

'''simple docstring'''

from math import floor, sqrt





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Any        =       0.0
					UpperCAmelCase		:							Optional[Any]        =       1.0
					UpperCAmelCase		:							int        =       int(sqrt(__magic_name__							)							)
					UpperCAmelCase		:							List[str]        =       root
					UpperCAmelCase		:							Dict        =       0
					while integer_part != 2 * root:
										UpperCAmelCase		:							Dict        =       denominator * integer_part - numerator
										UpperCAmelCase		:							Union[str, Any]        =       (n - numerator**2) / denominator
										UpperCAmelCase		:							List[str]        =       int((root + numerator) / denominator							)
										period += 1
					return period





def 				lowercase (					__magic_name__ = 1_0000							):



					'''simple docstring'''







					UpperCAmelCase		:							Optional[int]        =       0
					for i in range(2			,   n + 1							):
										UpperCAmelCase		:							Optional[int]        =       sqrt(__magic_name__							)
										if sr - floor(__magic_name__							) != 0 and continuous_fraction_period(__magic_name__							) % 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
a       :      List[Any]  	=			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
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

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