แก้ ลืมเปลี่ยนจำนวน its ใน js

.eslintrc

@@ -0,0 +1,19 @@
+parserOptions:
+  sourceType: module
+
+extends:
+  "standard"
+rules:
+  no-cond-assign: 0
+  no-console: 0
+  semi:
+    - error
+    - always
+  no-return-assign: 0
+  one-var: 0
+env:
+  es6: true
+
+globals:
+  console: false
+  performance: false

examples/example-papaparsing.html

@@ -101,8 +101,8 @@
         <br/>
         <label title="Number of iterations before the simulation is stopped">
           Iterations
-          <output id="iterationsSliderOutput">300</output>
-          <input type="range" min="5" max="5000" value="300" step="5" oninput="d3.select('#iterationsSliderOutput').text(value); ITERATIONS=value;">
+          <output id="iterationsSliderOutput">100</output>
+          <input type="range" min="5" max="1000" value="100" step="5" oninput="d3.select('#iterationsSliderOutput').text(value); ITERATIONS=value;">
         </label>
         <br/>
         <label title="Attribute used for coloring nodes">

examples/js/algos/hybrid.js

@@ -1,10 +1,10 @@
 /**
  * Initialize the hybrid layout algorithm and start simulation.
  */
-function startHybridSimulation() {
-  console.log("startHybridSimulation");
+function startHybridSimulation () {
+  console.log('startHybridSimulation');
   springForce = false;
-  d3.selectAll(".nodes").remove();
+  d3.selectAll('.nodes').remove();
   manualStop = false;
   simulation.stop();
   p1 = performance.now();
@@ -17,33 +17,33 @@ function startHybridSimulation() {
     .neighbourSize(NEIGHBOUR_SIZE)
     .sampleSize(SAMPLE_SIZE)
     .stableVelocity(0) // Change here
-    .distance(distance)
+    .distance(distance);
 
   let forceFull = d3.forceNeighbourSampling()
     .neighbourSize(FULL_NEIGHBOUR_SIZE)
     .sampleSize(FULL_SAMPLE_SIZE)
     .stableVelocity(0) // Change here
-    .distance(distance)
+    .distance(distance);
 
   let hybridSimulation = d3.hybridSimulation(simulation, forceSample, forceFull)
     .sampleIterations(ITERATIONS)
     .fullIterations(FULL_ITERATIONS)
-    .numPivots(PIVOTS ? NUM_PIVOTS:-1)
+    .numPivots(PIVOTS ? NUM_PIVOTS : -1)
     .interpFindTuneIts(INTERP_ENDING_ITS)
     .interpDistanceFn(distance)
-    .on("sampleTick", ticked)
-    .on("fullTick", ticked)
-    .on("startInterp", startedFull)
-    .on("end", ended);
+    .on('sampleTick', ticked)
+    .on('fullTick', ticked)
+    .on('startInterp', startedFull)
+    .on('end', ended);
 
   let sample = hybridSimulation.subSet();
   addNodesToDOM(sample);
 
   hybridSimulation.restart();
 
-  function startedFull() {
-    console.log("startedFull");
-    d3.selectAll(".nodes").remove();
+  function startedFull () {
+    console.log('startedFull');
+    d3.selectAll('.nodes').remove();
     addNodesToDOM(nodes);
   }
 }

examples/js/algos/linkForce.js

@@ -1,8 +1,8 @@
 /**
  * Initialize the link force algorithm and start simulation.
  */
-function startLinkSimulation() {
-  console.log("startLinkSimulation")
+function startLinkSimulation () {
+  console.log('startLinkSimulation');
   springForce = false;
   alreadyRanIterations = 0;
   manualStop = true;
@@ -12,26 +12,25 @@ function startLinkSimulation() {
 
   if (tweakedVerOfLink) {
     force = d3.forceLinkCompleteGraph()
-              .distance(function (n, m) {
-                return distanceFunction(n, m, props, norm);
-              })
-              .stableVelocity(0) // Change here
-              .onStableVelo(ended);
-  }
-  else {
-    for (i = nodes.length-1; i >= 1; i--) {
-      for (j = i-1; j >= 0; j--) {
+      .distance(function (n, m) {
+        return distanceFunction(n, m, props, norm);
+      })
+      .stableVelocity(0) // Change here
+      .onStableVelo(ended);
+  } else {
+    for (i = nodes.length - 1; i >= 1; i--) {
+      for (j = i - 1; j >= 0; j--) {
         links.push({
           source: nodes[i],
-          target: nodes[j],
+          target: nodes[j]
         });
       }
     }
     force = d3.forceLink()
-              .distance(function (n) {
-               return distanceFunction(n.source, n.target, props, norm);
-              })
-             .links(links);
+      .distance(function (n) {
+        return distanceFunction(n.source, n.target, props, norm);
+      })
+      .links(links);
   }
 
   /* Add force
@@ -51,9 +50,9 @@ function startLinkSimulation() {
   simulation
     .alphaDecay(0)
     .alpha(1)
-    .on("tick", ticked)
-    .on("end", ended)
-    //.velocityDecay(0.8)
-    .force(forceName,force)
+    .on('tick', ticked)
+    .on('end', ended)
+    // .velocityDecay(0.8)
+    .force(forceName, force)
     .restart();
 }

examples/js/algos/neighbourSampling.js

@@ -1,28 +1,28 @@
 /**
  * Initialize the Chalmers' 1996 algorithm and start simulation.
  */
-function startNeighbourSamplingSimulation() {
-  console.log("startNeighbourSamplingSimulation");
-  //springForce = true;
+function startNeighbourSamplingSimulation () {
+  console.log('startNeighbourSamplingSimulation');
+  // springForce = true;
   alreadyRanIterations = 0;
   manualStop = true;
   simulation.stop();
   p1 = performance.now();
 
   let force = d3.forceNeighbourSampling()
-                  .neighbourSize(NEIGHBOUR_SIZE)
-                  .sampleSize(SAMPLE_SIZE)
-                  .distance(function (s, t) {
-                    return distanceFunction(s, t, props, norm);
-                  })
-                  .stableVelocity(0) // Change here
-                  .onStableVelo(ended);
+    .neighbourSize(NEIGHBOUR_SIZE)
+    .sampleSize(SAMPLE_SIZE)
+    .distance(function (s, t) {
+      return distanceFunction(s, t, props, norm);
+    })
+    .stableVelocity(0) // Change here
+    .onStableVelo(ended);
 
   simulation
     .alphaDecay(0)
     .alpha(1)
-    .on("tick", ticked)
-    .on("end", ended)
+    .on('tick', ticked)
+    .on('end', ended)
     .force(forceName, force);
   // Restart the simulation.
   simulation.restart();

examples/js/algos/otherAlgo.js

@@ -1,7 +1,7 @@
 /**
  * Initialize the t-SNE algorithm and start simulation.
 */
-function starttSNE() {
+function starttSNE () {
   springForce = false;
   simulation.stop();
   p1 = performance.now();
@@ -25,20 +25,20 @@ function starttSNE() {
 /**
  * Initialize the Barnes-Hut algorithm and start simulation.
 */
-function startBarnesHutSimulation() {
-  console.log("startBarnesHutSimulation")
+function startBarnesHutSimulation () {
+  console.log('startBarnesHutSimulation');
   alreadyRanIterations = 0;
   manualStop = false;
   springForce = false;
   p1 = performance.now();
 
   simulation.alphaDecay(1 - Math.pow(0.001, 1 / ITERATIONS))
-      .on("tick", ticked)
-      .on("end", ended)
-      .force(forceName, d3.forceBarnesHut()
-        // The distance function that will be used to calculate distances
-        //  between nodes.
-        .distance(function(s, t) { return distanceFunction(s, t, props, norm); }));
+    .on('tick', ticked)
+    .on('end', ended)
+    .force(forceName, d3.forceBarnesHut()
+    // The distance function that will be used to calculate distances
+    //  between nodes.
+      .distance(function (s, t) { return distanceFunction(s, t, props, norm); }));
   // Restart the simulation.
   simulation.alpha(1).restart();
 }

examples/js/distances/cosineSimilarity.js

@@ -5,14 +5,14 @@
  * @param  {array} properties - the properties of the nodes.
  * @return {number}            the distance between source and target nodes.
  */
-function calculateCosineSimilarity(source, target, properties, normArgs) {
+function calculateCosineSimilarity (source, target, properties, normArgs) {
   var numerator = 0.0;
 
   // console.log(properties);
   // Iterate through every column of data
   for (var i = 0; i < properties.length; i++) {
     property = properties[i];
-    if (property.toLowerCase() !== "class" && property.toLowerCase() !== "app" && property.toLowerCase() !== "user" && property.toLowerCase() !== "weekday") {
+    if (property.toLowerCase() !== 'class' && property.toLowerCase() !== 'app' && property.toLowerCase() !== 'user' && property.toLowerCase() !== 'weekday') {
       var s = source[property],
         t = target[property];
 
@@ -26,7 +26,7 @@ function calculateCosineSimilarity(source, target, properties, normArgs) {
   return Math.abs(numerator / denominator);
 }
 
-function squareRooted(node, properties, normArgs) {
+function squareRooted (node, properties, normArgs) {
   var sum = 0.0;
 
   for (var i = 0, s; i < properties.length; i++) {

examples/js/distances/diceDissimilarity.js

@@ -5,14 +5,14 @@
  * @param  {array} properties - the properties of the nodes.
  * @return {number}            the distance between source and target nodes.
  */
-function calculateDiceDissimilarity(source, target, properties, normArgs) {
+function calculateDiceDissimilarity (source, target, properties, normArgs) {
   var notShared = 0.0;
 
   // console.log(properties);
   // Iterate through every column of data
   for (var i = 0; i < properties.length; i++) {
     property = properties[i];
-    if (property.toLowerCase() !== "class" && property.toLowerCase() !== "app" && property.toLowerCase() !== "user" && property.toLowerCase() !== "weekday") {
+    if (property.toLowerCase() !== 'class' && property.toLowerCase() !== 'app' && property.toLowerCase() !== 'user' && property.toLowerCase() !== 'weekday') {
       var s = source[property],
         t = target[property];
 

examples/js/distances/distance.js

@@ -6,23 +6,23 @@
  * @param  {object} normArgs   - the normalization arguments.
  * @return {number}            the distance between source and target nodes.
  */
-function calculateDistance(source, target, properties, normArgs) {
+function calculateDistance (source, target, properties, normArgs) {
   var val1 = 0.0, val2 = 0.0,
-      sumDiff = 0.0,
-      ordDiff = 1.0,
-      ORD_FACTOR = 0.75,
-      cols = 0,
-      average = normArgs.avg,
-      sigma = normArgs.sig,
-      st_dev = normArgs.st_d;
+    sumDiff = 0.0,
+    ordDiff = 1.0,
+    ORD_FACTOR = 0.75,
+    cols = 0,
+    average = normArgs.avg,
+    sigma = normArgs.sig,
+    st_dev = normArgs.st_d;
 
   // Iterate through every column of data
   for (var i = 0; i < properties.length; i++) {
     property = properties[i];
-    if (source.hasOwnProperty(property) && target.hasOwnProperty(property)
-        && property.toLowerCase() !== "index" && property.toLowerCase() !== "type") {
+    if (source.hasOwnProperty(property) && target.hasOwnProperty(property) &&
+        property.toLowerCase() !== 'index' && property.toLowerCase() !== 'type') {
       var s = source[property],
-          t = target[property];
+        t = target[property];
 
       // Comparing Floats and Integers
       if ((isNumeric(s) && isNumeric(t))) {
@@ -32,7 +32,7 @@ function calculateDistance(source, target, properties, normArgs) {
           val1 = (val1 - average[i]) / (st_dev[i] * sigma[i]);
           val2 = (val2 - average[i]) / (st_dev[i] * sigma[i]);
         }
-        sumDiff += (val1-val2) * (val1-val2);
+        sumDiff += (val1 - val2) * (val1 - val2);
         cols++;
       // Comparing strings
       } else if (/[a-zA-Z]/.test(s) && /[a-zA-Z]/.test(t) && s === t) {
@@ -42,9 +42,8 @@ function calculateDistance(source, target, properties, normArgs) {
         // Comparing Dates
         var parsedDateS = Date.parse(s);
         var parsedDateT = Date.parse(t);
-        if (isNaN(s) && !isNaN(parsedDateS)
-            && isNaN(t) && !isNaN(parsedDateT)) {
-
+        if (isNaN(s) && !isNaN(parsedDateS) &&
+            isNaN(t) && !isNaN(parsedDateT)) {
           val1 = parsedDateS.valueOf(),
           val2 = parsedDateT.valueOf();
 
@@ -52,7 +51,7 @@ function calculateDistance(source, target, properties, normArgs) {
             val1 = (val1 - average[i]) / (st_dev[i] * sigma[i]);
             val2 = (val2 - average[i]) / (st_dev[i] * sigma[i]);
           }
-          sumDiff += (val1-val2) * (val1-val2);
+          sumDiff += (val1 - val2) * (val1 - val2);
           cols++;
         }
       }
@@ -62,9 +61,9 @@ function calculateDistance(source, target, properties, normArgs) {
   sumDiff *= ordDiff;
 
   if (cols > 0) {
-    sumDiff *= properties.length/cols;
+    sumDiff *= properties.length / cols;
   }
 
-  //console.log(sumDiff);
+  // console.log(sumDiff);
   return sumDiff;
 }

examples/js/distances/distancePokerHands.js

@@ -6,33 +6,33 @@
  * @param  {node} target
  * @return {number}      the distance between source and target nodes.
  */
-function calculateDistancePoker(source, target) {
+function calculateDistancePoker (source, target) {
   var sumDiff = 0.0,
-      ordDiff = 1.0,
-      ORD_FACTOR = 1.5,
-      cards = ["C1", "C2", "C3", "C4", "C5"],
-      cols = 0;
+    ordDiff = 1.0,
+    ORD_FACTOR = 1.5,
+    cards = ['C1', 'C2', 'C3', 'C4', 'C5'],
+    cols = 0;
 
   // Iterate through cards
   for (var i = 0; i < cards.length; i++) {
     card = cards[i];
     if (source.hasOwnProperty(card) && target.hasOwnProperty(card)) {
       var s = parseInt(source[card]),
-          t = parseInt(target[card]);
+        t = parseInt(target[card]);
       // Calculate the squared difference.
-      sumDiff += (s-t) * (s-t);
+      sumDiff += (s - t) * (s - t);
     }
   }
 
