mindspore.Tensor.var

Tensor.var(axis=None, ddof=0, keepdims=False)[source]

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)]) – Axis or axes along which the variance is computed. The default is to compute the variance of the flattened array. Default: None .
ddof (int) – 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) – Default: False .

Returns

Variance tensor.

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