- planning.py shows how to use this graph with graph_search.
- graph_sparsification.py shows how to sparsify a graph
- batch_sparsification.py efficiently sparsify a graph with as a batch
- incremental_api.py shows how to extend with sparsity
To instantiate a graph:
graph = AsymMesh(n=10_000, k=6, dim=2, img_dim=[2], kernel_fn=l2, embed_fn=id2D, d_max=20) graph.extend(xys, images=xys, meta=xys) graph.update_zs() graph.update_edges()Most of the time it is better to enforce sparsity of the graph by only adding new vertices when there is no existing vertex that is close-by. The new dedupe API allows us to do this in a batch fashion:
spots = graph.dedupe(images=xys, r_min=r_min) xys = xys[spots] ds = graph.to_goal(zs_2=xys) if ds.size == 0: graph.extend(xys, images=xys, meta=xys) else: m = ds.min(axis=-1) >= r_min if m.sum() > 0: graph.extend(xys[m], images=xys[m], meta=xys[m]) graph.update_edges()| Dense | Sparse | Details |
|---|---|---|
 |  | 10x more edges in the dense graph in comparison to the sparse graph. |
Import this module in the method module.