ashenfad · July 20, 2016 22:08
diff --git a/README.md b/README.md
diff --git a/index.html b/index.html

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diff --git a/model.json b/model.json
 {
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0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2 ], [ 0, 0, 20, 16, 0, 0, 0, 0, 1, 0, 4, 0, 0, 5, 0, 1, 0, 11, 5, 21, 0, 76, 0, 0, 0, 0, 0, 6, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 2, 0, 114, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 5, 0, 0, 0, 0, 0 ], [ 0, 51, 1, 0, 4, 0, 0, 0, 23, 4, 0, 0, 1, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 14, 0, 0, 0, 0, 0, 0, 0, 3, 52 ], [ 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 2, 0, 0, 0, 97, 1, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 32, 0 ], [ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 1, 0, 0, 0, 0, 0, 1, 3, 0, 0, 205, 0, 0, 0, 1, 0, 0, 2, 10, 0, 0, 7, 0, 0, 2, 27, 1 ], [ 0, 0, 6, 0, 3, 123, 1, 0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 132, 29, 61, 0, 1, 0, 51, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 4, 0, 1 ], [ 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0 ], [ 0, 3, 1, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ], [ 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 7, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 100, 4, 3, 0, 0, 0, 0, 0, 52, 34, 2, 2, 0, 5, 0, 0, 0, 0, 12, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 10, 0, 1, 42, 9, 0, 0, 1, 0 ], [ 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 0, 0, 0, 0, 0, 0, 0, 0, 1, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3 ], [ 0, 0, 1, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 6, 0, 0, 22, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 6, 0, 0, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 4, 0, 2, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0 ], [ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 8, 4, 1, 0, 7, 0, 0, 0, 8, 0, 0, 2, 5, 3, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 7, 0, 0, 0, 28, 0, 15, 0, 0, 0, 0, 0, 0, 20, 1 ], [ 0, 1, 0, 2, 0, 1, 5, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 21, 0 ], [ 0, 6, 0, 38, 0, 0, 0, 0, 0, 0, 0, 1, 45, 78, 0, 0, 1, 0, 0, 0, 0, 4, 0, 9, 0, 1, 1, 17, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0 ], [ 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 15, 0, 0, 3, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 0, 0, 0, 0, 0, 0 ], [ 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 2, 0, 13, 0, 1, 0, 10, 10, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 9, 0, 0, 0, 0, 11 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 17, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 1, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 1, 0, 0, 4 ], [ 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 10, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0 ], [ 1, 0, 1, 10, 0, 0, 0, 0, 1, 0, 0, 1, 0, 85, 0, 26, 6, 10, 10, 0, 36, 0, 0, 0, 29, 1, 0, 1, 0, 2, 27, 0, 10, 0, 22, 0, 0, 0, 0, 10 ], [ 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 7, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 55, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 13, 2, 5, 0, 0, 1, 1, 0, 0, 0, 0 ], [ 1, 0, 0, 7, 0, 0, 0, 0, 4, 0, 0, 2, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 28 ], [ 0, 3, 0, 91, 0, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 1, 0, 2, 2, 2, 0, 0, 11, 1, 0, 0, 0, 0, 0, 0, 0, 3 ], [ 0, 0, 0, 17, 2, 21, 11, 0, 0, 0, 0, 3, 0, 0, 2, 0, 16, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 9, 6, 0, 0, 1, 2, 0, 0, 10, 0, 256 ], [ 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 4, 0, 3, 1, 6, 0, 4, 12, 0, 0, 0, 0, 0, 0, 0, 0, 2 ], [ 0, 8, 0, 43, 28, 0, 0, 0, 3, 0, 0, 0, 0, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 8, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 11, 0, 0, 0, 0, 1, 0, 13, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 2, 0, 1, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 1, 2, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0 ], [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ], [ 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 0, 4, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 10, 1, 2, 0, 0, 4, 0, 0, 11 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 19, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 0, 0 ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0 ] ],
    "nwsum" : [ 13638, 13755, 14449, 13402, 13676, 14078, 15302, 13035, 15297, 12904, 16278, 13538, 17126, 14149, 13630, 15623, 13189, 14895, 14683, 13107, 12473, 13283, 20802, 18293, 14938, 12320, 13939, 12259, 15077, 13212, 12925, 14222, 15970, 20825, 16072, 17962, 14967, 13615, 16393, 13521 ]
  }
 }
diff --git a/slda-dist.js b/slda-dist.js

