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import gym | |
import tensorflow as tf | |
import os | |
import datetime | |
import stable_baselines | |
from stable_baselines.common.policies import MlpPolicy | |
from stable_baselines.bench import Monitor | |
from stable_baselines.common.vec_env.dummy_vec_env import DummyVecEnv |
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# Sample code for building a multi-layer perceptron | |
# that predicts the brightness of a light bulb based | |
# on the month, weekday, hour and minute. | |
import numpy as np | |
from keras.models import Sequential | |
from keras.layers.core import Dense, Activation | |
from keras.utils import np_utils | |
from sklearn import preprocessing |
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def max_sum_subarray(array): | |
"""выдает длину и сумму подстроки, которая дает максимальную сумму | |
на массиве np.random.rand(1000)-0.5) работает в 37 раз быстрее, | |
чем алогиртм с лекции """ | |
n = len(array) | |
# all prefix sum compute | |
prefix_sum = [0] | |
for i in range(n): | |
prefix_sum.append(prefix_sum[-1] + array[i]) |
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from collections import defaultdict | |
n, m = list(map(int, input().split())) | |
graph = defaultdict(list) | |
for i in range(m): | |
v_from, v_to = list(map(int, input().split())) | |
graph[v_from].append(v_to) | |
graph[v_to].append(v_from) |
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heat | id | time | |
---|---|---|---|
0 | 301 | 25.3 | |
0 | 302 | 24.2 | |
0 | 303 | 29.2 | |
0 | 304 | 28.4 | |
0 | 305 | 27.3 | |
0 | 306 | 27.1 | |
0 | 307 | 28.0 | |
0 | 308 | 28.2 | |
1 | 201 | 27.8 |
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