t-SNE - t-SNE - 5.3 English - 68552

AOCL API Guide (68552)

Document_ID
68552
Release_Date
2026-05-13
Version
5.3 English

The following options are supported.

Table 4.15 Table of Options for t-SNE.#

Option name

Type

Default

Description

Constraints

mixed precision

string

\(s=\) no

Whether to use mixed precision iterative refinement, in which lower precision arithmetic is used before switching to the working precision for the final iterations.

\(s=\) no, or yes.

init

string

\(s=\) pca

Initialization method for the embedding.

\(s=\) pca, random, or supplied.

low precision max_iter

integer

\(i=200\)

If mixed precision iterative refinement is enabled, maximum number of iterations for the low precision phase.

\(1 \le i\)

check data

string

\(s=\) no

Check input data for NaNs prior to performing computation.

\(s=\) no, or yes.

n_components

integer

\(i=2\)

Number of embedding dimensions.

\(1 \le i \le 3\)

max_iter

integer

\(i=1000\)

Maximum number of gradient descent iterations.

\(1 \le i\)

seed

integer

\(i=0\)

Seed for random number generation; set to -1 for non-deterministic results.

\(-1 \le i\)

perplexity

real

\(r=30\)

Target perplexity for conditional probabilities.

\(1 \le r\)

n_iter_without_progress

integer

\(i=300\)

Stop if no progress is made for this many iterations.

\(0 \le i\)

early exaggeration

real

\(r=12\)

Exaggeration factor for early iterations.

\(1 \le r\)

storage order

string

\(s=\) column-major

Whether data is supplied and returned in row- or column-major order.

\(s=\) c, column-major, f, fortran, or row-major.

learning rate

real

\(r=-1\)

Gradient descent learning rate. Use any non-positive value for auto: max(N / early_exaggeration / 4, 50).

There are no constraints on \(r\).

min_grad_norm

real

\(r=1e-07\)

Stop if the gradient norm is below this threshold.

\(0 \le r\)

theta

real

\(r=0.5\)

Barnes-Hut approximation parameter (0 for exact).

\(0 \le r \le 1\)

low precision min_grad_norm

real

\(r=0.0001\)

If mixed precision iterative refinement is enabled, gradient norm convergence threshold for the low precision phase.

\(0 \le r\)