c_weighted_jaccard_sparse: Sparse weighted Jaccard similarity via C++

In natcpp: Fast C++ Primitives for the 'NeuroAnatomy Toolbox'

Sparse weighted Jaccard similarity via C++

Description

Compute the weighted Jaccard similarity matrix for a dgCMatrix, returning a sparse result. Uses weighted_jaccard_sparse_fill to compute min-sums only for column (or row) pairs that share at least one non-zero feature, then normalises to similarity. Only the upper triangle is computed, taking advantage of the symmetry of the Jaccard index.

Usage

c_weighted_jaccard_sparse(
  x,
  transpose = FALSE,
  display_progress = TRUE,
  threads = NULL,
  triangle = FALSE,
  distance = FALSE
)

Arguments

x	A dgCMatrix (sparse column-compressed matrix)
transpose	If FALSE (default), compare columns; if TRUE, compare rows.
display_progress	Whether to show a text progress bar (default TRUE).
threads	Number of threads for parallel computation. The default NULL applies the package thread policy (respecting getOption("Ncpus") and the OMP_THREAD_LIMIT environment variable, else 2). Set to 0 to use all available cores.
triangle	If TRUE, return a symmetric dsCMatrix (upper triangle only). If FALSE (default), return a general dgCMatrix.
distance	If TRUE, return distance (1 - similarity) instead of similarity. Default FALSE. A warning is issued since sparse distance matrices are typically dense.

Value

A sparse similarity (or distance) matrix: dsCMatrix when triangle = TRUE, dgCMatrix otherwise.

See Also

c_weighted_jaccard_dense for the dense equivalent

Examples

## Not run: 
library(Matrix)
m <- sparseMatrix(i = c(1,2,1,2,3,3), j = c(1,1,2,2,2,3),
                  x = c(4,2,1,3,3,1), dims = c(3,3))
c_weighted_jaccard_sparse(m)

## End(Not run)

natcpp documentation built on Sept. 26, 2026, 9:06 a.m.