Rust-based diffusion maps — diffusion

Performs diffusion maps dimensionality reduction on the input data. This function provides a user-friendly interface with input validation before calling the Rust implementation.

Usage

diffusion_maps(
  data,
  knn = NULL,
  n_dim = 2L,
  k = 5L,
  knn_method = c("kmknn", "balltree", "hnsw", "annoy", "nndescent", "exhaustive"),
  nn_params = params_nn(),
  dm_params = params_diffusion_maps(),
  seed = 42L,
  .verbose = TRUE
)

Arguments

data: Numerical matrix or data frame. The data to embed of shape samples x features. Will be coerced to a matrix.
knn: Optional NearestNeighbours class. If provided, diffusion maps will skip the k-nearest neighbour graph generation and use this one. Defaults to NULL.
n_dim: Integer. Number of dimensions in the embedding space. Defaults to 2L.
k: Integer. Number of nearest neighbours to consider for the affinity graph. Defaults to 5L.
knn_method: Character. (Approximate) Nearest neighbour method to use. One of "kmknn", "hnsw", "annoy", "nndescent", "balltree", or "exhaustive". Defaults to "kmknn".
nn_params: Named list. Nearest neighbour search parameters, see params_nn().
dm_params: Named list. Diffusion maps algorithm parameters, see params_diffusion_maps().
seed: Integer. Random seed for reproducibility. Defaults to 42L.
.verbose: Logical. Controls verbosity. Defaults to TRUE.

Value

A numerical matrix with dimensions samples x n_dim containing the diffusion maps embedding.