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mindspore.ops.Svd

class mindspore.ops.Svd(full_matrices=False, compute_uv=True)

Computes the singular value decompositions of one or more matrices.

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

Parameters

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

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

Inputs:

• input (Tensor) - Tensor of the matrices to be decomposed. The shape should be $(\ast ,M,N)$, the supported dtype are float32 and float64.

Outputs:

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

• u (Tensor) - Left singular vectors. If compute_uv is False , u will be zero value. 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 be zero value. The shape is $(\ast ,N,P)$. If full_matrices is True , the shape will be $(\ast ,N,N)$.

Supported Platforms:

GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, set_context
>>> from mindspore import ops
>>> set_context(device_target="CPU")
>>> svd = ops.Svd(full_matrices=True, compute_uv=True)
>>> a = Tensor(np.array([[1, 2], [-4, -5], [2, 1]]).astype(np.float32))
>>> s, u, v = svd(a)
>>> 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]]