 /*
  Given a BigML SLDA resource, will return a function that computes
  the cosine distance between two topics (referenced by their index)
 */
 function cosineDistFn(resource) {
    var nw = resource.model.nw;
    return function (a, b) {
        var dot = 0;
        var aSqr = 0;
        var bSqr = 0;
        for (i in nw) {
            var aVal = nw[i][a];
            var bVal = nw[i][b];
            dot += aVal * bVal;
            aSqr += aVal * aVal;
            bSqr += bVal * bVal;
        }
        var similarity = dot / (Math.sqrt(aSqr) * Math.sqrt(bSqr));
        var distance = 2 * Math.acos(similarity) / Math.PI;
        return distance;
    };
 }

 /*
  Given a BigML SLDA resource, returns a distance matrix for the
  topics
 */
 function distMatrix (resource) {
    var topics = resource.model.topic_count;
    var distFn = cosineDistFn(resource);
    var matrix = new Array(topics);
    for (var a = 0; a < topics; a++) {
        matrix[a] = new Array(topics);
        for (var b = 0; b < topics; b++) {
            if (a == b) {
                matrix[a][b] = 0;
            } else if (a > b) {
                matrix[a][b] = matrix[b][a];
            } else {
                matrix[a][b] = distFn(a, b);
            }
        }
    }
    return matrix;
 }
diff --git a/thumbnail.png b/thumbnail.png
diff --git a/tsne.js b/tsne.js
 // from https://github.com/karpathy/tsnejs

 // create main global object
 var tsnejs = tsnejs || { REVISION: 'ALPHA' };

 (function(global) {
  "use strict";

  // utility function
  var assert = function(condition, message) {
    if (!condition) { throw message || "Assertion failed"; }
  }

  // syntax sugar
  var getopt = function(opt, field, defaultval) {
    if(opt.hasOwnProperty(field)) {
      return opt[field];
    } else {
      return defaultval;
    }
  }

  // return 0 mean unit standard deviation random number
  var return_v = false;
  var v_val = 0.0;
  var gaussRandom = function() {
    if(return_v) { 
      return_v = false;
      return v_val; 
    }
    var u = 2*Math.random()-1;
    var v = 2*Math.random()-1;
    var r = u*u + v*v;
    if(r == 0 || r > 1) return gaussRandom();
    var c = Math.sqrt(-2*Math.log(r)/r);
    v_val = v*c; // cache this for next function call for efficiency
    return_v = true;
    return u*c;
  }

  // return random normal number
  var randn = function(mu, std){ return mu+gaussRandom()*std; }

  // utilitity that creates contiguous vector of zeros of size n
  var zeros = function(n) {
    if(typeof(n)==='undefined' || isNaN(n)) { return []; }
    if(typeof ArrayBuffer === 'undefined') {
      // lacking browser support
      var arr = new Array(n);
      for(var i=0;i<n;i++) { arr[i]= 0; }
      return arr;
    } else {
      return new Float64Array(n); // typed arrays are faster
    }
  }

  // utility that returns 2d array filled with random numbers
  // or with value s, if provided
  var randn2d = function(n,d,s) {
    var uses = typeof s !== 'undefined';
    var x = [];
    for(var i=0;i<n;i++) {
      var xhere = [];
      for(var j=0;j<d;j++) { 
        if(uses) {
          xhere.push(s); 
        } else {
          xhere.push(randn(0.0, 1e-4)); 
        }
      }
      x.push(xhere);
    }
    return x;
  }

  // compute L2 distance between two vectors
  var L2 = function(x1, x2) {
    var D = x1.length;
    var d = 0;
    for(var i=0;i<D;i++) { 
      var x1i = x1[i];
      var x2i = x2[i];
      d += (x1i-x2i)*(x1i-x2i);
    }
    return d;
  }

  // compute pairwise distance in all vectors in X
  var xtod = function(X) {
    var N = X.length;
    var dist = zeros(N * N); // allocate contiguous array
    for(var i=0;i<N;i++) {
      for(var j=i+1;j<N;j++) {
        var d = L2(X[i], X[j]);
        dist[i*N+j] = d;
        dist[j*N+i] = d;
      }
    }
    return dist;
  }

