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# scipy.linalg.pinv

`scipy.linalg.``pinv`(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 a least-squares solver.

  Parameters : | a : (M, N) array_like Matrix to be pseudo-inverted. cond, rcond : float, optional Cutoff for ‘small’ singular values in the least-squares solver. Singular values smaller than `rcond * largest_singular_value` are considered zero. 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 computation does not converge.

  Examples

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

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