




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		:				List[str]       			=		pd.read_csv('sample_data.csv', header=None)
		__lowerCAmelCase		:				Optional[Any]       			=		df.shape[:1][0]
		# If you're using some other dataset input the target column
		__lowerCAmelCase		:				Union[str, Any]       			=		df.iloc[:, 1:2]
		__lowerCAmelCase		:				Tuple       			=		actual_data.values.reshape(len_data, 1)
		__lowerCAmelCase		:				Dict       			=		MinMaxScaler().fit_transform(actual_data)
		__lowerCAmelCase		:				Union[str, Any]       			=		10
		__lowerCAmelCase		:				Optional[Any]       			=		5
		__lowerCAmelCase		:				List[Any]       			=		20
		__lowerCAmelCase		:				Optional[Any]       			=		len_data - periods * look_back
		__lowerCAmelCase		:				List[str]       			=		actual_data[:division]
		__lowerCAmelCase		:				Tuple       			=		actual_data[division - look_back :]
		__lowerCAmelCase			,     __lowerCAmelCase		:				List[str]       			=		[], []
		__lowerCAmelCase			,     __lowerCAmelCase		:				List[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		:				Tuple       			=		np.array(train_x)
		__lowerCAmelCase		:				Any       			=		np.array(test_x)
		__lowerCAmelCase		:				Dict       			=		np.array([list(i.ravel()) for i in train_y])
		__lowerCAmelCase		:				str       			=		np.array([list(i.ravel()) for i in test_y])

		__lowerCAmelCase		:				Union[str, Any]       			=		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')
		__lowerCAmelCase		:				List[str]       			=		model.fit(
		    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
		)
		__lowerCAmelCase		:				Union[str, Any]       			=		model.predict(x_test)












from __future__ import annotations




def 			__magic_name__				( A		:					list   ):






			'''simple docstring'''







			if len(A   ) == 0:
						return []
			a    ,		a    =			min(A   ), max(A   )
			a    =			int(max_value - min_value   ) + 1
			a    =			[[] for _ in range(A   )]

			for i in my_list:
						buckets[int(i - min_value   )].append(A   )

			return [v for bucket in buckets for v in sorted(A   )]


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

		testmod()
		assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
		assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]






