


'''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__    					=      pd.read_csv('''sample_data.csv''', header=None)
  UpperCamelCase__    					=      df.shape[:1][0]
  # If you're using some other dataset input the target column
  UpperCamelCase__    					=      df.iloc[:, 1:2]
  UpperCamelCase__    					=      actual_data.values.reshape(len_data, 1)
  UpperCamelCase__    					=      MinMaxScaler().fit_transform(actual_data)
  UpperCamelCase__    					=      1_0
  UpperCamelCase__    					=      5
  UpperCamelCase__    					=      2_0
  UpperCamelCase__    					=      len_data - periods * look_back
  UpperCamelCase__    					=      actual_data[:division]
  UpperCamelCase__    					=      actual_data[division - look_back :]
  UpperCamelCase__					,   UpperCamelCase__    					=      [], []
  UpperCamelCase__					,   UpperCamelCase__    					=      [], []

  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__    					=      np.array(train_x)
  UpperCamelCase__    					=      np.array(test_x)
  UpperCamelCase__    					=      np.array([list(i.ravel()) for i in train_y])
  UpperCamelCase__    					=      np.array([list(i.ravel()) for i in test_y])

  UpperCamelCase__    					=      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__    					=      model.fit(
      x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
  )
  UpperCamelCase__    					=      model.predict(x_test)




'''simple docstring'''
def 					a__       (					lowerCAmelCase__						)    ->	int:
    UpperCAmelCase__			:		Tuple													=				0
    while num > 0:
        digit_sum += num % 10
        num //= 10
    return digit_sum





def 					a__       (					lowerCAmelCase__ = 1_00						)    ->	int:
    UpperCAmelCase__			:		Dict													=				1
    UpperCAmelCase__			:		str													=				2

    for i in range(2  ,					max_n + 1						):
        UpperCAmelCase__			:		Tuple													=				pre_numerator
        UpperCAmelCase__			:		Tuple													=				2 * i // 3 if i % 3 == 0 else 1
        UpperCAmelCase__			:		str													=				cur_numerator
        UpperCAmelCase__			:		List[str]													=				e_cont * pre_numerator + temp

    return sum_digits(lowerCAmelCase__						)


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