   // Class of poker hands describes the similarities the best
   // so give it more priority than checking the differences between cards.
-  if (source.hasOwnProperty("CLASS") && target.hasOwnProperty("CLASS")) {
-    var s = parseInt(source["CLASS"]),
-        t = parseInt(target["CLASS"]);
+  if (source.hasOwnProperty('CLASS') && target.hasOwnProperty('CLASS')) {
+    var s = parseInt(source['CLASS']),
+      t = parseInt(target['CLASS']);
 
     // If classes differ, then scale them by a factor.
     if (s !== t) {
-      ordDiff *= (ORD_FACTOR * (Math.abs(s-t)))
+      ordDiff *= (ORD_FACTOR * (Math.abs(s - t)));
     }
   }
 
@@ -40,4 +40,4 @@ function calculateDistancePoker(source, target) {
   sumDiff *= ordDiff;
 
   return sumDiff;
-}
+}

examples/js/distances/euclideanDistance.js

@@ -5,14 +5,14 @@
  * @param  {array} properties - the properties of the nodes.
  * @return {number}            the distance between source and target nodes.
  */
-function calculateEuclideanDistance(source, target, properties, normArgs) {
+function calculateEuclideanDistance (source, target, properties, normArgs) {
   var sumDiff = 0.0;
 
   // console.log(normArgs);
   // Iterate through every column of data
   for (var i = 0; i < properties.length; i++) {
     property = properties[i];
-    if (property.toLowerCase() !== "class" && property.toLowerCase() !== "app" && property.toLowerCase() !== "user" && property.toLowerCase() !== "weekday" && property.toLowerCase() !== "type") {
+    if (property.toLowerCase() !== 'class' && property.toLowerCase() !== 'app' && property.toLowerCase() !== 'user' && property.toLowerCase() !== 'weekday' && property.toLowerCase() !== 'type') {
       var s = source[property],
         t = target[property];
 

examples/js/distances/euclideanDistanceInTSNE.js

@@ -5,7 +5,7 @@
  * @param  {array} properties - the properties of the nodes.
  * @return {number}            the distance between source and target nodes.
  */
-function calculateEuclideanDistanceTSNE(source, target, properties, normArgs) {
+function calculateEuclideanDistanceTSNE (source, target, properties, normArgs) {
   var dotProduct = 0.0,
     sumX = 0.0,
     sumY = 0.0;
@@ -15,7 +15,7 @@ function calculateEuclideanDistanceTSNE(source, target, properties, normArgs) {
   for (var i = 0; i < properties.length; i++) {
     property = properties[i];
     if (source.hasOwnProperty(property) && target.hasOwnProperty(property) &&
-      property.toLowerCase() !== "class") {
+      property.toLowerCase() !== 'class') {
       var s = source[property],
         t = target[property];
 

examples/js/distances/jaccardDissimilarity.js

@@ -5,14 +5,14 @@
  * @param  {array} properties - the properties of the nodes.
  * @return {number}            the distance between source and target nodes.
  */
-function calculateJaccardDissimilarity(source, target, properties, normArgs) {
+function calculateJaccardDissimilarity (source, target, properties, normArgs) {
   var notShared = 0.0;
 
   // console.log(properties);
   // Iterate through every column of data
   for (var i = 0; i < properties.length; i++) {
     property = properties[i];
-    if (property.toLowerCase() !== "class" && property.toLowerCase() !== "app" && property.toLowerCase() !== "user" && property.toLowerCase() !== "weekday") {
+    if (property.toLowerCase() !== 'class' && property.toLowerCase() !== 'app' && property.toLowerCase() !== 'user' && property.toLowerCase() !== 'weekday') {
       var s = source[property],
         t = target[property];
 

examples/js/distances/manhattanDistance.js

@@ -5,7 +5,7 @@
  * @param  {array} properties - the properties of the nodes.
  * @return {number}            the distance between source and target nodes.
  */
-function calculateManhattanDistance(source, target, properties, normArgs) {
+function calculateManhattanDistance (source, target, properties, normArgs) {
   var sum = 0.0,
     cols = 0;
 
@@ -13,7 +13,7 @@ function calculateManhattanDistance(source, target, properties, normArgs) {
   // Iterate through every column of data
   for (var i = 0; i < properties.length; i++) {
     property = properties[i];
-    if (property.toLowerCase() !== "class" && property.toLowerCase() !== "app" && property.toLowerCase() !== "user" && property.toLowerCase() !== "weekday") {
+    if (property.toLowerCase() !== 'class' && property.toLowerCase() !== 'app' && property.toLowerCase() !== 'user' && property.toLowerCase() !== 'weekday') {
       var s = source[property],
         t = target[property];
 

examples/js/distances/normalization.js

@@ -3,7 +3,7 @@
  * @param  {array} nodes
  * @return {object}    that contains the normalization parameters.
  */
-function calculateNormalization(nodes) {
+function calculateNormalization (nodes) {
   var STANDARD_DEV = 2.0,
     properties = Object.keys(nodes[0]),
     sums = calculateSums(nodes, properties),
@@ -23,10 +23,8 @@ function calculateNormalization(nodes) {
   };
 }
 
-
-function standardDevation(nodes, properties, avg) {
-
-  var stDev = new Array(properties.length).fill(0)
+function standardDevation (nodes, properties, avg) {
+  var stDev = new Array(properties.length).fill(0);
 
   for (var i = 0; i < properties.length; i++) {
     var sum = 0;
@@ -48,11 +46,10 @@ function standardDevation(nodes, properties, avg) {
       sum += Math.pow(val - propAvg, 2);
     });
 
-    stDev[i] = Math.sqrt(sum/nodes.length);
+    stDev[i] = Math.sqrt(sum / nodes.length);
   }
 
-  return stDev;
-
+  return stDev;
 }
 
 // Calculate the sum of values and the squared sum
@@ -63,7 +60,7 @@ function standardDevation(nodes, properties, avg) {
  * @return {object}       that contains arrays with sum of values
  *     and the squared sums.
  */
-function calculateSums(nodes, properties) {
+function calculateSums (nodes, properties) {
   var sumOfValues = new Array(properties.length).fill(0),
     sumOfSquares = new Array(properties.length).fill(0);
 
@@ -90,4 +87,4 @@ function calculateSums(nodes, properties) {
     sumOfVal: sumOfValues,
     sumOfSq: sumOfSquares
   };
-}
+}

examples/js/distances/numeric.js

@@ -3,6 +3,6 @@
  * @param  {object}  n - object to check.
  * @return {Boolean}   true, if it is a number, false otherwise.
  */
-function isNumeric(n) {
+function isNumeric (n) {
   return !isNaN(parseFloat(n)) && isFinite(n);
-}
+}

