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.
- 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 :
- old_output :
- 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.
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.
- 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.