python-scipyHow can I use the Radial Basis Function (RBF) in Python with SciPy?

Radial Basis Function (RBF) is a type of kernel-based machine learning algorithm which can be used for both classification and regression problems. The SciPy library in Python provides a convenient implementation of the RBF algorithm through its rbf function. The following example code shows how to use the rbf function to fit a model to a dataset of points:

from scipy.interpolate import Rbf
import numpy as np

# Generate some data
x = np.linspace(0, 10, 10)
y = np.sin(x)

# Fit a radial basis function
rbf = Rbf(x, y)

# Print the fitted model
print(rbf)

Output example

<scipy.interpolate.rbf.Rbf object at 0x7f9c8bb7d400>

The code consists of the following parts:

1. Importing the Rbf function from the scipy.interpolate library and the numpy library.
2. Generating some data points using the linspace function from numpy.
3. Fitting a radial basis function to the data points using the Rbf function from scipy.interpolate.
4. Printing the fitted model.

Helpful links

• SciPy RBF Documentation
• NumPy Documentation

Edit this code on GitHub