def test_single_training_data(self):
classifier = NaiveBayesClassifier()
classifier.train((('A', 'a'),))
self.failUnless(classifier.label('a') == 'A')
distribution = classifier.label_distribution('a')
self.failUnlessEqual(len(distribution), 1)
self.failUnless('A' in distribution)
self.failUnless(distribution['A'] == 0.0, distribution)
def test_single_training_data(self):
classifier = NaiveBayesClassifier()
classifier.train((('A', 'a'), ))
self.failUnless(classifier.label('a') == 'A')
distribution = classifier.label_distribution('a')
self.failUnlessEqual(len(distribution), 1)
self.failUnless('A' in distribution)
self.failUnless(distribution['A'] == 0.0, distribution)
def test_single_class_mixed_training_data(self):
classifier = NaiveBayesClassifier()
classifier.train((('A', 'a'),('A', 'a'),('B', 'a')))
self.failUnless(classifier.label('a') == 'A')
distribution = classifier.label_distribution('a')
self.failUnlessEqual(len(distribution), 2)
self.failUnless('A' in distribution)
correct_distribution = Counter()
correct_distribution['A'] = (2.0 / 3.0)**3
correct_distribution['B'] = (1.0 / 3.0)**3
correct_distribution.normalize()
correct_distribution.log()
self.failUnlessAlmostEqual(distribution['A'], correct_distribution['A'])
self.failUnlessAlmostEqual(distribution['B'], correct_distribution['B'])
def test_single_class_mixed_training_data(self):
classifier = NaiveBayesClassifier()
classifier.train((('A', 'a'), ('A', 'a'), ('B', 'a')))
self.failUnless(classifier.label('a') == 'A')
distribution = classifier.label_distribution('a')
self.failUnlessEqual(len(distribution), 2)
self.failUnless('A' in distribution)
correct_distribution = Counter()
correct_distribution['A'] = (2.0 / 3.0)**3
correct_distribution['B'] = (1.0 / 3.0)**3
correct_distribution.normalize()
correct_distribution.log()
self.failUnlessAlmostEqual(distribution['A'],
correct_distribution['A'])
self.failUnlessAlmostEqual(distribution['B'],
correct_distribution['B'])
