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 = np.array([[0.05], [.25], [0.61], [0.95]])
y = np.array([0.738513784857542, -0.210367746201974, -0.489015457891476, 12.3033138316612])
krig1 = MultiFiCoKrigingSurrogate()
krig1.train(x, y)
new_x = np.array([0.5])
mu, sigma = krig1.predict(x[0])
assert_near_equal(mu, [[y[0]]], 1e-4)
assert_near_equal(sigma, [[0.]], 1e-4)
mu, sigma = krig1.predict(new_x)
assert_near_equal(mu, [[-2.0279]], 1e-3)
assert_near_equal(sigma, [[1.3408]], 1e-3)
# Test with theta setting instead of estimation
krig2 = MultiFiCoKrigingSurrogate(theta=0.1, normalize=False)
krig2.train(x, y)
mu, sigma = krig2.predict(x[0])
assert_near_equal(mu, [[y[0]]], 1e-4)
assert_near_equal(sigma, [[.0]], 1e-4)
mu, sigma = krig2.predict(new_x)
assert_near_equal(mu, [[-1.2719]], 1e-3)
assert_near_equal(sigma, [[0.0439]], 1e-3)
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])
mu, sigma = krig1.predict(x[0])
assert_rel_error(self, mu, y[0], 1e-4)
assert_rel_error(self, sigma, 0., 1e-4)
mu, sigma = krig1.predict(new_x)
assert_rel_error(self, mu, -2.0279, 1e-3)
assert_rel_error(self, sigma, 1.3408, 1e-3)
# Test with theta setting instead of estimation
krig2 = MultiFiCoKrigingSurrogate(theta=0.1)
krig2.train(x, y)
mu, sigma = krig2.predict(x[0])
assert_rel_error(self, mu, y[0], 1e-4)
assert_rel_error(self, sigma, .0, 1e-4)
mu, sigma = krig2.predict(new_x)
assert_rel_error(self, mu, -1.2719, 1e-3)
assert_rel_error(self, sigma, 0.0439, 1e-3)
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])
mu, sigma = krig1.predict(x[0])
assert_rel_error(self, mu, y[0], 1e-4)
assert_rel_error(self, sigma, 0., 1e-4)
mu, sigma = krig1.predict(new_x)
assert_rel_error(self, mu, -2.0279, 1e-3)
assert_rel_error(self, sigma, 1.3408, 1e-3)
# Test with theta setting instead of estimation
krig2 = MultiFiCoKrigingSurrogate(theta=0.1)
krig2.train(x, y)
mu, sigma = krig2.predict(x[0])
assert_rel_error(self , mu, y[0], 1e-4)
assert_rel_error(self, sigma, .0, 1e-4)
mu, sigma = krig2.predict(new_x)
assert_rel_error(self, mu, -1.2719, 1e-3)
assert_rel_error(self, sigma, 0.0439, 1e-3)
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)
mu, sigma = krig1.predict([-2., 0.])
assert_rel_error(self, mu, branin(x[0]), 1e-5)
assert_rel_error(self, sigma, 0., 1e-5)
mu, sigma = krig1.predict([5., 5.])
assert_rel_error(self, mu, 22, 1)
assert_rel_error(self, sigma, 13, 1)
# Test with theta setting instead of estimation
krig2 = MultiFiCoKrigingSurrogate(theta=[0.1])
krig1.train(x, y)
mu, sigma = krig1.predict([-2., 0.])
assert_rel_error(self, mu, branin(x[0]), 1e-5)
assert_rel_error(self, sigma, 0., 1e-5)
mu, sigma = krig1.predict([5., 5.])
assert_rel_error(self, mu, 22, 1)
assert_rel_error(self, sigma, 13, 1)
def test_normalization(self):
# This dataset is ill conditioned if not normalized.
size = 100
x = np.random.random((size, 1))
y = np.random.random(size)
krig = MultiFiCoKrigingSurrogate(normalize=True)
krig.train(x, y)
# Make sure we aren't singular. We will get a few warnings during
# training.
krig.predict(0.5)
assert_near_equal(sigma, [[0.]], 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.*np.pi**2.))*x[0]**2.+5.*x[0]/np.pi-6.)**2.+10.*(1.-1./(8.*np.pi))*np.cos(x[0])+10.
return y
x = np.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 = np.array([branin(case) for case in x])
krig1 = MultiFiCoKrigingSurrogate()
krig1.train(x, y)
mu, sigma = krig1.predict([-2., 0.])
assert_near_equal(mu, [[branin(x[0])]], 1e-5)
assert_near_equal(sigma, [[0.]], 1e-5)
mu, sigma = krig1.predict([5., 5.])
assert_near_equal(mu, [[22]], 1)
assert_near_equal(sigma, [[13]], 1)
# Test with theta setting instead of estimation
krig2 = MultiFiCoKrigingSurrogate(theta=[0.1])
krig1.train(x, y)
mu, sigma = krig1.predict([-2., 0.])
assert_near_equal(mu, [[branin(x[0])]], 1e-5)