def test_posterior(self):
"""Check the posterior over weights function finds the minimum."""
clf = RVC()
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6], [7, 8]])
clf.phi = clf._apply_kernel(x, y)
clf.alpha_ = np.ones(3)
clf.m_ = np.ones(3)
clf.t = np.array([1, 0])
clf.beta_ = None
clf._posterior()
m_target = np.array([-9.157e-03, -5.049e-08, 2.794e-05])
sigma_target = np.array([
[1, -4.294e-10, -3.052e-03],
[-4.294e-10, 1, -1.875e-08],
[-3.052e-03, -1.875e-08, 6.667e-01]
])
np.testing.assert_allclose(clf.m_, m_target, rtol=1e-3)
np.testing.assert_allclose(clf.sigma_, sigma_target, rtol=1e-3)
def test_log_posterior(self):
"""Check _log_posterior returns the correct value and jacobian."""
clf = RVC()
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6], [7, 8]])
phi = clf._apply_kernel(x, y)
alpha = np.ones(3)
m = np.ones(3)
t = np.array([1, 0])
log_p, jacobian = clf._log_posterior(m, alpha, phi, t)
j_target = np.array([1.013, 1, 1.466])
self.assertAlmostEqual(log_p, 3.140, places=3)
np.testing.assert_allclose(jacobian, j_target, rtol=1e-3)
def test_hessian(self):
"""Check the hessian function returns the correct values."""
clf = RVC()
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6], [7, 8]])
phi = clf._apply_kernel(x, y)
alpha = np.ones(3)
m = np.ones(3)
t = np.array([1, 0])
hessian = clf._hessian(m, alpha, phi, t)
h_target = np.array([[1, 4.018e-10, 3.571e-03],
[4.018e-10, 1, 2.194e-08],
[3.571e-03, 2.194e-08, 1.392]])
np.testing.assert_allclose(hessian, h_target, rtol=1e-3)
