dukantic/Perceptron_Pizz2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

init lfs

Browse Source
This commit is contained in:
dukantic
2026-03-29 20:21:25 +02:00
commit 5b3ebf8645
39 changed files with 2413 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

674
LICENSE Normal file
View File

@@ -0,0 +1,674 @@
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://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
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
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.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
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.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
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
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.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
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
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
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.
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
Corresponding Source. Regardless of what server hosts the
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,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
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
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
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
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
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
unpacking, reading or copying.
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
be treated as though they were included in this License, to the extent
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
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
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
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
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
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) 2025 dukantic
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) 2025 dukantic
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

217
README.md Normal file
View File

@@ -0,0 +1,217 @@
> Dorian HAMDANI, Aubin DORIVAL, Rémi SCHIRRA
# Table of Contents
- [🇬🇧 Perceptron Report](#-perceptron-report)
- [Introduction](#introduction)
- [Running the Program](#running-the-program)
- [Build](#build)
- [Execution](#execution)
- [Structure](#structure)
- [Comparison](#comparison)
- [Linear Perceptron](#linear-perceptron)
- [Linear Data](#linear-data-accuracy-1000)
- [Moon Data](#moon-data-accuracy-809)
- [Two Circles](#two-circles-accuracy-330)
- [General Perceptron](#general-perceptron)
- [Linear Data](#linear-data-accuracy-1000-1)
- [Moon Data](#moon-data-accuracy-974)
- [Two Circles](#two-circles-accuracy-1000)
- [MNIST Usage](#mnist-usage)
- [Our Experience](#our-experience)
- [🇫🇷 Rapport Perceptron](#-rapport-perceptron)
- [Introduction](#introduction-1)
- [Lancer le programme](#lancer-le-programme)
- [Build](#build-1)
- [Exécution](#exécution)
- [Structure](#structure-1)
- [Comparaison](#comparaison)
- [Perceptron Linéaire](#perceptron-lineaire)
- [Linear Data](#linear-data-accuracy-1000-2)
- [Moon Data](#moon-data-accurancy-809)
- [Two Circles](#two-circles-accuracy-330-1)
- [Perceptron Générale](#perceptron-générale)
- [Linear Data](#linear-data-accuracy-1000-3)
- [Moon Data](#moon-data-accuracy-974-1)
- [Two Circles](#two-circles-accuracy-1000-1)
- [Utilisation MNIST](#utilisation-mnist)
- [Notre expérience](#notre-expérience)
# 🇬🇧 Perceptron Report
## Introduction
This report presents the perceptron project, implemented in Java, that we successfully carried out without particular difficulties.
## Running the Program
### Build
```bash
javac -d build src/main/java/fr/perceptron/*.java
```
### Execution
MNIST (training):
```bash
java -cp build fr.perceptron.MnistMain
```
MNIST (results):
```bash
java -cp build fr.perceptron.MnistWindow
```
2D Dataset:
```bash
java -cp build fr.perceptron.Main
```
## Structure
We structured the project as follows:
- Main
- NeuralNetwork
- Layers
- DataSet
- DataPoint
The main execution file allows choosing the layer sizes used in the perceptron, as well as the dataset. The program will convert them into an easy-to-use format: a `DataSet` composed of `DataPoint`.
## Comparison
We first developed a linear version of the perceptron, without any modular approach. Then, in the second part, we designed a more flexible version, which allowed us to adapt the perceptron to different situations much more easily. Below is a comparison of the results obtained with the same dataset on both versions of the perceptron:
### Linear Perceptron
#### Linear Data (Accuracy: 100.0%)
![linear_data_linear](images/linear_data_linear.png)
#### Moon Data (Accuracy: 80.9%)
![moon_data_linear](images/moon_data_linear.png)
#### Two Circles (Accuracy: 33.0%)
![twocircles_data_linear](images/twocircles_data_linear.png)
### General Perceptron
#### Linear Data (Accuracy: 100.0%)
![linear_data](images/linear_data.GIF)
#### Moon Data (Accuracy: 97.4%)
![moon_data](images/moon_data.GIF)
#### Two Circles (Accuracy: 100.0%)
![twocircles_data](images/twocircles_data.GIF)
