


from __future__ import annotations

import math

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	None:




									"""simple docstring"""





									_UpperCAmelCase					=      size
									# approximate the overall size of segment tree with given value
									_UpperCAmelCase					=      [0 for i in range(0      ,   4 * size						)]
									# create array to store lazy update
									_UpperCAmelCase					=      [0 for i in range(0      ,   4 * size						)]
									_UpperCAmelCase					=      [0 for i in range(0      ,   4 * size						)]  # flag for lazy update
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									return idx * 2
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									return idx * 2 + 1
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	None:




									"""simple docstring"""





									if left_element == right_element:
																_UpperCAmelCase					=      a[left_element - 1]
									else:
																_UpperCAmelCase					=      (left_element + right_element) // 2
																self.build(self.left(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																self.build(self.right(_SCREAMING_SNAKE_CASE						)      ,   mid + 1      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      max(
																    self.segment_tree[self.left(_SCREAMING_SNAKE_CASE						)]      ,   self.segment_tree[self.right(_SCREAMING_SNAKE_CASE						)]						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	bool:




									"""simple docstring"""





									if self.flag[idx] is True:
																_UpperCAmelCase					=      self.lazy[idx]
																_UpperCAmelCase					=      False
																if left_element != right_element:
																							_UpperCAmelCase					=      self.lazy[idx]
																							_UpperCAmelCase					=      self.lazy[idx]
																							_UpperCAmelCase					=      True
																							_UpperCAmelCase					=      True

									if right_element < a or left_element > b:
																return True
									if left_element >= a and right_element <= b:
																_UpperCAmelCase					=      val
																if left_element != right_element:
																							_UpperCAmelCase					=      val
																							_UpperCAmelCase					=      val
																							_UpperCAmelCase					=      True
																							_UpperCAmelCase					=      True
																return True
									_UpperCAmelCase					=      (left_element + right_element) // 2
									self.update(self.left(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									self.update(self.right(_SCREAMING_SNAKE_CASE						)      ,   mid + 1      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      max(
									    self.segment_tree[self.left(_SCREAMING_SNAKE_CASE						)]      ,   self.segment_tree[self.right(_SCREAMING_SNAKE_CASE						)]						)
									return True
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	int | float:




									"""simple docstring"""





									if self.flag[idx] is True:
																_UpperCAmelCase					=      self.lazy[idx]
																_UpperCAmelCase					=      False
																if left_element != right_element:
																							_UpperCAmelCase					=      self.lazy[idx]
																							_UpperCAmelCase					=      self.lazy[idx]
																							_UpperCAmelCase					=      True
																							_UpperCAmelCase					=      True
									if right_element < a or left_element > b:
																return -math.inf
									if left_element >= a and right_element <= b:
																return self.segment_tree[idx]
									_UpperCAmelCase					=      (left_element + right_element) // 2
									_UpperCAmelCase					=      self.query(self.left(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.query(self.right(_SCREAMING_SNAKE_CASE						)      ,   mid + 1      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return max(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)






		def __str__(      self						)			->	str:




									"""simple docstring"""





									return str([self.query(1      ,   1      ,   self.size      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for i in range(1      ,   self.size + 1						)]						)


if __name__ == "__main__":
		lowerCAmelCase__  :int             =							[1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
		lowerCAmelCase__  :List[Any]             =							1_5
		lowerCAmelCase__  :Tuple             =							SegmentTree(size)
		segt.build(1, 1, size, A)
		print(segt.query(1, 1, size, 4, 6))
		print(segt.query(1, 1, size, 7, 1_1))
		print(segt.query(1, 1, size, 7, 1_2))
		segt.update(1, 1, size, 1, 3, 1_1_1)
		print(segt.query(1, 1, size, 1, 1_5))
		segt.update(1, 1, size, 7, 8, 2_3_5)
		print(segt)




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__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :str             =							[], []

		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__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
