Compute Adaptive Radius-kNN Graphs Across k Values

Creates a sequence of adaptive-radius graphs by varying the k.scale parameter passed to create.rknn.graph(type = "adaptive.radius"). By default, ANN-backed construction uses the batched C++ backend while the direct all-pairs reference path uses the scalar R loop. Set backend = "r" to force the scalar loop for ANN parity checks.

Usage

create.rknn.graphs(X, k.values, ..., backend = c("auto", "cpp", "r"))

Arguments

X

Numeric matrix or data frame with observations in rows.

k.values

Strictly increasing integer vector, each between 1 and n - 1. Returned graphs follow this order.

...

Additional arguments forwarded to create.rknn.graph() with type = "adaptive.radius". The arguments type, k.scale, and radius are reserved by this plural constructor.

backend

Character scalar. "auto" uses the batched C++ ANN backend when radius.search = "ann" and the scalar R loop when radius.search = "all.pairs". "cpp" forces the batched C++ ANN backend and therefore requires radius.search = "ann". "r" forces the scalar R loop and is retained as a reference parity backend.

Value

A list of class "rknn_graphs" with components:

graphs

A named list of adaptive-radius graph objects, one per k.

k_statistics

A data frame with one row per k containing edge, component, and MST-repair counts.

timing

Present only when forwarded options request graph timing; contains backend-specific construction timing rows.

Attributes include k.values and n_vertices.

See also

create.rknn.graph()

Examples

X <- matrix(c(0, 1, 3, 4), ncol = 1)
result <- create.rknn.graphs(X, k.values = seq.int(1, 2), radius.search = "all.pairs",
    graph.detail = "minimal",
    prune.method = "none")
names(result$graphs)
#> [1] "1" "2"
result$k_statistics
#>   idx k n_edges n_edges_before_pruning n_edges_after_pruning
#> 1   1 1       2                      2                     2
#> 2   2 2       5                      5                     5
#>   n_components_before n_components_after n_mst_edges_added
#> 1                   2                  2                 0
#> 2                   1                  1                 0