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Gauss-HermiteE quadrature.

Computes the sample points and weights for Gauss-HermiteE quadrature.
These sample points and weights will correctly integrate polynomials of
degree :math:`2*deg - 1` or less over the interval :math:`[-\inf, \inf]`
with the weight function :math:`f(x) = \exp(-x^2/2)`.

Parameters
----------
deg : int(more...)

        def hermegauss(deg):
    """
    Gauss-HermiteE quadrature.

    Computes the sample points and weights for Gauss-HermiteE quadrature.
    These sample points and weights will correctly integrate polynomials of
    degree :math:`2*deg - 1` or less over the interval :math:`[-\inf, \inf]`
    with the weight function :math:`f(x) = \exp(-x^2/2)`.

    Parameters
    ----------
    deg : int
        Number of sample points and weights. It must be >= 1.

    Returns
    -------
    x : ndarray
        1-D ndarray containing the sample points.
    y : ndarray
        1-D ndarray containing the weights.

    Notes
    -----

    .. versionadded::1.7.0

    The results have only been tested up to degree 100, higher degrees may
    be problematic. The weights are determined by using the fact that

    .. math:: w_k = c / (He'_n(x_k) * He_{n-1}(x_k))

    where :math:`c` is a constant independent of :math:`k` and :math:`x_k`
    is the k'th root of :math:`He_n`, and then scaling the results to get
    the right value when integrating 1.

    """
    ideg = int(deg)
    if ideg != deg or ideg < 1:
        raise ValueError("deg must be a non-negative integer")

    # first approximation of roots. We use the fact that the companion
    # matrix is symmetric in this case in order to obtain better zeros.
    c = np.array([0]*deg + [1])
    m = hermecompanion(c)
    x = la.eigvals(m)
    x.sort()

    # improve roots by one application of Newton
    dy = hermeval(x, c)
    df = hermeval(x, hermeder(c))
    x -= dy/df

    # compute the weights. We scale the factor to avoid possible numerical
    # overflow.
    fm = hermeval(x, c[1:])
    fm /= np.abs(fm).max()
    df /= np.abs(df).max()
    w = 1/(fm * df)

    # for Hermite_e we can also symmetrize
    w = (w + w[::-1])/2
    x = (x - x[::-1])/2

    # scale w to get the right value
    w *= np.sqrt(2*np.pi) / w.sum()

    return x, w
        


src/n/u/nupic-linux64-HEAD/lib64/python2.6/site-packages/numpy/polynomial/tests/test_hermite_e.py   nupic-linux64(Download)
    def test_100(self):
        x, w = herme.hermegauss(100)
 
        # test orthogonality. Note that the results need to be normalized,
        # otherwise the huge values that can arise from fast growing

src/n/u/numpy-1.8.1/numpy/polynomial/tests/test_hermite_e.py   numpy(Download)
    def test_100(self):
        x, w = herme.hermegauss(100)
 
        # test orthogonality. Note that the results need to be normalized,
        # otherwise the huge values that can arise from fast growing