

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

      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    :Tuple      =			np.array(train_x)
      __a    :str      =			np.array(test_x)
      __a    :Optional[Any]      =			np.array([list(i.ravel()) for i in train_y])
      __a    :int      =			np.array([list(i.ravel()) for i in test_y])

      __a    :Dict      =			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')
      __a    :Optional[Any]      =			model.fit(
          x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
      )
      __a    :Dict      =			model.predict(x_test)







lowerCamelCase__       		=  8.3_1_4_4_5_9_8






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float	):




     '''simple docstring'''






     if temperature < 0:
          raise Exception("""Temperature cannot be less than 0 K"""	)
     if molar_mass <= 0:
          raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""	)
     else:
          return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # example
     lowerCamelCase__       		=  300
     lowerCamelCase__       		=  28
     lowerCamelCase__       		=  rms_speed_of_molecule(temperature, molar_mass)
     print(f'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


