def test_compute_K(self):
"""Tests if K is correctly computed.
"""
S = self.S_2x3
expected_K = np.zeros((S.shape[0], S.shape[0]))
for i in range(0, S.shape[0]):
for j in range(0, S.shape[0]):
s1 = np.array([S[i, :]])
s2 = np.array([S[j, :]])
exponent = (-self.a_1 * np.power(npla.norm(s1 - s2), 2) /
(2 * (self.sigma_05 ** 2)))
expected_K[i, j] = np.exp(exponent)
crkr = CrKr(S, self.C_2x2, self.D_2x3,
self.ridge_factor_05, self.sigma_05, self.a_1)
assert_true(np.allclose(expected_K, crkr._compute_K()))
def test_compute_K(self):
"""Tests if K is correctly computed.
"""
S = self.S_2x3
expected_K = np.zeros((S.shape[0], S.shape[0]))
for i in range(0, S.shape[0]):
for j in range(0, S.shape[0]):
s1 = np.array([S[i, :]])
s2 = np.array([S[j, :]])
exponent = (-self.a_1 * np.power(npla.norm(s1 - s2), 2) /
(2 * (self.sigma_05**2)))
expected_K[i, j] = np.exp(exponent)
crkr = CrKr(S, self.C_2x2, self.D_2x3, self.ridge_factor_05,
self.sigma_05, self.a_1)
assert_true(np.allclose(expected_K, crkr._compute_K()))
def test_delta_mean(self):
"""Tests if delta mean is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[0, 0, 0]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dm = np.dot(k.T,
np.dot(np.linalg.inv(K + ridge_factor * C), D))
assert_true(np.allclose(expected_dm, crkr._delta_mean(k, K)))
def test_delta_mean(self):
"""Tests if delta mean is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[0, 0, 0]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dm = np.dot(k.T, np.dot(np.linalg.inv(K + ridge_factor * C),
D))
assert_true(np.allclose(expected_dm, crkr._delta_mean(k, K)))
def test_delta_variance(self):
"""Tests if delta variance is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[1, 1, 1]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dv = (a + ridge_factor -
np.dot(k.T, np.dot(npla.inv(K + ridge_factor * C), k)))
assert_true(np.allclose(expected_dv, crkr._delta_variance(k, K)))
def test_delta_variance(self):
"""Tests if delta variance is correctly computed.
"""
S = self.S_2x3
C = self.C_2x2
D = self.D_2x3
ridge_factor = self.ridge_factor_05
sigma = self.sigma_05
a = self.a_1
crkr = CrKr(S, C, D, ridge_factor, sigma, a)
new_s = np.array([[1, 1, 1]])
k = crkr._compute_k(new_s)
K = crkr._compute_K()
expected_dv = (a +
ridge_factor -
np.dot(k.T, np.dot(npla.inv(K + ridge_factor * C), k)))
assert_true(np.allclose(expected_dv, crkr._delta_variance(k, K)))
