





from collections import Counter

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

lowercase    : Optional[int]    	=  datasets.load_iris()

lowercase    : Optional[int]    	=  np.array(data["""data"""])
lowercase    : int    	=  np.array(data["""target"""])
lowercase    : Dict    	=  data["""target_names"""]

lowercase	,lowercase	,lowercase	,lowercase    : List[str]    	=  train_test_split(X, y)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return np.linalg.norm(np.array(lowerCAmelCase					) - np.array(lowerCAmelCase					)					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase=5					):
       _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]))













from __future__ import annotations

lowercase    : int    	=  {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}

class  __A:

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						graph
            # mapping node to its parent in resulting breadth first tree
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						source_vertex

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						{self.source_vertex}
            _UpperCamelCase              =						None
            _UpperCamelCase              =						[self.source_vertex]  # first in first out queue

            while queue:
                   _UpperCamelCase              =						queue.pop(0      )
                   for adjacent_vertex in self.graph[vertex]:
                          if adjacent_vertex not in visited:
                                 visited.add(A      )
                                 _UpperCamelCase              =						vertex
                                 queue.append(A      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if target_vertex == self.source_vertex:
                   return self.source_vertex

            _UpperCamelCase              =						self.parent.get(A      )
            if target_vertex_parent is None:
                   _UpperCamelCase              =						(
                       F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
                   )
                   raise ValueError(A      )

            return self.shortest_path(A      ) + F'''->{target_vertex}'''


if __name__ == "__main__":
       lowercase    : Tuple    	=  Graph(graph, """G""")
       g.breath_first_search()
       print(g.shortest_path("""D"""))
       print(g.shortest_path("""G"""))
       print(g.shortest_path("""Foo"""))






