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