details, see `numpy.linalg.lstsq`.

    Warns
    -----
    RankWarning
        The rank of the coefficient matrix in the least-squares fit is
        deficient. The warning is only raised if ``full == False``.  The
        warnings can be turned off by

        >>> import warnings
        >>> warnings.simplefilter('ignore', np.RankWarning)

    See Also
    --------
    numpy.polynomial.chebyshev.chebfit
    numpy.polynomial.legendre.legfit
    numpy.polynomial.laguerre.lagfit
    numpy.polynomial.polynomial.polyfit
    numpy.polynomial.hermite_e.hermefit
    hermval : Evaluates a Hermite series.
    hermvander : Vandermonde matrix of Hermite series.
    hermweight : Hermite weight function
    numpy.linalg.lstsq : Computes a least-squares fit from the matrix.
    scipy.interpolate.UnivariateSpline : Computes spline fits.

    Notes
    -----
    The solution is the coefficients of the Hermite series `p` that
    minimizes the sum of the weighted squared errors

    .. math:: E = \sum_j w_j^2 * |y_j - p(x_j)|^2,

    where the :math:`w_j` are the weights. This problem is solved by
    setting up the (typically) overdetermined matrix equation

    .. math:: V(x) * c = w * y,

    where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
    coefficients to be solved for, `w` are the weights, `y` are the
    observed values.  This equation is then solved using the singular value
    decomposition of `V`.

    If some of the singular values of `V` are so small that they are
    neglected, then a `RankWarning` will be issued. This means that the
    coefficient values may be poorly determined. Using a lower order fit
    will usually get rid of the warning.  The `rcond` parameter can also be
    set to a value smaller than its default, but the resulting fit may be
    spurious and have large contributions from roundoff error.

    Fits using Hermite series are probably most useful when the data can be
    approximated by ``sqrt(w(x)) * p(x)``, where `w(x)` is the Hermite
    weight. In that case the weight ``sqrt(w(x[i]))`` should be used
    together with data values ``y[i]/sqrt(w(x[i]))``. The weight function is
    available as `hermweight`.

    References
    ----------
    .. [1] Wikipedia, "Curve fitting",
           https://en.wikipedia.org/wiki/Curve_fitting

    Examples
    --------
    >>> from numpy.polynomial.hermite import hermfit, hermval
    >>> x = np.linspace(-10, 10)
    >>> err = np.random.randn(len(x))/10
    >>> y = hermval(x, [1, 2, 3]) + err
    >>> hermfit(x, y, 2)
    array([1.0218, 1.9986, 2.9999]) # may vary

    )