def initialize_training_set(self, ntrain, enable_import=True, sampling=None, **kwargs):
import_successful = ReductionMethod.initialize_training_set(self, self.EIM_approximation.mu_range, ntrain, enable_import, sampling, **kwargs)
# Since exact evaluation is required, we cannot use a distributed training set
self.training_set.distributed_max = False
# Also initialize the map from parameter values to snapshots container index
self._training_set_parameters_to_snapshots_container_index = dict((mu, mu_index) for (mu_index, mu) in enumerate(self.training_set))
return import_successful
def initialize_training_set(self,
ntrain,
enable_import=True,
sampling=None,
**kwargs):
return ReductionMethod.initialize_training_set(
self, self.truth_problem.mu_range, ntrain, enable_import,
sampling, **kwargs)
def initialize_training_set(self,
ntrain,
enable_import=True,
sampling=None,
**kwargs):
assert enable_import
import_successful = ReductionMethod.initialize_training_set(
self, self.SCM_approximation.mu_range, ntrain, enable_import,
sampling, **kwargs)
self.SCM_approximation.training_set = self.training_set
return import_successful