def test_post_collected_tweets(mock_get, test_status):
"""Verify post_collected_tweets method returns None
Notes:
* Mock PostUpdate without making real call to Twitter API
"""
expected_num_of_ids = 4
status_id_file = Path(__file__).parent.joinpath(
"../conf/list_of_status_ids_replied_to.txt"
)
list_of_status_ids_replied_to = list(file_reader(status_id_file))
assert len(list_of_status_ids_replied_to) == expected_num_of_ids, (
f"Expected number ({expected_num_of_ids}) of status ids replied to "
f"did not match actual ({len(list_of_status_ids_replied_to)})"
)
quoted_tweet = test_status("post_reply_response")
mock_get.return_value = quoted_tweet
response = post_collected_tweets(quoted_tweets=[Tweet(quoted_tweet)])
new_list_of_status_ids_replied_to = list(file_reader(status_id_file))
assert (
str(quoted_tweet.in_reply_to_status_id) == new_list_of_status_ids_replied_to[-1]
), "Expected status id to be last item in list"
assert response
# clean up by overwriting file with original list
with status_id_file.open(mode="w") as f:
for line in list_of_status_ids_replied_to:
f.write(line + "\n")
def main():
if len(sys.argv) == 3:
paths = [sys.argv[1], sys.argv[2]]
else:
print('please give point cloud file and vehicle trajectory')
return
plat, plon, palt, pit = np.array(file_reader(paths[0])).T
trajlat, trajlon, trajalt, trajit = np.array(file_reader(paths[1])).T
trajectory = np.array([[trajlat[0], trajlon[0]],
[trajlat[-1], trajlon[-1]]])
traj_direction = trajectory[1] - trajectory[0]
new_road_points = road_surface_detection(plat, plon, palt, pit, trajectory)
scan_line = scan_line_generator(new_road_points, trajectory)
scan_line = boundary_selection(scan_line)
intensity = intensity_selection(scan_line)
refine_list = lane_marking_refinement(intensity, traj_direction)
line_cluster = lane_marking_selection(refine_list, traj_direction)
ls = lane_marking_generation(line_cluster)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(plat, plon, ',')
for l in ls:
ax.plot(l[0], l[1], color='black', linestyle='-')
plt.savefig('result.png')
plt.show()
def main():
# prepare data
print("Split ratio should be a decimal value between 0 and 1")
split = float(
input("Enter split ratio for the training and testing set : "))
print("enter a positive odd integer value for K nearest neighbour : ")
k = int(input("define K for the evaluation : "))
training_set, test_set = util.file_reader(split)
print('Train set: ' + repr(len(training_set)))
print('Test set: ' + repr(len(test_set)))
# generate predictions
predictions = []
for x in range(len(test_set)):
neighbors = get_neighbours(training_set, test_set[x], k)
result = get_response(neighbors)
predictions.append(result)
# print('> predicted=' + repr(result) + ', actual=' + repr(test_set[x][-1]))
accuracy = get_accuracy(test_set, predictions)
print('Accuracy: ' + repr(accuracy) + '%')
def robin_karp(str, substr, d, q):
n = len(str)
m = len(substr)
h = pow(d, m - 1) % q
substr_hash = 0
str_hash = 0
for s in range(m):
substr_hash = (d * substr_hash + ord(substr[s])) % q
str_hash = (d * str_hash + ord(str[s])) % q
for i in range(n - m + 1):
if substr_hash == str_hash:
match = True
for j in range(m):
if substr[j] != str[i + j]:
match = False
break
if match:
return i
if i < n - m:
str_hash = (d * (str_hash - ord(str[i]) * h) + ord(str[i + m])) % q
if str_hash < 0:
str_hash = str_hash + q
return -1
if __name__ == '__main__':
content = file_reader("data.txt")
print(robin_karp(content, 'А табаку-то вчера дал? То-то, брат. Ну, на, Бог с тобой', 256, 101))
accuracy = 0
output_length = len(output)
for i in range(output_length):
k = 0
for j in range(len(output[i])):
if results[i][j] == output[i][j]:
k += 1
if k == 4:
accuracy += 1
print('total no of inputs : ', output_length)
print("Accurately predicted : ", accuracy)
return (accuracy / output_length) * 100
if __name__ == '__main__':
training, testing, classes = util.file_reader()
input_matrix1, output_matrix1 = get_matrices(testing)
input_matrix = np.array([[0.997, 0.9991, 0.9985, 0.9978, 0.9988, 0.9984],
[0.334, 1.194, 1.172, 3.335, 0.981, 0.979],
[1.172, 0.334, 1.194, 0.981, 3.335, 0.979],
[1.194, 1.172, 0.334, 0.981, 0.979, 3.335],
[0.471, 0.650, 0.986, 5.379, 5.379, 0.983],
[0.986, 0.471, 0.650, 0.984, 5.379, 5.379],
[0.471, 0.986, 0.650, 5.379, 0.984, 5.379],
[0.205, 0.205, 1.188, 7.187, 7.855, 0.985],
[1.188, 0.205, 0.205, 0.985, 7.187, 7.855],
[0.205, 1.188, 0.205, 7.187, 0.985, 7.855]])
output_matrix = np.array([[0, 0, 0, 0],
[1, 0, 0, 1],