  // compute (p_{i|j} + p_{j|i})/(2n)
  var d2p = function(D, perplexity, tol) {
    var Nf = Math.sqrt(D.length); // this better be an integer
    var N = Math.floor(Nf);
    assert(N === Nf, "D should have square number of elements.");
    var Htarget = Math.log(perplexity); // target entropy of distribution
    var P = zeros(N * N); // temporary probability matrix

    var prow = zeros(N); // a temporary storage compartment
    for(var i=0;i<N;i++) {
      var betamin = -Infinity;
      var betamax = Infinity;
      var beta = 1; // initial value of precision
      var done = false;
      var maxtries = 50;

      // perform binary search to find a suitable precision beta
      // so that the entropy of the distribution is appropriate
      var num = 0;
      while(!done) {
        //debugger;

        // compute entropy and kernel row with beta precision
        var psum = 0.0;
        for(var j=0;j<N;j++) {
          var pj = Math.exp(- D[i*N+j] * beta);
          if(i===j) { pj = 0; } // we dont care about diagonals
          prow[j] = pj;
          psum += pj;
        }
        // normalize p and compute entropy
        var Hhere = 0.0;
        for(var j=0;j<N;j++) {
          var pj = prow[j] / psum;
          prow[j] = pj;
          if(pj > 1e-7) Hhere -= pj * Math.log(pj);
        }

        // adjust beta based on result
        if(Hhere > Htarget) {
          // entropy was too high (distribution too diffuse)
          // so we need to increase the precision for more peaky distribution
          betamin = beta; // move up the bounds
          if(betamax === Infinity) { beta = beta * 2; }
          else { beta = (beta + betamax) / 2; }

        } else {
          // converse case. make distrubtion less peaky
          betamax = beta;
          if(betamin === -Infinity) { beta = beta / 2; }
          else { beta = (beta + betamin) / 2; }
        }

        // stopping conditions: too many tries or got a good precision
        num++;
        if(Math.abs(Hhere - Htarget) < tol) { done = true; }
        if(num >= maxtries) { done = true; }
      }

      // console.log('data point ' + i + ' gets precision ' + beta + ' after ' + num + ' binary search steps.');
      // copy over the final prow to P at row i
      for(var j=0;j<N;j++) { P[i*N+j] = prow[j]; }

    } // end loop over examples i

    // symmetrize P and normalize it to sum to 1 over all ij
    var Pout = zeros(N * N);
    var N2 = N*2;
    for(var i=0;i<N;i++) {
      for(var j=0;j<N;j++) {
        Pout[i*N+j] = Math.max((P[i*N+j] + P[j*N+i])/N2, 1e-100);
      }
    }

    return Pout;
  }

  // helper function
  function sign(x) { return x > 0 ? 1 : x < 0 ? -1 : 0; }

  var tSNE = function(opt) {
    var opt = opt || {};
    this.perplexity = getopt(opt, "perplexity", 30); // effective number of nearest neighbors
    this.dim = getopt(opt, "dim", 2); // by default 2-D tSNE
    this.epsilon = getopt(opt, "epsilon", 10); // learning rate

    this.iter = 0;
  }

  tSNE.prototype = {

    // this function takes a set of high-dimensional points
    // and creates matrix P from them using gaussian kernel
    initDataRaw: function(X) {
      var N = X.length;
      var D = X[0].length;
      assert(N > 0, " X is empty? You must have some data!");
      assert(D > 0, " X[0] is empty? Where is the data?");
      var dists = xtod(X); // convert X to distances using gaussian kernel
      this.P = d2p(dists, this.perplexity, 1e-4); // attach to object
      this.N = N; // back up the size of the dataset
      this.initSolution(); // refresh this
    },

    // this function takes a given distance matrix and creates
    // matrix P from them.
    // D is assumed to be provided as a list of lists, and should be symmetric
    initDataDist: function(D) {
      var N = D.length;
      assert(N > 0, " X is empty? You must have some data!");
      // convert D to a (fast) typed array version
      var dists = zeros(N * N); // allocate contiguous array
      for(var i=0;i<N;i++) {
        for(var j=i+1;j<N;j++) {
          var d = D[i][j];
          dists[i*N+j] = d;
          dists[j*N+i] = d;
        }
      }
      this.P = d2p(dists, this.perplexity, 1e-4);
      this.N = N;
      this.initSolution(); // refresh this
    },

