Scikit-learn example

sklearn_example.py

import random
from sklearn import svm
from sklearn.grid_search import GridSearchCV
import pylab
import numpy as np


data = []
labels = []

# generate data
pylab.figure()
for i in xrange(25):
    x = random.uniform(0, 1)
    y = random.uniform(0, 1)
    data.append([x, y])
    if x < y:
        pylab.plot([x], [y], 'ro')
        labels.append(1)
    else:
        pylab.plot([x], [y], 'bo')
        labels.append(0)
#
# pylab.show()


# make a classifier

tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                     'C': [1, 10, 100, 1000]},
                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
clf = GridSearchCV(svm.SVC(C=1), tuned_parameters)
clf.fit(np.array(data), np.array(labels), cv=5)

# verify with new data
for i in xrange(1000):
    x = random.uniform(0, 1)
    y = random.uniform(0, 1)
    data.append([x, y])
    if clf.predict([x, y])[0] == 1:
        pylab.plot([x], [y], 'rx')
        labels.append(0)
    else:
        pylab.plot([x], [y], 'bx')
        labels.append(1)
#

pylab.show()