แก้ ลืมเปลี่ยนจำนวน its ใน js

.eslintrc

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

LICENSE

@@ -1,678 +1,24 @@
-GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
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-OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-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

@@ -1,240 +1,240 @@
-# d3-spring-model
-
-This module implements three force-directed layout algorithms to visualize high-dimensional data in 2D space.
-1. Basic spring model algorithm. In this model, every data point (node) pairs are connected with a spring that pushes or pulls, depending on the difference between 2D and high-dimensional distance. This is a tweaked version of [D3's force link](https://github.com/d3/d3-force#forceLink) with functionalities removed to improve performance and lower the memory usage.
-1. Neighbour and Sampling algorithm. It uses stochastic sampling to find the best neighbours for high-dimensional data and creates the layout in 2 dimensions.
-1. Hybrid layout algorithm. It performs Neighbour and Sampling algorithm on a subset of data before interpolating the rest onto the 2D space. Neighbour and Sampling algorithm may also be run over the full dataset at the end to refine placement.
-During the interpolation, each node have to find a parent, a closest node that has already been plotted on the 2D space. Two methods of of finding the parents have been implemented.
-   1. Bruteforce searching. This method takes more time but guaranteed that the parent found is the best one.
-   1. Pivot-based searching. This method introduce a one-off pre-processing time but will make parent finding of each node faster. The parent found may not be the best one but should still be near enough to provide good results.
-
-These algorithms are useful for producing visualizations that show relationships between the data. For instance:
-
-![Iris data set](img/IrisLink.png)
-![Part of Poker Hands data set](img/Poker3000Link.png)
-
-### Authors
-Pitchaya Boonsarngsuk
-
-Based on [d3-neighbour-sampling](https://github.com/sReeper/d3-neighbour-sampling) by Remigijus Bartasius and Matthew Chalmers under MIT license.
-
-Based on [d3-force](https://github.com/d3/d3-force) by Mike Bostock under BSD 3-Clause license.
-
-### Reference
-- Chalmers, M. ["A linear iteration time layout algorithm for visualising high-dimensional data."](http://dl.acm.org/citation.cfm?id=245035) Proceedings of the 7th conference on Visualization'96. IEEE Computer Society Press, 1996.
-- Morrison, A., Ross, G. & Chalmers, M. ["A Hybrid Layout Algorithm for Sub-Quadratic Multidimensional Scaling."](https://dl.acm.org/citation.cfm?id=857191.857738) INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization, 2002
-- Morrison, A. & Chalmers, M. ["Improving hybrid MDS with pivot-based searching."](https://dl.acm.org/citation.cfm?id=1947387) INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization, 2003
-
-## Usage
-
-Download the [latest release](https://git.win32exe.tech/brian/d3-spring-model/releases) and load either the full and minified version alongside [D3 4.0](https://github.com/d3/d3).
-```html
-<script src="https://d3js.org/d3.v4.min.js"></script>
-<script src="d3-spring-model.min.js"></script>
-<script>
-
-var simulation = d3.forceSimulation(nodes);
-
-</script>
-```
-
-## File structure
-- [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
-
-## Building
-```bash
-npm run build  # Clean build folder and build the module into a single js file.
-npm run minify # Minify the built js file.
-npm run zip    # Zip built files and documents for release.
-```
-See [package.json](package.json) for more details.
-
-## API Reference
-
-### Spring Model
-
-The model connect every nodes together with a "spring", a link force that pushes linked nodes together or apart according to the desired distance. The strength of the "spring" force is proportional to the difference between the linked nodes’ distance and the target distance.
-
-The implementation is based on [d3.forceLink()](https://github.com/d3/d3-force#forceLink) with the list of springs locked down so that every nodes are connected to each other. This comes with the benefit of huge memory usage decrease and lower the initialization time.
-
-<a name="forceLinkFullyConnected" href="#forceLinkFullyConnected">#</a> d3.**forceLinkFullyConnected**() [<>](src/link.js "Source")
-
-Creates a new link force with default parameters.
-
-<a name="springLink_distance" href="#springLink_distance">#</a> *springLink*.**distance**([<i>distance</i>])
-
-If *distance* is specified, sets the distance accessor to the specified number or function, re-evaluates the distance accessor for each link, and returns this force. If *distance* is not specified, returns the current distance accessor, which defaults to:
-
-```js
-function distance() {
-  return 30;
-}
-```
-
-The distance accessor is invoked for each pair of node. If it is a function, the two nodes will be passed as the two arguments as follow:
-
-```js
-function distance(nodeA, nodeB) { return NumberDistanceBetweenAandB; }
-```
-
-The resulting number is then stored internally, such that the distance of each link is only recomputed when the force is initialized or when this method is called with a new *distance*, and not on every application of the force.
-
-<a name="springLink_iterations" href="#springLink_iterations">#</a> *springLink*.**iterations**([*iterations*])
-
-If *iterations* is specified, sets the number of iterations per application to the specified number and returns this force. If *iterations* is not specified, returns the current iteration count which defaults to 1. Increasing the number of iterations greatly increases the rigidity of the constraint, but also increases the runtime cost to evaluate the force.
-
-<a name="springLink_latestAccel" href="#springLink_latestAccel">#</a> *springLink*.**latestAccel**()
-
-Returns the average velocity changes of the latest iteration.
-
-<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.
-
-<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.
-
-### Neighbour and Sampling
-
-The neighbour and sampling algorithm simplifies the model by only calculating the spring force of each node against several nearby and random nodes, at the cost of providing less accurate layout. In order for it to work properly, a distance function should be specified.
-
-<a name="forceNeighbourSampling" href="#forceNeighbourSampling">#</a> d3.**forceNeighbourSampling**() [<>](src/neighbourSampling.js "Source")
-
-Initializes the Neighbour and Sampling force with default parameters.
-
-<a name="neighbourSampling_distance" href="#neighbourSampling_distance">#</a> *neighbourSampling*.**distance**([*distance*]) [<>](https://github.com/sReeper/d3-neighbour-sampling/blob/master/src/neighbourSampling.js#L230 "Source")
-
-If *distance* is specified, sets the distance accessor to the specified number or function, re-evaluates the distance accessor for each link, and returns this force. If *distance* is not specified, returns the current distance accessor, which defaults to:
-
-```js
-function distance() {
-  return 300;
-}
-```
-
-The distance accessor is invoked for each pair of node. If it is a function, the two nodes will be passed as the two arguments as follow:
-
-```js
-function distance(nodeA, nodeB) { return NumberDistanceBetweenAandB; }
-```
-
-<a name="neighbourSampling_neighbourSize" href="#neighbourSampling_neighbourSize">#</a> *neighbourSampling*.**neighbourSize**([*neighbourSize*])
-
-If *neighbourSize* is specified, sets the neighbour set size to the specified number and returns this force. If *neighbourSize* is not specified, returns the current value, which defaults to 10.
-
-<a name="neighbourSampling_sampleSize" href="#neighbourSampling_sampleSize">#</a> *neighbourSampling*.**sampleSize**([*sampleSize*])
-
-If *sampleSize* is specified, sets the sample set size to the specified number and returns this force. If *sampleSize* is not specified, returns the current value, which defaults to 10.
-
-<a name="neighbourSampling_latestAccel" href="#neighbourSampling_latestAccel">#</a> *neighbourSampling*.**latestAccel**()
-
-Returns the average velocity changes of the latest iteration.
-
-<a name="neighbourSampling_stableVelocity" href="#neighbourSampling_stableVelocity">#</a> *neighbourSampling*.**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](#neighbourSampling_latestForce) 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.
-
-<a name="neighbourSampling_onStableVelo" href="#neighbourSampling_onStableVelo">#</a> *neighbourSampling*.**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.
-
-### Hybrid Layout Simulation - TO WRITE
-
-The hybrid layout algorithm reduces the computation power usage even further by performing neighbour and sampling algorithm on only $\sqrt{n}$ sample subset of the data, and interpolating the rest in. Neighbour and sampling algorithm may also be ran again over the full dataset after the interpolation to refine the layout. This algorithm is only recommended for visualizing larger dataset.
-
-<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.
-
-*forceSample* 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* may be absent, null, or undefined to skip the final refinement.
-
-*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.
-
-<a name="hybrid_simulation" href="#hybrid_simulation">#</a> *hybrid*.**simulation**([*simulation*])
-
-If *simulation* is specified, sets the [d3.forceSimulation](https://github.com/d3/d3-force#forceSimulation) to the given object and returns this layout simulation. Node list will be refreshed. If *simulation* is not specified, returns the current value, which defaults to 20.
-
-<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.
-
-<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.
-
-<a name="hybrid_forceSample" href="#hybrid_forceSample">#</a> *hybrid*.**forceSample**([*force*])
-
-If *force* is specified, sets the neighbour and sampling force to run on the $\sqrt{n}$ samples before interpolation and returns this layout simulation. The same limitation applies: [stableVelocity](neighbourSampling_stableVelocity) may be 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. If *force* is not specified, returns the current force object.
-
-<a name="hybrid_forceFull" href="#hybrid_forceFull">#</a> *hybrid*.**forceFull**([*force*])
-
-If *force* is specified, sets the neighbour and sampling force to run on the whole dataset after interpolation and returns this layout simulation. If set to null, the process will be skipped. If *force* is not specified, returns the current force object.
-
-<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.
-
-<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.
-
-<a name="hybrid_numPivots" href="#hybrid_numPivots">#</a> *hybrid*.**numPivots**([*number*])
-
-If *number* is specified, sets the number of pivots used to find parents during the interpolation process and returns this layout simulation. If *number* is less than 1, brute-force method will be used instead. If *number* is not specified, returns the current value, which defaults to 0 (brute-force method).
-
-<a name="hybrid_interpDistanceFn" href="#hybrid_interpDistanceFn">#</a> *hybrid*.**interpDistanceFn**([*distance*])
-
-If *distance* is specified, sets the distance accessor used during the interpolation process to the specified number or function and returns this layout simulation. If *distance* is not specified, returns the current distance accessor, which defaults to the one provided by the force for full dataset or
-
-```js
-function distance() {
-  return 300;
-}
-```
-
-If *distance* is a function, two nodes will be passed as the two arguments as follow:
-
-```js
-function distance(nodeA, nodeB) { return NumberDistanceBetweenAandB; }
-```
-<a name="hybrid_interpFindTuneIts" href="#hybrid_interpFindTuneIts">#</a> *hybrid*.**interpFindTuneIts**([*number*])
-
-During the interpolation, each node will find a "parent", a near sample node whose 2D location is known. The parent will be used to find an initial location for the node. After that, spring forces are applied to the node against $\sqrt{\sqrt{n}}$ samples to fine-tune the location for a *number* of iterations. This is not to be confused with the neighbour and sampling refinement after the entire interpolation process is completed.
-
-If *number* is specified, sets the number of refinement during the interpolation process and returns this layout simulation. If *number* is not specified, returns the current value, which defaults to 20.
-
-<a name="hybrid_on" href="#hybrid_on">#</a> <i>hybrid</i>.<b>on</b>(*typenames*, [*listener*])
-
-If *listener* is specified, sets the event *listener* for the specified *typenames* and returns this layout simulation. If an event listener was already registered for the same type and name, the existing listener is removed before the new listener is added. If *listener* is null, removes the current event listeners for the specified *typenames*, if any. If *listener* is not specified, returns the first currently-assigned listener matching the specified *typenames*, if any. When a specified event is dispatched, each *listener* will be invoked with the `this` context as the simulation.
-
-The *typenames* is a string containing one or more *typename* separated by whitespace. Each *typename* is a *type*, optionally followed by a period (`.`) and a *name*, such as `tick.foo` and `tick.bar`; the name allows multiple listeners to be registered for the same *type*. The *type* must be one of the following:
-
-* `sampleTick` - after each update of the simulation on the $\sqrt{n}$ subset.
-* `fullTick` - after each update of the simulation on the full dataset.
-* `startInterp` - just before the interpolation process
-* `end` - after the hybrid sumulation ends.
-
-Note that *tick* events are not dispatched when [*simulation*.tick](#simulation_tick) is called manually; events are only dispatched by the internal timer and are intended for interactive rendering of the simulation. To affect the simulation, register [forces](#simulation_force) instead of modifying nodes’ positions or velocities inside a tick event listener.
-
-See [*dispatch*.on](https://github.com/d3/d3-dispatch#dispatch_on) for details.
-
-<a name="hybrid_restart" href="#hybrid_restart">#</a> *hybrid*.**restart**()
-
-Start or continue the simulation where it was left off and returns this layout simulation.
-
-<a name="hybrid_stop" href="#hybrid_stop">#</a> *hybrid*.**stop**()
-
-Stops the simulation, if it is running, and returns this layout simulation. If the it has already stopped, this method does nothing.
-
-### Miscellaneous
-
-<a name="calculateStress" href="#calculateStress">#</a> d3.**calculateStress**(*nodes*, *distance*) [<>](src/stress.js "Source")
-
-Calculate stress of a whole system, based on sum-of-squared errors of inter-object distances. *nodes* is the array of all nodes in the system and *distance* is the function to calculate the desired distance between two node objects. *distance* is expected to have the same prototype as the one in [springLink](#springLink_distance).
+# d3-spring-model
+
+This module implements three force-directed layout algorithms to visualize high-dimensional data in 2D space.
+1. Basic spring model algorithm. In this model, every data point (node) pairs are connected with a spring that pushes or pulls, depending on the difference between 2D and high-dimensional distance. This is a tweaked version of [D3's force link](https://github.com/d3/d3-force#forceLink) with functionalities removed to improve performance and lower the memory usage.
+1. Neighbour and Sampling algorithm. It uses stochastic sampling to find the best neighbours for high-dimensional data and creates the layout in 2 dimensions.
+1. Hybrid layout algorithm. It performs Neighbour and Sampling algorithm on a subset of data before interpolating the rest onto the 2D space. Neighbour and Sampling algorithm may also be run over the full dataset at the end to refine placement.
+During the interpolation, each node have to find a parent, a closest node that has already been plotted on the 2D space. Two methods of of finding the parents have been implemented.
+   1. Bruteforce searching. This method takes more time but guaranteed that the parent found is the best one.
+   1. Pivot-based searching. This method introduce a one-off pre-processing time but will make parent finding of each node faster. The parent found may not be the best one but should still be near enough to provide good results.
+
+These algorithms are useful for producing visualizations that show relationships between the data. For instance:
+
+![Iris data set](img/IrisLink.png)
+![Part of Poker Hands data set](img/Poker3000Link.png)
+
+### Authors
+Pitchaya Boonsarngsuk
+
+Based on [d3-neighbour-sampling](https://github.com/sReeper/d3-neighbour-sampling) by Remigijus Bartasius and Matthew Chalmers under MIT license.
+
+Based on [d3-force](https://github.com/d3/d3-force) by Mike Bostock under BSD 3-Clause license.
+
+### Reference
+- Chalmers, M. ["A linear iteration time layout algorithm for visualising high-dimensional data."](http://dl.acm.org/citation.cfm?id=245035) Proceedings of the 7th conference on Visualization'96. IEEE Computer Society Press, 1996.
+- Morrison, A., Ross, G. & Chalmers, M. ["A Hybrid Layout Algorithm for Sub-Quadratic Multidimensional Scaling."](https://dl.acm.org/citation.cfm?id=857191.857738) INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization, 2002
+- Morrison, A. & Chalmers, M. ["Improving hybrid MDS with pivot-based searching."](https://dl.acm.org/citation.cfm?id=1947387) INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization, 2003
+
+## Usage
+
+Download the [latest release](https://git.win32exe.tech/brian/d3-spring-model/releases) and load either the full and minified version alongside [D3 4.0](https://github.com/d3/d3).
+```html
+<script src="https://d3js.org/d3.v4.min.js"></script>
+<script src="d3-spring-model.min.js"></script>
+<script>
+
+var simulation = d3.forceSimulation(nodes);
+
+</script>
+```
+
+## File structure
+- [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
+- [examples/](examples) An example page implementing the library
+- [img/](img) Images for this readme file
+
+## Building
+```bash
+npm run build  # Clean build folder and build the module into a single js file.
+npm run minify # Minify the built js file.
+npm run zip    # Zip built files and documents for release.
+```
+See [package.json](package.json) for more details.
+
+## API Reference
+
+### Spring Model
+
+The model connect every nodes together with a "spring", a link force that pushes linked nodes together or apart according to the desired distance. The strength of the "spring" force is proportional to the difference between the linked nodes’ distance and the target distance.
+
+The implementation is based on [d3.forceLink()](https://github.com/d3/d3-force#forceLink) with the list of springs locked down so that every nodes are connected to each other. This comes with the benefit of huge memory usage decrease and lower the initialization time.
+
+<a name="forceLinkFullyConnected" href="#forceLinkFullyConnected">#</a> d3.**forceLinkFullyConnected**() [<>](src/link.js "Source")
+
+Creates a new tweaked link force with default parameters.
+
+<a name="springLink_distance" href="#springLink_distance">#</a> *springLink*.**distance**([<i>distance</i>])
+
+If *distance* is specified, sets the distance accessor to the specified number or function, re-evaluates the distance accessor for each link, and returns this force. If *distance* is not specified, returns the current distance accessor, which defaults to:
+
+```js
+function distance() {
+  return 30;
+}
+```
+
+The distance accessor is invoked for each pair of node. If it is a function, the two nodes will be passed as the two arguments as follow:
+
+```js
+function distance(nodeA, nodeB) { return NumberDistanceBetweenAandB; }
+```
+
+The resulting number is then stored internally, such that the distance of each link is only recomputed when the force is initialized or when this method is called with a new *distance*, and not on every application of the force.
+
+<a name="springLink_iterations" href="#springLink_iterations">#</a> *springLink*.**iterations**([*iterations*])
+
+If *iterations* is specified, sets the number of iterations per application to the specified number and returns this force. If *iterations* is not specified, returns the current iteration count which defaults to 1. Increasing the number of iterations greatly increases the rigidity of the constraint, but also increases the runtime cost to evaluate the force.
+
+<a name="springLink_latestAccel" href="#springLink_latestAccel">#</a> *springLink*.**latestAccel**()
+
+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](#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](#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
+
+The neighbour and sampling algorithm simplifies the model by only calculating the spring force of each node against several nearby and random nodes, at the cost of providing less accurate layout. In order for it to work properly, a distance function should be specified.
+
+<a name="forceNeighbourSampling" href="#forceNeighbourSampling">#</a> d3.**forceNeighbourSampling**() [<>](src/neighbourSampling.js "Source")
+
+Initializes the Neighbour and Sampling force with default parameters.
+
+<a name="neighbourSampling_distance" href="#neighbourSampling_distance">#</a> *neighbourSampling*.**distance**([*distance*]) [<>](https://github.com/sReeper/d3-neighbour-sampling/blob/master/src/neighbourSampling.js#L230 "Source")
+
+If *distance* is specified, sets the distance accessor to the specified number or function, re-evaluates the distance accessor for each link, and returns this force. If *distance* is not specified, returns the current distance accessor, which defaults to:
+
+```js
+function distance() {
+  return 300;
+}
+```
+
+The distance accessor is invoked for each pair of node. If it is a function, the two nodes will be passed as the two arguments as follow:
+
+```js
+function distance(nodeA, nodeB) { return NumberDistanceBetweenAandB; }
+```
+
+<a name="neighbourSampling_neighbourSize" href="#neighbourSampling_neighbourSize">#</a> *neighbourSampling*.**neighbourSize**([*neighbourSize*])
+
+If *neighbourSize* is specified, sets the neighbour set size to the specified number and returns this force. If *neighbourSize* is not specified, returns the current value, which defaults to 10.
+
+<a name="neighbourSampling_sampleSize" href="#neighbourSampling_sampleSize">#</a> *neighbourSampling*.**sampleSize**([*sampleSize*])
+
+If *sampleSize* is specified, sets the sample set size to the specified number and returns this force. If *sampleSize* is not specified, returns the current value, which defaults to 10.
+
+<a name="neighbourSampling_latestAccel" href="#neighbourSampling_latestAccel">#</a> *neighbourSampling*.**latestAccel**()
+
+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*])
+
+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](#neighbourSampling_latestForce) 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.
+
+<a name="neighbourSampling_onStableVelo" href="#neighbourSampling_onStableVelo">#</a> *neighbourSampling*.**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.
+
+### Hybrid Layout Simulation
+
+The hybrid layout algorithm reduces the computation power usage even further by performing neighbour and sampling algorithm on only $\sqrt{n}$ sample subset of the data, and interpolating the rest in. Neighbour and sampling algorithm may also be ran again over the full dataset after the interpolation to refine the layout. This algorithm is only recommended for visualizing larger dataset.
+
+<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 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 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 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.
+
+<a name="hybrid_simulation" href="#hybrid_simulation">#</a> *hybrid*.**simulation**([*simulation*])
+
+If *simulation* is specified, sets the [d3.forceSimulation](https://github.com/d3/d3-force#forceSimulation) to the given object and returns this layout simulation. Node list will be refreshed. If *simulation* is not specified, returns the current value, which defaults to 20.
+
+<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 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 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*])
+
+If *force* is specified, sets the neighbour and sampling force to run on the $\sqrt{n}$ samples before interpolation and returns this layout simulation. The same limitation applies: [stableVelocity](neighbourSampling_stableVelocity) may be 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. If *force* is not specified, returns the current force object.
+
+<a name="hybrid_forceFull" href="#hybrid_forceFull">#</a> *hybrid*.**forceFull**([*force*])
+
+If *force* is specified, sets the neighbour and sampling force to run on the whole dataset after interpolation and returns this layout simulation. The same limitation applies: [stableVelocity](neighbourSampling_stableVelocity) may be 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. If set to null, the process will be skipped. If *force* is not specified, returns the current force object.
+
+<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 [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 [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*])
+
+If *number* is specified, sets the number of pivots used to find parents during the interpolation process and returns this layout simulation. If *number* is less than 1, brute-force method will be used instead. If *number* is not specified, returns the current value, which defaults to 0 (brute-force method).
+
+<a name="hybrid_interpDistanceFn" href="#hybrid_interpDistanceFn">#</a> *hybrid*.**interpDistanceFn**([*distance*])
+
+If *distance* is specified, sets the distance accessor used during the interpolation process to the specified number or function and returns this layout simulation. If *distance* is not specified, returns the current distance accessor, which defaults to the one provided by the force for full dataset or
+
+```js
+function distance() {
+  return 300;
+}
+```
+
+If *distance* is a function, two nodes will be passed as the two arguments as follow:
+
+```js
+function distance(nodeA, nodeB) { return NumberDistanceBetweenAandB; }
+```
+<a name="hybrid_interpFindTuneIts" href="#hybrid_interpFindTuneIts">#</a> *hybrid*.**interpFindTuneIts**([*number*])
+
+During the interpolation, each node will find a "parent", a near sample node whose 2D location is known. The parent will be used to find an initial location for the node. After that, spring forces are applied to the node against $\sqrt{\sqrt{n}}$ samples to fine-tune the location for a *number* of iterations. This is not to be confused with the neighbour and sampling refinement after the entire interpolation process is completed.
+
+If *number* is specified, sets the number of refinement during the interpolation process and returns this layout simulation. If *number* is not specified, returns the current value, which defaults to 20.
+
+<a name="hybrid_on" href="#hybrid_on">#</a> <i>hybrid</i>.<b>on</b>(*typenames*, [*listener*])
+
+If *listener* is specified, sets the event *listener* for the specified *typenames* and returns this layout simulation. If an event listener was already registered for the same type and name, the existing listener is removed before the new listener is added. If *listener* is null, removes the current event listeners for the specified *typenames*, if any. If *listener* is not specified, returns the first currently-assigned listener matching the specified *typenames*, if any. When a specified event is dispatched, each *listener* will be invoked with the `this` context as the simulation.
+
+The *typenames* is a string containing one or more *typename* separated by whitespace. Each *typename* is a *type*, optionally followed by a period (`.`) and a *name*, such as `tick.foo` and `tick.bar`; the name allows multiple listeners to be registered for the same *type*. The *type* must be one of the following:
+
+* `sampleTick` - after each update of the simulation on the $\sqrt{n}$ subset.
+* `fullTick` - after each update of the simulation on the full dataset.
+* `startInterp` - just before the interpolation process
+* `end` - after the hybrid sumulation ends.
+
+Note that *tick* events are not dispatched when [*simulation*.tick](#simulation_tick) is called manually; events are only dispatched by the internal timer and are intended for interactive rendering of the simulation. To affect the simulation, register [forces](#simulation_force) instead of modifying nodes’ positions or velocities inside a tick event listener.
+
+See [*dispatch*.on](https://github.com/d3/d3-dispatch#dispatch_on) for details.
+
+<a name="hybrid_restart" href="#hybrid_restart">#</a> *hybrid*.**restart**()
+
+Start or continue the simulation where it was left off and returns this layout simulation.
+
+<a name="hybrid_stop" href="#hybrid_stop">#</a> *hybrid*.**stop**()
+
+Stops the simulation, if it is running, and returns this layout simulation. If the it has already stopped, this method does nothing.
+
+### Miscellaneous
+
+<a name="calculateStress" href="#calculateStress">#</a> d3.**calculateStress**(*nodes*, *distance*) [<>](src/stress.js "Source")
+
+Calculate stress of a whole system, based on sum-of-squared errors of inter-object distances. *nodes* is the array of all nodes in the system and *distance* is the function to calculate the desired distance between two node objects. *distance* is expected to have the same prototype as the one in [springLink](#springLink_distance).

