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696
LICENSE
696
LICENSE
@@ -1,678 +1,24 @@
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 3, 29 June 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU General Public License is a free, copyleft license for
|
||||
software and other kinds of works.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
the GNU General Public License is intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users. We, the Free Software Foundation, use the
|
||||
GNU General Public License for most of our software; it applies also to
|
||||
any other work released this way by its authors. You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to prevent others from denying you
|
||||
these rights or asking you to surrender the rights. Therefore, you have
|
||||
certain responsibilities if you distribute copies of the software, or if
|
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you modify it: responsibilities to respect the freedom of others.
|
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|
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For example, if you distribute copies of such a program, whether
|
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gratis or for a fee, you must pass on to the recipients the same
|
||||
freedoms that you received. You must make sure that they, too, receive
|
||||
or can get the source code. And you must show them these terms so they
|
||||
know their rights.
|
||||
|
||||
Developers that use the GNU GPL protect your rights with two steps:
|
||||
(1) assert copyright on the software, and (2) offer you this License
|
||||
giving you legal permission to copy, distribute and/or modify it.
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||||
|
||||
For the developers' and authors' protection, the GPL clearly explains
|
||||
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|
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|
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Some devices are designed to deny users access to install or run
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|
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|
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||||
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|
||||
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|
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Finally, every program is threatened constantly by software patents.
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States should not allow patents to restrict development and use of
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|
||||
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The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU General Public License.
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|
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"Copyright" also means copyright-like laws that apply to other kinds of
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||||
works, such as semiconductor masks.
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"The Program" refers to any copyrightable work licensed under this
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A "covered work" means either the unmodified Program or a work based
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|
||||
To "propagate" a work means to do anything with it that, without
|
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permission, would make you directly or secondarily liable for
|
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infringement under applicable copyright law, except executing it on a
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To "convey" a work means any kind of propagation that enables other
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An interactive user interface displays "Appropriate Legal Notices"
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The "source code" for a work means the preferred form of the work
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A "Standard Interface" means an interface that either is an official
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The "System Libraries" of an executable work include anything, other
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The "Corresponding Source" for a work in object code form means all
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|
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|
||||
The Corresponding Source need not include anything that users
|
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|
||||
Source.
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||||
|
||||
The Corresponding Source for a work in source code form is that
|
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same work.
|
||||
|
||||
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|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
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|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
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in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
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|
||||
Conveying under any other circumstances is permitted solely under
|
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the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
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|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
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|
||||
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|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
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|
||||
|
||||
4. Conveying Verbatim Copies.
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||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
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|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
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|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
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|
||||
and execute modified versions of a covered work in that User Product from
|
||||
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|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
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|
||||
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|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
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|
||||
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|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
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|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
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|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
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|
||||
removal in certain cases when you modify the work.) You may place
|
||||
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|
||||
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|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
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|
