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:
- Importing the
Rbf
function from thescipy.interpolate
library and thenumpy
library. - Generating some data points using the
linspace
function fromnumpy
. - Fitting a radial basis function to the data points using the
Rbf
function fromscipy.interpolate
. - Printing the fitted model.
Helpful links
More of Python Scipy
- How do I use the trapz function in Python SciPy?
- How do I create a 2D array of zeros using Python and NumPy?
- How do I use Python XlsxWriter to write a NumPy array to an Excel file?
- How do I convert a Python numpy array to a list?
- How do I use the scipy ttest_ind function in Python?
- How do I calculate the mode using Python Scipy Stats?
- How can I use Python Scipy to generate random numbers?
- How do I convert a Python numpy.ndarray to a list?
- How can I use Python and SciPy to generate a uniform distribution?
See more codes...