examples/js/example-papaparsing.js

@@ -1,409 +1,400 @@
-// Get the width and heigh of the SVG element.
-var width = +document.getElementById('svg').clientWidth,
-  height = +document.getElementById('svg').clientHeight;
-
-var svg = d3.select("svg")
-  .call(d3.zoom().scaleExtent([0.0001, 1000000]).on("zoom", function () {
-    svg.attr("transform", d3.event.transform);
-  }))
-  .append("g");
-
-var div = d3.select("body").append("div")
-  .attr("class", "tooltip")
-  .style("opacity", 0);
-
-var brush = d3.brush()
-  .extent([[-9999999, -9999999], [9999999, 9999999]])
-  .on("end", brushEnded);
-
-svg.append("g")
-  .attr("class", "brush")
-  .call(brush);
-
-//var intercom = Intercom.getInstance();
-
-//intercom.on("select", unSelectNodes);
-
-var nodes, // as in Data points
-  node, // as in SVG object that have all small circles on screen
-  props,
-  norm,
-  p1 = 0,
-  p2 = 0,
-  size,
-  distanceFunction,
-  simulation,
-  velocities = [],
-  rendering = true, // Rendering during the execution.
-  forceName = "forces",
-  springForce = false,
-  tooltipWidth = 0,
-  fileName = "",
-  selectedData,
-  clickedIndex = -1,
-  paused = false,
-  alreadyRanIterations,
-  tweakedVerOfLink,
-  manualStop = false;
-
-// Default parameters
-var MULTIPLIER = 50,
-  PERPLEXITY = 30,
-  LEARNING_RATE = 10,
-  NEIGHBOUR_SIZE = 10,
-  SAMPLE_SIZE = 10,
-  PIVOTS = false,
-  NUM_PIVOTS = 3,
-  ITERATIONS = 300,
-  FULL_ITERATIONS = 20,
-  NODE_SIZE = 10,
-  COLOR_ATTRIBUTE = "",
-  FULL_NEIGHBOUR_SIZE = 10,
-  FULL_SAMPLE_SIZE = 10,
-  INTERP_ENDING_ITS = 20;
-
-// Create a color scheme for a range of numbers.
-var color = d3.scaleOrdinal(d3.schemeCategory10);
-
-$(document).ready(function() {
-  distanceFunction = calculateDistance;
-  d3.select('#startSimulation').on('click', startHybridSimulation);
-  $("#HLParameters").show();
-});
-
-/**
- * Parse the data from the provided csv file using Papa Parse library
- * @param  {file} evt - csv file.
- */
-function parseFile(evt) {
-  // Clear the previous nodes
-  d3.selectAll(".nodes").remove();
-  springForce = false;
-
-  fileName = evt.target.files[0].name;
-  Papa.parse(evt.target.files[0], {
-    header: true,
-    dynamicTyping: true,
-    skipEmptyLines: true,
-    complete: function (results) {
-      processData(results.data, results.error);
-    }
-  });
-}
-
-/**
- * Process the data and pass it into D3 force simulation.
- * @param  {array} data
- * @param  {object} error
- */
-function processData(data, error) {
-  if (error) throw error.message;
-
-  nodes = data;
-  size = nodes.length;
-  simulation = d3.forceSimulation();
-
-  // Calculate normalization parameters for distance fns
-  norm = calculateNormalization(nodes);
-  props = Object.keys(nodes[0]); // Properties to consider by distance fn
-
-  COLOR_ATTRIBUTE = props[props.length-1];
-
-  var opts = document.getElementById('color_attr').options;
-
-  props.forEach(function (d) {
-    opts.add(new Option(d, d, (d === COLOR_ATTRIBUTE) ? true : false));
-  });
-  opts.selectedIndex = props.length-1;
-  //props.pop(); //Hide Iris index / last column from the distance function
-  
-
-  //Put the nodes at (0,0)
-  nodes.forEach(function (d) {
-    d.x = 0;
-    d.y = 0;
-  });
-
-  addNodesToDOM(nodes);
-
-  // Pass the nodes to the D3 force simulation.
-  simulation
-    .nodes(nodes)
-    .stop();
-
-  ticked();
-};
-
-function addNodesToDOM(data) {
-  node = svg.append("g")
-    .attr("class", "nodes")
-    .selectAll("circle")
-    .data(data)
-    .enter().append("circle")
-    .attr("r", NODE_SIZE)
-    .attr("transform", "translate(" + width / 2 + "," + height / 2 + ")")
-    // Color code the data points by a property (for Poker Hands,
-    // it is a CLASS property).
-    .attr("fill", function (d) {
-      return color(d[COLOR_ATTRIBUTE]);
-    })
-    .on("mouseover", function (d) {
-      div.transition()
-        .duration(200)
-        .style("opacity", .9);
-      div.html(formatTooltip(d))
-        .style("left", (d3.event.pageX) + "px")
-        .style("top", (d3.event.pageY - (15 * props.length)) + "px")
-        .style("width", (6 * tooltipWidth) + "px")
-        .style("height", (14 * props.length) + "px");
-      highlightOnHover(d[COLOR_ATTRIBUTE]);
-    })
-    .on("mouseout", function (d) {
-      div.transition()
-        .duration(500)
-        .style("opacity", 0);
-      node.attr("opacity", 1);
-    })
-    .on("click", function (d) {
-      console.log("click", clickedIndex);
-      if (clickedIndex !== d.index) {
-        if (springForce) {
-          highlightNeighbours(Array.from(simulation.force(forceName).nodeNeighbours(d.index).keys()));
-          clickedIndex = d.index;
-        }
-      } else {
-        node.attr("r", NODE_SIZE).attr("stroke-width", 0);
-        clickedIndex = -1;
-      }
-    });
-    if (selectedData)
-      unSelectNodes(selectedData);
-}
-
-function ticked() {
-  alreadyRanIterations++;
-  // If rendering is selected, then draw at every iteration.
-  if (rendering === true) {
-    node // Each sub-circle in the SVG, update cx and cy
-      .attr("cx", function (d) {
-        return d.x*MULTIPLIER;
-      })
-      .attr("cy", function (d) {
-        return d.y*MULTIPLIER;
-      });
-  }
-  // Legacy: Emit the distribution data to allow the drawing of the bar graph
-  //if (springForce) {
-  //  intercom.emit("passedData", simulation.force(forceName).distributionData());
-  //}
-  if(manualStop && alreadyRanIterations == ITERATIONS) {
-    ended();
-  }
-}
-
-function ended() {
-  simulation.stop();
-  simulation.force(forceName, null);
-  if (rendering !== true) { // Never drawn anything before? Now it's time.
-    node
-      .attr("cx", function (d) {
-        return d.x*MULTIPLIER;
-      })
-      .attr("cy", function (d) {
-        return d.y*MULTIPLIER;
-      });
-  }
-
-  if (p1 !== 0) {
-    // Performance time measurement
-    p2 = performance.now();
-    console.log("Execution time: " + (p2 - p1));
-    p1 = 0;
-    p2 = 0;
-  }
-}
-
-function brushEnded() {
-  var s = d3.event.selection,
-    results = [];
-
-  if (s) {
-
-    var x0 = s[0][0] - width / 2,
-      y0 = s[0][1] - height / 2,
-      x1 = s[1][0] - width / 2,
-      y1 = s[1][1] - height / 2;
-
-    if (nodes) {
-      var sel = node.filter(function (d) {
-        if (d.x > x0 && d.x < x1 && d.y > y0 && d.y < y1) {
-          return true;
-        }
-        return false;
-      }).data();
-
-      results = sel.map(function (a) { return a.index; });
-    }
-
-    //intercom.emit("select", { name: fileName, indices: results });
-
-    d3.select(".brush").call(brush.move, null);
-  }
-}
-
-
-/**
- * Format the tooltip for the data
- * @param {*} node
- */
-function formatTooltip(node) {
-  var textString = "",
-    temp = "";
-
-  tooltipWidth = 0;
-  props.forEach(function (element) {
-    temp = element + ": " + node[element] + "<br/>";
-    textString += temp;
-    if (temp.length > tooltipWidth) {
-      tooltipWidth = temp.length;
-    }
-  });
-  return textString;
-}
-
-/**
- * Halt the execution.
- */
-function stopSimulation() {
-  simulation.stop();
-  if (typeof hybridSimulation !== 'undefined') {
-    hybridSimulation.stop();
-  }
-}
-
-/**
- * Calculate the average values of the array.
- * @param  {array} array
- * @return {number}  the mean of the array.
- */
-function getAverage(array) {
-  console.log("getAverage", array);
-  var total = 0;
-  for (var i = 0; i < array.length; i++) {
-    total += array[i];
-  }
-  return total / array.length;
-}
-
-/**
- * Deselect the nodes to match the selection from other window.
- * @param {*} data
- */
-function unSelectNodes(data) {
-  selectedData = data;
-  if (fileName === data.name && nodes) {
-    node
-      .classed("notSelected", function (d) {
-        if (data.indices.indexOf(d.index) < 0) {
-          return true;
-        }
-        return false;
-      });
-  }
-}
-
-
-/**
- * Highlight the neighbours for neighbour and sampling algorithm
- * @param {*} indices
- */
-function highlightNeighbours(indices) {
-  node
-    .attr("r", function (d) {
-      if (indices.indexOf(d.index) >= 0) {
-        return NODE_SIZE * 2;
-      }
-      return NODE_SIZE;
-    })
-    .attr("stroke-width", function (d) {
-      if (indices.indexOf(d.index) >= 0) {
-        return NODE_SIZE * 0.2 + "px";
-      }
-      return "0px";
-    })
-    .attr("stroke", "white");
-}
-
-
-/**
- * Highlight all the nodes with the same class on hover
- * @param {*} highlighValue
- */
-function highlightOnHover(highlighValue) {
-  node.attr("opacity", function (d) {
-    return (highlighValue === d[COLOR_ATTRIBUTE]) ? 1 : 0.3;
-  });
-}
-
-/**
- * Color the nodes according to given attribute.
- */
-function colorToAttribute() {
-  node.attr("fill", function (d) {
-    return color(d[COLOR_ATTRIBUTE])
-  });
-}
-
-
-/**
- * Update the distance range.
-
-function updateDistanceRange() {
-  if (springForce) {
-    simulation.force(forceName).distanceRange(SELECTED_DISTANCE);
-  }
-}
-
-
-/**
- * Implemented pause/resume functionality
- */
-function pauseUnPause() {
-  if (simulation) {
-    if (paused) {
-      simulation.force(forceName);
-      simulation.restart();
-      d3.select("#pauseButton").text("Pause");
-      paused = false;
-    } else {
-      simulation.stop();
-      d3.select("#pauseButton").text("Resume");
-      paused = true;
-    }
-  }
-}
-
-
-/**
- * Average distances for each node.
- * @param {*} dataNodes
- * @param {*} properties
- * @param {*} normalization
-
-function calculateAverageDistance(dataNodes, properties, normalization) {
-  var sum = 0,
-    n = nodes.length;
-
-  for (var i = 0; i < n; i++) {
-    var sumNode = 0;
-    for (var j = 0; j < n; j++) {
-      if (i !== j) {
-        sumNode += distanceFunction(nodes[i], nodes[j], properties, normalization);
-        // console.log(sumNode);
-      }
-    }
-    sum += sumNode / (n - 1);
-  }
-
-  return sum / n;
-}*/
+// Get the width and heigh of the SVG element.
+var width = +document.getElementById('svg').clientWidth,
+  height = +document.getElementById('svg').clientHeight;
+
+var svg = d3.select('svg')
+  .call(d3.zoom().scaleExtent([0.0001, 1000000]).on('zoom', function () {
+    svg.attr('transform', d3.event.transform);
+  }))
+  .append('g');
+
+var div = d3.select('body').append('div')
+  .attr('class', 'tooltip')
+  .style('opacity', 0);
+
+var brush = d3.brush()
+  .extent([[-9999999, -9999999], [9999999, 9999999]])
+  .on('end', brushEnded);
+
+svg.append('g')
+  .attr('class', 'brush')
+  .call(brush);
+
+// var intercom = Intercom.getInstance();
+
+// intercom.on("select", unSelectNodes);
+
+var nodes, // as in Data points
+  node, // as in SVG object that have all small circles on screen
+  props,
+  norm,
+  p1 = 0,
+  p2 = 0,
+  size,
+  distanceFunction,
+  simulation,
+  velocities = [],
+  rendering = true, // Rendering during the execution.
+  forceName = 'forces',
+  springForce = false,
+  tooltipWidth = 0,
+  fileName = '',
+  selectedData,
+  clickedIndex = -1,
+  paused = false,
+  alreadyRanIterations,
+  tweakedVerOfLink,
+  manualStop = false;
+
+// Default parameters
+var MULTIPLIER = 50,
+  PERPLEXITY = 30,
+  LEARNING_RATE = 10,
+  NEIGHBOUR_SIZE = 10,
+  SAMPLE_SIZE = 10,
+  PIVOTS = false,
+  NUM_PIVOTS = 3,
+  ITERATIONS = 100,
+  FULL_ITERATIONS = 20,
+  NODE_SIZE = 10,
+  COLOR_ATTRIBUTE = '',
+  FULL_NEIGHBOUR_SIZE = 10,
+  FULL_SAMPLE_SIZE = 10,
+  INTERP_ENDING_ITS = 20;
+
+// Create a color scheme for a range of numbers.
+var color = d3.scaleOrdinal(d3.schemeCategory10);
+
+$(document).ready(function () {
+  distanceFunction = calculateDistance;
+  d3.select('#startSimulation').on('click', startHybridSimulation);
+  $('#HLParameters').show();
+});
+
+/**
+ * Parse the data from the provided csv file using Papa Parse library
+ * @param  {file} evt - csv file.
+ */
+function parseFile (evt) {
+  // Clear the previous nodes
+  d3.selectAll('.nodes').remove();
+  springForce = false;
+
+  fileName = evt.target.files[0].name;
+  Papa.parse(evt.target.files[0], {
+    header: true,
+    dynamicTyping: true,
+    skipEmptyLines: true,
+    complete: function (results) {
+      processData(results.data, results.error);
+    }
+  });
+}
+
+/**
+ * Process the data and pass it into D3 force simulation.
+ * @param  {array} data
+ * @param  {object} error
+ */
+function processData (data, error) {
+  if (error) throw error.message;
+
+  nodes = data;
+  size = nodes.length;
+  simulation = d3.forceSimulation();
+
+  // Calculate normalization parameters for distance fns
+  norm = calculateNormalization(nodes);
+  props = Object.keys(nodes[0]); // Properties to consider by distance fn
+
+  COLOR_ATTRIBUTE = props[props.length - 1];
+
+  var opts = document.getElementById('color_attr').options;
+
+  props.forEach(function (d) {
+    opts.add(new Option(d, d, (d === COLOR_ATTRIBUTE)));
+  });
+  opts.selectedIndex = props.length - 1;
+  // props.pop(); //Hide Iris index / last column from the distance function
+
+  // Put the nodes at (0,0)
+  nodes.forEach(function (d) {
+    d.x = 0;
+    d.y = 0;
+  });
+
+  addNodesToDOM(nodes);
+
+  // Pass the nodes to the D3 force simulation.
+  simulation
+    .nodes(nodes)
+    .stop();
+
+  ticked();
+};
+
+function addNodesToDOM (data) {
+  node = svg.append('g')
+    .attr('class', 'nodes')
+    .selectAll('circle')
+    .data(data)
+    .enter().append('circle')
+    .attr('r', NODE_SIZE)
+    .attr('transform', 'translate(' + width / 2 + ',' + height / 2 + ')')
+    // Color code the data points by a property (for Poker Hands,
+    // it is a CLASS property).
+    .attr('fill', function (d) {
+      return color(d[COLOR_ATTRIBUTE]);
+    })
+    .on('mouseover', function (d) {
+      div.transition()
+        .duration(200)
+        .style('opacity', 0.9);
+      div.html(formatTooltip(d))
+        .style('left', (d3.event.pageX) + 'px')
+        .style('top', (d3.event.pageY - (15 * props.length)) + 'px')
+        .style('width', (6 * tooltipWidth) + 'px')
+        .style('height', (14 * props.length) + 'px');
+      highlightOnHover(d[COLOR_ATTRIBUTE]);
+    })
+    .on('mouseout', function (d) {
+      div.transition()
+        .duration(500)
+        .style('opacity', 0);
+      node.attr('opacity', 1);
+    })
+    .on('click', function (d) {
+      console.log('click', clickedIndex);
+      if (clickedIndex !== d.index) {
+        if (springForce) {
+          highlightNeighbours(Array.from(simulation.force(forceName).nodeNeighbours(d.index).keys()));
+          clickedIndex = d.index;
+        }
+      } else {
+        node.attr('r', NODE_SIZE).attr('stroke-width', 0);
+        clickedIndex = -1;
+      }
+    });
+  if (selectedData) { unSelectNodes(selectedData); }
+}
+
+function ticked () {
+  alreadyRanIterations++;
+  // If rendering is selected, then draw at every iteration.
+  if (rendering === true) {
+    node // Each sub-circle in the SVG, update cx and cy
+      .attr('cx', function (d) {
+        return d.x * MULTIPLIER;
+      })
+      .attr('cy', function (d) {
+        return d.y * MULTIPLIER;
+      });
+  }
+  // Legacy: Emit the distribution data to allow the drawing of the bar graph
+  // if (springForce) {
+  //  intercom.emit("passedData", simulation.force(forceName).distributionData());
+  // }
+  if (manualStop && alreadyRanIterations === ITERATIONS) {
+    ended();
+  }
+}
+
+function ended () {
+  simulation.stop();
+  simulation.force(forceName, null);
+  if (rendering !== true) { // Never drawn anything before? Now it's time.
+    node
+      .attr('cx', function (d) {
+        return d.x * MULTIPLIER;
+      })
+      .attr('cy', function (d) {
+        return d.y * MULTIPLIER;
+      });
+  }
+
+  if (p1 !== 0) {
+    // Performance time measurement
+    p2 = performance.now();
+    console.log('Execution time: ' + (p2 - p1));
+    p1 = 0;
+    p2 = 0;
+  }
+}
+
+function brushEnded () {
+  var s = d3.event.selection,
+    results = [];
+
+  if (s) {
+    var x0 = s[0][0] - width / 2,
+      y0 = s[0][1] - height / 2,
+      x1 = s[1][0] - width / 2,
+      y1 = s[1][1] - height / 2;
+
+    if (nodes) {
+      var sel = node.filter(function (d) {
+        if (d.x > x0 && d.x < x1 && d.y > y0 && d.y < y1) {
+          return true;
+        }
+        return false;
+      }).data();
+
+      results = sel.map(function (a) { return a.index; });
+    }
+
+    // intercom.emit("select", { name: fileName, indices: results });
+
+    d3.select('.brush').call(brush.move, null);
+  }
+}
+
+/**
+ * Format the tooltip for the data
+ * @param {*} node
+ */
+function formatTooltip (node) {
+  var textString = '',
+    temp = '';
+
+  tooltipWidth = 0;
+  props.forEach(function (element) {
+    temp = element + ': ' + node[element] + '<br/>';
+    textString += temp;
+    if (temp.length > tooltipWidth) {
+      tooltipWidth = temp.length;
+    }
+  });
+  return textString;
+}
+
+/**
+ * Halt the execution.
+ */
+function stopSimulation () {
+  simulation.stop();
+  if (typeof hybridSimulation !== 'undefined') {
+    hybridSimulation.stop();
+  }
+}
+
+/**
+ * Calculate the average values of the array.
+ * @param  {array} array
+ * @return {number}  the mean of the array.
+ */
+function getAverage (array) {
+  console.log('getAverage', array);
+  var total = 0;
+  for (var i = 0; i < array.length; i++) {
+    total += array[i];
+  }
+  return total / array.length;
+}
+
+/**
+ * Deselect the nodes to match the selection from other window.
+ * @param {*} data
+ */
+function unSelectNodes (data) {
+  selectedData = data;
+  if (fileName === data.name && nodes) {
+    node
+      .classed('notSelected', function (d) {
+        if (data.indices.indexOf(d.index) < 0) {
+          return true;
+        }
+        return false;
+      });
+  }
+}
+
+/**
+ * Highlight the neighbours for neighbour and sampling algorithm
+ * @param {*} indices
+ */
+function highlightNeighbours (indices) {
+  node
+    .attr('r', function (d) {
+      if (indices.indexOf(d.index) >= 0) {
+        return NODE_SIZE * 2;
+      }
+      return NODE_SIZE;
+    })
+    .attr('stroke-width', function (d) {
+      if (indices.indexOf(d.index) >= 0) {
+        return NODE_SIZE * 0.2 + 'px';
+      }
+      return '0px';
+    })
+    .attr('stroke', 'white');
+}
+
+/**
+ * Highlight all the nodes with the same class on hover
+ * @param {*} highlighValue
+ */
+function highlightOnHover (highlighValue) {
+  node.attr('opacity', function (d) {
+    return (highlighValue === d[COLOR_ATTRIBUTE]) ? 1 : 0.3;
+  });
+}
+
+/**
+ * Color the nodes according to given attribute.
+ */
+function colorToAttribute () {
+  node.attr('fill', function (d) {
+    return color(d[COLOR_ATTRIBUTE]);
+  });
+}
+
+/**
+ * Update the distance range.
+
+function updateDistanceRange() {
+  if (springForce) {
+    simulation.force(forceName).distanceRange(SELECTED_DISTANCE);
+  }
+}
+
+/**
+ * Implemented pause/resume functionality
+ */
+function pauseUnPause () {
+  if (simulation) {
+    if (paused) {
+      simulation.force(forceName);
+      simulation.restart();
+      d3.select('#pauseButton').text('Pause');
+      paused = false;
+    } else {
+      simulation.stop();
+      d3.select('#pauseButton').text('Resume');
+      paused = true;
+    }
+  }
+}
+
+/**
+ * Average distances for each node.
+ * @param {*} dataNodes
+ * @param {*} properties
+ * @param {*} normalization
+
+function calculateAverageDistance(dataNodes, properties, normalization) {
+  var sum = 0,
+    n = nodes.length;
+
+  for (var i = 0; i < n; i++) {
+    var sumNode = 0;
+    for (var j = 0; j < n; j++) {
+      if (i !== j) {
+        sumNode += distanceFunction(nodes[i], nodes[j], properties, normalization);
+        // console.log(sumNode);
+      }
+    }
+    sum += sumNode / (n - 1);
+  }
+
+  return sum / n;
+} */

index.js

@@ -1,17 +1,17 @@
 export {default as forceNeighbourSampling}
-  from "./src/neighbourSampling";
+  from './src/neighbourSampling';
 
