Lower range of iterations

.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

LICENSE

@@ -1,678 +1,24 @@
-GNU GENERAL PUBLIC LICENSE
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-PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
-  16. Limitation of Liability.
-
-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    {one line to give the program's name and a brief idea of what it does.}
-    Copyright (C) {year}  {name of author}
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    d3-spring-model  Copyright (C) 2018 Pitchaya Boonsarngsuk
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<http://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.
-
+The MIT License (MIT)
+
+Copyright (c) 2018 Pitchaya Boonsarngsuk
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
 
 
 

README.md

@@ -42,8 +42,8 @@ var simulation = d3.forceSimulation(nodes);
 - [index.js](index.js) Export list of the module
 - [src/](src) Source code of the module
 - [package.json](package.json) Node.js moudle descriptor with build scripts
-- [img](img) Images for this readme file
-- [examples](examples) An example page running all the algorithms implemented
+- [examples/](examples) An example page implementing the library
+- [img/](img) Images for this readme file
 
 ## Building
 ```bash
@@ -63,7 +63,7 @@ The implementation is based on [d3.forceLink()](https://github.com/d3/d3-force#f
 
 <a name="forceLinkFullyConnected" href="#forceLinkFullyConnected">#</a> d3.**forceLinkFullyConnected**() [<>](src/link.js "Source")
 
-Creates a new link force with default parameters.
+Creates a new tweaked link force with default parameters.
 
 <a name="springLink_distance" href="#springLink_distance">#</a> *springLink*.**distance**([<i>distance</i>])
 
@@ -89,15 +89,15 @@ If *iterations* is specified, sets the number of iterations per application to t
 
 <a name="springLink_latestAccel" href="#springLink_latestAccel">#</a> *springLink*.**latestAccel**()
 
-Returns the average velocity changes of the latest iteration.
+Returns the average velocity changes of the latest iteration. The value is only calculated if [threshold checking](#springLink_stableVelocity) is enabled.
 
 <a name="springLink_stableVelocity" href="#springLink_stableVelocity">#</a> *springLink*.**stableVelocity**([*threshold*])
 
-If *threshold* is specified, sets a threshold and returns this force. When the average velocity changes of the system goes below the threshold, the function [onStableVelo's handler](#springLink_onStableVelo) will be called. Set it to 0 or less or remove the [handler](#neighbourSampling_latestForce) to disable the threshold checking. If *threshold* is not specified, returns the current value, which defaults to 0.
+If *threshold* is specified, sets a threshold and returns this force. When the average velocity changes of the system goes below the threshold, the function [onStableVelo's handler](#springLink_onStableVelo) will be called. Set it to 0 or less or remove the [handler](#springLink_onStableVelo) to disable the threshold checking. If *threshold* is not specified, returns the current value, which defaults to 0.
 
 <a name="springLink_onStableVelo" href="#springLink_onStableVelo">#</a> *springLink*.**onStableVelo**([*handler*])
 
-If *handler* is specified, sets a handler function which will be called at the end of each iteration if the average velocity changes of the system goes below the [threshold](#neighbourSampling_stableVelocity), and returns this force. To remove the handler, change it to null. If *threshold* is not specified, returns the current value, which defaults to null.
+If *handler* is specified, sets a handler function which will be called at the end of each iteration if the average velocity changes of the system goes below the [threshold](#springLink_stableVelocity), and returns this force. To remove the handler, change it to null. If *threshold* is not specified, returns the current value, which defaults to null.
 
 ### Neighbour and Sampling
 
@@ -133,7 +133,7 @@ If *sampleSize* is specified, sets the sample set size to the specified number a
 
 <a name="neighbourSampling_latestAccel" href="#neighbourSampling_latestAccel">#</a> *neighbourSampling*.**latestAccel**()
 
-Returns the average velocity changes of the latest iteration.
+Returns the average velocity changes of the latest iteration. The value is only calculated if [threshold checking](#neighbourSampling_stableVelocity) is enabled.
 
 <a name="neighbourSampling_stableVelocity" href="#neighbourSampling_stableVelocity">#</a> *neighbourSampling*.**stableVelocity**([*threshold*])
 
@@ -149,11 +149,11 @@ The hybrid layout algorithm reduces the computation power usage even further by
 
 <a name="hybrid" href="#hybrid">#</a> d3.**hybridSimulation**(*simulation*, *forceSample*, [*forceFull*]) [<>](src/hybridSimulation.js "Source")
 
-Creates a new hybrid layout simulation default parameters. The simulation will takeover control of [d3.forceSimulation](https://github.com/d3/d3-force#forceSimulation) provided (*simulation* parameter). *forceSample* and *forceFull* are pre-configured [d3.forceNeighbourSampling](#forceNeighbourSampling) forces to be run over the $\sqrt{n}$ samples and full dataset respectively. While unsupported, other D3 forces such as [d3.forceLinkFullyConnected](forceLinkFullyConnected) may also work.
+Creates a new hybrid layout simulation default parameters. The simulation will take control of the provided [d3.forceSimulation](https://github.com/d3/d3-force#forceSimulation) (the *simulation* parameter). *forceSample* and *forceFull* are pre-configured [d3.forceNeighbourSampling](#forceNeighbourSampling) forces to be run over the $\sqrt{n}$ samples and full dataset respectively. While unsupported, other D3 forces such as [d3.forceLinkFullyConnected](forceLinkFullyConnected) may also work.
 
-*forceSample* and *forceFull* may have [stableVelocity](neighbourSampling_stableVelocity) configured to end the simulation and begin the interpolation phase early, but any [handler](neighbourSampling_onStableVelo) functions will be replaced be hybridSimulation's own internal function.
+*forceSample* and *forceFull* may have [stableVelocity](neighbourSampling_stableVelocity) configured to end the relevant phase early, but any [handler](neighbourSampling_onStableVelo) functions will be replaced be hybridSimulation's own internal function.
 
-*forceFull* may also be absent, null, or undefined to skip the final refinement.
+*forceFull* may also be absent, null, or undefined to skip the final phase.
 
 *simulation* should have already been loaded with nodes. If there are any changes in the list of nodes, the simulation have to be re-set using the [.simulation](#hybrid_simulation) method.
 
@@ -163,11 +163,11 @@ If *simulation* is specified, sets the [d3.forceSimulation](https://github.com/d
 
 <a name="hybrid_subSet" href="#hybrid_subSet">#</a> *hybrid*.**subSet**()
 
-Returns the list of nodes in the $\sqrt{n}$ sample set. This is randomly selected on initialization or the nodes list have been refreshed by [.simulation](#hybrid_simulation) method. These nodes will be placed on 2D space from the beginning.
+Returns the list of nodes in the $\sqrt{n}$ sample set. This is randomly selected on initialization or after the nodes list have been refreshed by [.simulation](#hybrid_simulation) method. These nodes will be placed on 2D space from the beginning.
 
 <a name="hybrid_nonSubSet" href="#hybrid_nonSubSet">#</a> *hybrid*.**nonSubSet**()
 
-Returns the list of nodes outside of the $\sqrt{n}$ sample set. This is randomly selected on initialization or the nodes list have been refreshed by [.simulation](#hybrid_simulation) method. These nodes will be interpolated onto 2D space later on.
+Returns the list of nodes outside of the $\sqrt{n}$ sample set. This is randomly selected on initialization or after the nodes list have been refreshed by [.simulation](#hybrid_simulation) method. These nodes will be interpolated onto 2D space later on.
 
 <a name="hybrid_forceSample" href="#hybrid_forceSample">#</a> *hybrid*.**forceSample**([*force*])
 
@@ -179,11 +179,11 @@ If *force* is specified, sets the neighbour and sampling force to run on the who
 
 <a name="hybrid_sampleIterations" href="#hybrid_sampleIterations">#</a> *hybrid*.**sampleIterations**([*iterations*])
 
-If *iterations* is specified, sets the number of iterations to run neighbour and sampling on the $\sqrt{n}$ samples before interpolation and returns this layout simulation. If *iterations* is not specified, returns the current value, which defaults to 300.
+If *iterations* is specified, sets the number of iterations to run neighbour and sampling on the $\sqrt{n}$ samples before interpolation and returns this layout simulation. If *iterations* is not specified, returns the current value, which defaults to 300. If [stableVelocity](neighbourSampling_stableVelocity) is set on [forceSample](#hybrid_forceSample), the phase may end before the number of iteration reaches the specied value.
 
 <a name="hybrid_fullIterations" href="#hybrid_fullIterations">#</a> *hybrid*.**fullIterations**([*iterations*])
 
-If *iterations* is specified, sets the number of iterations to run neighbour and sampling on the whole dataset after interpolation and returns this layout simulation. If set to a number less than 1, the process will be skipped. If *iterations* is not specified, returns the current value, which defaults to 20.
+If *iterations* is specified, sets the number of iterations to run neighbour and sampling on the whole dataset after interpolation and returns this layout simulation. If set to a number less than 1, the process will be skipped. If *iterations* is not specified, returns the current value, which defaults to 20. If [stableVelocity](neighbourSampling_stableVelocity) is set on [forceFull](#hybrid_forceFull), the phase may end before the number of iteration reaches the specied value.
 
 <a name="hybrid_numPivots" href="#hybrid_numPivots">#</a> *hybrid*.**numPivots**([*number*])
 

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">
@@ -221,11 +221,11 @@
       <p>Select distance function:</p>
       <div id="distance">
         <input type="radio" name="distance" checked onclick="distanceFunction=calculateDistance"> General<br>
-        <input type="radio" name="distance" onclick="distanceFunction=calculateEuclideanDistance"> Euclidean<br>
-        <input type="radio" name="distance" onclick="distanceFunction=calculateManhattanDistance"> Manhattan<br>
-        <input type="radio" name="distance" onclick="distanceFunction=calculateJaccardDissimilarity"> Jaccard<br>
-        <input type="radio" name="distance" onclick="distanceFunction=calculateDiceDissimilarity"> Dice<br>
-        <input type="radio" name="distance" onclick="distanceFunction=calculateCosineSimilarity"> Cosine<br>
+        <input type="radio" name="distance" onclick="distanceFunction=calculateEuclideanDistance"> Euclidean (must be numbers only)<br>
+        <input type="radio" name="distance" onclick="distanceFunction=calculateManhattanDistance"> Manhattan (not tested)<br>
+        <input type="radio" name="distance" onclick="distanceFunction=calculateJaccardDissimilarity"> Jaccard (not tested)<br>
+        <input type="radio" name="distance" onclick="distanceFunction=calculateDiceDissimilarity"> Dice (not tested)<br>
+        <input type="radio" name="distance" onclick="distanceFunction=calculateCosineSimilarity"> Cosine (not tested)<br>
         <input type="radio" name="distance" onclick="distanceFunction=calculateDistancePoker"> Poker Hands<br>
       </div>
     </div>
@@ -239,11 +239,11 @@
