def prepare_transform(self, ts_to_be_rescaled):
"""Prepare the model for temporal resampling by computing DTW alignment path between the reference time series
and a time series to be rescaled or a set of time series to be rescaled.
If ts_to_be_rescaled contains a single time series, all series from the dataset will be rescaled using the
DTW path between that time series and the reference one, otherwise, the X array given at transform time
should have the same number of time series (X.shape[0]) as ts_to_be_rescaled.
Parameters
----------
ts_to_be_rescaled : numpy.ndarray
A time series dataset of base modalities of shape (n_ts, sz, d) with
``d = self.reference_series_.shape[-1]``
"""
ts_to_be_rescaled = to_time_series_dataset(ts_to_be_rescaled)
# Now ts_to_be_rescaled is of shape n_ts, sz, d
# with d = self.reference_series.shape[-1]
self.saved_dtw_paths_ = []
for ts in ts_to_be_rescaled:
end = first_non_finite_index(ts)
resampled_ts = _resampled(ts[:end],
n_samples=self.n_samples,
kind=self.interp_kind)
if self.metric == "dtw":
path, d = dtw_path(self.reference_series_, resampled_ts)
elif self.metric == "lrdtw":
path, d = lr_dtw_path(self.reference_series_,
resampled_ts,
gamma=self.gamma_lr_dtw)
else:
raise ValueError("Unknown alignment function")
self.saved_dtw_paths_.append(path)
def DTW_update(self,
s1,
s2,
w,
gl_const=self.gl_const,
scr=self.scr,
ims=self.ims):
"""find the best path"""
best_path, distance = dtw_path(s1,
s2,
global_constraint=gl_const,
sakoe_chiba_radius=scr,
itakura_max_slope=ims)
x_cords = []
y_cords = []
# print(w)
for i in best_path:
x_cords += [round(i[0] * w + i[1] * (1 - w))]
y_cords += [s1[i[0]] * w + s2[i[1]] * (1 - w)]
s3 = []
for j in range(len(s1)):
sublistj = [
y_cords[k] for k in np.where(np.isin(np.array(x_cords), j))[0]
]
if len(sublistj) != 0:
s3 += [sum(sublistj) / len(sublistj)]
else:
s3 += [s3[-1]]
return s3
def DynamicTimeWarping(seismic_data, grid, window_width):
path_info = []
start = time.time()
for i in range(0, len(grid), 1):
reference_trace = seismic_data[:, grid[i][0], grid[i][1]]
reference_trace = np.trim_zeros(reference_trace, 'b')
block_size = 0
for ii in range(0, len(grid), 1):
if abs(grid[i][0] - grid[ii][0]) <= window_width and abs(
grid[i][1] - grid[ii][1]) <= window_width:
block_size = block_size + 1
matched_trace = seismic_data[:, grid[ii][0], grid[ii][1]]
matched_trace = np.trim_zeros(matched_trace, 'b')
path, sim = metrics.dtw_path(
reference_trace, matched_trace
) #print(grid[i][0], grid[i][1], grid[ii][0], grid[ii][1], sim)
#if sim < similarity_score:
PI = [grid[i][0], grid[i][1], grid[ii][0], grid[ii][1], path]
path_info.append(PI)
stop = time.time()
t = (stop - start) / 60
clear_output()
print('reference trace:', grid[i][0], grid[i][1], '(', i, 'out of:',
len(grid), ')', ' time in minutes: ', t, 'Sub-block size:',
block_size)
return path_info
def regrid_horizons_DTW(seismic_data, grid, grid_step,
filtered_horizons_binary, dense_grid, window_size):
horizontal_warp_distance = window_size * grid_step
dense_path_info = []
start = time.time()
for i in range(0, len(grid)):
reference_trace = seismic_data[:, grid[i][0], grid[i][1]]
reference_trace = np.trim_zeros(reference_trace, 'b')
block_size = 0
for iii in range(0, len(dense_grid)):
if abs(grid[i][0] -
dense_grid[iii][0]) <= horizontal_warp_distance and abs(
grid[i][1] -
dense_grid[iii][1]) <= horizontal_warp_distance:
block_size = block_size + 1
matched_trace = seismic_data[:, dense_grid[iii][0],
dense_grid[iii][1]]
matched_trace = np.trim_zeros(matched_trace, 'b')
path, sim = metrics.dtw_path(reference_trace, matched_trace)
PI = [
grid[i][0], grid[i][1], dense_grid[iii][0],
dense_grid[iii][1], path
]
dense_path_info.append(PI)
stop = time.time()
t = (stop - start) / 60
clear_output()
print('(', i, 'out of:', len(grid), ')', ' time in minutes: ', t,
'Sub-block size:', block_size)
regridded_horizons = []
it = 0
for reflector in filtered_horizons_binary:
new_horizon = []
it = it + 1
for i in range(0, len(reflector)):
events = []
reference_trace = seismic_data[:, reflector[i][1], reflector[i][2]]
reference_trace = np.trim_zeros(reference_trace, 'b')
event = reflector[i][0]
for k in range(0, len(dense_path_info)):
if dense_path_info[k][0] == reflector[i][
1] and dense_path_info[k][1] == reflector[i][2]:
path = dense_path_info[k][4]
for ll in range(0, len(path)):
if path[ll][0] == event:
point = [
path[ll][1], dense_path_info[k][2],
dense_path_info[k][3]
]
event_dense_path = path[ll][1]
new_horizon.append(point)
regridded_horizons.append(new_horizon)
return regridded_horizons, dense_path_info
def _petitjean_assignment(self, X, barycenter):
n = X.shape[0]
assign = ([[] for _ in range(self.barycenter_size)], [[] for _ in range(self.barycenter_size)])
for i in range(n):
path, _ = dtw_path(X[i], barycenter)
for pair in path:
assign[0][pair[1]].append(i)
assign[1][pair[1]].append(pair[0])
return assign
def eval_model(self,
net,
loader,
batch_size,
gamma,
verbose=1,
target_mean=0,
target_std=0):
criterion = torch.nn.MSELoss()
losses_mse = []
