lbsStatistics module
lbsStatistics
Classes
- class Statistics
- A class storing descriptive statistics.
Functions
- def Hamming_distance(arrangement_1, arrangement_2)
- Compute Hamming distance between two arrangements.
- def compute_all_reachable_arrangements(objects: tuple, arrangement: tuple, alpha: float, beta: float, gamma: float, w_max: float, n_ranks: int, logger: logging.Logger, max_objects: int = None)
- Compute all arrangements reachable by moving up to a maximum number of objects.
- def compute_arrangement_works(objects: tuple, arrangement: tuple, alpha: float, beta: float, gamma: float) -> dict
- Return a dictionary with works of rank objects.
- def compute_function_statistics(population, fct) -> Statistics
- Compute descriptive statistics of a function over a population.
- def compute_load(objects: tuple, rank_object_ids: list) -> float
- Return a load as a sum of all object loads.
- def compute_min_max_arrangements_work(objects: tuple, alpha: float, beta: float, gamma: float, n_ranks: int, sanity_checks = True, logger: typing.Optional[logging.Logger] = None)
- Compute all possible arrangements with repetition and minimax work.
- def compute_pairwise_reachable_arrangements(objects: tuple, arrangement: tuple, alpha: float, beta: float, gamma: float, w_max: float, from_id: int, to_id: int, n_ranks: int, max_objects: typing.Optional[int] = None, logger: typing.Optional[logging.Logger] = None)
- Compute arrangements reachable by moving up to a maximum number of objects from one rank to another.
- def compute_volume(objects: tuple, rank_object_ids: list, direction: str) -> float
- Return a volume of rank objects
- def error_out(distribution_name, parameters, logger: logging.Logger)
- Logs an error indicating not enough parameters for a distribution.
- def initialize()
- Seed pseudo-random number generators.
- def inverse_transform_sample(cmf)
- Sample from distribution defined by cumulative mass function This is a.k.a. the Smirnov transform.
- def min_Hamming_distance(arrangement, arrangement_list)
- Compute minimum Hamming distance from arrangement to list of arrangements.
- def print_function_statistics(values, function, var_name, logger: logging.Logger)
- Compute and report descriptive statistics of function values.
- def print_subset_statistics(subset_name, subset_size, set_name, set_size, logger: logging.Logger)
- Compute and report descriptive statistics of subset vs. full set.
- def recursively_compute_transitions(stack: list, visited: dict, objects: tuple, arrangement: tuple, alpha: float, beta: float, gamma: float, w_max: float, w_min_max: float, n_ranks: int, logger: logging.Logger, max_objects: typing.Optional[int] = None)
- Recursively compute all possible transitions to reachable arrangements from initial one.
- def sampler(distribution_name, parameters, logger: logging.Logger)
- Return a pseudo-random number generator based of requested type.
- def summarize_statistics_tuples(var_name, stats, key_tuples: list, logger: logging.Logger)
- Print pretty summary of statistics one tuple per row.