Python | Numpy np.laggauss() method

`np.laggauss()` Computes the sample points and weights for Gauss-Laguerre quadrature. These sample points and weights will correctly integrate polynomials of degree `2*deg - 1 `or less over the interval `[0, inf]` with the weight function `f(x) = exp(-x)`

Syntax : `np.laggauss(deg)`
Parameters:
deg :[int] Number of sample points and weights. It must be >= 1.

Return : 1.[ndarray] 1-D ndarray containing the sample points.
2.[ndarray] 1-D ndarray containing the weights.

Code #1 :

 `# Python program explaining ` `# numpy.laggauss() method  ` `   `  `# importing numpy as np   ` `# and numpy.polynomial.laguerre module as geek  ` `import` `numpy as np  ` `import` `numpy.polynomial.laguerre as geek ` `   `  `# Input degree = 2 ` ` `  `degree ``=` `2`  `    `  `# using np.laggauss() method  ` `res ``=` `geek.laggauss(degree)  ` ` `  `# Resulting array of sample point and weight ` `print` `(res)  `

Output:

```(array([ 0.58578644,  3.41421356]), array([ 0.85355339,  0.14644661]))
```

Code #2 :

 `# Python program explaining ` `# numpy.laggauss() method  ` `   `  `# importing numpy as np   ` `# and numpy.polynomial.laguerre module as geek  ` `import` `numpy as np  ` `import` `numpy.polynomial.laguerre as geek ` `   `  `# Input degree ` `degree ``=` `3` `   `  `# using np.laggauss() method  ` `res ``=` `geek.laggauss(degree)  ` ` `  `# Resulting array of sample point and weight ` `print` `(res)  `

Output:

```(array([ 0.41577456,  2.29428036,  6.28994508]), array([ 0.71109301,  0.27851773,  0.01038926]))

```
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