






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

 lowerCamelCase      :       List[str]    =						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''')
 lowerCamelCase      :       List[str]    =						model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 lowerCamelCase      :       List[str]    =						model.predict(x_test)














from math import pow, sqrt





def    __lowerCAmelCase   (    *__snake_case							):
     __lowerCAmelCase             =       len(__snake_case							) > 0 and all(value > 0.0 for value in values							)
     return result





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     return (
         round(sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case							)
         else ValueError("Input Error: Molar mass values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(effusion_rate * sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(effusion_rate / sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(molar_mass / pow(effusion_rate_a / effusion_rate_a       ,			2							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(pow(effusion_rate_a / effusion_rate_a       ,			2							) / molar_mass       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )






