def test_prediction(self):
print """Test basic classification"""
classifier = NaiveBayes()
classifier.train(self.training_set)
predictions = [classifier.classify_instance(x) for x in self.training_set]
self.assertEqual(predictions[0], 'cat')
self.assertEqual(predictions[1], 'dog')
self.assertEqual(predictions[2], 'cat')
self.assertEqual(predictions[3], 'dog')
self.assertEqual(predictions[4], 'dog')
count_x_y[woof_index, dog_index] == (2+1) count_y = classifier.count_y_table count_y[cat_index] == 3 count_y[dog_index] == 2 """Test probability tables Regardless of what kind of smoothing you do, the signs have to be right """ classifier.train(training_set) p_x_given_y = classifier.p_x_given_y_table p_x_given_y[meow_index, cat_index] > p_x_given_y[meow_index, dog_index] p_x_given_y[bark_index, cat_index] < p_x_given_y[bark_index, dog_index] p_y = classifier.p_y_table p_y[cat_index] > p_y[dog_index] """Test smoothing Zero counts must not result in zero proability """ p_x_given_y[bark_index, cat_index] != 0 p_x_given_y[purr_index, dog_index] != 0 """Test basic classification""" predictions = [classifier.classify_instance(x) for x in training_set] predictions == ['cat', 'cat', 'cat', 'dog', 'dog']