def calculate_objective_function(
self, param: parametrization.Parametrization) -> np.ndarray:
# TODO(logansu): Remove hack. This is hack because multiple
# parametrizations are currently handled as a single composite
# parametrization.
if isinstance(param, parametrization.CompositeParam):
constraints = []
for subparam in param._params:
constraints += self._build_constraints(subparam.to_vector(),
subparam._num_pixels)
else:
constraints = self._build_constraints(param.to_vector(),
param._num_pixels)
return np.hstack(constraints)
def calculate_gradient(
self, param: parametrization.Parametrization) -> np.ndarray:
# TODO(logansu): Remove hack. This is hack because multiple
# parametrizations are currently handled as a single composite
# parametrization.
if isinstance(param, parametrization.CompositeParam):
# The gradient is block-diagonal, with one block per
# sub-parametrization.
grad_blocks = []
for subparam in param._params:
grad_blocks.append(
self._build_constraint_grads(subparam.to_vector()))
return scipy.linalg.block_diag(*grad_blocks)
return self._build_constraint_grads(param.to_vector())
def calculate_gradient(self, param: Parametrization) -> float:
"""Returns zero. """
return np.zeros(self.value_shape + (len(param.to_vector()), ))