estimator
estimator#
import tensorwaves.estimator
Defines estimators which estimate a model’s ability to represent the data.
All estimators have to implement the Estimator
interface.
- class UnbinnedNLL(model: Union[Function, Model], dataset: Mapping[str, ndarray], phsp_dataset: Mapping[str, ndarray], phsp_volume: float = 1.0, backend: Union[str, tuple, dict] = 'numpy', use_caching: bool = False, fixed_parameters: Optional[Dict[str, Union[complex, float]]] = None)[source]#
Bases:
tensorwaves.interfaces.Estimator
Unbinned negative log likelihood estimator.
- Parameters
model – A model that should be compared to the dataset.
dataset – The dataset used for the comparison. The model has to be evaluatable with this dataset.
phsp_set – A phase space dataset, which is used for the normalization. The model has to be evaluatable with this dataset. When correcting for the detector efficiency use a phase space sample, that passed the detector reconstruction.