
"""simple docstring"""



class       lowerCAmelCase__				:







     def __init__(							self			:			Optional[int]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       name
       _snake_case					     =       value
       _snake_case					     =       weight







     def __repr__(							self			:			List[Any]			):
       return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''







     def      lowercase	(							self			:			Union[str, Any]			):
       return self.value







     def      lowercase	(							self			:			Any			):
       return self.name







     def      lowercase	(							self			:			str			):
       return self.weight







     def      lowercase	(							self			:			Optional[int]			):
       return self.value / self.weight






def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       str		)     ->					Optional[Any]:
  _snake_case					     =       []
  for i in range(len(lowercase__		)		):
    menu.append(Things(name[i] ,					value[i] ,					weight[i]		)		)
  return menu
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       List[str]		)     ->					List[Any]:
  _snake_case					     =       sorted(lowercase__ ,					key=lowercase__ ,					reverse=lowercase__		)
  _snake_case					     =       []
  _snake_case					     =       0.0, 0.0
  for i in range(len(lowercase__		)		):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]		)
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)
def 					_UpperCAmelCase      ( )     ->					Optional[Any]:
  pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()







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]))