    // (re)initializes the solution to random
    initSolution: function() {
      // generate random solution to t-SNE
      this.Y = randn2d(this.N, this.dim); // the solution
      this.gains = randn2d(this.N, this.dim, 1.0); // step gains to accelerate progress in unchanging directions
      this.ystep = randn2d(this.N, this.dim, 0.0); // momentum accumulator
      this.iter = 0;
    },

    // return pointer to current solution
    getSolution: function() {
      return this.Y;
    },

    // perform a single step of optimization to improve the embedding
    step: function() {
      this.iter += 1;
      var N = this.N;

      var cg = this.costGrad(this.Y); // evaluate gradient
      var cost = cg.cost;
      var grad = cg.grad;

      // perform gradient step
      var ymean = zeros(this.dim);
      for(var i=0;i<N;i++) {
        for(var d=0;d<this.dim;d++) {
          var gid = grad[i][d];
          var sid = this.ystep[i][d];
          var gainid = this.gains[i][d];

          // compute gain update
          var newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
          if(newgain < 0.01) newgain = 0.01; // clamp
          this.gains[i][d] = newgain; // store for next turn

          // compute momentum step direction
          var momval = this.iter < 250 ? 0.5 : 0.8;
          var newsid = momval * sid - this.epsilon * newgain * grad[i][d];
          this.ystep[i][d] = newsid; // remember the step we took

          // step!
          this.Y[i][d] += newsid; 

          ymean[d] += this.Y[i][d]; // accumulate mean so that we can center later
        }
      }

      // reproject Y to be zero mean
      for(var i=0;i<N;i++) {
        for(var d=0;d<this.dim;d++) {
          this.Y[i][d] -= ymean[d]/N;
        }
      }

      //if(this.iter%100===0) console.log('iter ' + this.iter + ', cost: ' + cost);
      return cost; // return current cost
    },

    // for debugging: gradient check
    debugGrad: function() {
      var N = this.N;

      var cg = this.costGrad(this.Y); // evaluate gradient
      var cost = cg.cost;
      var grad = cg.grad;

      var e = 1e-5;
      for(var i=0;i<N;i++) {
        for(var d=0;d<this.dim;d++) {
          var yold = this.Y[i][d];

          this.Y[i][d] = yold + e;
          var cg0 = this.costGrad(this.Y);

          this.Y[i][d] = yold - e;
          var cg1 = this.costGrad(this.Y);
          
          var analytic = grad[i][d];
          var numerical = (cg0.cost - cg1.cost) / ( 2 * e );
          console.log(i + ',' + d + ': gradcheck analytic: ' + analytic + ' vs. numerical: ' + numerical);

          this.Y[i][d] = yold;
        }
      }
    },

    // return cost and gradient, given an arrangement
    costGrad: function(Y) {
      var N = this.N;
      var dim = this.dim; // dim of output space
      var P = this.P;

      var pmul = this.iter < 100 ? 4 : 1; // trick that helps with local optima

      // compute current Q distribution, unnormalized first
      var Qu = zeros(N * N);
      var qsum = 0.0;
      for(var i=0;i<N;i++) {
        for(var j=i+1;j<N;j++) {
          var dsum = 0.0;
          for(var d=0;d<dim;d++) {
            var dhere = Y[i][d] - Y[j][d];
            dsum += dhere * dhere;
          }
          var qu = 1.0 / (1.0 + dsum); // Student t-distribution
          Qu[i*N+j] = qu;
          Qu[j*N+i] = qu;
          qsum += 2 * qu;
        }
      }
      // normalize Q distribution to sum to 1
      var NN = N*N;
      var Q = zeros(NN);
      for(var q=0;q<NN;q++) { Q[q] = Math.max(Qu[q] / qsum, 1e-100); }

      var cost = 0.0;
      var grad = [];
      for(var i=0;i<N;i++) {
        var gsum = new Array(dim); // init grad for point i
        for(var d=0;d<dim;d++) { gsum[d] = 0.0; }
        for(var j=0;j<N;j++) {
          cost += - P[i*N+j] * Math.log(Q[i*N+j]); // accumulate cost (the non-constant portion at least...)
          var premult = 4 * (pmul * P[i*N+j] - Q[i*N+j]) * Qu[i*N+j];
          for(var d=0;d<dim;d++) {
            gsum[d] += premult * (Y[i][d] - Y[j][d]);
          }
        }
        grad.push(gsum);
      }

      return {cost: cost, grad: grad};
    }
  }

  global.tSNE = tSNE; // export tSNE class
 })(tsnejs);


