





'''simple docstring'''




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__    :      str						=    np.array(data['''data'''])
lowercase__    :      Optional[Any]						=    np.array(data['''target'''])
lowercase__    :      Tuple						=    data['''target_names''']

lowercase__	,							lowercase__	,							lowercase__	,							lowercase__    :      Optional[int]						=    train_test_split(X, y)






def        _lowerCAmelCase			(  __snake_case  : str ,							__snake_case  : List[Any]			)     ->     Optional[Any]:
     return np.linalg.norm(np.array(__snake_case			) - np.array(__snake_case			)			)






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




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()