


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					      =				[], []

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








from __future__ import annotations







class 	snake_case_       :
					def __init__(				self       :List[str]      ,__snake_case       :int				) ->   None:
												a__					=					data
												a__					=					None
												a__					=					None






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):  # In Order traversal of the tree
							if tree:
														display(tree.left       )
														print(tree.data       )
														display(tree.right       )






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):
							return 1 + max(depth_of_tree(tree.left       ) ,   depth_of_tree(tree.right       )       ) if tree else 0






def 							__lowercase       (			__lowerCAmelCase							:    Node       ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left       ) and is_full_binary_tree(tree.right       )
							else:
														return not tree.left and not tree.right






def 							__lowercase       (			):  # Main function for testing.
							a__					=					Node(1       )
							a__					=					Node(2       )
							a__					=					Node(3       )
							a__					=					Node(4       )
							a__					=					Node(5       )
							a__					=					Node(6       )
							a__					=					Node(7       )
							a__					=					Node(8       )
							a__					=					Node(9       )

							print(is_full_binary_tree(__lowerCAmelCase       )       )
							print(depth_of_tree(__lowerCAmelCase       )       )
							print('Tree is: '       )
							display(__lowerCAmelCase       )


if __name__ == "__main__":
						main()




