aggregated_similarities: Aggregation similarities for sparse 3d representations
In vspinu/simdist: High performance distance and similarity functions

These are similarity counterparts of aggregated_distances, which see. Generally similarities are computed from their distance counterparts through s=1-d or s=1/(1+d), but this need not always be the case. See the source code for the exact computation.

asim_centroid(x, y = NULL, vecs, ptrans = NULL, strans = NULL,
  by = c("primary", "secondary", "row", "column"), precompute = TRUE,
  dist_type = "cosine")

asim_semantic_min_sum(x, y = NULL, vecs, ptrans = NULL, strans = NULL,
  by = c("primary", "secondary", "row", "column"), precompute = TRUE,
  dist_type = "cosine")

asim_semantic_min_max(x, y = NULL, vecs, ptrans = NULL, strans = NULL,
  by = c("primary", "secondary", "row", "column"), precompute = TRUE,
  dist_type = "cosine")

asim_rwmd(x, y = NULL, vecs, ptrans = NULL, strans = NULL,
  by = c("primary", "secondary", "row", "column"), precompute = TRUE,
  dist_type = "cosine")

`x`	sparse or dense objects supported by `simdist`. See `simdist-package` for description of supported types and related terminology.
`y`	sparse or dense objects supported by `simdist`. See `simdist-package` for description of supported types and related terminology.
`vecs`	Dense matrix with columns
`ptrans`	Primary and secondary transformations. Can be either a function, string or a numeric vector. When a function, it must take 3 arguments - an object supported by `simdist` distance measures, names of a dimension ("primary" or "secondary") and logical na.rm. When a string it must name a transformation function without the semantic prefix (norm_, scale_ or trans_); see `link{transformations}`. When numeric vector it specifies weights to scale along the corresponding dimension.
`strans`	Primary and secondary transformations. Can be either a function, string or a numeric vector. When a function, it must take 3 arguments - an object supported by `simdist` distance measures, names of a dimension ("primary" or "secondary") and logical na.rm. When a string it must name a transformation function without the semantic prefix (norm_, scale_ or trans_); see `link{transformations}`. When numeric vector it specifies weights to scale along the corresponding dimension.
`by`	Dimension along which to perform distance computation. For all supported data structures computation along primary dimension is more or as efficient than along the secondary dimension.
`precompute`	logical Weather to optimize the computation for speed and precompute individual distances. The computation is method specific bug generally should be `TRUE` (the default) unless memory usage is a concern.
`dist_type`	distance to use across individual vectors in `vecs`