diff --git a/js/som.js b/js/som.js
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+// Javascript implementation pf Kohonen's Self Organizing Map
+// Based on http://www.ai-junkie.com/ann/som/som1.html
+
+var mapWidth = 800,
+    mapHeight = 800;
+
+function getDistance(weight, inputVector) {
+	var distance = 0;
+	for (var i = 0; i <weight.length; i++) {
+		distance += (inputVector[i] - weight[i]) * (inputVector[i] - weight[i]);
+	}
+	return Math.sqrt(distance);
+}
+
+function makeRandomWeights(vSize, eSize) {
+	var weights = [];
+  if(typeof ArrayBuffer === 'undefined') {
+    // lacking browser support
+	  while (weights.length < vSize) {
+      var arr = new Array(eSize);
+      for(var i = 0; i < eSize; i++) { arr[i]= Math.random(); }
+      weights.push(arr);
+    }
+  } else {
+	  while (weights.length < vSize) {
+      var arr = new Float64Array(eSize);
+      for(var i = 0; i < eSize; i++) { arr[i]= Math.random(); }
+      weights.push(arr);
+    }
+  }
+  return weights;
+}
+
+function getBMUIndex(weights, target) {
+	var BMUIndex = 0;
+	var bestScore = 99999;
+	
+	for (i=0; i < weights.length; i++) {
+		distance = getDistance(weights[i], target);
+		if (distance < bestScore) {
+			bestScore = distance;
+			BMUIndex = i;
+		}
+	}
+	return BMUIndex;
+}
+
+function convertIndexToXY(idx, dimW) {
+	var x = parseInt(idx % dimW,10);
+	var y = parseInt((idx / dimW),10);
+	return [x,y];
+}
+
+
+function getEucledianDistance(coord1, coord2) {
+    return (coord1[0] - coord2[0]) * (coord1[0] - coord2[0]) + (coord1[1] - coord2[1]) * (coord1[1] - coord2[1]);
+}
+
+// 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
+    }
+  }
+
+// 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;
+}
+
+function dotproduct(a,b) {
+    var n = 0, lim = Math.min(a.length,b.length);
+    for (var i = 0; i < lim; i++) n += a[i] * b[i];
+    return n;
+ }
+
+function vecsum(a,b) {
+  var lim = a.length;
+  var sum = new Array(lim);
+  for (var i = 0; i < lim; i++) sum[i] = a[i] + b[i];
+  return sum;
+ }
+
+function norm2(a) {var sumsqr = 0; for (var i = 0; i < a.length; i++) sumsqr += a[i]*a[i]; return Math.sqrt(sumsqr);}
+
+function cosine_sim(x, y) {
+    xnorm = norm2(x);
+    if(!xnorm) return 0;
+    ynorm = norm2(y);
+    if(!ynorm) return 0;
+    return dotproduct(x, y) / (xnorm * ynorm);
+}
+
+function makeSOM(data, training_iterations) {
+	var dimW = 6;
+	var dimH = 6;
+  
+	var radius = (dimW * dimH) / 2;
+	var learning_rate = 1;
+	var time_constant = training_iterations / Math.log(radius);
+	var inputs = xtod(data.vecs);
+  var dimI = data.vecs.length;
+	var weights = makeRandomWeights(dimW * dimH, dimI);
+	var radius_decaying = 0;
+	var learning_rate_decaying = 0;
+	var svg;
+  var no_targets = (data.target.match(/.[ |]+./g) || []).length+1;
+//  var avg, avgsim1, avgsim2, minsim;
+  var refIndex;
+  var colorScale;
+  
+  if(no_targets > 1) {
+    refIndex=1;
+    colorScale = d3.scale.linear()
+      .range(['green', 'yellow', 'red']) // or use hex values
+      .domain([-1, 0, 1]);
+    
+    // avg = vecsum(inputs.slice(0, dimI), inputs.slice(dimI, 2*dimI));
+    // avgsim1 = cosine_sim(inputs.slice(0, dimI), avg);
+    // avgsim2 = cosine_sim(inputs.slice(dimI, 2*dimI), avg);
+
+    $("#somcolor2").css("background-color", colorScale(0));
+    $("#somcolor1").css("background-color", colorScale(-1));
+    $("#somcolor3").css("background-color", colorScale(1));
+  } else {
+    refIndex = data.words.length-1;
+    colorScale = d3.scale.linear()
+      .range(['white', 'red'])
+      .domain([-1, 1]);
+    $("#somcolor1").css("background-color", colorScale(1));
+    $("#somcolor3").css("background-color", colorScale(-1));
+  }
+  
+  $("#somword1").html(data.words[0]);
+  $("#somword2").html(data.words[refIndex]);
+  minsim = cosine_sim(inputs.slice(0, dimI), inputs.slice(refIndex*dimI, (refIndex+1)*dimI));
