplot as plt

2. Use the following data file, which includes comma-separated arithmetical data:

in_file = 'data_multivar.txt'a = []b = []with open(in_file, 'r') as f:  for line in f.readlines():    data = [float(x) for x in line.split(',')]    a.append(data[:-1])    b.append(data[-1])a = np.array(a)b = np.array(b)

3. Construct a Naive Bayes classifier:

classification_gaussiannb = GaussianNB()classification_gaussiannb.fit(a, b)b_pred = classification_gaussiannb.predict(a)

4. Calculate the accuracy of Naive Bayes:

correctness = 100.0 * (b == b_pred).sum() / a.shape[0]print "correctness of the classification =", round(correctness, ...