

'''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'''







import heapq as hq
import math
from collections.abc import Iterator


class    A__ :



    def __init__(   self						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           A_		   = str(id_    )
           A_		   = None
           A_		   = None
           A_		   = []
           A_		   = {}  # {vertex:distance}



    def __lt__(   self						,			UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           return self.key < other.key



    def __repr__(   self    )  -> Dict:





           '''simple docstring'''
           return self.id



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           self.neighbors.append(UpperCamelCase__    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = weight

def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       # add the neighbors:
       graph[a - 1].add_neighbor(graph[b - 1]			)
       graph[b - 1].add_neighbor(graph[a - 1]			)
       # add the edges:
       graph[a - 1].add_edge(graph[b - 1],    UpperCAmelCase__			)
       graph[b - 1].add_edge(graph[a - 1],    UpperCAmelCase__			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     list:
       A_		   = []
       for u in graph:
              A_		   = math.inf
              A_		   = None
       A_		   = 0
       A_		   = graph[:]
       while q:
              A_		   = min(UpperCAmelCase__			)
              q.remove(UpperCAmelCase__			)
              for v in u.neighbors:
                     if (v in q) and (u.edges[v.id] < v.key):
                            A_		   = u
                            A_		   = u.edges[v.id]
       for i in range(1,    len(UpperCAmelCase__			)			):
              a.append((int(graph[i].id			) + 1, int(graph[i].pi.id			) + 1)			)
       return a
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Iterator[tuple]:
       for u in graph:
              A_		   = math.inf
              A_		   = None
       A_		   = 0

       A_		   = list(UpperCAmelCase__			)
       hq.heapify(UpperCAmelCase__			)

       while h:
              A_		   = hq.heappop(UpperCAmelCase__			)
              for v in u.neighbors:
                     if (v in h) and (u.edges[v.id] < v.key):
                            A_		   = u
                            A_		   = u.edges[v.id]
                            hq.heapify(UpperCAmelCase__			)

       for i in range(1,    len(UpperCAmelCase__			)			):
              yield (int(graph[i].id			) + 1, int(graph[i].pi.id			) + 1)
def  UpperCAmelCase__      ( )     ->     None:
       pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
