var

计算输入数组 x 的方差。

参数

名称	类型	描述	默认值
x		实值浮点数据类型的输入数组。	必需
axis		计算方差的轴或多个轴。默认情况下，方差是在整个数组上计算的。如果是一个整数元组，则在多个轴上计算方差。默认值：None。	None
correction		自由度调整。将此参数设置为非 0 的值，会根据 N-c 调整方差计算中的除数，其中 N 对应于计算方差的元素总数，c 对应于所提供的自由度调整。在计算总体的方差时，将此参数设置为 0 是标准选择（即，提供的数组包含构成整个总体的数据）。在计算无偏样本方差时，将此参数设置为 1 是标准选择（即，提供的数组包含从更大总体中采样的数据；这通常被称为贝塞尔校正）。默认值：0。	0.0
keepdims		如果为 True，则缩减的轴将作为单例维度包含在结果中，因此结果与输入数组兼容。否则，如果为 False，则缩减的轴（维度）不包含在结果中。默认值：False。	False

返回值

名称	类型	描述
out	数组	如果方差是在整个数组上计算的，则返回一个包含方差的零维数组；否则，返回一个包含方差的非零维数组。返回的数组必须与 x 具有相同的数据类型。

特殊情况

设 N 等于计算方差所涉及的元素数量。

• 如果 N - correction 小于或等于 0，则方差为 NaN。
• 如果 x_i 为 NaN，则方差为 NaN（即，NaN 值会传播）。

示例

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

源代码位于 sparse/numba_backend/_common.py

def var(x, /, *, axis=None, correction=0.0, keepdims=False):
    """
    Calculates the variance of the input array ``x``.

    Parameters
    ----------
    x: array
        input array of a real-valued floating-point data type.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which variances are computed.
        By default, the variance is computed over the entire array.
        If a tuple of integers, variances are computed over multiple axes. Default: ``None``.
    correction: Union[int, float]
        degrees of freedom adjustment. Setting this parameter to a value other than ``0``
        has the effect of adjusting the divisor during the calculation of the variance according to ``N-c``
        where ``N`` corresponds to the total number of elements over which the variance is computed and ``c``
        corresponds to the provided degrees of freedom adjustment.
        When computing the variance of a population, setting this parameter to ``0`` is the standard choice
        (i.e., the provided array contains data constituting an entire population).
        When computing the unbiased sample variance, setting this parameter to ``1`` is the standard choice
        (i.e., the provided array contains data sampled from a larger population; this is commonly referred
        to as Bessel's correction). Default: ``0``.
    keepdims: bool
        if ``True``, the reduced axes are included in the result as singleton dimensions, and,
        accordingly, the result is compatible with the input array.
        Otherwise, if ``False``, the reduced axes (dimensions) are not included in the result. Default: ``False``.

    Returns
    -------
    out: array
        if the variance was computed over the entire array, a zero-dimensional array containing the variance;
        otherwise, a non-zero-dimensional array containing the variances.
        The returned array must have the same data type as ``x``.

    Special Cases
    -------------
    Let ``N`` equal the number of elements over which to compute the variance.

    -   If ``N - correction`` is less than or equal to ``0``, the variance is ``NaN``.
    -   If ``x_i`` is ``NaN``, the variance is ``NaN`` (i.e., ``NaN`` values propagate).

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