def test_prob_density_function(self):
print("test_prob_density_function")
# Creating object of classifier for unit testing
nb = NaiveBayesClassifier()
nb.mean = [1]
nb.variance = [3]
# Testing probability calc with known calculation.
self.assertAlmostEqual(nb.prob_den_func(0, 3), 0.11825507)
nb.mean = [1, 2]
nb.variance = [3, 1]
self.assertAlmostEqual(nb.prob_den_func(1, 0.1), 0.06561581)
def test_pred(self):
print("test_pred")
nb = NaiveBayesClassifier()
nb.mean = [1, 2]
nb.variance = [3, 1]
# testing condition where it is not trained
self.assertEqual(nb.predict([1.4, 12, 3, 9]), None)
self.assertEqual(nb.predict([2, 3, 4, 5]), None)
self.assertEqual(nb.predict([1]), None)
self.assertEqual(nb.predict([2, -3, 4, -5, -7, -7]), None)
nb.mean = [1, 1]
nb.variance = [1, 5]
nb.in_classes = [1, 0]
nb.prior_probs = [0.3, 0.2]
pred = nb.predict([5, 3, 4, 7])
# Testing condition simulating trained model.
self.assertEqual(pred[0], [0])
self.assertEqual(pred[1], [0])
self.assertEqual(pred[2], [0])
self.assertEqual(pred[3], [0])