 <script src="js/lib/jquery-3.1.1.js"></script>
 <script src="js/lib/intercom.js"></script>
 <script src="../build/d3-spring-model.js"></script>
-<script src="js/src/example-papaparsing.js"></script>
-<script src="js/src/example-papaparsing/hybrid.js"></script>
-<script src="js/src/example-papaparsing/linkForce.js"></script>
-<script src="js/src/example-papaparsing/neighbourSampling.js"></script>
-<script src="js/src/example-papaparsing/otherAlgo.js"></script>
+<script src="js/example-papaparsing.js"></script>
+<script src="js/algos/hybrid.js"></script>
+<script src="js/algos/linkForce.js"></script>
+<script src="js/algos/neighbourSampling.js"></script>
+<script src="js/algos/otherAlgo.js"></script>
 <script src="js/distances/distancePokerHands.js"></script>
 <script src="js/distances/distance.js"></script>
 <script src="js/distances/euclideanDistance.js"></script>

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();
@@ -16,34 +16,34 @@ function startHybridSimulation() {
   let forceSample = d3.forceNeighbourSampling()
     .neighbourSize(NEIGHBOUR_SIZE)
     .sampleSize(SAMPLE_SIZE)
-    .stableVelocity(0.43)
-    .distance(distance)
+    .stableVelocity(0) // Change here
+    .distance(distance);
 
   let forceFull = d3.forceNeighbourSampling()
     .neighbourSize(FULL_NEIGHBOUR_SIZE)
     .sampleSize(FULL_SAMPLE_SIZE)
-    .stableVelocity(0.6)
-    .distance(distance)
+    .stableVelocity(0) // Change here
+    .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;
@@ -11,19 +11,18 @@ function startLinkSimulation() {
   let links = [], force;
 
   if (tweakedVerOfLink) {
-    force = d3.forceLinkFullyConnected()
+    force = d3.forceLinkCompleteGraph()
       .distance(function (n, m) {
         return distanceFunction(n, m, props, norm);
       })
-              .stableVelocity(0) //TODO
+      .stableVelocity(0) // Change here
       .onStableVelo(ended);
-  }
-  else {
-    for (i = nodes.length-1; i >= 1; i--) {
-      for (j = i-1; j >= 0; j--) {
+  } 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]
         });
       }
     }
@@ -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

@@ -0,0 +1,29 @@
+/**
+ * Initialize the Chalmers' 1996 algorithm and start simulation.
+ */
+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);
+
+  simulation
+    .alphaDecay(0)
+    .alpha(1)
+    .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)
+    .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); }));
+      .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

@@ -6,7 +6,7 @@
  * @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,
@@ -19,8 +19,8 @@ function calculateDistance(source, target, properties, normArgs) {
   // 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" ) {
+    if (source.hasOwnProperty(property) && target.hasOwnProperty(property) &&
+        property.toLowerCase() !== 'index' && property.toLowerCase() !== 'type') {
       var s = source[property],
         t = target[property];
 
@@ -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

@@ -6,11 +6,11 @@
  * @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"],
+    cards = ['C1', 'C2', 'C3', 'C4', 'C5'],
     cols = 0;
 
   // Iterate through cards
@@ -20,19 +20,19 @@ function calculateDistancePoker(source, target) {
       var s = parseInt(source[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)));
     }
   }
 

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") {
+    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;
-
 }
 
 // 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);
 

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

@@ -2,27 +2,27 @@
 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);
+var svg = d3.select('svg')
+  .call(d3.zoom().scaleExtent([0.0001, 1000000]).on('zoom', function () {
+    svg.attr('transform', d3.event.transform);
   }))
-  .append("g");
+  .append('g');
 
-var div = d3.select("body").append("div")
-  .attr("class", "tooltip")
-  .style("opacity", 0);
+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);
+  .on('end', brushEnded);
 
-svg.append("g")
-  .attr("class", "brush")
+svg.append('g')
+  .attr('class', 'brush')
   .call(brush);
 
-var intercom = Intercom.getInstance();
+// var intercom = Intercom.getInstance();
 
-intercom.on("select", unSelectNodes);
+// intercom.on("select", unSelectNodes);
 
 var nodes, // as in Data points
   node, // as in SVG object that have all small circles on screen
@@ -35,10 +35,10 @@ var nodes, // as in Data points
   simulation,
   velocities = [],
   rendering = true, // Rendering during the execution.
-  forceName = "forces",
+  forceName = 'forces',
   springForce = false,
   tooltipWidth = 0,
-  fileName = "",
+  fileName = '',
   selectedData,
   clickedIndex = -1,
   paused = false,
@@ -57,7 +57,7 @@ var MULTIPLIER = 50,
   ITERATIONS = 300,
   FULL_ITERATIONS = 20,
   NODE_SIZE = 10,
-  COLOR_ATTRIBUTE = "",
+  COLOR_ATTRIBUTE = '',
   FULL_NEIGHBOUR_SIZE = 10,
   FULL_SAMPLE_SIZE = 10,
   INTERP_ENDING_ITS = 20;
@@ -65,24 +65,22 @@ var MULTIPLIER = 50,
 // Create a color scheme for a range of numbers.
 var color = d3.scaleOrdinal(d3.schemeCategory10);
 
-
-$(document).ready(function() {
+$(document).ready(function () {
   distanceFunction = calculateDistance;
   d3.select('#startSimulation').on('click', startHybridSimulation);
-  $("#HLParameters").show();
+  $('#HLParameters').show();
 });
 
 /**
  * Parse the data from the provided csv file using Papa Parse library
  * @param  {file} evt - csv file.