## MNIST Usage
On 2D datasets, once the perceptron is trained on the data, we can display a window that predicts the data separation for each pixel of the screen:
On the handwritten digit dataset, after training the perceptron, we can visualize the results on dataset images or even on a digit drawn by hand:
![mnist](images/mnist.png)
However, some cases can be ambiguous and lead to a detection error, as in this example (a 2 is predicted while the image is actually labeled as a 7):
![mnist_error](images/mnist_error.png)
Nevertheless, the perceptron achieved a success rate of 97.24%.
## Our Experience
Starting from very abstract knowledge in the field of artificial intelligence, this perceptron project taught us a lot.
First, regarding understanding, a significant amount of time was devoted to studying the course material, as it was necessary to fully grasp and connect the mathematical formulas.
The implementation, which we initially thought would be quick, also required a lot of time, due to the rigor involved and the complexity of debugging. However, the object-oriented Java implementation approach allowed us to realize certain points: training a perceptron is actually similar to solving a system, and we also understood why AI training is performed on the GPU: because the calculations are simple and intuitively parallelizable.
Finally, one problem we encountered was the interpretation of the results. We initially thought that the perceptron would output the equation of a line/hyperplane (linear or not) separating the data, but in fact, the perceptron classifies new data based on what it has “learned.”
# 🇫🇷 Rapport Perceptron
## Introduction
Ce rapport présente le projet de perceptron, implémenté en Java que nous avons mené à bien, sans difficultés particulière.
## Lancer le programme
### Build
```bash
javac -d build src/main/java/fr/perceptron/*.java
```
### Exécution
MNIST (entraînement) :
```bash
java -cp build fr.perceptron.MnistMain
```
MNIST (résultats) :
```
java -cp build fr.perceptron.MnistWindow
```
Jeu de donnée 2D :
```bash
java -cp build fr.perceptron.Main
```
## Structure
Nous avons structuré le projet de la manière suivante :
- Main
- NeuralNetwork
- Layers
- DataSet
- DataPoint
Le fichier d'exécution principal permet le choix des tailles des couches utilisées dans le perceptron, et des données utilisée. Le programme se chargera de les convertir en un format facile à utiliser : un DataSet composé de DataPoint.
## Comparaison
Nous avons commencé par développer une version linéaire du perceptron, sans aucune approche modulaire. Ensuite, dans la deuxième partie, nous avons conçu une version
plus flexible, ce qui nous a permis d'adapter le perceptron à différentes situations de manière bien plus aisée. Voici une comparaison des résultats obtenus avec le
même jeu de données sur les deux versions du perceptron :
### Perceptron Lineaire
#### Linear Data (Accuracy: 100.0%)
![linear_data_linear](images/linear_data_linear.png)
#### Moon Data (Accurancy 80.9%)
![moon_data_linear](images/moon_data_linear.png)
#### Two Circles (Accuracy: 33.0%)
![twocircles_data_linear](images/twocircles_data_linear.png)
### Perceptron Générale
#### Linear Data (Accuracy: 100.0%)
![linear_data](images/linear_data.GIF)
#### Moon Data (Accuracy: 97.4%)
![moon_data](images/moon_data.GIF)
#### Two Circles (Accuracy: 100.0%)
![twocircles_data](images/twocircles_data.GIF)
## Utilisation MNIST
Sur les jeux de données 2D, nous pouvons, après avoir entraîné le perceptron sur les données, afficher un fenêtre qui prédit la séparation des données pour chaque pixel de l'écran :
Sur le jeu de donnée d'écriture manuscrite, après entraînement du perceptron, on peut visualiser les résultats sur des images du jeu de donnée ou bien un chiffre dessiné à la main :
![mnist](images/mnist.png)
Néanmoins, certains cas peuvent être ambigües et provoquer une erreur de détection comme ici (un 2 est prédit alors que l'image est marquée étant un 7) :
![mnist_error](images/mnist_error.png)
Le perceptron a tout de même un taux de 97.24% de bonne réponses.
## Notre expérience
A partir de nos connaissances très abstraites sur le domaine de l'intelligence artificielle, ce projet de perceptron nous a beaucoup apprit.
A commencer par la compréhension, une période de temps non négligeable a été consacrée à la lecture du cours, il a fallu réussir à comprendre et lier les formules mathématiques entre elles.
L'implémentation, que nous pensions au départ être rapide, a également demandé beaucoup de temps, en raison de la rigueur demandée et la détection de bug plus complexe. L'approche d'implémentation en Java (POO) nous a néanmoins permit de réaliser certains points : l'entraînement d'un perceptron ressemble en fait à la résolution d'un système, et nous avons aussi comprit pourquoi l'entraînement d'IA est exécutée sur la carte graphique : car les calculs effectués sont simples et intuitivement parallélisable.
Pour finir, un problème que nous avons rencontré, a été l'interprétation des résultats. Nous pensions que le perceptron renverrai l'équation d'une droite / hyperplan (linéaire ou non) séparant les données, mais en fait, le perceptron classifie une donnée nouvelle en fonction de ce qu'il a "apprit".

