Dense weighted Jaccard similarity via C++ — c_weighted_jaccard

Compute the full weighted Jaccard similarity matrix for a dgCMatrix using an adaptive dense accumulation strategy. For small output matrices, uses a feature-oriented loop; for large outputs, switches to a column-oriented loop for better cache performance.

Usage

c_weighted_jaccard_dense(
  x,
  transpose = FALSE,
  threads = 4L,
  triangle = FALSE,
  distance = FALSE
)

Arguments

x: A dgCMatrix (sparse column-compressed matrix)
transpose: If FALSE, compare columns; if TRUE, compare rows
threads: Number of threads (default 4). Set to 0 for all cores.
triangle: If TRUE, return only the lower triangle as a flat numeric vector in dist layout. If FALSE (default), return a full square matrix.
distance: If TRUE, return distance (1 - similarity) instead of similarity. Default FALSE.

Value

A dense numeric similarity matrix, or a numeric vector in dist layout when triangle = TRUE.