Kirill Setdekov ksetdekov

## plot_decision_regions_ex.ipynb

      
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                ksetdekov
                / plot_decision_regions_ex.ipynb
            
            
              Created
              June 1, 2021 20:28
            
              
                plot_decision_regions example
              
          
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## heats.csv

          
            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

## connectivity.py
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)

## max_sum_subarray.py
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])

## neural_network_lighting.py
# 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

## ppo_RL_train.py


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
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
	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)
	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])
	# 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


	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