mindspore.numpy.std

mindspore.numpy.std(x, axis=None, ddof=0, keepdims=False)[source]

Computes the standard deviation along the specified axis. The standard deviation is the square root of the average of the squared deviations from the mean, i.e., \(std = sqrt(mean(abs(x - x.mean())**2))\).

Returns the standard deviation, 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

x (Tensor) – A Tensor to be calculated.
axis (Union[None, int, tuple(int)]) –
Axis or axes along which the standard deviation is computed. Default: None .

If None , compute the standard deviation of the flattened array.
ddof (int) – Means Delta Degrees of Freedom. The divisor used in calculations is \(N - ddof\), where \(N\) represents the number of elements. Default: 0.
keepdims – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input tensor. If the default value is passed, then keepdims will not be passed through to the std method of sub-classes of tensor, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised. Default: False .

Returns

Standard deviation tensor.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> input_x = np.array([1., 2., 3., 4.])
>>> output = np.std(input_x)
>>> print(output)
1.118034