Modify comments, more non-functional code cleanup

src/hybridSimulation.js

@@ -108,9 +108,9 @@ export default function (sim, forceS, forceF) {
 
   function sampleEnded() {
     event.call("startInterp");
-
     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;

src/interpolation/interpBruteForce.js

@@ -2,10 +2,14 @@ import {pointOnCircle, takeSampleFrom} from "./helpers";
 import {placeNearToNearestNeighbour} from "./interpCommon";
 
 /**
- * Perform interpolation where the nearest neighbour is found by brute-force.
+ * Perform interpolation where the "parent" node is found by brute-force.
+ * A "parent" of a node to be interpolated is a node whose position in 2D space
+ * is already known and have the least high-dimensional distance to the node in
+ * question.
  * For each point to be interpolated:
- * - Phase 1: find a nearest beighbour by comparing high-d distance against
-              each point already in the graph.
+ * - Phase 1: find the "parent" by comparing high-d distance against every
+              points already plotted on the graph.
+              this is essentially a nearest neighbour finding problem.
  * - Phase 2 and 3 are passed onto placeNearToNearestNeighbour
  * @param  {list} sampleSet      - nodes already plotted on the 2D graph
  * @param  {list} remainderSet   - nodes to be interpolated onto the graph

src/interpolation/interpCommon.js

@@ -1,6 +1,29 @@
 import {pointOnCircle, sumDistError} from "./helpers";
 import jiggle from "../jiggle";
 
+/**
+ * Phase 2 and 3 of each node to be interpolated.
+ * After a parent and high-dimensional distance to parent is found, imagine a
+ * circle around the parent with the distance as radius.
+ * Phase 2: Determine the quadrant of the circle the node is likely to be in.
+ *          Then, perform a binary search on the quardrant to find the best
+ *          position on the quadrant and place the node there.
+ *          The sum of ( difference between high-low-d distances between the
+ *          node and other sampled nodes ) is used to determine the prefered
+ *          position (lower is better).
+ * Phase 3: A subset of points whose 2D position is known are selected.
+            For several iterations:
+ *            Calculate the total force between the node and other nodes in the subset
+ *            Apply the force to the node's position
+ * @param  {object} node           - the node to be interpolated
+ * @param  {object} nearNeighbour  - the parent of the node
+ * @param  {number} radius         - high-dimensional distance to the parent
+ * @param  {list} sampleSubset     - subset for phase 3, list of nodes
+ * @param  {list} realDistances    - high-dimensional distances from the node to each
+                                     of the node in sampleSubset, list of numbers
+                                     index must correspond to sampleSubset
+ * @param  {Integer} endingIts     - Number of iterations for phase 3
+ */
 export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleSubset, realDistances, endingIts) {
   let
     dist0 = sumDistError(pointOnCircle(nearNeighbour.x, nearNeighbour.y, 0, radius), sampleSubset, realDistances),
@@ -39,6 +62,7 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
     }
   }
 
+  // Determine the angle
   let angle = binarySearchMin(lowBound, highBound,
                              function(angle){
                                return sumDistError(pointOnCircle(nearNeighbour.x, nearNeighbour.y, angle, radius), sampleSubset, realDistances);
@@ -47,6 +71,7 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
   node.x = newPoint.x;
   node.y = newPoint.y;
 
+  // Phase 3
   let
     multiplier = 1/sampleSubset.length,
     sumForces;
@@ -60,16 +85,15 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
 function sumForcesToSample(node, samples, sampleCache) {
   let nodeVx = 0,
       nodeVy = 0,
-      x, y, l;
+      x, y, l, i, sample;
 
-  for (let i = samples.length-1; i >=0 ; i--) {
-    let sample = samples[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
     x = node.x - sample.x || jiggle();
     y = node.y - sample.y || jiggle();
     l = Math.sqrt(x * x + y * y);
-
     l = (l - sampleCache[i]) / l;
     x *= l, y *= l;
     nodeVx -= x;

src/interpolation/interpolationPivots.js

@@ -2,10 +2,10 @@ import {pointOnCircle, takeSampleFrom} from "./helpers";
 import {placeNearToNearestNeighbour} from "./interpCommon";
 
 /**
- * Perform interpolation where the near neighbour is estimated by pivot-based searching.
+ * Perform interpolation where the "parent" node is is estimated by pivot-based searching.
  * - Pre-processing: assign random samples as pivots,
  *                   put the others in each pivot's bucket.
- *                   ie. non-pivot sample X may be in
+ *                   ie. a non-pivot sample X may be in
  *                   - bucket 3 of pivot A,
  *                   - bucket 1 of pivot B,
  *                   - bucket 5 of pivot C,
@@ -13,7 +13,8 @@ import {placeNearToNearestNeighbour} from "./interpCommon";
  * For each point to be interpolated:
  * - Phase 1: for each pivot: compare distance against the pivot
  *                            compare against other points in the same bucket of that pivot
- *            note down the nearest neighbour found
+ *            note down the parent found
+ *            this is essentially a near neighbour estimation problem.
  * - Phase 2 and 3 are passed onto placeNearToNearestNeighbour
  * @param  {list} sampleSet      - nodes already plotted on the 2D graph
  * @param  {list} remainderSet   - nodes to be interpolated onto the graph