SVM.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.datasets import make_blobs

X, Y = make_blobs(n_samples=100, centers=2, random_state=6)

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)

ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()

xx = np.linspace(xlim[0], xlim[1], 30)
yy = np.linspace(ylim[0], ylim[1], 30)

YY, XX = np.meshgrid(yy, xx)

xy = np.vstack([XX.ravel(), YY.ravel()]).T

Z = clf.decision_function(xy).reshape(XX.shape)

ax.contour(XX, YY, Z, colors='g', levels=[-1, 0, 1], alpha=0.5, linestyles=['-.', '-', '-.'])
ax.scatter(clf.support_vectors_[:,0], clf.support_vectors_[:,1], s=100, linewidth=1, facecolors='none')

plt.show()