def get_loader(mode, task_id, data_id, train_size, val=False, ques_id=0):
'''
mode = 'train' or 'test'
task_id = 4, 15, 16, 18, 19
info_type = 'edge_types', 'graphs', 'labels', 'node_ids', 'question_types',
data_id = 1~10, random generated 10 dataset
train_size = split train set and validation set
val = validation or train
ques_id = some task have different questions, default is 0
Returns a data loader for the desired split
'''
edge_type = get_data_types(utils.get_file_location(mode=mode, task_id=task_id, info_type='edge_types', data_id=data_id))
node_type = get_data_types(utils.get_file_location(mode=mode, task_id=task_id, info_type='node_ids', data_id=data_id))
question_type = get_data_types(utils.get_file_location(mode=mode, task_id=task_id, info_type='question_types', data_id=data_id))
label_type = get_data_types(utils.get_file_location(mode=mode, task_id=task_id, info_type='labels', data_id=data_id))
if len(label_type) == 0: label_type = node_type
split = BABI(
train_size = train_size,
datapath = utils.get_file_location(mode=mode, task_id=task_id, info_type='graphs', data_id=data_id),
edge_type = edge_type,
node_type = node_type,
question_type = question_type,
label_type = label_type,
is_validation = val,
task_id = task_id,
question_id = ques_id,
)
loader = torch.utils.data.DataLoader(
split,
batch_size=config.BATCH_SIZE,
shuffle=(mode == 'train'),
pin_memory=True,
num_workers=config.NUM_WORKER,
)
return split, loader
import numpy as np
from utils import get_file_location
with open(get_file_location(day=11)) as f:
data = np.array([[seat for seat in row] for row in f.read().split('\n')])
moves_dict = {
'up': {
'x': 0,
'y': -1
},
'up_right': {
'x': 1,
'y': -1
},
'right': {
'x': 1,
'y': 0
},
'down_right': {
'x': 1,
'y': 1
},
'down': {
'x': 0,
'y': 1
},
'down_left': {
'x': -1,
'y': 1
import random
from utils import get_file_location
with open(get_file_location(day=8)) as f:
data = [[x.split(' ')[0], int(x.split(' ')[1])]
for x in f.read().split('\n')]
# data = list(zip(random.sample(range(0,len(data)), len(data)), data))
with open(get_file_location(day=8)) as f:
data = [[x.split(' ')[0], int(x.split(' ')[1])]
for x in f.read().split('\n')]
class CorruptnessFinder:
def __init__(self, data):
self.data = data
self.iterator = 0
self.accumulator = 0
self.used_list, self.corrupt_moves = [], []
self.corrupt_flag = True
def _parse_move(self, move):
return move[0], move[1]
def _execute_action(self, move):
move_action, move_quantity = self._parse_move(move)
if move_action == 'jmp':
self.iterator += move_quantity
elif move_action == 'acc':
self.accumulator += move_quantity