def test_optimal_mu(self):
x, y = get_xy()
gpr = GPR()
gpr.fit(x, y, optimization_restart=10)
new_mu = gpr.optimal_mu(np.array([[-1, 1], [-1, 1], [-1, 1], [-1, 1]]))
np.testing.assert_array_almost_equal(np.abs(new_mu),
np.ones(4).reshape(1, -1))
def test_predict_03(self):
np.random.seed(1)
x, y = get_xy()
gpr = GPR()
gpr.fit(x, y, optimization_restart=50)
test_y = gpr.predict(x)
np.testing.assert_array_almost_equal(y, test_y, decimal=6)
def test_predict_02(self):
np.random.seed(42)
x, y = get_xy()
gpr = GPR()
gpr.fit(x, y, optimization_restart=50)
test_y, variance = gpr.predict(x[:4], return_variance=True)
true_var = np.array([[5.761689e-06, 2.017326e-06],
[5.761686e-06, 2.017325e-06],
[5.761692e-06, 2.017327e-06],
[5.761695e-06, 2.017328e-06]])
np.testing.assert_array_almost_equal(true_var, variance, decimal=6)
def test_predict_02(self):
np.random.seed(42)
x, y = get_xy()
gpr = GPR()
gpr.fit(x, y, optimization_restart=50)
test_y, variance = gpr.predict(x[:4], return_variance=True)
true_var = np.array([[0.0242762038, 0.0029760019],
[0.0244903294, 0.0030022514],
[0.0247701249, 0.0030365513],
[0.0232064664, 0.0028448636]])
np.testing.assert_array_almost_equal(true_var, variance, decimal=6)
def test_predict_01(self):
x, y = get_xy()
gpr = GPR()
gpr.fit(x, y, optimization_restart=50)
test_y, variance = gpr.predict(x, return_variance=True)
np.testing.assert_array_almost_equal(y, test_y, decimal=6)
def test_types(self):
x, y = get_xy()
gpr = GPR()
gpr.fit(x[:, 0], y[:, 0])
assert isinstance(gpr.model,
sklearn.gaussian_process.GaussianProcessRegressor)
def test_fit(self):
x, y = get_xy()
gpr = GPR()
gpr.fit(x, y)
assert isinstance(gpr.model, GPy.models.GPRegression)
def test_fit_mono(self):
x, y = get_xy()
gpr = GPR()
gpr.fit(x[:, 0], y[:, 0])
assert isinstance(gpr.model, GPy.models.GPRegression)