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68 lines
3.0 KiB
Python
68 lines
3.0 KiB
Python
# Stubs for random
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# Ron Murawski <ron@horizonchess.com>
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# Updated by Jukka Lehtosalo
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# based on http://docs.python.org/3.2/library/random.html
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# ----- random classes -----
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import _random
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from typing import (
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Any, TypeVar, Sequence, List, Callable, AbstractSet, Union
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)
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_T = TypeVar('_T')
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class Random(_random.Random):
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def __init__(self, x: Any = ...) -> None: ...
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def seed(self, a: Any = ..., version: int = ...) -> None: ...
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def getstate(self) -> tuple: ...
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def setstate(self, state: tuple) -> None: ...
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def getrandbits(self, k: int) -> int: ...
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def randrange(self, start: int, stop: Union[int, None] = ..., step: int = ...) -> int: ...
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def randint(self, a: int, b: int) -> int: ...
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def choice(self, seq: Sequence[_T]) -> _T: ...
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def shuffle(self, x: List[Any], random: Union[Callable[[], float], None] = ...) -> None: ...
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def sample(self, population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
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def random(self) -> float: ...
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def uniform(self, a: float, b: float) -> float: ...
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def triangular(self, low: float = ..., high: float = ...,
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mode: float = ...) -> float: ...
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def betavariate(self, alpha: float, beta: float) -> float: ...
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def expovariate(self, lambd: float) -> float: ...
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def gammavariate(self, alpha: float, beta: float) -> float: ...
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def gauss(self, mu: float, sigma: float) -> float: ...
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def lognormvariate(self, mu: float, sigma: float) -> float: ...
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def normalvariate(self, mu: float, sigma: float) -> float: ...
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def vonmisesvariate(self, mu: float, kappa: float) -> float: ...
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def paretovariate(self, alpha: float) -> float: ...
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def weibullvariate(self, alpha: float, beta: float) -> float: ...
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# SystemRandom is not implemented for all OS's; good on Windows & Linux
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class SystemRandom(Random):
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...
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# ----- random function stubs -----
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def seed(a: Any = ..., version: int = ...) -> None: ...
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def getstate() -> object: ...
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def setstate(state: object) -> None: ...
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def getrandbits(k: int) -> int: ...
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def randrange(start: int, stop: Union[None, int] = ..., step: int = ...) -> int: ...
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def randint(a: int, b: int) -> int: ...
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def choice(seq: Sequence[_T]) -> _T: ...
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def shuffle(x: List[Any], random: Union[Callable[[], float], None] = ...) -> None: ...
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def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
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def random() -> float: ...
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def uniform(a: float, b: float) -> float: ...
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def triangular(low: float = ..., high: float = ...,
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mode: float = ...) -> float: ...
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def betavariate(alpha: float, beta: float) -> float: ...
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def expovariate(lambd: float) -> float: ...
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def gammavariate(alpha: float, beta: float) -> float: ...
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def gauss(mu: float, sigma: float) -> float: ...
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def lognormvariate(mu: float, sigma: float) -> float: ...
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def normalvariate(mu: float, sigma: float) -> float: ...
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def vonmisesvariate(mu: float, kappa: float) -> float: ...
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def paretovariate(alpha: float) -> float: ...
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def weibullvariate(alpha: float, beta: float) -> float: ...
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