def create_np():
names_dict, label_list = points_dict()
for file_tmp in os.listdir("./Data/CoordinatesInput"):
if file_tmp.endswith(".txt"):
str_file = file_tmp.title()
str_file_pre = './Data/CoordinatesInput/%s.txt' % (str_file[:-4])
str_file_np = './Data/npData/%s' % (str_file[:-4])
with open(str_file_pre, 'r') as f:
print(str_file)
all_coordinates = []
for line in f:
line = line.strip('\n')
tmp = ''
coordinates_list = []
for sign in line:
if sign != '[' and sign != ']' and sign != "'" and sign != '"' and sign != ' ':
if sign == ',':
coordinates_list.append(tmp)
tmp = ''
else:
tmp += sign
coordinates_list.append(tmp)
all_coordinates.append(coordinates_list)
if len(all_coordinates) > 0:
data = np.zeros([len(all_coordinates), 6])
for idx, item1 in enumerate(all_coordinates):
for idy, item2 in enumerate(item1):
data[idx][idy] = float(item2)
tmp = data[:, :2]
unique_coordinate = npi.unique(tmp)
label = npi.indices(unique_coordinate, tmp)
max_cluster = np.max(label) + 1
count = np.zeros(max_cluster)
for i in label:
count[i] += 1
temp = count
idc = np.zeros(max_cluster)
for i in range(max_cluster):
idx = np.argmax(temp)
idc[idx] = max_cluster - (i + 1)
temp[idx] = 0
for idx, count_label in enumerate(idc):
if count_label < max_cluster - math.floor(max_cluster * 0.1):
label[np.where(label == idx)] = max_cluster
j = 0
for i in range(max_cluster + 1):
tmp = np.where(label == i)
if len(tmp[0]) > 0:
data[np.where(label == i), 5] = label_list[names_dict[str_file[:-4]]][j]
j += 1
np.save(str_file_np, data)
def main():
unique_trajectories = np.load('./Data/trajectories_uniques.npy')
lst_labels = []
with open('./Data/Labels/labels_uniques_%d.txt' % k1, 'r') as f:
for line in f:
tmp = line.strip('\n')
lst_labels.append(int(tmp))
lst_labels_ls = []
str_file = './Data/clusters_trajectories/labels_%d_%d.txt' % (k1, k2)
with open(str_file, 'r') as f:
for line in f:
tmp = line.strip('\n')
lst_labels_ls.append(int(tmp))
items = []
with open('./Data/cross_validation.txt', 'r') as f:
for line in f:
tmp = line.strip('\n')
items.append(int(tmp))
lst_labels_true = []
for label in lst_labels:
lst_labels_true.append(lst_labels_ls[label])
trajectory_dict = {}
for i, trajectory in enumerate(unique_trajectories):
tmp = []
for stop_area in trajectory:
tmp.append(int(float(stop_area)))
trajectory_dict[repr(tmp)] = lst_labels_true[i]
names_dict, label_list = points_dict()
n = len(names_dict)
results_cv = np.zeros([n, 1])
results = np.load('./Data/Results_RNN/blstm_results.npy')
max_results = np.argmax(results)
parameters = np.load('./Data/Results_RNN/blstm2_parameters_cv%d.npy' % max_results)
for i, file_tmp in enumerate(os.listdir("./Data/Trajectories")):
str_file1 = file_tmp.title()
str_file1 = str_file1[:-4]
with open('./Data/Trajectories/%s.txt' % (str_file1), 'r') as f:
ls_testing = []
trajectories_testing = []
for line in f:
lst = re.split(r'[ ]', line)
lst.pop(-1)
tmp = []
for stop_area in lst:
tmp.append(int(float(stop_area)))
trajectories_testing.append(tmp)
ls_testing.append(trajectory_dict[repr(tmp)])
ls_training= []
trajectories_traininig = []
for file_tmp in os.listdir("./Data/Trajectories"):
str_file2 = file_tmp.title()
str_file2 = str_file2[:-4]
if str_file1 != str_file2:
with open('./Data/Trajectories/%s.txt' % (str_file2), 'r') as f:
for line in f:
lst = re.split(r'[ ]', line)
lst.pop(-1)
tmp = []
for stop_area in lst:
tmp.append(int(float(stop_area)))
trajectories_traininig.append(tmp)
ls_training.append(trajectory_dict[repr(tmp)])
print('run test blstm %s %d' % (str_file1, i))
run_test(trajectories_traininig, trajectories_testing, ls_training, ls_testing, parameters, str_file1)