BIN
data/linear_data_eval.csv LFS Normal file
View File

Binary file not shown.
1 version https://git-lfs.github.com/spec/v1
2 oid sha256:9c397c8008c26015cd169a26338766818cba1f82198912b2a5db5d546e9fe4d8
3 size 7641

BIN
data/linear_data_train.csv LFS Normal file
View File

Binary file not shown.
1 version https://git-lfs.github.com/spec/v1
2 oid sha256:5f1850d656667a07c8a8ce4eb59c15fc3a78e65ea49a27734d4c2c11f6aa22d0
3 size 38125

BIN
data/moon_data_eval.csv LFS Normal file
View File

Binary file not shown.
1 version https://git-lfs.github.com/spec/v1
2 oid sha256:381f736b9a6e6aab5ca8ffe9f4b701cb90b64b67a7b81cbc540a09ae29426358
3 size 32171

BIN
data/moon_data_train.csv LFS Normal file
View File

Binary file not shown.
1 version https://git-lfs.github.com/spec/v1
2 oid sha256:cb9f707cdedff00287ca9315331f7ae4030ca6b62fd71c3bd65c256dc054ef0d
3 size 64383

BIN
data/twocircles_data_eval.csv LFS Normal file
View File

Binary file not shown.
1 version https://git-lfs.github.com/spec/v1
2 oid sha256:fef1fc1dfa6a1686338161fb6f59bfe4fff3938503e3f7101414a56547ae1515
3 size 3718

BIN
data/twocircles_data_train.csv LFS Normal file
View File

Binary file not shown.
1 version https://git-lfs.github.com/spec/v1
2 oid sha256:d4f2d4f3c35e8dd243b1073a38f9d668d72acc7e9bdcd824ed9b257718fd7f7b
3 size 18700

BIN
images/linear_data.GIF LFS Normal file
View File

Binary file not shown.

BIN
images/linear_data_linear.png LFS Normal file
View File

Binary file not shown.

BIN
images/linear_data_linear.png~ LFS Normal file
View File

Binary file not shown.

BIN
images/mnist.png LFS Normal file
View File

Binary file not shown.

BIN
images/mnist.png~ LFS Normal file
View File

Binary file not shown.

BIN
images/mnist_error.png LFS Normal file
View File

Binary file not shown.

BIN
images/mnist_error.png~ LFS Normal file
View File

Binary file not shown.

BIN
images/moon_data.GIF LFS Normal file
View File

Binary file not shown.

BIN
images/moon_data_linear.png LFS Normal file
View File

Binary file not shown.

BIN
images/moon_data_linear.png~ LFS Normal file
View File

Binary file not shown.

BIN
images/twocircles_data.GIF LFS Normal file
View File

Binary file not shown.

BIN
images/twocircles_data_linear.png LFS Normal file
View File

Binary file not shown.

BIN
images/twocircles_data_linear.png~ LFS Normal file
View File

Binary file not shown.

BIN
mnist/cours_technologie_web.pdf LFS Normal file
View File

Binary file not shown.

BIN
mnist/t10k-images-idx3-ubyte.gz LFS Normal file
View File

Binary file not shown.

BIN
mnist/t10k-labels-idx1-ubyte.gz LFS Normal file
View File

Binary file not shown.

