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@thearn
Last active December 19, 2015 01:39
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Scikit-learn example
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()
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