




def 							_UpperCAmelCase  (UpperCamelCase_ : int = 1000000       ):
    '''simple docstring'''
    _lowerCAmelCase    :    List[str]										=      set(range(3      ,    UpperCamelCase_      ,    2       )       )
    primes.add(2       )
    for p in range(3      ,    UpperCamelCase_      ,    2       ):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p      ,    UpperCamelCase_      ,    UpperCamelCase_       )       )       )

    _lowerCAmelCase    :    Optional[int]										=      [float(UpperCamelCase_       ) for n in range(limit + 1       )]

    for p in primes:
        for n in range(UpperCamelCase_      ,    limit + 1      ,    UpperCamelCase_       ):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]       )       )


if __name__ == "__main__":
 print(F'''{solution() = }''')







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


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''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

