Julia implementation of L.J.P. van der Maaten and G.E. Hintons t-SNE visualisation technique.

The scripts in the `examples`

folder require `Plots`

, `MLDatasets`

and `RDatasets`

Julia packages.

`julia> Pkg.add("TSne")`

`tsne(X, ndim, reduce_dims, max_iter, perplexit; [keyword arguments])`

Apply t-SNE (t-Distributed Stochastic Neighbor Embedding) to `X`

,
i.e. embed its points (rows) into `ndims`

dimensions preserving close neighbours.
Returns the pointsÃ—`ndims`

matrix of calculated embedded coordinates.

`X`

: AbstractMatrix or AbstractVector. If`X`

is a matrix, then rows are observations and columns are features.`ndims`

: Dimension of the embedded space.`reduce_dims`

the number of the first dimensions of`X`

PCA to use for t-SNE, if 0, all available dimension are used`max_iter`

: Maximum number of iterations for the optimization- `perplexity': The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. Different values can result in significantly different results

**Optional Arguments**

`distance`

if`true`

, specifies that`X`

is a distance matrix, if of type`Function`

or`Distances.SemiMetric`

, specifies the function to use for calculating the distances between the rows (or elements, if`X`

is a vector) of`X`

`pca_init`

whether to use the first`ndims`

of`X`

PCA as the initial t-SNE layout, if`false`

(the default), the method is initialized with the random layout`max_iter`

how many iterations of t-SNE to do`perplexity`

the number of "effective neighbours" of a datapoint, typical values are from 5 to 50, the default is 30`verbose`

output informational and diagnostic messages`progress`

display progress meter during t-SNE optimization`min_gain`

,`eta`

,`initial_momentum`

,`final_momentum`

,`momentum_switch_iter`

,`stop_cheat_iter`

,`cheat_scale`

low-level parameters of t-SNE optimization`extended_output`

if`true`

, returns a tuple of embedded coordinates matrix, point perplexities and final Kullback-Leibler divergence

```
using TSne, Statistics, MLDatasets
rescale(A; dims=1) = (A .- mean(A, dims=dims)) ./ max.(std(A, dims=dims), eps())
alldata, allabels = MNIST.traindata(Float64);
data = reshape(permutedims(alldata[:, :, 1:2500], (3, 1, 2)),
2500, size(alldata, 1)*size(alldata, 2));
# Normalize the data, this should be done if there are large scale differences in the dataset
X = rescale(data, dims=1);
Y = tsne(X, 2, 50, 1000, 20.0);
using Plots
theplot = scatter(Y[:,1], Y[:,2], marker=(2,2,:auto,stroke(0)), color=Int.(allabels[1:size(Y,1)]))
Plots.pdf(theplot, "myplot.pdf")
```

`julia demo-csv.jl haveheader --labelcol=5 iris-headers.csv`

Creates `myplot.pdf`

with t-SNE result visualized using `Gadfly.jl`

.