

'''simple docstring'''



import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
  UpperCAmelCase					:			List[Any]       =	pd.read_csv('sample_data.csv', header=None)
  UpperCAmelCase					:			Union[str, Any]       =	df.shape[:1][0]
  # If you're using some other dataset input the target column
  UpperCAmelCase					:			Dict       =	df.iloc[:, 1:2]
  UpperCAmelCase					:			str       =	actual_data.values.reshape(len_data, 1)
  UpperCAmelCase					:			str       =	MinMaxScaler().fit_transform(actual_data)
  UpperCAmelCase					:			int       =	1_0
  UpperCAmelCase					:			Any       =	5
  UpperCAmelCase					:			Optional[int]       =	2_0
  UpperCAmelCase					:			List[Any]       =	len_data - periods * look_back
  UpperCAmelCase					:			Tuple       =	actual_data[:division]
  UpperCAmelCase					:			Optional[Any]       =	actual_data[division - look_back :]
  UpperCAmelCase  ,	UpperCAmelCase					:			Dict       =	[], []
  UpperCAmelCase  ,	UpperCAmelCase					:			Tuple       =	[], []

  for i in range(0, len(train_data) - forward_days - look_back + 1):
    train_x.append(train_data[i : i + look_back])
    train_y.append(train_data[i + look_back : i + look_back + forward_days])
  for i in range(0, len(test_data) - forward_days - look_back + 1):
    test_x.append(test_data[i : i + look_back])
    test_y.append(test_data[i + look_back : i + look_back + forward_days])
  UpperCAmelCase					:			int       =	np.array(train_x)
  UpperCAmelCase					:			Optional[int]       =	np.array(test_x)
  UpperCAmelCase					:			Any       =	np.array([list(i.ravel()) for i in train_y])
  UpperCAmelCase					:			Any       =	np.array([list(i.ravel()) for i in test_y])

  UpperCAmelCase					:			List[Any]       =	Sequential()
  model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='mean_squared_error', optimizer='adam')
  UpperCAmelCase					:			Dict       =	model.fit(
      x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
  )
  UpperCAmelCase					:			Optional[int]       =	model.predict(x_test)









'''simple docstring'''



from math import isqrt







def   a__			(    a__		):




     """simple docstring"""


     return all(number % divisor != 0 for divisor in range(2 ,       isqrt(a__		) + 1		)		)







def   a__			(    a__ = 10**6		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = 1
     __SCREAMING_SNAKE_CASE      = 7
     while prime_candidate < max_prime:
          primes_count += is_prime(a__		)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


if __name__ == "__main__":
  print(f"""{solution() = }""")






