tSNE implementation from a pre-computed kNN graph

Wraps the tSNE implementation in manifolds-rs. You have two optimiser options: "bh", which tends to be faster on smaller data sets, and "fft" for large data sets. This version takes a pre-computed kNN graph, please see new_nearest_neighbour().

Usage

rs_tsne_from_knn_gpu(
  embd,
  knn_data,
  n_dim,
  perplexity,
  approx_type,
  tsne_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 tSNE dimensions to return. Needs to be two, others are not supported.

perplexity

Numeric. The tSNE perplexity parameter.

approx_type

String. One of c("fft", "bh"). Which of the two approximations to use.

tsne_params

Named list. List that contains all of the key parameters for the tSNE 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 tSNE embeddings.