mindspore.Tensor.var

Tensor.var(axis=None, ddof=0, keepdims=False) → Tensor

Compute the variance along the specified axis.

The variance is the average of the squared deviations from the mean, i.e., $v a r = m e a n (a b s (x - x . m e a n ()) * * 2)$ .

Return the variance, which is computed for the flattened array by default, otherwise over the specified axis.

Note

Numpy arguments dtype, out and where are not supported.

Parameters

axis (Union[None, int, tuple(int)], optional) – Axis or axes along which the variance is computed. The default is to compute the variance of the flattened array. Default: None .
ddof (int, optional) – Means Delta Degrees of Freedom. Default: 0 . The divisor used in calculations is $N - d d o f$ , where $N$ represents the number of elements.
keepdims (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. Default: False .

Returns

Variance tensor.

Raises

TypeError – If axis is not one of the followings: None, int, tuple.
TypeError – If ddof is not an int.
TypeError – If keepdims is not a bool.
ValueError – If axis is out of range $[- s e l f . n d i m, s e l f . n d i m)$ .

See also

mindspore.Tensor.mean(): Reduce a dimension of a tensor by averaging all elements in the dimension.
mindspore.Tensor.std(): Compute the standard deviation along the specified axis.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> input_x = Tensor(np.array([1., 2., 3., 4.], np.float32))
>>> output = input_x.var()
>>> print(output)
1.25

Tensor.var(dim=None, *, correction=1, keepdim=False) → Tensor

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 ( $δ^{2}$ ) is calculated as:

δ^{2} = \frac{1}{max (0, N - δ N)} \sum_{i = 0}^{N - 1} (x_{i} - \bar{x})^{2}

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

Parameters

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 self 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 self.
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 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 $[- s e l f . n d i m, s e l f . n d i m)$ .

Supported Platforms:: Ascend

Examples

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