





"""simple docstring"""





from __future__ import annotations



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list ):


							'''simple docstring'''







							if len(SCREAMING_SNAKE_CASE ) == 0:
														return []
							__lowerCamelCase     ,    __lowerCamelCase   :   str       =min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Union[str, Any]       =int(max_value - min_value ) + 1
							__lowerCamelCase   :   list[list]       =[[] for _ in range(SCREAMING_SNAKE_CASE )]

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

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


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]











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

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

							_UpperCamelCase					      =   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')
							_UpperCamelCase					      =   model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							_UpperCamelCase					      =   model.predict(x_test)




