
"""simple docstring"""




from collections import deque

from .hash_table import HashTable


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def __init__(     self ,				*A_ ,				**A_       )     ->	List[str]:





         """simple docstring"""




         super().__init__(*A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			str       =     deque([]       ) if self.values[key] is None else self.values[key]
         self.values[key].appendleft(A_       )
         _lowercase:			Optional[int]       =     self.values[key]
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         return (
             sum(self.charge_factor - len(A_       ) for slot in self.values       )
             / self.size_table
             * self.charge_factor
         )



       def 							lowercase_							(     self ,				A_ ,				A_=None       )     ->	Any:





         """simple docstring"""




         if not (
             len(self.values[key]       ) == self.charge_factor and self.values.count(A_       ) == 0
         ):
           return key
         return super()._collision_resolution(A_ ,				A_       )


"""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
