def man(query, subject):
"""Manhattan metric"""
return ldtw.dist_manhatten(ldtw.TimeSeries(query), ldtw.TimeSeries(subject))
query = dtw.TimeSeries([m.cos(i/64.0) * m.exp(-0.5*(i-512)**2/256**2) for i in range(1024)])
subject = dtw.TimeSeries([m.cos(i/64.0+1) * m.exp(-0.5*(i-512)**2/256**2) for i in range(1024)])
# plot query and subject for a quick overview
pl.title("query and subject")
pl.plot(query)
pl.plot(subject)
pl.show()
# do for Euclidean and Manhatten mode
for mode, name in [(True, 'Euclidean'), (False, 'Manhatten')]:
# calculate naive L_p-norm
print dtw.dist_euclidean(query, subject) \
if mode == True else \
dtw.dist_manhatten(query, subject)
# calculate a bunch of windowed DTWs (error should decrease monotonically)
for window in range(0, max(len(subject), len(query)), 32):
gamma = dtw.WarpingPath()
print dtw.dist_cdtw_backtrace(query, subject, window, gamma, mode)
pl.plot(*zip(*[node[::-1] for node in gamma]))
# calculate full DTW
print dtw.dist_dtw(query, subject, mode)
# plot objective function H(i,j) := |query(i)-subject(j)|
pl.imshow([[abs(query[i]-subject[j]) for j in range(len(subject))]
for i in range(len(query))], aspect="auto")
pl.title(name)
pl.show()
for i in range(1024)
])
# plot query and subject for a quick overview
pl.title("query and subject")
pl.plot(query)
pl.plot(subject)
pl.show()
# do for Euclidean and Manhatten mode
for mode, name in [(True, 'Euclidean'), (False, 'Manhatten')]:
# calculate naive L_p-norm
print dtw.dist_euclidean(query, subject) \
if mode == True else \
dtw.dist_manhatten(query, subject)
# calculate a bunch of windowed DTWs (error should decrease monotonically)
for window in range(0, max(len(subject), len(query)), 32):
gamma = dtw.WarpingPath()
print dtw.dist_cdtw_backtrace(query, subject, window, gamma, mode)
pl.plot(*zip(*[node[::-1] for node in gamma]))
# calculate full DTW
print dtw.dist_dtw(query, subject, mode)
# plot objective function H(i,j) := |query(i)-subject(j)|
pl.imshow([[abs(query[i] - subject[j]) for j in range(len(subject))]
for i in range(len(query))],
aspect="auto")
pl.title(name)
def man(query, subject):
"""Manhattan metric"""
return ldtw.dist_manhatten(ldtw.TimeSeries(query),
ldtw.TimeSeries(subject))
