Function var

python/paddle/tensor/stat.py:186–304

Computes the variance of ``x`` along ``axis`` . .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``. For example, ``var(input=tensor_x, dim=1, ...)`` is equivalent to ``var(x=tensor_x, axi

(
    x: Tensor,
    axis: int | Sequence[int] | None = None,
    unbiased: bool | None = None,
    keepdim: bool = False,
    name: str | None = None,
    *,
    correction: float = 1,
    out: Tensor | None = None,
)

@param_two_alias(["x", "input"], ["axis", "dim"])
def var(
    x: Tensor,
    axis: int | Sequence[int] | None = None,
    unbiased: bool | None = None,
    keepdim: bool = False,
    name: str | None = None,
    *,
    correction: float = 1,
    out: Tensor | None = None,
) -> Tensor:
    """
    Computes the variance of ``x`` along ``axis`` .

    .. note::
        Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``.
        For example, ``var(input=tensor_x, dim=1, ...)`` is equivalent to ``var(x=tensor_x, axis=1, ...)``.

    Args:
        x (Tensor): The input Tensor with data type float16, float32, float64.
            alias: ``input``.
        axis (int|list|tuple|None, optional): The axis along which to perform variance calculations. ``axis`` should be int, list(int) or tuple(int).
            alias: ``dim``.

            - If ``axis`` is a list/tuple of dimension(s), variance is calculated along all element(s) of ``axis`` . ``axis`` or element(s) of ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
            - If ``axis`` or element(s) of ``axis`` is less than 0, it works the same way as :math:`axis + D` .
            - If ``axis`` is None, variance is calculated over all elements of ``x``. Default is None.

        unbiased (bool, optional): Whether to use the unbiased estimation. If ``unbiased`` is True, the divisor used in the computation is :math:`N - 1`, where :math:`N` represents the number of elements along ``axis`` , otherwise the divisor is :math:`N`. Default is True.
        keep_dim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the input unless keep_dim is true. Default is False.
        name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
        correction (int|float, optional): Difference between the sample size and sample degrees of freedom.
            Defaults to 1 (Bessel's correction). If unbiased is specified, this parameter is ignored.
        out (Tensor|None, optional): Output tensor. Default is None.

    Returns:
        Tensor, results of variance along ``axis`` of ``x``, with the same data type as ``x``.

    Examples:
        .. code-block:: pycon

            >>> import paddle

            >>> x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
            >>> out1 = paddle.var(x)
            >>> print(out1.numpy())
            2.6666667
            >>> out2 = paddle.var(x, axis=1)
            >>> print(out2.numpy())
            [1.         4.3333335]
    """
    if unbiased is not None and correction != 1:
        raise ValueError("Only one of unbiased and correction may be given")

    if unbiased is not None:
        actual_correction = 1.0 if unbiased else 0.0
    else:
        actual_correction = float(correction)