cuda_ml_tsne: Perform t-distributed stochastic neighbor embedding.

In cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries

Perform t-distributed stochastic neighbor embedding.

Description

t-distributed stochastic neighbor embedding (t-SNE) for visualizing high- dimensional data.

Usage

cuda_ml_tsne(
  x,
  n_components = 2L,
  n_neighbors = ceiling(3 * perplexity),
  method = c("barnes_hut", "fft", "exact"),
  angle = 0.5,
  n_iter = 1000L,
  learning_rate = 200,
  learning_rate_method = c("adaptive", "none"),
  perplexity = 30,
  perplexity_max_iter = 100L,
  perplexity_tol = 1e-05,
  early_exaggeration = 12,
  late_exaggeration = 1,
  exaggeration_iter = 250L,
  min_grad_norm = 1e-07,
  pre_momentum = 0.5,
  post_momentum = 0.8,
  square_distances = TRUE,
  seed = NULL
)

Arguments

x	The input matrix or data frame. Each data point should be a row and should consist of numeric values only.
n_components	Dimension of the embedded space.
n_neighbors	The number of datapoints to use in the attractive forces. Default: ceiling(3 * perplexity).
method	T-SNE method, must be one of {"barnes_hut", "fft", "exact"}. The "exact" method will be more accurate but slower. Both "barnes_hut" and "fft" methods are fast approximations.
angle	Valid values are between 0.0 and 1.0, which trade off speed and accuracy, respectively. Generally, these values are set between 0.2 and 0.8. (Barnes-Hut only.)
n_iter	Maximum number of iterations for the optimization. Should be at least 250. Default: 1000L.
learning_rate	Learning rate of the t-SNE algorithm, usually between (10, 1000). If the learning rate is too high, then t-SNE result could look like a cloud / ball of points.
learning_rate_method	Must be one of {"adaptive", "none"}. If "adaptive", then learning rate, early exaggeration, and perplexity are automatically tuned based on input size. Default: "adaptive".
perplexity	The target value of the conditional distribution's perplexity (see https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding for details).
perplexity_max_iter	The number of epochs the best Gaussian bands are found for. Default: 100L.
perplexity_tol	Stop optimizing the Gaussian bands when the conditional distribution's perplexity is within this desired tolerance compared to its target value. Default: 1e-5.
early_exaggeration	Controls the space between clusters. Not critical to tune this. Default: 12.0.
late_exaggeration	Controls the space between clusters. It may be beneficial to increase this slightly to improve cluster separation. This will be applied after exaggeration_iter iterations (FFT only).
exaggeration_iter	Number of exaggeration iterations. Default: 250L.
min_grad_norm	If the gradient norm is below this threshold, the optimization will be stopped. Default: 1e-7.
pre_momentum	During the exaggeration iteration, more forcefully apply gradients. Default: 0.5.
post_momentum	During the late phases, less forcefully apply gradients. Default: 0.8.
square_distances	Whether TSNE should square the distance values.
seed	Seed to the pseudorandom number generator. Setting this can make repeated runs look more similar. Note, however, that this highly parallelized t-SNE implementation is not completely deterministic between runs, even with the same seed being used for each run. Default: NULL.

Value

A matrix containing the embedding of the input data in a low- dimensional space, with each row representing an embedded data point.

Examples

library(cuda.ml)

if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  oils <- modeldata::oils
  oil_predictors <- oils |>
    subset(select = -class) |>
    scale()

  embedding <- cuda_ml_tsne(oil_predictors, method = "exact")

  set.seed(0)
  print(kmeans(embedding, centers = 7))
}

cuda.ml documentation built on Aug. 21, 2026, 9:14 a.m.