pygsti.drivers.bootstrap.create_bootstrap_dataset#
- create_bootstrap_dataset(input_data_set, generation_method, input_model=None, seed=None, outcome_labels=None, verbosity=1)#
Creates a DataSet used for generating bootstrapped error bars.
- Parameters:
input_data_set (DataSet) – The data set to use for generating the “bootstrapped” data set.
generation_method ({ 'nonparametric', 'parametric' }) – The type of dataset to generate. ‘parametric’ generates a DataSet with the same circuits and sample counts as input_data_set but using the probabilities in input_model (which must be provided). ‘nonparametric’ generates a DataSet with the same circuits and sample counts as input_data_set using the count frequencies of input_data_set as probabilities.
input_model (Model, optional) – The model used to compute the probabilities for circuits when generation_method is set to ‘parametric’. If ‘nonparametric’ is selected, this argument must be set to None (the default).
seed (int, optional) – A seed value for numpy’s random number generator.
outcome_labels (list, optional) – The list of outcome labels to include in the output dataset. If None are specified, defaults to the spam labels of input_data_set.
verbosity (int, optional) – How verbose the function output is. If 0, then printing is suppressed. If 1 (or greater), then printing is not suppressed.
- Return type: