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import builtins |
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from collections.abc import Callable |
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from typing import Any, overload, Literal |
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|
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import numpy as np |
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from numpy import ( |
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dtype, |
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float32, |
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float64, |
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int8, |
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int16, |
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int32, |
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int64, |
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int_, |
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long, |
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uint8, |
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uint16, |
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uint32, |
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uint64, |
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uint, |
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ulong, |
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) |
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from numpy.random.bit_generator import BitGenerator |
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from numpy._typing import ( |
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ArrayLike, |
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NDArray, |
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_ArrayLikeFloat_co, |
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_ArrayLikeInt_co, |
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_DoubleCodes, |
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_DTypeLikeBool, |
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_DTypeLikeInt, |
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_DTypeLikeUInt, |
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_Float32Codes, |
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_Float64Codes, |
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_Int8Codes, |
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_Int16Codes, |
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_Int32Codes, |
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_Int64Codes, |
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_IntCodes, |
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_LongCodes, |
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_ShapeLike, |
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_SingleCodes, |
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_SupportsDType, |
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_UInt8Codes, |
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_UInt16Codes, |
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_UInt32Codes, |
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_UInt64Codes, |
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_UIntCodes, |
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_ULongCodes, |
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) |
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_DTypeLikeFloat32 = ( |
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dtype[float32] |
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| _SupportsDType[dtype[float32]] |
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| type[float32] |
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| _Float32Codes |
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| _SingleCodes |
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) |
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_DTypeLikeFloat64 = ( |
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dtype[float64] |
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| _SupportsDType[dtype[float64]] |
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| type[float] |
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| type[float64] |
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| _Float64Codes |
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| _DoubleCodes |
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) |
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class RandomState: |
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_bit_generator: BitGenerator |
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def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ... |
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def __repr__(self) -> str: ... |
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def __str__(self) -> str: ... |
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def __getstate__(self) -> dict[str, Any]: ... |
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def __setstate__(self, state: dict[str, Any]) -> None: ... |
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def __reduce__(self) -> tuple[Callable[[BitGenerator], RandomState], tuple[BitGenerator], dict[str, Any]]: ... |
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def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ... |
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@overload |
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def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ... |
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@overload |
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def get_state( |
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self, legacy: Literal[True] = ... |
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) -> dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]: ... |
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def set_state( |
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self, state: dict[str, Any] | tuple[str, NDArray[uint32], int, int, float] |
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) -> None: ... |
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@overload |
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def random_sample(self, size: None = ...) -> float: ... |
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@overload |
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def random_sample(self, size: _ShapeLike) -> NDArray[float64]: ... |
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@overload |
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def random(self, size: None = ...) -> float: ... |
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@overload |
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def random(self, size: _ShapeLike) -> NDArray[float64]: ... |
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@overload |
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def beta(self, a: float, b: float, size: None = ...) -> float: ... |
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@overload |
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def beta( |
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self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
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) -> NDArray[float64]: ... |
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@overload |
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def exponential(self, scale: float = ..., size: None = ...) -> float: ... |
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@overload |
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def exponential( |
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self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... |
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) -> NDArray[float64]: ... |
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@overload |
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def standard_exponential(self, size: None = ...) -> float: ... |
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@overload |
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def standard_exponential(self, size: _ShapeLike) -> NDArray[float64]: ... |
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@overload |
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def tomaxint(self, size: None = ...) -> int: ... |
