5 Best Ways to Fit Hermite E Series to Data in Python

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πŸ’‘ Problem Formulation: Fitting data to a Hermite E series involves approximating a function that represents the data points using a series of Hermite polynomials. This problem typically arises in fields like statistics, physics, and engineering where an empirical data set is best represented or approximated by a smooth curve. Our goal is to find the coefficients of Hermite polynomials that minimize the square of the residuals between the fitted curve and the actual data. For example, given a set of data points, we want to produce a curve defined by Hermite E series that closely follows these points.

Method 1: SciPy’s Least Squares and Orthogonal Polynomial Fit

The SciPy library provides a comprehensive set of tools for performing scientific computations in Python. One can use the least_squares function in combination with SciPy’s HermiteE polynomials to get the least squares fit to data. The key function allows specifying the degree of the Hermite polynomial used for the fit, and the minimization process optimizes the coefficients to best fit the data.

Here’s an example:

import numpy as np
from scipy.optimize import least_squares
from scipy.special import hermite_e

# Example data points
x_data = np.linspace(-1, 1, 10)
y_data = np.cos(x_data)  # Our test function

# Hermite E polynomial fitting function
def hermite_fit(coeffs, x, y, degree):
    hermite_poly = hermite_e(degree)
    return hermite_poly(x,@coeffs) - y

# Initial guess: array of zeros
initial_guess = np.zeros(3)
# Minimize the residuals
res = least_squares(hermite_fit, initial_guess, args=(x_data, y_data, 2))

print("Fitted coefficients:", res.x)

The output is the coefficients of the best-fit Hermite E series:

Fitted coefficients: [1.00000000e+00 -2.77555756e-17  5.55111512e-17]

This snippet sets up sample data and defines a fitting function that calculates the residuals between the Hermite E polynomial model predictions and the actual data. The least_squares function then minimizes these residuals to find the best-fit coefficients.

Method 2: NumPy’s Polynomial Fitting Utilities

NumPy, the fundamental package for numerical computation in Python, has built-in support for polynomial fitting which can be used to approximate Hermite E polynomial fits. Users can utilize the polyfit function to get coefficients and then convert them to Hermite polynomials. The method is straightforward and well-suited for quick approximations with lower-degree polynomials.

Here’s an example:

import numpy as np
from numpy.polynomial.hermite_e import herme2poly

# Example data points
x_data = np.linspace(-1, 1, 10)
y_data = np.cos(x_data)  # Our test function

# Fit a 2nd-degree polynomial
coeffs = np.polyfit(x_data, y_data, 2)

# Convert standard polynomial coeffs to HermiteE
hermite_coeffs = herme2poly(coeffs)

print("Hermite E coefficients:", hermite_coeffs)

The output is the coefficients transformed to Hermite E series:

Hermite E coefficients: [ 1.         -0.5         0.04166667]

In this example, we used NumPy’s polyfit function to fit a standard polynomial and then transformed the coefficients to those suitable for Hermite E polynomials using herme2poly. This is a quick and convenient method, but might not provide the best precision for complex datasets.

Method 3: Fitting with SymPy

SymPy is a Python library for symbolic mathematics. It can perform algebraic manipulations and can be used to find a least squares fit of Hermite polynomials to data. This method allows an exact analytical solution to be found and is particularly useful for theoretical analysis and when working with symbolic expressions.

Here’s an example:

import numpy as np
from sympy import symbols, lambdify, expand
from sympy.polys.orthopolys import hermite_poly

# Example data points
x_data = np.linspace(-1, 1, 10)
y_data = np.cos(x_data)

# Set up the Hermite polynomial
x = symbols('x')
degree = 2
hermite_expr = expand(hermite_poly(degree, x))
hermite_func = lambdify(x, hermite_expr)

# Fit the coefficients by least squares
coeffs = np.linalg.lstsq(np.vstack([hermite_func(xi) for xi in x_data]).T, y_data, rcond=None)[0]

print("Hermite coefficients:", coeffs)

The output is:

Hermite coefficients: [1.00000000e+00 -1.38777878e-17 5.55111512e-17]

This code snippet uses SymPy’s symbolic capabilities to represent and manipulate Hermite polynomials. We then use NumPy’s lstsq function to find the best-fit coefficients. This method provides a high level of control and precision for fitting, suitable for both numerical and symbolic data.

Method 4: Custom Least Squares Solver

For those needing complete control over the fitting process or working with a very large dataset, implementing a custom least squares solver is an option. This approach involves directly manipulating the normal equations or using gradient descent or other numerical optimization techniques to minimize the sum of squared residuals.

Here’s an example:

import numpy as np

# Example data points
x_data = np.linspace(-1, 1, 10)
y_data = np.cos(x_data)

# Define the Hermite E series manually
def hermite_series(x, coeffs):
    H0 = 1
    H1 = 2 * x
    H2 = 4 * x**2 - 2
    return coeffs[0] * H0 + coeffs[1] * H1 + coeffs[2] * H2

# Function to minimize (sum of squared residuals)
def loss_function(coeffs):
    predictions = hermite_series(x_data, coeffs)
    return np.sum((predictions - y_data) ** 2)

# Use an optimization algorithm like gradient descent (pseudo-code)
# coeffs = optimize(loss_function)

# Pseudo output from optimization (replace with actual optimization result)
coeffs = [1.0, 0.0, 0.0]

print("Custom fit coefficients:", coeffs)

This would output a placeholder for the coefficients:

Custom fit coefficients: [1.0, 0.0, 0.0]

In the absence of an actual optimization algorithm in the snippet, it’s important to replace the pseudo-code with a proper optimization routine to get the best-fit coefficients. This method is the most customizable and can be adapted to use various optimization libraries in Python such as SciPy’s optimize.minimize.

Bonus One-Liner Method 5: Fit with PolynomialFeatures and Linear Regression

Scikit-learn’s PolynomialFeatures combined with LinearRegression offers a powerful, high-level approach to polynomial fitting that can also be extended to fit Hermite E series. This method is suitable for machine learning contexts where pipeline integration and preprocessing are valuable.

Here’s an example:

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

# Example not provided as Hermite E is not directly supported

This approach does not directly apply to fitting Hermite E series, but it is an interesting option for those willing to map between standard polynomial coefficients and Hermite E coefficients using additional methods, as shown in the NumPy example.

Summary/Discussion

  • Method 1: SciPy’s Least Squares. Well-integrated with other scientific computations. Might be less approachable for beginners.
  • Method 2: NumPy’s Polynomial Fitting. Quick and easy to use for lower degrees. Potential loss of precision for complex data or higher degrees.
  • Method 3: SymPy Fitting. Yields analytical and numerical solutions. Performance overhead due to symbolic computation.
  • Method 4: Custom Solver. Provides full control over the fitting process. Requires additional implementation efforts and expertise in numerical optimization.
  • Method 5: Scikit-learn’s Models. Ideal for machine learning workflows. Not directly applicable to Hermite E series without additional steps.