SVC kernels

poly_svc.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm

x1 = np.genfromtxt("class1.csv", delimiter = ",")
x2 = np.genfromtxt("class2.csv", delimiter = ",")
x3 = np.genfromtxt("class3.csv", delimiter = ",")

y1 = np.zeros(x1.shape[0])
y2 = np.ones(x2.shape[0])
y3 = np.arange(x3.shape[0])
y3.fill(2)

x = np.concatenate((x1, x2, x3), axis = 0)
y = np.concatenate((y1, y2, y3))

xmin, xmax = x[:, 0].min() - 0.1, x[:, 0].max() + 0.1
ymin, ymax = x[:, 1].min() - 0.1, x[:, 1].max() + 0.1

clf = svm.SVC(kernel = 'poly')

y_pred = clf.fit(x, y).predict(x)
print "Number of mislabeled points: %d" % (y != y_pred).sum()

xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.01), np.arange(ymin, ymax, 0.01))
xnew = np.c_[xx.ravel(), yy.ravel()]
ynew = clf.fit(x, y).predict(xnew).reshape(xx.shape)

fig = plt.figure(1)
#plt.set_cmap(plt.cm.Paired)
plt.pcolormesh(xx, yy, ynew)
plt.plot(x1[:, 0], x1[:, 1], 'ob', x2[:, 0], x2[:, 1], 'or', x3[:, 0], x3[:, 1], 'og')
plt.savefig("poly_svc.png")

rbf_svc.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm

x1 = np.genfromtxt("class1.csv", delimiter = ",")
x2 = np.genfromtxt("class2.csv", delimiter = ",")
x3 = np.genfromtxt("class3.csv", delimiter = ",")

y1 = np.zeros(x1.shape[0])
y2 = np.ones(x2.shape[0])
y3 = np.arange(x3.shape[0])
y3.fill(2)

x = np.concatenate((x1, x2, x3), axis = 0)
y = np.concatenate((y1, y2, y3))

xmin, xmax = x[:, 0].min() - 0.1, x[:, 0].max() + 0.1
ymin, ymax = x[:, 1].min() - 0.1, x[:, 1].max() + 0.1

clf = svm.SVC(kernel = 'rbf')

y_pred = clf.fit(x, y).predict(x)
print "Number of mislabeled points: %d" % (y != y_pred).sum()

xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.01), np.arange(ymin, ymax, 0.01))
xnew = np.c_[xx.ravel(), yy.ravel()]
ynew = clf.fit(x, y).predict(xnew).reshape(xx.shape)

fig = plt.figure(1)
#plt.set_cmap(plt.cm.Paired)
plt.pcolormesh(xx, yy, ynew)
plt.plot(x1[:, 0], x1[:, 1], 'ob', x2[:, 0], x2[:, 1], 'or', x3[:, 0], x3[:, 1], 'og')
plt.savefig("rbf_svc.png")