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# scipy.linalg.pinv2

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

  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 : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in 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.

  Examples

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