svm.py

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

#we create 40 separable points
X, y = make_blobs(n_samples=40, centers=2, random_state=20)

#fit the model, don't regularize for illustration purposes
clf = svm.SVC(kernel = 'linear', C=1)
clf.fit(X,y)
plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)

#plot decision function
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()

#create grid to evaluate model
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)

#plot decision boundary and margins
ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1],
           alpha=0.5,
           linestyles=['--', '-', '--'])

#plot support vectors
ax.scatter(clf.support_vectors_[:, 0],
           clf.support_vectors_[:, 1], s=100,
           linewidth=1, facecolors='none')

newData1 = [[3,4], [5,6]]
newData2 = [[2,3], [4,5]]
print(clf.predict(newData1))
print(clf.predict(newData2))

plt.show()