



'''simple docstring'''
from collections import Counter

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

a_					=	datasets.load_iris()

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

a_		,			a_		,			a_		,			a_					=	train_test_split(X, y)





def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  Union[str, Any]							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  str,		UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Optional[int]=5							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=zip(_SCREAMING_SNAKE_CASE,		_SCREAMING_SNAKE_CASE							)
      # List of distances of all points from the point to be classified
      SCREAMING_SNAKE_CASE__       :			Tuple													=[]
      for data_point in data:
            SCREAMING_SNAKE_CASE__       :			int													=euclidean_distance(data_point[0],		_SCREAMING_SNAKE_CASE							)
            distances.append((distance, data_point[1])							)
      # Choosing 'k' points with the least distances.
      SCREAMING_SNAKE_CASE__       :			List[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
      SCREAMING_SNAKE_CASE__       :			Tuple													=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]))







'''simple docstring'''
from __future__ import annotations

import os
from collections.abc import Mapping

a_					=	tuple[int, int]






class        __SCREAMING_SNAKE_CASE :




 def __init__( self		:							Any   , __lowercase		:							set[int]   , __lowercase		:							Mapping[EdgeT, int] )		->  None:
       SCREAMING_SNAKE_CASE__       :			set[int]													=vertices
       SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={
           (min(__lowercase ), max(__lowercase )): weight for edge, weight in edges.items()
       }




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							EdgeT   , __lowercase		:							int )		->  None:
       self.vertices.add(edge[0] )
       self.vertices.add(edge[1] )
       SCREAMING_SNAKE_CASE__       :			List[str]													=weight




 def        __magic_name__		( self		:							Optional[Any] )		->  Graph:
       SCREAMING_SNAKE_CASE__       :			Graph													=Graph({min(self.vertices )}   , {} )
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int

       while len(subgraph.vertices ) < len(self.vertices ):
             SCREAMING_SNAKE_CASE__       :			Any													=max(self.edges.values() ) + 1
             for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                         if weight < min_weight:
                               SCREAMING_SNAKE_CASE__       :			List[str]													=edge
                               SCREAMING_SNAKE_CASE__       :			Any													=weight

             subgraph.add_edge(__lowercase   , __lowercase )

       return subgraph





def 	_a(					UpperCamelCase__			:  str = "p107_network.txt"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													=os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
      SCREAMING_SNAKE_CASE__       :			str													=os.path.join(UpperCamelCase__,		UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={}
      SCREAMING_SNAKE_CASE__       :			list[str]
      SCREAMING_SNAKE_CASE__       :			int
      SCREAMING_SNAKE_CASE__       :			int

      with open(UpperCamelCase__							) as f:
            SCREAMING_SNAKE_CASE__       :			Any													=f.read().strip().split('''\n'''							)

      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[line.split(''','''							) for line in data]

      for edgea in range(1,		len(UpperCamelCase__							)							):
            for edgea in range(UpperCamelCase__							):
                  if adjaceny_matrix[edgea][edgea] != "-":
                        SCREAMING_SNAKE_CASE__       :			List[Any]													=int(adjaceny_matrix[edgea][edgea]							)

      SCREAMING_SNAKE_CASE__       :			Graph													=Graph(set(range(len(UpperCamelCase__							)							)							),		UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Graph													=graph.prims_algorithm()

      SCREAMING_SNAKE_CASE__       :			int													=sum(graph.edges.values()							)
      SCREAMING_SNAKE_CASE__       :			int													=sum(subgraph.edges.values()							)

      return initial_total - optimal_total


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