def test_probability_of(self):
pd = ProbabilityDistribution(("var1", "var2", "var3"))
pd.set(0.05, (False, False, False))
pd.set(0.01, (False, True, False))
pd.set(0.2, (True, True, True))
expected_probability = 0.06
result = pd.probability_of({"var3": False})
self.assertAlmostEqual(expected_probability, result, places=5)
def test_probability_of(self):
pd = ProbabilityDistribution(("var1", "var2", "var3"))
pd.set(0.05, (False, False, False))
pd.set(0.01, (False, True, False))
pd.set(0.2, (True, True, True))
expected_probability = 0.06
result = pd.probability_of({"var3" : False})
self.assertAlmostEqual(expected_probability, result, places=5)
def test_aima_example(self):
pd = ProbabilityDistribution(("ToothAche", "Cavity", "Catch"))
pd.set(0.108, (True, True, True))
pd.set(0.012, (True, True, False))
pd.set(0.072, (False, True, True))
pd.set(0.008, (False, True, False))
pd.set(0.016, (True, False, True))
pd.set(0.064, (True, False, False))
pd.set(0.144, (False, False, True))
pd.set(0.008, (False, False, False))
q = Query("Cavity", {"ToothAche" : True})
(true_distr, false_distr) = EnumerationJointAsk().ask(q, pd)
self.assertAlmostEqual(0.6, true_distr, places=4)
self.assertAlmostEqual(0.4, false_distr, places=4)
def test_aima_example(self):
pd = ProbabilityDistribution(("ToothAche", "Cavity", "Catch"))
pd.set(0.108, (True, True, True))
pd.set(0.012, (True, True, False))
pd.set(0.072, (False, True, True))
pd.set(0.008, (False, True, False))
pd.set(0.016, (True, False, True))
pd.set(0.064, (True, False, False))
pd.set(0.144, (False, False, True))
pd.set(0.008, (False, False, False))
q = Query("Cavity", {"ToothAche": True})
(true_distr, false_distr) = EnumerationJointAsk().ask(q, pd)
self.assertAlmostEqual(0.6, true_distr, places=4)
self.assertAlmostEqual(0.4, false_distr, places=4)
def test_set_with_less_values_then_variables(self):
pd = ProbabilityDistribution(("var1", "var2", "var3"))
self.assertRaises(ValueError, pd.set, 0.1, (True, False))