numpy.polynomial.hermite.hermgauss — NumPy v1.11 Manual (original) (raw)

numpy.polynomial.hermite.hermgauss(deg)[source]¶

Gauss-Hermite quadrature.

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

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

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

$w_k = c / (H'_n(x_k) * H_{n-1}(x_k))$

where $c$ is a constant independent of $k$ and $x_k$ is the k’th root of $H_n$ , and then scaling the results to get the right value when integrating 1.