The input may have been computed on CUDA, but graph assembly itself is
currently performed on the CPU with a sparse Matrix.
Union graphs retain an edge observed in either direction, whereas mutual
graphs require both directed neighbour relations. When the two directions
have different weights, the undirected edge retains the stronger affinity.
Usage
cuda_knn_graph(
neighbors,
weighting = c("binary", "distance", "gaussian"),
symmetrize = c("union", "mutual"),
sigma = NULL
)
Arguments
- neighbors
A cuda_knn() result or compatible list.
- weighting
Edge weighting: binary, inverse-distance, or Gaussian.
- symmetrize
Keep the union or only mutual nearest-neighbour edges.
- sigma
Gaussian bandwidth. Defaults to the median positive distance.
Value
A cuda_graph list containing sparse adjacency, counts of
vertices and undirected edges, weighting, symmetrize,
source_device, and the graph-assembly backend. Named kNN observations
are retained as adjacency dimnames and in vertex_names.
Examples
index <- matrix(c(2, 3, 1, 3, 1, 2), 3, byrow = TRUE)
distance <- matrix(c(1, 2, 1, 1, 2, 1), 3, byrow = TRUE)
cuda_knn_graph(list(index = index, distance = distance))
#> <cuda_graph vertices=3 edges=3 weighting=binary source_device=unknown compute=cpu backend=Matrix>
