





'''simple docstring'''




class  SCREAMING_SNAKE_CASE    :


     def __init__(						self						,	_UpperCAmelCase = ""						,	_UpperCAmelCase = False):

          '''simple docstring'''






          __A						: dict[str, RadixNode]      =       {}

          # A node will be a leaf if the tree contains its word
          __A						: Optional[int]      =       is_leaf

          __A						: int      =       prefix


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Optional[Any]      =       0
          for q, w in zip(self.prefix						,	_UpperCAmelCase):
               if q != w:
                    break

               x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          for word in words:
               self.insert(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if self.prefix == word:
               __A						: Tuple      =       True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
               __A						: Any      =       RadixNode(prefix=_UpperCAmelCase						,	is_leaf=_UpperCAmelCase)

          else:
               __A						: List[str]      =       self.nodes[word[0]]
               __A ,__A ,__A						: Tuple      =       incoming_node.match(
                   _UpperCAmelCase)

               # Case 3: The node prefix is equal to the matching
               # Solution: We insert remaining word on the next node
               if remaining_prefix == "":
                    self.nodes[matching_string[0]].insert(_UpperCAmelCase)

               # Case 4: The word is greater equal to the matching
               # Solution: Create a node in between both nodes, change
               # prefixes and add the new node for the remaining word
               else:
                    __A						: Optional[int]      =       remaining_prefix

                    __A						: Dict      =       self.nodes[matching_string[0]]
                    __A						: Dict      =       RadixNode(_UpperCAmelCase						,	_UpperCAmelCase)
                    __A						: Optional[Any]      =       aux_node

                    if remaining_word == "":
                         __A						: Union[str, Any]      =       True
                    else:
                         self.nodes[matching_string[0]].insert(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Any      =       self.nodes.get(word[0]						,	_UpperCAmelCase)
          if not incoming_node:
               return False
          else:
               __A ,__A ,__A						: List[str]      =       incoming_node.match(
                   _UpperCAmelCase)
               # If there is remaining prefix, the word can't be on the tree
               if remaining_prefix != "":
                    return False
               # This applies when the word and the prefix are equal
               elif remaining_word == "":
                    return incoming_node.is_leaf
               # We have word remaining so we check the next node
               else:
                    return incoming_node.find(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Optional[Any]      =       self.nodes.get(word[0]						,	_UpperCAmelCase)
          if not incoming_node:
               return False
          else:
               __A ,__A ,__A						: Optional[Any]      =       incoming_node.match(
                   _UpperCAmelCase)
               # If there is remaining prefix, the word can't be on the tree
               if remaining_prefix != "":
                    return False
               # We have word remaining so we check the next node
               elif remaining_word != "":
                    return incoming_node.delete(_UpperCAmelCase)
               else:
                    # If it is not a leaf, we don't have to delete
                    if not incoming_node.is_leaf:
                         return False
                    else:
                         # We delete the nodes if no edges go from it
                         if len(incoming_node.nodes) == 0:
                              del self.nodes[word[0]]
                              # We merge the current node with its only child
                              if len(self.nodes) == 1 and not self.is_leaf:
                                   __A						: Union[str, Any]      =       list(self.nodes.values())[0]
                                   __A						: List[str]      =       merging_node.is_leaf
                                   self.prefix += merging_node.prefix
                                   __A						: Any      =       merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                         elif len(incoming_node.nodes) > 1:
                              __A						: Tuple      =       False
                         # If there is 1 edge, we merge it with its child
                         else:
                              __A						: int      =       list(incoming_node.nodes.values())[0]
                              __A						: Optional[int]      =       merging_node.is_leaf
                              incoming_node.prefix += merging_node.prefix
                              __A						: List[Any]      =       merging_node.nodes

                         return True


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase = 0):

          '''simple docstring'''






          if self.prefix != "":
               print('-' * height						,	self.prefix						,	'  (leaf)' if self.is_leaf else '')

          for value in self.nodes.values():
               value.print_tree(height + 1)






def        _lowerCAmelCase			(  )     ->     bool:
     __A						: List[Any]      =       'banana bananas bandana band apple all beast'.split()
     __A						: Tuple      =       RadixNode()
     root.insert_many(__snake_case			)

     assert all(root.find(__snake_case			) for word in words			)
     assert not root.find('bandanas'			)
     assert not root.find('apps'			)
     root.delete('all'			)
     assert not root.find('all'			)
     root.delete('banana'			)
     assert not root.find('banana'			)
     assert root.find('bananas'			)

     return True






def        _lowerCAmelCase			(  )     ->     None:
     assert test_trie()






def        _lowerCAmelCase			(  )     ->     None:
     __A						: Union[str, Any]      =       RadixNode()
     __A						: Tuple      =       'banana bananas bandanas bandana band apple all beast'.split()
     root.insert_many(__snake_case			)

     print('Words:' ,							__snake_case			)
     print('Tree:'			)
     root.print_tree()


if __name__ == "__main__":
  main()






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

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)