-export { default as forceBarnesHut}
-  from "./src/barnesHut";
+export {default as forceBarnesHut}
+  from './src/barnesHut';
 
-export { default as tSNE}
-  from "./src/t-sne";
+export {default as tSNE}
+  from './src/t-sne';
 
-export { default as forceLinkCompleteGraph}
-  from "./src/link";
+export {default as forceLinkCompleteGraph}
+  from './src/link';
 
-export { default as hybridSimulation}
-  from "./src/hybridSimulation";
+export {default as hybridSimulation}
+  from './src/hybridSimulation';
 
-export { getStress as calculateStress }
-  from "./src/stress";
+export {getStress as calculateStress}
+  from './src/stress';

package.json

@@ -12,11 +12,19 @@
   "main": "build/d3-spring-model.js",
   "jsnext:main": "index",
   "scripts": {
+    "lintcheck": "eslint index.js src",
+    "lintfix": "eslint index.js src --fix",
     "build": "rm -rf build && mkdir build && rollup -g d3-force:d3,d3-dispatch:d3,d3-quadtree:d3,d3-collection:d3 -f umd -n d3 -o build/d3-spring-model.js -- index.js",
     "minify": "node_modules/uglify-es/bin/uglifyjs build/d3-spring-model.js -c -m -o build/d3-spring-model.min.js",
     "zip": "zip -j build/d3-spring-model.zip -- LICENSE README.md build/d3-spring-model.js build/d3-spring-model.min.js"
   },
   "devDependencies": {
+    "eslint": "4",
+    "eslint-config-standard": "^11.0.0",
+    "eslint-plugin-import": "^2.9.0",
+    "eslint-plugin-node": "^6.0.1",
+    "eslint-plugin-promise": "^3.7.0",
+    "eslint-plugin-standard": "^3.0.1",
     "rollup": "0.36",
     "uglify-js": "git+https://github.com/mishoo/UglifyJS2.git#harmony"
   },

src/barnesHut.js

@@ -1,158 +1,153 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
-import {x, y} from "./xy";
-import {quadtree} from "d3-quadtree";
-
-/**
- * The refinement of the existing Barnes-Hut implementation in D3
- * to fit the use case of the project. Previously the algorithm stored
- * strength as internal node, now the random child is stored as internal
- * node and the force calculations are done between the node and that internal
- * object if they are sufficiently far away.
- * The check to see if the nodes are far away was also changed to the one described in original Barnes-Hut paper.
- * @return {force} calculated forces.
- */
-export default function() {
-  var nodes,
-      node,
-      alpha,
-      distance = constant(300),
-      theta = 0.5;
-
-  /**
-   * Constructs a quadtree at every iteration and apply the forces by visiting
-   * each node in a tree.
-   * @param  {number} _ - controls the stopping of the
-   *     particle simulations.
-   */
-  function force(_) {
-    var i, n = nodes.length, tree = quadtree(nodes, x, y).visitAfter(accumulate);
-    for (alpha = _, i = 0; i < n; ++i) {
-      node = nodes[i], tree.visit(apply);
-    }
-  }
-
-  /**
-   * Function used during the tree construction to fill out the nodes with
-   * correct data. Internal nodes acquire the random child while the leaf
-   * nodes accumulate forces from coincident quadrants.
-   * @param  {quadrant} quad - node representing the quadrant in quadtree.
-   */
-  function accumulate(quad) {
-    var q, d, children = [];
-
-    // For internal nodes, accumulate forces from child quadrants.
-    if (quad.length) {
-      for (var i = 0; i < 4; ++i) {
-        if ((q = quad[i]) && (d = q.data)) {
-          children.push(d);
-        }
-      }
-      // Choose a random child.
-      quad.data = children[Math.floor(Math.random() * children.length)];
-      quad.x = quad.data.x;
-      quad.y = quad.data.y;
-    }
-
-    // For leaf nodes, accumulate forces from coincident quadrants.
-    else {
-      q = quad;
-      q.x = q.data.x;
-      q.y = q.data.y;
-    }
-  }
-
-  /**
-   * Function that applies the forces for each node. If the objects are
-   * far away, the approximation is made. Otherwise, forces are calculated
-   * directly between the nodes.
-   * @param  {quadrant} quad - node representing the quadrant in quadtree.
-   * @param  {number} x1   - lower x bound of the node.
-   * @param  {number} _    - lower y bound of the node.
-   * @param  {number} x2   - upper x bound of the node.
-   * @return {boolean}     - true if the approximation was applied.
-   */
-  function apply(quad, x1, _, x2) {
-
-    var x = quad.data.x + quad.data.vx - node.x - node.vx,
-        y = quad.data.y + quad.data.vy - node.y - node.vy,
-        w = x2 - x1,
-        l = Math.sqrt(x * x + y * y);
-
-    // Apply the Barnes-Hut approximation if possible.
-    // Limit forces for very close nodes; randomize direction if coincident.
-    if (w / l < theta) {
-      if (x === 0) x = jiggle(), l += x * x;
-      if (y === 0) y = jiggle(), l += y * y;
-      if (quad.data) {
-        l = (l - +distance(node, quad.data)) / l * alpha;
-        x *= l, y *= l;
-        quad.data.vx -= x;
-        quad.data.vy -= y;
-        node.vx += x;
-        node.vy += y;
-      }
-      return true;
-    }
-
-    // Otherwise, process points directly.
-    else if (quad.length) return;
-
-    // Limit forces for very close nodes; randomize direction if coincident.
-    if (quad.data !== node || quad.next) {
-      if (x === 0) x = jiggle(), l += x * x;
-      if (y === 0) y = jiggle(), l += y * y;
-    }
-
-    do if (quad.data !== node) {
-      l = (l - +distance(node, quad.data)) / l * alpha;
-      x *= l, y *= l;
-      quad.data.vx -= x;
-      quad.data.vy -= y;
-      node.vx += x;
-      node.vy += y;
-    } while (quad = quad.next);
-  }
-
-   /**
-    * Calculates the stress. Basically, it computes the difference between
-    * high dimensional distance and real distance. The lower the stress is,
-    * the better layout.
-    * @return {number} - stress of the layout.
-    */
-  function getStress() {
-    var totalDiffSq = 0, totalHighDistSq = 0;
-    for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
-      for (var j = 0; j < nodes.length; j++) {
-        if (i !== j) {
-          source = nodes[i], target = nodes[j];
-          realDist = Math.hypot(target.x-source.x, target.y-source.y);
-          highDist = +distance(nodes[i], nodes[j]);
-          totalDiffSq += Math.pow(realDist-highDist, 2);
-          totalHighDistSq += highDist * highDist;
-        }
-      }
-    }
-    return Math.sqrt(totalDiffSq/totalHighDistSq);
-  }
-
-  // API for initializing the algorithm, setting parameters and querying
-  // metrics.
-  force.initialize = function(_) {
-    nodes = _;
-  };
-
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
-  };
-
-  force.theta = function(_) {
-    return arguments.length ? (theta = _, force) : theta;
-  };
-
-  force.stress = function() {
-    return getStress();
-  };
-
-  return force;
-}
+import constant from './constant';
+import jiggle from './jiggle';
+import {x, y} from './xy';
+import {quadtree} from 'd3-quadtree';
+
+/**
+ * The refinement of the existing Barnes-Hut implementation in D3
+ * to fit the use case of the project. Previously the algorithm stored
+ * strength as internal node, now the random child is stored as internal
+ * node and the force calculations are done between the node and that internal
+ * object if they are sufficiently far away.
+ * The check to see if the nodes are far away was also changed to the one described in original Barnes-Hut paper.
+ * @return {force} calculated forces.
+ */
+export default function () {
+  var nodes,
+    node,
+    alpha,
+    distance = constant(300),
+    theta = 0.5;
+
+    /**
+   * Constructs a quadtree at every iteration and apply the forces by visiting
+   * each node in a tree.
+   * @param  {number} _ - controls the stopping of the
+   *     particle simulations.
+   */
+  function force (_) {
+    var i, n = nodes.length, tree = quadtree(nodes, x, y).visitAfter(accumulate);
+    for (alpha = _, i = 0; i < n; ++i) {
+      node = nodes[i]; tree.visit(apply);
+    }
+  }
+
+  /**
+   * Function used during the tree construction to fill out the nodes with
+   * correct data. Internal nodes acquire the random child while the leaf
+   * nodes accumulate forces from coincident quadrants.
+   * @param  {quadrant} quad - node representing the quadrant in quadtree.
+   */
+  function accumulate (quad) {
+    var q, d, children = [];
+
+    // For internal nodes, accumulate forces from child quadrants.
+    if (quad.length) {
+      for (var i = 0; i < 4; ++i) {
+        if ((q = quad[i]) && (d = q.data)) {
+          children.push(d);
+        }
+      }
+      // Choose a random child.
+      quad.data = children[Math.floor(Math.random() * children.length)];
+      quad.x = quad.data.x;
+      quad.y = quad.data.y;
+    } else { // For leaf nodes, accumulate forces from coincident quadrants.
+      q = quad;
+      q.x = q.data.x;
+      q.y = q.data.y;
+    }
+  }
+
+  /**
+   * Function that applies the forces for each node. If the objects are
+   * far away, the approximation is made. Otherwise, forces are calculated
+   * directly between the nodes.
+   * @param  {quadrant} quad - node representing the quadrant in quadtree.
+   * @param  {number} x1   - lower x bound of the node.
+   * @param  {number} _    - lower y bound of the node.
+   * @param  {number} x2   - upper x bound of the node.
+   * @return {boolean}     - true if the approximation was applied.
+   */
+  function apply (quad, x1, _, x2) {
+    var x = quad.data.x + quad.data.vx - node.x - node.vx,
+      y = quad.data.y + quad.data.vy - node.y - node.vy,
+      w = x2 - x1,
+      l = Math.sqrt(x * x + y * y);
+
+    // Apply the Barnes-Hut approximation if possible.
+    // Limit forces for very close nodes; randomize direction if coincident.
+    if (w / l < theta) {
+      if (x === 0) { x = jiggle(); l += x * x; }
+      if (y === 0) { y = jiggle(); l += y * y; }
+      if (quad.data) {
+        l = (l - +distance(node, quad.data)) / l * alpha;
+        x *= l; y *= l;
+        quad.data.vx -= x;
+        quad.data.vy -= y;
+        node.vx += x;
+        node.vy += y;
+      }
+      return true;
+    } else if (quad.length) return; // Otherwise, process points directly.
+
+    // Limit forces for very close nodes; randomize direction if coincident.
+    if (quad.data !== node || quad.next) {
+      if (x === 0) { x = jiggle(); l += x * x; }
+      if (y === 0) { y = jiggle(); l += y * y; }
+    }
+
+    do {
+      if (quad.data !== node) {
+        l = (l - +distance(node, quad.data)) / l * alpha;
+        x *= l; y *= l;
+        quad.data.vx -= x;
+        quad.data.vy -= y;
+        node.vx += x;
+        node.vy += y;
+      }
+    } while (quad = quad.next);
+  }
+
+  /**
+    * Calculates the stress. Basically, it computes the difference between
+    * high dimensional distance and real distance. The lower the stress is,
+    * the better layout.
+    * @return {number} - stress of the layout.
+    */
+  function getStress () {
+    var totalDiffSq = 0, totalHighDistSq = 0;
+    for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
+      for (var j = 0; j < nodes.length; j++) {
+        if (i !== j) {
+          source = nodes[i]; target = nodes[j];
+          realDist = Math.hypot(target.x - source.x, target.y - source.y);
+          highDist = +distance(nodes[i], nodes[j]);
+          totalDiffSq += Math.pow(realDist - highDist, 2);
+          totalHighDistSq += highDist * highDist;
+        }
+      }
+    }
+    return Math.sqrt(totalDiffSq / totalHighDistSq);
+  }
+
+  // API for initializing the algorithm, setting parameters and querying
+  // metrics.
+  force.initialize = function (_) {
+    nodes = _;
+  };
+
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
+  };
+
+  force.theta = function (_) {
+    return arguments.length ? (theta = _, force) : theta;
+  };
+
+  force.stress = function () {
+    return getStress();
+  };
+
+  return force;
+}

src/constant.js

@@ -1,8 +1,8 @@
 /**
  * @return a constant defined by x.
  */
-export default function(x) {
-  return function() {
+export default function (x) {
+  return function () {
     return x;
   };
 }

