return set(range(0, self.sequence_size)) == set(factors)
def humanize(x):
return map(lambda e: e + 1, x)
def machinize(x):
return map(lambda e: e - 1, x)
def solution(idx):
K, C, S = map(int, util.list_input())
sequence_size = K
complexity = C
tester_count = S
# K진수의 C자리 숫자(0이 패딩)에서 모든 수를 뽑는 문제와 같음
# 한번에 C자리 뽑을 수 있으므로 C*S가 K보다 작으면 망함
f = Fractal(sequence_size, complexity)
indexset = humanize(f.search_indexset())
if len(indexset) > tester_count:
util.print_case(idx, 'IMPOSSIBLE')
else:
util.print_case(idx, ' '.join(map(str, indexset)))
if __name__ == '__main__':
count = util.int_input() # float_input, list_input
util.loop(count, solution)
def linear_search(digit, count):
indigit = digit - 2
solutions = []
for i in range(0, 2**indigit):
i_bin = bin(i)[2:]
padded = ('0' * indigit) + i_bin
str_coin = '1' + padded[-indigit:] + '1'
assert len(str_coin) == digit
coin = JamCoin.from_str(str_coin)
divisors = coin.validate()
if divisors:
solutions.append((coin, divisors))
if len(solutions) >= count:
break
return solutions
def solution(idx):
N, J = map(int, util.list_input())
util.print_case(idx, '')
answers = linear_search(N, J)
assert len(answers) == J
for coin, divisors in answers:
print coin, ' '.join(map(str, divisors))
if __name__ == '__main__':
count = util.int_input()
util.loop(count, solution)
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import sys
from datetime import datetime
from datasets import *
import random, pdb, sys
import numpy as np
import tqdm, util
from models import *
model = CPDModel(node_features=(8, 100),
edge_features=(1, 32),
hidden_dim=(16, 100))
optimizer = tf.keras.optimizers.Adam()
util.load_checkpoint(model, optimizer, sys.argv[1])
_, _, testset = cath_dataset(
3000) # fix this to only give individual amino acids
loss, acc, confusion = util.loop(testset, model, train=False)
print('ALL TEST PERPLEXITY {}, ACCURACY {}'.format(np.exp(loss), acc))
util.save_confusion(confusion)
_, _, testset = cath_dataset(3000, filter_file='../data/test_split_L100.json')
loss, acc, confusion = util.loop(testset, model, train=False)
print('SHORT TEST PERPLEXITY {}, ACCURACY {}'.format(np.exp(loss), acc))
util.save_confusion(confusion)
_, _, testset = cath_dataset(3000, filter_file='../data/test_split_sc.json')
loss, acc, confusion = util.loop(testset, model, train=False)
print('SINGLE CHAIN TEST PERPLEXITY {}, ACCURACY {}'.format(np.exp(loss), acc))
util.save_confusion(confusion)