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@overload |
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|
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def tomaxint(self, size: _ShapeLike) -> NDArray[int64]: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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) -> int: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: type[bool] = ..., |
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) -> bool: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: type[np.bool] = ..., |
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) -> np.bool: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: type[int] = ..., |
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) -> int: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., |
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) -> uint8: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., |
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) -> uint16: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., |
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) -> uint32: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., |
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) -> uint: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., |
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) -> ulong: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., |
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) -> uint64: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., |
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) -> int8: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., |
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) -> int16: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., |
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) -> int32: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[int_] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., |
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) -> int_: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[long] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., |
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) -> long: ... |
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@overload |
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def randint( |
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self, |
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low: int, |
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high: None | int = ..., |
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size: None = ..., |
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dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., |
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) -> int64: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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) -> NDArray[long]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: _DTypeLikeBool = ..., |
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) -> NDArray[np.bool]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., |
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) -> NDArray[int8]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., |
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) -> NDArray[int16]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., |
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) -> NDArray[int32]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., |
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) -> NDArray[int64]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., |
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) -> NDArray[uint8]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., |
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) -> NDArray[uint16]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., |
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) -> NDArray[uint32]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., |
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) -> NDArray[uint64]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[long] | type[int] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ..., |
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) -> NDArray[long]: ... |
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@overload |
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def randint( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
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size: None | _ShapeLike = ..., |
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dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ..., |
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) -> NDArray[ulong]: ... |
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def bytes(self, length: int) -> builtins.bytes: ... |
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@overload |
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def choice( |
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self, |
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a: int, |
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size: None = ..., |
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replace: bool = ..., |
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p: None | _ArrayLikeFloat_co = ..., |
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) -> int: ... |
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@overload |
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def choice( |
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self, |
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a: int, |
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size: _ShapeLike = ..., |
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replace: bool = ..., |
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p: None | _ArrayLikeFloat_co = ..., |
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) -> NDArray[long]: ... |
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@overload |
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def choice( |
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self, |
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a: ArrayLike, |
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size: None = ..., |
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replace: bool = ..., |
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p: None | _ArrayLikeFloat_co = ..., |
|
) -> Any: ... |
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@overload |
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def choice( |
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self, |
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a: ArrayLike, |
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size: _ShapeLike = ..., |
|
replace: bool = ..., |
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p: None | _ArrayLikeFloat_co = ..., |
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) -> NDArray[Any]: ... |
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@overload |
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def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ... |
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@overload |
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def uniform( |
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self, |
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low: _ArrayLikeFloat_co = ..., |
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high: _ArrayLikeFloat_co = ..., |
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size: None | _ShapeLike = ..., |
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) -> NDArray[float64]: ... |
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@overload |
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def rand(self) -> float: ... |
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@overload |
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def rand(self, *args: int) -> NDArray[float64]: ... |
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@overload |
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def randn(self) -> float: ... |
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@overload |
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def randn(self, *args: int) -> NDArray[float64]: ... |
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@overload |
