'''simple docstring'''




import math

def _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		[]
       __UpperCAmelCase  :				Tuple       				=		2
       __UpperCAmelCase  :				int       				=		int(math.sqrt(lowerCamelCase__						)						)  # Size of every segment
       __UpperCAmelCase  :				str       				=		[True] * (end + 1)
       __UpperCAmelCase  :				int       				=		[]

       while start <= end:
              if temp[start] is True:
                     in_prime.append(lowerCamelCase__						)
                     for i in range(start * start							,    end + 1							,    lowerCamelCase__						):
                            __UpperCAmelCase  :				List[str]       				=		False
              start += 1
       prime += in_prime

       __UpperCAmelCase  :				Optional[int]       				=		end + 1
       __UpperCAmelCase  :				str       				=		min(2 * end							,    lowerCamelCase__						)

       while low <= n:
              __UpperCAmelCase  :				int       				=		[True] * (high - low + 1)
              for each in in_prime:
                     __UpperCAmelCase  :				int       				=		math.floor(low / each						) * each
                     if t < low:
                            t += each

                     for j in range(lowerCamelCase__							,    high + 1							,    lowerCamelCase__						):
                            __UpperCAmelCase  :				int       				=		False

              for j in range(len(lowerCamelCase__						)						):
                     if temp[j] is True:
                            prime.append(j + low						)

              __UpperCAmelCase  :				str       				=		high + 1
              __UpperCAmelCase  :				List[Any]       				=		min(high + end							,    lowerCamelCase__						)

       return prime


print(sieve(10**6))







'''simple docstring'''




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

_a							,					_a							,					_a							,					_a   :	Any 							=   train_test_split(X, y)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return np.linalg.norm(np.array(lowerCamelCase__						) - np.array(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		zip(lowerCamelCase__							,    lowerCamelCase__						)
       # List of distances of all points from the point to be classified
       __UpperCAmelCase  :				int       				=		[]
       for data_point in data:
              __UpperCAmelCase  :				Optional[Any]       				=		euclidean_distance(data_point[0]							,    lowerCamelCase__						)
              distances.append((distance, data_point[1])						)
       # Choosing 'k' points with the least distances.
       __UpperCAmelCase  :				Union[str, Any]       				=		[i[1] for i in sorted(lowerCamelCase__						)[:k]]
       # Most commonly occurring class among them
       # is the class into which the point is classified
       __UpperCAmelCase  :				Dict       				=		Counter(lowerCamelCase__						).most_common(1						)[0][0]
       return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))






