def align_signal(s, t, w=5, has_time=True):
"""
every row of s or t is a time series
every column is dimensions of signal at one time point
w size is symmetric. w=5 means the window has size 11.
"""
if has_time:
s = s[:, 1:]
t = t[:, 1:]
dist_fun = euclidean_distances
dist, cost, acc, path = dtw(s, t, dist_fun)
path = np.array(path)
warped_t = t[path[1, :], :]
new_t = np.zeros(s.shape)
for i in range(warped_t.shape[0]):
new_t[path[0, i], :] = warped_t[i, :]
if has_time:
Ts = np.arange(1, s.shape[0]+1)
Ts = Ts.reshape(-1, 1)
new_t = np.hstack((Ts, new_t))
return new_t
def align_signal(s, t, w=5, has_time=True):
"""
every row of s or t is a time series
every column is dimensions of signal at one time point
w size is symmetric. w=5 means the window has size 11.
"""
if has_time:
s = s[:, 1:]
t = t[:, 1:]
dist_fun = euclidean_distances
dist, cost, acc, path = dtw(s, t, dist_fun)
path = np.array(path)
warped_t = t[path[1, :], :]
new_t = np.zeros(s.shape)
for i in range(warped_t.shape[0]):
new_t[path[0, i], :] = warped_t[i, :]
if has_time:
Ts = np.arange(1, s.shape[0]+1)
Ts = Ts.reshape(-1, 1)
new_t = np.hstack((Ts, new_t))
return new_t
def dtw_distance(s, t):
"""
every row of s or t is a time series
every column is dimensions of signal at one time point
w size is symmetric. w=5 means the window has size 11.
"""
has_time = True
if has_time:
s = s[:, 1:]
t = t[:, 1:]
dist_fun = euclidean_distances
dist, cost, acc, path = dtw(s, t, dist_fun)
return dist