In this article, we will be looking toward the approach to evaluating a Hermite series at points x broadcast over the columns of the coefficient in Python and NumPy.
Example:
Array: [1,2,3,4,5],[6,7,8,9,10]]
Result: [ 73. 100. 131. 166. 205.]
Explanation: Hermite series at points x broadcast over the columns
Numpy np.hermeval() method
To evaluate a Hermite series at a tuple of points x broadcast over the columns of the coefficient, the user needs to call the hermite.hermval() method of the Numpy library in Python. Further, the user needs to pass the first parameter to the function which is the x, where x is a list or tuple, the 2nd parameter is C, which is an array of coefficients, and the 3rd parameter tensor, if True, the shape of the coefficient array is extended with ones on the right, one for each dimension of x.
Syntax : np.hermeval(x, series,tensor)
Parameter:
- x: list or tuple
- series: array of coefficient
- tensor, if True, the shape of the coefficient array is extended with ones on the right, one for each dimension of x. Scalars have dimension 0 for this action.
Return : Return the evaluated hermite series.
Example:
In this example, we created a 2-D array of 10 data points and further created a tuple name x., then with the use of the hermite.hermeval() method we pass the required parameters with tensor set to false to evaluate the Hermite series at points x broadcast over the columns in Python.
Python3
import numpy as np from numpy.polynomial import hermite a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) # Dimensions of Array print("\nDimensions of Array:\n", a.ndim) # Shape of the array print("\nShape of Array:\n", a.shape) # Tuple x = [6, 7, 8, 9, 10] # To evaluate a Hermite series at # points x broadcast over the columns hermite.hermval(x, a, tensor=False) |
Output:
Dimensions of Array: 2 Shape of Array: (2, 5) array([ 73., 100., 131., 166., 205.])
Example:
In this example, we created a 2-D array of 10 data points and further created a tuple name x., then with the use of the hermite.hermeval() method we pass the required parameters with tensor set to true to evaluate the Hermite series at points x broadcast over the columns in Python.
Python3
import numpy as np from numpy.polynomial import hermite a = np.array([[1,2,3,4,5],[6,7,8,9,10]]) # Dimensions of Array print("\nDimensions of Array:\n",a.ndim) # Shape of the array print("\nShape of Array:\n",a.shape) # Tuple x = [6,7,8,9,10] # To evaluate a Hermite series at # points x broadcast over the columns hermite.hermval(x,a,tensor=True) |
Output:
Dimensions of Array:
2
Shape of Array:
(2, 5)
array([[ 73., 85., 97., 109., 121.],
[ 86., 100., 114., 128., 142.],
[ 99., 115., 131., 147., 163.],
[112., 130., 148., 166., 184.],
[125., 145., 165., 185., 205.]])
