




from collections import Counter

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

_lowerCAmelCase						:   Union[str, Any]		=   datasets.load_iris()

_lowerCAmelCase						:   Union[str, Any]		=   np.array(data["data"])
_lowerCAmelCase						:   Optional[Any]		=   np.array(data["target"])
_lowerCAmelCase						:   int		=   data["target_names"]

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   List[Any]=5	):




 """simple docstring"""







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











import math



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	):




 """simple docstring"""







 UpperCAmelCase__							 =						math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

 return exponent == int(_lowerCAmelCase	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   float = 1 / 1_2345	):




 """simple docstring"""







 UpperCAmelCase__							 =						0
 UpperCAmelCase__							 =						0

 UpperCAmelCase__							 =						3
 while True:
  UpperCAmelCase__							 =						(integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(_lowerCAmelCase	):
   UpperCAmelCase__							 =						int(_lowerCAmelCase	)
   total_partitions += 1
   if check_partition_perfect(_lowerCAmelCase	):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(_lowerCAmelCase	)
  integer += 1


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





