# Code source: Gael Varoqueux
# Modified for Documentation merge by Jaques Grobler

import numpy as np
import pylab as pl
from sklearn import svm


Y = [-1] * 4 + [1] * 5

X = np.array([
    [-2, 4],
    [4, 1],
    [2, -2],
    [1, 1],
    [1, 6],
    [2, 4],
    [6, 2],
    [7, 7],
    [8, 8],
])



# fit the model
clf = svm.SVC(kernel='linear', C=0.1)
clf.fit(X, Y)

    # get the separating hyperplane
w = clf.coef_[0]


a = -w[0] / w[1]
b = -(clf.intercept_[0]) / w[1]
xx = np.linspace(-10, 10)
yy = a * xx + b

# plot the parallels to the separating hyperplane that pass through the
# support vectors
margin = 1 / np.sqrt(np.sum(clf.coef_ ** 2))
yy_down = yy + a * margin
yy_up = yy - a * margin

pl.plot(xx, yy, 'k-')
pl.plot(xx, yy_down, 'k--')
pl.plot(xx, yy_up, 'k--')

pl.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
           s=80, facecolors='red', zorder=10)
pl.scatter(X[:, 0], X[:, 1], c=Y, facecolors='black', zorder=10)


pl.show()