kernel-examples.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
__author__="Josh Montague"
__license__="MIT License"

import sys
import json

import matplotlib.pyplot as plt
try:
    import seaborn as sns
except ImportError as e:
    sys.stderr.write("seaborn not installed. Using default matplotlib templates.")

import numpy as np
from sklearn.svm import SVC 

# adapted from:
# http://scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html

# 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', 'sigmoid'):
    #clf = SVC(kernel=kernel) 
    clf = SVC(kernel=kernel, gamma=2)
    clf.fit(X, Y)

    # plot the line, the points, and the nearest vectors to the plane
    plt.figure(fignum, figsize=(8, 6))
    plt.clf()

    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
                facecolors='none', zorder=10, s=300)
    plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired, s=100)

    plt.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)
    plt.figure(fignum, figsize=(4, 3))
    #plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
    plt.pcolormesh(XX, YY, Z > 0, alpha=0.1)
    plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],
                levels=[-.5, 0, .5])

    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)
    plt.title('{}'.format(kernel))

    #plt.xticks(())
    #plt.yticks(())
    fignum = fignum + 1

plt.show()