def test_collect_counts(self):
classifier = NaiveBayes()
classifier._collect_counts(self.training_set)
cat_index = classifier.label_codebook.get_index('cat')
dog_index = classifier.label_codebook.get_index('dog')
purr_index = classifier.feature_codebook.get_index('purr')
meow_index = classifier.feature_codebook.get_index('meow')
bark_index = classifier.feature_codebook.get_index('bark')
woof_index = classifier.feature_codebook.get_index('woof')
print """Test collecting counts
If any of these fails, check if you have updated the codebooks
and check if the counts have been collected correctly without smoothing"""
count_x_y = classifier.count_x_y_table
self.assertEqual(count_x_y[purr_index, cat_index], 2)
self.assertEqual(count_x_y[meow_index, cat_index], 2)
self.assertEqual(count_x_y[bark_index, cat_index], 0)
self.assertEqual(count_x_y[woof_index, cat_index], 1)
self.assertEqual(count_x_y[purr_index, dog_index], 0)
self.assertEqual(count_x_y[meow_index, dog_index], 1)
self.assertEqual(count_x_y[bark_index, dog_index], 1)
self.assertEqual(count_x_y[woof_index, dog_index], 2)
count_y = classifier.count_y_table
self.assertEqual(count_y[cat_index], 3)
self.assertEqual(count_y[dog_index], 2)
from helper import Instance
from naive_bayes import NaiveBayes
cat1 = Instance(label='cat', data=[1,0], raw_data=['purr' ,'purr' ,'meow'])
cat2 = Instance(label='cat', data=[0,2],raw_data=['meow','woof'])
cat3 = Instance(label='cat', data=[1],raw_data=['purr'])
dog1 = Instance(label='dog', data=[3,2],raw_data=['bark','woof'])
dog2 = Instance(label='dog', data=[2,0],raw_data=['woof','meow'])
training_set = [cat1, cat2, cat3, dog1, dog2]
classifier = NaiveBayes()
classifier._collect_counts(training_set)
cat_index = classifier.label_codebook.get_index('cat')
dog_index = classifier.label_codebook.get_index('dog')
purr_index = classifier.feature_codebook.get_index('purr')
meow_index = classifier.feature_codebook.get_index('meow')
bark_index = classifier.feature_codebook.get_index('bark')
woof_index = classifier.feature_codebook.get_index('woof')
"""Test counting"""
count_x_y = classifier.count_table
count_x_y[purr_index, cat_index] == (2+1)
count_x_y[meow_index, cat_index] == (2+1)
count_x_y[bark_index, cat_index] == (0+1)
count_x_y[woof_index, cat_index] == (1+1)
count_x_y[purr_index, dog_index] == (0+1)
count_x_y[meow_index, dog_index] == (1+1)
count_x_y[bark_index, dog_index] == (1+1)
