def test_reset_y_star_event(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 1, 1]])
     ei.current = [NormalDistribution(mu=1,sigma=1),
                   NormalDistribution(mu=1,sigma=1),
                   NormalDistribution(mu=1,sigma=1)]
     ei.execute()
     ei.target = array([[2, 2, 2]])
     ei.execute()
     self.assertEqual(ei.y_star.all(), array([2, 2, 2]).all())
 def test_reset_y_star_event(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 1, 1]])
     ei.current = [
         NormalDistribution(mu=1, sigma=1),
         NormalDistribution(mu=1, sigma=1),
         NormalDistribution(mu=1, sigma=1)
     ]
     ei.execute()
     ei.target = array([[2, 2, 2]])
     ei.execute()
     self.assertEqual(ei.y_star.all(), array([2, 2, 2]).all())
 def test_ei_2obj(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 10], [1, -10]])
     ei.current = [NormalDistribution(mu=1, sigma=1),
                   NormalDistribution(mu=0, sigma=1)]
     ei.calc_switch = "EI"
     ei.execute()
     self.assertAlmostEqual([5.0], ei.EI,1)
     self.assertEqual(0.5, ei.PI,6)
 def test_ei_nobj(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 1, 1]])
     list_of_cases = [Case(outputs=[("y1", 1), ("y2", 1), ("y3", 1)])]
     ei.criteria = ['y1', 'y2', 'y3']
     ei.current = [NormalDistribution(mu=1, sigma=1),
                   NormalDistribution(mu=1, sigma=1),
                   NormalDistribution(mu=1, sigma=1)]
     ei.execute()
     self.assertAlmostEqual(0.875,ei.PI,1)
 def test_ei_2obj(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 10], [1, -10]])
     ei.current = [
         NormalDistribution(mu=1, sigma=1),
         NormalDistribution(mu=0, sigma=1)
     ]
     ei.calc_switch = "EI"
     ei.execute()
     self.assertAlmostEqual([5.0], ei.EI, 1)
     self.assertEqual(0.5, ei.PI, 6)
 def test_ei_calc_switch(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 1, 1]])
     ei.current = [NormalDistribution(mu=1, sigma=1),
                   NormalDistribution(mu=1, sigma=1),
                   NormalDistribution(mu=1, sigma=1)]
     ei.calc_switch = 'EI'
     try:
         ei.execute()
     except ValueError,err:
         self.assertEqual(str(err),': EI calculations not supported'
                          ' for more than 2 objectives')
 def test_ei_nobj(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 1, 1]])
     list_of_cases = [Case(outputs=[("y1", 1), ("y2", 1), ("y3", 1)])]
     ei.criteria = ['y1', 'y2', 'y3']
     ei.current = [
         NormalDistribution(mu=1, sigma=1),
         NormalDistribution(mu=1, sigma=1),
         NormalDistribution(mu=1, sigma=1)
     ]
     ei.execute()
     self.assertAlmostEqual(0.875, ei.PI, 1)
 def test_ei_calc_switch(self):
     ei = MultiObjExpectedImprovement()
     ei.target = array([[1, 1, 1]])
     ei.current = [
         NormalDistribution(mu=1, sigma=1),
         NormalDistribution(mu=1, sigma=1),
         NormalDistribution(mu=1, sigma=1)
     ]
     ei.calc_switch = 'EI'
     try:
         ei.execute()
     except ValueError, err:
         self.assertEqual(
             str(err), ': EI calculations not supported'
             ' for more than 2 objectives')