
"""simple docstring"""






from collections import Counter

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

UpperCAmelCase        =				datasets.load_iris()

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

UpperCAmelCase        =				train_test_split(X, y)




def __magic_name__	(				_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		List[Any]			)					->						List[str]:

	'''simple docstring'''




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




def __magic_name__	(				_lowerCamelCase:		List[Any],					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		List[Any],					_lowerCamelCase:		Union[str, Any]=5			)					->						str:

	'''simple docstring'''




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







'''simple docstring'''





import functools





def   a_   (		_UpperCAmelCase    :      list[int] ,_UpperCAmelCase    :      list[int]				)    ->  int:

      # Validation
      if not isinstance(_UpperCAmelCase ,_UpperCAmelCase				) or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for day in days				):
            raise ValueError('The parameter days should be a list of integers'				)

      if len(_UpperCAmelCase				) != 3 or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for cost in costs				):
            raise ValueError('The parameter costs should be a list of three integers'				)

      if len(_UpperCAmelCase				) == 0:
            return 0

      if min(_UpperCAmelCase				) <= 0:
            raise ValueError('All days elements should be greater than 0'				)

      if max(_UpperCAmelCase				) >= 3_66:
            raise ValueError('All days elements should be less than 366'				)

      __snake_case :   str													=    set(_UpperCAmelCase				)

      @functools.cache
      def dynamic_programming(_UpperCAmelCase    :      int				) -> int:
            if index > 3_65:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1				)

            return min(
                costs[0] + dynamic_programming(index + 1				) ,costs[1] + dynamic_programming(index + 7				) ,costs[2] + dynamic_programming(index + 30				) ,)

      return dynamic_programming(1				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



