Diffusion maps implementation with pre-computed kNN

Leverages the Diffusion Maps implementation in manifolds-rs - a very fast Rust-based implementation. This version uses a pre-computed kNN graph, please see new_nearest_neighbour().

Usage

rs_diffusion_maps_from_knn(
  embd,
  knn_data,
  n_dim,
  k,
  dm_params,
  seed,
  use_high_precision,
  verbose
)

Arguments

embd

Numerical matrix. The data to use to generate the embeddings. Should be of dimensions samples x features.

knn_data

NearestNeighbours class from R.

n_dim

Integer. Number of dimensions to return.

k

Integer. Number of nearest neighbours to consider.

dm_params

Named list. List that contains all of the key parameters for the diffusion maps generation.

seed

Integer. Seed for reproducibility.

use_high_precision

Optional logical. Controls fp32 vs fp64 for. If NULL will use sensible default thresholding.

verbose

Integer. If 0L -> silent or 1L for normal verbosity; 2L for detailed verbosity.

Value

The diffusion maps embeddings.