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
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.inspection import DecisionBoundaryDisplay

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

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, 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), Y)

ax = plt.gca()

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

Total running time of the script: ( 0 minutes 0.091 seconds)

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