1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
|
from __future__ import annotations
from ._array_object import Array
from typing import Optional, Tuple, Union
import numpy as np
def max(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))
def mean(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
return Array._new(np.asarray(np.mean(x._array, axis=axis, keepdims=keepdims)))
def min(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))
def prod(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
return Array._new(np.asarray(np.prod(x._array, axis=axis, keepdims=keepdims)))
def std(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array:
# Note: the keyword argument correction is different here
return Array._new(np.asarray(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
def sum(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
return Array._new(np.asarray(np.sum(x._array, axis=axis, keepdims=keepdims)))
def var(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array:
# Note: the keyword argument correction is different here
return Array._new(np.asarray(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
|