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();
@@ -16,33 +16,34 @@ function startHybridSimulation() {
   let forceSample = d3.forceNeighbourSampling()
     .neighbourSize(NEIGHBOUR_SIZE)
     .sampleSize(SAMPLE_SIZE)
-    .stableVelocity(0)
-    .distance(distance)
+    .stableVelocity(0) // Change here
+    .distance(distance);
 
   let forceFull = d3.forceNeighbourSampling()
     .neighbourSize(FULL_NEIGHBOUR_SIZE)
     .sampleSize(FULL_SAMPLE_SIZE)
-    .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;
@@ -11,27 +11,26 @@ function startLinkSimulation() {
   let links = [], force;
 
   if (tweakedVerOfLink) {
-    force = d3.forceLinkFullyConnected()
-              .distance(function (n, m) {
-                return distanceFunction(n, m, props, norm);
-              })
-              .stableVelocity(0.000001) //TODO
-              .onStableVelo(ended);
-  }
-  else {
-    for (i = nodes.length-1; i >= 1; i--) {
-      for (j = i-1; j >= 0; j--) {
+    force = d3.forceLinkCompleteGraph()
+      .distance(function (n, m) {
+        return distanceFunction(n, m, props, norm);
+      })
+      .stableVelocity(0) // Change here
+      .onStableVelo(ended);
+  } else {
+    for (i = nodes.length - 1; i >= 1; i--) {
+      for (j = i - 1; j >= 0; j--) {
         links.push({
           source: nodes[i],
-          target: nodes[j],
+          target: nodes[j]
         });
       }
     }
     force = d3.forceLink()
-              .distance(function (n) {
-               return distanceFunction(n.source, n.target, props, norm);
-              })
-             .links(links);
+      .distance(function (n) {
+        return distanceFunction(n.source, n.target, props, norm);
+      })
+      .links(links);
   }
 
   /* Add force
@@ -51,9 +50,9 @@ function startLinkSimulation() {
   simulation
     .alphaDecay(0)
     .alpha(1)
-    .on("tick", ticked)
-    .on("end", ended)
-    //.velocityDecay(0.8)
-    .force(forceName,force)
+    .on('tick', ticked)
+    .on('end', ended)
+    // .velocityDecay(0.8)
+    .force(forceName, force)
     .restart();
 }

examples/js/algos/neighbourSampling.js

@@ -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)
-      .force(forceName, d3.forceBarnesHut()
-        // The distance function that will be used to calculate distances
-        //  between nodes.
-        .distance(function(s, t) { return distanceFunction(s, t, props, norm); }));
+    .on('tick', ticked)
+    .on('end', ended)
+    .force(forceName, d3.forceBarnesHut()
+    // The distance function that will be used to calculate distances
+    //  between nodes.
+      .distance(function (s, t) { return distanceFunction(s, t, props, norm); }));
   // Restart the simulation.
   simulation.alpha(1).restart();
 }

