"""
==================================================
Plot different SVM classifiers in the iris dataset
==================================================

Comparison of different linear SVM classifiers on the iris dataset. It
will plot the decision surface for four different SVM classifiers.

"""
print __doc__

import numpy as np
import pylab as pl
from scikits.learn import svm, datasets

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
                     # avoid this ugly slicing by using a two-dim dataset
Y = iris.target

h=.02 # step size in the mesh

# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
svc     = svm.SVC(kernel='linear').fit(X, Y)
rbf_svc = svm.SVC(kernel='poly').fit(X, Y)
nu_svc  = svm.NuSVC(kernel='linear').fit(X,Y)
lin_svc = svm.LinearSVC().fit(X, Y)

# create a mesh to plot in
x_min, x_max = X[:,0].min()-1, X[:,0].max()+1
y_min, y_max = X[:,1].min()-1, X[:,1].max()+1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                     np.arange(y_min, y_max, h))

# title for the plots
titles = ['SVC with linear kernel',
          'SVC with polynomial (degree 3) kernel',
          'NuSVC with linear kernel',
          'LinearSVC (linear kernel)']


pl.set_cmap(pl.cm.Paired)

for i, clf in enumerate((svc, rbf_svc, nu_svc, lin_svc)):
    # Plot the decision boundary. For that, we will asign a color to each
    # point in the mesh [x_min, m_max]x[y_min, y_max].
    pl.subplot(2, 2, i+1)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.set_cmap(pl.cm.Paired)
    pl.contourf(xx, yy, Z)
    pl.axis('tight')

    # Plot also the training points
    pl.scatter(X[:,0], X[:,1], c=Y)

    pl.title(titles[i])

pl.axis('tight')
pl.show()