Haskell-tSNE

Available on Hackage: tsne

A pure Haskell implementation of the t-SNE data dimension reduction algorithm.

• The tsne2D and tsne3D functions take a list of high dimension values and generates a sequence of solutions. Each solution is a list of 2D/3D positions, one for each input value.
• Rather slow. It's not at all optimized yet. Internally it uses lists of plain vanilla Haskell boxed Doubles, which probably accounts for a a fair chunk of the slowness.
• Based on the tSNEJS JavaScript implementation (thanks!)

See it in action on YouTube in my 3D t-SNE visualization application TeaSneeze.

Example

Using the Python scikit-learn digits data set.

#!haskell

import Data.Default(def)
import Data.Algorithm.TSNE

main :: IO ()
main = do
    -- print results out as they are produced forever
    forTsne3D printTsneResult def exampleInput

printTsneResult :: TSNEOutput3D -> IO ()
printTsneResult r = do
    putStrLn $ "iteration: " ++ (show.tsneIteration3D) r
    putStrLn $ "cost: " ++ (show.tsneCost3D) r

-- first 20 values of the digits dataset
exampleInput :: [[Double]]
exampleInput = [
        [0.0,0.0,5.0,13.0,9.0,1.0,0.0,0.0,0.0,0.0,13.0,15.0,10.0,15.0,5.0,0.0,0.0,3.0,15.0,2.0,0.0,11.0,8.0,0.0,0.0,4.0,12.0,0.0,0.0,8.0,8.0,0.0,0.0,5.0,8.0,0.0,0.0,9.0,8.0,0.0,0.0,4.0,11.0,0.0,1.0,12.0,7.0,0.0,0.0,2.0,14.0,5.0,10.0,12.0,0.0,0.0,0.0,0.0,6.0,13.0,10.0,0.0,0.0,0.0],
        [0.0,0.0,0.0,12.0,13.0,5.0,0.0,0.0,0.0,0.0,0.0,11.0,16.0,9.0,0.0,0.0,0.0,0.0,3.0,15.0,16.0,6.0,0.0,0.0,0.0,7.0,15.0,16.0,16.0,2.0,0.0,0.0,0.0,0.0,1.0,16.0,16.0,3.0,0.0,0.0,0.0,0.0,1.0,16.0,16.0,6.0,0.0,0.0,0.0,0.0,1.0,16.0,16.0,6.0,0.0,0.0,0.0,0.0,0.0,11.0,16.0,10.0,0.0,0.0],
        [0.0,0.0,0.0,4.0,15.0,12.0,0.0,0.0,0.0,0.0,3.0,16.0,15.0,14.0,0.0,0.0,0.0,0.0,8.0,13.0,8.0,16.0,0.0,0.0,0.0,0.0,1.0,6.0,15.0,11.0,0.0,0.0,0.0,1.0,8.0,13.0,15.0,1.0,0.0,0.0,0.0,9.0,16.0,16.0,5.0,0.0,0.0,0.0,0.0,3.0,13.0,16.0,16.0,11.0,5.0,0.0,0.0,0.0,0.0,3.0,11.0,16.0,9.0,0.0],
        [0.0,0.0,7.0,15.0,13.0,1.0,0.0,0.0,0.0,8.0,13.0,6.0,15.0,4.0,0.0,0.0,0.0,2.0,1.0,13.0,13.0,0.0,0.0,0.0,0.0,0.0,2.0,15.0,11.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,12.0,12.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,10.0,8.0,0.0,0.0,0.0,8.0,4.0,5.0,14.0,9.0,0.0,0.0,0.0,7.0,13.0,13.0,9.0,0.0,0.0],
