Did I find the right examples for you? yes no

All Samples(1)  |  Call(1)  |  Derive(0)  |  Import(0)
Return the companion matrix of c.

The usual companion matrix of the Laguerre polynomials is already
symmetric when `c` is a basis Laguerre polynomial, so no scaling is
applied.

Parameters
----------
c : array_like
    1-D array of Laguerre series coefficients ordered from low to high(more...)

        def lagcompanion(c):
    """
    Return the companion matrix of c.

    The usual companion matrix of the Laguerre polynomials is already
    symmetric when `c` is a basis Laguerre polynomial, so no scaling is
    applied.

    Parameters
    ----------
    c : array_like
        1-D array of Laguerre series coefficients ordered from low to high
        degree.

    Returns
    -------
    mat : ndarray
        Companion matrix of dimensions (deg, deg).

    Notes
    -----

    .. versionadded::1.7.0

    """
    accprod = np.multiply.accumulate
    # c is a trimmed copy
    [c] = pu.as_series([c])
    if len(c) < 2:
        raise ValueError('Series must have maximum degree of at least 1.')
    if len(c) == 2:
        return np.array([[1 + c[0]/c[1]]])

    n = len(c) - 1
    mat = np.zeros((n, n), dtype=c.dtype)
    top = mat.reshape(-1)[1::n+1]
    mid = mat.reshape(-1)[0::n+1]
    bot = mat.reshape(-1)[n::n+1]
    top[...] = -np.arange(1, n)
    mid[...] = 2.*np.arange(n) + 1.
    bot[...] = top
    mat[:, -1] += (c[:-1]/c[-1])*n
    return mat
        


src/n/u/numpy-1.8.1/numpy/polynomial/tests/test_laguerre.py   numpy(Download)
    def test_linear_root(self):
        assert_(lag.lagcompanion([1, 2])[0, 0] == 1.5)
 
 
class TestGauss(TestCase):