def test_is_prod(self):
kern = self.se_0
cov = Covariance(kern)
self.assertFalse(cov.is_prod())
kern = self.se_0 + self.se_0
cov = Covariance(kern)
self.assertFalse(cov.is_prod())
kern = self.se_0 * self.se_0
cov = Covariance(kern)
self.assertTrue(cov.is_prod())
def expand_full_kernel(self, covariance: Covariance) -> List[Covariance]:
"""Expand full kernel.
:param covariance: Covariance to expand.
:return:
"""
result = self.expand_single_kernel(covariance)
if covariance is None:
pass
elif not covariance.is_base():
for i, operand in enumerate(covariance.raw_kernel.parts):
covariance_operand = Covariance(operand)
for e in self.expand_full_kernel(covariance_operand):
new_operands = covariance.raw_kernel.parts[:i] + [e.raw_kernel] \
+ covariance.raw_kernel.parts[i + 1:]
new_operands = [op.copy() for op in new_operands]
if covariance.is_prod():
prod_kern = new_operands[0]
for part in new_operands[1:]:
prod_kern *= part
result.append(Covariance(prod_kern))
elif covariance.is_sum():
prod_kern = new_operands[0]
for part in new_operands[1:]:
prod_kern += part
result.append(Covariance(prod_kern))
else:
raise TypeError(
f'Unknown combination kernel class {covariance.__class__.__name__}'
)
return result