Rust-based t-SNE

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

Usage

tsne(
  data,
  knn = NULL,
  n_dim = 2L,
  perplexity = 30,
  approx_type = c("bh", "fft"),
  knn_method = c("balltree", "hnsw", "annoy", "nndescent", "exhaustive"),
  nn_params = params_nn(),
  tsne_params = params_tsne(),
  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, t-SNE 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. Currently only 2L is supported. Defaults to 2L.

perplexity

Numeric. Perplexity parameter, related to the number of nearest neighbours used in manifold learning. Typical values are between 5 and 50. Defaults to 30.0.

approx_type

Character. Approximation method for computing repulsive forces. One of "bh" for Barnes-Hut or "fft" for FFT-accelerated interpolation. Defaults to "bh".

knn_method

Character. Approximate nearest neighbour algorithm to use. One of "hnsw", "annoy", "nndescent", "balltree", or "exhaustive". Defaults to "balltree".

nn_params

Named list. Nearest neighbour search parameters, see params_nn().

tsne_params

Named list. t-SNE algorithm parameters, see params_tsne().

seed

Integer. Random seed for reproducibility.

.verbose

Logical. Controls verbosity. Defaults to TRUE.

Value

A numerical matrix with dimensions samples x n_dim containing the t-SNE embedding.

Details

The number of neighbours will be `3 * perplexity“, as this is a usual default in tSNE.