
"""simple docstring"""



from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def 					_UpperCAmelCase      ( )     ->					int:
  _snake_case					     =       [randint(-10_00 ,					10_00		) for i in range(10		)]
  _snake_case					     =       randint(-50_00 ,					50_00		)
  return (arr, r)


UpperCAmelCase__          = make_dataset()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Optional[Any]		)     ->					Tuple:
  for triplet in permutations(lowercase__ ,					3		):
    if sum(lowercase__		) == target:
      return tuple(sorted(lowercase__		)		)
  return (0, 0, 0)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       List[Any]		)     ->					Optional[int]:
  arr.sort()
  _snake_case					     =       len(lowercase__		)
  for i in range(n - 1		):
    _snake_case					     =       i + 1, n - 1
    while left < right:
      if arr[i] + arr[left] + arr[right] == target:
        return (arr[i], arr[left], arr[right])
      elif arr[i] + arr[left] + arr[right] < target:
        left += 1
      elif arr[i] + arr[left] + arr[right] > target:
        right -= 1
  return (0, 0, 0)
def 					_UpperCAmelCase      ( )     ->					List[Any]:
  _snake_case					     =       '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
  _snake_case					     =       '''
triplet_sum1(*dataset)
'''
  _snake_case					     =       '''
triplet_sum2(*dataset)
'''
  _snake_case					     =       repeat(setup=lowercase__ ,					stmt=lowercase__ ,					repeat=5 ,					number=1_00_00		)
  _snake_case					     =       repeat(setup=lowercase__ ,					stmt=lowercase__ ,					repeat=5 ,					number=1_00_00		)
  return (min(lowercase__		), min(lowercase__		))


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase__          = solution_times()
 print(F"The time for naive implementation is {times[0]}.")
 print(F"The time for optimized implementation is {times[1]}.")







from collections import Counter

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

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).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]))