examples/js/distances/cosineSimilarity.js

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

examples/js/distances/diceDissimilarity.js

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

examples/js/distances/distance.js

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

examples/js/distances/distancePokerHands.js

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

examples/js/distances/euclideanDistance.js

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

examples/js/distances/euclideanDistanceInTSNE.js

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

examples/js/distances/jaccardDissimilarity.js

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

examples/js/distances/manhattanDistance.js

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

examples/js/distances/normalization.js

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

examples/js/distances/numeric.js

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

examples/js/example-papaparsing.js

@@ -1,425 +1,400 @@
-// Get the width and heigh of the SVG element.
-var width = +document.getElementById('svg').clientWidth,
-  height = +document.getElementById('svg').clientHeight;
-
-var svg = d3.select("svg")
-  .call(d3.zoom().scaleExtent([0.0001, 1000000]).on("zoom", function () {
-    svg.attr("transform", d3.event.transform);
-  }))
-  .append("g");
-
-var div = d3.select("body").append("div")
-  .attr("class", "tooltip")
-  .style("opacity", 0);
-
-var brush = d3.brush()
-  .extent([[-9999999, -9999999], [9999999, 9999999]])
-  .on("end", brushEnded);
-
-svg.append("g")
-  .attr("class", "brush")
-  .call(brush);
-
-var intercom = Intercom.getInstance();
-
-intercom.on("select", unSelectNodes);
-
-var nodes, // as in Data points
-  node, // as in SVG object that have all small circles on screen
-  props,
-  norm,
-  p1 = 0,
-  p2 = 0,
-  size,
-  distanceFunction,
-  simulation,
-  velocities = [],
-  rendering = true, // Rendering during the execution.
-  forceName = "forces",
-  springForce = false,
-  tooltipWidth = 0,
-  fileName = "",
-  selectedData,
-  clickedIndex = -1,
-  paused = false,
-  alreadyRanIterations,
-  tweakedVerOfLink,
-  manualStop = false;
-
-// Default parameters
-var MULTIPLIER = 50,
-  PERPLEXITY = 30,
-  LEARNING_RATE = 10,
-  NEIGHBOUR_SIZE = 10,
-  SAMPLE_SIZE = 10,
-  PIVOTS = false,
-  NUM_PIVOTS = 3,
-  ITERATIONS = 300,
-  FULL_ITERATIONS = 20,
-  NODE_SIZE = 10,
-  COLOR_ATTRIBUTE = "",
-  FULL_NEIGHBOUR_SIZE = 10,
-  FULL_SAMPLE_SIZE = 10,
-  INTERP_ENDING_ITS = 20;
-
-// Create a color scheme for a range of numbers.
-var color = d3.scaleOrdinal(d3.schemeCategory10);
-
-
-$(document).ready(function() {
-  distanceFunction = calculateDistance;
-  d3.select('#startSimulation').on('click', startHybridSimulation);
-  $("#HLParameters").show();
-});
-
-/**
- * Parse the data from the provided csv file using Papa Parse library
- * @param  {file} evt - csv file.
- */
-function parseFile(evt) {
-  // Clear the previous nodes
-  d3.selectAll(".nodes").remove();
-  springForce = false;
-
-  fileName = evt.target.files[0].name;
-
-  Papa.parse(evt.target.files[0], {
-    header: true,
-    dynamicTyping: true,
-    skipEmptyLines: true,
-    complete: function (results) {
-      processData(results.data, results.error);
-    }
-  });
-}
-
-/**
- * Process the data and pass it into D3 force simulation.
- * @param  {array} data
- * @param  {object} error
- */
-function processData(data, error) {
-  if (error) throw error.message;
-
-  nodes = data;
-  size = nodes.length;
-  simulation = d3.forceSimulation();
-
-  // Calculate normalization arguments and get the list of
-  // properties of the nodes.
-  norm = calculateNormalization(nodes); // Used with distance fn
-  props = Object.keys(nodes[0]); // Properties to consider by distance fn
-  COLOR_ATTRIBUTE = props[props.length-1];
-
-  var opts = document.getElementById('color_attr').options;
-
-  props.forEach(function (d) {
-    opts.add(new Option(d, d, (d === COLOR_ATTRIBUTE) ? true : false));
-  });
-  opts.selectedIndex = props.length-1;
-
-  //props.pop(); //Hide Iris index / last column from distance function
-
-  //Put the nodes in random starting positions
-  //TODO Change this back
-  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);
-
-  // Pass the nodes to the D3 force simulation.
-  simulation
-    .nodes(nodes)
-    .stop();
-
-  ticked();
-};
-
-function addNodesToDOM(data) {
-  node = svg.append("g")
-    .attr("class", "nodes")
-    .selectAll("circle")
-    .data(data)
-    .enter().append("circle")
-    .attr("r", NODE_SIZE)
-    .attr("transform", "translate(" + width / 2 + "," + height / 2 + ")")
-    // Color code the data points by a property (for Poker Hands,
-    // it is a CLASS property).
-    .attr("fill", function (d) {
-      return color(d[COLOR_ATTRIBUTE]);
-    })
-    .on("mouseover", function (d) {
-      div.transition()
-        .duration(200)
-        .style("opacity", .9);
-      div.html(formatTooltip(d))
-        .style("left", (d3.event.pageX) + "px")
-        .style("top", (d3.event.pageY - (15 * props.length)) + "px")
-        .style("width", (6 * tooltipWidth) + "px")
-        .style("height", (14 * props.length) + "px");
-      highlightOnHover(d[COLOR_ATTRIBUTE]);
-    })
-    .on("mouseout", function (d) {
-      div.transition()
-        .duration(500)
-        .style("opacity", 0);
-      node.attr("opacity", 1);
-    })
-    .on("click", function (d) {
-      console.log("click", clickedIndex);
-      if (clickedIndex !== d.index) {
-        if (springForce) {
-          highlightNeighbours(Array.from(simulation.force(forceName).nodeNeighbours(d.index).keys()));
-          clickedIndex = d.index;
-        }
-      } else {
-        node.attr("r", NODE_SIZE).attr("stroke-width", 0);
-        clickedIndex = -1;
-      }
-    });
-    if (selectedData)
-      unSelectNodes(selectedData);
-}
-
-function ticked() {
-  //console.log("ticked");
-  alreadyRanIterations++;
-  // If rendering is selected, then draw at every iteration.
-  if (rendering === true) {
-    node // Each sub-circle in the SVG, update cx and cy
-      .attr("cx", function (d) {
-        return d.x*MULTIPLIER;
-      })
-      .attr("cy", function (d) {
-        return d.y*MULTIPLIER;
-      });
-  }
-  // Emit the distribution data to allow the drawing of the bar graph
-  if (springForce) {
-    intercom.emit("passedData", simulation.force(forceName).distributionData());
-  }
-  if(manualStop && alreadyRanIterations == ITERATIONS) {
-    ended();
-  }
-}
-
-function ended() {
-  simulation.stop();
-  simulation.force(forceName, null);
-  console.log("ended");
-  if (rendering !== true) { // Never drawn anything before? Now it's time.
-    node
-      .attr("cx", function (d) {
-        return d.x*MULTIPLIER;
-      })
-      .attr("cy", function (d) {
-        return d.y*MULTIPLIER;
-      });
-  }
-
-  if (p1 !== 0) {
-    // Performance time measurement
-    p2 = performance.now();
-    console.log("Execution time: " + (p2 - p1));
-    // 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());
-    p1 = 0;
-    p2 = 0;
-  }
-}
-
-function brushEnded() {
-  var s = d3.event.selection,
-    results = [];
-
-  if (s) {
-
-    var x0 = s[0][0] - width / 2,
-      y0 = s[0][1] - height / 2,
-      x1 = s[1][0] - width / 2,
-      y1 = s[1][1] - height / 2;
-
-    if (nodes) {
-      var sel = node.filter(function (d) {
-        if (d.x > x0 && d.x < x1 && d.y > y0 && d.y < y1) {
-          return true;
-        }
-        return false;
-      }).data();
-
-      results = sel.map(function (a) { return a.index; });
-    }
-
-    intercom.emit("select", { name: fileName, indices: results });
-
-    d3.select(".brush").call(brush.move, null);
-  }
-}
-
-
-/**
- * Format the tooltip for the data
- * @param {*} node
- */
-function formatTooltip(node) {
-  var textString = "",
-    temp = "";
-
-  tooltipWidth = 0;
-  props.forEach(function (element) {
-    temp = element + ": " + node[element] + "<br/>";
-    textString += temp;
-    if (temp.length > tooltipWidth) {
-      tooltipWidth = temp.length;
-    }
-  });
-  return textString;
-}
-
-/**
- * Halt the execution.
- */
-function stopSimulation() {
-  console.log("stopSimulation");
-  simulation.stop();
-  if (typeof hybridSimulation !== 'undefined') {
-    hybridSimulation.stop();
-  }
-}
-
-/**
- * Calculate the average values of the array.
- * @param  {array} array
- * @return {number}  the mean of the array.
- */
-function getAverage(array) {
-  console.log("getAverage", array);
-  var total = 0;
-  for (var i = 0; i < array.length; i++) {
-    total += array[i];
-  }
-  return total / array.length;
-}
-
-/**
- * Deselect the nodes to match the selection from other window.
- * @param {*} data
- */
-function unSelectNodes(data) {
-  selectedData = data;
-  if (fileName === data.name && nodes) {
-    node
-      .classed("notSelected", function (d) {
-        if (data.indices.indexOf(d.index) < 0) {
-          return true;
-        }
-        return false;
-      });
-  }
-}
-
-
-/**
- * Highlight the neighbours for neighbour and sampling algorithm
- * @param {*} indices
- */
-function highlightNeighbours(indices) {
-  node
-    .attr("r", function (d) {
-      if (indices.indexOf(d.index) >= 0) {
-        return NODE_SIZE * 2;
-      }
-      return NODE_SIZE;
-    })
-    .attr("stroke-width", function (d) {
-      if (indices.indexOf(d.index) >= 0) {
-        return NODE_SIZE * 0.2 + "px";
-      }
-      return "0px";
-    })
-    .attr("stroke", "white");
-}
-
-
-/**
- * Highlight all the nodes with the same class on hover
- * @param {*} highlighValue
- */
-function highlightOnHover(highlighValue) {
-  node.attr("opacity", function (d) {
-    return (highlighValue === d[COLOR_ATTRIBUTE]) ? 1 : 0.3;
-  });
-}
-
-/**
- * Color the nodes according to given attribute.
- */
-function colorToAttribute() {
-  node.attr("fill", function (d) {
-    return color(d[COLOR_ATTRIBUTE])
-  });
-}
-
-
-/**
- * Update the distance range.
-
-function updateDistanceRange() {
-  if (springForce) {
-    simulation.force(forceName).distanceRange(SELECTED_DISTANCE);
-  }
-}
-
-
-/**
- * Implemented pause/resume functionality
- */
-function pauseUnPause() {
-  if (simulation) {
-    if (paused) {
-      simulation.force(forceName);
-      simulation.restart();
-      d3.select("#pauseButton").text("Pause");
-      paused = false;
-    } else {
-      simulation.stop();
-      d3.select("#pauseButton").text("Resume");
-      paused = true;
-    }
-  }
-}
-
-
-/**
- * Average distances for each node.
- * @param {*} dataNodes
- * @param {*} properties
- * @param {*} normalization
-
-function calculateAverageDistance(dataNodes, properties, normalization) {
-  var sum = 0,
-    n = nodes.length;
-
-  for (var i = 0; i < n; i++) {
-    var sumNode = 0;
-    for (var j = 0; j < n; j++) {
-      if (i !== j) {
-        sumNode += distanceFunction(nodes[i], nodes[j], properties, normalization);
-        // console.log(sumNode);
-      }
-    }
-    sum += sumNode / (n - 1);
-  }
-
-  return sum / n;
-}*/
+// Get the width and heigh of the SVG element.
+var width = +document.getElementById('svg').clientWidth,
+  height = +document.getElementById('svg').clientHeight;
+
+var svg = d3.select('svg')
+  .call(d3.zoom().scaleExtent([0.0001, 1000000]).on('zoom', function () {
+    svg.attr('transform', d3.event.transform);
+  }))
+  .append('g');
+
+var div = d3.select('body').append('div')
+  .attr('class', 'tooltip')
+  .style('opacity', 0);
+
+var brush = d3.brush()
+  .extent([[-9999999, -9999999], [9999999, 9999999]])
+  .on('end', brushEnded);
+
+svg.append('g')
+  .attr('class', 'brush')
+  .call(brush);
+
+// var intercom = Intercom.getInstance();
+
+// intercom.on("select", unSelectNodes);
+
+var nodes, // as in Data points
+  node, // as in SVG object that have all small circles on screen
+  props,
+  norm,
+  p1 = 0,
+  p2 = 0,
+  size,
+  distanceFunction,
+  simulation,
+  velocities = [],
+  rendering = true, // Rendering during the execution.
+  forceName = 'forces',
+  springForce = false,
+  tooltipWidth = 0,
+  fileName = '',
+  selectedData,
+  clickedIndex = -1,
+  paused = false,
+  alreadyRanIterations,
+  tweakedVerOfLink,
+  manualStop = false;
+
+// Default parameters
+var MULTIPLIER = 50,
+  PERPLEXITY = 30,
+  LEARNING_RATE = 10,
+  NEIGHBOUR_SIZE = 10,
+  SAMPLE_SIZE = 10,
+  PIVOTS = false,
+  NUM_PIVOTS = 3,
+  ITERATIONS = 100,
+  FULL_ITERATIONS = 20,
+  NODE_SIZE = 10,
+  COLOR_ATTRIBUTE = '',
+  FULL_NEIGHBOUR_SIZE = 10,
+  FULL_SAMPLE_SIZE = 10,
+  INTERP_ENDING_ITS = 20;
+
+// Create a color scheme for a range of numbers.
+var color = d3.scaleOrdinal(d3.schemeCategory10);
+
+$(document).ready(function () {
+  distanceFunction = calculateDistance;
+  d3.select('#startSimulation').on('click', startHybridSimulation);
+  $('#HLParameters').show();
+});
+
+/**
+ * Parse the data from the provided csv file using Papa Parse library
+ * @param  {file} evt - csv file.
+ */
+function parseFile (evt) {
+  // Clear the previous nodes
+  d3.selectAll('.nodes').remove();
+  springForce = false;
+
+  fileName = evt.target.files[0].name;
+  Papa.parse(evt.target.files[0], {
+    header: true,
+    dynamicTyping: true,
+    skipEmptyLines: true,
+    complete: function (results) {
+      processData(results.data, results.error);
+    }
+  });
+}
+
+/**
+ * Process the data and pass it into D3 force simulation.
+ * @param  {array} data
+ * @param  {object} error
+ */
+function processData (data, error) {
+  if (error) throw error.message;
+
+  nodes = data;
+  size = nodes.length;
+  simulation = d3.forceSimulation();
+
+  // Calculate normalization parameters for distance fns
+  norm = calculateNormalization(nodes);
+  props = Object.keys(nodes[0]); // Properties to consider by distance fn
+
+  COLOR_ATTRIBUTE = props[props.length - 1];
+
+  var opts = document.getElementById('color_attr').options;
+
+  props.forEach(function (d) {
+    opts.add(new Option(d, d, (d === COLOR_ATTRIBUTE)));
+  });
+  opts.selectedIndex = props.length - 1;
+  // props.pop(); //Hide Iris index / last column from the distance function
+
+  // Put the nodes at (0,0)
+  nodes.forEach(function (d) {
+    d.x = 0;
+    d.y = 0;
+  });
+
+  addNodesToDOM(nodes);
+
+  // Pass the nodes to the D3 force simulation.
+  simulation
+    .nodes(nodes)
+    .stop();
+
+  ticked();
+};
+
+function addNodesToDOM (data) {
+  node = svg.append('g')
+    .attr('class', 'nodes')
+    .selectAll('circle')
+    .data(data)
+    .enter().append('circle')
+    .attr('r', NODE_SIZE)
+    .attr('transform', 'translate(' + width / 2 + ',' + height / 2 + ')')
+    // Color code the data points by a property (for Poker Hands,
+    // it is a CLASS property).
+    .attr('fill', function (d) {
+      return color(d[COLOR_ATTRIBUTE]);
+    })
+    .on('mouseover', function (d) {
+      div.transition()
+        .duration(200)
+        .style('opacity', 0.9);
+      div.html(formatTooltip(d))
+        .style('left', (d3.event.pageX) + 'px')
+        .style('top', (d3.event.pageY - (15 * props.length)) + 'px')
+        .style('width', (6 * tooltipWidth) + 'px')
+        .style('height', (14 * props.length) + 'px');
+      highlightOnHover(d[COLOR_ATTRIBUTE]);
+    })
+    .on('mouseout', function (d) {
+      div.transition()
+        .duration(500)
+        .style('opacity', 0);
+      node.attr('opacity', 1);
+    })
+    .on('click', function (d) {
+      console.log('click', clickedIndex);
+      if (clickedIndex !== d.index) {
+        if (springForce) {
+          highlightNeighbours(Array.from(simulation.force(forceName).nodeNeighbours(d.index).keys()));
+          clickedIndex = d.index;
+        }
+      } else {
+        node.attr('r', NODE_SIZE).attr('stroke-width', 0);
+        clickedIndex = -1;
+      }
+    });
+  if (selectedData) { unSelectNodes(selectedData); }
+}
+
+function ticked () {
+  alreadyRanIterations++;
+  // If rendering is selected, then draw at every iteration.
+  if (rendering === true) {
+    node // Each sub-circle in the SVG, update cx and cy
+      .attr('cx', function (d) {
+        return d.x * MULTIPLIER;
+      })
+      .attr('cy', function (d) {
+        return d.y * MULTIPLIER;
+      });
+  }
+  // Legacy: Emit the distribution data to allow the drawing of the bar graph
+  // if (springForce) {
+  //  intercom.emit("passedData", simulation.force(forceName).distributionData());
+  // }
+  if (manualStop && alreadyRanIterations === ITERATIONS) {
+    ended();
+  }
+}
+
+function ended () {
+  simulation.stop();
+  simulation.force(forceName, null);
+  if (rendering !== true) { // Never drawn anything before? Now it's time.
+    node
+      .attr('cx', function (d) {
+        return d.x * MULTIPLIER;
+      })
+      .attr('cy', function (d) {
+        return d.y * MULTIPLIER;
+      });
+  }
+
+  if (p1 !== 0) {
+    // Performance time measurement
+    p2 = performance.now();
+    console.log('Execution time: ' + (p2 - p1));
+    p1 = 0;
+    p2 = 0;
+  }
+}
+
+function brushEnded () {
+  var s = d3.event.selection,
+    results = [];
+
+  if (s) {
+    var x0 = s[0][0] - width / 2,
+      y0 = s[0][1] - height / 2,
+      x1 = s[1][0] - width / 2,
+      y1 = s[1][1] - height / 2;
+
+    if (nodes) {
+      var sel = node.filter(function (d) {
+        if (d.x > x0 && d.x < x1 && d.y > y0 && d.y < y1) {
+          return true;
+        }
+        return false;
+      }).data();
+
+      results = sel.map(function (a) { return a.index; });
+    }
+
+    // intercom.emit("select", { name: fileName, indices: results });
+
+    d3.select('.brush').call(brush.move, null);
+  }
+}
+
+/**
+ * Format the tooltip for the data
+ * @param {*} node
+ */
+function formatTooltip (node) {
+  var textString = '',
+    temp = '';
+
+  tooltipWidth = 0;
+  props.forEach(function (element) {
+    temp = element + ': ' + node[element] + '<br/>';
+    textString += temp;
+    if (temp.length > tooltipWidth) {
+      tooltipWidth = temp.length;
+    }
+  });
+  return textString;
+}
+
+/**
+ * Halt the execution.
+ */
+function stopSimulation () {
+  simulation.stop();
+  if (typeof hybridSimulation !== 'undefined') {
+    hybridSimulation.stop();
+  }
+}
+
+/**
+ * Calculate the average values of the array.
+ * @param  {array} array
+ * @return {number}  the mean of the array.
+ */
+function getAverage (array) {
+  console.log('getAverage', array);
+  var total = 0;
+  for (var i = 0; i < array.length; i++) {
+    total += array[i];
+  }
+  return total / array.length;
+}
+
+/**
+ * Deselect the nodes to match the selection from other window.
+ * @param {*} data
+ */
+function unSelectNodes (data) {
+  selectedData = data;
+  if (fileName === data.name && nodes) {
+    node
+      .classed('notSelected', function (d) {
+        if (data.indices.indexOf(d.index) < 0) {
+          return true;
+        }
+        return false;
+      });
+  }
+}
+
+/**
+ * Highlight the neighbours for neighbour and sampling algorithm
+ * @param {*} indices
+ */
+function highlightNeighbours (indices) {
+  node
+    .attr('r', function (d) {
+      if (indices.indexOf(d.index) >= 0) {
+        return NODE_SIZE * 2;
+      }
+      return NODE_SIZE;
+    })
+    .attr('stroke-width', function (d) {
+      if (indices.indexOf(d.index) >= 0) {
+        return NODE_SIZE * 0.2 + 'px';
+      }
+      return '0px';
+    })
+    .attr('stroke', 'white');
+}
+
+/**
+ * Highlight all the nodes with the same class on hover
+ * @param {*} highlighValue
+ */
+function highlightOnHover (highlighValue) {
+  node.attr('opacity', function (d) {
+    return (highlighValue === d[COLOR_ATTRIBUTE]) ? 1 : 0.3;
+  });
+}
+
+/**
+ * Color the nodes according to given attribute.
+ */
+function colorToAttribute () {
+  node.attr('fill', function (d) {
+    return color(d[COLOR_ATTRIBUTE]);
+  });
+}
+
+/**
+ * Update the distance range.
+
+function updateDistanceRange() {
+  if (springForce) {
+    simulation.force(forceName).distanceRange(SELECTED_DISTANCE);
+  }
+}
+
+/**
+ * Implemented pause/resume functionality
+ */
+function pauseUnPause () {
+  if (simulation) {
+    if (paused) {
+      simulation.force(forceName);
+      simulation.restart();
+      d3.select('#pauseButton').text('Pause');
+      paused = false;
+    } else {
+      simulation.stop();
+      d3.select('#pauseButton').text('Resume');
+      paused = true;
+    }
+  }
+}
+
+/**
+ * Average distances for each node.
+ * @param {*} dataNodes
+ * @param {*} properties
+ * @param {*} normalization
+
+function calculateAverageDistance(dataNodes, properties, normalization) {
+  var sum = 0,
+    n = nodes.length;
+
+  for (var i = 0; i < n; i++) {
+    var sumNode = 0;
+    for (var j = 0; j < n; j++) {
+      if (i !== j) {
+        sumNode += distanceFunction(nodes[i], nodes[j], properties, normalization);
+        // console.log(sumNode);
+      }
+    }
+    sum += sumNode / (n - 1);
+  }
+
+  return sum / n;
+} */

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

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

src/constant.js

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

src/hybridSimulation.js

@@ -1,196 +1,204 @@
-import {dispatch} from "d3-dispatch";
-import constant from "./constant";
-import interpBruteForce from "./interpolation/interpBruteForce";
-import interpolationPivots from "./interpolation/interpolationPivots";
-import {takeSampleFrom} from "./interpolation/helpers";
-
-/**
- * An implementation of Chalmers, Morrison, and Ross' 2002 hybrid layout
- * algorithm with an option to use the 2003 pivot-based near neighbour searching
- * method.
- * It performs the 1996 neighbour sampling spring simulation model, on a
- * "sample set", sqrt(n) samples of the data.
- * Other data points are then interpolated into the model.
- * Finally, another spring simulation may be performed on the entire dataset to
- * clean up the model.
- * @param {object} sim    - D3 Simulation object
- * @param {object} forceS - Pre-configured D3 force object for the sample set.
-                            The ending handler will be re-configured.
-                            Neighbour sampling force is expected, but other D3
-                            forces may also work.
- * @param {object} forceF - Pre-configured D3 force object for the simultion ran
-                            on the entire dataset at the end.
-                            Neighbour sampling force is expected, but other D3
-                            forces may also work.
-                            The force should not have any ending condition.
- */
-export default function (sim, forceS, forceF) {
-  var
-    SAMPLE_ITERATIONS = 300,
-    FULL_ITERATIONS   = 20,
-    interpDistanceFn,
-    NUM_PIVOTS         = 0,
-    INTERP_FINE_ITS   = 20,
-    sample = [],
-    remainder = [],
-    simulation = sim,
-    forceSample = forceS,
-    forceFull = forceF,
-    event = d3.dispatch("sampleTick", "fullTick", "startInterp", "end"),
-    initAlready = false,
-    nodes,
-    alreadyRanIterations,
-    hybrid;
-
-    if(simulation != undefined) initSimulation();
-    if(forceS != undefined || forceF != undefined) initForces();
-
-  // Performed on first run
-  function initialize() {
-    initAlready = true;
-    console.log("Initializing Hybrid");
-    alreadyRanIterations = 0;
-    simulation
-      .on("tick", sampleTick)
-      .on("end", sampleEnded)
-      .nodes(sample)
-      .force("Sample force", forceSample);
-    console.log("Initialized Hybrid");
-  }
-
-  function initForces(){
-    if (forceSample.onStableVelo) {
-      forceSample.onStableVelo(sampleEnded);
-    }
-
-    // Set default value for interpDistanceFn if not been specified yet
-    if(interpDistanceFn === undefined) {
-      if(forceFull.distance == 'function')
-        interpDistanceFn = forceFull.distance();
-      else
-        interpDistanceFn = constant(300);
-    }
-  }
-
-  function initSimulation(){
-    nodes = simulation.nodes();
-    simulation
-      .stop()
-      .alphaDecay(0)
-      .alpha(1)
-
-    let sets = takeSampleFrom(nodes, Math.sqrt(nodes.length));
-    sample = sets.sample;
-    remainder = sets.remainder;
-  }
-
-  // Sample simulation ticked 1 frame, keep track of number of iterations here.
-  function sampleTick() {
-    event.call("sampleTick");
-    if(++alreadyRanIterations >= SAMPLE_ITERATIONS){
-      sampleEnded();
-    }
-  }
-
-  // Full simulation ticked 1 frame, keep track of number of iterations here.
-  function fullTick() {
-    event.call("fullTick");
-    if(++alreadyRanIterations >= FULL_ITERATIONS){
-      simulation.stop();
-      initAlready = false;
-      simulation.force("Full force", null);
-      event.call("end");
-    }
-  }
-
-  function sampleEnded() {
-    simulation.stop();
-    simulation.force("Sample force", null);
-    // Reset velocity of all nodes
-    for (let i=sample.length-1; i>=0; i--){
-      sample[i].vx=0;
-      sample[i].vy=0;
-    }
-
-    event.call("startInterp");
-    if (NUM_PIVOTS>=1) {
-      interpolationPivots(sample, remainder, NUM_PIVOTS, interpDistanceFn, INTERP_FINE_ITS);
-    } else {
-      interpBruteForce(sample, remainder, interpDistanceFn, INTERP_FINE_ITS);
-    }
-
-    event.call("fullTick");
-    alreadyRanIterations = 0;
-    simulation
-      .on("tick", null)
-      .on("end", null) // The ending condition should be iterations count
-      .nodes(nodes);
-
-    if (FULL_ITERATIONS<1 || forceF === undefined || forceF === null) {
-      event.call("end");
-      return;
-    }
-    simulation
-      .on("tick", fullTick)
-      .force("Full force", forceFull)
-      .restart();
-  }
-
-  return hybrid = {
-    restart: function () {
-      if(!initAlready) initialize();
-      simulation.restart();
-      return hybrid;
-    },
-
-    stop: function () {
-      simulation.stop();
-      return hybrid;
-    },
-
-    numPivots: function (_) {
-      return arguments.length ? (NUM_PIVOTS = +_, hybrid) : NUM_PIVOTS;
-    },
-
-    sampleIterations: function (_) {
-      return arguments.length ? (SAMPLE_ITERATIONS = +_, hybrid) : SAMPLE_ITERATIONS;
-    },
-
-    fullIterations: function (_) {
-      return arguments.length ? (FULL_ITERATIONS = +_, hybrid) : FULL_ITERATIONS;
-    },
-
-    interpFindTuneIts: function (_) {
-      return arguments.length ? (INTERP_FINE_ITS = +_, hybrid) : INTERP_FINE_ITS;
-    },
-
-    on: function (name, _) {
-      return arguments.length > 1 ? (event.on(name, _), hybrid) : event.on(name);
-    },
-
-    subSet: function (_) {
-      return arguments.length ? (sample = _, hybrid) : sample;
-    },
-
-    nonSubSet: function (_) {
-      return arguments.length ? (remainder = _, hybrid) : remainder;
-    },
-
-    interpDistanceFn: function (_) {
-      return arguments.length ? (interpDistanceFn = typeof _ === "function" ? _ : constant(+_), hybrid) : interpDistanceFn;
-    },
-
-    simulation: function (_) {
-      return arguments.length ? (toInit = true, simulation = _, hybrid) : simulation;
-    },
-
-    forceSample: function (_) {
-      return arguments.length ? (forceSample = _, initForces(), hybrid) : forceSample;
-    },
-
-    forceFull: function (_) {
-      return arguments.length ? (forceFull = _, initForces(), hybrid) : forceFull;
-    },
-
-  };
-}
+import {dispatch} from 'd3-dispatch';
+import constant from './constant';
+import interpBruteForce from './interpolation/interpBruteForce';
+import interpolationPivots from './interpolation/interpolationPivots';
+import {takeSampleFrom} from './interpolation/helpers';
+
+/**
+ * An implementation of Chalmers, Morrison, and Ross' 2002 hybrid layout
+ * algorithm with an option to use the 2003 pivot-based near neighbour searching
+ * method.
+ * It performs the 1996 neighbour sampling spring simulation model, on a
+ * "sample set", sqrt(n) samples of the data.
+ * Other data points are then interpolated into the model.
+ * Finally, another spring simulation may be performed on the entire dataset to
+ * clean up the model.
+ * @param {object} sim    - D3 Simulation object
+ * @param {object} forceS - Pre-configured D3 force object for the sample set.
+                            The ending handler will be re-configured.
+                            Neighbour sampling force is expected, but other D3
+                            forces may also work.
+ * @param {object} forceF - Pre-configured D3 force object for the simultion ran
+                            on the entire dataset at the end.
+                            Neighbour sampling force is expected, but other D3
+                            forces may also work.
+                            The force should not have any ending condition.
+ */
+export default function (sim, forceS, forceF) {
+  var
+    SAMPLE_ITERATIONS = 300,
+    FULL_ITERATIONS = 20,
+    interpDistanceFn,
+    NUM_PIVOTS = 0,
+    INTERP_FINE_ITS = 20,
+    sample = [],
+    remainder = [],
+    simulation = sim,
+    forceSample = forceS,
+    forceFull = forceF,
+    event = dispatch('sampleTick', 'fullTick', 'startInterp', 'end'),
+    initAlready = false,
+    nodes,
+    alreadyRanIterations,
+    hybrid;
+
+  if (simulation !== undefined) initSimulation();
+  if (forceS !== undefined || forceF !== undefined) initForces();
+
+  // Performed on first run
+  function initialize () {
+    initAlready = true;
+    alreadyRanIterations = 0;
+    simulation
+      .on('tick', sampleTick)
+      .on('end', sampleEnded)
+      .nodes(sample)
+      .force('Sample force', forceSample);
+    console.log('Initialized Simulation for Hybrid');
+  }
+
+  function initForces () {
+    if (forceSample.onStableVelo) {
+      forceSample.onStableVelo(sampleEnded);
+    }
+
+    if (forceFull.onStableVelo) {
+      forceFull.onStableVelo(fullEnded);
+    }
+
+    // Set default value for interpDistanceFn if not been specified yet
+    if (interpDistanceFn === undefined) {
+      if (forceFull.distance === 'function') {
+        interpDistanceFn = forceFull.distance();
+      } else {
+        interpDistanceFn = constant(300);
+      }
+    }
+  }
+
+  function initSimulation () {
+    nodes = simulation.nodes();
+    simulation
+      .stop()
+      .alphaDecay(0)
+      .alpha(1);
+
+    let sets = takeSampleFrom(nodes, Math.sqrt(nodes.length));
+    sample = sets.sample;
+    remainder = sets.remainder;
+  }
+
+  // Sample simulation ticked 1 frame, keep track of number of iterations here.
+  function sampleTick () {
+    event.call('sampleTick');
+    if (alreadyRanIterations++ >= SAMPLE_ITERATIONS) {
+      sampleEnded();
+    }
+  }
+
+  // Full simulation ticked 1 frame, keep track of number of iterations here.
+  function fullTick () {
+    event.call('fullTick');
+    if (alreadyRanIterations++ >= FULL_ITERATIONS) {
+      fullEnded();
+    }
+  }
+
+  function fullEnded () {
+    simulation.stop();
+    initAlready = false;
+    simulation.force('Full force', null);
+    event.call('end');
+  }
+
+  function sampleEnded () {
+    simulation.stop();
+    simulation.force('Sample force', null);
+    // Reset velocity of all nodes
+    for (let i = sample.length - 1; i >= 0; i--) {
+      sample[i].vx = 0;
+      sample[i].vy = 0;
+    }
+
+    event.call('startInterp');
+    if (NUM_PIVOTS >= 1) {
+      interpolationPivots(sample, remainder, NUM_PIVOTS, interpDistanceFn, INTERP_FINE_ITS);
+    } else {
+      interpBruteForce(sample, remainder, interpDistanceFn, INTERP_FINE_ITS);
+    }
+
+    event.call('fullTick');
+    alreadyRanIterations = 0;
+    simulation
+      .on('tick', null)
+      .on('end', null) // The ending condition should be iterations count
+      .nodes(nodes);
+
+    if (FULL_ITERATIONS < 1 || forceF === undefined || forceF === null) {
+      event.call('end');
+      return;
+    }
+    simulation
+      .on('tick', fullTick)
+      .force('Full force', forceFull)
+      .restart();
+  }
+
+  return hybrid = {
+    restart: function () {
+      if (!initAlready) initialize();
+      simulation.restart();
+      return hybrid;
+    },
+
+    stop: function () {
+      simulation.stop();
+      return hybrid;
+    },
+
+    numPivots: function (_) {
+      return arguments.length ? (NUM_PIVOTS = +_, hybrid) : NUM_PIVOTS;
+    },
+
+    sampleIterations: function (_) {
+      return arguments.length ? (SAMPLE_ITERATIONS = +_, hybrid) : SAMPLE_ITERATIONS;
+    },
+
+    fullIterations: function (_) {
+      return arguments.length ? (FULL_ITERATIONS = +_, hybrid) : FULL_ITERATIONS;
+    },
+
+    interpFindTuneIts: function (_) {
+      return arguments.length ? (INTERP_FINE_ITS = +_, hybrid) : INTERP_FINE_ITS;
+    },
+
+    on: function (name, _) {
+      return arguments.length > 1 ? (event.on(name, _), hybrid) : event.on(name);
+    },
+
+    subSet: function (_) {
+      return arguments.length ? (sample = _, hybrid) : sample;
+    },
+
+    nonSubSet: function (_) {
+      return arguments.length ? (remainder = _, hybrid) : remainder;
+    },
+
+    interpDistanceFn: function (_) {
+      return arguments.length ? (interpDistanceFn = typeof _ === 'function' ? _ : constant(+_), hybrid) : interpDistanceFn;
+    },
+
+    simulation: function (_) {
+      return arguments.length ? (initAlready = false, simulation = _, hybrid) : simulation;
+    },
+
+    forceSample: function (_) {
+      return arguments.length ? (forceSample = _, initForces(), hybrid) : forceSample;
+    },
+
+    forceFull: function (_) {
+      return arguments.length ? (forceFull = _, initForces(), hybrid) : forceFull;
+    }
+
+  };
+}

src/interpolation/helpers.js

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

src/interpolation/interpBruteForce.js

@@ -1,48 +1,47 @@
-import {pointOnCircle, takeSampleFrom} from "./helpers";
-import {placeNearToNearestNeighbour} from "./interpCommon";
-
-/**
- * Perform interpolation where the "parent" node is found by brute-force.
- * A "parent" of a node to be interpolated is a node whose position in 2D space
- * is already known and have the least high-dimensional distance to the node in
- * question.
- * For each point to be interpolated:
- * - Phase 1: find the "parent" by comparing high-d distance against every
-              points already plotted on the graph.
-              this is essentially a nearest neighbour finding problem.
- * - Phase 2 and 3 are passed onto placeNearToNearestNeighbour
- * @param  {list} sampleSet      - nodes already plotted on the 2D graph
- * @param  {list} remainderSet   - nodes to be interpolated onto the graph
- * @param  {function} distanceFn - f(nodex, nodey) that calculate high-dimensional
- *                                 distance between 2 nodes
- * @param  {number} endingIts    - for phase 3, how many iterations to refine the
- *                                 placement of each interpolated point
- */
-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--) {
-    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--) {
-      sample = sampleSet[j];
-      dist = distanceFn(node, sample);
-      if (nearestSample === undefined || dist < minDist) {
-        minDist = dist;
-        nearestSample = sample;
-      }
-
-      index = sampleSubset.indexOf(sample);
-      if (index !== -1)
-        sampleSubsetDistanceCache[index] = dist;
-    }
-
-    placeNearToNearestNeighbour(node, nearestSample, minDist, sampleSubset, sampleSubsetDistanceCache, endingIts);
-  }
-}
+import {takeSampleFrom} from './helpers';
+import {placeNearToNearestNeighbour} from './interpCommon';
+
+/**
+ * Perform interpolation where the "parent" node is found by brute-force.
+ * A "parent" of a node to be interpolated is a node whose position in 2D space
+ * is already known and have the least high-dimensional distance to the node in
+ * question.
+ * For each point to be interpolated:
+ * - Phase 1: find the "parent" by comparing high-d distance against every
+              points already plotted on the graph.
+              this is essentially a nearest neighbour finding problem.
+ * - Phase 2 and 3 are passed onto placeNearToNearestNeighbour
+ * @param  {list} sampleSet      - nodes already plotted on the 2D graph
+ * @param  {list} remainderSet   - nodes to be interpolated onto the graph
+ * @param  {function} distanceFn - f(nodex, nodey) that calculate high-dimensional
+ *                                 distance between 2 nodes
+ * @param  {number} endingIts    - for phase 3, how many iterations to refine the
+ *                                 placement of each interpolated point
+ */
+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--) {
+    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--) {
+      sample = sampleSet[j];
+      dist = distanceFn(node, sample);
+      if (nearestSample === undefined || dist < minDist) {
+        minDist = dist;
+        nearestSample = sample;
+      }
+
+      index = sampleSubset.indexOf(sample);
+      if (index !== -1) { sampleSubsetDistanceCache[index] = dist; }
+    }
+
+    placeNearToNearestNeighbour(node, nearestSample, minDist, sampleSubset, sampleSubsetDistanceCache, endingIts);
+  }
+}

src/interpolation/interpCommon.js

@@ -1,5 +1,5 @@
-import {pointOnCircle, sumDistError} from "./helpers";
-import jiggle from "../jiggle";
+import {pointOnCircle, sumDistError} from './helpers';
+import jiggle from '../jiggle';
 
 /**
  * Phase 2 and 3 of each node to be interpolated.
@@ -24,9 +24,9 @@ import jiggle from "../jiggle";
                                      index must correspond to sampleSubset
  * @param  {Integer} endingIts     - Number of iterations for phase 3
  */
-export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleSubset, realDistances, endingIts) {
+export function placeNearToNearestNeighbour (node, nearNeighbour, radius, sampleSubset, realDistances, endingIts) {
   let
-    sumDistErrorByAngle = function(angle){
+    sumDistErrorByAngle = function (angle) {
       return sumDistError(pointOnCircle(nearNeighbour.x, nearNeighbour.y, angle, radius), sampleSubset, realDistances);
     },
     dist0 = sumDistErrorByAngle(0),
@@ -36,17 +36,11 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
     lowBound = 0.0,
     highBound = 0.0;
 
-  // Determine the closest quadrant
-  if (dist0 == dist180) {
-    if (dist90 > dist270)
-      lowBound = highBound = 270;
-    else
-      lowBound = highBound = 90;
-  } else if (dist90 == dist270) {
-    if (dist0 > dist180)
-      lowBound = highBound = 180;
-    else
-      lowBound = highBound = 0;
+    // Determine the closest quadrant
+  if (dist0 === dist180) {
+    if (dist90 > dist270) { lowBound = highBound = 270; } else { lowBound = highBound = 90; }
+  } else if (dist90 === dist270) {
+    if (dist0 > dist180) { lowBound = highBound = 180; } else { lowBound = highBound = 0; }
   } else if (dist0 > dist180) {
     if (dist90 > dist270) {
       lowBound = 180;
@@ -55,39 +49,37 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
       lowBound = 90;
       highBound = 180;
     }
+  } else if (dist90 > dist270) {
+    lowBound = 270;
+    highBound = 360;
   } else {
-    if (dist90 > dist270) {
-      lowBound = 270;
-      highBound = 360;
-    } else {
-      lowBound = 0;
-      highBound = 90;
-    }
+    lowBound = 0;
+    highBound = 90;
   }
 
   // Determine the angle
-  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;
+    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

@@ -1,135 +1,136 @@
-import {pointOnCircle, takeSampleFrom} from "./helpers";
-import {placeNearToNearestNeighbour} from "./interpCommon";
-
-/**
- * Perform interpolation where the "parent" node is is estimated by pivot-based searching.
- * - Pre-processing: assign random samples as pivots,
- *                   put the others in each pivot's bucket.
- *                   ie. a non-pivot sample X may be in
- *                   - bucket 3 of pivot A,
- *                   - bucket 1 of pivot B,
- *                   - bucket 5 of pivot C,
- *                   all at the same time
- * For each point to be interpolated:
- * - Phase 1: for each pivot: compare distance against the pivot
- *                            compare against other points in the same bucket of that pivot
- *            note down the parent found
- *            this is essentially a near neighbour estimation problem.
- * - Phase 2 and 3 are passed onto placeNearToNearestNeighbour
- * @param  {list} sampleSet      - nodes already plotted on the 2D graph
- * @param  {list} remainderSet   - nodes to be interpolated onto the graph
- * @param  {function} distanceFn - f(nodex, nodey) that calculate high-dimensional
- *                                 distance between 2 nodes
- * @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) {
-  // Pivot based parent finding
-  let numBuckets = Math.floor(Math.sqrt(sampleSet.length));
-  let numNonPivots = sampleSet.length - numPivots;
-  let sets = takeSampleFrom(sampleSet, numPivots);
-  let pivots = sets.sample;
-  let nonPivotSamples = sets.remainder;
-
-  let pivotsBuckets = []; // [ For each Pivot:[For each bucket:[each point in bucket]] ]
-  for (let i = 0; i < numPivots; i++) {
-    pivotsBuckets[i] = [];
-    for (let j = 0; j < numBuckets; j++) {
-      pivotsBuckets[i][j] = [];
-    }
-  }
-
-  // Pre-calculate distance between each non-pivot sample to each pivot
-  // At the same time, determine the bucket width for each pivot based on furthest non-pivot sample
-  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++)
-    distCache[i] = [];
-
-  for (let j = 0; j < numPivots; j++) {
-    let pivot = pivots[j];
-    let maxDist = -1;
-
-    for (let i = 0; i < numNonPivots; i++) {
-      let sample = nonPivotSamples[i];
-      distCache[i][j] = distanceFn(pivot, sample);
-      if (distCache[i][j] > maxDist)
-        maxDist = distCache[i][j];
-    }
-
-    bucketWidths.push(maxDist / numBuckets);
-  }
-
-  // Put samples (pivot not included) into buckets
-  for (let j = 0; j < numPivots; j++) {
-    let bucketWidth = bucketWidths[j];
-    for (let i = 0; i < numNonPivots; i++) {
-      let sample = nonPivotSamples[i];
-      let bucketNumber = Math.floor(distCache[i][j] / bucketWidth);
-      if (bucketNumber >= numBuckets) {
-        bucketNumber = numBuckets - 1;
-      } else if (bucketNumber < 0) {  // Should never be negative anyway
-        bucketNumber = 0;
-      }
-      pivotsBuckets[j][bucketNumber].push(sample);
-    }
-  }
-  // ---------------------------------------------------------------------
-
-
-  let sampleSubset = takeSampleFrom(sampleSet, Math.sqrt(sampleSet.length)).sample;
-  //Plot each of the remainder nodes
-  for (let i = remainderSet.length-1; i>=0; i--) {
-    let node = remainderSet[i];
-    let sampleSubsetDistanceCache = [],
-      minDist, nearSample;
-
-    // Pivot based parent search
-    for (let p = 0; p < numPivots; p++) {
-      let pivot = pivots[p];
-      let bucketWidth = bucketWidths[p];
-
-      let dist = distanceFn(node, pivot);
-      let index = sampleSubset.indexOf(pivot);
-      if (index !== -1) {
-        sampleSubsetDistanceCache[index] = dist;
-      }
-      if (minDist === undefined || dist < minDist){
-        minDist = dist;
-        nearSample = pivot;
-      }
-
-      let bucketNumber = Math.floor(dist / bucketWidth);
-      if (bucketNumber >= numBuckets) {
-        bucketNumber = numBuckets - 1;
-      } else if (bucketNumber < 0) {  // Should never be negative anyway
-        bucketNumber = 0;
-      }
-
-      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 {
-          dist = distanceFn(candidateNode, node);
-          if (index !== -1)
-            sampleSubsetDistanceCache[index] = dist;
-        }
-
-        if (dist < minDist){
-          minDist = dist;
-          nearSample = candidateNode;
-        }
-      }
-    }
-
-    // Fill in holes in cache
-    for (let k = 0; k < sampleSubset.length; k++) {
-      if (sampleSubsetDistanceCache[k] === undefined)
-        sampleSubsetDistanceCache[k] = distanceFn(node, sampleSubset[k]);
-    }
-    placeNearToNearestNeighbour(node, nearSample, minDist, sampleSubset, sampleSubsetDistanceCache, endingIts);
-  }
-}
+import {takeSampleFrom} from './helpers';
+import {placeNearToNearestNeighbour} from './interpCommon';
+
+/**
+ * Perform interpolation where the "parent" node is is estimated by pivot-based searching.
+ * - Pre-processing: assign random samples as pivots,
+ *                   put the others in each pivot's bucket.
+ *                   ie. a non-pivot sample X may be in
+ *                   - bucket 3 of pivot A,
+ *                   - bucket 1 of pivot B,
+ *                   - bucket 5 of pivot C,
+ *                   all at the same time
+ * For each point to be interpolated:
+ * - Phase 1: for each pivot: compare distance against the pivot
+ *                            compare against other points in the same bucket of that pivot
+ *            note down the parent found
+ *            this is essentially a near neighbour estimation problem.
+ * - Phase 2 and 3 are passed onto placeNearToNearestNeighbour
+ * @param  {list} sampleSet      - nodes already plotted on the 2D graph
+ * @param  {list} remainderSet   - nodes to be interpolated onto the graph
+ * @param  {function} distanceFn - f(nodex, nodey) that calculate high-dimensional
+ *                                 distance between 2 nodes
+ * @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) {
+  // Pivot based parent finding
+  let numBuckets = Math.floor(Math.sqrt(sampleSet.length));
+  let numNonPivots = sampleSet.length - numPivots;
+  let sets = takeSampleFrom(sampleSet, numPivots);
+  let pivots = sets.sample;
+  let nonPivotSamples = sets.remainder;
+
+  let pivotsBuckets = []; // [ For each Pivot:[For each bucket:[each point in bucket]] ]
+  for (let i = 0; i < numPivots; i++) {
+    pivotsBuckets[i] = [];
+    for (let j = 0; j < numBuckets; j++) {
+      pivotsBuckets[i][j] = [];
+    }
+  }
+
+  // Pre-calculate distance between each non-pivot sample to each pivot
+  // At the same time, determine the bucket width for each pivot based on furthest non-pivot sample
+  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++) {
+    distCache[i] = [];
+  }
+
+  for (let j = 0; j < numPivots; j++) {
+    let pivot = pivots[j];
+    let maxDist = -1;
+
+    for (let i = 0; i < numNonPivots; i++) {
+      let sample = nonPivotSamples[i];
+      distCache[i][j] = distanceFn(pivot, sample);
+      if (distCache[i][j] > maxDist) {
+        maxDist = distCache[i][j];
+      }
+    }
+
+    bucketWidths.push(maxDist / numBuckets);
+  }
+
+  // Put samples (pivot not included) into buckets
+  for (let j = 0; j < numPivots; j++) {
+    let bucketWidth = bucketWidths[j];
+    for (let i = 0; i < numNonPivots; i++) {
+      let sample = nonPivotSamples[i];
+      let bucketNumber = Math.floor(distCache[i][j] / bucketWidth);
+      if (bucketNumber >= numBuckets) {
+        bucketNumber = numBuckets - 1;
+      } else if (bucketNumber < 0) { // Should never be negative anyway
+        bucketNumber = 0;
+      }
+      pivotsBuckets[j][bucketNumber].push(sample);
+    }
+  }
+  // ---------------------------------------------------------------------
+
+  let sampleSubset = takeSampleFrom(sampleSet, Math.sqrt(sampleSet.length)).sample;
+  // Plot each of the remainder nodes
+  for (let i = remainderSet.length - 1; i >= 0; i--) {
+    let node = remainderSet[i];
+    let sampleSubsetDistanceCache = [],
+      minDist, nearSample;
+
+    // Pivot based parent search
+    for (let p = 0; p < numPivots; p++) {
+      let pivot = pivots[p];
+      let bucketWidth = bucketWidths[p];
+
+      let dist = distanceFn(node, pivot);
+      let index = sampleSubset.indexOf(pivot);
+      if (index !== -1) {
+        sampleSubsetDistanceCache[index] = dist;
+      }
+      if (minDist === undefined || dist < minDist) {
+        minDist = dist;
+        nearSample = pivot;
+      }
+
+      let bucketNumber = Math.floor(dist / bucketWidth);
+      if (bucketNumber >= numBuckets) {
+        bucketNumber = numBuckets - 1;
+      } else if (bucketNumber < 0) { // Should never be negative anyway
+        bucketNumber = 0;
+      }
+
+      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 {
+          dist = distanceFn(candidateNode, node);
+          if (index !== -1) { sampleSubsetDistanceCache[index] = dist; }
+        }
+
+        if (dist < minDist) {
+          minDist = dist;
+          nearSample = candidateNode;
+        }
+      }
+    }
+
+    // Fill in holes in cache
+    for (let k = 0; k < sampleSubset.length; k++) {
+      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

@@ -1,107 +1,110 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
-
-/**
- * Modified link force algorithm
- * - simplify calculations for parameters locked for spring model
- * - replace the use of links {} with loop. greatly reduce memory usage
- * - removed other unused functions
- * Alpha should be constant 1 for accurate simulation
- */
-export default function() {
-  var dataSizeFactor,
-      distance = constant(30),
-      distances = [],
-      nodes,
-      stableVelocity = 0,
-      stableVeloHandler = null,
-      latestVelocityDiff = 0,
-      iterations = 1;
-
-  function force(alpha) {
-    let n = nodes.length;
-    // Cache old velocity for comparison later
-    if (stableVeloHandler!==null && stableVelocity>=0) {
-      for (let i = n-1, node; i>=0; i--) {
-        node = nodes[i];
-        node.oldvx = node.vx;
-        node.oldvy = node.vy;
-      }
-    }
-    // Each iteration in a tick
-    for (var k = 0, source, target, i, j, x, y, l; k < iterations; ++k) {
-      // For each link
-      for (i = 1; i < n; i++) for (j = 0; j < i; j++) {
-        // jiggle so l won't be zero and divide by zero error after this
-        source = nodes[i];
-        target = nodes[j];
-        x = target.x + target.vx - source.x - source.vx || jiggle();
-        y = target.y + target.vy - source.y - source.vy || jiggle();
-        l = Math.sqrt(x * x + y * y);
-        l = (l - distances[i*(i-1)/2+j]) / l  * dataSizeFactor * alpha;
-        x *= l, y *= l;
-        target.vx -= x;
-        target.vy -= y;
-        source.vx += x;
-        source.vy += y;
-      }
-    }
-
-    // Calculate velocity changes, aka force applied.
-    if (stableVeloHandler!==null && stableVelocity>=0) {
-      let velocityDiff = 0;
-      for (let i = n-1, node; i>=0; i--) {
-        node = nodes[i];
-        velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
-      }
-      velocityDiff /= n*(n-1);
-      latestVelocityDiff = velocityDiff;
-
-      if(velocityDiff<stableVelocity){
-        stableVeloHandler();
-      }
-    }
-  }
-
-  function initialize() {
-    if (!nodes) return;
-    // 0.5 to divide the force to two part for source and target node
-    dataSizeFactor = 0.5/(nodes.length-1);
-    initializeDistance();
-  }
-
-  function initializeDistance() {
-    if (!nodes) return;
-    for (let i = 1, n = nodes.length; i < n; i++) {
-      for (let j = 0; j < i; j++) {
-        distances.push(distance(nodes[i], nodes[j]));
-      }
-    }
-  }
-
-  force.initialize = function(_) {
-    nodes = _;
-    initialize();
-  };
-
-  force.iterations = function(_) {
-    return arguments.length ? (iterations = +_, force) : iterations;
-  };
-
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), initializeDistance(), force) : distance;
-  };
-
-  force.latestAccel = function () {
-    return latestVelocityDiff;
-  };
-
-  force.onStableVelo = function (_) {
-    return arguments.length ? (stableVeloHandler = _, force) : stableVeloHandler;
-  };
-
-  force.stableVelocity = function (_) {
-    return arguments.length ? (stableVelocity = _, force) : stableVelocity;
-  };
-  return force;
-}
+import constant from './constant';
+import jiggle from './jiggle';
+
+/**
+ * Modified link force algorithm
+ * - simplify calculations for parameters locked for spring model
+ * - replace the use of links {} with loop. greatly reduce memory usage
+ * - removed other unused functions
+ * Alpha should be constant 1 for accurate simulation
+ */
+export default function () {
+  var dataSizeFactor,
+    distance = constant(30),
+    distances = [],
+    nodes,
+    stableVelocity = 0,
+    stableVeloHandler = null,
+    latestVelocityDiff = 0,
+    iterations = 1;
+
+  function force (alpha) {
+    let n = nodes.length;
+    // Cache old velocity for comparison later
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
+      for (let i = n - 1, node; i >= 0; i--) {
+        node = nodes[i];
+        node.oldvx = node.vx;
+        node.oldvy = node.vy;
+      }
+    }
+
+    // Each iteration in a tick
+    for (var k = 0, source, target, i, j, x, y, l; k < iterations; ++k) {
+      // For each link
+      for (i = 1; i < n; i++) {
+        for (j = 0; j < i; j++) {
+        // jiggle so l won't be zero and divide by zero error after this
+          source = nodes[i];
+          target = nodes[j];
+          x = target.x + target.vx - source.x - source.vx || jiggle();
+          y = target.y + target.vy - source.y - source.vy || jiggle();
+          l = Math.sqrt(x * x + y * y);
+          l = (l - distances[i * (i - 1) / 2 + j]) / l * dataSizeFactor * alpha;
+          x *= l; y *= l;
+          target.vx -= x;
+          target.vy -= y;
+          source.vx += x;
+          source.vy += y;
+        }
+      }
+    }
+
+    // Calculate velocity changes, aka force applied.
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
+      let velocityDiff = 0;
+      for (let i = n - 1, node; i >= 0; i--) {
+        node = nodes[i];
+        velocityDiff += Math.abs(Math.hypot(node.vx - node.oldvx, node.vy - node.oldvy));
+      }
+      velocityDiff /= n;
+      latestVelocityDiff = velocityDiff;
+
+      if (velocityDiff < stableVelocity) {
+        stableVeloHandler();
+      }
+    }
+  }
+
+  function initialize () {
+    if (!nodes) return;
+    // 0.5 to divide the force to two part for source and target node
+    dataSizeFactor = 0.5 / (nodes.length - 1);
+    initializeDistance();
+  }
+
+  function initializeDistance () {
+    if (!nodes) return;
+    for (let i = 1, n = nodes.length; i < n; i++) {
+      for (let j = 0; j < i; j++) {
+        distances.push(distance(nodes[i], nodes[j]));
+      }
+    }
+  }
+
+  force.initialize = function (_) {
+    nodes = _;
+    initialize();
+  };
+
+  force.iterations = function (_) {
+    return arguments.length ? (iterations = +_, force) : iterations;
+  };
+
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), initializeDistance(), force) : distance;
+  };
+
+  force.latestAccel = function () {
+    return latestVelocityDiff;
+  };
+
+  force.onStableVelo = function (_) {
+    return arguments.length ? (stableVeloHandler = _, force) : stableVeloHandler;
+  };
+
+  force.stableVelocity = function (_) {
+    return arguments.length ? (stableVelocity = _, force) : stableVelocity;
+  };
+  return force;
+}

src/neighbourSampling.js

@@ -1,206 +1,203 @@
-import constant from "./constant";
-import jiggle from "./jiggle";
-import {getStress} from "./stress";
-
-/**
- * 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) {
-  return b[1] - a[1];
-}
-
-export default function () {
-  var neighbours = [],
-    distance = constant(300),
-    nodes,
-    neighbourSize = 10,
-    sampleSize = 10,
-    stableVelocity = 0,
-    stableVeloHandler = null,
-    dataSizeFactor,
-    latestVelocityDiff = 0;
-
-  /**
-   * Apply spring forces at each simulation iteration.
-   * @param  {number} alpha - multiplier for amount of force applied
-   */
-  function force(alpha) {
-    let n = nodes.length;
-    // Cache old velocity for comparison later
-    if (stableVeloHandler!==null && stableVelocity>=0) {
-      for (let i = n-1, node; i>=0; i--) {
-        node = nodes[i];
-        node.oldvx = node.vx;
-        node.oldvy = node.vy;
-      }
-    }
-
-    for (let i = n-1, node, samples; i>=0; i--) {
-      node = nodes[i];
-      samples = createRandomSamples(i);
-
-      for (let [neighbourID, highDDist] of neighbours[i]) {
-        setVelocity(node, nodes[neighbourID], highDDist, alpha);
-      }
-
-      for (let [sampleID, highDDist] of samples) {
-        setVelocity(node, nodes[sampleID], highDDist, alpha);
-      }
-
-      neighbours[i] = findNewNeighbours(neighbours[i], samples);
-    }
-
-    // Calculate velocity changes, aka force applied.
-    if (stableVeloHandler!==null && stableVelocity>=0) {
-      let velocityDiff = 0;
-      for (let i = n-1, node; i>=0; i--) {
-        node = nodes[i];
-        velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
-      }
-      velocityDiff /= n*(neighbourSize+sampleSize);
-      latestVelocityDiff = velocityDiff;
-
-      if(velocityDiff<stableVelocity){
-        stableVeloHandler();
-      }
-    }
-  }
-
-  /**
-   * Apply force to both source and target nodes.
-   * @param {number} source  - source node object
-   * @param {number} target  - target node object
-   * @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) {
-    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;
-    // Set the calculated velocites for both nodes.
-    target.vx -= x;
-    target.vy -= y;
-    source.vx += x;
-    source.vy += y;
-  }
-
-  // Called on nodes change and added to a simulation
-  function initialize() {
-    if (!nodes) return;
-
-    // Initialize for each node some random neighbours.
-    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));
-    }
-
-    initDataSizeFactor();
-  }
-
-  function initDataSizeFactor(){
-    dataSizeFactor = 0.5/(neighbourSize+sampleSize);
-  }
-
-  /**
-   * Generates an array of array[index, high-d distance to the node of index]
-   * where all indices are different and the size is as specified unless
-   * impossible (may be due to too large size requested)
-   * @param  {number} index  - index of a node to calculate distance against
-   * @param  {array} exclude - indices of the nodes to ignore.
-   * @param  {number} size   - max number of elements in the map to return.
-   * @return {array}
-   */
-  function pickRandomNodesFor(index, exclude, size) {
-    let randElements = [];
-    let max = nodes.length;
-
-    for (let i = 0; i < size; i++) {
-      // Stop when no new elements can be found.
-      if (randElements.length + exclude.length >= nodes.length) {
-        break;
-      }
-
-      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));
-      }
-      randElements.push(rand);
-    }
-    for(let i=randElements.length-1, rand; i>=0; i--){
-      rand = randElements[i];
-      randElements[i] = [rand, distance(nodes[index], nodes[rand])];
-    }
-    return randElements;
-  }
-
-  /**
-   * Generates a map {index: high-dimensional distance to the node of index}
-   * to be used as samples set for the node of the specified index.
-   * @param  {number} index - index of the node to generate sample for
-   * @return {map}
-   */
-  function createRandomSamples(index) {
-    // Ignore the current neighbours of the node and itself.
-    let exclude = [index];
-    exclude = exclude.concat(Array.from(neighbours[index].keys()));
-    return new Map(pickRandomNodesFor(index, exclude, sampleSize));
-  }
-
-  /**
-   * Compares the elements from sample set to the neighbour set and replaces the
-   * elements in the neighbour set if any better neighbours are found.
-   * @param  {map} neighbours - map of neighbours
-   * @param  {map} samples    - map of samples
-   * @return {map}            - new map of neighbours
-   */
-  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 = _;
-    initialize();
-  };
-
-  force.neighbourSize = function (_) {
-    return arguments.length ? (neighbourSize = +_, initialize(), force) : neighbourSize;
-  };
-
-  force.neighbours = function () {
-    return neighbours;
-  };
-
-  force.sampleSize = function (_) {
-    return arguments.length ? (sampleSize = +_, initDataSizeFactor(), force) : sampleSize;
-  };
-
-  force.distance = function (_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
-  };
-
-  force.latestAccel = function () {
-    return latestVelocityDiff;
-  };
-
-  force.onStableVelo = function (_) {
-    return arguments.length ? (stableVeloHandler = _, force) : stableVeloHandler;
-  };
-
-  force.stableVelocity = function (_) {
-    return arguments.length ? (stableVelocity = _, force) : stableVelocity;
-  };
-  return force;
-}
+import constant from './constant';
+import jiggle from './jiggle';
+/**
+ * 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) {
+  return b[1] - a[1];
+}
+
+export default function () {
+  var neighbours = [],
+    distance = constant(300),
+    nodes,
+    neighbourSize = 10,
+    sampleSize = 10,
+    stableVelocity = 0,
+    stableVeloHandler = null,
+    dataSizeFactor,
+    latestVelocityDiff = 0;
+
+    /**
+   * Apply spring forces at each simulation iteration.
+   * @param  {number} alpha - multiplier for amount of force applied
+   */
+  function force (alpha) {
+    let n = nodes.length;
+    // Cache old velocity for comparison later
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
+      for (let i = n - 1, node; i >= 0; i--) {
+        node = nodes[i];
+        node.oldvx = node.vx;
+        node.oldvy = node.vy;
+      }
+    }
+
+    for (let i = n - 1, node, samples; i >= 0; i--) {
+      node = nodes[i];
+      samples = createRandomSamples(i);
+
+      for (let [neighbourID, highDDist] of neighbours[i]) {
+        setVelocity(node, nodes[neighbourID], highDDist, alpha);
+      }
+
+      for (let [sampleID, highDDist] of samples) {
+        setVelocity(node, nodes[sampleID], highDDist, alpha);
+      }
+
+      neighbours[i] = findNewNeighbours(neighbours[i], samples);
+    }
+
+    // Calculate velocity changes, aka force applied.
+    if (stableVeloHandler !== null && stableVelocity >= 0) {
+      let velocityDiff = 0;
+      for (let i = n - 1, node; i >= 0; i--) {
+        node = nodes[i];
+        velocityDiff += Math.abs(Math.hypot(node.vx - node.oldvx, node.vy - node.oldvy));
+      }
+      velocityDiff /= n;
+      latestVelocityDiff = velocityDiff;
+
+      if (velocityDiff < stableVelocity) {
+        stableVeloHandler();
+      }
+    }
+  }
+
+  /**
+   * Apply force to both source and target nodes.
+   * @param {number} source  - source node object
+   * @param {number} target  - target node object
+   * @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) {
+    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;
+    // Set the calculated velocites for both nodes.
+    target.vx -= x;
+    target.vy -= y;
+    source.vx += x;
+    source.vy += y;
+  }
+
+  // Called on nodes change and added to a simulation
+  function initialize () {
+    if (!nodes) return;
+
+    // Initialize for each node some random neighbours.
+    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));
+    }
+
+    initDataSizeFactor();
+  }
+
+  function initDataSizeFactor () {
+    dataSizeFactor = 0.5 / (neighbourSize + sampleSize);
+  }
+
+  /**
+   * Generates an array of array[index, high-d distance to the node of index]
+   * where all indices are different and the size is as specified unless
+   * impossible (may be due to too large size requested)
+   * @param  {number} index  - index of a node to calculate distance against
+   * @param  {array} exclude - indices of the nodes to ignore.
+   * @param  {number} size   - max number of elements in the map to return.
+   * @return {array}
+   */
+  function pickRandomNodesFor (index, exclude, size) {
+    let randElements = [];
+    let max = nodes.length;
+
+    for (let i = 0; i < size; i++) {
+      // Stop when no new elements can be found.
+      if (randElements.length + exclude.length >= nodes.length) {
+        break;
+      }
+
+      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);
+      }
+      randElements.push(rand);
+    }
+    for (let i = randElements.length - 1, rand; i >= 0; i--) {
+      rand = randElements[i];
+      randElements[i] = [rand, distance(nodes[index], nodes[rand])];
+    }
+    return randElements;
+  }
+
+  /**
+   * Generates a map {index: high-dimensional distance to the node of index}
+   * to be used as samples set for the node of the specified index.
+   * @param  {number} index - index of the node to generate sample for
+   * @return {map}
+   */
+  function createRandomSamples (index) {
+    // Ignore the current neighbours of the node and itself.
+    let exclude = [index];
+    exclude = exclude.concat(Array.from(neighbours[index].keys()));
+    return new Map(pickRandomNodesFor(index, exclude, sampleSize));
+  }
+
+  /**
+   * Compares the elements from sample set to the neighbour set and replaces the
+   * elements in the neighbour set if any better neighbours are found.
+   * @param  {map} neighbours - map of neighbours
+   * @param  {map} samples    - map of samples
+   * @return {map}            - new map of neighbours
+   */
+  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 = _;
+    initialize();
+  };
+
+  force.neighbourSize = function (_) {
+    return arguments.length ? (neighbourSize = +_, initialize(), force) : neighbourSize;
+  };
+
+  force.neighbours = function () {
+    return neighbours;
+  };
+
+  force.sampleSize = function (_) {
+    return arguments.length ? (sampleSize = +_, initDataSizeFactor(), force) : sampleSize;
+  };
+
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
+  };
+
+  force.latestAccel = function () {
+    return latestVelocityDiff;
+  };
+
+  force.onStableVelo = function (_) {
+    return arguments.length ? (stableVeloHandler = _, force) : stableVeloHandler;
+  };
+
+  force.stableVelocity = function (_) {
+    return arguments.length ? (stableVelocity = _, force) : stableVelocity;
+  };
+  return force;
+}

src/stress.js

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

src/t-sne.js

@@ -1,374 +1,374 @@
-import constant from "./constant";
-
-/**
- * Set the node id accessor to the specified i.
- * @param  {node} d - node.
- * @param  {accessor} i - id accessor.
- * @return {accessor}  - node id accessor.
- */
-function index(d, i) {
-  return i;
-}
-
-/**
- * t-SNE implementation in D3 by using the code existing in tsnejs
- * (https://github.com/karpathy/tsnejs) to compute the solution.
- */
-export default function() {
-  var id = index,
-    distance = constant(300),
-    nodes,
-    perplexity = 30,
-    learningRate = 10,
-    iteration = 0,
-    dim = 2,
-    N, // length of the nodes.
-    P, // probability matrix.
-    Y, // solution.
-    gains,
-    ystep;
-
-  /**
-   * Make a step in t-SNE algorithm and set the velocities for the nodes
-   * to accumulate the values from solution.
-   */
-  function force() {
-    // Make a step at each iteration.
-    step();
-    var solution = getSolution();
-
-    // Set the velocity for each node using the solution.
-    for (var i = 0; i < nodes.length; i++) {
-      nodes[i].vx += solution[i][0];
-      nodes[i].vy += solution[i][1];
-    }
-  }
-
-  /**
-   * Calculates the random number from Gaussian distribution.
-   * @return {number} random number.
-   */
-  function gaussRandom() {
-    let u = 2 * Math.random() - 1;
-    let v = 2 * Math.random() - 1;
-    let r = u * u + v * v;
-    if (r == 0 || r > 1) return gaussRandom();
-    return u * Math.sqrt(-2 * Math.log(r) / r);
-  }
-
-  /**
-   * Return the normalized number.
-   * @return {number} normalized random number from Gaussian distribution.
-   */
-  function randomN() {
-    return gaussRandom() * 1e-4;
-  }
-
-  function sign(x) {
-    return x > 0 ? 1 : x < 0 ? -1 : 0;
-  }
-
-  /**
-   * Create an array of length n filled with zeros.
-   * @param  {number} n - length of array.
-   * @return {Float64Array}   - array of zeros with length n.
-   */
-  function zeros(n) {
-    if (typeof(n) === 'undefined' || isNaN(n)) {
-      return [];
-    }
-    return new Float64Array(n); // typed arrays are faster
-  }
-
-  // Returns a 2d array of random numbers
-  /**
-   * Creates a 2d array filled with random numbers.
-   * @param  {number} n - rows.
-   * @param  {number} d - columns.
-   * @return {array}   - 2d array
-   */
-  function random2d(n, d) {
-    var x = [];
-    for (var i = 0; i < n; i++) {
-      var y = [];
-      for (var j = 0; j < d; j++) {
-        y.push(randomN());
-      }
-      x.push(y);
-    }
-    return x;
-  }
-
-  /**
-   * Compute the probability matrix using the provided data.
-   * @param  {array} data       - nodes.
-   * @param  {number} perplexity - used to calculate entropy of distribution.
-   * @param  {number} tol        - limit for entropy difference.
-   * @return {2d array}          - 2d matrix containing probabilities.
-   */
-  function d2p(data, perplexity, tol) {
-    N = Math.floor(data.length);
-    var Htarget = Math.log(perplexity); // target entropy of distribution.
-    var P1 = zeros(N * N); // temporary probability matrix.
-
-    var prow = zeros(N); // a temporary storage compartment.
-    for (var i = 0; i < N; i++) {
-      var betamin = -Infinity;
-      var betamax = Infinity;
-      var beta = 1; // initial value of precision.
-      var done = false;
-      var maxtries = 50;
-
-      // Perform binary search to find a suitable precision beta
-      // so that the entropy of the distribution is appropriate.
-      var num = 0;
-      while (!done) {
-        // Compute entropy and kernel row with beta precision.
-        var psum = 0.0;
-        for (var j = 0; j < N; j++) {
-          var pj = Math.exp(-distance(data[i], data[j]) * beta);
-          // Ignore the diagonals
-          if (i === j) {
-            pj = 0;
-          }
-          prow[j] = pj;
-          psum += pj;
-        }
-        // Normalize p and compute entropy.
-        var Hhere = 0.0;
-        for (j = 0; j < N; j++) {
-          if (psum == 0) {
-            pj = 0;
-          } else {
-            pj = prow[j] / psum;
-          }
-          prow[j] = pj;
-          if (pj > 1e-7) {
-            Hhere -= pj * Math.log(pj);
-          }
-        }
-
-        // Adjust beta based on result.
-        if (Hhere > Htarget) {
-          // Entropy was too high (distribution too diffuse)
-          // so we need to increase the precision for more peaky distribution.
-          betamin = beta; // move up the bounds.
-          if (betamax === Infinity) {
-            beta = beta * 2;
-          } else {
-            beta = (beta + betamax) / 2;
-          }
-
-        } else {
-          // Converse case. Make distrubtion less peaky.
-          betamax = beta;
-          if (betamin === -Infinity) {
-            beta = beta / 2;
-          } else {
-            beta = (beta + betamin) / 2;
-          }
-        }
-
-        // Stopping conditions: too many tries or got a good precision.
-        num++;
-        if (Math.abs(Hhere - Htarget) < tol || num >= maxtries) {
-          done = true;
-        }
-      }
-
-      // Copy over the final prow to P1 at row i
-      for (j = 0; j < N; j++) {
-        P1[i * N + j] = prow[j];
-      }
-
-    }
-
-    // Symmetrize P and normalize it to sum to 1 over all ij
-    var Pout = zeros(N * N);
-    var N2 = N * 2;
-    for (i = 0; i < N; i++) {
-      for (j = 0; j < N; j++) {
-        Pout[i * N + j] = Math.max((P1[i * N + j] + P1[j * N + i]) / N2, 1e-100);
-      }
-    }
-    return Pout;
-  }
-
-  /**
-   * Initialize a starting (random) solution.
-   */
-  function initSolution() {
-    Y = random2d(N, dim);
-    // Step gains to accelerate progress in unchanging directions.
-    gains = random2d(N, dim, 1.0);
-    // Momentum accumulator.
-    ystep = random2d(N, dim, 0.0);
-    iteration = 0;
-  }
-
-  /**
-   * @return {2d array} the solution.
-   */
-  function getSolution() {
-    return Y;
-  }
-
-  /**
-   * Do a single step (iteration) for the layout.
-   * @return {number} the current cost.
-   */
-  function step() {
-    iteration += 1;
-
-    var cg = costGrad(Y); // Evaluate gradient.
-    var cost = cg.cost;
-    var grad = cg.grad;
-
-    // Perform gradient step.
-    var ymean = zeros(dim);
-    for (var i = 0; i < N; i++) {
-      for (var d = 0; d < dim; d++) {
-        var gid = grad[i][d];
-        var sid = ystep[i][d];
-        var gainid = gains[i][d];
-
-        // Compute gain update.
-        var newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
-        if (newgain < 0.01) {
-          newgain = 0.01;
-        }
-        gains[i][d] = newgain;
-
-        // Compute momentum step direction.
-        var momval = iteration < 250 ? 0.5 : 0.8;
-        var newsid = momval * sid - learningRate * newgain * grad[i][d];
-        ystep[i][d] = newsid;
-
-        // Do the step.
-        Y[i][d] += newsid;
-
-        // Accumulate mean so that we can center later.
-        ymean[d] += Y[i][d];
-      }
-    }
-
-    // Reproject Y to have the zero mean.
-    for (i = 0; i < N; i++) {
-      for (d = 0; d < dim; d++) {
-        Y[i][d] -= ymean[d] / N;
-      }
-    }
-    return cost;
-  }
-
-  /**
-   * Calculate the cost and the gradient.
-   * @param  {2d array} Y - the current solution to evaluate.
-   * @return {object}   that contains a cost and a gradient.
-   */
-  function costGrad(Y) {
-
-    var pmul = iteration < 100 ? 4 : 1;
-
-    // Compute current Q distribution, unnormalized first.
-    var Qu = zeros(N * N);
-    var qsum = 0.0;
-    for (var i = 0; i < N; i++) {
-      for (var j = i + 1; j < N; j++) {
-        var dsum = 0.0;
-        for (var d = 0; d < dim; d++) {
-          var dhere = Y[i][d] - Y[j][d];
-          dsum += dhere * dhere;
-        }
-        var qu = 1.0 / (1.0 + dsum); // Student t-distribution.
-        Qu[i * N + j] = qu;
-        Qu[j * N + i] = qu;
-        qsum += 2 * qu;
-      }
-    }
-    // Normalize Q distribution to sum to 1.
-    var NN = N * N;
-    var Q = zeros(NN);
-    for (var q = 0; q < NN; q++) {
-      Q[q] = Math.max(Qu[q] / qsum, 1e-100);
-    }
-
-    var cost = 0.0;
-    var grad = [];
-    for (i = 0; i < N; i++) {
-      var gsum = new Array(dim); // Initialize gradiet for point i.
-      for (d = 0; d < dim; d++) {
-        gsum[d] = 0.0;
-      }
-      for (j = 0; j < N; j++) {
-        // Accumulate the cost.
-        cost += -P[i * N + j] * Math.log(Q[i * N + j]);
-        var premult = 4 * (pmul * P[i * N + j] - Q[i * N + j]) * Qu[i * N + j];
-        for (d = 0; d < dim; d++) {
-          gsum[d] += premult * (Y[i][d] - Y[j][d]);
-        }
-      }
-      grad.push(gsum);
-    }
-
-    return {
-      cost: cost,
-      grad: grad
-    };
-  }
-
-  /**
-   * Calculates the stress. Basically, it computes the difference between
-   * high dimensional distance and real distance. The lower the stress is,
-   * the better layout.
-   * @return {number} - stress of the layout.
-   */
-  function getStress() {
-    var totalDiffSq = 0,
-      totalHighDistSq = 0;
-    for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
-      for (var j = 0; j < nodes.length; j++) {
-        if (i !== j) {
-          source = nodes[i], target = nodes[j];
-          realDist = Math.hypot(target.x - source.x, target.y - source.y);
-          highDist = +distance(nodes[i], nodes[j]);
-          totalDiffSq += Math.pow(realDist - highDist, 2);
-          totalHighDistSq += highDist * highDist;
-        }
-      }
-    }
-    return Math.sqrt(totalDiffSq / totalHighDistSq);
-  }
-
-  // API for initializing the algorithm, setting parameters and querying
-  // metrics.
-  force.initialize = function(_) {
-    nodes = _;
-    N = nodes.length;
-    // Initialize the probability matrix.
-    P = d2p(nodes, perplexity, 1e-4);
-    initSolution();
-  };
-
-  force.id = function(_) {
-    return arguments.length ? (id = _, force) : id;
-  };
-
-  force.distance = function(_) {
-    return arguments.length ? (distance = typeof _ === "function" ? _ : constant(+_), force) : distance;
-  };
-
-  force.stress = function() {
-    return getStress();
-  };
-
-  force.learningRate = function(_) {
-    return arguments.length ? (learningRate = +_, force) : learningRate;
-  };
-
-  force.perplexity = function(_) {
-    return arguments.length ? (perplexity = +_, force) : perplexity;
-  };
-
-  return force;
-}
+/* eslint-disable block-scoped-var */
+import constant from './constant';
+
+/**
+ * Set the node id accessor to the specified i.
+ * @param  {node} d - node.
+ * @param  {accessor} i - id accessor.
+ * @return {accessor}  - node id accessor.
+ */
+function index (d, i) {
+  return i;
+}
+
+/**
+ * t-SNE implementation in D3 by using the code existing in tsnejs
+ * (https://github.com/karpathy/tsnejs) to compute the solution.
+ */
+export default function () {
+  var id = index,
+    distance = constant(300),
+    nodes,
+    perplexity = 30,
+    learningRate = 10,
+    iteration = 0,
+    dim = 2,
+    N, // length of the nodes.
+    P, // probability matrix.
+    Y, // solution.
+    gains,
+    ystep;
+
+    /**
+   * Make a step in t-SNE algorithm and set the velocities for the nodes
+   * to accumulate the values from solution.
+   */
+  function force () {
+    // Make a step at each iteration.
+    step();
+    var solution = getSolution();
+
+    // Set the velocity for each node using the solution.
+    for (var i = 0; i < nodes.length; i++) {
+      nodes[i].vx += solution[i][0];
+      nodes[i].vy += solution[i][1];
+    }
+  }
+
+  /**
+   * Calculates the random number from Gaussian distribution.
+   * @return {number} random number.
+   */
+  function gaussRandom () {
+    let u = 2 * Math.random() - 1;
+    let v = 2 * Math.random() - 1;
+    let r = u * u + v * v;
+    if (r === 0 || r > 1) {
+      return gaussRandom();
+    }
+    return u * Math.sqrt(-2 * Math.log(r) / r);
+  }
+
+  /**
+   * Return the normalized number.
+   * @return {number} normalized random number from Gaussian distribution.
+   */
+  function randomN () {
+    return gaussRandom() * 1e-4;
+  }
+
+  function sign (x) {
+    return x > 0 ? 1 : x < 0 ? -1 : 0;
+  }
+
+  /**
+   * Create an array of length n filled with zeros.
+   * @param  {number} n - length of array.
+   * @return {Float64Array}   - array of zeros with length n.
+   */
+  function zeros (n) {
+    if (typeof n === 'undefined' || isNaN(n)) {
+      return [];
+    }
+    return new Float64Array(n); // typed arrays are faster
+  }
+
+  // Returns a 2d array of random numbers
+  /**
+   * Creates a 2d array filled with random numbers.
+   * @param  {number} n - rows.
+   * @param  {number} d - columns.
+   * @return {array}   - 2d array
+   */
+  function random2d (n, d) {
+    var x = [];
+    for (var i = 0; i < n; i++) {
+      var y = [];
+      for (var j = 0; j < d; j++) {
+        y.push(randomN());
+      }
+      x.push(y);
+    }
+    return x;
+  }
+
+  /**
+   * Compute the probability matrix using the provided data.
+   * @param  {array} data       - nodes.
+   * @param  {number} perplexity - used to calculate entropy of distribution.
+   * @param  {number} tol        - limit for entropy difference.
+   * @return {2d array}          - 2d matrix containing probabilities.
+   */
+  function d2p (data, perplexity, tol) {
+    N = Math.floor(data.length);
+    var Htarget = Math.log(perplexity); // target entropy of distribution.
+    var P1 = zeros(N * N); // temporary probability matrix.
+
+    var prow = zeros(N); // a temporary storage compartment.
+    for (var i = 0; i < N; i++) {
+      var betamin = -Infinity;
+      var betamax = Infinity;
+      var beta = 1; // initial value of precision.
+      var done = false;
+      var maxtries = 50;
+
+      // Perform binary search to find a suitable precision beta
+      // so that the entropy of the distribution is appropriate.
+      var num = 0;
+      while (!done) {
+        // Compute entropy and kernel row with beta precision.
+        var psum = 0.0;
+        for (var j = 0; j < N; j++) {
+          var pj = Math.exp(-distance(data[i], data[j]) * beta);
+          // Ignore the diagonals
+          if (i === j) {
+            pj = 0;
+          }
+          prow[j] = pj;
+          psum += pj;
+        }
+        // Normalize p and compute entropy.
+        var Hhere = 0.0;
+        for (j = 0; j < N; j++) {
+          if (psum === 0) {
+            pj = 0;
+          } else {
+            pj = prow[j] / psum;
+          }
+          prow[j] = pj;
+          if (pj > 1e-7) {
+            Hhere -= pj * Math.log(pj);
+          }
+        }
+
+        // Adjust beta based on result.
+        if (Hhere > Htarget) {
+          // Entropy was too high (distribution too diffuse)
+          // so we need to increase the precision for more peaky distribution.
+          betamin = beta; // move up the bounds.
+          if (betamax === Infinity) {
+            beta = beta * 2;
+          } else {
+            beta = (beta + betamax) / 2;
+          }
+        } else {
+          // Converse case. Make distrubtion less peaky.
+          betamax = beta;
+          if (betamin === -Infinity) {
+            beta = beta / 2;
+          } else {
+            beta = (beta + betamin) / 2;
+          }
+        }
+
+        // Stopping conditions: too many tries or got a good precision.
+        num++;
+        if (Math.abs(Hhere - Htarget) < tol || num >= maxtries) {
+          done = true;
+        }
+      }
+
+      // Copy over the final prow to P1 at row i
+      for (j = 0; j < N; j++) {
+        P1[i * N + j] = prow[j];
+      }
+    }
+
+    // Symmetrize P and normalize it to sum to 1 over all ij
+    var Pout = zeros(N * N);
+    var N2 = N * 2;
+    for (i = 0; i < N; i++) {
+      for (j = 0; j < N; j++) {
+        Pout[i * N + j] = Math.max((P1[i * N + j] + P1[j * N + i]) / N2, 1e-100);
+      }
+    }
+    return Pout;
+  }
+
+  /**
+   * Initialize a starting (random) solution.
+   */
+  function initSolution () {
+    Y = random2d(N, dim);
+    // Step gains to accelerate progress in unchanging directions.
+    gains = random2d(N, dim, 1.0);
+    // Momentum accumulator.
+    ystep = random2d(N, dim, 0.0);
+    iteration = 0;
+  }
+
+  /**
+   * @return {2d array} the solution.
+   */
+  function getSolution () {
+    return Y;
+  }
+
+  /**
+   * Do a single step (iteration) for the layout.
+   * @return {number} the current cost.
+   */
+  function step () {
+    iteration += 1;
+
+    var cg = costGrad(Y); // Evaluate gradient.
+    var cost = cg.cost;
+    var grad = cg.grad;
+
+    // Perform gradient step.
+    var ymean = zeros(dim);
+    for (var i = 0; i < N; i++) {
+      for (var d = 0; d < dim; d++) {
+        var gid = grad[i][d];
+        var sid = ystep[i][d];
+        var gainid = gains[i][d];
+
+        // Compute gain update.
+        var newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
+        if (newgain < 0.01) {
+          newgain = 0.01;
+        }
+        gains[i][d] = newgain;
+
+        // Compute momentum step direction.
+        var momval = iteration < 250 ? 0.5 : 0.8;
+        var newsid = momval * sid - learningRate * newgain * grad[i][d];
+        ystep[i][d] = newsid;
+
+        // Do the step.
+        Y[i][d] += newsid;
+
+        // Accumulate mean so that we can center later.
+        ymean[d] += Y[i][d];
+      }
+    }
+
+    // Reproject Y to have the zero mean.
+    for (i = 0; i < N; i++) {
+      for (d = 0; d < dim; d++) {
+        Y[i][d] -= ymean[d] / N;
+      }
+    }
+    return cost;
+  }
+
+  /**
+   * Calculate the cost and the gradient.
+   * @param  {2d array} Y - the current solution to evaluate.
+   * @return {object}   that contains a cost and a gradient.
+   */
+  function costGrad (Y) {
+    var pmul = iteration < 100 ? 4 : 1;
+
+    // Compute current Q distribution, unnormalized first.
+    var Qu = zeros(N * N);
+    var qsum = 0.0;
+    for (var i = 0; i < N; i++) {
+      for (var j = i + 1; j < N; j++) {
+        var dsum = 0.0;
+        for (var d = 0; d < dim; d++) {
+          var dhere = Y[i][d] - Y[j][d];
+          dsum += dhere * dhere;
+        }
+        var qu = 1.0 / (1.0 + dsum); // Student t-distribution.
+        Qu[i * N + j] = qu;
+        Qu[j * N + i] = qu;
+        qsum += 2 * qu;
+      }
+    }
+    // Normalize Q distribution to sum to 1.
+    var NN = N * N;
+    var Q = zeros(NN);
+    for (var q = 0; q < NN; q++) {
+      Q[q] = Math.max(Qu[q] / qsum, 1e-100);
+    }
+
+    var cost = 0.0;
+    var grad = [];
+    for (i = 0; i < N; i++) {
+      var gsum = new Array(dim); // Initialize gradiet for point i.
+      for (d = 0; d < dim; d++) {
+        gsum[d] = 0.0;
+      }
+      for (j = 0; j < N; j++) {
+        // Accumulate the cost.
+        cost += -P[i * N + j] * Math.log(Q[i * N + j]);
+        var premult = 4 * (pmul * P[i * N + j] - Q[i * N + j]) * Qu[i * N + j];
+        for (d = 0; d < dim; d++) {
+          gsum[d] += premult * (Y[i][d] - Y[j][d]);
+        }
+      }
+      grad.push(gsum);
+    }
+
+    return {
+      cost: cost,
+      grad: grad
+    };
+  }
+
+  /**
+   * Calculates the stress. Basically, it computes the difference between
+   * high dimensional distance and real distance. The lower the stress is,
+   * the better layout.
+   * @return {number} - stress of the layout.
+   */
+  function getStress () {
+    var totalDiffSq = 0,
+      totalHighDistSq = 0;
+    for (var i = 0, source, target, realDist, highDist; i < nodes.length; i++) {
+      for (var j = 0; j < nodes.length; j++) {
+        if (i !== j) {
+          source = nodes[i]; target = nodes[j];
+          realDist = Math.hypot(target.x - source.x, target.y - source.y);
+          highDist = +distance(nodes[i], nodes[j]);
+          totalDiffSq += Math.pow(realDist - highDist, 2);
+          totalHighDistSq += highDist * highDist;
+        }
+      }
+    }
+    return Math.sqrt(totalDiffSq / totalHighDistSq);
+  }
+
+  // API for initializing the algorithm, setting parameters and querying
+  // metrics.
+  force.initialize = function (_) {
+    nodes = _;
+    N = nodes.length;
+    // Initialize the probability matrix.
+    P = d2p(nodes, perplexity, 1e-4);
+    initSolution();
+  };
+
+  force.id = function (_) {
+    return arguments.length ? (id = _, force) : id;
+  };
+
+  force.distance = function (_) {
+    return arguments.length ? (distance = typeof _ === 'function' ? _ : constant(+_), force) : distance;
+  };
+
+  force.stress = function () {
+    return getStress();
+  };
+
+  force.learningRate = function (_) {
+    return arguments.length ? (learningRate = +_, force) : learningRate;
+  };
+
+  force.perplexity = function (_) {
+    return arguments.length ? (perplexity = +_, force) : perplexity;
+  };
+
+  return force;
+}

src/xy.js

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