




import random





def 				A__ (snake_case   :		list		,	snake_case   :		Any				)     ->						tuple:
					__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      [], [], []
					for element in data:
										if element < pivot:
															less.append(snake_case				)
										elif element > pivot:
															greater.append(snake_case				)
										else:
															equal.append(snake_case				)
					return less, equal, greater







def 				A__ (snake_case   :		list		,	snake_case   :		int				)     ->						Dict:
					# index = len(items) // 2 when trying to find the median
					#   (value of index when items is sorted)

					# invalid input
					if index >= len(snake_case				) or index < 0:
										return None

					__UpperCamelCase					:	Optional[int]             =      items[random.randint(0		,	len(snake_case				) - 1				)]
					__UpperCamelCase					:	Tuple             =      0
					__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Optional[Any]             =      _partition(snake_case		,	snake_case				)
					__UpperCamelCase					:	List[str]             =      len(snake_case				)
					__UpperCamelCase					:	Tuple             =      len(snake_case				)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(snake_case		,	snake_case				)
					# must be in larger
					else:
										return quick_select(snake_case		,	index - (m + count)				)












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__":
	a__		     =     pd.read_csv('''sample_data.csv''', header=None)
	a__		     =     df.shape[:1][0]
	# If you're using some other dataset input the target column
	a__		     =     df.iloc[:, 1:2]
	a__		     =     actual_data.values.reshape(len_data, 1)
	a__		     =     MinMaxScaler().fit_transform(actual_data)
	a__		     =     10
	a__		     =     5
	a__		     =     20
	a__		     =     len_data - periods * look_back
	a__		     =     actual_data[:division]
	a__		     =     actual_data[division - look_back :]
	a__     ,							a__		     =     [], []
	a__     ,							a__		     =     [], []

	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])
	a__		     =     np.array(train_x)
	a__		     =     np.array(test_x)
	a__		     =     np.array([list(i.ravel()) for i in train_y])
	a__		     =     np.array([list(i.ravel()) for i in test_y])

	a__		     =     Sequential()
	model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
	model.add(LSTM(64, input_shape=(128, 1)))
	model.add(Dense(forward_days))
	model.compile(loss='''mean_squared_error''', optimizer='''adam''')
	a__		     =     model.fit(
	    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
	)
	a__		     =     model.predict(x_test)






