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Module « numpy.linalg »

Fonction svdvals - module numpy.linalg

Signature de la fonction svdvals

def svdvals(x) 

Description

help(numpy.linalg.svdvals)

Returns the singular values of a matrix (or a stack of matrices) ``x``.
When x is a stack of matrices, the function will compute the singular
values for each matrix in the stack.

This function is Array API compatible.

Calling ``np.svdvals(x)`` to get singular values is the same as
``np.svd(x, compute_uv=False, hermitian=False)``.

Parameters
----------
x : (..., M, N) array_like
    Input array having shape (..., M, N) and whose last two
    dimensions form matrices on which to perform singular value
    decomposition. Should have a floating-point data type.

Returns
-------
out : ndarray
    An array with shape (..., K) that contains the vector(s)
    of singular values of length K, where K = min(M, N).

See Also
--------
scipy.linalg.svdvals : Compute singular values of a matrix.

Examples
--------

>>> np.linalg.svdvals([[1, 2, 3, 4, 5],
...                    [1, 4, 9, 16, 25],
...                    [1, 8, 27, 64, 125]])
array([146.68862757,   5.57510612,   0.60393245])

Determine the rank of a matrix using singular values:

>>> s = np.linalg.svdvals([[1, 2, 3],
...                        [2, 4, 6],
...                        [-1, 1, -1]]); s
array([8.38434191e+00, 1.64402274e+00, 2.31534378e-16])
>>> np.count_nonzero(s > 1e-10)  # Matrix of rank 2
2

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