bhtsne

Barnes-Hut and vanilla implementations of the t-SNE algorithm written in Rust. The tree-accelerated version of the algorithm is described with fine detail in this paper by Laurens van der Maaten. The vanilla version of the algorithm is described in this other paper by G. Hinton and Laurens van der Maaten. Additional implementations of the algorithm, including this one, are listed at this page.

Usage

Add this line to your Cargo.toml: toml [dependencies] bhtsne = "0.4.1"

Documentation

The docs are available on crates.io.

Example

```rust use bhtsne;

const N: usize = 150; // Number of vectors to embed. const D: usize = 4; // The dimensionality of the // original space. const THETA: f32 = 0.5; // Parameter used by the Barnes-Hut algorithm. // When set to 0.0 the exact t-SNE version is // used instead.

const PERPLEXITY: f32 = 10.0; // Perplexity of the conditional distribution. const MAXITER: u64 = 2000; // Number of fitting iterations. const NODIMS: usize = 2; // Dimensionality of the embedded space.

// Loads the data from a csv file skipping the first row, // treating it as headers and skipping the 5th column, // treating it as a class label. // Do note that you can also use f64s. let mut data: Vec = bhtsne::load_csv("iris.csv", true, Some(4));

// The vector where the bi-dimensional embedding // will be stored. let mut y: Vec = vec![0.0; N * NO_DIMS];

// Runs t-SNE. bhtsne::run( &mut data, N, D, &mut y, NODIMS, PERPLEXITY, THETA, false, MAXITER, 250, 250, );

// Writes the embedding to a csv file. bhtsne::writecsv("irisembedding.csv", y, NO_DIMS); ```

MNIST embedding

The following embedding has been obtained by preprocessing the MNIST dataset using PCA to reduce its dimensionality to 50. It took approximately 17 mins on a 2.0GHz quad-core 10th-generation i5 MacBook Pro.