




import operator as op




def 					lowerCamelCase__       (						A__       :					Optional[int]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       __lowerCamelCase   =						lambda A__  ,			A__	: int(x / y	)  # noqa: E731 integer division operation
       __lowerCamelCase   =						{
           """^""": op.pow,
           """*""": op.mul,
           """/""": div,
           """+""": op.add,
           """-""": op.sub,
       }  # operators & their respective operation

       # print table header
       print("""Symbol""".center(8	)  ,			"""Action""".center(12	)  ,			"""Stack"""  ,			sep=""" | """	)
       print("""-""" * (30 + len(_lowercase	))	)

       for x in post_fix:
              if x.isdigit():  # if x in digit
                     stack.append(_lowercase	)  # append x to stack
                     # output in tabular format
                     print(x.rjust(8	)  ,			("""push(""" + x + """)""").ljust(12	)  ,			""",""".join(_lowercase	)  ,			sep=""" | """	)
              else:
                     __lowerCamelCase   =						stack.pop()  # pop stack
                     # output in tabular format
                     print("""""".rjust(8	)  ,			("""pop(""" + b + """)""").ljust(12	)  ,			""",""".join(_lowercase	)  ,			sep=""" | """	)

                     __lowerCamelCase   =						stack.pop()  # pop stack
                     # output in tabular format
                     print("""""".rjust(8	)  ,			("""pop(""" + a + """)""").ljust(12	)  ,			""",""".join(_lowercase	)  ,			sep=""" | """	)

                     stack.append(
                         str(opr[x](int(_lowercase	)  ,			int(_lowercase	)	)	)	)  # evaluate the 2 values popped from stack & push result to stack
                     # output in tabular format
                     print(
                         x.rjust(8	)  ,			("""push(""" + a + x + b + """)""").ljust(12	)  ,			""",""".join(_lowercase	)  ,			sep=""" | """  ,			)

       return int(stack[0]	)


if __name__ == "__main__":
       UpperCAmelCase_									=	input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
       print('\n\tResult = ', solve(Postfix))






"""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__":
						__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(1_28, input_shape=(look_back, 1), return_sequences=True))
						model.add(LSTM(64, input_shape=(1_28, 1)))
						model.add(Dense(forward_days))
						model.compile(loss="mean_squared_error", optimizer="adam")
						__a      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						__a      =				model.predict(x_test)
