



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

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

						_snake_case		=  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")
						_snake_case		=  model.fit(
						    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
						)
						_snake_case		=  model.predict(x_test)


'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     print(self.vertex       )
     for i in self.vertex:
        print(_a							,' -> '							,' -> '.join([str(_a       ) for j in self.vertex[i]]       )       )


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

     # call the recursive helper function
     for i in range(len(self.vertex       )       ):
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

     # Recur for all the vertices that are adjacent to this node
     for i in self.vertex:
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


if __name__ == "__main__":
  __lowerCAmelCase         =    Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3



