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"""
=========================================
SVM: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machine classifier with
linear kernel.
"""
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.datasets import make_blobs
from sklearn.inspection import DecisionBoundaryDisplay
# we create 40 separable points
X, y = make_blobs(n_samples=40, centers=2, random_state=6)
# fit the model, don't regularize for illustration purposes
clf = svm.SVC(kernel="linear", C=1000)
clf.fit(X, y)
plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
# plot the decision function
ax = plt.gca()
DecisionBoundaryDisplay.from_estimator(
clf,
X,
plot_method="contour",
colors="k",
levels=[-1, 0, 1],
alpha=0.5,
linestyles=["--", "-", "--"],
ax=ax,
)
# plot support vectors
ax.scatter(
clf.support_vectors_[:, 0],
clf.support_vectors_[:, 1],
s=100,
linewidth=1,
facecolors="none",
edgecolors="k",
)
plt.show()
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