clusters_from_spanning_forest¶
- fast_plscan.clusters_from_spanning_forest(sorted_mst, num_points, *, min_cluster_size=2.0, max_cluster_size=inf, persistence_measure='size', sample_weights=None)¶
Compute PLSCAN clusters from a sorted minimum spanning forest.
- Parameters:
sorted_mst (
SpanningTree) – A sorted (partial) minimum spanning forest.num_points (
int) – The number of points in the sorted minimum spanning forest.min_cluster_size (
float, default:2.0) – The minimum size of a cluster.max_cluster_size (
float, default:inf) – The maximum size of a cluster.persistence_measure (
str, default:'size') – Selects a persistence measure. Valid options are “size”, “distance”, “density”, “size-distance”, and “size-density”. The “size”, “distance”, and “density” options compute persistence as the range of size/distance/density values for which clusters are leaves. The “size-distance” and “size-density” options compute bi-persistence as the distance/density – minimum cluster size areas for which clusters are leaves. Density is computed as exp(-dist).sample_weights (
ndarray[tuple[int],dtype[single]] |None, default:None) – Sample weights for the points in the sorted minimum spanning tree. If None, all samples are considered equally weighted.
- Return type:
tuple[Labelling,ndarray[tuple[int],dtype[uintc]],PersistenceTrace,LeafTree,CondensedTree,LinkageTree]- Returns:
labels – Essentially a tuple of cluster labels and membership probabilities for each point.
trace – A trace of the total (bi-)persistence per minimum cluster size.
leaf_tree – A leaf tree with cluster-leaves at minimum cluster sizes.
condensed_tree – A condensed tree with the cluster merge distances.
linkage_tree – A single linkage dendrogram of the sorted minimum spanning tree. (order matches the input sorted_mst!)