




from collections import Counter

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

_UpperCAmelCase		:  Optional[Any]	=   datasets.load_iris()

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

_UpperCAmelCase	,	_UpperCAmelCase	,	_UpperCAmelCase	,	_UpperCAmelCase		:  List[Any]	=   train_test_split(X, y)



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  return np.linalg.norm(np.array(lowerCamelCase      ) - np.array(lowerCamelCase      )      )



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=5      ):
  lowercase			:Optional[int]							=  zip(lowerCamelCase,	lowerCamelCase      )
  # List of distances of all points from the point to be classified
  lowercase			:Any							=  []
  for data_point in data:
    lowercase			:Dict							=  euclidean_distance(data_point[0],	lowerCamelCase      )
    distances.append((distance, data_point[1])      )
  # Choosing 'k' points with the least distances.
  lowercase			:Any							=  [i[1] for i in sorted(lowerCamelCase      )[:k]]
  # Most commonly occurring class among them
  # is the class into which the point is classified
  lowercase			:Union[str, Any]							=  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]))









def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  # Return True if there is node that has not iterated.
  lowercase			:Union[str, Any]							=  [False] * len(lowerCamelCase      )
  lowercase			:Union[str, Any]							=  []
  queue.append(lowerCamelCase      )
  lowercase			:Optional[Any]							=  True

  while queue:
    lowercase			:Optional[Any]							=  queue.pop(0      )
    for ind in range(len(graph[u]      )      ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowerCamelCase      )
        lowercase			:Dict							=  True
        lowercase			:Dict							=  u

  return visited[t]



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  # This array is filled by BFS and to store path
  lowercase			:Optional[int]							=  [-1] * (len(lowerCamelCase      ))
  lowercase			:int							=  0
  while bfs(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
    lowercase			:int							=  float("Inf"      )
    lowercase			:Any							=  sink

    while s != source:
      # Find the minimum value in select path
      lowercase			:Any							=  min(lowerCamelCase,	graph[parent[s]][s]      )
      lowercase			:Dict							=  parent[s]

    max_flow += path_flow
    lowercase			:Union[str, Any]							=  sink

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


_UpperCAmelCase		:  Optional[Any]	=   [
    [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],
]

_UpperCAmelCase	,	_UpperCAmelCase		:  Any	=   0, 5







print(ford_fulkerson(graph, source, sink))



