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mindspore.mint.var

mindspore.mint.var(input, dim=None, *, correction=1, keepdim=False)

Calculates the variance over the dimensions specified by dim. dim can be a single dimension, list of dimensions, or None to reduce over all dimensions.

The variance (${\delta }^2$) is calculated as:

$${\delta }^2=\frac{1}{max(0,N-\delta N)}\sum _{i=0}^{N-1}(x_i-\overline{x}{)}^2$$

where $x$ is the sample set of elements, $\overline{x}$ is the sample mean, $N$ is the number of samples and $\delta N$ is the correction.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• input (Tensor) – The tensor used to calculate the variance.

• dim (None, int, tuple(int), optional) – The dimension or dimensions to reduce. Defaults to None. If None, all dimensions are reduced.

Keyword Arguments

• correction (int, optional) – The difference between the sample size and sample degrees of freedom. Defaults to Bessel’s correction. Defaults to 1.

• keepdim (bool, optional) – Whether the output tensor has dim retained or not. If True , keep these reduced dimensions and the length is 1. If False, don't keep these dimensions. Defaults to False.

Returns

Tensor, the variance. Suppose the shape of input is $(x_0,x_1,...,x_R)$:

• If dim is () and keepdim is set to False , returns a 0-D Tensor, indicating the variance of all elements in input.

• If dim is int, e.g. 1 and keepdim is set to False , then the returned Tensor has shape $(x_0,x_2,...,x_R)$.

• If dim is tuple(int) or list(int), e.g. (1, 2) and keepdim is set to False , then the returned Tensor has shape $(x_0,x_3,...,x_R)$.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If input is not in bfloat16, float16, flaot32.

• TypeError – If dim is not one of the followings: None, int, list, tuple.

• TypeError – If correction is not an int.

• TypeError – If keepdim is not a bool.

• ValueError – If dim is out of range $[-input.ndim,input.ndim)$.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> input = Tensor([[8, 2, 1], [5, 9, 3], [4, 6, 7]], mindspore.float32)
>>> output = mint.var(input, dim=0, correction=1, keepdim=True)
>>> print(output)
[[ 4.333333, 12.333333, 9.333333]]