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# scipy.linalg.pinv2

`scipy.linalg.``pinv2`(a, cond=None, rcond=None)

  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 : array, shape (M, N) 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.
  Returns: | B : array, shape (N, M) Raises LinAlgError if SVD computation does not converge :

  Examples

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

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