


from collections import Counter

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

_snake_case          =							datasets.load_iris()

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

_snake_case      ,					_snake_case      ,					_snake_case      ,					_snake_case          =							train_test_split(X, y)



def   snake_case						(				_a:     Optional[Any]		,   _a:     List[Any]							)->		Union[str, Any]:
      '''simple docstring'''


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



def   snake_case						(				_a:     List[Any]		,   _a:     List[Any]		,   _a:     Optional[Any]		,   _a:     List[str]		,   _a:     List[Any]=5							)->		Optional[Any]:
      '''simple docstring'''


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


def   snake_case						(				_a:     List[Any]		,   _a:     Any		,   _a:     str		,   _a:     List[Any]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[]
      queue.append(_a							)
      lowerCamelCase__           =		True

      while queue:
            lowerCamelCase__           =		queue.pop(0							)
            for ind in range(len(graph[u]							)							):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a							)
                        lowerCamelCase__           =		True
                        lowerCamelCase__           =		u

      return visited[t]



def   snake_case						(				_a:     List[Any]		,   _a:     str		,   _a:     List[str]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[-1] * (len(_a							))
      lowerCamelCase__           =		0
      while bfs(_a		,   _a		,   _a		,   _a							):
            lowerCamelCase__           =		float('Inf'							)
            lowerCamelCase__           =		sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase__           =		min(_a		,   graph[parent[s]][s]							)
                  lowerCamelCase__           =		parent[s]

            max_flow += path_flow
            lowerCamelCase__           =		sink

            while v != source:
                  lowerCamelCase__           =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase__           =		parent[v]
      return max_flow


_snake_case          =							[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

_snake_case      ,					_snake_case          =							0, 5






print(ford_fulkerson(graph, source, sink))



