deltakit.decode.noise_sources.CombinedIndependent

class deltakit.decode.noise_sources.CombinedIndependent(internal_sources: tuple[MonteCarloNoise[CodeT, ErrorT], ...])

Bases: MonteCarloNoise[tuple[CodeT, …], tuple[ErrorT, …]], Generic[CodeT, ErrorT]

Class to combine several independent noise sources into one combined model, where each error returned from one of the internal sources becomes an element in a tuple.

Methods

CombinedIndependent.as_exhaustive_sequential_model	Return the equivalent exhaustive model for this noise model.
CombinedIndependent.build_batch_error_generator	Given some representation of a code, return a generator of batches of errors for that code.
CombinedIndependent.build_split_batch_error_generators	Given some representation of a code, return num_splits number of batch generators of errors for that code and the respective sizes for those generators.
CombinedIndependent.error_generator	Given some representation of a code, return a generator of errors for that code.
CombinedIndependent.field_values	Return the values of data that characterises this noise model.
CombinedIndependent.get_rng	Return a numpy random number generator, using the member data seed.
CombinedIndependent.importance_sampling_decomposition	Expresses the independent error distribution as a statistical mixture of other error distributions.
CombinedIndependent.split_error_generator	Given some representation of a code, return num_splits number of generators of errors for that code and the respective sizes for those generators.