        [0.0,0.0,0.0,1.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,8.0,0.0,0.0,0.0,0.0,0.0,1.0,13.0,6.0,2.0,2.0,0.0,0.0,0.0,7.0,15.0,0.0,9.0,8.0,0.0,0.0,5.0,16.0,10.0,0.0,16.0,6.0,0.0,0.0,4.0,15.0,16.0,13.0,16.0,1.0,0.0,0.0,0.0,0.0,3.0,15.0,10.0,0.0,0.0,0.0,0.0,0.0,2.0,16.0,4.0,0.0,0.0],
        [0.0,0.0,12.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,16.0,16.0,14.0,0.0,0.0,0.0,0.0,13.0,16.0,15.0,10.0,1.0,0.0,0.0,0.0,11.0,16.0,16.0,7.0,0.0,0.0,0.0,0.0,0.0,4.0,7.0,16.0,7.0,0.0,0.0,0.0,0.0,0.0,4.0,16.0,9.0,0.0,0.0,0.0,5.0,4.0,12.0,16.0,4.0,0.0,0.0,0.0,9.0,16.0,16.0,10.0,0.0,0.0],
        [0.0,0.0,0.0,12.0,13.0,0.0,0.0,0.0,0.0,0.0,5.0,16.0,8.0,0.0,0.0,0.0,0.0,0.0,13.0,16.0,3.0,0.0,0.0,0.0,0.0,0.0,14.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,12.0,7.0,2.0,0.0,0.0,0.0,0.0,13.0,16.0,13.0,16.0,3.0,0.0,0.0,0.0,7.0,16.0,11.0,15.0,8.0,0.0,0.0,0.0,1.0,9.0,15.0,11.0,3.0,0.0],
        [0.0,0.0,7.0,8.0,13.0,16.0,15.0,1.0,0.0,0.0,7.0,7.0,4.0,11.0,12.0,0.0,0.0,0.0,0.0,0.0,8.0,13.0,1.0,0.0,0.0,4.0,8.0,8.0,15.0,15.0,6.0,0.0,0.0,2.0,11.0,15.0,15.0,4.0,0.0,0.0,0.0,0.0,0.0,16.0,5.0,0.0,0.0,0.0,0.0,0.0,9.0,15.0,1.0,0.0,0.0,0.0,0.0,0.0,13.0,5.0,0.0,0.0,0.0,0.0],
        [0.0,0.0,9.0,14.0,8.0,1.0,0.0,0.0,0.0,0.0,12.0,14.0,14.0,12.0,0.0,0.0,0.0,0.0,9.0,10.0,0.0,15.0,4.0,0.0,0.0,0.0,3.0,16.0,12.0,14.0,2.0,0.0,0.0,0.0,4.0,16.0,16.0,2.0,0.0,0.0,0.0,3.0,16.0,8.0,10.0,13.0,2.0,0.0,0.0,1.0,15.0,1.0,3.0,16.0,8.0,0.0,0.0,0.0,11.0,16.0,15.0,11.0,1.0,0.0],
        [0.0,0.0,11.0,12.0,0.0,0.0,0.0,0.0,0.0,2.0,16.0,16.0,16.0,13.0,0.0,0.0,0.0,3.0,16.0,12.0,10.0,14.0,0.0,0.0,0.0,1.0,16.0,1.0,12.0,15.0,0.0,0.0,0.0,0.0,13.0,16.0,9.0,15.0,2.0,0.0,0.0,0.0,0.0,3.0,0.0,9.0,11.0,0.0,0.0,0.0,0.0,0.0,9.0,15.0,4.0,0.0,0.0,0.0,9.0,12.0,13.0,3.0,0.0,0.0],
        [0.0,0.0,1.0,9.0,15.0,11.0,0.0,0.0,0.0,0.0,11.0,16.0,8.0,14.0,6.0,0.0,0.0,2.0,16.0,10.0,0.0,9.0,9.0,0.0,0.0,1.0,16.0,4.0,0.0,8.0,8.0,0.0,0.0,4.0,16.0,4.0,0.0,8.0,8.0,0.0,0.0,1.0,16.0,5.0,1.0,11.0,3.0,0.0,0.0,0.0,12.0,12.0,10.0,10.0,0.0,0.0,0.0,0.0,1.0,10.0,13.0,3.0,0.0,0.0],
        [0.0,0.0,0.0,0.0,14.0,13.0,1.0,0.0,0.0,0.0,0.0,5.0,16.0,16.0,2.0,0.0,0.0,0.0,0.0,14.0,16.0,12.0,0.0,0.0,0.0,1.0,10.0,16.0,16.0,12.0,0.0,0.0,0.0,3.0,12.0,14.0,16.0,9.0,0.0,0.0,0.0,0.0,0.0,5.0,16.0,15.0,0.0,0.0,0.0,0.0,0.0,4.0,16.0,14.0,0.0,0.0,0.0,0.0,0.0,1.0,13.0,16.0,1.0,0.0],