src/hybridSimulation.js

@@ -1,203 +1,204 @@
-import {dispatch} from "d3-dispatch";
-import constant from "./constant";
-import interpBruteForce from "./interpolation/interpBruteForce";
-import interpolationPivots from "./interpolation/interpolationPivots";
-import {takeSampleFrom} from "./interpolation/helpers";
-
-/**
- * An implementation of Chalmers, Morrison, and Ross' 2002 hybrid layout
- * algorithm with an option to use the 2003 pivot-based near neighbour searching
- * method.
- * It performs the 1996 neighbour sampling spring simulation model, on a
- * "sample set", sqrt(n) samples of the data.
- * Other data points are then interpolated into the model.
- * Finally, another spring simulation may be performed on the entire dataset to
- * clean up the model.
- * @param {object} sim    - D3 Simulation object
- * @param {object} forceS - Pre-configured D3 force object for the sample set.
-                            The ending handler will be re-configured.
-                            Neighbour sampling force is expected, but other D3
-                            forces may also work.
- * @param {object} forceF - Pre-configured D3 force object for the simultion ran
-                            on the entire dataset at the end.
-                            Neighbour sampling force is expected, but other D3
-                            forces may also work.
-                            The force should not have any ending condition.
- */
-export default function (sim, forceS, forceF) {
-  var
-    SAMPLE_ITERATIONS = 300,
-    FULL_ITERATIONS   = 20,
-    interpDistanceFn,
-    NUM_PIVOTS         = 0,
-    INTERP_FINE_ITS   = 20,
-    sample = [],
-    remainder = [],
-    simulation = sim,
-    forceSample = forceS,
-    forceFull = forceF,
-    event = d3.dispatch("sampleTick", "fullTick", "startInterp", "end"),
-    initAlready = false,
-    nodes,
-    alreadyRanIterations,
-    hybrid;
-
-    if(simulation != undefined) initSimulation();
-    if(forceS != undefined || forceF != undefined) initForces();
-
-  // Performed on first run
-  function initialize() {
-    initAlready = true;
-    alreadyRanIterations = 0;
-    simulation
-      .on("tick", sampleTick)
-      .on("end", sampleEnded)
-      .nodes(sample)
-      .force("Sample force", forceSample);
-    console.log("Initialized Simulation for Hybrid");
-  }
-
-  function initForces(){
-    if (forceSample.onStableVelo) {
-      forceSample.onStableVelo(sampleEnded);
-    }
-
-    if (forceFull.onStableVelo) {
-      forceFull.onStableVelo(fullEnded);
-    }
-
-    // Set default value for interpDistanceFn if not been specified yet
-    if(interpDistanceFn === undefined) {
-      if(forceFull.distance == 'function')
-        interpDistanceFn = forceFull.distance();
-      else
-        interpDistanceFn = constant(300);
-    }
-  }
-
-  function initSimulation(){
-    nodes = simulation.nodes();
-    simulation
-      .stop()
-      .alphaDecay(0)
-      .alpha(1)
-
-    let sets = takeSampleFrom(nodes, Math.sqrt(nodes.length));
-    sample = sets.sample;
-    remainder = sets.remainder;
-  }
-
-  // Sample simulation ticked 1 frame, keep track of number of iterations here.
-  function sampleTick() {
-    event.call("sampleTick");
-    if(alreadyRanIterations++ >= SAMPLE_ITERATIONS){
-      sampleEnded();
-    }
-  }
-
-  // Full simulation ticked 1 frame, keep track of number of iterations here.
-  function fullTick() {
-    event.call("fullTick");
-    if(alreadyRanIterations++ >= FULL_ITERATIONS){
-      fullEnded();
-    }
-  }
-
-  function fullEnded() {
-    simulation.stop();
-    initAlready = false;
-    simulation.force("Full force", null);
-    event.call("end");
-  }
-
-  function sampleEnded() {
-    simulation.stop();
-    simulation.force("Sample force", null);
-    // Reset velocity of all nodes
-    for (let i=sample.length-1; i>=0; i--){
-      sample[i].vx=0;
-      sample[i].vy=0;
-    }
-
-    event.call("startInterp");
-    if (NUM_PIVOTS>=1) {
-      interpolationPivots(sample, remainder, NUM_PIVOTS, interpDistanceFn, INTERP_FINE_ITS);
-    } else {
-      interpBruteForce(sample, remainder, interpDistanceFn, INTERP_FINE_ITS);
-    }
-
-    event.call("fullTick");
-    alreadyRanIterations = 0;
-    simulation
-      .on("tick", null)
-      .on("end", null) // The ending condition should be iterations count
-      .nodes(nodes);
-
-    if (FULL_ITERATIONS<1 || forceF === undefined || forceF === null) {
-      event.call("end");
-      return;
-    }
-    simulation
-      .on("tick", fullTick)
-      .force("Full force", forceFull)
-      .restart();
-  }
-
-  return hybrid = {
-    restart: function () {
-      if(!initAlready) initialize();
-      simulation.restart();
-      return hybrid;
-    },
-
-    stop: function () {
-      simulation.stop();
-      return hybrid;
-    },
-
-    numPivots: function (_) {
-      return arguments.length ? (NUM_PIVOTS = +_, hybrid) : NUM_PIVOTS;
-    },
-
-    sampleIterations: function (_) {
-      return arguments.length ? (SAMPLE_ITERATIONS = +_, hybrid) : SAMPLE_ITERATIONS;
-    },
-
-    fullIterations: function (_) {
-      return arguments.length ? (FULL_ITERATIONS = +_, hybrid) : FULL_ITERATIONS;
-    },
-
-    interpFindTuneIts: function (_) {
-      return arguments.length ? (INTERP_FINE_ITS = +_, hybrid) : INTERP_FINE_ITS;
-    },
-
-    on: function (name, _) {
-      return arguments.length > 1 ? (event.on(name, _), hybrid) : event.on(name);
-    },
-
-    subSet: function (_) {
-      return arguments.length ? (sample = _, hybrid) : sample;
-    },
-
-    nonSubSet: function (_) {
-      return arguments.length ? (remainder = _, hybrid) : remainder;
-    },
-
-    interpDistanceFn: function (_) {
-      return arguments.length ? (interpDistanceFn = typeof _ === "function" ? _ : constant(+_), hybrid) : interpDistanceFn;
-    },
-
-    simulation: function (_) {
-      return arguments.length ? (toInit = true, simulation = _, hybrid) : simulation;
-    },
-
-    forceSample: function (_) {
-      return arguments.length ? (forceSample = _, initForces(), hybrid) : forceSample;
-    },
-
-    forceFull: function (_) {
-      return arguments.length ? (forceFull = _, initForces(), hybrid) : forceFull;
-    },
-
-  };
-}
+import {dispatch} from 'd3-dispatch';
+import constant from './constant';
+import interpBruteForce from './interpolation/interpBruteForce';
+import interpolationPivots from './interpolation/interpolationPivots';
+import {takeSampleFrom} from './interpolation/helpers';
+
+/**
+ * An implementation of Chalmers, Morrison, and Ross' 2002 hybrid layout
+ * algorithm with an option to use the 2003 pivot-based near neighbour searching
+ * method.
+ * It performs the 1996 neighbour sampling spring simulation model, on a
+ * "sample set", sqrt(n) samples of the data.
+ * Other data points are then interpolated into the model.
+ * Finally, another spring simulation may be performed on the entire dataset to
+ * clean up the model.
+ * @param {object} sim    - D3 Simulation object
+ * @param {object} forceS - Pre-configured D3 force object for the sample set.
+                            The ending handler will be re-configured.
+                            Neighbour sampling force is expected, but other D3
+                            forces may also work.
+ * @param {object} forceF - Pre-configured D3 force object for the simultion ran
+                            on the entire dataset at the end.
+                            Neighbour sampling force is expected, but other D3
+                            forces may also work.
+                            The force should not have any ending condition.
+ */
+export default function (sim, forceS, forceF) {
+  var
+    SAMPLE_ITERATIONS = 300,
+    FULL_ITERATIONS = 20,
+    interpDistanceFn,
+    NUM_PIVOTS = 0,
+    INTERP_FINE_ITS = 20,
+    sample = [],
+    remainder = [],
+    simulation = sim,
+    forceSample = forceS,
+    forceFull = forceF,
+    event = dispatch('sampleTick', 'fullTick', 'startInterp', 'end'),
+    initAlready = false,
+    nodes,
+    alreadyRanIterations,
+    hybrid;
+
+  if (simulation !== undefined) initSimulation();
+  if (forceS !== undefined || forceF !== undefined) initForces();
+
+  // Performed on first run
+  function initialize () {
+    initAlready = true;
+    alreadyRanIterations = 0;
+    simulation
+      .on('tick', sampleTick)
+      .on('end', sampleEnded)
+      .nodes(sample)
+      .force('Sample force', forceSample);
+    console.log('Initialized Simulation for Hybrid');
+  }
+
+  function initForces () {
+    if (forceSample.onStableVelo) {
+      forceSample.onStableVelo(sampleEnded);
+    }
+
+    if (forceFull.onStableVelo) {
+      forceFull.onStableVelo(fullEnded);
+    }
+
+    // Set default value for interpDistanceFn if not been specified yet
+    if (interpDistanceFn === undefined) {
+      if (forceFull.distance === 'function') {
+        interpDistanceFn = forceFull.distance();
+      } else {
+        interpDistanceFn = constant(300);
+      }
+    }
+  }
+
+  function initSimulation () {
+    nodes = simulation.nodes();
+    simulation
+      .stop()
+      .alphaDecay(0)
+      .alpha(1);
+
+    let sets = takeSampleFrom(nodes, Math.sqrt(nodes.length));
+    sample = sets.sample;
+    remainder = sets.remainder;
+  }
+
+  // Sample simulation ticked 1 frame, keep track of number of iterations here.
+  function sampleTick () {
+    event.call('sampleTick');
+    if (alreadyRanIterations++ >= SAMPLE_ITERATIONS) {
+      sampleEnded();
+    }
+  }
+
+  // Full simulation ticked 1 frame, keep track of number of iterations here.
+  function fullTick () {
+    event.call('fullTick');
+    if (alreadyRanIterations++ >= FULL_ITERATIONS) {
+      fullEnded();
+    }
+  }
+
+  function fullEnded () {
+    simulation.stop();
+    initAlready = false;
+    simulation.force('Full force', null);
+    event.call('end');
+  }
+
+  function sampleEnded () {
+    simulation.stop();
+    simulation.force('Sample force', null);
+    // Reset velocity of all nodes
+    for (let i = sample.length - 1; i >= 0; i--) {
+      sample[i].vx = 0;
+      sample[i].vy = 0;
+    }
+
+    event.call('startInterp');
+    if (NUM_PIVOTS >= 1) {
+      interpolationPivots(sample, remainder, NUM_PIVOTS, interpDistanceFn, INTERP_FINE_ITS);
+    } else {
+      interpBruteForce(sample, remainder, interpDistanceFn, INTERP_FINE_ITS);
+    }
+
+    event.call('fullTick');
+    alreadyRanIterations = 0;
+    simulation
+      .on('tick', null)
+      .on('end', null) // The ending condition should be iterations count
+      .nodes(nodes);
+
+    if (FULL_ITERATIONS < 1 || forceF === undefined || forceF === null) {
+      event.call('end');
+      return;
+    }
+    simulation
+      .on('tick', fullTick)
+      .force('Full force', forceFull)
+      .restart();
+  }
+
+  return hybrid = {
+    restart: function () {
+      if (!initAlready) initialize();
+      simulation.restart();
+      return hybrid;
+    },
+
+    stop: function () {
+      simulation.stop();
+      return hybrid;
+    },
+
+    numPivots: function (_) {
+      return arguments.length ? (NUM_PIVOTS = +_, hybrid) : NUM_PIVOTS;
+    },
+
+    sampleIterations: function (_) {
+      return arguments.length ? (SAMPLE_ITERATIONS = +_, hybrid) : SAMPLE_ITERATIONS;
+    },
+
+    fullIterations: function (_) {
+      return arguments.length ? (FULL_ITERATIONS = +_, hybrid) : FULL_ITERATIONS;
+    },
+
+    interpFindTuneIts: function (_) {
+      return arguments.length ? (INTERP_FINE_ITS = +_, hybrid) : INTERP_FINE_ITS;
+    },
+
+    on: function (name, _) {
+      return arguments.length > 1 ? (event.on(name, _), hybrid) : event.on(name);
+    },
+
+    subSet: function (_) {
+      return arguments.length ? (sample = _, hybrid) : sample;
+    },
+
+    nonSubSet: function (_) {
+      return arguments.length ? (remainder = _, hybrid) : remainder;
+    },
+
+    interpDistanceFn: function (_) {
+      return arguments.length ? (interpDistanceFn = typeof _ === 'function' ? _ : constant(+_), hybrid) : interpDistanceFn;
+    },
+
+    simulation: function (_) {
+      return arguments.length ? (initAlready = false, simulation = _, hybrid) : simulation;
+    },
+
+    forceSample: function (_) {
+      return arguments.length ? (forceSample = _, initForces(), hybrid) : forceSample;
+    },
+
+    forceFull: function (_) {
+      return arguments.length ? (forceFull = _, initForces(), hybrid) : forceFull;
+    }
+
+  };
+}

src/interpolation/helpers.js

@@ -8,26 +8,26 @@
                       sample is the list of selected objects while
                       remainder is the list of those unselected.
  */
-export function takeSampleFrom(sourceList, amount) {
+export function takeSampleFrom (sourceList, amount) {
   let randElements = [],
-      max = sourceList.length,
-      swap = false;
+    max = sourceList.length,
+    swap = false;
 
   if (amount >= max) {
     return {sample: sourceList, remainder: {}};
   }
 
   // If picking more than half of the entire set, random to pick the remainder instead
-  if (amount > Math.ceil(max/2)){
+  if (amount > Math.ceil(max / 2)) {
     amount = max - amount;
     swap = true;
   }
 
   for (let i = 0; i < amount; ++i) {
-    let rand = sourceList[Math.floor((Math.random() * max))];
+    let rand = sourceList[Math.floor(Math.random() * max)];
     // Re-random until suitable value is found.
     while (randElements.includes(rand)) {
-      rand = sourceList[Math.floor((Math.random() * max))];
+      rand = sourceList[Math.floor(Math.random() * max)];
     }
     randElements.push(rand);
   }
@@ -35,13 +35,12 @@ export function takeSampleFrom(sourceList, amount) {
     return !randElements.includes(obj);
   });
 
-  if(swap) {
+  if (swap) {
     return {
       sample: remainder,
       remainder: randElements
     };
-  }
-  else {
+  } else {
     return {
       sample: randElements,
       remainder: remainder
@@ -58,14 +57,14 @@ export function takeSampleFrom(sourceList, amount) {
  * @param {number} r
  * @return {object} - coordinate {x: number, y: number} of the point
  */
-export function pointOnCircle(h, k, angle, r) {
+export function pointOnCircle (h, k, angle, r) {
   return {
-    x: h + r*Math.cos(toRadians(angle)),
-    y: k + r*Math.sin(toRadians(angle))
+    x: h + r * Math.cos(toRadians(angle)),
+    y: k + r * Math.sin(toRadians(angle))
   };
 }
 
-function toRadians(degrees) {
+function toRadians (degrees) {
   return degrees * (Math.PI / 180);
 }
 
