mindspore.ops.cond

mindspore.ops.cond(A, p=None)

Returns the matrix norm or vector norm of a given tensor.

p is the calculation mode of norm. The following norm modes are supported.

p	norm for matrices	norm for vectors
None (default)	2-norm (see below)	2-norm (see below)
'fro'	Frobenius norm	– not supported –
'nuc'	nuclear norm	– not supported –
inf	\(max(sum(abs(x), dim=1))\)	\(max(abs(x))\)
-inf	\(min(sum(abs(x), dim=1))\)	\(min(abs(x))\)
0	– not supported –	\(sum(x != 0)\)
1	\(max(sum(abs(x), dim=0))\)	as below
-1	\(min(sum(abs(x), dim=0))\)	as below
2	largest singular value	as below
-2	smallest singular value	as below
other int or float	– not supported –	\(sum(abs(x)^{p})^{(1 / p)}\)

Note

Currently, complex numbers are not supported.

Parameters

• A (Tensor) – Tensor of shape \((*, n)\) or \((*, m, n)\) where * is zero or more batch dimensions.

• p (Union[int, float, inf, -inf, 'fro', 'nuc'], optional) – norm’s mode. Refer to the table above for behavior. Default: None.

Returns

Tensor, the result of norm calculation on the specified dimension, dim, has the same dtype as A.

Raises

• TypeError – If A is a vector and p is a str.

• ValueError – If A is a matrices and p is not in valid mode.

• ValueError – If A is a matrix and p is an integer that is not in [1, -1, 2, -2].

Supported Platforms:

GPU CPU

Examples

>>> import mindspore as ms
>>> x = ms.Tensor([[1.0, 0.0, -1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 1.0]])
>>> print(ms.ops.cond(x))
1.4142
>>> print(ms.ops.cond(x, 'fro'))
1.1623