allennlp.nn.chu_liu_edmonds#

decode_mst#

decode_mst(energy:numpy.ndarray, length:int, has_labels:bool=True) -> Tuple[numpy.ndarray, numpy.ndarray]

Note: Counter to typical intuition, this function decodes the maximum spanning tree.

Decode the optimal MST tree with the Chu-Liu-Edmonds algorithm for maximum spanning arborescences on graphs.

Parameters

  • energy : numpy.ndarray, required. A tensor with shape (num_labels, timesteps, timesteps) containing the energy of each edge. If has_labels is False, the tensor should have shape (timesteps, timesteps) instead.
  • length : int, required. The length of this sequence, as the energy may have come from a padded batch.
  • has_labels : bool, optional, (default = True) Whether the graph has labels or not.

chu_liu_edmonds#

chu_liu_edmonds(length:int, score_matrix:numpy.ndarray, current_nodes:List[bool], final_edges:Dict[int, int], old_input:numpy.ndarray, old_output:numpy.ndarray, representatives:List[Set[int]])

Applies the chu-liu-edmonds algorithm recursively to a graph with edge weights defined by score_matrix.

Note that this function operates in place, so variables will be modified.

Parameters

  • length : int, required. The number of nodes.
  • score_matrix : numpy.ndarray, required. The score matrix representing the scores for pairs of nodes.
  • current_nodes : List[bool], required. The nodes which are representatives in the graph. A representative at it's most basic represents a node, but as the algorithm progresses, individual nodes will represent collapsed cycles in the graph.
  • final_edges : Dict[int, int], required. An empty dictionary which will be populated with the nodes which are connected in the maximum spanning tree.
  • old_input : numpy.ndarray, required.
  • old_output : numpy.ndarray, required.
  • representatives : List[Set[int]], required. A list containing the nodes that a particular node is representing at this iteration in the graph.

Returns

Nothing - all variables are modified in place.