def test_dK_dtheta_5():
n = 4
theta = np.array(
[ 2.59193443e-02, 0.00000000e+00, 6.83797216e-07, 3.08837678e-03,
0.00000000e+00, 2.56956907e-02, -1.48051536e+00, -1.51759911e+00,
-1.34983215e+00, -1.22431771e+00])
size = len(theta)
dK1 = np.zeros((size, n, n))
dK2 = np.zeros((size, n, n))
dK3 = np.zeros((size, n, n))
for u in range(size):
_ratematrix.dK_dtheta_ij(theta, n, u, A=None, out=dK1[u, :, :])
for i in range(n):
for j in range(n):
_ratematrix.dK_dtheta_u(theta, n, i, j, out=dK2[:, i, j])
for i in range(n):
for j in range(n):
dKij = np.zeros(size)
_ratematrix.dK_dtheta_u(theta, n, i, j, out=dKij)
dK3[:, i, j] = dKij
np.testing.assert_almost_equal(dK1, dK2)
np.testing.assert_almost_equal(dK1, dK3)
np.testing.assert_almost_equal(dK2, dK3)
def test_dK_dtheta_5():
n = 4
theta = np.array([
2.59193443e-02, 0.00000000e+00, 6.83797216e-07, 3.08837678e-03,
0.00000000e+00, 2.56956907e-02, -1.48051536e+00, -1.51759911e+00,
-1.34983215e+00, -1.22431771e+00
])
size = len(theta)
dK1 = np.zeros((size, n, n))
dK2 = np.zeros((size, n, n))
dK3 = np.zeros((size, n, n))
for u in range(size):
_ratematrix.dK_dtheta_ij(theta, n, u, A=None, out=dK1[u, :, :])
for i in range(n):
for j in range(n):
_ratematrix.dK_dtheta_u(theta, n, i, j, out=dK2[:, i, j])
for i in range(n):
for j in range(n):
dKij = np.zeros(size)
_ratematrix.dK_dtheta_u(theta, n, i, j, out=dKij)
dK3[:, i, j] = dKij
np.testing.assert_almost_equal(dK1, dK2)
np.testing.assert_almost_equal(dK1, dK3)
np.testing.assert_almost_equal(dK2, dK3)
def test_dK_dtheta_4():
# check that the dot product part of dK_dtheta_u works
n = 4
theta = example_theta(n)
A = random.randn(len(theta), len(theta))
for i in range(n):
for j in range(n):
grad = np.zeros(len(theta))
_ratematrix.dK_dtheta_u(theta, n, i, j, out=grad)
gradprod1 = np.dot(grad, A)
gradprod2 = np.zeros(len(theta))
grad2 = np.zeros(len(theta))
_ratematrix.dK_dtheta_u(theta,
n,
i,
j,
out=grad2,
A=A,
out2=gradprod2)
np.testing.assert_almost_equal(grad, grad2)
np.testing.assert_almost_equal(gradprod1, gradprod2)
np.testing.assert_almost_equal(np.dot(grad2, A), gradprod2)
def test_dK_dtheta_4():
# check that the dot product part of dK_dtheta_u works
n = 4
theta = example_theta(n)
A = random.randn(len(theta), len(theta))
for i in range(n):
for j in range(n):
grad = np.zeros(len(theta))
_ratematrix.dK_dtheta_u(theta, n, i, j, out=grad)
gradprod1 = np.dot(grad, A)
gradprod2 = np.zeros(len(theta))
grad2 = np.zeros(len(theta))
_ratematrix.dK_dtheta_u(theta, n, i, j, out=grad2, A=A, out2=gradprod2)
np.testing.assert_almost_equal(grad, grad2)
np.testing.assert_almost_equal(gradprod1, gradprod2)
np.testing.assert_almost_equal(np.dot(grad2, A), gradprod2)
def test_dK_dtheta_3():
# test dK_dtheta_ij vs dK_dtheta_u. both return slices of the same 3D
# tensor, so by repeated calls to both functions we can build the whole
# tensor using both approaches and check that they're equal.
for n in [3, 4]:
theta = example_theta(n)
dKuij1 = np.zeros((len(theta), n, n))
dKuij2 = np.zeros((len(theta), n, n))
for u in range(len(theta)):
_ratematrix.dK_dtheta_ij(theta, n, u, None, dKuij1[u])
for i in range(n):
for j in range(n):
_ratematrix.dK_dtheta_u(theta, n, i, j, out=dKuij2[:, i, j])
np.testing.assert_array_almost_equal(dKuij1, dKuij2)
def test_dK_dtheta_3():
# test dK_dtheta_ij vs dK_dtheta_u. both return slices of the same 3D
# tensor, so by repeated calls to both functions we can build the whole
# tensor using both approaches and check that they're equal.
for n in [3, 4]:
theta = example_theta(n)
dKuij1 = np.zeros((len(theta), n, n))
dKuij2 = np.zeros((len(theta), n, n))
for u in range(len(theta)):
_ratematrix.dK_dtheta_ij(theta, n, u, None, dKuij1[u])
for i in range(n):
for j in range(n):
_ratematrix.dK_dtheta_u(theta, n, i, j, out=dKuij2[:, i, j])
np.testing.assert_array_almost_equal(dKuij1, dKuij2)
