# allennlp.nn.chu_liu_edmonds¶

allennlp.nn.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]])[source]

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. Nothing - all variables are modified in place.
allennlp.nn.chu_liu_edmonds.decode_mst(energy: numpy.ndarray, length: int, has_labels: bool = True) → Tuple[numpy.ndarray, numpy.ndarray][source]

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.