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import sys
from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union, overload
from numpy import dtype, float32, float64, int64, integer, ndarray
from numpy.random import BitGenerator
from numpy.typing import ArrayLike, DTypeLike, _ArrayLikeFloat_co, _ArrayLikeInt_co, _ShapeLike, _DoubleCodes, _SingleCodes
if sys.version_info >= (3, 8):
from typing import Literal
else:
from typing_extensions import Literal
class Generator:
# COMPLETE
_bit_generator: BitGenerator
_poisson_lam_max: float
def __init__(self, bit_generator: BitGenerator) -> None: ...
def __repr__(self) -> str: ...
def __str__(self) -> str: ...
# Pickling support:
def __getstate__(self) -> Dict[str, Any]: ...
def __setstate__(self, state: Dict[str, Any]): ...
def __reduce__(self) -> Tuple[Callable[[str], BitGenerator], Tuple[str], Dict[str, Any]]: ...
@property
def bit_generator(self) -> BitGenerator: ...
def bytes(self, length: int) -> str: ...
# TODO: Needs overloading
def standard_cauchy(
self, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
# TODO: Needs overloading and specific dtypes
def standard_exponential(
self,
size: Optional[Union[_ShapeLike]] = ...,
dtype: DTypeLike = ...,
method: Literal["zig", "inv"] = ...,
out=Union[None, ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]]],
) -> Union[float, ndarray[Any, Any]]: ...
# TODO: Needs typing
def random(
self,
size: Optional[_ShapeLike] = ...,
dtype: DTypeLike = ...,
out: Union[None, ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]]] = ...,
): ...
def beta(
self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
): ...
def exponential(self, scale: _ArrayLikeFloat_co = ..., size: Union[None, _ShapeLike] = ...): ...
def integers(
self,
low: _ArrayLikeInt_co,
high: Optional[_ArrayLikeInt_co] = ...,
size: Union[None, _ShapeLike] = ...,
dtype=...,
endpoint: bool = ...,
) -> ndarray[Any, dtype[integer]]: ...
# TODO: Use a TypeVar _T here to get away from Any output? Should be int->ndarray[Any,dtype[int64]], ArrayLike[_T] -> Union[_T, ndarray[Any,Any]]
def choice(
self,
a: ArrayLike,
size: Union[None, _ShapeLike] = ...,
replace: bool = ...,
p=...,
axis=...,
shuffle: bool = ...,
) -> Any: ...
def uniform(
self,
low: _ArrayLikeFloat_co = ...,
high: _ArrayLikeFloat_co = ...,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
@overload
def standard_normal(
self,
size: None = ...,
dtype: DTypeLike = ...,
out: None = ...,
) -> float: ...
# TODO: How to literal dtype?
@overload
def standard_normal(
self,
size: _ShapeLike = ...,
dtype: DTypeLike = ...,
out: Union[None, ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]]] = ...,
) -> Union[ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]]]: ...
def normal(
self,
loc: _ArrayLikeFloat_co = ...,
scale: _ArrayLikeFloat_co = ...,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def standard_gamma(
self,
shape,
size: Union[None, _ShapeLike] = ...,
dtype=...,
out: Union[None, ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]]] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def gamma(
self,
shape: _ArrayLikeFloat_co,
scale: _ArrayLikeFloat_co = ...,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def f(
self,
dfnum: _ArrayLikeFloat_co,
dfden: _ArrayLikeFloat_co,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def noncentral_f(
self,
dfnum: _ArrayLikeFloat_co,
dfden: _ArrayLikeFloat_co,
nonc: _ArrayLikeFloat_co,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def chisquare(
self, df: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def noncentral_chisquare(
self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def standard_t(
self, df: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def vonmises(
self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def pareto(
self, a: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def weibull(
self, a: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def power(
self, a: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def laplace(
self,
loc: _ArrayLikeFloat_co = ...,
scale: _ArrayLikeFloat_co = ...,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def gumbel(
self,
loc: _ArrayLikeFloat_co = ...,
scale: _ArrayLikeFloat_co = ...,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def logistic(
self,
loc: _ArrayLikeFloat_co = ...,
scale: _ArrayLikeFloat_co = ...,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def lognormal(
self,
mean: _ArrayLikeFloat_co = ...,
sigma: _ArrayLikeFloat_co = ...,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def rayleigh(
self, scale: _ArrayLikeFloat_co = ..., size: Union[None, _ShapeLike] = ...
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def wald(
self,
mean: _ArrayLikeFloat_co,
scale: _ArrayLikeFloat_co,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
def triangular(
self,
left: _ArrayLikeFloat_co,
mode: _ArrayLikeFloat_co,
right: _ArrayLikeFloat_co,
size: Union[None, _ShapeLike] = ...,
) -> Union[float, ndarray[Any, dtype[float64]]]: ...
# Complicated, discrete distributions:
def binomial(
self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[int, ndarray[Any, dtype[int64]]]: ...
def negative_binomial(
self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[int, ndarray[Any, dtype[int64]]]: ...
def poisson(
self, lam: _ArrayLikeFloat_co = ..., size: Union[None, _ShapeLike] = ...
) -> Union[int, ndarray[Any, dtype[int64]]]: ...
def zipf(
self, a: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[int, ndarray[Any, dtype[int64]]]: ...
def geometric(
self, p: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[int, ndarray[Any, dtype[int64]]]: ...
def hypergeometric(
self,
ngood: _ArrayLikeInt_co,
nbad: _ArrayLikeInt_co,
nsample: _ArrayLikeInt_co,
size: Union[None, _ShapeLike] = ...,
) -> Union[int, ndarray[Any, dtype[int64]]]: ...
def logseries(
self, p: _ArrayLikeFloat_co, size: Union[None, _ShapeLike] = ...
) -> Union[int, ndarray[Any, dtype[int64]]]: ...
# Multivariate distributions:
# TODO: Really need 1-d array like floating and 2-d array-like floating. Using Sequence[float] ??
def multivariate_normal(
self,
mean: Sequence[float],
cov: Sequence[Sequence[float]],
size: Union[None, _ShapeLike] = ...,
check_valid: Literal["warn", "raise", "ignore"] = ...,
tol: float = ...,
*,
method: Literal["svd", "eigh", "cholesky"] = ...
): ...
# TODO: Need 1-d array like floating. Using Sequence[float] ??
def multinomial(
self, n: _ArrayLikeInt_co, pvals: Sequence[float], size: Union[None, _ShapeLike] = ...
): ...
# TODO: Need 1-d array like integers. Using Sequence[int] ??
def multivariate_hypergeometric(
self,
colors: Sequence[int],
nsample: int,
size: Union[None, _ShapeLike] = ...,
method: Literal["marginals", "count"] = ...,
): ...
# TODO: Need 1-d array like floating. Using Sequence[float] ??
def dirichlet(
self, alpha: Sequence[float], size: Union[None, _ShapeLike] = ...
) -> ndarray[Any, dtype[float64]]: ...
def permuted(
self, x: ArrayLike, *, axis: Optional[int] = ..., out: Optional[ndarray[Any, Any]] = ...
) -> ndarray[Any, Any]: ...
def shuffle(self, x: ArrayLike, axis: int = ...) -> Sequence[Any]: ...
@overload
def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]: ...
@overload
def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]: ...
def default_rng(seed=None) -> Generator: ...
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