def assign_label(self):
all_labeled_y, all_labeled_x = read_input('./datas/all_label.p')
all_labeled_imgs = self.encoder.predict(all_labeled_x)
all_unlabeled_x = read_input('./datas/all_unlabel.p')
all_unlabeled_imgs = self.encoder.predict(all_unlabeled_x)
index = -1
for unlabeled_img in tqdm(all_unlabeled_imgs):
index += 1
target_img = all_unlabeled_x[index]
target_img = target_img.reshape((1,) + target_img.shape)
all_distance = [euclidean_distance(unlabeled_img, labeled_img) for labeled_img in all_labeled_imgs]
index_of_max = min(range(len(all_distance)), key = lambda i: all_distance[i])
assigned_label = all_labeled_y[index_of_max]
all_labeled_x = np.concatenate((all_labeled_x, target_img), axis=0)
all_labeled_y = np.concatenate((all_labeled_y, np.array([assigned_label])), axis=0)
# # for debug
# origin_img = all_labeled_x[index_of_max]
# target_img = np.reshape(target_img, (3, 32, 32))
# origin_img = np.reshape(origin_img, (3, 32, 32))
# pyplot.figure(figsize=[4, 4])
# pyplot.subplot(2, 2, 1)
# pyplot.imshow(toimage(origin_img))
# pyplot.subplot(2, 2, 2)
# pyplot.imshow(toimage(target_img))
# pyplot.show()
output_dict = dict({'data': all_labeled_x, 'labels': all_labeled_y})
output_path = 'datas/relabeled_img.p'
import pickle
with open(output_path, 'wb') as handle:
print("Save output at %s " % output_path)
pickle.dump(output_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)
def test_trees_on_the_path(self):
tree_map = read_input("test_input")
self.assertEqual(trees_on_the_path(tree_map, 1, 1), 2)
self.assertEqual(trees_on_the_path(tree_map, 3, 1), 7)
self.assertEqual(trees_on_the_path(tree_map, 5, 1), 3)
self.assertEqual(trees_on_the_path(tree_map, 7, 1), 4)
self.assertEqual(trees_on_the_path(tree_map, 1, 2), 2)
def setUp(self):
"""setUp test.
:return:
"""
super(PizzaTestCase, self).setUp()
try:
unlink(input_file)
except FileNotFoundError:
pass
with open(input_file, 'wb') as file_descriptor:
file_descriptor.write(input_text)
self.pizza = read_input(input_file)
from datetime import datetime
from main import (
decode_seats,
find_my_seat,
read_input,
)
# [TEST1] Verify that highest ID is correctly found from test dataset
input = read_input(path='input_test.txt')
decoded_file = decode_seats(enc=input)
if decoded_file[-1] != 820:
raise Exception(
f'{datetime.now()} - ERROR: Highest value should be 820, but got \'{decoded_file[-1]}\''
)
# [TEST2] Verify that missing id '3' (my seat) is correctly found from test dataset
decoded_sim = [
1,
2,
4,
5,
]
if find_my_seat(seats=decoded_sim) != 3:
raise Exception(f'{datetime.now()} - ERROR: My seat was not found')
print(f'{datetime.now()} - OK: all tests passed!')
import pytest
from main import (Problem1ApplyRules, Problem1SourrandingSeatsFinder,
Problem2ApplyRules, Problem2SourrandingSeatsFinder,
SeatLayout, read_input, solver)
finder_1 = Problem1SourrandingSeatsFinder()
rules_1 = Problem1ApplyRules()
finder_2 = Problem2SourrandingSeatsFinder()
rules_2 = Problem2ApplyRules()
test_inputs = [
(read_input("./input_test_1_1.txt"), finder_1, rules_1, 37),
(read_input("./input_test_1_2.txt"), finder_1, rules_1, 11),
(read_input("./input_test_2_1.txt"), finder_2, rules_2, 5),
(read_input("./input_test_2_2.txt"), finder_2, rules_2, 9),
(read_input("./input_test_1_1.txt"), finder_2, rules_2, 26),
]
@pytest.mark.parametrize("test_input,finder,rules,expected", test_inputs)
def test(test_input, finder, rules, expected):
seat_layout = SeatLayout.load(finder=finder, rules=rules, input_str=test_input)
assert solver(seat_layout) == expected
def _get_tiles_corner_tiles():
tiles = read_input("test_input")
search_matches(tiles)
corner_tiles = search_corners(tiles)
return tiles, corner_tiles
# # for debug
# origin_img = all_labeled_x[index_of_max]
# target_img = np.reshape(target_img, (3, 32, 32))
# origin_img = np.reshape(origin_img, (3, 32, 32))
# pyplot.figure(figsize=[4, 4])
# pyplot.subplot(2, 2, 1)
# pyplot.imshow(toimage(origin_img))
# pyplot.subplot(2, 2, 2)
# pyplot.imshow(toimage(target_img))
# pyplot.show()
output_dict = dict({'data': all_labeled_x, 'labels': all_labeled_y})
output_path = 'datas/relabeled_img.p'
import pickle
with open(output_path, 'wb') as handle:
print("Save output at %s " % output_path)
pickle.dump(output_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)
if __name__ == "__main__":
x = read_input('./datas/all_unlabel.p')
np.random.shuffle(x)
x_train, x_test = x[:-500], x[-500:]
x_train = x_train.astype(np.float32) / 255.
x_test = x_test.astype(np.float32) / 255.
autoencoder = AutoEncoder(x_train, x_test)
autoencoder.start_train()
autoencoder.predict(x_test[:10])
autoencoder.assign_label()
def test_read_input_ok(monkeypatch):
monkeypatch.setattr('sys.stdin', io.StringIO('a\nb\nc\nd\n'))
assert main.read_input() == ['a', 'b', 'c', 'd']
import pytest
from main import problem_1, problem_2, read_input
input_1 = read_input("./input_test_1.txt")
input_2 = read_input("./input_test_2.txt")
testdata_1 = [(input_1, 7 * 5), (input_2, 22 * 10)]
testdata_2 = [(input_1, 8), (input_2, 19208)]
@pytest.mark.parametrize("test_input,expected", testdata_1)
def test_1(test_input, expected):
assert problem_1(test_input) == expected
@pytest.mark.parametrize("test_input,expected", testdata_2)
def test_2(test_input, expected):
assert problem_2(test_input) == expected
def test_read_file(self):
read = read_input('test_input.txt')
self.assertEqual(read, [
'C - 3 - 4', 'M - 2 - 1', 'T - 1 - 2 - 3',
'A - Lara - 1 - 1 - N - AADA'
], "Should be 6")
def setUp(self):
self.rules, self.messages = read_input("test-input")
def setUp(self):
self.layout = read_input("test_input")