 // export the library to window, or to module in nodejs
 (function(lib) {
  "use strict";
  if (typeof module === "undefined" || typeof module.exports === "undefined") {
    window.tsnejs = lib; // in ordinary browser attach library to window
  } else {
    module.exports = lib; // in nodejs
  }
 })(tsnejs);

	<!DOCTYPE html>
	<html>
	<meta charset="utf-8">
	<style>
	#words {
	position: absolute;
	top: 50px;
	left: 800px;

	display: flex;
	flex-direction: column;
	}
	</style>
	<head>
	<script src="//d3js.org/d3.v3.min.js" charset="utf-8"></script>
	<script src="//cdnjs.cloudflare.com/ajax/libs/seedrandom/2.4.0/seedrandom.min.js"></script>
	<script src="slda-dist.js" charset="utf-8"></script>
	<script src="tsne.js" charset="utf-8"></script>
	</head>
	<body>
	<svg width="850" height="500"></svg>
	<div id="words"></div>
	<script>
	var width = 850;
	var height = 500;

	var colors = d3.scale.category20();

	d3.json("model.json", function(error, resource) {
	var topics = resource.model.topic_count;
	var topicMeans = resource.model.topic_means;
	var topTerms = resource.model.top_terms;
	var coords = tsneLayout(resource, "defaultseed");
	var range = Array.apply(null, Array(topics)).map(function (_, i) {return i;});

	// scale the tsne coordinates relative to the svg size
	coords = scaleDimension(width, width/10, 0, coords);
	coords = scaleDimension(height, height/10, 1, coords);

	var words = d3.select("#words");
	var colorFn = function (i, lightness) {
	return d3.lab(lightness,
	(200 * coords[i][0] / width) - 100,
	(200 * coords[i][1] / height) - 100);
	};

	var svg = d3.select("svg");
	svg.style("width", width);
	svg.style("height", height);
	svg.selectAll(".dot").data(range).enter()
	.append("circle")
	.attr("class", "dot")
	.attr("r", function (i) {
	// set topic radius relative to the 'topic_means' score
	var r = topicMeans[i];
	r = r * r * 10000;
	return r;
	})
	.on("mouseenter", function (i) {
	words.selectAll(".word").remove();
	for (var k in topTerms[i]) {
	words.append("text")
	.attr("class", "word")
	.text(topTerms[i][k][0]);
	}
	})
	.on("mouseleave", function (i) {
	words.selectAll(".word").remove();
	})
	.attr("cx", function (i) {return coords[i][0];})
	.attr("cy", function (i) {return coords[i][1];})
	.style("fill", function (i) { return colorFn(i, 80); })
	.style("stroke", function (i) { return colorFn(i, 65); });
	});

	function scaleDimension(dimSize, buffer, dimIndex, coords) {
	var minDim = coords[0][dimIndex];
	var maxDim = coords[0][dimIndex];
	for (var i in coords) {
	minDim = Math.min(minDim, coords[i][dimIndex]);
	maxDim = Math.max(maxDim, coords[i][dimIndex]);
	}
	var diff = maxDim - minDim;
	var dimScale = dimSize - 2 * buffer;

	for (var i in coords) {
	coords[i][dimIndex] = (((coords[i][dimIndex] - minDim) / diff) * dimScale) + buffer;
	}
	return coords;
	}

	function tsneLayout(resource, seed) {
	Math.seedrandom(seed);
	var topics = resource.model.topic_count;
	var perplexity = Math.round(topics / 5);
	perplexity = Math.max(perplexity, 2);
	var tsne = new tsnejs.tSNE({epsilon: 10, perplexity: perplexity});
	tsne.initDataDist(distMatrix(resource));
	for (var k = 0; k < 300; k++) {
	tsne.step();
	}
	return tsne.getSolution();
	}
	</script>
	</body>
	</html>

