mirror of
https://github.com/davidhalter/typeshed.git
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88 lines
3.8 KiB
Python
88 lines
3.8 KiB
Python
import _random
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import sys
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from typing import AbstractSet, Any, Callable, Iterable, List, MutableSequence, Optional, Sequence, Tuple, TypeVar, Union
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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[Any, ...]: ...
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def setstate(self, state: Tuple[Any, ...]) -> None: ...
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def getrandbits(self, __k: int) -> int: ...
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def randrange(self, start: int, stop: Optional[int] = ..., step: int = ...) -> int: ...
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def randint(self, a: int, b: int) -> int: ...
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if sys.version_info >= (3, 9):
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def randbytes(self, n: int) -> bytes: ...
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def choice(self, seq: Sequence[_T]) -> _T: ...
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def choices(
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self,
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population: Sequence[_T],
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weights: Optional[Sequence[float]] = ...,
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*,
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cum_weights: Optional[Sequence[float]] = ...,
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k: int = ...,
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) -> List[_T]: ...
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def shuffle(self, x: MutableSequence[Any], random: Optional[Callable[[], float]] = ...) -> None: ...
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if sys.version_info >= (3, 9):
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def sample(
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self, population: Union[Sequence[_T], AbstractSet[_T]], k: int, *, counts: Optional[Iterable[_T]] = ...
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) -> List[_T]: ...
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else:
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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 = ..., mode: Optional[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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# ----- 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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if sys.version_info >= (3, 9):
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def randbytes(n: int) -> bytes: ...
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def choice(seq: Sequence[_T]) -> _T: ...
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def choices(
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population: Sequence[_T],
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weights: Optional[Sequence[float]] = ...,
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*,
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cum_weights: Optional[Sequence[float]] = ...,
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k: int = ...,
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) -> List[_T]: ...
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def shuffle(x: MutableSequence[Any], random: Optional[Callable[[], float]] = ...) -> None: ...
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if sys.version_info >= (3, 9):
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def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int, *, counts: Optional[Iterable[_T]] = ...) -> List[_T]: ...
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else:
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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 = ..., mode: Optional[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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