losses_dtw = []
losses_tdi = []
for i, data in enumerate(loader, 0):
loss_mse, loss_dtw, loss_tdi = torch.tensor(0), torch.tensor(
0), torch.tensor(0)
# get the inputs
inputs, target = data
# inputs, target, breakpoints = data
inputs = torch.tensor(inputs, dtype=torch.float32).to(self.device)
target = torch.tensor(target, dtype=torch.float32).to(self.device)
# batch_size, N_output = target.shape[0:2]
outputs = net(inputs)
# MSE
loss_mse = criterion(target, outputs)
loss_dtw, loss_tdi = 0, 0
# DTW and TDI
for k in range(batch_size):
target_k_cpu = target[k, :,
0:1].view(-1).detach().cpu().numpy()
output_k_cpu = outputs[k, :,
0:1].view(-1).detach().cpu().numpy()
loss_dtw += dtw(target_k_cpu, output_k_cpu)
path, sim = dtw_path(target_k_cpu, output_k_cpu)
Dist = 0
for i, j in path:
Dist += (i - j) * (i - j)
loss_tdi += Dist / (self.N_output * self.N_output)
loss_dtw = loss_dtw / batch_size
loss_tdi = loss_tdi / batch_size
# print statistics
losses_mse.append(loss_mse.item())
losses_dtw.append(loss_dtw)
losses_tdi.append(loss_tdi)
## TODO plotting eval
print(' Eval mse= ',
np.array(losses_mse).mean(), ' dtw= ',
np.array(losses_dtw).mean(), ' tdi= ',
np.array(losses_tdi).mean())
def evaluate_iteration(model, criterion, X_test_left, X_test_right, y_test):
model.eval()
x_test_left = np.transpose(X_test_left, [1, 0, 2])
x_test_right = np.transpose(X_test_right, [1, 0, 2])
y_test = np.transpose(y_test, [1, 0, 2])
x_test_left_tensor = numpy_to_tvar(x_test_left)
x_test_right_tensor = numpy_to_tvar(x_test_right)
y_test_tensor = numpy_to_tvar(y_test)
output, atten = model(x_test_left_tensor,
x_test_right_tensor, y_test_tensor, 0)
loss = criterion(output, y_test_tensor)
loss_mse, loss_dtw, loss_tdi = 0, 0, 0
loss_mae, loss_RMSLE, loss_RMSE = 0, 0, 0
for k in range(BATCH_SIZE):
target_k_cpu = y_test_tensor[:, k, 0:1].view(-1).detach().cpu().numpy()
output_k_cpu = output[:, k, 0:1].view(-1).detach().cpu().numpy()
loss_dtw += dtw(target_k_cpu, output_k_cpu)
path, sim = dtw_path(target_k_cpu, output_k_cpu)
Dist = 0
for i, j in path:
Dist += (i-j)*(i-j)
loss_tdi += Dist / (N_output*N_output)
loss_mae += mean_absolute_error(target_k_cpu, output_k_cpu)
loss_RMSLE += np.sqrt(mean_squared_error(target_k_cpu, output_k_cpu))
loss_RMSE += np.sqrt(mean_squared_error(target_k_cpu, output_k_cpu))
loss_dtw = loss_dtw / BATCH_SIZE
loss_tdi = loss_tdi / BATCH_SIZE
loss_mae = loss_mae / BATCH_SIZE
loss_RMSLE = loss_RMSLE / BATCH_SIZE
loss_RMSE = loss_RMSE / BATCH_SIZE
# # metric
# output_numpy = output.cpu().data.numpy()
# y_test_numpy = y_test_tensor.cpu().data.numpy()
# loss_mae = mean_absolute_error(y_test_numpy,output_numpy)
# loss_RMSLE = np.sqrt(mean_squared_error(y_test_numpy,output_numpy))
# loss_RMSE = np.sqrt(mean_squared_error(y_test_numpy,output_numpy))
# test_loss_meter.add(loss.item())
# plot_result(output, y_test_tensor)
# show_attention(x_test_left_tensor, x_test_right_tensor,output,atten)
# plt.show()
return loss.item(), loss_mae, loss_RMSLE, loss_RMSE, loss_dtw
def _get_similarity(self, seq1_features, seq2_features):
path, dist = dtw_path(np.array(seq1_features), np.array(seq2_features))
similarities_per_path = []
for i in range(len(path)):
cosine_sim = self.cosine_score(
torch.Tensor(seq1_features[path[i][0]]),
torch.Tensor(seq2_features[path[i][1]])).numpy()
similarities_per_path.append(cosine_sim)
total_path_similarity = sum(similarities_per_path) / len(path)
return total_path_similarity
def true_dilate(target, pred, alpha): # target, pred [seq_length]
N_output = target.shape[0]
loss_dtw = dtw(target, pred)
path, sim = dtw_path(target, pred)
Dist = 0
for ii, jj in path:
Dist += (ii - jj) * (ii - jj)
loss_tdi = Dist / (N_output * N_output)
loss_dilate = alpha * loss_dtw + (1 - alpha) * loss_tdi
return loss_dtw, loss_tdi, loss_dilate
def _petitjean_assignment(X, barycenter, metric_params=None):
if metric_params is None:
metric_params = {}
n = X.shape[0]
barycenter_size = barycenter.shape[0]
assign = ([[] for _ in range(barycenter_size)],
[[] for _ in range(barycenter_size)])
for i in range(n):
path, _ = dtw_path(X[i], barycenter, **metric_params)
for pair in path:
assign[0][pair[1]].append(i)
assign[1][pair[1]].append(pair[0])
return assign
def _applying_dtw_to_clusters(
self,
adata1,
adata2,
genename,
min_percentile_outlier=0,
max_percentile_outlier=100
):
expression1 = adata1.raw.to_adata()[:, genename].X
expression2 = adata2.raw.to_adata()[:, genename].X
if not isinstance(expression1, np.ndarray):
expression1 = expression1.toarray()
if not isinstance(expression2, np.ndarray):
expression2 = expression2.toarray()
# when all or most of the gene expressions are 0, np.percentile causes error
# catching error by try and except, but it needs to be fixed
# excluding cells that have too low or too high gene expression
try:
min_outlier, max_outlier = np.percentile(
expression1, q=[min_percentile_outlier, max_percentile_outlier]
)
gene_expression1 = expression1[(expression1 <= max_outlier) & (
expression1 >= min_outlier)]
ordered_cells1 = np.array(adata1[(
