
import math




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Dict       )  ->     List[Any]:
						if 0 not in (x, y):
												# We use the relation x^y = y*log10(x), where 10 is the base.
												return y * math.logaa(SCREAMING_SNAKE_CASE_       )
						else:
												if x == 0:  # 0 raised to any number is 0
																		return 0
												elif y == 0:
																		return 1  # any number raised to 0 is 1
						raise AssertionError("""This should never happen"""       )


if __name__ == "__main__":  # Main function
		# Read two numbers from input and typecast them to int using map function.
		# Here x is the base and y is the power.
		A		: Dict								=     '''Enter the base and the power separated by a comma: '''
		A							,      A		: str								=     map(int, input(prompt).split(''','''))
		A							,      A		: Optional[int]								=     map(int, input(prompt).split(''','''))

		# We find the log of each number, using the function res(), which takes two
		# arguments.
		A		: Tuple								=     res(xa, ya)
		A		: List[Any]								=     res(xa, ya)

		# We check for the largest number
		if resa > resa:
				print('''Largest number is''', xa, '''^''', ya)
		elif resa > resa:
				print('''Largest number is''', xa, '''^''', ya)
		else:
				print('''Both are equal''')

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