def test_posterior(self):
"""Check the posterior over weights function returns mean and covar."""
clf = RVR()
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.beta_ = 1
clf.y = np.array([1, 1])
clf._posterior()
m_target = np.array([6.103885e-03, 3.750334e-08, 6.666294e-01])
sigma_target = np.array([
[9.997764e-01, -1.373791e-09, -6.103885e-03],
[-1.373791e-09, 1.000000e+00, -3.750334e-08],
[-6.103885e-03, -3.750334e-08, 3.333706e-01]
])
np.testing.assert_allclose(clf.m_, m_target)
np.testing.assert_allclose(clf.sigma_, sigma_target)
def test_predict(self):
"""Check the predict function works with pre-set values."""
clf = RVR(kernel='linear', bias_used=False)
clf.relevance_ = np.array([[1, 1]])
clf.m_ = np.array([1])
y = clf.predict(np.array([1, 1]))
self.assertEqual(y, 2)
