def test_load_data(self):
data = load_data('example.txt')
self.assertIn('rules', data)
self.assertIn('your_ticket', data)
self.assertIn('nearby_tickets', data)
self.assertIn('class', data['rules'])
self.assertIn('row', data['rules'])
self.assertIn('seat', data['rules'])
self.assertIsNone(assert_array_equal((1, 3),
data['rules']['class'][0]))
self.assertIsNone(assert_array_equal((5, 7),
data['rules']['class'][1]))
self.assertIsNone(assert_array_equal((6, 11), data['rules']['row'][0]))
self.assertIsNone(assert_array_equal((33, 44),
data['rules']['row'][1]))
self.assertIsNone(
assert_array_equal((13, 40), data['rules']['seat'][0]))
self.assertIsNone(
assert_array_equal((45, 50), data['rules']['seat'][1]))
self.assertIsNone(assert_array_equal([7, 1, 14], data['your_ticket']))
self.assertIsNone(
assert_array_equal([7, 3, 47], data['nearby_tickets'][0]))
self.assertIsNone(
assert_array_equal([40, 4, 50], data['nearby_tickets'][1]))
self.assertIsNone(
assert_array_equal([55, 2, 20], data['nearby_tickets'][2]))
self.assertIsNone(
assert_array_equal([38, 6, 12], data['nearby_tickets'][3]))
def test_determine_coumn_rules(self):
data = load_data('example.txt')
valid_tickets, error_rate = validate_tickets(data)
column_rules = determine_column_rules(data, valid_tickets)
self.assertEqual(column_rules[0], 'row')
self.assertEqual(column_rules[1], 'class')
self.assertEqual(column_rules[2], 'seat')
def find_encryption_weakness(input_filename, preamble_size):
fail_number = find_invalid_number(input_filename, preamble_size)
if fail_number is not None:
data = load_data(input_filename)
for i in range(len(data)):
for j in range(i + 1, len(data) + 1):
snip = data[i:j]
if np.sum(snip) == fail_number:
return np.min(snip) + np.max(snip)
dataset = sys.argv[1]
dataset_dir = 'Datasets/{}/'.format(dataset.title())
drop_columns = []
if dataset == 'housing':
target_column = 'SalePrice'
drop_columns = ['Id']
elif dataset == 'grades':
target_column = 'finalgrade'
else:
raise Exception('Unknown dataset')
df = load_data(dataset_dir + 'train.csv', target_column, drop_columns)
train, test = split_train_test(df)
train = TrainData(train, target_column)
test = TestData(test, target_column, train.get_imputation_map(),
train.get_categorical_maps())
with open(dataset_dir + 'config.json', 'r') as f:
gbrt_config = json.load(f)
gbrt = GBRT(**gbrt_config)
train_errors, test_errors = gbrt.fit(train, test)
fig = plt.figure()
fig.suptitle('MSE as function of NumberOfBasisFunctions')
plt.plot(range(1,
def test_example(self):
from part1 import validate_tickets
data = load_data('example.txt')
valid_tickets, error_rate = validate_tickets(data)
self.assertEqual(error_rate, 71)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input_filename', type=str)
args = parser.parse_args()
play_game(*build_cards(load_data(args.input_filename)))
def test_load_data(self):
from part1 import load_data
initial_state = np.array([['.', '#', '.'], ['.', '.', '#'],
['#', '#', '#']])
data = load_data('example1.txt')
self.assertIsNone(assert_array_equal(initial_state, data))