Awesome
go-tsne
A Go implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE), a prize-winning technique for dimensionality reduction particularly well suited for visualizing high-dimensional datasets.
<p float="left"> <img src="https://github.com/danaugrs/go-tsne/blob/master/examples/mnist2d/mnist2d.gif" alt="mnist2d" width="49%" /> <img src="https://github.com/danaugrs/go-tsne/blob/master/examples/mnist3d/mnist3d.gif" alt="mnist3d" width="49%" /> </p>Usage
Import this library:
import "github.com/danaugrs/go-tsne/tsne"
Create the TSNE object:
t := tsne.NewTSNE(2, 300, 100, 300, true)
The parameters are
- Number of output dimensions
- Perplexity
- Learning rate
- Max number of iterations
- Verbosity
There are two ways to start the t-SNE embedding optimization. The regular way is to provide an n by d matrix where each row is a datapoint and each column is a dimension:
Y := t.EmbedData(X, nil)
The alternative is to provide a distance matrix directly:
Y := t.EmbedDistances(D, nil)
In either case, the returned matrix Y will contain the final embedding.
For more fine-grained control, a step function can be provided in either case:
Y := t.EmbedData(X, func(iter int, divergence float64, embedding mat.Matrix) bool {
fmt.Printf("Iteration %d: divergence is %v\n", iter, divergence)
return false
})
The step function has access to the iteration, the current divergence, and the embedding optimized so far.
You can return true to halt the optimization.
Examples
Two examples are provided - mnist2d and mnist3d. They both use the same data - a subset of MNIST with 2500 handwritten digits. mnist2d generates plots throughout the optimization process, and mnist3d shows the optimization happening in real-time, in 3D. mnist3d depends on G3N.
To run an example, cd to the example's directory, build it, and execute it, e.g:
cd examples/mnist2d
go build
./mnist2d
Support
I hope you enjoy using and learning from go-tsne as much as I enjoyed writing it.
If you come across any issues, please report them.