	/*
	Given a BigML SLDA resource, will return a function that computes
	the cosine distance between two topics (referenced by their index)
	*/
	function cosineDistFn(resource) {
	var nw = resource.model.nw;
	return function (a, b) {
	var dot = 0;
	var aSqr = 0;
	var bSqr = 0;
	for (i in nw) {
	var aVal = nw[i][a];
	var bVal = nw[i][b];
	dot += aVal * bVal;
	aSqr += aVal * aVal;
	bSqr += bVal * bVal;
	}
	var similarity = dot / (Math.sqrt(aSqr) * Math.sqrt(bSqr));
	var distance = 2 * Math.acos(similarity) / Math.PI;
	return distance;
	};
	}

	/*
	Given a BigML SLDA resource, returns a distance matrix for the
	topics
	*/
	function distMatrix (resource) {
	var topics = resource.model.topic_count;
	var distFn = cosineDistFn(resource);
	var matrix = new Array(topics);
	for (var a = 0; a < topics; a++) {
	matrix[a] = new Array(topics);
	for (var b = 0; b < topics; b++) {
	if (a == b) {
	matrix[a][b] = 0;
	} else if (a > b) {
	matrix[a][b] = matrix[b][a];
	} else {
	matrix[a][b] = distFn(a, b);
	}
	}
	}
	return matrix;
	}
	// from https://github.com/karpathy/tsnejs

	// create main global object
	var tsnejs = tsnejs \|\| { REVISION: 'ALPHA' };

	(function(global) {
	"use strict";

	// utility function
	var assert = function(condition, message) {
	if (!condition) { throw message \|\| "Assertion failed"; }
	}

	// syntax sugar
	var getopt = function(opt, field, defaultval) {
	if(opt.hasOwnProperty(field)) {
	return opt[field];
	} else {
	return defaultval;
	}
	}

	// return 0 mean unit standard deviation random number
	var return_v = false;
	var v_val = 0.0;
	var gaussRandom = function() {
	if(return_v) {
	return_v = false;
	return v_val;
	}
	var u = 2*Math.random()-1;
	var v = 2*Math.random()-1;
	var r = uu + vv;
	if(r == 0 \|\| r > 1) return gaussRandom();
	var c = Math.sqrt(-2*Math.log(r)/r);
	v_val = v*c; // cache this for next function call for efficiency
	return_v = true;
	return u*c;
	}

	// return random normal number
	var randn = function(mu, std){ return mu+gaussRandom()*std; }

	// utilitity that creates contiguous vector of zeros of size n
	var zeros = function(n) {
	if(typeof(n)==='undefined' \|\| isNaN(n)) { return []; }
	if(typeof ArrayBuffer === 'undefined') {
	// lacking browser support
	var arr = new Array(n);
	for(var i=0;i<n;i++) { arr[i]= 0; }
	return arr;
	} else {
	return new Float64Array(n); // typed arrays are faster
	}
	}

	// utility that returns 2d array filled with random numbers
	// or with value s, if provided
	var randn2d = function(n,d,s) {
	var uses = typeof s !== 'undefined';
	var x = [];
	for(var i=0;i<n;i++) {
	var xhere = [];
	for(var j=0;j<d;j++) {
	if(uses) {
	xhere.push(s);
	} else {
	xhere.push(randn(0.0, 1e-4));
	}
	}
	x.push(xhere);
	}
	return x;
	}

	// compute L2 distance between two vectors
	var L2 = function(x1, x2) {
	var D = x1.length;
	var d = 0;
	for(var i=0;i<D;i++) {
	var x1i = x1[i];
	var x2i = x2[i];
	d += (x1i-x2i)*(x1i-x2i);
	}
	return d;
	}

	// compute pairwise distance in all vectors in X
	var xtod = function(X) {
	var N = X.length;
	var dist = zeros(N * N); // allocate contiguous array
	for(var i=0;i<N;i++) {
	for(var j=i+1;j<N;j++) {
	var d = L2(X[i], X[j]);
	dist[i*N+j] = d;
	dist[j*N+i] = d;
	}
	}
	return dist;
	}

	// compute (p_{i\|j} + p_{j\|i})/(2n)
	var d2p = function(D, perplexity, tol) {
	var Nf = Math.sqrt(D.length); // this better be an integer
	var N = Math.floor(Nf);
	assert(N === Nf, "D should have square number of elements.");
	var Htarget = Math.log(perplexity); // target entropy of distribution
	var P = zeros(N * N); // temporary probability matrix

	var prow = zeros(N); // a temporary storage compartment
	for(var i=0;i<N;i++) {
	var betamin = -Infinity;
	var betamax = Infinity;
	var beta = 1; // initial value of precision
	var done = false;
	var maxtries = 50;

