from math import sqrt



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5						,    int(sqrt(SCREAMING_SNAKE_CASE			) + 1			)						,    6			):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = 10_001			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	0
    __UpperCamelCase  :Optional[int]    						=	1
    while count != nth and number < 3:
        number += 1
        if is_prime(SCREAMING_SNAKE_CASE			):
            count += 1
    while count != nth:
        number += 2
        if is_prime(SCREAMING_SNAKE_CASE			):
            count += 1
    return number


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






from collections import Counter

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

__lowercase											=      datasets.load_iris()

__lowercase											=      np.array(data['''data'''])
__lowercase											=      np.array(data['''target'''])
__lowercase											=      data['''target_names''']

__lowercase				, __lowercase				, __lowercase				, __lowercase											=      train_test_split(X, y)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=5			):
    '''simple docstring'''







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





