Acs 2

numpy-stubs/__init__.pyi

@@ -658,6 +658,9 @@ def asarray(a: ndarray, dtype: Type[str]) -> ndarray[str_]: ...
 def atleast_2d(ary: _Coercable) -> ndarray: ...
 @overload
 def atleast_2d(ar: _Coercable, *ary: _Coercable) -> List[ndarray]: ...
+def bincount(
+    x: _ArrayLike[_DType], weights: _ArrayLike = ..., minlength: int = ...
+) -> ndarray[_DType]: ...
 @overload
 def ceil(a: _FloatLike) -> _FloatLike: ...
 @overload
@@ -712,7 +715,7 @@ def isin(element: Sequence[_DType], test_element: _DType) -> ndarray[_DType]: ..
 @overload
 def isnan(x: float64) -> bool: ...
 @overload
-def isnan(x: ndarray[_DType]) -> ndarray[bool_]: ...
+def isnan(x: _ArrayLike[_DType]) -> ndarray[bool_]: ...
 @overload
 def ix_(x: ndarray[_DType]) -> ndarray[_DType]: ...
 @overload

numpy-stubs/random.pyi

@@ -63,9 +63,9 @@ def exponential(scale: _FloatLike) -> _Float: ...
 @overload
 def exponential(scale: _FloatLike, size: Sequence[_IntLike]) -> ndarray[float64]: ...
 @overload
-def exponential(scale: Sequence[_FloatLike], size: Sequence[_IntLike]) -> ndarray[float64]: ...
-def geometric(p: float, size: _IntLike) -> ndarray[float64]: ...
-def get_state() -> Tuple[str, ndarray[_Int], _IntLike, _IntLike, _FloatLike]: ...
+def multinomial(
+    n: int, pvals: _ArrayLike[_Float] = ..., size: Union[int, Tuple[int, ...]] = ...
+) -> ndarray[_Int]: ...
 def normal(loc: float, scale: float, size: Union[int, Tuple[int, ...]]) -> ndarray[float64]: ...
 @overload
 def permutation(size: int) -> ndarray[int64]: ...