expression1 <= max_outlier) & (expression1 >= min_outlier)].obs_names.to_list())
except IndexError as e:
gene_expression1 = expression1
ordered_cells1 = np.array(adata1.obs_names.to_list())
# excluding cells that have too low or too high gene expression
try:
min_outlier, max_outlier = np.percentile(
expression2, q=[min_percentile_outlier, max_percentile_outlier]
)
gene_expression2 = expression2[(
expression2 <= max_outlier) & (expression2 >= min_outlier)]
ordered_cells2 = np.array(adata2[(
expression2 <= max_outlier) & (expression2 >= min_outlier)].obs_names.to_list())
except IndexError as e:
gene_expression2 = expression2
ordered_cells2 = np.array(adata2.obs_names.to_list())
path, dist = dtw_path(
gene_expression1, gene_expression2)
return ordered_cells1, ordered_cells2, path, dist
def ntu_similarity(net,
config,
x,
f,
y,
window_size=16,
slide=2,
visibility=False,
dist='cosine',
use_all_joints_on_each_bp=False):
if use_all_joints_on_each_bp:
body_parts = config.body_parts_entire_body if not visibility else config.body_parts_invis_entire_body
else:
body_parts = config.body_parts if not visibility else config.body_parts_invis
bps = list(body_parts._fields)
cand_framesPerBP, metric, querys = ntu_similarity_prepart(
net, x, f, y, body_parts, bps, dist, slide, window_size)
finals = [[] for _ in range(4)]
for query_idx, query in enumerate(querys):
for bp in range(len(bps)):
path, _ = dtw_path(query[bp], cand_framesPerBP[bp])
similarities = []
for i in range(len(path)):
if dist == "cosine":
metric_sim = metric(
torch.Tensor(query[bp][path[i][0]]),
torch.Tensor(
cand_framesPerBP[bp][path[i][1]])).numpy()
else:
metric_sim = metric(
torch.Tensor(query[bp][path[i][0]]).unsqueeze(0),
torch.Tensor(cand_framesPerBP[bp][
path[i][1]]).unsqueeze(0)).numpy()[0]
similarities.append(metric_sim)
final_sim = np.mean(similarities)
finals[query_idx].append(final_sim)
# origin, flipped, arm_flipped, leg_flipped
final_sim_calculated = [np.mean(final_v) for final_v in finals
] # different paths don't `np.mean(_, axis=1)`
if dist == 'cosine':
return tuple(final_sim_calculated)
else:
return tuple([1 / item for item in final_sim_calculated])
def plot_DWT_matrix(comb_df: pd.DataFrame, targ:str, sample_idx : int):
frag_df = comb_df[comb_df['ID'] == sample_idx]
seq1 = frag_df[targ].values.reshape(1,-1,1)
seq2 = frag_df['OEP'].values.reshape(1,-1,1)
path, sim = metrics.dtw_path(seq1[0], seq2[0])
sz = frag_df.shape[0]
plt.figure(1, figsize=(7,6))
left, bottom = 0.01, 0.1
w_ts = h_ts = 0.2
left_h = left + w_ts + 0.02
width = height = 0.65
bottom_h = bottom + height + 0.02
rect_s_y = [left, bottom, w_ts, height]
rect_gram = [left_h, bottom, width, height]
rect_s_x = [left_h, bottom_h, width - 0.13, h_ts]
ax_gram = plt.axes(rect_gram)
ax_s_x = plt.axes(rect_s_x)
ax_s_y = plt.axes(rect_s_y)
mat = cdist(seq1[0], seq2[0])
sns.heatmap(mat, cmap = 'jet', ax = ax_gram)
ax_gram.axis("off")
ax_gram.invert_yaxis()
ax_gram.autoscale(False)
ax_gram.plot([j for (i, j) in path], [i for (i, j) in path], "w--",
linewidth=2.)
ax_s_x.plot(np.arange(sz), seq2[0], color = '#510385', linewidth=1.8, label = 'OEP')
ax_s_x.set_xlim((0, sz - 1))
ax_s_x.axis("off")
ax_s_x.set(title = f"Mẫu số {sample_idx}")
ax_s_y.plot(-seq1[0], np.arange(sz), color = '#850318', linewidth=1.8, label = targ)
ax_s_y.set_ylim((0, sz - 1))
ax_s_y.axis("off")
plt.legend()
plt.show()
def get_dtw_distance_mse(query, reference):
'''
Parameters:
query : 1D array
reference : 1D array
'''
if len(query) > len(reference):
query, reference = reference, query
try:
path, distance = tsm.dtw_path(query,
reference,
global_constraint='sakoe_chiba')
warped_reference = np.zeros(len(query))
for pair in path:
warped_reference[pair[0]] = reference[pair[1]]
mse = rms_difference(query, warped_reference)
return distance, mse
except ValueError:
return (1000, 100)
def _mm_assignment(X, barycenter, weights, metric_params=None):
"""Computes item assignement based on DTW alignments and return cost as a
bonus.
Parameters
----------
X : numpy.array of shape (n, sz, d)
Time-series to be averaged
barycenter : numpy.array of shape (barycenter_size, d)
Barycenter as computed at the current step of the algorithm.
weights: array
Weights of each X[i]. Must be the same size as len(X).
metric_params: dict or None (default: None)
DTW constraint parameters to be used.
See :ref:`tslearn.metrics.dtw_path <fun-tslearn.metrics.dtw_path>` for
a list of accepted parameters
If None, no constraint is used for DTW computations.
Returns
-------
list of index pairs
Warping paths
float
Current alignment cost
"""
if metric_params is None:
metric_params = {}
n = X.shape[0]
cost = 0.
list_p_k = []
for i in range(n):
path, dist_i = dtw_path(barycenter, X[i], **metric_params)
cost += dist_i**2 * weights[i]
list_p_k.append(path)
cost /= weights.sum()
return list_p_k, cost
def get_column_wise_dtw_metrics(query, reference):
'''
Calculates dtw distances between columns.
Transposes input arrays (columns become rows),
calculates DTW distance using euclidean norm and MSE between aligned columns.