        [0.0,0.0,5.0,12.0,1.0,0.0,0.0,0.0,0.0,0.0,15.0,14.0,7.0,0.0,0.0,0.0,0.0,0.0,13.0,1.0,12.0,0.0,0.0,0.0,0.0,2.0,10.0,0.0,14.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,16.0,1.0,0.0,0.0,0.0,0.0,0.0,6.0,15.0,0.0,0.0,0.0,0.0,0.0,9.0,16.0,15.0,9.0,8.0,2.0,0.0,0.0,3.0,11.0,8.0,13.0,12.0,4.0],
        [0.0,2.0,9.0,15.0,14.0,9.0,3.0,0.0,0.0,4.0,13.0,8.0,9.0,16.0,8.0,0.0,0.0,0.0,0.0,6.0,14.0,15.0,3.0,0.0,0.0,0.0,0.0,11.0,14.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,15.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,15.0,4.0,0.0,0.0,1.0,5.0,6.0,13.0,16.0,6.0,0.0,0.0,2.0,12.0,12.0,13.0,11.0,0.0,0.0],
        [0.0,0.0,0.0,8.0,15.0,1.0,0.0,0.0,0.0,0.0,1.0,14.0,13.0,1.0,1.0,0.0,0.0,0.0,10.0,15.0,3.0,15.0,11.0,0.0,0.0,7.0,16.0,7.0,1.0,16.0,8.0,0.0,0.0,9.0,16.0,13.0,14.0,16.0,5.0,0.0,0.0,1.0,10.0,15.0,16.0,14.0,0.0,0.0,0.0,0.0,0.0,1.0,16.0,10.0,0.0,0.0,0.0,0.0,0.0,10.0,15.0,4.0,0.0,0.0],
        [0.0,5.0,12.0,13.0,16.0,16.0,2.0,0.0,0.0,11.0,16.0,15.0,8.0,4.0,0.0,0.0,0.0,8.0,14.0,11.0,1.0,0.0,0.0,0.0,0.0,8.0,16.0,16.0,14.0,0.0,0.0,0.0,0.0,1.0,6.0,6.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,16.0,3.0,0.0,0.0,0.0,1.0,5.0,15.0,13.0,0.0,0.0,0.0,0.0,4.0,15.0,16.0,2.0,0.0,0.0,0.0],
        [0.0,0.0,0.0,8.0,15.0,1.0,0.0,0.0,0.0,0.0,0.0,12.0,14.0,0.0,0.0,0.0,0.0,0.0,3.0,16.0,7.0,0.0,0.0,0.0,0.0,0.0,6.0,16.0,2.0,0.0,0.0,0.0,0.0,0.0,7.0,16.0,16.0,13.0,5.0,0.0,0.0,0.0,15.0,16.0,9.0,9.0,14.0,0.0,0.0,0.0,3.0,14.0,9.0,2.0,16.0,2.0,0.0,0.0,0.0,7.0,15.0,16.0,11.0,0.0],
        [0.0,0.0,1.0,8.0,15.0,10.0,0.0,0.0,0.0,3.0,13.0,15.0,14.0,14.0,0.0,0.0,0.0,5.0,10.0,0.0,10.0,12.0,0.0,0.0,0.0,0.0,3.0,5.0,15.0,10.0,2.0,0.0,0.0,0.0,16.0,16.0,16.0,16.0,12.0,0.0,0.0,1.0,8.0,12.0,14.0,8.0,3.0,0.0,0.0,0.0,0.0,10.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,9.0,0.0,0.0,0.0],
        [0.0,0.0,10.0,7.0,13.0,9.0,0.0,0.0,0.0,0.0,9.0,10.0,12.0,15.0,2.0,0.0,0.0,0.0,4.0,11.0,10.0,11.0,0.0,0.0,0.0,0.0,1.0,16.0,10.0,1.0,0.0,0.0,0.0,0.0,12.0,13.0,4.0,0.0,0.0,0.0,0.0,0.0,12.0,1.0,12.0,0.0,0.0,0.0,0.0,1.0,10.0,2.0,14.0,0.0,0.0,0.0,0.0,0.0,11.0,14.0,5.0,0.0,0.0,0.0],
        [0.0,0.0,6.0,14.0,4.0,0.0,0.0,0.0,0.0,0.0,11.0,16.0,10.0,0.0,0.0,0.0,0.0,0.0,8.0,14.0,16.0,2.0,0.0,0.0,0.0,0.0,1.0,12.0,12.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,11.0,0.0,0.0,0.0,1.0,4.0,4.0,7.0,16.0,2.0,0.0,0.0,7.0,16.0,16.0,13.0,11.0,1.0]
    ]