@@ -80,7 +79,7 @@ function toRadians(degrees) {
                                  that of samples.
  * @return {number} - Sum of distances differences
  */
-export function sumDistError(node, samples, realDistances) {
+export function sumDistError (node, samples, realDistances) {
   let total = 0.0;
   for (let i = 0; i < samples.length; i++) {
     let sample = samples[i];

src/interpolation/interpBruteForce.js

@@ -1,5 +1,5 @@
-import {takeSampleFrom} from "./helpers";
-import {placeNearToNearestNeighbour} from "./interpCommon";
+import {takeSampleFrom} from './helpers';
+import {placeNearToNearestNeighbour} from './interpCommon';
 
 /**
  * Perform interpolation where the "parent" node is found by brute-force.
@@ -18,19 +18,19 @@ import {placeNearToNearestNeighbour} from "./interpCommon";
  * @param  {number} endingIts    - for phase 3, how many iterations to refine the
  *                                 placement of each interpolated point
  */
-export default function(sampleSet, remainderSet, distanceFn, endingIts) {
+export default function (sampleSet, remainderSet, distanceFn, endingIts) {
   let
     sampleSubset = takeSampleFrom(sampleSet, Math.sqrt(sampleSet.length)).sample,
     sampleSubsetDistanceCache = [];
 
-  // For each datapoint "node" to be interpolated
-  for (let i = remainderSet.length-1; i>=0; i--) {
+    // For each datapoint "node" to be interpolated
+  for (let i = remainderSet.length - 1; i >= 0; i--) {
     let
       node = remainderSet[i],
       nearestSample, minDist, sample, dist, index;
 
     // For each datapoint "sample" in the sample set
-    for (let j = sampleSet.length-1; j>=0; j--) {
+    for (let j = sampleSet.length - 1; j >= 0; j--) {
       sample = sampleSet[j];
       dist = distanceFn(node, sample);
       if (nearestSample === undefined || dist < minDist) {
@@ -39,8 +39,7 @@ export default function(sampleSet, remainderSet, distanceFn, endingIts) {
       }
 
       index = sampleSubset.indexOf(sample);
-      if (index !== -1)
-        sampleSubsetDistanceCache[index] = dist;
+      if (index !== -1) { sampleSubsetDistanceCache[index] = dist; }
     }
 
     placeNearToNearestNeighbour(node, nearestSample, minDist, sampleSubset, sampleSubsetDistanceCache, endingIts);

src/interpolation/interpCommon.js

@@ -1,5 +1,5 @@
-import {pointOnCircle, sumDistError} from "./helpers";
-import jiggle from "../jiggle";
+import {pointOnCircle, sumDistError} from './helpers';
+import jiggle from '../jiggle';
 
 /**
  * Phase 2 and 3 of each node to be interpolated.
@@ -24,9 +24,9 @@ import jiggle from "../jiggle";
                                      index must correspond to sampleSubset
  * @param  {Integer} endingIts     - Number of iterations for phase 3
  */
-export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleSubset, realDistances, endingIts) {
+export function placeNearToNearestNeighbour (node, nearNeighbour, radius, sampleSubset, realDistances, endingIts) {
   let
-    sumDistErrorByAngle = function(angle){
+    sumDistErrorByAngle = function (angle) {
       return sumDistError(pointOnCircle(nearNeighbour.x, nearNeighbour.y, angle, radius), sampleSubset, realDistances);
     },
     dist0 = sumDistErrorByAngle(0),
@@ -36,17 +36,11 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
     lowBound = 0.0,
     highBound = 0.0;
 
-  // Determine the closest quadrant
-  if (dist0 == dist180) {
-    if (dist90 > dist270)
-      lowBound = highBound = 270;
-    else
-      lowBound = highBound = 90;
-  } else if (dist90 == dist270) {
-    if (dist0 > dist180)
-      lowBound = highBound = 180;
-    else
-      lowBound = highBound = 0;
+    // Determine the closest quadrant
+  if (dist0 === dist180) {
+    if (dist90 > dist270) { lowBound = highBound = 270; } else { lowBound = highBound = 90; }
+  } else if (dist90 === dist270) {
+    if (dist0 > dist180) { lowBound = highBound = 180; } else { lowBound = highBound = 0; }
   } else if (dist0 > dist180) {
     if (dist90 > dist270) {
       lowBound = 180;
@@ -55,14 +49,12 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
       lowBound = 90;
       highBound = 180;
     }
+  } else if (dist90 > dist270) {
+    lowBound = 270;
+    highBound = 360;
   } else {
-    if (dist90 > dist270) {
-      lowBound = 270;
-      highBound = 360;
-    } else {
-      lowBound = 0;
-      highBound = 90;
-    }
+    lowBound = 0;
+    highBound = 90;
   }
 
   // Determine the angle
@@ -73,21 +65,21 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
 
   // Phase 3
   let
-    multiplier = 1/sampleSubset.length,
+    multiplier = 1 / sampleSubset.length,
     sumForces;
   for (let i = 0; i < endingIts; i++) {
     sumForces = sumForcesToSample(node, sampleSubset, realDistances);
-    node.x += sumForces.x*multiplier;
-    node.y += sumForces.y*multiplier;
+    node.x += sumForces.x * multiplier;
+    node.y += sumForces.y * multiplier;
   }
 }
 
-function sumForcesToSample(node, samples, sampleCache) {
+function sumForcesToSample (node, samples, sampleCache) {
   let nodeVx = 0,
-      nodeVy = 0,
-      x, y, l, i, sample;
+    nodeVy = 0,
+    x, y, l, i, sample;
 
-  for (i = samples.length-1; i >=0 ; i--) {
+  for (i = samples.length - 1; i >= 0; i--) {
     sample = samples[i];
 
     // jiggle so l won't be zero and divide by zero error after this
@@ -95,7 +87,7 @@ function sumForcesToSample(node, samples, sampleCache) {
     y = node.y - sample.y || jiggle();
     l = Math.sqrt(x * x + y * y);
     l = (l - sampleCache[i]) / l;
-    x *= l, y *= l;
+    x *= l; y *= l;
     nodeVx -= x;
     nodeVy -= y;
   }
@@ -110,27 +102,27 @@ function sumForcesToSample(node, samples, sampleCache) {
  * @param  {function(x)} fn - function that takes in a number x and returns a number
  * @return {integer}        - an integer x where f(x) is minimum
  */
-function binarySearchMin(lb, hb, fn) {
+function binarySearchMin (lb, hb, fn) {
   while (lb <= hb) {
-    if(lb === hb) return lb;
+    if (lb === hb) return lb;
 
-    if(hb-lb == 1) {
+    if (hb - lb === 1) {
       if (fn(lb) >= fn(hb)) return hb;
       else return lb;
     }
 
     let
-      range = hb-lb,
-      valLowerHalf = fn(lb + range/4),
-      valHigherHalf = fn(lb + range*3/4);
+      range = hb - lb,
+      valLowerHalf = fn(lb + range / 4),
+      valHigherHalf = fn(lb + range * 3 / 4);
 
-    if (valLowerHalf > valHigherHalf)
+    if (valLowerHalf > valHigherHalf) {
       lb = Math.floor((lb + hb) / 2);
-    else if (valLowerHalf < valHigherHalf)
+    } else if (valLowerHalf < valHigherHalf) {
       hb = Math.ceil((lb + hb) / 2);
-    else {
-      lb += Math.floor(range/4);
-      hb -= Math.ceil(range/4);
+    } else {
+      lb += Math.floor(range / 4);
+      hb -= Math.ceil(range / 4);
     }
   }
   return -1;

src/interpolation/interpolationPivots.js

@@ -1,5 +1,5 @@
-import {takeSampleFrom} from "./helpers";
-import {placeNearToNearestNeighbour} from "./interpCommon";
+import {takeSampleFrom} from './helpers';
+import {placeNearToNearestNeighbour} from './interpCommon';
 
 /**
  * Perform interpolation where the "parent" node is is estimated by pivot-based searching.
@@ -23,7 +23,7 @@ import {placeNearToNearestNeighbour} from "./interpCommon";
  * @param  {number} endingIts    - for phase 3, how many iterations to refine the
  *                                 placement of each interpolated point
  */
-export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIts) {
+export default function (sampleSet, remainderSet, numPivots, distanceFn, endingIts) {
   // Pivot based parent finding
   let numBuckets = Math.floor(Math.sqrt(sampleSet.length));
   let numNonPivots = sampleSet.length - numPivots;
@@ -44,8 +44,9 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
   let distCache = []; // [ For each non-pivot sample:[For each Pivot: distance] ]
   let bucketWidths = []; // [ For each Pivot: width of each bucket ]
 
-  for (let i = 0; i < nonPivotSamples.length; i++)
+  for (let i = 0; i < nonPivotSamples.length; i++) {
     distCache[i] = [];
+  }
 
   for (let j = 0; j < numPivots; j++) {
     let pivot = pivots[j];
@@ -54,8 +55,9 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
     for (let i = 0; i < numNonPivots; i++) {
       let sample = nonPivotSamples[i];
       distCache[i][j] = distanceFn(pivot, sample);
-      if (distCache[i][j] > maxDist)
+      if (distCache[i][j] > maxDist) {
         maxDist = distCache[i][j];
+      }
     }
 
     bucketWidths.push(maxDist / numBuckets);
@@ -69,7 +71,7 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
       let bucketNumber = Math.floor(distCache[i][j] / bucketWidth);
       if (bucketNumber >= numBuckets) {
         bucketNumber = numBuckets - 1;
-      } else if (bucketNumber < 0) {  // Should never be negative anyway
+      } else if (bucketNumber < 0) { // Should never be negative anyway
         bucketNumber = 0;
       }
       pivotsBuckets[j][bucketNumber].push(sample);
@@ -77,10 +79,9 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
   }
   // ---------------------------------------------------------------------
 
-
   let sampleSubset = takeSampleFrom(sampleSet, Math.sqrt(sampleSet.length)).sample;
-  //Plot each of the remainder nodes
-  for (let i = remainderSet.length-1; i>=0; i--) {
+  // Plot each of the remainder nodes
+  for (let i = remainderSet.length - 1; i >= 0; i--) {
     let node = remainderSet[i];
     let sampleSubsetDistanceCache = [],
       minDist, nearSample;
@@ -95,7 +96,7 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
       if (index !== -1) {
         sampleSubsetDistanceCache[index] = dist;
       }
-      if (minDist === undefined || dist < minDist){
+      if (minDist === undefined || dist < minDist) {
         minDist = dist;
         nearSample = pivot;
       }
@@ -103,22 +104,21 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
       let bucketNumber = Math.floor(dist / bucketWidth);
       if (bucketNumber >= numBuckets) {
         bucketNumber = numBuckets - 1;
-      } else if (bucketNumber < 0) {  // Should never be negative anyway
+      } else if (bucketNumber < 0) { // Should never be negative anyway
         bucketNumber = 0;
       }
 
-      for (let j = pivotsBuckets[p][bucketNumber].length-1; j>=0; j--) {
+      for (let j = pivotsBuckets[p][bucketNumber].length - 1; j >= 0; j--) {
         let candidateNode = pivotsBuckets[p][bucketNumber][j];
         let index = sampleSubset.indexOf(candidateNode);
-        if (index !== -1 && sampleSubsetDistanceCache[index] !== undefined)
-          dist = sampleSubsetDistanceCache[index]
-        else {
+        if (index !== -1 && sampleSubsetDistanceCache[index] !== undefined) {
+          dist = sampleSubsetDistanceCache[index];
+        } else {
           dist = distanceFn(candidateNode, node);
-          if (index !== -1)
-            sampleSubsetDistanceCache[index] = dist;
+          if (index !== -1) { sampleSubsetDistanceCache[index] = dist; }
         }
 
-        if (dist < minDist){
+        if (dist < minDist) {
           minDist = dist;
           nearSample = candidateNode;
         }
@@ -127,8 +127,9 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
 
     // Fill in holes in cache
     for (let k = 0; k < sampleSubset.length; k++) {
-      if (sampleSubsetDistanceCache[k] === undefined)
+      if (sampleSubsetDistanceCache[k] === undefined) {
         sampleSubsetDistanceCache[k] = distanceFn(node, sampleSubset[k]);
+      }
     }
     placeNearToNearestNeighbour(node, nearSample, minDist, sampleSubset, sampleSubsetDistanceCache, endingIts);
   }

src/jiggle.js

@@ -1,7 +1,7 @@
 /**
  * @return {number} a very small non-zero random number.
  */
-export default function() {
+export default function () {
   let rand;
   do {
     rand = (Math.random() - 0.5) * 1e-6;