Parameters:
query : 2D array with shape n * m (n time frames, m features)
reference : 2D array with shape k * m (k time frames, m features)
'''
assert (len(query) == len(reference))
if len(query[0]) > len(reference[0]):
query, reference = reference, query
query = query.T
reference = reference.T
path, distance = tsm.dtw_path(query,
reference,
global_constraint='sakoe_chiba')
warped_reference = np.zeros(query.shape)
for pair in path:
warped_reference[pair[0]] = reference[pair[1]]
mse = np.linalg.norm(query - warped_reference) / len(query)
return distance, mse
def DTW_similarity(comb_df: pd.DataFrame, sampl_df: pd.DataFrame):
sim_df = pd.DataFrame(columns = ['frag','Evt_type','Tx_RIP', 'Th_Flow', 'Abd_RIP'])
for i in range(sampl_df.shape[0]):
evt_type = sampl_df.Resp_lab[i]
frag_df = comb_df[comb_df['ID'] == i][['Tx_RIP', 'Th_Flow', 'Abd_RIP', 'OEP']]
sims = [i, evt_type]
for j in product(frag_df.drop(['OEP'], axis = 1).columns,['OEP']):
targ = j[0]
seq1 = frag_df[targ].values.reshape(1,-1,1)
seq2 = frag_df['OEP'].values.reshape(1,-1,1)
_, sim = metrics.dtw_path(seq1[0],seq2[0])
sims.append(sim)
sim_df.loc[i] = sims
return sim_df
import numpy
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
from tslearn.generators import random_walks
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
from tslearn import metrics
numpy.random.seed(0)
n_ts, sz, d = 2, 100, 1
dataset = random_walks(n_ts=n_ts, sz=sz, d=d)
scaler = TimeSeriesScalerMeanVariance(mu=0., std=1.) # Rescale time series
dataset_scaled = scaler.fit_transform(dataset)
path, sim = metrics.dtw_path(dataset_scaled[0], dataset_scaled[1])
matrix_path = numpy.zeros((sz, sz), dtype=numpy.int)
for i, j in path:
matrix_path[i, j] = 1
plt.figure()
plt.subplot2grid((1, 3), (0, 0), colspan=2)
plt.plot(numpy.arange(sz), dataset_scaled[0, :, 0])
plt.plot(numpy.arange(sz), dataset_scaled[1, :, 0])
plt.subplot(1, 3, 3)
plt.imshow(matrix_path, cmap="gray_r")
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
from tslearn.generators import random_walks
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
from tslearn import metrics
numpy.random.seed(0)
n_ts, sz, d = 2, 100, 1
dataset = random_walks(n_ts=n_ts, sz=sz, d=d, random_state=5)
scaler = TimeSeriesScalerMeanVariance(mu=0., std=1.) # Rescale time series
dataset_scaled = scaler.fit_transform(dataset)
lcss_path, sim_lcss = metrics.lcss_path(dataset_scaled[0, :, 0],
dataset_scaled[1, :40, 0],
eps=1.5)
dtw_path, sim_dtw = metrics.dtw_path(dataset_scaled[0, :, 0],
dataset_scaled[1, :40, 0])
plt.figure(1, figsize=(8, 8))
plt.plot(dataset_scaled[0, :, 0], "b-", label='First time series')
plt.plot(dataset_scaled[1, :40, 0], "g-", label='Second time series')
for positions in lcss_path:
plt.plot([positions[0], positions[1]], [
dataset_scaled[0, positions[0], 0], dataset_scaled[1, positions[1], 0]
],
color='orange')
plt.legend()
plt.title("Time series matching with LCSS")
plt.figure(2, figsize=(8, 8))
-0.600, -0.606, -0.607, -0.604, -0.598, -0.589, -0.577, -0.558, -0.531,
-0.496, -0.454, -0.410, -0.364, -0.318, -0.276, -0.237, -0.203, -0.176,
-0.157, -0.145, -0.142, -0.145, -0.154, -0.168, -0.185, -0.206, -0.230,
-0.256, -0.286, -0.318, -0.351, -0.383, -0.414, -0.442, -0.467, -0.489,
-0.508, -0.523, -0.535, -0.544, -0.552, -0.557, -0.560, -0.560, -0.557,
-0.551, -0.542, -0.531, -0.519, -0.507, -0.494, -0.484, -0.476, -0.469,
-0.463, -0.456, -0.449, -0.442, -0.435, -0.431, -0.429, -0.430, -0.435,
-0.442, -0.452, -0.465, -0.479, -0.493, -0.506, -0.517, -0.526, -0.535,
-0.548, -0.567, -0.592, -0.622, -0.655, -0.690, -0.728, -0.764, -0.795,
-0.815, -0.823, -0.821])
s_y1 = numpy.concatenate((s_x, s_x)).reshape((-1, 1))
s_y2 = numpy.concatenate((s_x, s_x[::-1])).reshape((-1, 1))
sz = s_y1.shape[0]
path, sim = metrics.dtw_path(s_y1, s_y2)
plt.figure(1, figsize=(8, 8))
# definitions for the axes
left, bottom = 0.01, 0.1
w_ts = h_ts = 0.2
left_h = left + w_ts + 0.02
width = height = 0.65
bottom_h = bottom + height + 0.02
rect_s_y = [left, bottom, w_ts, height]
rect_gram = [left_h, bottom, width, height]
rect_s_x = [left_h, bottom_h, width, h_ts]
ax_gram = plt.axes(rect_gram)
def ntu_similarity_global_dtw(net,
config,
x,
f,
y,
min_num=2,
window_size=16,
slide=2,
visibility=False,
dist='cosine',
use_all_joints_on_each_bp=False):
if use_all_joints_on_each_bp:
body_parts = config.body_parts_entire_body if not visibility else config.body_parts_invis_entire_body
else:
body_parts = config.body_parts if not visibility else config.body_parts_invis
bps = list(body_parts._fields)
cand_framesPerBP, metric, querys = ntu_similarity_prepart(
net, x, f, y, body_parts, bps, dist, slide, window_size)
querys_bp_flatten = []
for query in querys:
query_bp_flatten = [
np.concatenate([bp[p_idx] for bp in query])
for p_idx in range(len(query[0]))
]
querys_bp_flatten.append(query_bp_flatten)
c_bp_flatten = [
np.concatenate([bp[p_idx] for bp in cand_framesPerBP])
for p_idx in range(len(cand_framesPerBP[0]))
]
paths = [
dtw_path(np.array(query_flatten), np.array(c_bp_flatten))[0]
for query_flatten in querys_bp_flatten
]
finals = [[] for _ in range(4)]
for idx, (query, path) in enumerate(zip(querys, paths)):
for path_idx in range(len(path)):
sims = []
for bp in range(len(bps)):
if dist == "cosine":
metric_sim = metric(
torch.Tensor(query[bp][path[path_idx][0]]),
torch.Tensor(
cand_framesPerBP[bp][path[path_idx][1]])).numpy()
else:
metric_sim = metric(
torch.Tensor(
query[bp][path[path_idx][0]]).unsqueeze(0),
torch.Tensor(cand_framesPerBP[bp][
path[path_idx][1]]).unsqueeze(0)).numpy()[0]
sims.append(metric_sim)
sims = np.sort(np.array(sims), axis=None)
sims = sims[:min_num]
finals[idx].append(np.mean(sims))
# origin, flipped, arm_flipped, leg_flipped
final_sim_calculated = [np.mean(final_v) for final_v in finals
] # different paths don't `np.mean(_, axis=1)`
if dist == 'cosine':
return tuple(final_sim_calculated)
else:
return tuple([1 / item for item in final_sim_calculated])
normer_tests = my_dtw_tests.plsm[j]['Time_pls'].values.astype('float64')
#create normalization values
normer_tests = dtw_wei(normer_tests,twind,b=5./((max_train-min_train)/2.)**2.,c=1.)
normer_tests = np.outer(normer_tests,np.ones(X_tests.shape[1]))
#include a time normalization factory
X_tests *= normer_tests
y_tests = my_dtw_tests.plsm[j].index.values
#get multi-parameter dtw solution
path, sim = metrics.dtw_path(X_train, X_tests)
#convert path into a numpy array
m = np.array(zip(*path))
fig, nax = plt.subplots(nrows=2,ncols=2,sharex=True,figsize=(12,12))
#x_vals = ['SPEED']+x_vals
for i,ax in enumerate(nax.flatten()):
parm = x_vals[i]
ax.plot(y_train[m[0,:]],my_dtw_train.plsm['Wind'][parm].values[m[0,:]])
ax.plot(y_train[m[0,:]],my_dtw_tests.plsm[j][parm].values[m[1,:]])
ax.set_ylabel(parm)
ax.set_xlabel('Time [UTC]')
fancy_plot(ax)
#ax.plot(my_dtw_train.plsm['Wind'].index.values.astype('float64'),X_train[:,0])
def visualise_test(self, nets, nets_name, batch_size, means_input,
stds_input, means_target, stds_target):
gen_test = iter(self.testloader)
losses_mse = []
losses_dtw = []
losses_tdi = []
for ind, (test_inputs, test_targets) in enumerate(gen_test):
print(ind)
test_inputs = torch.tensor(test_inputs,
dtype=torch.float32).to(self.device)
test_targets = torch.tensor(test_targets,
dtype=torch.float32).to(self.device)
criterion = torch.nn.MSELoss()
print(f"test_inputs tensor shape: {test_inputs.size()}, "
f"test_targets shape: {test_targets.size()}, ")
fig, axs = plt.subplots(1, 3, sharey='col', figsize=(15, 8))
for net_i, net in enumerate(nets):
test_preds = net(test_inputs).to(self.device)
loss_mse = criterion(test_targets, test_preds)
# DTW and TDI
loss_dtw, loss_tdi = 0, 0
for k in range(batch_size):
target_k_cpu = test_targets[k, :, 0:1].view(
-1).detach().cpu().numpy()
output_k_cpu = test_preds[k, :, 0:1].view(
-1).detach().cpu().numpy()
loss_dtw += dtw(target_k_cpu, output_k_cpu)
path, sim = dtw_path(target_k_cpu, output_k_cpu)
Dist = 0
for i, j in path:
Dist += (i - j) * (i - j)
loss_tdi += Dist / (self.N_output * self.N_output)
loss_dtw = loss_dtw / batch_size
loss_tdi = loss_tdi / batch_size
# print statistics
losses_mse.append(loss_mse.item())
losses_dtw.append(loss_dtw)
losses_tdi.append(loss_tdi)
input = test_inputs.detach().cpu().numpy()[0, :, :]
target = test_targets.detach().cpu().numpy()[0, :, :]
preds = test_preds.detach().cpu().numpy()[0, :, :]
print(
f"input np shape: {input.shape}, target np shape: {target.shape}, preds np shape: {preds.shape}"
)
print(
f"means_input: {means_input.shape}, stds_input: {stds_input.shape}"
)
## select target column in input
input = input[:, self.idx_tgt_col]
## Scaling back to original
input = input * stds_input[
ind, :, self.idx_tgt_col] + means_input[ind, :,
self.idx_tgt_col]
target = target * stds_target[
ind, self.idx_tgt_col] + means_target[ind,
self.idx_tgt_col]
preds = preds * stds_target[
ind, self.idx_tgt_col] + means_target[ind,
self.idx_tgt_col]
print(
f"input shape: {input.shape}, target shape: {target.shape}, preds shape: {preds.shape}"
)
print(
f"target plot shape: {np.concatenate([input[-1:], target.ravel()]).shape}"
f"preds plot shape: {np.concatenate([input[-1:], preds.ravel()]).shape}"
)
print(f"net_i: {net_i}")
axs[net_i].plot(range(0, self.N_input),
input,
label='input',
linewidth=1)
axs[net_i].plot(range(self.N_input - 1,
self.N_input + self.N_output),
np.concatenate([input[-1:],
target.ravel()]),
label='target',
linewidth=1)
axs[net_i].plot(range(self.N_input - 1,
self.N_input + self.N_output),
np.concatenate([input[-1:],
preds.ravel()]),
label='prediction',
linewidth=1)
# axs[i].xticks(range(0,40,2))
axs[net_i].legend()
axs[net_i].set_title(
f"{nets_name[net_i]} \n MSE: {round(loss_mse.item(), 3)}, DTW: {round(loss_dtw, 3)}. TDI: {round(loss_tdi, 3)}"
)
# plt.show()
plt.tight_layout()
plt.savefig(f"results/new_test_loader/{ind}.png")
plt.close()
print(' Test mse= ',
np.array(losses_mse).mean(), ' dtw= ',
np.array(losses_dtw).mean(), ' tdi= ',
np.array(losses_tdi).mean())
hashBucket9 = Lsh(seqID, m, 9)
resultlist = []
for i in range(len(hashBucket1)): # 遍历查询序列
value = ""
for j in range(len(hashbucket1)): # 遍历数据库序列
flag1 = (hashBucket1[i] == hashbucket1[j]).all() # 哈希族是否相等
flag2 = (hashBucket2[i] == hashbucket2[j]).all()
flag3 = (hashBucket3[i] == hashbucket3[j]).all()
flag4 = (hashBucket4[i] == hashbucket4[j]).all()
flag5 = (hashBucket5[i] == hashbucket5[j]).all()
flag6 = (hashBucket6[i] == hashbucket6[j]).all()
flag7 = (hashBucket7[i] == hashbucket7[j]).all()
flag8 = (hashBucket8[i] == hashbucket8[j]).all()
flag9 = (hashBucket9[i] == hashbucket9[j]).all()
if flag1 or flag2 or flag3 or flag4 or flag5 or flag6 or flag7 or flag8 or flag9: #只要有哈希族匹配就计算dtw
optimal_path, dtw_score = dtw_path(query[i], database[j])
if dtw_score <= threshold:
value += str(j) + " "
resultlist.append(value)
time_end = time.time()
time_avg = (time_end - time_start) / len(query)
print('LSH_DTW: ', time_avg)
time_start = time.time()
resultlist_true = []
for i in range(len(query)):
value = ""
for j in range(len(database)):
optimal_path, dtw_score = dtw_path(query[i], database[j])
if dtw_score <= threshold:
value += str(j) + " "
def evaluate(boardSeq, comment, bert_emb, intvals, cnn1, w_hidden2hidden,
boardenc2skill1, boardenc2skill2, comment2skill, comment2skill2,
decoderBoard1, decoderBoard2, w_hidden2board, decoderComment):
#
boardSeq = boardSeq.permute(1, 0, 2) #batch, 500, 512-> 500, batch, 512
boardSeq = boardSeq.to(dtype=torch.float32)
seq_len = boardSeq.size()[0]
batch_size = boardSeq.size()[1]
board_seq_encoded_enlarged = torch.zeros(seq_len, batch_size, d_model)
for i in range(seq_len):
board_seq_encoded_enlarged[i] = w_hidden2hidden(boardSeq[i])
board_seq_encoded_enlarged = board_seq_encoded_enlarged.permute(
1, 0, 2) #(input_len, bs, 768)-> (bs, input_len, 768)
skills = boardenc2skill1(board_seq_encoded_enlarged)
skills2 = boardenc2skill2(skills)
skill_len = skills.size()[1]
board_per_skill = 25 #max(1, int(10/skill_len))
#################Comment Encoding ##############################
max_length = comment.size()[1]
comment_skill = comment2skill(
bert_emb.view(batch_size, max_length,
d_model)) # expected n_skills * bs * 768
comment_skill2 = comment2skill2(comment_skill)
comm_skill_len = comment_skill.size()[1]
###################### SKILLS GENERATION 8#######################
decoder_input = torch.ones(1, batch_size, 768) # <SOS_index>
decoder_input_c = torch.ones(1, batch_size, 768) # <SOS_index>
skills2 = skills2.to(dtype=torch.float32)
comment_skill2 = comment_skill2.to(dtype=torch.float32)
decoder_input = decoder_input.to(dtype=torch.float32)
decoder_input_c = decoder_input_c.to(dtype=torch.float32)
count = 0
use_teacher_forcing = True if random.random() < t_ratio else False
generated_skills = torch.ones(16, batch_size, 768)
generated_skills_c = torch.ones(16, batch_size, 768)
for j in range(skills2.size()[1]):
decoder_hidden = skills2[:, j, :].view(
1, batch_size, d_model).to(dtype=torch.float32).repeat(1, 1, 1)
decoder_hidden_c = comment_skill2[:, j, :].view(
1, batch_size, d_model).to(dtype=torch.float32).repeat(1, 1, 1)
for i in range(4):
output, decoder_hidden = decoderBoard1(decoder_input,
decoder_hidden)
output_c, decoder_hidden_c = decoderBoard1(decoder_input_c,
decoder_hidden_c)
decoder_input = output
decoder_input_c = output_c
generated_skills[count] = output.view(batch_size, 768)
generated_skills_c[count] = output_c.view(batch_size, 768)
count = count + 1
generated_skills = generated_skills.permute(1, 0, 2)
generated_skills_c = generated_skills_c.permute(1, 0, 2)
#######################################################BOARD GENERATION FROM 8 ############################\
#decoder_input = torch.ones(1, bs, 84) # <SOS_index>
decoder_inputB = boardSeq.clone()[0, :, :].view(
1, batch_size, 512) #game start positions as start token
count = 0
use_teacher_forcing = True if random.random() < t_ratio else False
generated_boards = torch.ones(board_per_skill * 16, batch_size, 512)
for j in range(generated_skills.size()[1]):
decoder_hiddenB = generated_skills.clone()[:, j, :].view(
1, batch_size, d_model).to(dtype=torch.float32).repeat(1, 1, 1)
for i in range(board_per_skill):
outputB, decoder_hiddenB = decoderBoard2(decoder_inputB,
decoder_hiddenB)
if use_teacher_forcing and j * board_per_skill + i + 1 < seq_len:
decoder_inputB = boardSeq.clone()[i + 1 + j *
board_per_skill, :, :].view(
1, batch_size, 512)
else:
decoder_inputB = outputB
#print(output.shape)
generated_boards[count] = outputB.view(batch_size, 512)
count = count + 1
#######################################################BOARD GENERATION FROM 2 ############################\
#decoder_input = torch.ones(1, bs, 84) # <SOS_index>
decoder_inputB1 = boardSeq.clone()[0, :, :].view(
1, batch_size, 512) #game start positions as start token
count = 0
use_teacher_forcing = True if random.random() < t_ratio else False
generated_boards1 = torch.ones(board_per_skill * 4, batch_size, 512)
for j in range(skills2.size()[1]):
decoder_hiddenB1 = skills2.clone()[:, j, :].view(
1, batch_size, d_model).to(dtype=torch.float32).repeat(1, 1, 1)
for i in range(board_per_skill):
outputB1, decoder_hiddenB1 = decoderBoard2(decoder_inputB1,
decoder_hiddenB1)
if use_teacher_forcing and j * board_per_skill + i + 1 < seq_len:
decoder_inputB1 = boardSeq.clone()[i + 1 + j *
board_per_skill, :, :].view(
1, batch_size, 512)
else:
decoder_inputB1 = outputB1
#print(output.shape)
generated_boards1[count] = outputB1.view(batch_size, 512)
count = count + 1
criterion = SoftDTW(gamma=1.0, normalize=True)
if torch.cuda.device_count() > 1:
criterion = CriterionParallel(criterion)
generated_boards = generated_boards.permute(1, 0, 2)
generated_boards1 = generated_boards1.permute(1, 0, 2)
tar = boardSeq.permute(1, 0, 2).clone()[:, 1:, :]
generated_boards_cpu = generated_boards.detach().cpu().numpy()
generated_boards1_cpu = generated_boards1.detach().cpu().numpy()
tar_cpu = tar.detach().cpu().numpy()
intvals = intvals.detach().cpu().numpy()
intvals = intvals[0]
prefix = np.zeros(len(intvals))
# print(intvals)
tot_time = sum(intvals)
prefix[0] = intvals[0]
for i in range(1, len(intvals)):
prefix[i] = prefix[i - 1] + intvals[i]
multi = tot_time / 200.0
pred = []
prev = 0
path, dist = dtw_path(generated_boards_cpu[0], tar_cpu[0])
for prs in (path):
if (prs[0] + 1) % board_per_skill == 0 and prs[0] != 0:
pred.append((prs[1] - prev) * multi)
prev = prs[1]
# print(path)
pred1 = []
prev = 0
path1, dist1 = dtw_path(generated_boards1_cpu[0], tar_cpu[0])
for prs in (path1):
if (prs[0] + 1) % board_per_skill == 0 and prs[0] != 0:
pred1.append((prs[1] - prev) * multi)
prev = prs[1]
for i in range(1, len(pred)):
pred[i] += pred[i - 1]
for i in range(1, len(pred1)):
pred1[i] += pred1[i - 1]
pred = np.array(pred)
pred1 = np.array(pred1)
prefix = np.unique(prefix)
pred = np.unique(pred)
pred1 = np.unique(pred1)
# print(prefix)
# print(pred)
# print(pred1)
paths1, dists1 = dtw_path(prefix, pred)
paths2, dists2 = dtw_path(prefix, pred1)
# print(paths1)
# print(paths2)
curr = 0
tot_den = 0
tot_num = 0
prev_d = -1
prev_n = -1
track = 0
for nums in paths1:
fi = nums[0]
se = nums[1]
x2 = prefix[fi]
y2 = pred[se]
if fi == 0:
x1 = 0.0
else:
x1 = prefix[fi - 1]
if se == 0:
y1 = 0.0
else:
y1 = pred[se - 1]
tot_num += max(0.0, min(x2, y2) - max(x1, y1))
if prev_d != x2 and prev_n != x1:
tot_den += max(0.0, max(x2, y2) - min(x1, y1))
prev_d = x2
prev_n = x1
curr = 0
tot_den1 = 0
tot_num1 = 0
prev_d = -1
prev_n = -1
track = 0
for nums in paths2:
fi = nums[0]
se = nums[1]
x2 = prefix[fi]
y2 = pred1[se]
if fi == 0:
x1 = 0.0
else:
x1 = prefix[fi - 1]
if se == 0:
y1 = 0.0
else:
y1 = pred1[se - 1]
tot_num1 += max(0.0, min(x2, y2) - max(x1, y1))
if prev_d != x2 and prev_n != x1:
tot_den1 += max(0.0, max(x2, y2) - min(x1, y1))
prev_d = x2
prev_n = x1
# print(tot_den1)
# print(tot_num1)
# print("Alignment Score 1:",dists1)
# print("Alignment Score 2:",dists2)
# print("IoU Score 1:", tot_num/tot_den)
# print("IoU Score 2:", tot_num1/tot_den1)
return dists1, dists2, tot_num, tot_den, tot_num1, tot_den1
def evaluate(boardSeq, comment, bert_emb, intvals, cnn1, w_hidden2hidden,
boardenc2skill, comment2skill, decoderBoard, w_hidden2board,
decoderComment):
#
boardSeq = torch.transpose(boardSeq, 0,
1) #batch, 500, 512-> 500, batch, 512
# boardSeq = boardSeq.to(dtype = torch.float64)
seq_len = boardSeq.size()[0]
batch_size = boardSeq.size()[1]
board_seq_encoded_enlarged = torch.zeros(seq_len, batch_size, d_model)
for i in range(seq_len):
board_seq_encoded_enlarged[i] = w_hidden2hidden(boardSeq[i])
board_seq_encoded_enlarged = torch.transpose(
board_seq_encoded_enlarged, 0,
1) #(input_len, bs, 768)-> (bs, input_len, 768)
skills = boardenc2skill(board_seq_encoded_enlarged)
skill_len = skills.size()[1]
# print('skill', skills.shape)
# print(skill_len)
board_per_skill = 100 #max(1, int(10/skill_len))
# print(skills.shape)
#################Comment Encoding ##############################
#comment = torch.randn(batch_size, 12)
max_length = comment.size()[1]
#######################################################BOARD GENERATION############################\
#decoder_input = torch.ones(1, bs, 84) # <SOS_index>
decoder_inputB = boardSeq.clone()[0, :, :].view(
1, batch_size, 512) #game start positions as start token
count = 0
use_teacher_forcing = True if random.random() < t_ratio else False
generated_boards = torch.ones(board_per_skill * skill_len, batch_size, 512)
for j in range(skills.size()[1]):
decoder_hiddenB = skills.clone()[:, j, :].view(
1, batch_size, d_model).to(dtype=torch.float32).repeat(1, 1, 1)
# if use_cuda:
# decoder_hiddenB = decoder_hiddenB.to(device)
for i in range(board_per_skill):
outputB, decoder_hiddenB = decoderBoard(decoder_inputB,
decoder_hiddenB)
decoder_inputB = outputB
#print(output.shape)
generated_boards[count] = outputB.view(batch_size, 512)
count = count + 1
generated_boards = torch.transpose(generated_boards, 0, 1)
boardSeq = torch.transpose(boardSeq, 0,
1) #.view(batch_size, seq_len, 8*8*15)
tar = boardSeq.clone()[:, 1:, :]
# loss_dtw = criterion(generated_boards, tar)
generated_boards_cpu = generated_boards.detach().cpu().numpy()
tar_cpu = tar.detach().cpu().numpy()
intvals = intvals.detach().cpu().numpy()
intvals = intvals[0]
prefix = np.zeros(len(intvals))
# print(intvals)
tot_time = sum(intvals)
prefix[0] = intvals[0]
for i in range(1, len(intvals)):
prefix[i] = prefix[i - 1] + intvals[i]
multi = tot_time / 200.0
pred = []
prev = 0
path, dist = dtw_path(generated_boards_cpu[0], tar_cpu[0])
for prs in (path):
if (prs[0] + 1) % 25 == 0 and prs[0] != 0:
pred.append((prs[1] - prev) * multi)
prev = prs[1]
pred = np.array(pred)
prefix = np.unique(prefix)
pred = np.unique(pred)
paths1, dists1 = dtw_path(prefix, pred)
curr = 0
tot_den = 0
tot_num = 0
prev_d = -1
prev_n = -1
track = 0
for nums in paths1:
fi = nums[0]
se = nums[1]
x2 = prefix[fi]
y2 = pred[se]
if fi == 0:
x1 = 0.0
else:
x1 = prefix[fi - 1]
if se == 0:
y1 = 0.0
else:
y1 = pred[se - 1]
tot_num += max(0.0, min(x2, y2) - max(x1, y1))
if prev_d != x2 and prev_n != x1:
tot_den += max(0.0, max(x2, y2) - min(x1, y1))
prev_d = x2
prev_n = x1
return dists1, dists1, tot_num, tot_den, tot_num, tot_den
IAP_path_new = np.zeros((t_rescaled.shape[-1], 3))
IAP_path_new[:, 0] = PchipInterpolator(t, path[:, 0])(t_rescaled)
IAP_path_new[:, 1] = PchipInterpolator(t, path[:, 1])(t_rescaled)
IAP_path_new[:, 2] = PchipInterpolator(t, path[:, 2])(t_rescaled)
IAP_path_rescaled.append(IAP_path_new.tolist())
# 2-3) vector_deviations
# vector_path for each vector_traj
labels = []
for i, traj in enumerate(vector_trajs_rescaled):
traj = np.array(traj)
dtw_dists = []
for j, path in enumerate(vector_paths_rescaled):
path = np.array(path)
dtw_path, dtw_dist = metrics.dtw_path(traj[:, 1:4], path[:, 0:3])
dtw_dists.append(dtw_dist)
labels.append(np.argmin(dtw_dists))
# deviations
vector_trajs_updated, vector_trajs_dist, vector_deviations = [], [], []
for j, path in enumerate(vector_paths_rescaled):
path = np.array(path)
for i, traj in enumerate(vector_trajs_rescaled):
if labels[i] == j:
vector_trajs_updated.append(traj.tolist())
deviations = traj[:, 1:4] - path[:, 0:3]
vector_deviations.append(deviations.tolist())
traj_dist = np.sum([
def compute_DTW_on_iter(dataset,
iter,
numOfDrones,
drones,
per_series=True):
print('iter: ', iter)
dtw_results_dict = {
'iter': [],
'update_step': [],
'drone': [],
'comparison_drone': [],
'DTW_dist': []
}
# print('iter: ',iter )
dataset_iter = dataset.loc[dataset['iter'] == iter, :]
# cut the df by current update step-win size
update_step_ls = dataset_iter.update_step.unique()
# num of features (all columns - no sensor columns and label
num_of_features = dataset_iter.shape[1] - len(no_sensors_cols +
['label'])
# iterate over time steps
for update_step in update_step_ls:
current_seq = dataset_iter.loc[
(dataset_iter['update_step'] <= update_step)
& (dataset_iter['update_step'] > (update_step - win_size))]
# iterte over drones
for droneIidx in range(numOfDrones):
currentDrone = drones[droneIidx]
currentDroneDf = current_seq.loc[current_seq.drone ==
currentDrone, :]
# drop irrelevant cols and convert to numpy
currentDroneNp = currentDroneDf.drop(
no_sensors_cols + ['label'], 1).to_numpy()
if use_scaler:
scaled_currentDroneNp = StandardScaler().fit_transform(
currentDroneNp)
else:
scaled_currentDroneNp = currentDroneNp
for droneJidx in range(numOfDrones):
# dont compare drone to itself
if (droneIidx >= droneJidx): continue
# print(droneIidx, droneJidx)
otherDrone = drones[droneJidx]
otherDroneDf = current_seq.loc[current_seq.drone ==
otherDrone, :]
otherDroneNp = otherDroneDf.drop(
no_sensors_cols + ['label'], 1).to_numpy()
if use_scaler:
scaled_otherDroneNp = StandardScaler().fit_transform(
otherDroneNp)
else:
scaled_otherDroneNp = otherDroneNp
"""compute DTW"""
if per_series: # compute between each pair of series, return list
dist = [
dtw_path(scaled_currentDroneNp[:, i],
scaled_otherDroneNp[:, i])[1]
for i in range(num_of_features)
]
dist = np.array(dist)
else:
# path, dist = dtw_path(scaled_currentDroneNp, scaled_otherDroneNp)
path = ''
dist = dtw(scaled_currentDroneNp,
scaled_otherDroneNp,
window_type="sakoechiba",
window_args={
'window_size': 60
}).distance
# print('Iter {} updatestep {} DroneI {} DroneJ {} DTW {}'.format(iter,update_step,currentDrone, otherDrone, dist))
# save results of current drone
dtw_results_dict['iter'].append(iter)
dtw_results_dict['update_step'].append(update_step)
dtw_results_dict['drone'].append(currentDrone)
dtw_results_dict['comparison_drone'].append(otherDrone)
dtw_results_dict['DTW_dist'].append(
dist) # ; dtw_results_dict['DTW_path'].append(path)
# save results of other drone
dtw_results_dict['iter'].append(iter)
dtw_results_dict['update_step'].append(update_step)
dtw_results_dict['drone'].append(otherDrone)
dtw_results_dict['comparison_drone'].append(currentDrone)
dtw_results_dict['DTW_dist'].append(
dist) # ; dtw_results_dict['DTW_path'].append(path)
print('iter done: ', iter)
return dtw_results_dict
def make_one_folium(sz, a=1., noise=.1, resample_fun=None):
theta = np.linspace(0, 1, sz)
if resample_fun is not None:
theta = resample_fun(theta)
theta -= .5
theta *= .9 * np.pi
theta = theta.reshape((-1, 1))
r = a / 2 * (4 * np.cos(theta) - 1. / np.cos(theta))
x = r * np.cos(theta) + np.random.rand(sz, 1) * noise
y = r * np.sin(theta) + np.random.rand(sz, 1) * noise
return np.array(np.hstack((x, y)))
trajectory = make_one_folium(sz=30).dot(get_rot2d(np.pi + np.pi / 3))
rotated_trajectory = trajectory.dot(get_rot2d(np.pi / 4)) + np.array([0., 3.])
path_dtw, _ = dtw_path(trajectory, rotated_trajectory)
path_ctw, cca, _ = ctw_path(trajectory, rotated_trajectory,
max_iter=100, n_components=2)
plt.figure(figsize=(8, 4))
ax = plt.subplot(1, 2, 1)
for (i, j) in path_dtw:
ax.plot([trajectory[i, 0], rotated_trajectory[j, 0]],
[trajectory[i, 1], rotated_trajectory[j, 1]],
color='g' if i == j else 'r', alpha=.5)
plot_trajectory(trajectory, ax)
plot_trajectory(rotated_trajectory, ax)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title("DTW")
DTW_matrix[i, j] = cost + last_min
print("DTW Distance Matrix")
return DTW_matrix
def plot_matrix(cdist_matrix, path):
plt.plot([j for (i, j) in path], [i for (i, j) in path],
"w-",
linewidth=3.)
plt.imshow(cdist_matrix)
plt.show()
#ts1 = [1,3,1,2,1,1,1]
#ts2 = [1,1,3,1,2,1,1]
ts1 = [1, 3, 4, 9, 8, 2, 1, 5, 7, 3]
ts2 = [1, 6, 2, 3, 0, 9, 4, 3, 6, 3]
a = compute_DTW(ts1, ts2)
path, sim = metrics.dtw_path(ts1, ts2)
#arr = metrics.cdist_dtw(ts1, ts2)
print("Using tslearn library")
print(f"Path : {path}")
print(f"Similarity Score: {sim}")
#print(np.shape(arr))
#cdist_matrix = cdist([ts1], [ts2])
#print(cdist_matrix)
plot_matrix(a, path)
