def test_ei(self): ei = ExpectedImprovement() ei.target = 1.0 ei.current = NormalDistribution(mu=1, sigma=1) ei.execute() self.assertAlmostEqual([0.40], ei.EI, 2) self.assertAlmostEqual(0.5, ei.PI, 6)
def test_ei_zero_division(self): ei = ExpectedImprovement() ei.target = 1.0 ei.current = NormalDistribution(mu=1, sigma=0) ei.execute() self.assertEqual(0, ei.EI) self.assertEqual(0, ei.PI)
def test_ei_zero_division(self): ei = ExpectedImprovement() ei.target = 1.0 ei.current = NormalDistribution(mu=1, sigma=0) ei.execute() self.assertEqual(0,ei.EI) self.assertEqual(0,ei.PI)
def test_ei(self): ei = ExpectedImprovement() ei.target = 1.0 ei.current = NormalDistribution(mu=1, sigma=1) ei.execute() self.assertAlmostEqual([0.40],ei.EI,2) self.assertAlmostEqual(0.5,ei.PI,6)
def test_ei_zero_division(self): ei = ExpectedImprovement() ei.best_case = CaseSet(Case(outputs=[("y",1)])) ei.criteria = "y" ei.predicted_value = NormalDistribution(mu=1,sigma=0) ei.execute() self.assertEqual(0,ei.EI) self.assertEqual(0,ei.PI)
def test_ei(self): ei = ExpectedImprovement() ei.best_case = CaseSet(Case(outputs=[("y",1)])) ei.criteria = "y" ei.predicted_value = NormalDistribution(mu=1,sigma=1) ei.execute() self.assertAlmostEqual([0.91],ei.EI,2) self.assertAlmostEqual(0.5,ei.PI,6)
def test_ei_zero_division(self): ei = ExpectedImprovement() ei.best_case = CaseSet(Case(outputs=[("y", 1)])) ei.criteria = "y" ei.predicted_value = NormalDistribution(mu=1, sigma=0) ei.execute() self.assertEqual(0, ei.EI) self.assertEqual(0, ei.PI)
def test_ei(self): ei = ExpectedImprovement() ei.best_case = CaseSet(Case(outputs=[("y", 1)])) ei.criteria = "y" ei.predicted_value = NormalDistribution(mu=1, sigma=1) ei.execute() self.assertAlmostEqual([0.91], ei.EI, 2) self.assertAlmostEqual(0.5, ei.PI, 6)
def test_ei_bad_criteria(self): ei = ExpectedImprovement() ei.best_case = CaseSet(Case(outputs=[("y",1)])) ei.criteria = "x" ei.predicted_value = NormalDistribution(mu=1,sigma=1) try: ei.execute() except ValueError,err: self.assertEqual(str(err),": best_case did not have an output which " "matched the criteria, 'x'")
def test_ei_bad_criteria(self): ei = ExpectedImprovement() ei.best_case = CaseSet(Case(outputs=[("y", 1)])) ei.criteria = "x" ei.predicted_value = NormalDistribution(mu=1, sigma=1) try: ei.execute() except ValueError, err: self.assertEqual( str(err), ": best_case did not have an output which " "matched the criteria, 'x'")