mirror of
https://github.com/davidhalter/typeshed.git
synced 2025-12-06 20:24:30 +08:00
1c0403cf83
* random.sample: make counts keyword-only * random: mark positional-only args * random.triangular: fix type of mode * random: add randbytes Co-authored-by: hauntsaninja <>
80 lines
3.9 KiB
Python
80 lines
3.9 KiB
Python
# Stubs for random
|
|
# Ron Murawski <ron@horizonchess.com>
|
|
# Updated by Jukka Lehtosalo
|
|
|
|
# based on http://docs.python.org/3.2/library/random.html
|
|
|
|
# ----- random classes -----
|
|
|
|
import _random
|
|
import sys
|
|
from typing import Any, TypeVar, Sequence, Iterable, List, Callable, AbstractSet, Union, Optional, Tuple
|
|
|
|
_T = TypeVar('_T')
|
|
|
|
class Random(_random.Random):
|
|
def __init__(self, x: Any = ...) -> None: ...
|
|
def seed(self, a: Any = ..., version: int = ...) -> None: ...
|
|
def getstate(self) -> Tuple[Any, ...]: ...
|
|
def setstate(self, state: Tuple[Any, ...]) -> None: ...
|
|
def getrandbits(self, __k: int) -> int: ...
|
|
def randrange(self, start: int, stop: Union[int, None] = ..., step: int = ...) -> int: ...
|
|
def randint(self, a: int, b: int) -> int: ...
|
|
if sys.version_info >= (3, 9):
|
|
def randbytes(self, n: int) -> bytes: ...
|
|
def choice(self, seq: Sequence[_T]) -> _T: ...
|
|
if sys.version_info >= (3, 6):
|
|
def choices(self, population: Sequence[_T], weights: Optional[Sequence[float]] = ..., *, cum_weights: Optional[Sequence[float]] = ..., k: int = ...) -> List[_T]: ...
|
|
def shuffle(self, x: List[Any], random: Union[Callable[[], float], None] = ...) -> None: ...
|
|
if sys.version_info >= (3, 9):
|
|
def sample(self, population: Union[Sequence[_T], AbstractSet[_T]], k: int, *, counts: Optional[Iterable[_T]] = ...) -> List[_T]: ...
|
|
else:
|
|
def sample(self, population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
|
|
def random(self) -> float: ...
|
|
def uniform(self, a: float, b: float) -> float: ...
|
|
def triangular(self, low: float = ..., high: float = ..., mode: Optional[float] = ...) -> float: ...
|
|
def betavariate(self, alpha: float, beta: float) -> float: ...
|
|
def expovariate(self, lambd: float) -> float: ...
|
|
def gammavariate(self, alpha: float, beta: float) -> float: ...
|
|
def gauss(self, mu: float, sigma: float) -> float: ...
|
|
def lognormvariate(self, mu: float, sigma: float) -> float: ...
|
|
def normalvariate(self, mu: float, sigma: float) -> float: ...
|
|
def vonmisesvariate(self, mu: float, kappa: float) -> float: ...
|
|
def paretovariate(self, alpha: float) -> float: ...
|
|
def weibullvariate(self, alpha: float, beta: float) -> float: ...
|
|
|
|
# SystemRandom is not implemented for all OS's; good on Windows & Linux
|
|
class SystemRandom(Random):
|
|
...
|
|
|
|
# ----- random function stubs -----
|
|
def seed(a: Any = ..., version: int = ...) -> None: ...
|
|
def getstate() -> object: ...
|
|
def setstate(state: object) -> None: ...
|
|
def getrandbits(__k: int) -> int: ...
|
|
def randrange(start: int, stop: Union[None, int] = ..., step: int = ...) -> int: ...
|
|
def randint(a: int, b: int) -> int: ...
|
|
if sys.version_info >= (3, 9):
|
|
def randbytes(n: int) -> bytes: ...
|
|
def choice(seq: Sequence[_T]) -> _T: ...
|
|
if sys.version_info >= (3, 6):
|
|
def choices(population: Sequence[_T], weights: Optional[Sequence[float]] = ..., *, cum_weights: Optional[Sequence[float]] = ..., k: int = ...) -> List[_T]: ...
|
|
def shuffle(x: List[Any], random: Union[Callable[[], float], None] = ...) -> None: ...
|
|
if sys.version_info >= (3, 9):
|
|
def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int, *, counts: Optional[Iterable[_T]] = ...) -> List[_T]: ...
|
|
else:
|
|
def sample(population: Union[Sequence[_T], AbstractSet[_T]], k: int) -> List[_T]: ...
|
|
def random() -> float: ...
|
|
def uniform(a: float, b: float) -> float: ...
|
|
def triangular(low: float = ..., high: float = ...,
|
|
mode: Optional[float] = ...) -> float: ...
|
|
def betavariate(alpha: float, beta: float) -> float: ...
|
|
def expovariate(lambd: float) -> float: ...
|
|
def gammavariate(alpha: float, beta: float) -> float: ...
|
|
def gauss(mu: float, sigma: float) -> float: ...
|
|
def lognormvariate(mu: float, sigma: float) -> float: ...
|
|
def normalvariate(mu: float, sigma: float) -> float: ...
|
|
def vonmisesvariate(mu: float, kappa: float) -> float: ...
|
|
def paretovariate(alpha: float) -> float: ...
|
|
def weibullvariate(alpha: float, beta: float) -> float: ...
|