  */
-function parseFile(evt) {
+function parseFile (evt) {
   // Clear the previous nodes
-  d3.selectAll(".nodes").remove();
+  d3.selectAll('.nodes').remove();
   springForce = false;
 
   fileName = evt.target.files[0].name;
-
   Papa.parse(evt.target.files[0], {
     header: true,
     dynamicTyping: true,
@@ -98,39 +96,32 @@ function parseFile(evt) {
  * @param  {array} data
  * @param  {object} error
  */
-function processData(data, error) {
+function processData (data, error) {
   if (error) throw error.message;
 
   nodes = data;
   size = nodes.length;
   simulation = d3.forceSimulation();
 
-  // Calculate normalization arguments and get the list of
-  // properties of the nodes.
-  norm = calculateNormalization(nodes); // Used with distance fn
+  // 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];
+
+  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.add(new Option(d, d, (d === COLOR_ATTRIBUTE)));
   });
-  opts.selectedIndex = props.length-1;
+  opts.selectedIndex = props.length - 1;
+  // props.pop(); //Hide Iris index / last column from the distance function
 
-  //props.pop(); //Hide Iris index / last column from distance function
-
-  //Put the nodes in random starting positions
-  //TODO Change this back
+  // Put the nodes at (0,0)
   nodes.forEach(function (d) {
     d.x = 0;
     d.y = 0;
   });
-  /*
-  nodes.forEach(function (d) {
-    d.x = (Math.random()-0.5) * 100000;
-    d.y = (Math.random()-0.5) * 100000;
-  });*/
 
   addNodesToDOM(nodes);
 
@@ -142,115 +133,99 @@ function processData(data, error) {
   ticked();
 };
 
-function addNodesToDOM(data) {
-  node = svg.append("g")
-    .attr("class", "nodes")
-    .selectAll("circle")
+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 + ")")
+    .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) {
+    .attr('fill', function (d) {
       return color(d[COLOR_ATTRIBUTE]);
     })
-    .on("mouseover", function (d) {
+    .on('mouseover', function (d) {
       div.transition()
         .duration(200)
-        .style("opacity", .9);
+        .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");
+        .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) {
+    .on('mouseout', function (d) {
       div.transition()
         .duration(500)
-        .style("opacity", 0);
-      node.attr("opacity", 1);
+        .style('opacity', 0);
+      node.attr('opacity', 1);
     })
-    .on("click", function (d) {
-      console.log("click", clickedIndex);
+    .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);
+        node.attr('r', NODE_SIZE).attr('stroke-width', 0);
         clickedIndex = -1;
       }
     });
-    if (selectedData)
-      unSelectNodes(selectedData);
+  if (selectedData) { unSelectNodes(selectedData); }
 }
 
-function ticked() {
-  //console.log("ticked");
+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('cx', function (d) {
+        return d.x * MULTIPLIER;
       })
-      .attr("cy", function (d) {
-        return d.y*MULTIPLIER;
+      .attr('cy', function (d) {
+        return d.y * MULTIPLIER;
       });
-  }/*
-  if(alreadyRanIterations%10 != 1) stresses.push("");
-  else  stresses.push(d3.calculateStress(nodes, function (n, m) {
-    return distanceFunction(n, m, props, norm);
-  }));*/
-  // 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) {
+  // 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() {
+function ended () {
   simulation.stop();
   simulation.force(forceName, null);
-  console.log("ended");
-  //if (rendering !== true) { // Never drawn anything before? Now it's time.
+  if (rendering !== true) { // Never drawn anything before? Now it's time.
     node
-      .attr("cx", function (d) {
-        return d.x*MULTIPLIER;
+      .attr('cx', function (d) {
+        return d.x * MULTIPLIER;
       })
-      .attr("cy", function (d) {
-        return d.y*MULTIPLIER;
+      .attr('cy', function (d) {
+        return d.y * MULTIPLIER;
       });
-  //}
+  }
 
   if (p1 !== 0) {
     // Performance time measurement
     p2 = performance.now();
-    console.log("Execution time: " + (p2 - p1));
-    // Do not calculate stress for data sets bigger than 100 000.
-    // if (nodes.length <= 100000) {
-    //   console.log("Stress: ", simulation.force(forceName).stress());
-    // }
-    // console.log(simulation.force(forceName).nodeNeighbours());
+    console.log('Execution time: ' + (p2 - p1));
     p1 = 0;
     p2 = 0;
   }
-  console.log("Post stress", d3.calculateStress(nodes, function (n, m) {
-    return distanceFunction(n, m, props, norm);
-  }));
 }
 
-function brushEnded() {
+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,
@@ -267,24 +242,23 @@ function brushEnded() {
       results = sel.map(function (a) { return a.index; });
     }
 
-    intercom.emit("select", { name: fileName, indices: results });
+    // intercom.emit("select", { name: fileName, indices: results });
 
-    d3.select(".brush").call(brush.move, null);
+    d3.select('.brush').call(brush.move, null);
   }
 }
 
-
 /**
  * Format the tooltip for the data
  * @param {*} node
  */
-function formatTooltip(node) {
-  var textString = "",
-    temp = "";
+function formatTooltip (node) {
+  var textString = '',
+    temp = '';
 
   tooltipWidth = 0;
   props.forEach(function (element) {
-    temp = element + ": " + node[element] + "<br/>";
+    temp = element + ': ' + node[element] + '<br/>';
     textString += temp;
     if (temp.length > tooltipWidth) {
       tooltipWidth = temp.length;
@@ -296,8 +270,7 @@ function formatTooltip(node) {
 /**
  * Halt the execution.
  */
-function stopSimulation() {
-  console.log("stopSimulation");
+function stopSimulation () {
   simulation.stop();
   if (typeof hybridSimulation !== 'undefined') {
     hybridSimulation.stop();
@@ -309,8 +282,8 @@ function stopSimulation() {
  * @param  {array} array
  * @return {number}  the mean of the array.
  */
-function getAverage(array) {
-  console.log("getAverage", array);
+function getAverage (array) {
+  console.log('getAverage', array);
   var total = 0;
   for (var i = 0; i < array.length; i++) {
     total += array[i];
@@ -322,11 +295,11 @@ function getAverage(array) {
  * Deselect the nodes to match the selection from other window.
  * @param {*} data
  */
-function unSelectNodes(data) {
+function unSelectNodes (data) {
   selectedData = data;
   if (fileName === data.name && nodes) {
     node
-      .classed("notSelected", function (d) {
+      .classed('notSelected', function (d) {
         if (data.indices.indexOf(d.index) < 0) {
           return true;
         }
@@ -335,35 +308,33 @@ function unSelectNodes(data) {
   }
 }
 
-
 /**
  * Highlight the neighbours for neighbour and sampling algorithm
  * @param {*} indices
  */
-function highlightNeighbours(indices) {
+function highlightNeighbours (indices) {
   node
-    .attr("r", function (d) {
+    .attr('r', function (d) {
       if (indices.indexOf(d.index) >= 0) {
         return NODE_SIZE * 2;
       }
       return NODE_SIZE;
     })
-    .attr("stroke-width", function (d) {
+    .attr('stroke-width', function (d) {
       if (indices.indexOf(d.index) >= 0) {
-        return NODE_SIZE * 0.2 + "px";
+        return NODE_SIZE * 0.2 + 'px';
       }
-      return "0px";
+      return '0px';
     })
-    .attr("stroke", "white");
+    .attr('stroke', 'white');
 }
 
-
 /**
  * Highlight all the nodes with the same class on hover
  * @param {*} highlighValue
  */
-function highlightOnHover(highlighValue) {
-  node.attr("opacity", function (d) {
+function highlightOnHover (highlighValue) {
+  node.attr('opacity', function (d) {
     return (highlighValue === d[COLOR_ATTRIBUTE]) ? 1 : 0.3;
   });
 }
@@ -371,13 +342,12 @@ function highlightOnHover(highlighValue) {
 /**
  * Color the nodes according to given attribute.
  */
-function colorToAttribute() {
-  node.attr("fill", function (d) {
-    return color(d[COLOR_ATTRIBUTE])
+function colorToAttribute () {
+  node.attr('fill', function (d) {
+    return color(d[COLOR_ATTRIBUTE]);
   });
 }
 
-
 /**
  * Update the distance range.
 
@@ -387,26 +357,24 @@ function updateDistanceRange() {
   }
 }
 
-
 /**
  * Implemented pause/resume functionality
  */
-function pauseUnPause() {
+function pauseUnPause () {
   if (simulation) {
     if (paused) {
       simulation.force(forceName);
       simulation.restart();
-      d3.select("#pauseButton").text("Pause");
+      d3.select('#pauseButton').text('Pause');
       paused = false;
     } else {
       simulation.stop();
-      d3.select("#pauseButton").text("Resume");
+      d3.select('#pauseButton').text('Resume');
       paused = true;
     }
   }
 }
 
-
 /**
  * Average distances for each node.
  * @param {*} dataNodes
@@ -429,4 +397,4 @@ function calculateAverageDistance(dataNodes, properties, normalization) {
   }
 
   return sum / n;
-}*/
+} */

examples/js/src/example-papaparsing/neighbourSampling.js

@@ -1,29 +0,0 @@
-/**
- * Initialize the Chalmers' 1996 algorithm and start simulation.
- */
-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(0.000001) //TODO
-                  .onStableVelo(ended);
-
-  simulation
-    .alphaDecay(0)
-    .alpha(1)
-    .on("tick", ticked)
-    .on("end", ended)
-    .force(forceName, force);
-  // Restart the simulation.
-  simulation.restart();
-}

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 tSNE}
-  from "./src/t-sne";
-*/
-export { default as forceLinkFullyConnected}
-  from "./src/link";
+export {default as forceBarnesHut}
+  from './src/barnesHut';
 
-export { default as hybridSimulation}
-  from "./src/hybridSimulation";
+export {default as tSNE}
+  from './src/t-sne';
 
-export { getStress as calculateStress }
-  from "./src/stress";
+export {default as forceLinkCompleteGraph}
+  from './src/link';
+
+export {default as hybridSimulation}
+  from './src/hybridSimulation';
+
+export {getStress as calculateStress}
+  from './src/stress';

package.json

@@ -8,15 +8,23 @@
     "d3-spring-model",
     "force"
   ],
-  "license": "GPL-3.0-only",
+  "license": "MIT",
   "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

@@ -1,7 +1,7 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
-import {x, y} from "./xy";
-import {quadtree} from "d3-quadtree";
+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
@@ -12,7 +12,7 @@ import {quadtree} from "d3-quadtree";
  * 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() {
+export default function () {
   var nodes,
     node,
     alpha,
@@ -25,10 +25,10 @@ export default function() {
    * @param  {number} _ - controls the stopping of the
    *     particle simulations.
    */
-  function force(_) {
+  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);
+      node = nodes[i]; tree.visit(apply);
     }
   }
 
@@ -38,7 +38,7 @@ export default function() {
    * nodes accumulate forces from coincident quadrants.
    * @param  {quadrant} quad - node representing the quadrant in quadtree.
    */
-  function accumulate(quad) {
+  function accumulate (quad) {
     var q, d, children = [];
 
     // For internal nodes, accumulate forces from child quadrants.
@@ -52,10 +52,7 @@ export default function() {
       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 {
+    } else { // For leaf nodes, accumulate forces from coincident quadrants.
       q = quad;
       q.x = q.data.x;
       q.y = q.data.y;
@@ -72,8 +69,7 @@ export default function() {
    * @param  {number} x2   - upper x bound of the node.
    * @return {boolean}     - true if the approximation was applied.
    */
-  function apply(quad, x1, _, x2) {
-
+  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,
@@ -82,35 +78,34 @@ export default function() {
     // 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 (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;
+        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;
+    } 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;
+      if (x === 0) { x = jiggle(); l += x * x; }
+      if (y === 0) { y = jiggle(); l += y * y; }
     }
 
-    do if (quad.data !== node) {
+    do {
+      if (quad.data !== node) {
         l = (l - +distance(node, quad.data)) / l * alpha;
-      x *= l, y *= l;
+        x *= l; y *= l;
         quad.data.vx -= x;
         quad.data.vy -= y;
         node.vx += x;
         node.vy += y;
+      }
     } while (quad = quad.next);
   }
 
@@ -120,37 +115,37 @@ export default function() {
     * the better layout.
     * @return {number} - stress of the layout.
     */
-  function getStress() {
+  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);
+          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);
+          totalDiffSq += Math.pow(realDist - highDist, 2);
           totalHighDistSq += highDist * highDist;
         }
       }
     }
-    return Math.sqrt(totalDiffSq/totalHighDistSq);
+    return Math.sqrt(totalDiffSq / totalHighDistSq);
   }
 
   // API for initializing the algorithm, setting parameters and querying
   // metrics.
-  force.initialize = function(_) {
+  force.initialize = function (_) {
     nodes = _;
   };
 
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
   };
 
-  force.theta = function(_) {
+  force.theta = function (_) {
     return arguments.length ? (theta = _, force) : theta;
   };
 
-  force.stress = function() {
+  force.stress = function () {
     return getStress();
   };
 

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

@@ -1,8 +1,8 @@
-import {dispatch} from "d3-dispatch";
-import constant from "./constant";
-import interpBruteForce from "./interpolation/interpBruteForce";
-import interpolationPivots from "./interpolation/interpolationPivots";
-import {takeSampleFrom} from "./interpolation/helpers";
+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
@@ -36,30 +36,28 @@ export default function (sim, forceS, forceF) {
     simulation = sim,
     forceSample = forceS,
     forceFull = forceF,
-    event = d3.dispatch("sampleTick", "fullTick", "startInterp", "end"),
+    event = dispatch('sampleTick', 'fullTick', 'startInterp', 'end'),
     initAlready = false,
     nodes,
     alreadyRanIterations,
-    hybrid,
-    p0 = performance.now(), p1, p2, p3;
+    hybrid;
 
-    if(simulation != undefined) initSimulation();
-    if(forceS != undefined || forceF != undefined) initForces();
+  if (simulation !== undefined) initSimulation();
+  if (forceS !== undefined || forceF !== undefined) initForces();
 
   // Performed on first run
-  function initialize() {
+  function initialize () {
     initAlready = true;
-    console.log("Initializing Hybrid");
     alreadyRanIterations = 0;
     simulation
-      .on("tick", sampleTick)
-      .on("end", sampleEnded)
+      .on('tick', sampleTick)
+      .on('end', sampleEnded)
       .nodes(sample)
-      .force("Sample force", forceSample);
-    console.log("Initialized Hybrid");
+      .force('Sample force', forceSample);
+    console.log('Initialized Simulation for Hybrid');
   }
 
-  function initForces(){
+  function initForces () {
     if (forceSample.onStableVelo) {
       forceSample.onStableVelo(sampleEnded);
     }
@@ -69,20 +67,21 @@ export default function (sim, forceS, forceF) {
     }
 
     // Set default value for interpDistanceFn if not been specified yet
-    if(interpDistanceFn === undefined) {
-      if(forceFull.distance == 'function')
+    if (interpDistanceFn === undefined) {
+      if (forceFull.distance === 'function') {
         interpDistanceFn = forceFull.distance();
-      else
+      } else {
         interpDistanceFn = constant(300);
       }
     }
+  }
 
-  function initSimulation(){
+  function initSimulation () {
     nodes = simulation.nodes();
     simulation
       .stop()
       .alphaDecay(0)
-      .alpha(1)
+      .alpha(1);
 
     let sets = takeSampleFrom(nodes, Math.sqrt(nodes.length));
     sample = sets.sample;
@@ -90,72 +89,64 @@ export default function (sim, forceS, forceF) {
   }
 
   // Sample simulation ticked 1 frame, keep track of number of iterations here.
-  function sampleTick() {
-    event.call("sampleTick");
-    if(++alreadyRanIterations >= SAMPLE_ITERATIONS){
+  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){
+  function fullTick () {
+    event.call('fullTick');
+    if (alreadyRanIterations++ >= FULL_ITERATIONS) {
       fullEnded();
     }
   }
 
-  function fullEnded() {
-    p3 = performance.now();
+  function fullEnded () {
     simulation.stop();
     initAlready = false;
-    simulation.force("Full force", null);
-    console.log("Phase 1 takes", p1-p0);
-    console.log("Phase 2 takes", p2-p1);
-    console.log("Phase 3 takes", p3-p2);
-    console.log("Total", p3-p0);
-    console.log("Phase 3 iterations", alreadyRanIterations);
-    event.call("end");
+    simulation.force('Full force', null);
+    event.call('end');
   }
 
-  function sampleEnded() {
-    p1 = performance.now();
+  function sampleEnded () {
     simulation.stop();
-    simulation.force("Sample force", null);
+    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;
+    for (let i = sample.length - 1; i >= 0; i--) {
+      sample[i].vx = 0;
+      sample[i].vy = 0;
     }
 
-    event.call("startInterp");
-    if (NUM_PIVOTS>=1) {
+    event.call('startInterp');
+    if (NUM_PIVOTS >= 1) {
       interpolationPivots(sample, remainder, NUM_PIVOTS, interpDistanceFn, INTERP_FINE_ITS);
     } else {
       interpBruteForce(sample, remainder, interpDistanceFn, INTERP_FINE_ITS);
     }
-    p2 = performance.now();
 
-    event.call("fullTick");
+    event.call('fullTick');
     alreadyRanIterations = 0;
     simulation
-      .on("tick", null)
-      .on("end", null) // The ending condition should be iterations count
+      .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");
+    if (FULL_ITERATIONS < 1 || forceF === undefined || forceF === null) {
+      event.call('end');
       return;
     }
     simulation
-      .on("tick", fullTick)
-      .force("Full force", forceFull)
+      .on('tick', fullTick)
+      .force('Full force', forceFull)
       .restart();
   }
 
   return hybrid = {
     restart: function () {
-      if(!initAlready) initialize();
+      if (!initAlready) initialize();
       simulation.restart();
       return hybrid;
     },
@@ -194,11 +185,11 @@ export default function (sim, forceS, forceF) {
     },
 
     interpDistanceFn: function (_) {
-      return arguments.length ? (interpDistanceFn = typeof _ === "function" ? _ : constant(+_), hybrid) : interpDistanceFn;
+      return arguments.length ? (interpDistanceFn = typeof _ === 'function' ? _ : constant(+_), hybrid) : interpDistanceFn;
     },
 
     simulation: function (_) {
-      return arguments.length ? (toInit = true, simulation = _, hybrid) : simulation;
+      return arguments.length ? (initAlready = false, simulation = _, hybrid) : simulation;
     },
 
     forceSample: function (_) {
@@ -207,7 +198,7 @@ export default function (sim, forceS, forceF) {
 
     forceFull: function (_) {
       return arguments.length ? (forceFull = _, initForces(), hybrid) : forceFull;
-    },
+    }
 
   };
 }

src/interpolation/helpers.js

@@ -8,7 +8,7 @@
                       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;
@@ -18,16 +18,16 @@ export function takeSampleFrom(sourceList, amount) {
   }
 
   // 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 {pointOnCircle, 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 (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),
@@ -37,16 +37,10 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
     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;
+  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;
@@ -55,39 +49,37 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
       lowBound = 90;
       highBound = 180;
     }
-  } else {
-    if (dist90 > dist270) {
+  } else if (dist90 > dist270) {
     lowBound = 270;
     highBound = 360;
   } else {
     lowBound = 0;
     highBound = 90;
   }
-  }
 
   // Determine the angle
-  let angle = binarySearchMin(lowBound, highBound,sumDistErrorByAngle);
+  let angle = binarySearchMin(lowBound, highBound, sumDistErrorByAngle);
   let newPoint = pointOnCircle(nearNeighbour.x, nearNeighbour.y, angle, radius);
   node.x = newPoint.x;
   node.y = newPoint.y;
 
   // 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;
 
-  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 {pointOnCircle, 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];
@@ -54,9 +55,10 @@ 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);
   }
@@ -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;
       }
@@ -107,18 +108,17 @@ export default function(sampleSet, remainderSet, numPivots, distanceFn, endingIt
         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;
         }
@@ -127,9 +127,10 @@ 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

@@ -1,5 +1,5 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
+import constant from './constant';
+import jiggle from './jiggle';
 
 /**
  * Modified link force algorithm
@@ -8,7 +8,7 @@ import jiggle from "./jiggle";
  * - removed other unused functions
  * Alpha should be constant 1 for accurate simulation
  */
-export default function() {
+export default function () {
   var dataSizeFactor,
     distance = constant(30),
     distances = [],
@@ -18,60 +18,62 @@ export default function() {
     latestVelocityDiff = 0,
     iterations = 1;
 
-  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;
       }
     }
+
     // 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++) {
+      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;
+          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) {
+    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();
       }
-      else console.log(velocityDiff);
     }
   }
 
-  function initialize() {
+  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);
+    dataSizeFactor = 0.5 / (nodes.length - 1);
     initializeDistance();
   }
 
-  function initializeDistance() {
+  function initializeDistance () {
     if (!nodes) return;
     for (let i = 1, n = nodes.length; i < n; i++) {
       for (let j = 0; j < i; j++) {
@@ -80,17 +82,17 @@ export default function() {
     }
   }
 
-  force.initialize = function(_) {
+  force.initialize = function (_) {
     nodes = _;
     initialize();
   };
 
-  force.iterations = function(_) {
+  force.iterations = function (_) {
     return arguments.length ? (iterations = +_, force) : iterations;
   };
 
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), initializeDistance(), force) : distance;
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), initializeDistance(), force) : distance;
   };
 
   force.latestAccel = function () {

src/neighbourSampling.js

@@ -1,14 +1,12 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
-import {getStress} from "./stress";
-
+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];
 }
 
@@ -27,18 +25,18 @@ export default function () {
    * 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);
 
@@ -54,19 +52,18 @@ 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();
       }
-      else console.log(velocityDiff);
     }
   }
 
@@ -77,14 +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;
@@ -93,11 +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));
@@ -106,8 +103,8 @@ export default function () {
     initDataSizeFactor();
   }
 
-  function initDataSizeFactor(){
-    dataSizeFactor = 0.5/(neighbourSize+sampleSize);
+  function initDataSizeFactor () {
+    dataSizeFactor = 0.5 / (neighbourSize + sampleSize);
   }
 
   /**
@@ -119,7 +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;
 
@@ -129,14 +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])];
     }
@@ -149,7 +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()));
@@ -163,13 +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 = _;
@@ -189,7 +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

@@ -1,4 +1,5 @@
-import constant from "./constant";
+/* eslint-disable block-scoped-var */
+import constant from './constant';
 
 /**
  * Set the node id accessor to the specified i.
@@ -6,7 +7,7 @@ import constant from "./constant";
  * @param  {accessor} i - id accessor.
  * @return {accessor}  - node id accessor.
  */
-function index(d, i) {
+function index (d, i) {
   return i;
 }
 
@@ -14,7 +15,7 @@ function index(d, 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() {
+export default function () {
   var id = index,
     distance = constant(300),
     nodes,
@@ -32,7 +33,7 @@ export default function() {
    * Make a step in t-SNE algorithm and set the velocities for the nodes
    * to accumulate the values from solution.
    */
-  function force() {
+  function force () {
     // Make a step at each iteration.
     step();
     var solution = getSolution();
@@ -48,11 +49,13 @@ export default function() {
    * Calculates the random number from Gaussian distribution.
    * @return {number} random number.
    */
-  function gaussRandom() {
+  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();
+    if (r === 0 || r > 1) {
+      return gaussRandom();
+    }
     return u * Math.sqrt(-2 * Math.log(r) / r);
   }
 
@@ -60,11 +63,11 @@ export default function() {
    * Return the normalized number.
    * @return {number} normalized random number from Gaussian distribution.
    */
-  function randomN() {
+  function randomN () {
     return gaussRandom() * 1e-4;
   }
 
-  function sign(x) {
+  function sign (x) {
     return x > 0 ? 1 : x < 0 ? -1 : 0;
   }
 
@@ -73,8 +76,8 @@ export default function() {
    * @param  {number} n - length of array.
    * @return {Float64Array}   - array of zeros with length n.
    */
-  function zeros(n) {
-    if (typeof(n) === 'undefined' || isNaN(n)) {
+  function zeros (n) {
+    if (typeof n === 'undefined' || isNaN(n)) {
       return [];
     }
     return new Float64Array(n); // typed arrays are faster
@@ -87,7 +90,7 @@ export default function() {
    * @param  {number} d - columns.
    * @return {array}   - 2d array
    */
-  function random2d(n, d) {
+  function random2d (n, d) {
     var x = [];
     for (var i = 0; i < n; i++) {
       var y = [];
@@ -106,7 +109,7 @@ export default function() {
    * @param  {number} tol        - limit for entropy difference.
    * @return {2d array}          - 2d matrix containing probabilities.
    */
-  function d2p(data, perplexity, tol) {
+  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.
@@ -137,7 +140,7 @@ export default function() {
         // Normalize p and compute entropy.
         var Hhere = 0.0;
         for (j = 0; j < N; j++) {
-          if (psum == 0) {
+          if (psum === 0) {
             pj = 0;
           } else {
             pj = prow[j] / psum;
@@ -158,7 +161,6 @@ export default function() {
           } else {
             beta = (beta + betamax) / 2;
           }
-
         } else {
           // Converse case. Make distrubtion less peaky.
           betamax = beta;
@@ -180,7 +182,6 @@ export default function() {
       for (j = 0; j < N; j++) {
         P1[i * N + j] = prow[j];
       }
-
     }
 
     // Symmetrize P and normalize it to sum to 1 over all ij
@@ -197,7 +198,7 @@ export default function() {
   /**
    * Initialize a starting (random) solution.
    */
-  function initSolution() {
+  function initSolution () {
     Y = random2d(N, dim);
     // Step gains to accelerate progress in unchanging directions.
     gains = random2d(N, dim, 1.0);
@@ -209,7 +210,7 @@ export default function() {
   /**
    * @return {2d array} the solution.
    */
-  function getSolution() {
+  function getSolution () {
     return Y;
   }
 
@@ -217,7 +218,7 @@ export default function() {
    * Do a single step (iteration) for the layout.
    * @return {number} the current cost.
    */
-  function step() {
+  function step () {
     iteration += 1;
 
     var cg = costGrad(Y); // Evaluate gradient.
@@ -266,8 +267,7 @@ export default function() {
    * @param  {2d array} Y - the current solution to evaluate.
    * @return {object}   that contains a cost and a gradient.
    */
-  function costGrad(Y) {
-
+  function costGrad (Y) {
     var pmul = iteration < 100 ? 4 : 1;
 
     // Compute current Q distribution, unnormalized first.
@@ -323,13 +323,13 @@ export default function() {
    * the better layout.
    * @return {number} - stress of the layout.
    */
-  function getStress() {
+  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];
+          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);
@@ -342,7 +342,7 @@ export default function() {
 
   // API for initializing the algorithm, setting parameters and querying
   // metrics.
-  force.initialize = function(_) {
+  force.initialize = function (_) {
     nodes = _;
     N = nodes.length;
     // Initialize the probability matrix.
@@ -350,23 +350,23 @@ export default function() {
     initSolution();
   };
 
-  force.id = function(_) {
+  force.id = function (_) {
     return arguments.length ? (id = _, force) : id;
   };
 
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
   };
 
-  force.stress = function() {
+  force.stress = function () {
     return getStress();
   };
 
-  force.learningRate = function(_) {
+  force.learningRate = function (_) {
     return arguments.length ? (learningRate = +_, force) : learningRate;
   };
 
-  force.perplexity = function(_) {
+  force.perplexity = function (_) {
     return arguments.length ? (perplexity = +_, force) : perplexity;
   };
 

src/xy.js

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