




"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str				)       ->   list:






      '''simple docstring'''


      _A     =  [0] * len(_snake_case				)

      for i in range(1							,    len(_snake_case				)				):
            # use last results for better performance - dynamic programming
            _A     =  prefix_result[i - 1]
            while j > 0 and input_string[i] != input_string[j]:
                  _A     =  prefix_result[j - 1]

            if input_string[i] == input_string[j]:
                  j += 1
            _A     =  j

      return prefix_result






def 		_snake_case	(   _snake_case				:   str				)       ->   int:






      '''simple docstring'''


      return max(prefix_function(_snake_case				)				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



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		: Union[str, Any]								=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
A		: Union[str, Any]								=     dataset.iloc[:, 1:2].values
A		: Dict								=     dataset.iloc[:, 2].values


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


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




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_SNAKE_CASE_       )       )		,   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