mindspore.Tensor.var

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

Compute the variance along the specified axis.

The variance is the average of the squared deviations from the mean, i.e., \(var = mean(abs(x - x.mean())**2)\).

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 \(N - ddof\), 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