def  __UpperCamelCase    (						A ,							A							):
					_enforce_args(A ,							A							)
					if n == 0:
										return 0
					UpperCamelCase__										=      float('''-inf'''							)
					for i in range(1 ,							n + 1							):
										UpperCamelCase__										=      max(
										    A ,							prices[i - 1] + naive_cut_rod_recursive(n - i ,							A							)							)

					return max_revue







def  __UpperCamelCase    (						A ,							A							):
					_enforce_args(A ,							A							)
					UpperCamelCase__										=      [float('''-inf'''							) for _ in range(n + 1							)]
					return _top_down_cut_rod_recursive(A ,							A ,							A							)







def  __UpperCamelCase    (						A ,							A ,							A							):
					if max_rev[n] >= 0:
										return max_rev[n]
					elif n == 0:
										return 0
					else:
										UpperCamelCase__										=      float('''-inf'''							)
										for i in range(1 ,							n + 1							):
															UpperCamelCase__										=      max(
															    A ,							prices[i - 1] + _top_down_cut_rod_recursive(n - i ,							A ,							A							) ,							)

										UpperCamelCase__										=      max_revenue

					return max_rev[n]







def  __UpperCamelCase    (						A ,							A							):
					_enforce_args(A ,							A							)

					# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
					# length 0.
					UpperCamelCase__										=      [float('''-inf'''							) for _ in range(n + 1							)]
					UpperCamelCase__										=      0

					for i in range(1 ,							n + 1							):
										UpperCamelCase__										=      max_rev[i]
										for j in range(1 ,							i + 1							):
															UpperCamelCase__										=      max(A ,							prices[j - 1] + max_rev[i - j]							)

										UpperCamelCase__										=      max_revenue_i

					return max_rev[n]







def  __UpperCamelCase    (						A ,							A							):
					if n < 0:
										UpperCamelCase__										=      f"n must be greater than or equal to 0. Got n = {n}"
										raise ValueError(A							)

					if n > len(A							):
										UpperCamelCase__										=      (
										    '''Each integral piece of rod must have a corresponding price. '''
										    f"Got n = {n} but length of prices = {len(A							)}"
										)
										raise ValueError(A							)







def  __UpperCamelCase    (						):
					UpperCamelCase__										=      [6, 10, 12, 15, 20, 23]
					UpperCamelCase__										=      len(A							)

					# the best revenue comes from cutting the rod into 6 pieces, each
					# of length 1 resulting in a revenue of 6 * 6 = 36.
					UpperCamelCase__										=      36

					UpperCamelCase__										=      top_down_cut_rod(A ,							A							)
					UpperCamelCase__										=      bottom_up_cut_rod(A ,							A							)
					UpperCamelCase__										=      naive_cut_rod_recursive(A ,							A							)

					assert expected_max_revenue == max_rev_top_down
					assert max_rev_top_down == max_rev_bottom_up
					assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
					main()




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

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

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



