
"""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								=						[], []

							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(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""")
							lowerCAmelCase								=						model.fit(
							    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
							)
							lowerCAmelCase								=						model.predict(x_test)



from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

SCREAMING_SNAKE_CASE       :      Tuple												= (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= (4, 6, 2, 0, 8, 10, 3, -2)






@dataclass
class _lowerCamelCase:
       lowercase_ :	int
       lowercase_ :	Node | None






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase)       ->   None:




           """simple docstring"""

           _lowercase       :							Node | None      		=		None
           for i in sorted(lowerCamelCase,      reverse=lowerCamelCase):
               _lowercase       :							Tuple      		=		Node(lowerCamelCase,      self.head)
       def __iter__(					self)       ->   Iterator[int]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.head
           while node:
               yield node.data
               _lowercase       :							int      		=		node.next_node
       def __len__(					self)       ->   int:




           """simple docstring"""

           return sum(1 for _ in self)




       def __str__(					self)       ->   str:




           """simple docstring"""

           return " -> ".join([str(lowerCamelCase) for node in self])



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						SortedLinkedList:
    return SortedLinkedList(list(lowerCamelCase_ ) + list(lowerCamelCase_ ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 SCREAMING_SNAKE_CASE       :      int												= SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))


