


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

				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				:Union[str, Any]        =      np.array(train_x)
				lowerCAmelCase				:Tuple        =      np.array(test_x)
				lowerCAmelCase				:Optional[int]        =      np.array([list(i.ravel()) for i in train_y])
				lowerCAmelCase				:Dict        =      np.array([list(i.ravel()) for i in test_y])

				lowerCAmelCase				:Dict        =      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				:Optional[Any]        =      model.fit(
				    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
				)
				lowerCAmelCase				:List[Any]        =      model.predict(x_test)



'''simple docstring'''

import math





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     float		):





	"""simple docstring"""







	return math.pow(lowerCAmelCase		,		2		) - a





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	return 2 * x





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					2.0

	while start <= a:
		__magic_name__	:   List[str]					     =					math.pow(lowerCAmelCase		,		2		)

	return start





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     int = 9999		,		lowerCAmelCase    :     float = 0.00_0000_0000_0001		):





	"""simple docstring"""







	if a < 0:
		raise ValueError('math domain error'		)

	__magic_name__	:   Any					     =					get_initial_point(lowerCAmelCase		)

	for _ in range(lowerCAmelCase		):
		__magic_name__	:   List[str]					     =					value
		__magic_name__	:   Optional[int]					     =					value - fx(lowerCAmelCase		,		lowerCAmelCase		) / fx_derivative(lowerCAmelCase		)
		if abs(prev_value - value		) < tolerance:
			return value

	return value


if __name__ == "__main__":
							from doctest import testmod

							testmod()