src/link.js

@@ -1,108 +1,110 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
-
-/**
- * Modified link force algorithm
- * - simplify calculations for parameters locked for spring model
- * - replace the use of links {} with loop. greatly reduce memory usage
- * - removed other unused functions
- * Alpha should be constant 1 for accurate simulation
- */
-export default function() {
-  var dataSizeFactor,
-      distance = constant(30),
-      distances = [],
-      nodes,
-      stableVelocity = 0,
-      stableVeloHandler = null,
-      latestVelocityDiff = 0,
-      iterations = 1;
-
-  function force(alpha) {
-    let n = nodes.length;
-    // Cache old velocity for comparison later
-    if (stableVeloHandler!==null && stableVelocity>=0) {
-      for (let i = n-1, node; i>=0; i--) {
-        node = nodes[i];
-        node.oldvx = node.vx;
-        node.oldvy = node.vy;
-      }
-    }
-    
-    // Each iteration in a tick
-    for (var k = 0, source, target, i, j, x, y, l; k < iterations; ++k) {
-      // For each link
-      for (i = 1; i < n; i++) for (j = 0; j < i; j++) {
-        // jiggle so l won't be zero and divide by zero error after this
-        source = nodes[i];
-        target = nodes[j];
-        x = target.x + target.vx - source.x - source.vx || jiggle();
-        y = target.y + target.vy - source.y - source.vy || jiggle();
-        l = Math.sqrt(x * x + y * y);
-        l = (l - distances[i*(i-1)/2+j]) / l  * dataSizeFactor * alpha;
-        x *= l, y *= l;
-        target.vx -= x;
-        target.vy -= y;
-        source.vx += x;
-        source.vy += y;
-      }
-    }
-
-    // Calculate velocity changes, aka force applied.
-    if (stableVeloHandler!==null && stableVelocity>=0) {
-      let velocityDiff = 0;
-      for (let i = n-1, node; i>=0; i--) {
-        node = nodes[i];
-        velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
-      }
-      velocityDiff /= n;
-      latestVelocityDiff = velocityDiff;
-
-      if(velocityDiff<stableVelocity){
-        stableVeloHandler();
-      }
-    }
-  }
-
-  function initialize() {
-    if (!nodes) return;
-    // 0.5 to divide the force to two part for source and target node
-    dataSizeFactor = 0.5/(nodes.length-1);
-    initializeDistance();
-  }
-
-  function initializeDistance() {
-    if (!nodes) return;
-    for (let i = 1, n = nodes.length; i < n; i++) {
-      for (let j = 0; j < i; j++) {
-        distances.push(distance(nodes[i], nodes[j]));
-      }
-    }
-  }
-
-  force.initialize = function(_) {
-    nodes = _;
-    initialize();
-  };
-
-  force.iterations = function(_) {
-    return arguments.length ? (iterations = +_, force) : iterations;
-  };
-
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), initializeDistance(), force) : distance;
-  };
-
-  force.latestAccel = function () {
-    return latestVelocityDiff;
-  };
-
-  force.onStableVelo = function (_) {
-    return arguments.length ? (stableVeloHandler = _, force) : stableVeloHandler;
-  };
-
-  force.stableVelocity = function (_) {
-    return arguments.length ? (stableVelocity = _, force) : stableVelocity;
-  };
-  return force;
-}
+import constant from './constant';
+import jiggle from './jiggle';
+
+/**
+ * Modified link force algorithm
+ * - simplify calculations for parameters locked for spring model
+ * - replace the use of links {} with loop. greatly reduce memory usage
+ * - removed other unused functions
+ * Alpha should be constant 1 for accurate simulation
+ */
+export default function () {
+  var dataSizeFactor,
+    distance = constant(30),
+    distances = [],
+    nodes,
+    stableVelocity = 0,
+    stableVeloHandler = null,
+    latestVelocityDiff = 0,
+    iterations = 1;
+
+  function force (alpha) {
+    let n = nodes.length;
+    // Cache old velocity for comparison later
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
+      for (let i = n - 1, node; i >= 0; i--) {
+        node = nodes[i];
+        node.oldvx = node.vx;
+        node.oldvy = node.vy;
+      }
+    }
+
+    // Each iteration in a tick
+    for (var k = 0, source, target, i, j, x, y, l; k < iterations; ++k) {
+      // For each link
+      for (i = 1; i < n; i++) {
+        for (j = 0; j < i; j++) {
+        // jiggle so l won't be zero and divide by zero error after this
+          source = nodes[i];
+          target = nodes[j];
+          x = target.x + target.vx - source.x - source.vx || jiggle();
+          y = target.y + target.vy - source.y - source.vy || jiggle();
+          l = Math.sqrt(x * x + y * y);
+          l = (l - distances[i * (i - 1) / 2 + j]) / l * dataSizeFactor * alpha;
+          x *= l; y *= l;
+          target.vx -= x;
+          target.vy -= y;
+          source.vx += x;
+          source.vy += y;
+        }
+      }
+    }
+
+    // Calculate velocity changes, aka force applied.
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
+      let velocityDiff = 0;
+      for (let i = n - 1, node; i >= 0; i--) {
+        node = nodes[i];
+        velocityDiff += Math.abs(Math.hypot(node.vx - node.oldvx, node.vy - node.oldvy));
+      }
+      velocityDiff /= n;
+      latestVelocityDiff = velocityDiff;
+
+      if (velocityDiff < stableVelocity) {
+        stableVeloHandler();
+      }
+    }
+  }
+
+  function initialize () {
+    if (!nodes) return;
+    // 0.5 to divide the force to two part for source and target node
+    dataSizeFactor = 0.5 / (nodes.length - 1);
+    initializeDistance();
+  }
+
+  function initializeDistance () {
+    if (!nodes) return;
+    for (let i = 1, n = nodes.length; i < n; i++) {
+      for (let j = 0; j < i; j++) {
+        distances.push(distance(nodes[i], nodes[j]));
+      }
+    }
+  }
+
+  force.initialize = function (_) {
+    nodes = _;
+    initialize();
+  };
+
+  force.iterations = function (_) {
+    return arguments.length ? (iterations = +_, force) : iterations;
+  };
+
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), initializeDistance(), force) : distance;
+  };
+
+  force.latestAccel = function () {
+    return latestVelocityDiff;
+  };
+
+  force.onStableVelo = function (_) {
+    return arguments.length ? (stableVeloHandler = _, force) : stableVeloHandler;
+  };
+
+  force.stableVelocity = function (_) {
+    return arguments.length ? (stableVelocity = _, force) : stableVelocity;
+  };
+  return force;
+}

src/neighbourSampling.js

@@ -1,12 +1,12 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
+import constant from './constant';
+import jiggle from './jiggle';
 /**
  * An implementation of Chalmers' 1996 Neighbour and Sampling algorithm.
  * It uses random sampling to find the most suited neighbours from the
  * data set.
  */
 
-function sortDistances(a, b) {
+function sortDistances (a, b) {
   return b[1] - a[1];
 }
 
@@ -21,22 +21,22 @@ export default function () {
     dataSizeFactor,
     latestVelocityDiff = 0;
 
-  /**
+    /**
    * Apply spring forces at each simulation iteration.
    * @param  {number} alpha - multiplier for amount of force applied
    */
-  function force(alpha) {
+  function force (alpha) {
     let n = nodes.length;
     // Cache old velocity for comparison later
-    if (stableVeloHandler!==null && stableVelocity>=0) {
-      for (let i = n-1, node; i>=0; i--) {
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
+      for (let i = n - 1, node; i >= 0; i--) {
         node = nodes[i];
         node.oldvx = node.vx;
         node.oldvy = node.vy;
       }
     }
 
-    for (let i = n-1, node, samples; i>=0; i--) {
+    for (let i = n - 1, node, samples; i >= 0; i--) {
       node = nodes[i];
       samples = createRandomSamples(i);
 
@@ -52,16 +52,16 @@ export default function () {
     }
 
     // Calculate velocity changes, aka force applied.
-    if (stableVeloHandler!==null && stableVelocity>=0) {
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
       let velocityDiff = 0;
-      for (let i = n-1, node; i>=0; i--) {
+      for (let i = n - 1, node; i >= 0; i--) {
         node = nodes[i];
-        velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
+        velocityDiff += Math.abs(Math.hypot(node.vx - node.oldvx, node.vy - node.oldvy));
       }
       velocityDiff /= n;
       latestVelocityDiff = velocityDiff;
 
-      if(velocityDiff<stableVelocity){
+      if (velocityDiff < stableVelocity) {
         stableVeloHandler();
       }
     }
@@ -74,14 +74,14 @@ export default function () {
    * @param {number} dist    - high dimensional distance between the two nodes
    * @param {number} alpha   - multiplier for the amount of force applied
    */
-  function setVelocity(source, target, dist, alpha) {
+  function setVelocity (source, target, dist, alpha) {
     let x, y, l;
     // jiggle so l won't be zero and divide by zero error after this
     x = target.x + target.vx - source.x - source.vx || jiggle();
     y = target.y + target.vy - source.y - source.vy || jiggle();
     l = Math.sqrt(x * x + y * y);
     l = (l - dist) / l * dataSizeFactor * alpha;
-    x *= l, y *= l;
+    x *= l; y *= l;
     // Set the calculated velocites for both nodes.
     target.vx -= x;
     target.vy -= y;
@@ -90,11 +90,11 @@ export default function () {
   }
 
   // Called on nodes change and added to a simulation
-  function initialize() {
+  function initialize () {
     if (!nodes) return;
 
     // Initialize for each node some random neighbours.
-    for (let i = nodes.length-1; i>=0; i--) {
+    for (let i = nodes.length - 1; i >= 0; i--) {
       let neighbs = pickRandomNodesFor(i, [i], neighbourSize);
       // Sort the neighbour set by the distances.
       neighbours[i] = new Map(neighbs.sort(sortDistances));
@@ -103,8 +103,8 @@ export default function () {
     initDataSizeFactor();
   }
 
-  function initDataSizeFactor(){
-    dataSizeFactor = 0.5/(neighbourSize+sampleSize);
+  function initDataSizeFactor () {
+    dataSizeFactor = 0.5 / (neighbourSize + sampleSize);
   }
 
   /**
@@ -116,7 +116,7 @@ export default function () {
    * @param  {number} size   - max number of elements in the map to return.
    * @return {array}
    */
-  function pickRandomNodesFor(index, exclude, size) {
+  function pickRandomNodesFor (index, exclude, size) {
     let randElements = [];
     let max = nodes.length;
 
@@ -126,14 +126,14 @@ export default function () {
         break;
       }
 
-      let rand = Math.floor((Math.random() * max));
+      let rand = Math.floor(Math.random() * max);
       // Re-random until suitable value is found.
       while (randElements.includes(rand) || exclude.includes(rand)) {
-        rand = Math.floor((Math.random() * max));
+        rand = Math.floor(Math.random() * max);
       }
       randElements.push(rand);
     }
-    for(let i=randElements.length-1, rand; i>=0; i--){
+    for (let i = randElements.length - 1, rand; i >= 0; i--) {
       rand = randElements[i];
       randElements[i] = [rand, distance(nodes[index], nodes[rand])];
     }
@@ -146,7 +146,7 @@ export default function () {
    * @param  {number} index - index of the node to generate sample for
    * @return {map}
    */
-  function createRandomSamples(index) {
+  function createRandomSamples (index) {
     // Ignore the current neighbours of the node and itself.
     let exclude = [index];
     exclude = exclude.concat(Array.from(neighbours[index].keys()));
@@ -160,13 +160,12 @@ export default function () {
    * @param  {map} samples    - map of samples
    * @return {map}            - new map of neighbours
    */
-  function findNewNeighbours(neighbours, samples) {
+  function findNewNeighbours (neighbours, samples) {
     let combined = [...neighbours.entries()].concat([...samples.entries()]);
     combined = combined.sort(sortDistances);
     return new Map(combined.slice(0, neighbourSize));
   }
 
-
   // API for initializing the algorithm and setting parameters
   force.initialize = function (_) {
     nodes = _;
@@ -186,7 +185,7 @@ export default function () {
   };
 
   force.distance = function (_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
   };
 
   force.latestAccel = function () {

src/stress.js

@@ -4,12 +4,13 @@
   * to the better layout.
   * @return {number} - stress of the layout.
   */
-export function getStress(nodes, distance) {
-  let sumDiffSq = 0
+export function getStress (nodes, distance) {
+  let sumDiffSq = 0;
   let sumLowDDistSq = 0;
-  for (let j = nodes.length-1; j >= 1; j--) {
+  for (let j = nodes.length - 1; j >= 1; j--) {
     for (let i = 0; i < j; i++) {
-      let source = nodes[i], target = nodes[j];
+      let source = nodes[i];
+      let target = nodes[j];
       let lowDDist = Math.hypot(target.x - source.x, target.y - source.y);
       let highDDist = distance(source, target);
       sumDiffSq += Math.pow(highDDist - lowDDist, 2);

