"""simple docstring"""







from math import floor, sqrt


def   a__      (		UpperCAmelCase__       :			int			)->	int:
						UpperCAmelCase__						=		0.0
						UpperCAmelCase__						=		1.0
						UpperCAmelCase__						=		int(sqrt(UpperCAmelCase__			)			)
						UpperCAmelCase__						=		root
						UpperCAmelCase__						=		0
						while integer_part != 2 * root:
												UpperCAmelCase__						=		denominator * integer_part - numerator
												UpperCAmelCase__						=		(n - numerator**2) / denominator
												UpperCAmelCase__						=		int((root + numerator) / denominator			)
												period += 1
						return period


def   a__      (		UpperCAmelCase__       :			int = 1_0_0_0_0			)->	int:
						UpperCAmelCase__						=		0
						for i in range(2		,			n + 1			):
												UpperCAmelCase__						=		sqrt(UpperCAmelCase__			)
												if sr - floor(UpperCAmelCase__			) != 0 and continuous_fraction_period(UpperCAmelCase__			) % 2 == 1:
																		count_odd_periods += 1
						return count_odd_periods


if __name__ == "__main__":
						print(F"""{solution(int(input().strip()))}""")


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)

