svm.py

import numpy
def convertManyToOne(Y):
	newY = numpy.empty((0, 1))
	for i in xrange(len(Y)):
		for j in xrange(len(Y[i])):
			if Y[i][j] == 1:
				newY = numpy.vstack([newY, j])
				break
	return newY

import scipy.io as sio

X = sio.loadmat('X_scaled.mat')['X_scaled']
Y = sio.loadmat('Y.mat')['Y']
Y = convertManyToOne(Y)
Y = numpy.hstack(Y)
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)
from sklearn.svm import SVC
clf = SVC(verbose=True, cache_size = 700)
clf.fit(X_train, y_train)

print "Training Accuracy %.2f\n" % (clf.score(X_train, y_train))
print "Testing Accuracy %.2f\n" % (clf.score(X_test, y_test))