	// perform binary search to find a suitable precision beta
	// so that the entropy of the distribution is appropriate
	var num = 0;
	while(!done) {
	//debugger;

	// compute entropy and kernel row with beta precision
	var psum = 0.0;
	for(var j=0;j<N;j++) {
	var pj = Math.exp(- D[iN+j] beta);
	if(i===j) { pj = 0; } // we dont care about diagonals
	prow[j] = pj;
	psum += pj;
	}
	// normalize p and compute entropy
	var Hhere = 0.0;
	for(var j=0;j<N;j++) {
	var pj = prow[j] / psum;
	prow[j] = pj;
	if(pj > 1e-7) Hhere -= pj * Math.log(pj);
	}

	// adjust beta based on result
	if(Hhere > Htarget) {
	// entropy was too high (distribution too diffuse)
	// so we need to increase the precision for more peaky distribution
	betamin = beta; // move up the bounds
	if(betamax === Infinity) { beta = beta * 2; }
	else { beta = (beta + betamax) / 2; }

	} else {
	// converse case. make distrubtion less peaky
	betamax = beta;
	if(betamin === -Infinity) { beta = beta / 2; }
	else { beta = (beta + betamin) / 2; }
	}

	// stopping conditions: too many tries or got a good precision
	num++;
	if(Math.abs(Hhere - Htarget) < tol) { done = true; }
	if(num >= maxtries) { done = true; }
	}

	// console.log('data point ' + i + ' gets precision ' + beta + ' after ' + num + ' binary search steps.');
	// copy over the final prow to P at row i
	for(var j=0;j<N;j++) { P[i*N+j] = prow[j]; }

	} // end loop over examples i

	// symmetrize P and normalize it to sum to 1 over all ij
	var Pout = zeros(N * N);
	var N2 = N*2;
	for(var i=0;i<N;i++) {
	for(var j=0;j<N;j++) {
	Pout[iN+j] = Math.max((P[iN+j] + P[j*N+i])/N2, 1e-100);
	}
	}

	return Pout;
	}

	// helper function
	function sign(x) { return x > 0 ? 1 : x < 0 ? -1 : 0; }

	var tSNE = function(opt) {
	var opt = opt \|\| {};
	this.perplexity = getopt(opt, "perplexity", 30); // effective number of nearest neighbors
	this.dim = getopt(opt, "dim", 2); // by default 2-D tSNE
	this.epsilon = getopt(opt, "epsilon", 10); // learning rate

	this.iter = 0;
	}

	tSNE.prototype = {

	// this function takes a set of high-dimensional points
	// and creates matrix P from them using gaussian kernel
	initDataRaw: function(X) {
	var N = X.length;
	var D = X[0].length;
	assert(N > 0, " X is empty? You must have some data!");
	assert(D > 0, " X[0] is empty? Where is the data?");
	var dists = xtod(X); // convert X to distances using gaussian kernel
	this.P = d2p(dists, this.perplexity, 1e-4); // attach to object
	this.N = N; // back up the size of the dataset
	this.initSolution(); // refresh this
	},

	// this function takes a given distance matrix and creates
	// matrix P from them.
	// D is assumed to be provided as a list of lists, and should be symmetric
	initDataDist: function(D) {
	var N = D.length;
	assert(N > 0, " X is empty? You must have some data!");
	// convert D to a (fast) typed array version
	var dists = zeros(N * N); // allocate contiguous array
	for(var i=0;i<N;i++) {
	for(var j=i+1;j<N;j++) {
	var d = D[i][j];
	dists[i*N+j] = d;
	dists[j*N+i] = d;
	}
	}
	this.P = d2p(dists, this.perplexity, 1e-4);
	this.N = N;
	this.initSolution(); // refresh this
	},

	// (re)initializes the solution to random
	initSolution: function() {
	// generate random solution to t-SNE
	this.Y = randn2d(this.N, this.dim); // the solution
	this.gains = randn2d(this.N, this.dim, 1.0); // step gains to accelerate progress in unchanging directions
	this.ystep = randn2d(this.N, this.dim, 0.0); // momentum accumulator
	this.iter = 0;
	},

	// return pointer to current solution
	getSolution: function() {
	return this.Y;
	},

