You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
570 lines
19 KiB
570 lines
19 KiB
from collections.abc import Callable
|
|
from typing import Any, Union, overload, Literal
|
|
|
|
from numpy import (
|
|
bool_,
|
|
dtype,
|
|
float32,
|
|
float64,
|
|
int8,
|
|
int16,
|
|
int32,
|
|
int64,
|
|
int_,
|
|
ndarray,
|
|
uint,
|
|
uint8,
|
|
uint16,
|
|
uint32,
|
|
uint64,
|
|
)
|
|
from numpy.random.bit_generator import BitGenerator
|
|
from numpy._typing import (
|
|
ArrayLike,
|
|
_ArrayLikeFloat_co,
|
|
_ArrayLikeInt_co,
|
|
_DoubleCodes,
|
|
_DTypeLikeBool,
|
|
_DTypeLikeInt,
|
|
_DTypeLikeUInt,
|
|
_Float32Codes,
|
|
_Float64Codes,
|
|
_Int8Codes,
|
|
_Int16Codes,
|
|
_Int32Codes,
|
|
_Int64Codes,
|
|
_IntCodes,
|
|
_ShapeLike,
|
|
_SingleCodes,
|
|
_SupportsDType,
|
|
_UInt8Codes,
|
|
_UInt16Codes,
|
|
_UInt32Codes,
|
|
_UInt64Codes,
|
|
_UIntCodes,
|
|
)
|
|
|
|
_DTypeLikeFloat32 = Union[
|
|
dtype[float32],
|
|
_SupportsDType[dtype[float32]],
|
|
type[float32],
|
|
_Float32Codes,
|
|
_SingleCodes,
|
|
]
|
|
|
|
_DTypeLikeFloat64 = Union[
|
|
dtype[float64],
|
|
_SupportsDType[dtype[float64]],
|
|
type[float],
|
|
type[float64],
|
|
_Float64Codes,
|
|
_DoubleCodes,
|
|
]
|
|
|
|
class RandomState:
|
|
_bit_generator: BitGenerator
|
|
def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ...
|
|
def __repr__(self) -> str: ...
|
|
def __str__(self) -> str: ...
|
|
def __getstate__(self) -> dict[str, Any]: ...
|
|
def __setstate__(self, state: dict[str, Any]) -> None: ...
|
|
def __reduce__(self) -> tuple[Callable[[str], RandomState], tuple[str], dict[str, Any]]: ...
|
|
def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ...
|
|
@overload
|
|
def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ...
|
|
@overload
|
|
def get_state(
|
|
self, legacy: Literal[True] = ...
|
|
) -> dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]: ...
|
|
def set_state(
|
|
self, state: dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]
|
|
) -> None: ...
|
|
@overload
|
|
def random_sample(self, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def random_sample(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def random(self, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def random(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def beta(self, a: float, b: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def beta(
|
|
self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def exponential(self, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def exponential(
|
|
self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def standard_exponential(self, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def tomaxint(self, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def tomaxint(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: int,
|
|
high: None | int = ...,
|
|
) -> int: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: int,
|
|
high: None | int = ...,
|
|
size: None = ...,
|
|
dtype: _DTypeLikeBool = ...,
|
|
) -> bool: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: int,
|
|
high: None | int = ...,
|
|
size: None = ...,
|
|
dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
|
|
) -> int: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: _DTypeLikeBool = ...,
|
|
) -> ndarray[Any, dtype[bool_]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
|
|
) -> ndarray[Any, dtype[int8]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
|
|
) -> ndarray[Any, dtype[int16]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
|
|
) -> ndarray[Any, dtype[int32]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
|
|
) -> ndarray[Any, dtype[int64]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
|
|
) -> ndarray[Any, dtype[uint8]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
|
|
) -> ndarray[Any, dtype[uint16]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
|
|
) -> ndarray[Any, dtype[uint32]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
|
|
) -> ndarray[Any, dtype[uint64]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def randint( # type: ignore[misc]
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
|
|
) -> ndarray[Any, dtype[uint]]: ...
|
|
def bytes(self, length: int) -> bytes: ...
|
|
@overload
|
|
def choice(
|
|
self,
|
|
a: int,
|
|
size: None = ...,
|
|
replace: bool = ...,
|
|
p: None | _ArrayLikeFloat_co = ...,
|
|
) -> int: ...
|
|
@overload
|
|
def choice(
|
|
self,
|
|
a: int,
|
|
size: _ShapeLike = ...,
|
|
replace: bool = ...,
|
|
p: None | _ArrayLikeFloat_co = ...,
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def choice(
|
|
self,
|
|
a: ArrayLike,
|
|
size: None = ...,
|
|
replace: bool = ...,
|
|
p: None | _ArrayLikeFloat_co = ...,
|
|
) -> Any: ...
|
|
@overload
|
|
def choice(
|
|
self,
|
|
a: ArrayLike,
|
|
size: _ShapeLike = ...,
|
|
replace: bool = ...,
|
|
p: None | _ArrayLikeFloat_co = ...,
|
|
) -> ndarray[Any, Any]: ...
|
|
@overload
|
|
def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def uniform(
|
|
self,
|
|
low: _ArrayLikeFloat_co = ...,
|
|
high: _ArrayLikeFloat_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def rand(self) -> float: ...
|
|
@overload
|
|
def rand(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def randn(self) -> float: ...
|
|
@overload
|
|
def randn(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def random_integers(
|
|
self,
|
|
low: _ArrayLikeInt_co,
|
|
high: None | _ArrayLikeInt_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def standard_normal(self, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def standard_normal( # type: ignore[misc]
|
|
self, size: _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def normal(
|
|
self,
|
|
loc: _ArrayLikeFloat_co = ...,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def standard_gamma( # type: ignore[misc]
|
|
self,
|
|
shape: float,
|
|
size: None = ...,
|
|
) -> float: ...
