def cross_cov(self, inputs_1, inputs_2):
"""
:param inputs_1: np.array(nxd)
:param inputs_2: np.array(mxd)
:return: np.array(nxm)
"""
r2 = np.abs(
Distances.dist_square_length_scale(self.length_scale.value,
inputs_1, inputs_2))
r = np.sqrt(r2)
cov = (1.0 + np.sqrt(5) * r +
(5.0 / 3.0) * r2) * np.exp(-np.sqrt(5) * r)
return cov
def gradient_respect_distance_cross(cls,
ls,
inputs_1,
inputs_2,
second=False):
"""
:param ls: (ParameterEntity) length_scale
:param inputs_1: np.array(nxd)
:param inputs_2: np.array(mxd)
:param second: (boolean) Computes second derivative if it's True.
:return: np.array(nxm) or {'first': np.array(nxm), 'second': np.array(nxm)}
"""
r2 = np.abs(
Distances.dist_square_length_scale(ls.value, inputs_1, inputs_2))
r = np.sqrt(r2)
exp_r = np.exp(-np.sqrt(5) * r)
part_1 = (1.0 + np.sqrt(5) * r +
(5.0 / 3.0) * r2) * exp_r * (-np.sqrt(5))
part_2 = (exp_r * (np.sqrt(5) + (10.0 / 3.0) * r))
derivate_respect_to_r = part_1 + part_2
if not second:
return derivate_respect_to_r
part_0 = (10.0 / 3.0) * exp_r
part_1 = part_1 * (-np.sqrt(5.0))
part_3 = 2.0 * (
(10.0 / 3.0) * r + np.sqrt(5.0)) * exp_r * (-np.sqrt(5.0))
hessian_respect_to_r = part_0 + part_1 + part_3
sol = {
'first': derivate_respect_to_r,
'second': hessian_respect_to_r,
}
return sol
def test_hessian_distance_length_scale_respect_point(self):
params = np.array([1.0, 5.0])
point = np.array([[4.5, 7.5]])
inputs = np.array([[5.0, 6.0], [8.0, 9.0]])
result = Distances.gradient_distance_length_scale_respect_point(
params, point, inputs, second=True)
result = result['second']
dh = 0.00001
finite_diff = FiniteDifferences.second_order_central(
lambda x: np.sqrt(
Distances.dist_square_length_scale(
params, x.reshape((1, len(x))), inputs)), point[0, :],
np.array([dh]))
for i in xrange(2):
for j in xrange(2):
print i, j
npt.assert_almost_equal(
finite_diff[i, j],
np.array([[result[0, i, j], result[1, i, j]]]),
decimal=5)
def test_dist_square_length_scale(self):
assert Distances.dist_square_length_scale(self.ls, self.x1) == [[0.0]]