	// perform a single step of optimization to improve the embedding
	step: function() {
	this.iter += 1;
	var N = this.N;

	var cg = this.costGrad(this.Y); // evaluate gradient
	var cost = cg.cost;
	var grad = cg.grad;

	// perform gradient step
	var ymean = zeros(this.dim);
	for(var i=0;i<N;i++) {
	for(var d=0;d<this.dim;d++) {
	var gid = grad[i][d];
	var sid = this.ystep[i][d];
	var gainid = this.gains[i][d];

	// compute gain update
	var newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
	if(newgain < 0.01) newgain = 0.01; // clamp
	this.gains[i][d] = newgain; // store for next turn

	// compute momentum step direction
	var momval = this.iter < 250 ? 0.5 : 0.8;
	var newsid = momval * sid - this.epsilon * newgain * grad[i][d];
	this.ystep[i][d] = newsid; // remember the step we took

	// step!
	this.Y[i][d] += newsid;

	ymean[d] += this.Y[i][d]; // accumulate mean so that we can center later
	}
	}

	// reproject Y to be zero mean
	for(var i=0;i<N;i++) {
	for(var d=0;d<this.dim;d++) {
	this.Y[i][d] -= ymean[d]/N;
	}
	}

	//if(this.iter%100===0) console.log('iter ' + this.iter + ', cost: ' + cost);
	return cost; // return current cost
	},

	// for debugging: gradient check
	debugGrad: function() {
	var N = this.N;

	var cg = this.costGrad(this.Y); // evaluate gradient
	var cost = cg.cost;
	var grad = cg.grad;

	var e = 1e-5;
	for(var i=0;i<N;i++) {
	for(var d=0;d<this.dim;d++) {
	var yold = this.Y[i][d];

	this.Y[i][d] = yold + e;
	var cg0 = this.costGrad(this.Y);

	this.Y[i][d] = yold - e;
	var cg1 = this.costGrad(this.Y);

	var analytic = grad[i][d];
	var numerical = (cg0.cost - cg1.cost) / ( 2 * e );
	console.log(i + ',' + d + ': gradcheck analytic: ' + analytic + ' vs. numerical: ' + numerical);

	this.Y[i][d] = yold;
	}
	}
	},

	// return cost and gradient, given an arrangement
	costGrad: function(Y) {
	var N = this.N;
	var dim = this.dim; // dim of output space
	var P = this.P;

	var pmul = this.iter < 100 ? 4 : 1; // trick that helps with local optima

	// compute current Q distribution, unnormalized first
	var Qu = zeros(N * N);
	var qsum = 0.0;
	for(var i=0;i<N;i++) {
	for(var j=i+1;j<N;j++) {
	var dsum = 0.0;
	for(var d=0;d<dim;d++) {
	var dhere = Y[i][d] - Y[j][d];
	dsum += dhere * dhere;
	}
	var qu = 1.0 / (1.0 + dsum); // Student t-distribution
	Qu[i*N+j] = qu;
	Qu[j*N+i] = qu;
	qsum += 2 * qu;
	}
	}
	// normalize Q distribution to sum to 1
	var NN = N*N;
	var Q = zeros(NN);
	for(var q=0;q<NN;q++) { Q[q] = Math.max(Qu[q] / qsum, 1e-100); }

	var cost = 0.0;
	var grad = [];
	for(var i=0;i<N;i++) {
	var gsum = new Array(dim); // init grad for point i
	for(var d=0;d<dim;d++) { gsum[d] = 0.0; }
	for(var j=0;j<N;j++) {
	cost += - P[iN+j] Math.log(Q[i*N+j]); // accumulate cost (the non-constant portion at least...)
	var premult = 4 * (pmul * P[iN+j] - Q[iN+j]) * Qu[i*N+j];
	for(var d=0;d<dim;d++) {
	gsum[d] += premult * (Y[i][d] - Y[j][d]);
	}
	}
	grad.push(gsum);
	}

	return {cost: cost, grad: grad};
	}
	}

	global.tSNE = tSNE; // export tSNE class
	})(tsnejs);


	// export the library to window, or to module in nodejs
	(function(lib) {
	"use strict";
	if (typeof module === "undefined" \|\| typeof module.exports === "undefined") {
	window.tsnejs = lib; // in ordinary browser attach library to window
	} else {
	module.exports = lib; // in nodejs
	}
	})(tsnejs);