def dtw_warp(x, labels, slope_constraint="symmetric", use_window=True, dtw_type="normal"):
import utils.dtw as dtw
if use_window:
window = np.ceil(x.shape[1] / 10.).astype(int)
else:
window = None
orig_steps = np.arange(x.shape[1])
l = np.argmax(labels, axis=1) if labels.ndim > 1 else labels
ret = np.zeros_like(x)
for i, pat in enumerate(tqdm(x)):
# guarentees that same one isnt selected
choices = np.delete(np.arange(x.shape[0]), i)
# remove ones of different classes
choices = np.where(l[choices] == l[i])[0]
if choices.size > 0:
# pick random intra-class pattern
random_prototype = x[np.random.choice(choices)]
if dtw_type == "shape":
path = dtw.shape_dtw(random_prototype, pat, dtw.RETURN_PATH, slope_constraint=slope_constraint, window=window)
else:
path = dtw.dtw(random_prototype, pat, dtw.RETURN_PATH, slope_constraint=slope_constraint, window=window)
# Time warp
warped = pat[path[1]]
for dim in range(x.shape[2]):
ret[i,:,dim] = np.interp(orig_steps, np.linspace(0, x.shape[1]-1., num=warped.shape[0]), warped[:,dim]).T
else:
print("There is only one pattern of class %d, skipping timewarping"%l[i])
ret[i,:] = pat
return ret
def discriminative_guided_warp(x,
labels,
batch_size=6,
slope_constraint="symmetric",
use_window=True,
dtw_type="normal",
use_variable_slice=True):
import utils.dtw as dtw
if use_window:
window = np.ceil(x.shape[1] / 10.).astype(int)
else:
window = None
orig_steps = np.arange(x.shape[1])
l = np.argmax(labels, axis=1) if labels.ndim > 1 else labels
positive_batch = np.ceil(batch_size / 2).astype(int)
negative_batch = np.floor(batch_size / 2).astype(int)
ret = np.zeros_like(x)
warp_amount = np.zeros(x.shape[0])
for i, pat in enumerate(tqdm(x)):
# guarentees that same one isnt selected
choices = np.delete(np.arange(x.shape[0]), i)
# remove ones of different classes
positive = np.where(l[choices] == l[i])[0]
negative = np.where(l[choices] != l[i])[0]
if positive.size > 0 and negative.size > 0:
pos_k = min(positive.size, positive_batch)
neg_k = min(negative.size, negative_batch)
positive_prototypes = x[np.random.choice(positive,
pos_k,
replace=False)]
negative_prototypes = x[np.random.choice(negative,
neg_k,
replace=False)]
# vector embedding and nearest prototype in one
pos_aves = np.zeros((pos_k))
neg_aves = np.zeros((pos_k))
if dtw_type == "shape":
for p, pos_prot in enumerate(positive_prototypes):
for ps, pos_samp in enumerate(positive_prototypes):
if p != ps:
pos_aves[p] += (1. / (pos_k - 1.)) * dtw.shape_dtw(
pos_prot,
pos_samp,
dtw.RETURN_VALUE,
slope_constraint=slope_constraint,
window=window)
for ns, neg_samp in enumerate(negative_prototypes):
neg_aves[p] += (1. / neg_k) * dtw.shape_dtw(
pos_prot,
neg_samp,
dtw.RETURN_VALUE,
slope_constraint=slope_constraint,
window=window)
selected_id = np.argmax(neg_aves - pos_aves)
path = dtw.shape_dtw(positive_prototypes[selected_id],
pat,
dtw.RETURN_PATH,
slope_constraint=slope_constraint,
window=window)
else:
for p, pos_prot in enumerate(positive_prototypes):
for ps, pos_samp in enumerate(positive_prototypes):
if p != ps:
pos_aves[p] += (1. / (pos_k - 1.)) * dtw.dtw(
pos_prot,
pos_samp,
dtw.RETURN_VALUE,
slope_constraint=slope_constraint,
window=window)
for ns, neg_samp in enumerate(negative_prototypes):
neg_aves[p] += (1. / neg_k) * dtw.dtw(
pos_prot,
neg_samp,
dtw.RETURN_VALUE,
slope_constraint=slope_constraint,
window=window)
selected_id = np.argmax(neg_aves - pos_aves)
path = dtw.dtw(positive_prototypes[selected_id],
pat,
dtw.RETURN_PATH,
slope_constraint=slope_constraint,
window=window)
# Time warp
warped = pat[path[1]]
warp_path_interp = np.interp(
orig_steps, np.linspace(0,
x.shape[1] - 1.,
num=warped.shape[0]), path[1])
warp_amount[i] = np.sum(np.abs(orig_steps - warp_path_interp))
for dim in range(x.shape[2]):
ret[i, :, dim] = np.interp(
orig_steps,
np.linspace(0, x.shape[1] - 1., num=warped.shape[0]),
warped[:, dim]).T
else:
print("There is only one pattern of class %d" % l[i])
ret[i, :] = pat
warp_amount[i] = 0.
if use_variable_slice:
max_warp = np.max(warp_amount)
if max_warp == 0:
# unchanged
ret = window_slice(ret, reduce_ratio=0.95)
else:
for i, pat in enumerate(ret):
# Variable Sllicing
ret[i] = window_slice(pat[np.newaxis, :, :],
reduce_ratio=0.95 +
0.05 * warp_amount[i] / max_warp)[0]
return ret