
"""simple docstring"""




import random


class 			__magic_name__       :
       @staticmethod
       def 							lowercase_							(     A_       )     ->	tuple[list[int], list[int]]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     [ord(__UpperCamelCase       ) for i in text]
         _lowercase:			Optional[int]       =     []
         _lowercase:			Dict       =     []
         for i in plain:
           _lowercase:			Any       =     random.randint(1 ,				300       )
           _lowercase:			Union[str, Any]       =     (i + k) * k
           cipher.append(__UpperCamelCase       )
           key.append(__UpperCamelCase       )
         return cipher, key



       @staticmethod
       def 							lowercase_							(     A_ ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			int       =     []
         for i in range(len(__UpperCamelCase       )       ):
           _lowercase:			List[str]       =     int((cipher[i] - (key[i]) ** 2) / key[i]       )
           plain.append(chr(__UpperCamelCase       )       )
         return "".join(__UpperCamelCase       )


if __name__ == "__main__":
      A__   :					Union[str, Any]							=   Onepad().encrypt('Hello')
      print(c, k)
      print(Onepad().decrypt(c, k))


"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A__   :					Tuple							=   pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
A__   :					Optional[int]							=   dataset.iloc[:, 1:2].values
A__   :					Union[str, Any]							=   dataset.iloc[:, 2].values


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


A__   :					Union[str, Any]							=   PolynomialFeatures(degree=4)
A__   :					List[Any]							=   poly_reg.fit_transform(X)
A__   :					Dict							=   LinearRegression()
pol_reg.fit(X_poly, y)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  plt.scatter(_UpperCamelCase ,				_UpperCamelCase ,				color='''red'''						)
  plt.plot(_UpperCamelCase ,				pol_reg.predict(poly_reg.fit_transform(_UpperCamelCase						)						) ,				color='''blue'''						)
  plt.title('''Truth or Bluff (Linear Regression)'''						)
  plt.xlabel('''Position level'''						)
  plt.ylabel('''Salary'''						)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003
