class test(unittest.TestCase):
def setUp(self):
self.sax = SAX()
self.delta = 1.0e-10
def testEuclideanDistance(self):
sig1 = [ i for i in xrange(100) ]
sig2 = [ i + 0.5 for i in xrange(100) ]
lse = self.sax.euclidean_dist(sig1, sig2)
assert lse == 5.0
def testNormalizeOnRandom(self):
orig_sig = [ random.uniform(0, 1) for x in xrange(1000) ]
sig = self.sax.normalize(orig_sig)
# properly Z-normalized signal should have a mean
# very close to 0 and standard deviation very close to 1.0
assert abs(np.mean(sig)) < self.delta
assert abs(np.std(sig) - 1.0) < self.delta
def testPAA(self):
siglen = 100
M = 10
orig_sig = [ random.uniform(0, 1) for x in xrange(siglen) ]
paa_sig = self.sax.to_PAA(orig_sig, M)
assert len(paa_sig) == M
assert np.mean(self.sax.normalize(orig_sig[:M])) == paa_sig[0]
def testPAAexample(self):
orig_sig = [2.02, 2.33, 2.99, 6.85, 9.20, 8.80, 7.50, 6.00, 5.85, 3.85, 4.85, 3.85, 2.22, 1.45, 1.34]
M = 9
paa_sig = self.sax.to_PAA(orig_sig, M)
res_sig = [-0.9327168, -0.3699053, 1.383673, 1.391248, 0.6299752, 0.01641218, -0.05933634, -0.8387886, -1.220561]
assert len(paa_sig) == len(res_sig)
M = 5
paa_sig = self.sax.to_PAA(orig_sig, M)
res_sig2 = [-0.9379922, -0.0857173, 0.4738943, 1.444949, -0.8951336]
assert len(paa_sig) == len(res_sig2)
def setUp(self):
self.sax = SAX()
self.delta = 1.0e-10