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def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ... |
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@overload |
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def random_integers( |
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self, |
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low: _ArrayLikeInt_co, |
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high: None | _ArrayLikeInt_co = ..., |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[long]: ... |
|
@overload |
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def standard_normal(self, size: None = ...) -> float: ... |
|
@overload |
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def standard_normal( |
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self, size: _ShapeLike = ... |
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) -> NDArray[float64]: ... |
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@overload |
|
def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... |
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@overload |
|
def normal( |
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self, |
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loc: _ArrayLikeFloat_co = ..., |
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scale: _ArrayLikeFloat_co = ..., |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
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def standard_gamma( |
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self, |
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shape: float, |
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size: None = ..., |
|
) -> float: ... |
|
@overload |
|
def standard_gamma( |
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self, |
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shape: _ArrayLikeFloat_co, |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
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def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ... |
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@overload |
|
def gamma( |
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self, |
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shape: _ArrayLikeFloat_co, |
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scale: _ArrayLikeFloat_co = ..., |
|
size: None | _ShapeLike = ..., |
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) -> NDArray[float64]: ... |
|
@overload |
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def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ... |
|
@overload |
|
def f( |
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self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
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def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ... |
|
@overload |
|
def noncentral_f( |
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self, |
|
dfnum: _ArrayLikeFloat_co, |
|
dfden: _ArrayLikeFloat_co, |
|
nonc: _ArrayLikeFloat_co, |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def chisquare(self, df: float, size: None = ...) -> float: ... |
|
@overload |
|
def chisquare( |
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self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ... |
|
@overload |
|
def noncentral_chisquare( |
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self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def standard_t(self, df: float, size: None = ...) -> float: ... |
|
@overload |
|
def standard_t( |
|
self, df: _ArrayLikeFloat_co, size: None = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def standard_t( |
|
self, df: _ArrayLikeFloat_co, size: _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ... |
|
@overload |
|
def vonmises( |
|
self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def pareto(self, a: float, size: None = ...) -> float: ... |
|
@overload |
|
def pareto( |
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def weibull(self, a: float, size: None = ...) -> float: ... |
|
@overload |
|
def weibull( |
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def power(self, a: float, size: None = ...) -> float: ... |
|
@overload |
|
def power( |
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def standard_cauchy(self, size: None = ...) -> float: ... |
|
@overload |
|
def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ... |
|
@overload |
|
def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... |
|
@overload |
|
def laplace( |
|
self, |
|
loc: _ArrayLikeFloat_co = ..., |
|
scale: _ArrayLikeFloat_co = ..., |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... |
|
@overload |
|
def gumbel( |
|
self, |
|
loc: _ArrayLikeFloat_co = ..., |
|
scale: _ArrayLikeFloat_co = ..., |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ... |
|
@overload |
|
def logistic( |
|
self, |
|
loc: _ArrayLikeFloat_co = ..., |
|
scale: _ArrayLikeFloat_co = ..., |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ... |
|
@overload |
|
def lognormal( |
|
self, |
|
mean: _ArrayLikeFloat_co = ..., |
|
sigma: _ArrayLikeFloat_co = ..., |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def rayleigh(self, scale: float = ..., size: None = ...) -> float: ... |
|
@overload |
|
def rayleigh( |
|
self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def wald(self, mean: float, scale: float, size: None = ...) -> float: ... |
|
@overload |
|
def wald( |
|
self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ... |
|
@overload |
|
def triangular( |
|
self, |
|
left: _ArrayLikeFloat_co, |
|
mode: _ArrayLikeFloat_co, |
|
right: _ArrayLikeFloat_co, |
|
size: None | _ShapeLike = ..., |
|
) -> NDArray[float64]: ... |
|
@overload |
|
def binomial(self, n: int, p: float, size: None = ...) -> int: ... |
|
@overload |
|
def binomial( |
|
self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[long]: ... |
|
@overload |
|
def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ... |
|
@overload |
|
def negative_binomial( |
|
self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[long]: ... |
|
@overload |
|
def poisson(self, lam: float = ..., size: None = ...) -> int: ... |
|
@overload |
|
def poisson( |
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self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... |
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) -> NDArray[long]: ... |
|
@overload |
|
def zipf(self, a: float, size: None = ...) -> int: ... |
|
@overload |
|
def zipf( |
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self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
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) -> NDArray[long]: ... |
|
@overload |
|
def geometric(self, p: float, size: None = ...) -> int: ... |
|
@overload |
|
def geometric( |
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self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[long]: ... |
|
@overload |
|
def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ... |
|
@overload |
|
def hypergeometric( |
|
self, |
|
ngood: _ArrayLikeInt_co, |
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nbad: _ArrayLikeInt_co, |
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nsample: _ArrayLikeInt_co, |
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size: None | _ShapeLike = ..., |
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) -> NDArray[long]: ... |
|
@overload |
|
def logseries(self, p: float, size: None = ...) -> int: ... |
|
@overload |
|
def logseries( |
|
self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[long]: ... |
|
def multivariate_normal( |
|
self, |
|
mean: _ArrayLikeFloat_co, |
|
cov: _ArrayLikeFloat_co, |
|
size: None | _ShapeLike = ..., |
|
check_valid: Literal["warn", "raise", "ignore"] = ..., |
|
tol: float = ..., |
|
) -> NDArray[float64]: ... |
|
def multinomial( |
|
self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[long]: ... |
|
def dirichlet( |
|
self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
|
) -> NDArray[float64]: ... |
|
def shuffle(self, x: ArrayLike) -> None: ... |
|
@overload |
|
def permutation(self, x: int) -> NDArray[long]: ... |
|
@overload |
|
def permutation(self, x: ArrayLike) -> NDArray[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 |
|
|
|
sample = _rand.random_sample |
|
ranf = _rand.random_sample |
|
|
|
def set_bit_generator(bitgen: BitGenerator) -> None: |
|
... |
|
|
|
def get_bit_generator() -> BitGenerator: |
|
... |
|
|