def test_2d_1fi_cokriging(self): # CoKrigingSurrogate with one fidelity could be used as a KrigingSurrogate # Same test as for KrigingSurrogate... well with predicted test value adjustment def branin(x): y = (x[1]-(5.1/(4.*pi**2.))*x[0]**2.+5.*x[0]/pi-6.)**2.+10.*(1.-1./(8.*pi))*cos(x[0])+10. return y x = array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.], [4., 7.5], [-5., 9.], [5.5, 10.5], [10., 12.], [7., 13.5], [2.5, 15.]]) y = array([branin(case) for case in x]) krig1 = MultiFiCoKrigingSurrogate() krig1.train(x, y) pred1 = krig1.predict([-2., 0.]) self.assertAlmostEqual(branin(x[0]), pred1.mu, places=5) self.assertAlmostEqual(0., pred1.sigma, places=5) pred2 = krig1.predict([5., 5.]) self.assertAlmostEqual(22, pred2.mu, delta=1) self.assertAlmostEqual(13, pred2.sigma, delta=1) # Test with theta setting instead of estimation krig2 = MultiFiCoKrigingSurrogate(theta=[0.1]) krig1.train(x, y) pred1 = krig1.predict([-2., 0.]) self.assertAlmostEqual(branin(x[0]), pred1.mu, places=5) self.assertAlmostEqual(0., pred1.sigma, places=5) pred2 = krig1.predict([5., 5.]) self.assertAlmostEqual(22, pred2.mu, delta=1) self.assertAlmostEqual(13, pred2.sigma, delta=1)
def test_1d_1fi_cokriging(self): # CoKrigingSurrogate with one fidelity could be used as a KrigingSurrogate # Same test as for KrigingSurrogate... well with predicted test value adjustment x = array([[0.05], [.25], [0.61], [0.95]]) y = array([0.738513784857542, -0.210367746201974, -0.489015457891476, 12.3033138316612]) krig1 = MultiFiCoKrigingSurrogate() krig1.train(x, y) new_x = array([0.5]) pred1 = krig1.predict(x[0]) self.assertTrue(isinstance(pred1, NormalDistribution)) self.assertAlmostEqual(y[0] , pred1.mu, places=4) self.assertAlmostEqual(.0, pred1.sigma, places=4) pred2 = krig1.predict(new_x) self.assertTrue(isinstance(pred2, NormalDistribution)) self.assertAlmostEqual( -2.0279, pred2.mu, places=3) self.assertAlmostEqual(1.3408, pred2.sigma, places=3) # Test with theta setting instead of estimation krig2 = MultiFiCoKrigingSurrogate(theta=0.1) krig2.train(x, y) pred1 = krig2.predict(x[0]) self.assertTrue(isinstance(pred1, NormalDistribution)) self.assertAlmostEqual(y[0] , pred1.mu, places=4) self.assertAlmostEqual(.0, pred1.sigma, places=4) pred2 = krig2.predict(new_x) self.assertTrue(isinstance(pred2, NormalDistribution)) self.assertAlmostEqual( -1.2719, pred2.mu, places=3) self.assertAlmostEqual(0.0439, pred2.sigma, places=3)
self.assertAlmostEqual(0., pred.sigma, delta=0.02) def test_2d_1fi_cokriging(self): # CoKrigingSurrogate with one fidelity could be used as a KrigingSurrogate # Same test as for KrigingSurrogate... well with predicted test value adjustment def branin(x): y = (x[1]-(5.1/(4.*pi**2.))*x[0]**2.+5.*x[0]/pi-6.)**2.+10.*(1.-1./(8.*pi))*cos(x[0])+10. return y x = array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.], [4., 7.5], [-5., 9.], [5.5, 10.5], [10., 12.], [7., 13.5], [2.5, 15.]]) y = array([branin(case) for case in x]) krig1 = MultiFiCoKrigingSurrogate() krig1.train(x, y) pred1 = krig1.predict([-2., 0.]) self.assertAlmostEqual(branin(x[0]), pred1.mu, places=5) self.assertAlmostEqual(0., pred1.sigma, places=5) pred2 = krig1.predict([5., 5.]) self.assertAlmostEqual(22, pred2.mu, delta=1) self.assertAlmostEqual(13, pred2.sigma, delta=1) # Test with theta setting instead of estimation krig2 = MultiFiCoKrigingSurrogate(theta=[0.1]) krig1.train(x, y) pred1 = krig1.predict([-2., 0.]) self.assertAlmostEqual(branin(x[0]), pred1.mu, places=5)