def 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

    """
    a = asarray_chkfinite(a)
    u, s, vh = decomp_svd(a)
    t = u.dtype.char
    if rcond is not None:
        cond = rcond
    if cond in [None, -1]:
        cond = {0: feps*1e3, 1: eps*1e6}[_array_precision[t]]
    m, n = a.shape
    cutoff = cond*numpy.maximum.reduce(s)
    psigma = zeros((m, n), t)
    for i in range(len(s)):
        if s[i] > cutoff:
            psigma[i, i] = 1.0/conjugate(s[i])
    # XXX: use lapack/blas routines for dot
    return transpose(conjugate(dot(dot(u, psigma), vh)))
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tools/linalg.py/pinv2/original