





'''simple docstring'''



from __future__ import annotations

__magic_name__   :						List[str]    =			list[tuple[int, int]]

__magic_name__   :						Optional[int]    =			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__magic_name__   :						List[str]    =			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   ):
       _snake_case     		=				pos_x
       _snake_case     		=				pos_y
       _snake_case     		=				(pos_y, pos_x)
       _snake_case     		=				goal_x
       _snake_case     		=				goal_y
       _snake_case     		=				g_cost
       _snake_case     		=				parent
       _snake_case     		=				self.calculate_heuristic()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				abs(self.pos_x - self.goal_x	)
       _snake_case     		=				abs(self.pos_y - self.goal_y	)
       return dx + dy






     def __lt__(					self						,   lowerCamelCase	):
       return self.f_cost < other.f_cost







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				Node(start[1]						,   start[0]						,   goal[1]						,   goal[0]						,   0						,   lowerCamelCase	)
       _snake_case     		=				Node(goal[1]						,   goal[0]						,   goal[1]						,   goal[0]						,   99_999						,   lowerCamelCase	)

       _snake_case     		=				[self.start]
       _snake_case     		=				[]

       _snake_case     		=				False

     def 	UpperCamelCase(					self	):
       while self.open_nodes:
         # Open Nodes are sorted using __lt__
         self.open_nodes.sort()
         _snake_case     		=				self.open_nodes.pop(0	)

         if current_node.pos == self.target.pos:
           _snake_case     		=				True
           return self.retrace_path(lowerCamelCase	)

         self.closed_nodes.append(lowerCamelCase	)
         _snake_case     		=				self.get_successors(lowerCamelCase	)

         for child_node in successors:
           if child_node in self.closed_nodes:
             continue

           if child_node not in self.open_nodes:
             self.open_nodes.append(lowerCamelCase	)
           else:
             # retrieve the best current path
             _snake_case     		=				self.open_nodes.pop(self.open_nodes.index(lowerCamelCase	)	)

             if child_node.g_cost < better_node.g_cost:
               self.open_nodes.append(lowerCamelCase	)
             else:
               self.open_nodes.append(lowerCamelCase	)

       if not self.reached:
         return [self.start.pos]
       return None

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for action in delta:
         _snake_case     		=				parent.pos_x + action[1]
         _snake_case     		=				parent.pos_y + action[0]

         if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(lowerCamelCase	) - 1):
           continue

         if grid[pos_y][pos_x] != 0:
           continue

         successors.append(
             Node(
                 lowerCamelCase						,   lowerCamelCase						,   self.target.pos_y						,   self.target.pos_x						,   parent.g_cost + 1						,   lowerCamelCase						,   )	)
       return successors






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				node
       _snake_case     		=				[]
       while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)	)
         _snake_case     		=				current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
 __magic_name__   :						str    =			(0, 0)
 __magic_name__   :						Any    =			(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 print("""------""")

 __magic_name__   :						Any    =			GreedyBestFirst(init, goal)
 __magic_name__   :						Union[str, Any]    =			greedy_bf.search()
 if path:
  for pos_x, pos_y in path:
   __magic_name__   :						Optional[Any]    =			2

  for elem in grid:
   print(elem)









'''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__":
 __magic_name__   :						Union[str, Any]    =			pd.read_csv("""sample_data.csv""", header=None)
 __magic_name__   :						Optional[Any]    =			df.shape[:1][0]
 # If you're using some other dataset input the target column
 __magic_name__   :						str    =			df.iloc[:, 1:2]
 __magic_name__   :						Dict    =			actual_data.values.reshape(len_data, 1)
 __magic_name__   :						Tuple    =			MinMaxScaler().fit_transform(actual_data)
 __magic_name__   :						Union[str, Any]    =			10
 __magic_name__   :						Optional[int]    =			5
 __magic_name__   :						Any    =			20
 __magic_name__   :						int    =			len_data - periods * look_back
 __magic_name__   :						Union[str, Any]    =			actual_data[:division]
 __magic_name__   :						int    =			actual_data[division - look_back :]
 __magic_name__   ,    __magic_name__   :						List[str]    =			[], []
 __magic_name__   ,    __magic_name__   :						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])
 __magic_name__   :						List[str]    =			np.array(train_x)
 __magic_name__   :						List[str]    =			np.array(test_x)
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in train_y])
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in test_y])

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