BIN
mnist/train-images-idx3-ubyte.gz LFS Normal file
View File

Binary file not shown.

BIN
mnist/train-labels-idx1-ubyte.gz LFS Normal file
View File

Binary file not shown.

BIN
nn_256_128.dat Normal file
View File

Binary file not shown.

14
src/main/java/fr/perceptron/DataPoint.java Normal file
View File

@@ -0,0 +1,14 @@
package fr.perceptron;
import java.io.Serializable;
public class DataPoint implements Serializable {
public double[] inputs;
public double[] expectedOutputs;
public DataPoint() {
}
}

155
src/main/java/fr/perceptron/DataSet.java Normal file
View File

@@ -0,0 +1,155 @@
package fr.perceptron;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Random;
import java.util.stream.Stream;
public class DataSet {
public DataPoint[] points;
public int numPoints;
public int numBatches;
public int currentBatch;
public DataSet(int numBatches) {
this.numBatches = numBatches;
this.currentBatch = 0;
}
public void loadFromArray(double[][] inputs, double[] labels) {
if (inputs.length != labels.length) return;
int num = inputs.length;
this.points = new DataPoint[num];
this.numPoints = num;
for (int i=0; i<num; i++) {
DataPoint p = new DataPoint();
p.inputs = inputs[i];
p.expectedOutputs = new double[] {0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
p.expectedOutputs[(int) labels[i]] = 1;
this.points[i] = p;
}
}
public void loadDataSet(String path) {
int numLines = 0;
// count the number of lines in the file
try (Stream<String> lines = Files.lines(Paths.get(path))) {
numLines = (int) lines.count();
} catch (IOException e) {
e.printStackTrace();
}
// create the array of DataPoints
this.points = new DataPoint[numLines];
this.numPoints = numLines;
// read the file again and fill the array
try (BufferedReader br = new BufferedReader(new FileReader(path))) {
String line;
int i = 0;
while ((line = br.readLine()) != null) {
String[] values = line.split(",");
DataPoint point = new DataPoint();
point.inputs = new double[values.length - 1];
point.expectedOutputs = new double[1];
// fill the inputs
for (int j = 1; j < values.length; ++j) {
point.inputs[j - 1] = Double.parseDouble(values[j]);
}
// fill the expected output
point.expectedOutputs[0] = Double.parseDouble(values[0]);
this.points[i] = point;
i++;
}
} catch (IOException e) {
System.err.println("Error reading file: " + e.getMessage());
} catch (NumberFormatException e) {
System.err.println("Error parsing number: " + e.getMessage());
}
// check if the number of points is correct
if (this.points.length != numLines) {
System.err.println("Warning: file has " + numLines
+ " lines but " + this.points.length + " points were created.");
}
// reset the current batch
this.currentBatch = 0;
}
public DataSet generateBatch() {
// if the current batch is greater than the number of batches,
// return null
if (this.currentBatch >= this.numBatches) {
return null;
}
int batchSize = this.numPoints / this.numBatches;
// make sure the batch size is correct for the last batch
if (this.currentBatch == this.numBatches - 1) {
batchSize = this.numPoints - (this.numBatches - 1) * batchSize;
}
// generate a batch of data points
DataSet batch = new DataSet(0);
batch.points = new DataPoint[batchSize];
batch.numPoints = batchSize;
// fill the batch with the points
int batchStart = this.currentBatch * batchSize;
for (int i = 0; i < batchSize; ++i) {
// get the index of the point in the original data set
int index = batchStart + i;
batch.points[i] = new DataPoint();
batch.points[i].inputs = this.points[index].inputs;
batch.points[i].expectedOutputs = this.points[index].expectedOutputs;
}
// increment the current batch
this.currentBatch++;
return batch;
}
public void resetBatches() {
this.currentBatch = 0;
shuffle();
}
public void shuffle() {
Random rnd = new Random();
for (int i = numPoints - 1; i > 0; i--) {
int index = rnd.nextInt(i + 1);
DataPoint temp = points[i];
points[i] = points[index];
points[index] = temp;
}
}
// Ensure that the number of points per batch is set to 'size'
// and update the number of batches accordingly
// also reset the current batch to 0
public void batchesOf(int size) {
this.currentBatch = 0;
this.numBatches = this.numPoints / size;
if (this.numPoints % size != 0) {
this.numBatches++;
}
System.out.println("Number of batches: " + this.numBatches);
System.out.println("Batch size: " + size);
}
public void setNumBatches(int numBatches) {
this.numBatches = numBatches;
}
}

