"""simple docstring"""






def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:str						)					->       List[str]:
      '''simple docstring'''


      assert x is not None
      assert y is not None

      lowercase								=      len(lowerCAmelCase__						)
      lowercase								=      len(lowerCAmelCase__						)

      # declaring the array for storing the dp values
      lowercase								=      [[0] * (n + 1) for _ in range(m + 1						)]  # noqa: E741

      for i in range(1   ,						m + 1						):
            for j in range(1   ,						n + 1						):
                  lowercase								=      1 if x[i - 1] == y[j - 1] else 0

                  lowercase								=      max(l[i - 1][j]   ,						l[i][j - 1]   ,						l[i - 1][j - 1] + match						)

      lowercase								=      """"""
      lowercase				,				lowercase								=      m, n
      while i > 0 and j > 0:
            lowercase								=      1 if x[i - 1] == y[j - 1] else 0

            if l[i][j] == l[i - 1][j - 1] + match:
                  if match == 1:
                        lowercase								=      x[i - 1] + seq
                  i -= 1
                  j -= 1
            elif l[i][j] == l[i - 1][j]:
                  i -= 1
            else:
                  j -= 1

      return l[m][n], seq


if __name__ == "__main__":
       __lowerCAmelCase       :			Union[str, Any]   	="""AGGTAB"""
       __lowerCAmelCase       :			str   	="""GXTXAYB"""
       __lowerCAmelCase       :			str   	=4
       __lowerCAmelCase       :			List[str]   	="""GTAB"""

       __lowerCAmelCase	,	__lowerCAmelCase       :			Optional[int]   	=longest_common_subsequence(a, b)
       print("""len =""", ln, """, sub-sequence =""", subseq)
       import doctest

       doctest.testmod()




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

       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       :			int   	=np.array(train_x)
       __lowerCAmelCase       :			List[str]   	=np.array(test_x)
       __lowerCAmelCase       :			Dict   	=np.array([list(i.ravel()) for i in train_y])
       __lowerCAmelCase       :			str   	=np.array([list(i.ravel()) for i in test_y])

       __lowerCAmelCase       :			Optional[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""")
       __lowerCAmelCase       :			Optional[int]   	=model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase       :			Dict   	=model.predict(x_test)



