def __init__(self, truth_problem, **kwargs):
# Call parent
ReductionMethod.__init__(self, os.path.join(
"test_eim_approximation_14_tempdir", expression_type, basis_generation, "mock_problem"))
# Minimal subset of a DifferentialProblemReductionMethod
self.truth_problem = truth_problem
self.reduced_problem = None
def __init__(self, truth_problem, **kwargs):
# Call parent
ReductionMethod.__init__(self, os.path.join("test_eim_approximation_15_tempdir", expression_type, basis_generation, "mock_problem"))
# Minimal subset of a DifferentialProblemReductionMethod
self.truth_problem = truth_problem
self.reduced_problem = None
# I/O
self.folder["basis"] = os.path.join(self.truth_problem.folder_prefix, "basis")
# Gram Schmidt
self.GS = GramSchmidt(self.truth_problem.inner_product)
def __init__(self, SCM_approximation, folder_prefix):
# Call the parent initialization
ReductionMethod.__init__(self, folder_prefix)
# $$ OFFLINE DATA STRUCTURES $$ #
# High fidelity problem
self.SCM_approximation = SCM_approximation
# I/O
self.folder["post_processing"] = os.path.join(self.folder_prefix,
"post_processing")
self.greedy_selected_parameters = SCM_approximation.greedy_selected_parameters
self.greedy_error_estimators = GreedyErrorEstimatorsList()
def __init__(self, SCM_approximation, folder_prefix):
# Call the parent initialization
ReductionMethod.__init__(self, folder_prefix)
# $$ OFFLINE DATA STRUCTURES $$ #
# High fidelity problem
self.SCM_approximation = SCM_approximation
# I/O
self.folder["post_processing"] = os.path.join(self.folder_prefix, "post_processing")
self.greedy_selected_parameters = SCM_approximation.greedy_selected_parameters
self.greedy_error_estimators = GreedyErrorEstimatorsList()
# Get data that were temporarily store in the SCM_approximation
self.bounding_box_minimum_eigensolver_parameters = self.SCM_approximation._input_storage_for_SCM_reduction["bounding_box_minimum_eigensolver_parameters"]
self.bounding_box_maximum_eigensolver_parameters = self.SCM_approximation._input_storage_for_SCM_reduction["bounding_box_maximum_eigensolver_parameters"]
del self.SCM_approximation._input_storage_for_SCM_reduction
def __init__(self, EIM_approximation):
# Call the parent initialization
ReductionMethod.__init__(self, EIM_approximation.folder_prefix)
# $$ OFFLINE DATA STRUCTURES $$ #
# High fidelity problem
self.EIM_approximation = EIM_approximation
# Declare a new container to store the snapshots
self.snapshots_container = self.EIM_approximation.parametrized_expression.create_snapshots_container()
self._training_set_parameters_to_snapshots_container_index = dict()
# I/O
self.folder["snapshots"] = os.path.join(self.folder_prefix, "snapshots")
self.folder["post_processing"] = os.path.join(self.folder_prefix, "post_processing")
self.greedy_selected_parameters = GreedySelectedParametersList()
self.greedy_errors = GreedyErrorEstimatorsList()
#
# By default set a tolerance slightly larger than zero, in order to
# stop greedy iterations in trivial cases by default
self.tol = 1e-15