src/t-sne.js

@@ -1,374 +1,374 @@
-import constant from "./constant";
-
-/**
- * Set the node id accessor to the specified i.
- * @param  {node} d - node.
- * @param  {accessor} i - id accessor.
- * @return {accessor}  - node id accessor.
- */
-function index(d, i) {
-  return i;
-}
-
-/**
- * t-SNE implementation in D3 by using the code existing in tsnejs
- * (https://github.com/karpathy/tsnejs) to compute the solution.
- */
-export default function() {
-  var id = index,
-    distance = constant(300),
-    nodes,
-    perplexity = 30,
-    learningRate = 10,
-    iteration = 0,
-    dim = 2,
-    N, // length of the nodes.
-    P, // probability matrix.
-    Y, // solution.
-    gains,
-    ystep;
-
-  /**
-   * Make a step in t-SNE algorithm and set the velocities for the nodes
-   * to accumulate the values from solution.
-   */
-  function force() {
-    // Make a step at each iteration.
-    step();
-    var solution = getSolution();
-
-    // Set the velocity for each node using the solution.
-    for (var i = 0; i < nodes.length; i++) {
-      nodes[i].vx += solution[i][0];
-      nodes[i].vy += solution[i][1];
-    }
-  }
-
-  /**
-   * Calculates the random number from Gaussian distribution.
-   * @return {number} random number.
-   */
-  function gaussRandom() {
-    let u = 2 * Math.random() - 1;
-    let v = 2 * Math.random() - 1;
-    let r = u * u + v * v;
-    if (r == 0 || r > 1) return gaussRandom();
-    return u * Math.sqrt(-2 * Math.log(r) / r);
-  }
-
-  /**
-   * Return the normalized number.
-   * @return {number} normalized random number from Gaussian distribution.
-   */
-  function randomN() {
-    return gaussRandom() * 1e-4;
-  }
-
-  function sign(x) {
-    return x > 0 ? 1 : x < 0 ? -1 : 0;
-  }
-
-  /**
-   * Create an array of length n filled with zeros.
-   * @param  {number} n - length of array.
-   * @return {Float64Array}   - array of zeros with length n.
-   */
-  function zeros(n) {
-    if (typeof(n) === 'undefined' || isNaN(n)) {
-      return [];
-    }
-    return new Float64Array(n); // typed arrays are faster
-  }
-
-  // Returns a 2d array of random numbers
-  /**
-   * Creates a 2d array filled with random numbers.
-   * @param  {number} n - rows.
-   * @param  {number} d - columns.
-   * @return {array}   - 2d array
-   */
-  function random2d(n, d) {
-    var x = [];
-    for (var i = 0; i < n; i++) {
-      var y = [];
-      for (var j = 0; j < d; j++) {
-        y.push(randomN());
-      }
-      x.push(y);
-    }
-    return x;
-  }
-
-  /**
-   * Compute the probability matrix using the provided data.
-   * @param  {array} data       - nodes.
-   * @param  {number} perplexity - used to calculate entropy of distribution.
-   * @param  {number} tol        - limit for entropy difference.
-   * @return {2d array}          - 2d matrix containing probabilities.
-   */
-  function d2p(data, perplexity, tol) {
-    N = Math.floor(data.length);
-    var Htarget = Math.log(perplexity); // target entropy of distribution.
-    var P1 = 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) {
-        // Compute entropy and kernel row with beta precision.
-        var psum = 0.0;
-        for (var j = 0; j < N; j++) {
-          var pj = Math.exp(-distance(data[i], data[j]) * beta);
-          // Ignore the diagonals
-          if (i === j) {
-            pj = 0;
-          }
-          prow[j] = pj;
-          psum += pj;
-        }
-        // Normalize p and compute entropy.
-        var Hhere = 0.0;
-        for (j = 0; j < N; j++) {
-          if (psum == 0) {
-            pj = 0;
-          } else {
-            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 || num >= maxtries) {
-          done = true;
-        }
-      }
-
-      // Copy over the final prow to P1 at row i
-      for (j = 0; j < N; j++) {
-        P1[i * N + j] = prow[j];
-      }
-
-    }
-
-    // Symmetrize P and normalize it to sum to 1 over all ij
-    var Pout = zeros(N * N);
-    var N2 = N * 2;
-    for (i = 0; i < N; i++) {
-      for (j = 0; j < N; j++) {
-        Pout[i * N + j] = Math.max((P1[i * N + j] + P1[j * N + i]) / N2, 1e-100);
-      }
-    }
-    return Pout;
-  }
-
-  /**
-   * Initialize a starting (random) solution.
-   */
-  function initSolution() {
-    Y = random2d(N, dim);
-    // Step gains to accelerate progress in unchanging directions.
-    gains = random2d(N, dim, 1.0);
-    // Momentum accumulator.
-    ystep = random2d(N, dim, 0.0);
-    iteration = 0;
-  }
-
-  /**
-   * @return {2d array} the solution.
-   */
-  function getSolution() {
-    return Y;
-  }
-
-  /**
-   * Do a single step (iteration) for the layout.
-   * @return {number} the current cost.
-   */
-  function step() {
-    iteration += 1;
-
-    var cg = costGrad(Y); // Evaluate gradient.
-    var cost = cg.cost;
-    var grad = cg.grad;
-
-    // Perform gradient step.
-    var ymean = zeros(dim);
-    for (var i = 0; i < N; i++) {
-      for (var d = 0; d < dim; d++) {
-        var gid = grad[i][d];
-        var sid = ystep[i][d];
-        var gainid = 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;
-        }
-        gains[i][d] = newgain;
-
-        // Compute momentum step direction.
-        var momval = iteration < 250 ? 0.5 : 0.8;
-        var newsid = momval * sid - learningRate * newgain * grad[i][d];
-        ystep[i][d] = newsid;
-
-        // Do the step.
-        Y[i][d] += newsid;
-
-        // Accumulate mean so that we can center later.
-        ymean[d] += Y[i][d];
-      }
-    }
-
-    // Reproject Y to have the zero mean.
-    for (i = 0; i < N; i++) {
-      for (d = 0; d < dim; d++) {
-        Y[i][d] -= ymean[d] / N;
-      }
-    }
-    return cost;
-  }
-
-  /**
-   * Calculate the cost and the gradient.
-   * @param  {2d array} Y - the current solution to evaluate.
-   * @return {object}   that contains a cost and a gradient.
-   */
-  function costGrad(Y) {
-
-    var pmul = iteration < 100 ? 4 : 1;
-
-    // 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 (i = 0; i < N; i++) {
-      var gsum = new Array(dim); // Initialize gradiet for point i.
-      for (d = 0; d < dim; d++) {
-        gsum[d] = 0.0;
-      }
-      for (j = 0; j < N; j++) {
-        // Accumulate the cost.
-        cost += -P[i * N + j] * Math.log(Q[i * N + j]);
-        var premult = 4 * (pmul * P[i * N + j] - Q[i * N + j]) * Qu[i * N + j];
-        for (d = 0; d < dim; d++) {
-          gsum[d] += premult * (Y[i][d] - Y[j][d]);
-        }
-      }
-      grad.push(gsum);
-    }
-
-    return {
-      cost: cost,
-      grad: grad
-    };
-  }
-
-  /**
-   * Calculates the stress. Basically, it computes the difference between
-   * high dimensional distance and real distance. The lower the stress is,
-   * the better layout.
-   * @return {number} - stress of the layout.
-   */
-  function getStress() {
-    var totalDiffSq = 0,
-      totalHighDistSq = 0;
-    for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
-      for (var j = 0; j < nodes.length; j++) {
-        if (i !== j) {
-          source = nodes[i], target = nodes[j];
-          realDist = Math.hypot(target.x - source.x, target.y - source.y);
-          highDist = +distance(nodes[i], nodes[j]);
-          totalDiffSq += Math.pow(realDist - highDist, 2);
-          totalHighDistSq += highDist * highDist;
-        }
-      }
-    }
-    return Math.sqrt(totalDiffSq / totalHighDistSq);
-  }
-
-  // API for initializing the algorithm, setting parameters and querying
-  // metrics.
-  force.initialize = function(_) {
-    nodes = _;
-    N = nodes.length;
-    // Initialize the probability matrix.
-    P = d2p(nodes, perplexity, 1e-4);
-    initSolution();
-  };
-
-  force.id = function(_) {
-    return arguments.length ? (id = _, force) : id;
-  };
-
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
-  };
-
-  force.stress = function() {
-    return getStress();
-  };
-
-  force.learningRate = function(_) {
-    return arguments.length ? (learningRate = +_, force) : learningRate;
-  };
-
-  force.perplexity = function(_) {
-    return arguments.length ? (perplexity = +_, force) : perplexity;
-  };
-
-  return force;
-}
+/* eslint-disable block-scoped-var */
+import constant from './constant';
+
+/**
+ * Set the node id accessor to the specified i.
+ * @param  {node} d - node.
+ * @param  {accessor} i - id accessor.
+ * @return {accessor}  - node id accessor.
+ */
+function index (d, i) {
+  return i;
+}
+
+/**
+ * t-SNE implementation in D3 by using the code existing in tsnejs
+ * (https://github.com/karpathy/tsnejs) to compute the solution.
+ */
+export default function () {
+  var id = index,
+    distance = constant(300),
+    nodes,
+    perplexity = 30,
+    learningRate = 10,
+    iteration = 0,
+    dim = 2,
+    N, // length of the nodes.
+    P, // probability matrix.
+    Y, // solution.
+    gains,
+    ystep;
+
+    /**
+   * Make a step in t-SNE algorithm and set the velocities for the nodes
+   * to accumulate the values from solution.
+   */
+  function force () {
+    // Make a step at each iteration.
+    step();
+    var solution = getSolution();
+
+    // Set the velocity for each node using the solution.
+    for (var i = 0; i < nodes.length; i++) {
+      nodes[i].vx += solution[i][0];
+      nodes[i].vy += solution[i][1];
+    }
+  }
+
+  /**
+   * Calculates the random number from Gaussian distribution.
+   * @return {number} random number.
+   */
+  function gaussRandom () {
+    let u = 2 * Math.random() - 1;
+    let v = 2 * Math.random() - 1;
+    let r = u * u + v * v;
+    if (r === 0 || r > 1) {
+      return gaussRandom();
+    }
+    return u * Math.sqrt(-2 * Math.log(r) / r);
+  }
+
+  /**
+   * Return the normalized number.
+   * @return {number} normalized random number from Gaussian distribution.
+   */
+  function randomN () {
+    return gaussRandom() * 1e-4;
+  }
+
+  function sign (x) {
+    return x > 0 ? 1 : x < 0 ? -1 : 0;
+  }
+
+  /**
+   * Create an array of length n filled with zeros.
+   * @param  {number} n - length of array.
+   * @return {Float64Array}   - array of zeros with length n.
+   */
+  function zeros (n) {
+    if (typeof n === 'undefined' || isNaN(n)) {
+      return [];
+    }
+    return new Float64Array(n); // typed arrays are faster
+  }
+
+  // Returns a 2d array of random numbers
+  /**
+   * Creates a 2d array filled with random numbers.
+   * @param  {number} n - rows.
+   * @param  {number} d - columns.
+   * @return {array}   - 2d array
+   */
+  function random2d (n, d) {
+    var x = [];
+    for (var i = 0; i < n; i++) {
+      var y = [];
+      for (var j = 0; j < d; j++) {
+        y.push(randomN());
+      }
+      x.push(y);
+    }
+    return x;
+  }
+
+  /**
+   * Compute the probability matrix using the provided data.
+   * @param  {array} data       - nodes.
+   * @param  {number} perplexity - used to calculate entropy of distribution.
+   * @param  {number} tol        - limit for entropy difference.
+   * @return {2d array}          - 2d matrix containing probabilities.
+   */
+  function d2p (data, perplexity, tol) {
+    N = Math.floor(data.length);
+    var Htarget = Math.log(perplexity); // target entropy of distribution.
+    var P1 = 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) {
+        // Compute entropy and kernel row with beta precision.
+        var psum = 0.0;
+        for (var j = 0; j < N; j++) {
+          var pj = Math.exp(-distance(data[i], data[j]) * beta);
+          // Ignore the diagonals
+          if (i === j) {
+            pj = 0;
+          }
+          prow[j] = pj;
+          psum += pj;
+        }
+        // Normalize p and compute entropy.
+        var Hhere = 0.0;
+        for (j = 0; j < N; j++) {
+          if (psum === 0) {
+            pj = 0;
+          } else {
+            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 || num >= maxtries) {
+          done = true;
+        }
+      }
+
+      // Copy over the final prow to P1 at row i
+      for (j = 0; j < N; j++) {
+        P1[i * N + j] = prow[j];
+      }
+    }
+
+    // Symmetrize P and normalize it to sum to 1 over all ij
+    var Pout = zeros(N * N);
+    var N2 = N * 2;
+    for (i = 0; i < N; i++) {
+      for (j = 0; j < N; j++) {
+        Pout[i * N + j] = Math.max((P1[i * N + j] + P1[j * N + i]) / N2, 1e-100);
+      }
+    }
+    return Pout;
+  }
+
+  /**
+   * Initialize a starting (random) solution.
+   */
+  function initSolution () {
+    Y = random2d(N, dim);
+    // Step gains to accelerate progress in unchanging directions.
+    gains = random2d(N, dim, 1.0);
+    // Momentum accumulator.
+    ystep = random2d(N, dim, 0.0);
+    iteration = 0;
+  }
+
+  /**
+   * @return {2d array} the solution.
+   */
+  function getSolution () {
+    return Y;
+  }
+
+  /**
+   * Do a single step (iteration) for the layout.
+   * @return {number} the current cost.
+   */
+  function step () {
+    iteration += 1;
+
+    var cg = costGrad(Y); // Evaluate gradient.
+    var cost = cg.cost;
+    var grad = cg.grad;
+
+    // Perform gradient step.
+    var ymean = zeros(dim);
+    for (var i = 0; i < N; i++) {
+      for (var d = 0; d < dim; d++) {
+        var gid = grad[i][d];
+        var sid = ystep[i][d];
+        var gainid = 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;
+        }
+        gains[i][d] = newgain;
+
+        // Compute momentum step direction.
+        var momval = iteration < 250 ? 0.5 : 0.8;
+        var newsid = momval * sid - learningRate * newgain * grad[i][d];
+        ystep[i][d] = newsid;
+
+        // Do the step.
+        Y[i][d] += newsid;
+
+        // Accumulate mean so that we can center later.
+        ymean[d] += Y[i][d];
+      }
+    }
+
+    // Reproject Y to have the zero mean.
+    for (i = 0; i < N; i++) {
+      for (d = 0; d < dim; d++) {
+        Y[i][d] -= ymean[d] / N;
+      }
+    }
+    return cost;
+  }
+
+  /**
+   * Calculate the cost and the gradient.
+   * @param  {2d array} Y - the current solution to evaluate.
+   * @return {object}   that contains a cost and a gradient.
+   */
+  function costGrad (Y) {
+    var pmul = iteration < 100 ? 4 : 1;
+
+    // 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 (i = 0; i < N; i++) {
+      var gsum = new Array(dim); // Initialize gradiet for point i.
+      for (d = 0; d < dim; d++) {
+        gsum[d] = 0.0;
+      }
+      for (j = 0; j < N; j++) {
+        // Accumulate the cost.
+        cost += -P[i * N + j] * Math.log(Q[i * N + j]);
+        var premult = 4 * (pmul * P[i * N + j] - Q[i * N + j]) * Qu[i * N + j];
+        for (d = 0; d < dim; d++) {
+          gsum[d] += premult * (Y[i][d] - Y[j][d]);
+        }
+      }
+      grad.push(gsum);
+    }
+
+    return {
+      cost: cost,
+      grad: grad
+    };
+  }
+
+  /**
+   * Calculates the stress. Basically, it computes the difference between
+   * high dimensional distance and real distance. The lower the stress is,
+   * the better layout.
+   * @return {number} - stress of the layout.
+   */
+  function getStress () {
+    var totalDiffSq = 0,
+      totalHighDistSq = 0;
+    for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
+      for (var j = 0; j < nodes.length; j++) {
+        if (i !== j) {
+          source = nodes[i]; target = nodes[j];
+          realDist = Math.hypot(target.x - source.x, target.y - source.y);
+          highDist = +distance(nodes[i], nodes[j]);
+          totalDiffSq += Math.pow(realDist - highDist, 2);
+          totalHighDistSq += highDist * highDist;
+        }
+      }
+    }
+    return Math.sqrt(totalDiffSq / totalHighDistSq);
+  }
+
+  // API for initializing the algorithm, setting parameters and querying
+  // metrics.
+  force.initialize = function (_) {
+    nodes = _;
+    N = nodes.length;
+    // Initialize the probability matrix.
+    P = d2p(nodes, perplexity, 1e-4);
+    initSolution();
+  };
+
+  force.id = function (_) {
+    return arguments.length ? (id = _, force) : id;
+  };
+
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
+  };
+
+  force.stress = function () {
+    return getStress();
+  };
+
+  force.learningRate = function (_) {
+    return arguments.length ? (learningRate = +_, force) : learningRate;
+  };
+
+  force.perplexity = function (_) {
+    return arguments.length ? (perplexity = +_, force) : perplexity;
+  };
+
+  return force;
+}

src/xy.js

@@ -1,13 +1,13 @@
-/**
- * @return x value of a node
- */
-export function x(d) {
-  return d.x;
-}
-
-/**
- * @return y value of a node
- */
-export function y(d) {
-  return d.y;
-}
+/**
+ * @return x value of a node
+ */
+export function x (d) {
+  return d.x;
+}
+
+/**
+ * @return y value of a node
+ */
+export function y (d) {
+  return d.y;
+}