def apply_parameters(self,
id_to_param_value=None,
zero_offset: bool = False,
keep_zeros: bool = False):
"""Returns A, b after applying parameters (and reshaping).
Args:
id_to_param_value: (optional) dict mapping parameter ids to values
zero_offset: (optional) if True, zero out the constant offset in the
parameter vector
keep_zeros: (optional) if True, store explicit zeros in A where
parameters are affected
"""
def param_value(idx):
return (np.array(self.id_to_param[idx].value)
if id_to_param_value is None else id_to_param_value[idx])
param_vec = canonInterface.get_parameter_vector(
self.total_param_size,
self.param_id_to_col,
self.param_id_to_size,
param_value,
zero_offset=zero_offset)
if keep_zeros and self._P_mapping_nonzero is None:
self._P_mapping_nonzero = canonInterface.A_mapping_nonzero_rows(
self.P, self.x.size)
P, _ = canonInterface.get_matrix_from_tensor(
self.reduced_P,
param_vec,
self.x.size,
nonzero_rows=self._P_mapping_nonzero,
with_offset=False,
problem_data_index=self.problem_data_index_P)
q, d = canonInterface.get_matrix_from_tensor(self.q,
param_vec,
self.x.size,
with_offset=True)
q = q.toarray().flatten()
if keep_zeros and self._A_mapping_nonzero is None:
self._A_mapping_nonzero = canonInterface.A_mapping_nonzero_rows(
self.A, self.x.size)
A, b = canonInterface.get_matrix_from_tensor(
self.reduced_A,
param_vec,
self.x.size,
nonzero_rows=self._A_mapping_nonzero,
with_offset=True,
problem_data_index=self.problem_data_index_A)
return P, q, d, A, np.atleast_1d(b)
def apply_parameters(self,
id_to_param_value=None,
zero_offset=False,
keep_zeros=False):
"""Returns A, b after applying parameters (and reshaping).
Args:
id_to_param_value: (optional) dict mapping parameter ids to values
zero_offset: (optional) if True, zero out the constant offset in the
parameter vector
keep_zeros: (optional) if True, store explicit zeros in A where
parameters are affected
"""
if self.reduced_A is None:
# Form a reduced representation of A, for faster application of
# parameters.
if np.prod(self.A.shape) != 0:
reduced_A, indices, indptr, shape = (
canonInterface.reduce_problem_data_tensor(
self.A, self.x.size))
self.reduced_A = reduced_A
self.problem_data_index = (indices, indptr, shape)
else:
self.reduced_A = self.A
self.problem_data_index = None
def param_value(idx):
return (np.array(self.id_to_param[idx].value)
if id_to_param_value is None else id_to_param_value[idx])
param_vec = canonInterface.get_parameter_vector(
self.total_param_size,
self.param_id_to_col,
self.param_id_to_size,
param_value,
zero_offset=zero_offset)
c, d = canonInterface.get_matrix_from_tensor(self.c,
param_vec,
self.x.size,
with_offset=True)
c = c.toarray().flatten()
if keep_zeros and self._A_mapping_nonzero is None:
self._A_mapping_nonzero = canonInterface.A_mapping_nonzero_rows(
self.A, self.x.size)
A, b = canonInterface.get_matrix_from_tensor(
self.reduced_A,
param_vec,
self.x.size,
nonzero_rows=self._A_mapping_nonzero,
with_offset=True,
problem_data_index=self.problem_data_index)
return c, d, A, np.atleast_1d(b)
def apply_parameters(self,
id_to_param_value=None,
zero_offset=False,
keep_zeros=False):
"""Returns A, b after applying parameters (and reshaping).
Args:
id_to_param_value: (optional) dict mapping parameter ids to values
zero_offset: (optional) if True, zero out the constant offset in the
parameter vector
keep_zeros: (optional) if True, store explicit zeros in A where
parameters are affected
"""
def param_value(idx):
return (np.array(self.id_to_param[idx].value)
if id_to_param_value is None else id_to_param_value[idx])
param_vec = canonInterface.get_parameter_vector(
self.total_param_size,
self.param_id_to_col,
self.param_id_to_size,
param_value,
zero_offset=zero_offset)
# TODO special code to handle P in canonInterface.
if param_vec is None:
tensor_application = self.P
else:
if sp.issparse(self.P):
tensor_application = self.P @ sp.csc_matrix(param_vec[:, None])
else:
tensor_application = self.P @ param_vec
P = tensor_application.reshape((self.x.size, self.x.size),
order='F').tocsc()
q, d = canonInterface.get_matrix_and_offset_from_tensor(
self.q, param_vec, self.x.size)
q = q.toarray().flatten()
if keep_zeros and self._A_mapping_nonzero is None:
self._A_mapping_nonzero = canonInterface.A_mapping_nonzero_rows(
self.A, self.x.size)
A, b = canonInterface.get_matrix_and_offset_from_tensor(
self.A,
param_vec,
self.x.size,
nonzero_rows=self._A_mapping_nonzero)
return P, q, d, A, np.atleast_1d(b)