101
src/main/java/fr/perceptron/Layer.java Normal file
View File

@@ -0,0 +1,101 @@
package fr.perceptron;
import java.io.Serializable;
import java.util.Random;
/**
* The class Layer is one layer of the perceptron
*/
public class Layer implements Serializable {
public int numInputNeurons;
public int numNeurons;
public double[] weights;
public double[] biases;
public double[] rawLayerData;
private transient Random random;
public Layer(int numInputNeurons, int numNeurons, Random random) {
if (numInputNeurons < 1 || numNeurons < 1) {
throw new IllegalArgumentException("Invalid number of neurons");
}
this.random = random;
this.weights = new double[numInputNeurons * numNeurons];
this.biases = new double[numNeurons];
this.rawLayerData = new double[numNeurons];
this.numInputNeurons = numInputNeurons;
this.numNeurons = numNeurons;
this.initRandomWeights();
// this.initRandomBiases();
}
public Layer(int numInputNeurons, int numNeurons) {
this(numInputNeurons, numNeurons, new Random());
}
public void initRandomWeights() {
for (int i = 0; i < this.weights.length; ++i) {
this.weights[i] = random.nextDouble() * 2 - 1.0; // [-1.0; 1.0[ range
}
}
public void initRandomBiases() {
for (int i = 0; i < this.biases.length; ++i) {
this.biases[i] = random.nextDouble() * 2 - 1.0; // [-1.0; 1.0[ range
}
}
public double[] forward(double[] inputs) {
double[] outputs = new double[this.numNeurons];
for (int numOutput = 0; numOutput < this.numNeurons; ++numOutput) {
outputs[numOutput] = this.biases[numOutput];
for (int numInput = 0; numInput < this.numInputNeurons; ++numInput) {
outputs[numOutput] += this.getWeight(numInput, numOutput) * inputs[numInput];
}
this.rawLayerData[numOutput] = outputs[numOutput];
}
return activationFunction(outputs);
}
public static double activationFunction(double x) {
// Sigmoid function
return 1.0d / (1.0d + Math.exp(-x));
}
public static double activationDerivativeFunction(double x){
double temp = activationFunction(x);
return temp * (1.0d - temp);
}
public static double[] activationFunction(double[] inputs) {
double[] outputs = new double[inputs.length];
for (int i = 0; i < inputs.length; ++i) {
outputs[i] = activationFunction(inputs[i]);
}
return outputs;
}
public double getWeight(int neuronIn, int neuronOut) {
return this.weights[neuronOut * this.numInputNeurons + neuronIn];
}
public void setWeight(int neuronIn, int neuronOut, double v) {
this.weights[neuronOut * this.numInputNeurons + neuronIn] = v;
}
@Override
public String toString() {
return String.format("Layer[%d, %d]", this.numInputNeurons, this.numNeurons);
}
}