+
+  var itdom = document.getElementById("iterations");
+
+  var div = d3.select("#som2");
+
+	data.coords = [];
+  for(var i=0; i< data.words.length; i++) {
+    data.coords[i] = [Math.floor(dimW/2), Math.floor(dimH/2)];
+  }
+  
+  svg = div.append("svg")
+					 .attr("width", mapWidth)
+					 .attr("height", mapHeight);
+
+  var rects = svg.selectAll(".r")
+      .data(weights)
+      .enter().append("rect")
+			.attr("class", "r")
+      .attr("width", mapWidth/dimW)
+      .attr("height", mapHeight/dimH)
+      .attr("fill", "white")
+      .attr("z-index", "-1")
+      .attr("x", function(d, i) { return (i % dimW) * (mapWidth/dimW);})
+      .attr("y", function(d, i) { return (Math.floor(i / dimW) * (mapWidth/dimW)); })
+            
+
+  var g = svg.selectAll(".b")
+			.data(data.words)
+			.enter().append("g")
+			.attr("class", "u");
+	g.append("a")
+	  .attr("xlink:href", function(word) {return data.urlprefix+word;})
+	  .attr("title", function(d, i) {
+		  return "rank: "+i +"  "+"freq. rank: "+data.ranks[i].toString().replace(/\B(?=(\d{3})+(?!\d))/g, ",");
+	  })
+		.append("text")
+    .attr("text-anchor", "bottom")
+    .attr("font-size", 12)
+    .attr("fill", function(d) {
+			if(data.target.indexOf(" "+d+" ") >= 0) {
+				return "blue";
+			} else {
+				return "#333"
+			}
+	  })
+    .text(function(d) { return d; });
+  
+  var som_interval = setInterval(somStep, 0);
+  var it=0;
+  
+  function updateSOM() {
+    var oc = [];
+    for(var x = 0; x < dimW; x++) {
+      for(var y = 0; y < dimH; y++) {
+        oc[y*dimW+x]=1;
+      }
+    }
+    svg.selectAll('.u')
+      .data(data.coords)
+      .transition()
+      .attr("transform", function(d, i) { 
+			  return "translate(" +
+          (d[0]*(mapWidth/dimW)+4) + "," +
+          (d[1]*(mapHeight/dimH)+oc[d[1]*dimW+d[0]]++*14+4) + ")"; });
+
+    var colorFun = function(d, i) {
+      var sim1=cosine_sim(d, inputs.slice(0, dimI));
+      var sim2=cosine_sim(d, inputs.slice(dimI, 2*dimI));
+      var col;
+//      col = (sim1-avgsim1)/(1-avgsim1)-(sim2-avgsim2)/(1-avgsim2);
+      col = (sim2-sim1)/(1-minsim);
+//      console.log(Math.floor(i/dimW)+","+i%dimW+":"+(sim1-minsim)/(1-minsim)+ " " + (sim2-minsim)/(1-minsim) + "--> "+ col);
+      if(col > 1) col=1;
+      if(col < -1) col=-1;
+      return colorScale(col);
+    };
+    
+    if(it>training_iterations*.6) {
+      svg.selectAll(".r")
+        .data(weights)
+        .transition()
+        .attr("fill", colorFun);
+    }
+  }
+  
+  function somStep() {
+	  if(it++ >= training_iterations) {
+      updateSOM();
+      clearInterval(som_interval);
+      return;
+    }
+	  itdom.innerHTML = it;
+	  radius_decaying = radius * Math.exp(-it/time_constant);
+    learning_rate_decaying = learning_rate * Math.exp(-it/time_constant);
+    //learning_rate_decaying = learning_rate * Math.exp(-it/training_iterations);
+    
+    //pick a random input to train
+    var current=Math.floor(Math.random()*dimI)
+    var iv = inputs.slice(current*dimI, (current+1)*dimI);
+    // Determine the BMU
+    BMUIdx = getBMUIndex(weights, iv);
+    var coord1 = convertIndexToXY(BMUIdx, dimW);
+    data.coords[current] = coord1;
+    var widthSq = radius_decaying * radius_decaying;
+    for (var v in weights) {
+      var coord2 = convertIndexToXY(v, dimW);
+      var dist = getEucledianDistance(coord1, coord2);
+      // Determine if the weight is within the training radius
+      if (dist < widthSq) {
+        // console.log(dist, learning_rate_decaying, radius_decaying, it);
+        influence = Math.exp(-dist/(2*widthSq));
+        for (vidx = 0;vidx<weights[v].length;vidx++) {
+          weights[v][vidx] += influence * learning_rate_decaying * (iv[vidx] - weights[v][vidx]);
+        }
+      }
+    }
+//  }
+    if(it % 10 == 0) {
+      updateSOM();
+    }
+	}
+
+}