




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

   UpperCamelCase_						:      Tuple       =						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_						:      int       =						model.fit(
       x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
   )
   UpperCamelCase_						:      Optional[Any]       =						model.predict(x_test)








'''simple docstring'''





from decimal import Decimal, getcontext
from math import ceil, factorial





def      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("Undefined for non-integers"   )
     elif precision < 1:
          raise ValueError("Undefined for non-natural numbers"   )

     _snake_case							=				precision
     _snake_case							=				ceil(precision / 14   )
     _snake_case							=				426_880 * Decimal(10_005   ).sqrt()
     _snake_case							=				1
     _snake_case							=				13_591_409
     _snake_case							=				Decimal(_UpperCamelCase   )
     for k in range(1 ,						_UpperCamelCase   ):
          _snake_case							=				factorial(6 * k   ) // (factorial(3 * k   ) * factorial(_UpperCamelCase   ) ** 3)
          linear_term += 545_140_134
          exponential_term *= -262_537_412_640_768_000
          partial_sum += Decimal(multinomial_term * linear_term   ) / exponential_term
     return str(constant_term / partial_sum   )[:-1]


if __name__ == "__main__":
   UpperCamelCase_						:      Any       =						50
   print(F'The first {n} digits of pi is: {pi(n)}')