137
src/main/java/fr/perceptron/MNIST.java Normal file
View File

@@ -0,0 +1,137 @@
package fr.perceptron;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.util.zip.GZIPInputStream;
public class MNIST {
private static double[] labels_train;
private static double[][] images_train;
private static double[] labels_eval;
private static double[][] images_eval;
public static double[] getEvalLabels() {
return labels_eval;
}
public static double[][] getEvalImages() {
return images_eval;
}
public static double[] getTrainLabels() {
return labels_train;
}
public static double[][] getTrainImages() {
return images_train;
}
public static void loadEval() {
final String trainImagesPath = "./mnist/t10k-images-idx3-ubyte.gz";
final String trainLabelsPath = "./mnist/t10k-labels-idx1-ubyte.gz";
try {
InputStream imgIn = new GZIPInputStream(new FileInputStream(trainImagesPath));
InputStream lblIn = new GZIPInputStream(new FileInputStream(trainLabelsPath));
byte[] tempBuffer = new byte[16];
imgIn.read(tempBuffer, 0, 16);
lblIn.read(tempBuffer, 0, 8); // Les labels n'ont que 8 octets d'en-tête
byte[] dataBuffer = new byte[1];
labels_eval = new double[10000];
images_eval = new double[10000][784];
System.out.println("Start loading MNIST set.");
for (int i = 0; i < 10000; i++) {
lblIn.read(dataBuffer, 0, 1);
labels_eval[i] = dataBuffer[0] & 0xFF;
for (int j = 0; j < 784; j++) {
imgIn.read(dataBuffer, 0, 1);
double pixelVal = (dataBuffer[0] & 0xFF) / 255.0;
images_eval[i][j] = pixelVal;
}
}
System.out.println("MNIST set loaded.");
imgIn.close();
lblIn.close();
} catch (IOException e) {
e.printStackTrace();
}
}
public static void loadTrain() {
final String trainImagesPath = "./mnist/train-images-idx3-ubyte.gz";
final String trainLabelsPath = "./mnist/train-labels-idx1-ubyte.gz";
try {
InputStream imgIn = new GZIPInputStream(new FileInputStream(trainImagesPath));
InputStream lblIn = new GZIPInputStream(new FileInputStream(trainLabelsPath));
byte[] tempBuffer = new byte[16];
imgIn.read(tempBuffer, 0, 16);
lblIn.read(tempBuffer, 0, 8); // Les labels n'ont que 8 octets d'en-tête
byte[] dataBuffer = new byte[1];
labels_train = new double[60000];
images_train = new double[60000][784];
System.out.println("Start loading MNIST set.");
for (int i = 0; i < 60000; i++) {
lblIn.read(dataBuffer, 0, 1);
labels_train[i] = dataBuffer[0] & 0xFF;
for (int j = 0; j < 784; j++) {
imgIn.read(dataBuffer, 0, 1);
double pixelVal = (dataBuffer[0] & 0xFF) / 255.0;
images_train[i][j] = pixelVal;
}
}
System.out.println("MNIST set loaded.");
imgIn.close();
lblIn.close();
} catch (IOException e) {
e.printStackTrace();
}
}
public static void save() {
try {
System.out.println("Saving set to cache...");
ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream("data.dat"));
oos.writeObject(labels_train);
oos.writeObject(images_train);
oos.writeObject(labels_eval);
oos.writeObject(images_eval);
oos.close();
System.err.println("Set saved.");
} catch (Exception e) {
}
}
public static void load() {
try {
System.out.println("Loading cache...");
ObjectInputStream ois = new ObjectInputStream(new FileInputStream("data.dat"));
labels_train = (double[]) ois.readObject();
images_train = (double[][]) ois.readObject();
labels_eval = (double[]) ois.readObject();
images_eval = (double[][]) ois.readObject();
ois.close();
System.out.println("Cache loaded.");
} catch (Exception e) {
}
}
}

