In this article, we will cover how to evaluate a Hermite_e series at points x with a multidimensional coefficient array in Python using NumPy.
Example
Input: [[0 1] [2 3]] Output: [[ 6. 2.] [10. 4.]] Explanation: Hermite_e series at input points.
hermite_e.hermeval method
To evaluate a Hermite series at points x with a multidimensional coefficient array, NumPy provides a function called hermite_e.hermeval(). It takes two parameters x and c. whereas x is a tuple or list and c is an array of coefficients. This method is available in the hermite_e module in python, it returns a Hermite_e series at the given input points, Below is the syntax of the hermeval method.
Syntax: hermite_e.hermeval(x, c, tensor)
Parameter:
- x: a list or tuple
- c: an array of coefficients ordered
- tensor: boolean, optional
Return: Hermite_e series at points x
Example 1:
In this example, we are creating a coefficient of a multi-dimensional array with 5 elements and displaying the shape and dimensions of the array. After that, we are evaluating the Hermite_e series at points [4,1].
Python3
# import the numpy module import numpy # import hermite_se from numpy.polynomial import hermite_e # create array of coefficients with 5 elements each coefficients_data = numpy.array([[1, 2, 3, 4, 5], [3, 4, 2, 6, 7]]) # Display the coefficients print(coefficients_data) # get the shape print(f"\nShape of an array: {coefficients_data.shape}") # get the dimensions print(f"Dimension: {coefficients_data.ndim}") # Evaluate a Hermite_e series at points - [4,1] print("\nHermite_e series", hermite_e.hermeval( [4, 1], coefficients_data)) |
Output:
[[1 2 3 4 5] [3 4 2 6 7]] Shape of an array: (2, 5) Dimension: 2 Hermite_e series [[13. 4.] [18. 6.] [11. 5.] [28. 10.] [33. 12.]]
Example 2:
In this example, we are creating a coefficient of a multi-dimensional array with NumPy of shape 2×2 and displaying the shape and dimensions of the array. After that, we are evaluating the Hermite_e series at points [3,1].
Python3
# import the numpy module import numpy # import hermite_se from numpy.polynomial import hermite_e # create array of coefficients with 5 elements each coefficients_data = np.arange(4).reshape(2,2) # Display the coefficients print(coefficients_data) # get the shape print(f"\nShape of an array: {coefficients_data.shape}") # get the dimensions print(f"Dimension: {coefficients_data.ndim}") h = [3,1] # Evaluate a Hermite_e series at points - [3,1] print("\nHermite_e series", hermite_e.hermeval( h,coefficients_data)) |
Output:
[[0 1] [2 3]] Shape of an array: (2, 2) Dimension: 2 Hermite_e series [[ 6. 2.] [10. 4.]]
