scipy.linalg.pinv2(a, cond=None, rcond=None, return_rank=False, check_finite=True)[source]

Compute the (Moore-Penrose) pseudo-inverse of a matrix.

Calculate a generalized inverse of a matrix using its singular-value decomposition and including all ‘large’ singular values.

Parameters :

a : (M, N) array_like

Matrix to be pseudo-inverted.

cond, rcond : float or None

Cutoff for ‘small’ singular values. Singular values smaller than rcond*largest_singular_value are considered zero. If None or -1, suitable machine precision is used.

return_rank : bool, optional

if True, return the effective rank of the matrix

check_finite : boolean, optional

Whether to check the input matrixes contain only finite numbers. Disabling may give a performance gain, but may result to problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns :

B : (N, M) ndarray

The pseudo-inverse of matrix a.

rank : int

The effective rank of the matrix. Returned if return_rank == True

Raises :

LinAlgError :

If SVD computation does not converge.


>>> a = np.random.randn(9, 6)
>>> B = linalg.pinv2(a)
>>> np.allclose(a, dot(a, dot(B, a)))
>>> np.allclose(B, dot(B, dot(a, B)))

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