min

计算输入数组 x 的最小值。

参数

名称	类型	描述	默认值
x		输入数组。应具有实数值数据类型。	必需
axis		计算最小值的轴或多个轴。默认情况下，最小值必须在整个数组上计算。如果为整数元组，则最小值必须在多个轴上计算。默认值：None。	None
keepdims		如果为 True，则将缩减后的轴（维度）作为单例维度包含在结果中。因此，结果必须与输入数组兼容。否则，如果为 False，则不将缩减后的轴（维度）包含在结果中。默认值：False。	False

返回值

名称	类型	描述
out	数组	如果最小值是在整个数组上计算的，则返回一个包含最小值的零维数组。否则，返回一个包含最小值的非零维数组。返回的数组必须与 x 具有相同的数据类型。

特殊情况

对于浮点操作数，如果 x_i 为 NaN，则最小值为 NaN（即 NaN 值会传播）。

示例

>>> a = sparse.COO.from_numpy(np.array([[0, -1], [-2, 0]]))
>>> o = sparse.min(a, axis=1)
>>> o.todense()
array([-1, -2])

源代码位于 sparse/numba_backend/_common.py

def min(x, /, *, axis=None, keepdims=False):
    """
    Calculates the minimum value of the input array ``x``.

    Parameters
    ----------
    x: array
        input array. Should have a real-valued data type.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which minimum values are computed.
        By default, the minimum value must be computed over the entire array.
        If a tuple of integers, minimum values must be computed over multiple axes. Default: ``None``.
    keepdims: bool
        If ``True``, the reduced axes (dimensions) are included in the result as singleton dimensions.
        Accordingly, the result must be compatible with the input array.
        Otherwise, if ``False``, the reduced axes (dimensions) are not be included in the result. Default: ``False``.

    Returns
    -------
    out: array
        if the minimum value was computed over the entire array, a zero-dimensional array containing the minimum value.
        Otherwise, a non-zero-dimensional array containing the minimum values.
        The returned array must have the same data type as ``x``.

    Special Cases
    -------------
    For floating-point operands, if ``x_i`` is ``NaN``, the minimum value is ``NaN`` (i.e., ``NaN`` values propagate).

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, -1], [-2, 0]]))
    >>> o = sparse.min(a, axis=1)
    >>> o.todense()
    array([-1, -2])
    """
    return x.min(axis=axis, keepdims=keepdims)