|
|
@overload
|
|
def standard_gamma(
|
|
self,
|
|
shape: _ArrayLikeFloat_co,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def gamma(
|
|
self,
|
|
shape: _ArrayLikeFloat_co,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def f(
|
|
self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def noncentral_f(
|
|
self,
|
|
dfnum: _ArrayLikeFloat_co,
|
|
dfden: _ArrayLikeFloat_co,
|
|
nonc: _ArrayLikeFloat_co,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def chisquare(self, df: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def chisquare(
|
|
self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def noncentral_chisquare(
|
|
self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def standard_t(self, df: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def standard_t(
|
|
self, df: _ArrayLikeFloat_co, size: None = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def standard_t(
|
|
self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def vonmises(
|
|
self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def pareto(self, a: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def pareto(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def weibull(self, a: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def weibull(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def power(self, a: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def power(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def laplace(
|
|
self,
|
|
loc: _ArrayLikeFloat_co = ...,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def gumbel(
|
|
self,
|
|
loc: _ArrayLikeFloat_co = ...,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def logistic(
|
|
self,
|
|
loc: _ArrayLikeFloat_co = ...,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def lognormal(
|
|
self,
|
|
mean: _ArrayLikeFloat_co = ...,
|
|
sigma: _ArrayLikeFloat_co = ...,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def rayleigh(self, scale: float = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def rayleigh(
|
|
self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def wald(self, mean: float, scale: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def wald(
|
|
self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def triangular(
|
|
self,
|
|
left: _ArrayLikeFloat_co,
|
|
mode: _ArrayLikeFloat_co,
|
|
right: _ArrayLikeFloat_co,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def binomial(self, n: int, p: float, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def binomial(
|
|
self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def negative_binomial(
|
|
self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def poisson(self, lam: float = ..., size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def poisson(
|
|
self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def zipf(self, a: float, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def zipf(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def geometric(self, p: float, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def geometric(
|
|
self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def hypergeometric(
|
|
self,
|
|
ngood: _ArrayLikeInt_co,
|
|
nbad: _ArrayLikeInt_co,
|
|
nsample: _ArrayLikeInt_co,
|
|
size: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def logseries(self, p: float, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def logseries(
|
|
self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
def multivariate_normal(
|
|
self,
|
|
mean: _ArrayLikeFloat_co,
|
|
cov: _ArrayLikeFloat_co,
|
|
size: None | _ShapeLike = ...,
|
|
check_valid: Literal["warn", "raise", "ignore"] = ...,
|
|
tol: float = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
def multinomial(
|
|
self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int_]]: ...
|
|
def dirichlet(
|
|
self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
def shuffle(self, x: ArrayLike) -> None: ...
|
|
@overload
|
|
def permutation(self, x: int) -> ndarray[Any, dtype[int_]]: ...
|
|
@overload
|
|
def permutation(self, x: ArrayLike) -> ndarray[Any, Any]: ...
|
|
|
|
_rand: RandomState
|
|
|
|
beta = _rand.beta
|
|
binomial = _rand.binomial
|
|
bytes = _rand.bytes
|
|
chisquare = _rand.chisquare
|
|
choice = _rand.choice
|
|
dirichlet = _rand.dirichlet
|
|
exponential = _rand.exponential
|
|
f = _rand.f
|
|
gamma = _rand.gamma
|
|
get_state = _rand.get_state
|
|
geometric = _rand.geometric
|
|
gumbel = _rand.gumbel
|
|
hypergeometric = _rand.hypergeometric
|
|
laplace = _rand.laplace
|
|
logistic = _rand.logistic
|
|
lognormal = _rand.lognormal
|
|
logseries = _rand.logseries
|
|
multinomial = _rand.multinomial
|
|
multivariate_normal = _rand.multivariate_normal
|
|
negative_binomial = _rand.negative_binomial
|
|
noncentral_chisquare = _rand.noncentral_chisquare
|
|
noncentral_f = _rand.noncentral_f
|
|
normal = _rand.normal
|
|
pareto = _rand.pareto
|
|
permutation = _rand.permutation
|
|
poisson = _rand.poisson
|
|
power = _rand.power
|
|
rand = _rand.rand
|
|
randint = _rand.randint
|
|
randn = _rand.randn
|
|
random = _rand.random
|
|
random_integers = _rand.random_integers
|
|
random_sample = _rand.random_sample
|
|
rayleigh = _rand.rayleigh
|
|
seed = _rand.seed
|
|
set_state = _rand.set_state
|
|
shuffle = _rand.shuffle
|
|
standard_cauchy = _rand.standard_cauchy
|
|
standard_exponential = _rand.standard_exponential
|
|
standard_gamma = _rand.standard_gamma
|
|
standard_normal = _rand.standard_normal
|
|
standard_t = _rand.standard_t
|
|
triangular = _rand.triangular
|
|
uniform = _rand.uniform
|
|
vonmises = _rand.vonmises
|
|
wald = _rand.wald
|
|
weibull = _rand.weibull
|
|
zipf = _rand.zipf
|
|
# Two legacy that are trivial wrappers around random_sample
|
|
sample = _rand.random_sample
|
|
ranf = _rand.random_sample
|
|
|
|
def set_bit_generator(bitgen: BitGenerator) -> None:
|
|
...
|
|
|
|
def get_bit_generator() -> BitGenerator:
|
|
...
|
|
|