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mindspore.Tensor.svd

Tensor.svd(full_matrices=False, compute_uv=True)

Computes the singular value decompositions of one or more matrices.

Refer to mindspore.ops.svd() for more detail.

Parameters

• full_matrices (bool, optional) – If true, compute full-sized $U$ and $V$. If false, compute only the leading P singular vectors. P is the minimum of M and N. M, N is the row, col of the input matrix. Default: False.

• compute_uv (bool, optional) – If true, compute the left and right singular vectors. If false, compute only the singular values. Default: True.

Returns

• s (Tensor) - Singular values. The shape is $(\ast ,P)$.

• u (Tensor) - Left singular vectors. If compute_uv is False, u will not be returned. The shape is $(\ast ,M,P)$. If full_matrices is True, the shape will be $(\ast ,M,M)$.

• v (Tensor) - Right singular vectors. If compute_uv is False, v will not be returned. The shape is $(\ast ,P,N)$. If full_matrices is True, the shape will be $(\ast ,N,N)$.

Raises

• TypeError – If full_matrices or compute_uv is not the type of bool.

• TypeError – If the rank of input less than 2.

• TypeError – If the type of input is not one of the following dtype: mstype.float32, mstype.float64.

Supported Platforms:

GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, set_context
>>> set_context(device_target="CPU")
>>> a = Tensor(np.array([[1, 2], [-4, -5], [2, 1]]).astype(np.float32))
>>> s, u, v = a.svd(full_matrices=True, compute_uv=True)
>>> print(s)
[7.0652843 1.040081 ]
>>> print(u)
[[ 0.30821905 -0.48819482 0.81649697]
 [-0.90613353  0.11070572 0.40824813]
 [ 0.2896955   0.8656849  0.4082479 ]]
>>> print(v)
[[ 0.63863593 0.769509  ]
 [ 0.769509  -0.63863593]]