||||
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|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
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|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
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|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
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||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
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|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
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|
||||
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|
||||
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|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
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|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
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|
||||
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|
||||
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d3-spring-model Copyright (C) 2018 Pitchaya Boonsarngsuk
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|
||||
480
README.md
480
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:
|
||||
|
||||

|
||||

|
||||
|
||||
### 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:
|
||||
|
||||

|
||||

|
||||
|
||||
### 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).
|
||||
|
||||
1000001
examples/data/poker/poker1000000.data
Normal file
1000001
examples/data/poker/poker1000000.data
Normal file
File diff suppressed because it is too large
Load Diff
800000
examples/data/poker/poker200000.csv
Normal file
800000
examples/data/poker/poker200000.csv
Normal file
File diff suppressed because it is too large
Load Diff
750000
examples/data/poker/poker250000.csv
Normal file
750000
examples/data/poker/poker250000.csv
Normal file
File diff suppressed because it is too large
Load Diff
@@ -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>
|
||||
|
||||
@@ -16,12 +16,13 @@ function startHybridSimulation() {
|
||||
let forceSample = d3.forceNeighbourSampling()
|
||||
.neighbourSize(NEIGHBOUR_SIZE)
|
||||
.sampleSize(SAMPLE_SIZE)
|
||||
.stableVelocity(0)
|
||||
.stableVelocity(0) // Change here
|
||||
.distance(distance)
|
||||
|
||||
let forceFull = d3.forceNeighbourSampling()
|
||||
.neighbourSize(FULL_NEIGHBOUR_SIZE)
|
||||
.sampleSize(FULL_SAMPLE_SIZE)
|
||||
.stableVelocity(0) // Change here
|
||||
.distance(distance)
|
||||
|
||||
let hybridSimulation = d3.hybridSimulation(simulation, forceSample, forceFull)
|
||||
@@ -11,11 +11,11 @@ function startLinkSimulation() {
|
||||
let links = [], force;
|
||||
|
||||
if (tweakedVerOfLink) {
|
||||
force = d3.forceLinkFullyConnected()
|
||||
force = d3.forceLinkCompleteGraph()
|
||||
.distance(function (n, m) {
|
||||
return distanceFunction(n, m, props, norm);
|
||||
})
|
||||
.stableVelocity(0.000001) //TODO
|
||||
.stableVelocity(0) // Change here
|
||||
.onStableVelo(ended);
|
||||
}
|
||||
else {
|
||||
@@ -15,7 +15,7 @@ function startNeighbourSamplingSimulation() {
|
||||
.distance(function (s, t) {
|
||||
return distanceFunction(s, t, props, norm);
|
||||
})
|
||||
.stableVelocity(0.000001) //TODO
|
||||
.stableVelocity(0) // Change here
|
||||
.onStableVelo(ended);
|
||||
|
||||
simulation
|
||||
@@ -20,7 +20,7 @@ function calculateDistance(source, target, properties, normArgs) {
|
||||
for (var i = 0; i < properties.length; i++) {
|
||||
property = properties[i];
|
||||
if (source.hasOwnProperty(property) && target.hasOwnProperty(property)
|
||||
&& property.toLowerCase() !== "index" ) {
|
||||
&& property.toLowerCase() !== "index" && property.toLowerCase() !== "type") {
|
||||
var s = source[property],
|
||||
t = target[property];
|
||||
|
||||
|
||||
@@ -12,7 +12,7 @@ function calculateEuclideanDistance(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" && property.toLowerCase() !== "type") {
|
||||
var s = source[property],
|
||||
t = target[property];
|
||||
|
||||
|
||||
@@ -20,9 +20,9 @@ svg.append("g")
|
||||
.attr("class", "brush")
|
||||
.call(brush);
|
||||
|
||||
var intercom = Intercom.getInstance();
|
||||
//var intercom = Intercom.getInstance();
|
||||
|
||||
intercom.on("select", unSelectNodes);
|
||||
//intercom.on("select", unSelectNodes);
|
||||
|
||||
var nodes, // as in Data points
|
||||
node, // as in SVG object that have all small circles on screen
|
||||
@@ -65,7 +65,6 @@ var MULTIPLIER = 50,
|
||||
// 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);
|
||||
@@ -82,7 +81,6 @@ function parseFile(evt) {
|
||||
springForce = false;
|
||||
|
||||
fileName = evt.target.files[0].name;
|
||||
|
||||
Papa.parse(evt.target.files[0], {
|
||||
header: true,
|
||||
dynamicTyping: true,
|
||||
@@ -105,10 +103,10 @@ function processData(data, error) {
|
||||
size = nodes.length;
|
||||
simulation = d3.forceSimulation();
|
||||
|
||||
// Calculate normalization arguments and get the list of
|
||||
// properties of the nodes.
|
||||
norm = calculateNormalization(nodes); // Used with distance fn
|
||||
// Calculate normalization parameters for distance fns
|
||||
norm = calculateNormalization(nodes);
|
||||
props = Object.keys(nodes[0]); // Properties to consider by distance fn
|
||||
|
||||
COLOR_ATTRIBUTE = props[props.length-1];
|
||||
|
||||
var opts = document.getElementById('color_attr').options;
|
||||
@@ -117,20 +115,14 @@ function processData(data, error) {
|
||||
opts.add(new Option(d, d, (d === COLOR_ATTRIBUTE) ? true : false));
|
||||
});
|
||||
opts.selectedIndex = props.length-1;
|
||||
//props.pop(); //Hide Iris index / last column from the distance function
|
||||
|
||||
|
||||
//props.pop(); //Hide Iris index / last column from distance function
|
||||
|
||||
//Put the nodes in random starting positions
|
||||
//TODO Change this back
|
||||
//Put the nodes at (0,0)
|
||||
nodes.forEach(function (d) {
|
||||
d.x = 0;
|
||||
d.y = 0;
|
||||
});
|
||||
/*
|
||||
nodes.forEach(function (d) {
|
||||
d.x = (Math.random()-0.5) * 100000;
|
||||
d.y = (Math.random()-0.5) * 100000;
|
||||
});*/
|
||||
|
||||
addNodesToDOM(nodes);
|
||||
|
||||
@@ -189,7 +181,6 @@ function addNodesToDOM(data) {
|
||||
}
|
||||
|
||||
function ticked() {
|
||||
//console.log("ticked");
|
||||
alreadyRanIterations++;
|
||||
// If rendering is selected, then draw at every iteration.
|
||||
if (rendering === true) {
|
||||
@@ -201,10 +192,10 @@ function ticked() {
|
||||
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());
|
||||
}
|
||||
// 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();
|
||||
}
|
||||
@@ -213,7 +204,6 @@ function ticked() {
|
||||
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) {
|
||||
@@ -228,11 +218,6 @@ function ended() {
|
||||
// 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;
|
||||
}
|
||||
@@ -260,7 +245,7 @@ function brushEnded() {
|
||||
results = sel.map(function (a) { return a.index; });
|
||||
}
|
||||
|
||||
intercom.emit("select", { name: fileName, indices: results });
|
||||
//intercom.emit("select", { name: fileName, indices: results });
|
||||
|
||||
d3.select(".brush").call(brush.move, null);
|
||||
}
|
||||
@@ -290,7 +275,6 @@ function formatTooltip(node) {
|
||||
* Halt the execution.
|
||||
*/
|
||||
function stopSimulation() {
|
||||
console.log("stopSimulation");
|
||||
simulation.stop();
|
||||
if (typeof hybridSimulation !== 'undefined') {
|
||||
hybridSimulation.stop();
|
||||
34
index.js
34
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";
|
||||
|
||||
@@ -8,7 +8,7 @@
|
||||
"d3-spring-model",
|
||||
"force"
|
||||
],
|
||||
"license": "GPL-3.0-only",
|
||||
"license": "MIT",
|
||||
"main": "build/d3-spring-model.js",
|
||||
"jsnext:main": "index",
|
||||
"scripts": {
|
||||
|
||||
@@ -48,14 +48,13 @@ export default function (sim, forceS, forceF) {
|
||||
// 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");
|
||||
console.log("Initialized Simulation for Hybrid");
|
||||
}
|
||||
|
||||
function initForces(){
|
||||
@@ -63,6 +62,10 @@ export default function (sim, forceS, forceF) {
|
||||
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')
|
||||
@@ -87,7 +90,7 @@ export default function (sim, forceS, forceF) {
|
||||
// Sample simulation ticked 1 frame, keep track of number of iterations here.
|
||||
function sampleTick() {
|
||||
event.call("sampleTick");
|
||||
if(++alreadyRanIterations >= SAMPLE_ITERATIONS){
|
||||
if(alreadyRanIterations++ >= SAMPLE_ITERATIONS){
|
||||
sampleEnded();
|
||||
}
|
||||
}
|
||||
@@ -95,14 +98,18 @@ export default function (sim, forceS, forceF) {
|
||||
// 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");
|
||||
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);
|
||||
|
||||
@@ -1,48 +1,48 @@
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -66,7 +66,7 @@ export function placeNearToNearestNeighbour(node, nearNeighbour, radius, sampleS
|
||||
}
|
||||
|
||||
// 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;
|
||||
|
||||
@@ -1,175 +1,135 @@
|
||||
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 correct = 0, wrong = 0, percentsOff = [];
|
||||
|
||||
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]);
|
||||
}
|
||||
|
||||
let nearestSample, nearestMinDist, sample, dist;
|
||||
for (let j = sampleSet.length-1; j>=0; j--) {
|
||||
sample = sampleSet[j];
|
||||
dist = distanceFn(node, sample);
|
||||
if (nearestSample === undefined || dist < nearestMinDist) {
|
||||
nearestMinDist = dist;
|
||||
nearestSample = sample;
|
||||
}
|
||||
}
|
||||
|
||||
if(minDist-nearestMinDist == 0) correct++;
|
||||
else {
|
||||
wrong++;
|
||||
if(nearestMinDist!=0)percentsOff.push(minDist/nearestMinDist)
|
||||
}
|
||||
|
||||
placeNearToNearestNeighbour(node, nearSample, minDist, sampleSubset, sampleSubsetDistanceCache, endingIts);
|
||||
}
|
||||
console.log("Correct", correct);
|
||||
console.log("Wrong", wrong);
|
||||
console.log("Correct percent", 100*correct/(correct+wrong));
|
||||
StandardDeviation(percentsOff)
|
||||
}
|
||||
|
||||
function StandardDeviation(numbersArr) {
|
||||
//--CALCULATE AVAREGE--
|
||||
var total = 0;
|
||||
for(var key in numbersArr)
|
||||
total += numbersArr[key];
|
||||
var meanVal = total / numbersArr.length;
|
||||
console.log("Average percent off", meanVal);
|
||||
//--CALCULATE AVAREGE--
|
||||
|
||||
//--CALCULATE STANDARD DEVIATION--
|
||||
var SDprep = 0;
|
||||
for(var key in numbersArr)
|
||||
SDprep += Math.pow((parseFloat(numbersArr[key]) - meanVal),2);
|
||||
var SDresult = Math.sqrt(SDprep/numbersArr.length);
|
||||
//--CALCULATE STANDARD DEVIATION--
|
||||
console.log("SD percent off", SDresult);
|
||||
}
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -28,6 +28,7 @@ export default function() {
|
||||
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
|
||||
@@ -54,13 +55,12 @@ export default function() {
|
||||
node = nodes[i];
|
||||
velocityDiff += Math.abs(Math.hypot(node.vx-node.oldvx, node.vy-node.oldvy));
|
||||
}
|
||||
velocityDiff /= n*(n-1);
|
||||
velocityDiff /= n;
|
||||
latestVelocityDiff = velocityDiff;
|
||||
|
||||
if(velocityDiff<stableVelocity){
|
||||
stableVeloHandler();
|
||||
}
|
||||
else console.log(velocityDiff);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -1,206 +1,204 @@
|
||||
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;
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user