

'''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__":
      __lowerCamelCase						    =      pd.read_csv('''sample_data.csv''', header=None)
      __lowerCamelCase						    =      df.shape[:1][0]
      # If you're using some other dataset input the target column
      __lowerCamelCase						    =      df.iloc[:, 1:2]
      __lowerCamelCase						    =      actual_data.values.reshape(len_data, 1)
      __lowerCamelCase						    =      MinMaxScaler().fit_transform(actual_data)
      __lowerCamelCase						    =      10
      __lowerCamelCase						    =      5
      __lowerCamelCase						    =      20
      __lowerCamelCase						    =      len_data - periods * look_back
      __lowerCamelCase						    =      actual_data[:division]
      __lowerCamelCase						    =      actual_data[division - look_back :]
      __lowerCamelCase    , __lowerCamelCase						    =      [], []
      __lowerCamelCase    , __lowerCamelCase						    =      [], []

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

      __lowerCamelCase						    =      Sequential()
      model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
      model.add(LSTM(64, input_shape=(128, 1)))
      model.add(Dense(forward_days))
      model.compile(loss='''mean_squared_error''', optimizer='''adam''')
      __lowerCamelCase						    =      model.fit(
          x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
      )
      __lowerCamelCase						    =      model.predict(x_test)








'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[False] * (required_sum + 1) for _ in range(arr_len + 1			)]

       # for each arr value, a sum of zero(0) can be formed by not taking any element
       # hence True/1
       for i in range(arr_len + 1			):
              A_		   = True

       # sum is not zero and set is empty then false
       for i in range(1,    required_sum + 1			):
              A_		   = False

       for i in range(1,    arr_len + 1			):
              for j in range(1,    required_sum + 1			):
                     if arr[i - 1] > j:
                            A_		   = subset[i - 1][j]
                     if arr[i - 1] <= j:
                            A_		   = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

       return subset[arr_len][required_sum]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
