#!/usr/bin/python
# -*- coding: utf-8 -*-

"""
=========================================================
SVM-Kernels
=========================================================

Three different types of SVM-Kernels are displayed below.
The polynomial and RBF are especially useful when the
data-points are not linearly separable.


"""
print(__doc__)


# Code source: Gael Varoqueux
# License: BSD 3 clause

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


# Our dataset and targets
X = np.c_[(.4, -.7),
          (-1.5, -1),
          (-1.4, -.9),
          (-1.3, -1.2),
          (-1.1, -.2),
          (-1.2, -.4),
          (-.5, 1.2),
          (-1.5, 2.1),
          (1, 1),
          # --
          (1.3, .8),
          (1.2, .5),
          (.2, -2),
          (.5, -2.4),
          (.2, -2.3),
          (0, -2.7),
          (1.3, 2.1)].T
Y = [0] * 8 + [1] * 8

# figure number
fignum = 1

# fit the model
for kernel in ('linear', 'poly', 'rbf'):
    clf = svm.SVC(kernel=kernel, gamma=2)
    clf.fit(X, Y)

    # plot the line, the points, and the nearest vectors to the plane
    pl.figure(fignum, figsize=(4, 3))
    pl.clf()

    pl.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,
               facecolors='none', zorder=10)
    pl.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=pl.cm.Paired)

    pl.axis('tight')
    x_min = -3
    x_max = 3
    y_min = -3
    y_max = 3

    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(XX.shape)
    pl.figure(fignum, figsize=(4, 3))
    pl.pcolormesh(XX, YY, Z > 0, cmap=pl.cm.Paired)
    pl.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],
               levels=[-.5, 0, .5])

    pl.xlim(x_min, x_max)
    pl.ylim(y_min, y_max)

    pl.xticks(())
    pl.yticks(())
    fignum = fignum + 1
pl.show()