90
src/main/java/fr/perceptron/Main.java Normal file
View File

@@ -0,0 +1,90 @@
package fr.perceptron;
import java.io.BufferedWriter;
import java.io.FileWriter;
public class Main {
public static void main(String[] args) {
int[] layerSizes = { 2, 50, 10, 1 };
if (args.length > 0) {
layerSizes = new int[args.length];
for (int i = 0; i < args.length; ++i) {
layerSizes[i] = Integer.parseInt(args[i]);
}
}
String file = "data/twocircles_data_";
DataSet data = new DataSet(20);
DataSet eval = new DataSet(1);
data.loadDataSet(file + "train.csv");
eval.loadDataSet(file + "eval.csv");
NeuralNetwork nn = new NeuralNetwork(layerSizes);
System.out.println(nn + "\n");
Window window = new Window();
window.init(nn, data);
long startTime = System.currentTimeMillis();
long endTime, epochStartTime, elapsedTime;
double totalCost = 0.0;
double[] learnRate;
double[] NNCost;
learnRate = new double[500];
NNCost = new double[500];
double oldCost;
// Train the Neural Network
for (int i = 0; i < 500; i++) {
System.out.println("Epoch " + i);
epochStartTime = System.currentTimeMillis();
nn.train(data);
elapsedTime = System.currentTimeMillis();
System.out.println("Time elapsed: " + (elapsedTime - epochStartTime) + " ms");
oldCost = totalCost;
totalCost = nn.evaluateTotalCost(data);
System.out.println("Total cost: " + totalCost + " Learning rate: " + nn.learningRate);
window.update();
learnRate[i] = nn.learningRate;
NNCost[i] = totalCost;
if (totalCost < NeuralNetwork.COST_TOLERANCE) break;
if (Math.abs(oldCost - totalCost) < NeuralNetwork.COST_VARIATION_TOLERANCE) break;
data.resetBatches();
}
endTime = System.currentTimeMillis() - startTime;
System.out.println("Total training time: " + endTime + " ms (" + (endTime / 1000.0) + " s)");
window.update();
int correct = 0;
DataSet d = eval;
for (int i = 0; i < d.points.length; i++) {
double res = nn.calculateOutputs(d.points[i].inputs)[0];
correct += (res > 0.5 ? 1.0 : 0.0) == d.points[i].expectedOutputs[0] ? 1 : 0;
}
System.out.println("Correct points on eval set: " + correct + " / " + d.points.length);
System.out.println("Accuracy: " + (correct * 100.0 / d.points.length) + "%");
// Save the Neural Network
saveStats(learnRate, NNCost, null, "stats.txt");
}
public static void saveStats(double[] learnRate, double[] NNCost, double[] accuracies, String filename) {
// Save learn rate and cost in a text file, where each line contains:
// learnRate[i], NNCost[i]
try (BufferedWriter writer = new BufferedWriter(new FileWriter("stats/" + filename))) {
for (int i = 0; i < learnRate.length; i++) {
writer.write(learnRate[i] + "," + NNCost[i]);
if (accuracies != null) {
writer.write("," + accuracies[i]);
}
writer.newLine();
}
} catch (Exception e) {
e.printStackTrace();
}
}
}

101
src/main/java/fr/perceptron/MnistMain.java Normal file
View File

@@ -0,0 +1,101 @@
package fr.perceptron;
import java.io.File;
import java.io.IOException;
public class MnistMain {
public static void main(String[] args) {
int[] layerSizes = {
784, // 28 * 28 = 784
512, // 256 neurons in the hidden layer 1
// 128, // 128 neurons in the hidden layer 2
10 // 10 classes (0-9 digits)
};
if (args.length > 0) {
layerSizes = new int[args.length];
for (int i = 0; i < args.length; ++i) {
layerSizes[i] = Integer.parseInt(args[i]);
}
}
if (!new File("data.dat").exists()) {
MNIST.loadTrain();
MNIST.loadEval();
MNIST.save();
} else {
MNIST.load();
}
DataSet data = new DataSet(1);
DataSet eval = new DataSet(1);
data.loadFromArray(MNIST.getTrainImages(), MNIST.getTrainLabels());
data.batchesOf(1000);
eval.loadFromArray(MNIST.getEvalImages(), MNIST.getEvalLabels());
String cachedNN = "nn.dat";
NeuralNetwork nn;
System.out.println("Try loading cached NN...");
try {
nn = NeuralNetwork.load(cachedNN);
} catch (ClassNotFoundException | IOException e) {
System.out.println("Loading failed, creating new NN...");
nn = new NeuralNetwork(layerSizes);
}
System.out.println("Done.");
System.out.println(nn + "\n");
int numEpochs = 500;
long startTime = System.currentTimeMillis();
long endTime, epochStartTime, elapsedTime;
double[] learnRates = new double[numEpochs];
double[] dataAccuracies = new double[numEpochs];
// double[] NNCost = new double[numEpochs];
double[] evalAccuracies = new double[numEpochs];
// double cost = 0.0;
// Train the Neural Network
for (int i = nn.epoch; i < numEpochs; i++) {
System.out.println("Epoch " + i);
epochStartTime = System.currentTimeMillis();
nn.train(data);
elapsedTime = System.currentTimeMillis() - epochStartTime;
System.out.println("Time elapsed: " + elapsedTime + "ms (" + (elapsedTime / 1000.0) + "s)");
// cost = nn.evaluateTotalCost(data);
double dataAccuracy = nn.evaluateAccuracy(data);
double evalAccuracy = nn.evaluateAccuracy(eval);
System.out.println(String.format("Learning rate: %.10f", nn.learningRate));
System.out.println(String.format("Train data accuracy: %.5f%%", dataAccuracy * 100));
System.out.println(String.format("Eval data accuracy: %.5f%%", evalAccuracy * 100));
learnRates[i] = nn.learningRate;
// NNCost[i] = cost;
dataAccuracies[i] = dataAccuracy;
evalAccuracies[i] = evalAccuracy;
data.resetBatches();
// Main.saveStats(learnRates, NNCost, dataAccuracies, "stats.txt");
Main.saveStats(learnRates, dataAccuracies, evalAccuracies, "stats.txt");
try {
nn.save(cachedNN);
} catch (IOException e) {
System.out.println(e);
e.printStackTrace();
return;
}
}
endTime = System.currentTimeMillis() - startTime;
System.out.println("Total training time: " + endTime + "ms (" + (endTime / 1000.0) + "s)");
// Main.saveStats(learnRates, NNCost, dataAccuracies, "stats.txt");
Main.saveStats(learnRates, dataAccuracies, evalAccuracies, "stats.txt");
try {
nn.save(cachedNN);
} catch (IOException e) {
System.out.println(e);
e.printStackTrace();
return;
}
}
}

