SVM with custom kernel#

Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors.

3-Class classification using Support Vector Machine with custom kernel
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn import datasets, svm
from sklearn.inspection import DecisionBoundaryDisplay

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
Y = iris.target


def my_kernel(X, Y):
    """
    We create a custom kernel:

                 (2  0)
    k(X, Y) = X  (    ) Y.T
                 (0  1)
    """
    M = np.array([[2, 0], [0, 1.0]])
    return np.dot(np.dot(X, M), Y.T)


h = 0.02  # step size in the mesh

# we create an instance of SVM and fit out data.
clf = svm.SVC(kernel=my_kernel)
clf.fit(X, Y)

ax = plt.gca()
DecisionBoundaryDisplay.from_estimator(
    clf,
    X,
    cmap=plt.cm.Paired,
    ax=ax,
    response_method="predict",
    plot_method="pcolormesh",
    shading="auto",
)

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors="k")
plt.title("3-Class classification using Support Vector Machine with custom kernel")
plt.axis("tight")
plt.show()

Total running time of the script: (0 minutes 0.093 seconds)

Related examples

Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

Plot the support vectors in LinearSVC

Plot the support vectors in LinearSVC

Gallery generated by Sphinx-Gallery