mlmc.sample_storage.Memory
- class mlmc.sample_storage.Memory[source]
Sample’s data are stored in the main memory
Methods
__init__()chunks([level_id, n_samples])Create a generator yielding chunk specifications for collected data.
get_level_ids()Get list of available level IDs.
get_level_parameters()Get stored level parameters.
get_n_collected()Number of collected samples at each level :return: List
get_n_levels()Get number of levels :return: int
get_n_ops()Get number of operations on each level :return: List[float]
load_result_format()Load result format
load_scheduled_samples()n_finished()Number of finished samples on each level :return: List
sample_pairs()Sample results split to numpy arrays :return: List[Array[M, N, 2]]
sample_pairs_level(chunk_spec)Get samples for given level, chunks does not make sense in Memory storage so all data are retrieved at once :type chunk_spec: :param chunk_spec: object containing chunk identifier level identifier and chunk_slice - slice() object :return: np.ndarray
save_global_data(result_format[, ...])Save global metadata such as result format and level parameters.
save_n_ops(n_ops)Save number of operations :type n_ops: :param n_ops: Dict[_level_id, List[time, number of valid samples]] :return: None
save_result_format(res_spec)Save sample result format :type res_spec:
List[QuantitySpec] :param res_spec: List[QuantitySpec] :return: Nonesave_samples(successful_samples, failed_samples)Save successful samples - store result pairs
save_scheduled_samples(level_id, samples)Save scheduled sample ids :type level_id: :param level_id: int :type samples: :param samples: List[str] :return: None
unfinished_ids()We finished all samples in memory :return: