def trajectoryAlldistance(i, trjs):
trs_matrix = tdist.cdist(trjs, [trjs[i]], metric="hausdorff")
cPickle.dump(
trs_matrix,
open(
'./distance_compution/hausdorff_distance/hausdorff_distance_' +
str(i), 'w'))
trs_matrix = tdist.cdist(trjs, [trjs[i]], metric="lcss", eps=200)
cPickle.dump(
trs_matrix,
open('./distance_compution/LCSS_distance/LCSS_distance_' + str(i),
'w'))
#
trs_matrix = tdist.cdist(trjs, [trjs[i]], metric="edr", eps=200)
cPickle.dump(
trs_matrix,
open('./distance_compution/EDR_distance/EDR_distance_' + str(i), 'w'))
#
trs_matrix = tdist.cdist(trjs, [trjs[i]], metric="dtw")
cPickle.dump(
trs_matrix,
open('./distance_compution/DTW_distance/DTW_distance_' + str(i), 'w'))
print 'complete: ' + str(i)
def trajectory_distance_batch(i,
batch_trjs,
trjs,
metric_type="hausdorff",
data_name='porto'):
if metric_type == 'lcss' or metric_type == 'edr':
trs_matrix = tdist.cdist(batch_trjs,
trjs,
metric=metric_type,
eps=0.003)
# elif metric_type=='erp':
# trs_matrix = tdist.cdist(batch_trjs, trjs, metric=metric_type, eps=0.003)
else:
trs_matrix = tdist.cdist(batch_trjs, trjs, metric=metric_type)
cPickle.dump(
trs_matrix,
open(
'./features/' + data_name + '_' + metric_type + '_distance_' +
str(i), 'w'))
print 'complete: ' + str(i)
def trajectoryAlldistance(i,trjs):
trs_matrix = tdist.cdist(trjs, [trjs[i]],metric="hausdorff")
with open('./distance_compution/hausdorff_distance/hausdorff_distance_' + str(i), 'wb') as f:
pickle.dump(trs_matrix, f, pickle.HIGHEST_PROTOCOL)
trs_matrix = tdist.cdist(trjs, [trjs[i]],metric="lcss",eps=200)
with open('./distance_compution/LCSS_distance/LCSS_distance_' + str(i), 'wb') as f:
pickle.dump(trs_matrix, f, pickle.HIGHEST_PROTOCOL)
trs_matrix = tdist.cdist(trjs, [trjs[i]],metric="edr",eps=200)
with open('./distance_compution/EDR_distance/EDR_distance_' + str(i), 'wb') as f:
pickle.dump(trs_matrix, f, pickle.HIGHEST_PROTOCOL)
trs_matrix = tdist.cdist(trjs, [trjs[i]],metric="dtw")
with open('./distance_compution/DTW_distance/DTW_distance_'+str(i), 'wb') as f:
pickle.dump(trs_matrix, f , pickle.HIGHEST_PROTOCOL)
print(('complete: '+str(i)))
import numpy as np
import traj_dist.distance as tdist
import pickle
traj_list = pickle.load(open("/Users/bguillouet/These/trajectory_distance/data/benchmark_trajectories.pkl", "rb"))[:10]
traj_A = traj_list[0]
traj_B = traj_list[1]
# Simple distance
dist = tdist.sspd(traj_A, traj_B)
print(dist)
# Pairwise distance
pdist = tdist.pdist(traj_list, metric="sspd")
print(pdist)
# Distance between two list of trajectories
cdist = tdist.cdist(traj_list, traj_list, metric="sspd")
print(cdist)
# read_name='traffic_set_idx_06260152.txt'
# read_name='traffic_set_idx_06260227.txt'
# read_name='traffic_set_idx_06280622.txt'
# read_name='traffic_set_idx_06290219.txt'
train_set, train_labels, test_set, truth_labels = read_traffic_dataset(
resample=True, randomize=True)
train_num = len(train_labels)
test_num = len(truth_labels)
dm_eucl = np.zeros((train_num, test_num))
for i in range(train_num):
print(i)
for j in range(test_num):
dm_eucl[i, j] = eucdist(train_set[i], test_set[j])
dm_dtw = tdist.cdist(train_set, test_set, metric='dtw')
dm_sspd = tdist.cdist(train_set, test_set, metric='sspd')
dm_lcss = tdist.cdist(train_set, test_set, metric='lcss', eps=0.05)
dm_edr = tdist.cdist(train_set, test_set, metric='edr', eps=0.05)
dm_erp = tdist.cdist(train_set,
test_set,
metric='erp',
g=np.zeros(2, dtype=float))
dm_fre = tdist.cdist(train_set, test_set, metric='frechet')
dm_hau = tdist.cdist(train_set, test_set, metric='hausdorff')
np.savez_compressed('traffic_dm_eucl', dm_eucl)
np.savez_compressed('traffic_dm_dtw', dm_dtw)
np.savez_compressed('traffic_dm_sspd', dm_sspd)
np.savez_compressed('traffic_dm_lcss', dm_lcss)
np.savez_compressed('traffic_dm_edr', dm_edr)
def trajectoryAlldistance(i,trjs):
trs_matrix = tdist.cdist(trjs, [trjs[i]],metric="edr",eps=0.05)
#print(trs_matrix)
cPickle.dump(trs_matrix, open('./distance_compution/EDR_distance/EDR_distance_' + str(i), 'w'))
print 'complete: '+str(i)
import numpy as np
# Three 2-D Trajectory
traj_A = np.array([[-122.39534, 37.77678],[-122.3992 , 37.77631],[-122.40235, 37.77594],[-122.40553, 37.77848],
[-122.40801, 37.78043],[-122.40837, 37.78066],[-122.41103, 37.78463],[-122.41207, 37.78954],
[-122.41252, 37.79232],[-122.41316, 37.7951 ],[-122.41392, 37.7989 ],[-122.41435, 37.80129],
[-122.41434, 37.80129]])
traj_B = np.array([[-122.39472, 37.77672],[-122.3946 , 37.77679],[-122.39314, 37.77846],[-122.39566, 37.78113],
[-122.39978, 37.78438],[-122.40301, 37.78708],[-122.4048 , 37.78666],[-122.40584, 37.78564],
[-122.40826, 37.78385],[-122.41061, 37.78321],[-122.41252, 37.78299]])
traj_C = np.array([[-122.39542, 37.77665],[-122.3988 , 37.77417],[-122.41042, 37.76944],[-122.41459, 37.77016],
[-122.41462, 37.77013]])
traj_list = [traj_A, traj_B, traj_C]
import traj_dist.distance as tdist
# Simple distance
dist = tdist.sspd(traj_A,traj_B)
print(dist)
# Pairwise distance
pdist = tdist.pdist(traj_list,metric="sspd")
print(pdist)
# Distance between two list of trajectories
cdist = tdist.cdist(traj_list, traj_list,metric="sspd")
print(cdist)