estimator

Defines estimators which estimate a model’s ability to represent the data.

All estimators have to implement the Estimator interface.

class UnbinnedNLL(model: tensorwaves.interfaces.Function, dataset: dict, phsp_set: dict)[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 evaluateable with this dataset.

  • phsp_set – A phase space dataset, which is used for the normalization. The model has to be evaluateable with this dataset. When correcting for the detector efficiency use a phase space sample, that passed the detector reconstruction.

__call__() → float[source]

Evaluate discrepancy.

gradient() → numpy.ndarray[source]

Compute the gradient of the data set.

property parameters

Get dict of parameters.

update_parameters(new_parameters: dict) → None[source]

Update the collection of parameters.

kinematics interfaces