Evaluate Hermite series at points x when coefficients are multi-dimensional using NumPy in Python
Last Updated :
13 Jun, 2022
In this article, we will cover how to evaluate a Hermite series at points x with a multidimensional coefficient array in Python using NumPy.
Example
Input: [[11 12][13 14]]
Output: [[37. 63.][40. 68.]]
Explanation: Hermite series at points x.
NumPy.polynomial.hermite.hermval method
To evaluate a Hermite series at points x with a multidimensional coefficient array, NumPy provides a function called hermite.hermval(). It takes two parameters x and c. whereas x is a tuple or list. It is considered a scalar. But, the parameter x should support multiplication and addition within itself and with the elements of c. If c is a 1-D array, then it will have the same shape as x. If c is multidimensional, then the shape of the result depends on the value of the tensor.
Syntax: polynomial.hermite.hermval(x,c, tensor)
Parameter:
- x: array_like
- c: Array of coefficient
- tensor: boolean(optional).
Return: An Hermite series at points x.
Example 1:
In the first example, let us consider a 2D array and evaluate a Hermite series at point x. Import the necessary packages and pass the appropriate parameters as shown.
Python3
import numpy as np
from numpy.polynomial import hermite
c = np.array([[ 11 , 12 ], [ 13 , 14 ]])
print (f 'The coefficient array is {c}' )
print (f 'The shape of the array is {c.shape}' )
print (f 'The dimension of the array is {c.ndim}D' )
print (f 'The datatype of the array is {c.dtype}' )
res = hermite.hermval([ 1 , 2 ], c)
print (f 'Resultant series ---> {res}' )
|
Output:
The coefficient array is
[[11 12]
[13 14]]
The shape of the array is (2, 2)
The dimension of the array is 2D
The datatype of the array is int32
Resultant series --->
[[37. 63.]
[40. 68.]]
Example 2:
In the second example, let us consider a 3D array and evaluate a Hermite series at point x. Import the necessary packages and pass the appropriate parameters as shown
Python3
import numpy as np
from numpy.polynomial import hermite
c = np.arange( 27 ).reshape( 3 , 3 , 3 )
print (f 'The coefficient array is {c}' )
print (f 'The shape of the array is {c.shape}' )
print (f 'The dimension of the array is {c.ndim}D' )
print (f 'The datatype of the array is {c.dtype}' )
res = hermite.hermval([ 17 , 22 ], c)
print (f 'Resultant series ---> {res}' )
|
Output:
The coefficient array is
[[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]]
[[ 9 10 11]
[12 13 14]
[15 16 17]]
[[18 19 20]
[21 22 23]
[24 25 26]]]
The shape of the array is (3, 3, 3)
The dimension of the array is 3D
The datatype of the array is int32
Resultant series --->
[[[21078. 35208.]
[22267. 37187.]
[23456. 39166.]]
[[24645. 41145.]
[25834. 43124.]
[27023. 45103.]]
[[28212. 47082.]
[29401. 49061.]
[30590. 51040.]]]
Share your thoughts in the comments
Please Login to comment...