330
src/main/java/fr/perceptron/MnistWindow.java Normal file
View File

@@ -0,0 +1,330 @@
package fr.perceptron;
import javax.swing.BorderFactory;
import javax.swing.BoxLayout;
import javax.swing.JButton;
import javax.swing.JComponent;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import java.awt.BorderLayout;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.FlowLayout;
import java.awt.Graphics;
import java.awt.event.MouseAdapter;
import java.awt.event.MouseEvent;
import java.awt.event.MouseMotionAdapter;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.Arrays;
import java.util.Random;
public class MnistWindow {
private int PXSIZE = 20;
private DataSet data;
private final JFrame frame;
private final DrawingPanel panel;
private final Random random = new Random();
private BarComponent[] bars;
private int currentPointIndex = 0;
private JLabel expectedResult;
private JLabel predictedResult;
private NeuralNetwork nn;
public MnistWindow(DataSet data, NeuralNetwork nn) {
this.data = data;
this.nn = nn;
frame = new JFrame("MNIST Viewer");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
panel = new DrawingPanel(Arrays.copyOf(data.points[0].inputs, data.points[0].inputs.length));
bars = new BarComponent[10];
JPanel rightPanel = new JPanel();
rightPanel.setLayout(new BoxLayout(rightPanel, BoxLayout.Y_AXIS));
// Create digit bars
for (int i = 0; i < 10; i++) {
JPanel barPanel = new JPanel(new FlowLayout(FlowLayout.LEFT, 5, 0));
barPanel.add(new JLabel(Integer.toString(i)));
BarComponent bar = new BarComponent();
bar.setPreferredSize(new Dimension(200, 20));
bars[i] = bar;
barPanel.add(bar);
rightPanel.add(barPanel);
}
expectedResult = new JLabel();
predictedResult = new JLabel();
rightPanel.add(predictedResult);
rightPanel.add(expectedResult);
double acc = 0;
for (DataPoint d : data.points) {
double expected = getDataValue(d.expectedOutputs);
double predicted = getDataValue(softmax(nn.calculateOutputs(d.inputs)));
if (expected == predicted) {
acc += 1;
}
}
acc /= data.points.length;
// Create buttons
JPanel buttonPanel = new JPanel();
JButton resetButton = new JButton("Reset");
resetButton.addActionListener(e -> panel.reset());
JButton nextButton = new JButton("Next");
nextButton.addActionListener(e -> showRandomPoint(false));
JButton nextErrorButton = new JButton("Next error");
nextErrorButton.addActionListener(e -> showRandomPoint(true));
buttonPanel.add(resetButton);
buttonPanel.add(nextButton);
buttonPanel.add(nextErrorButton);
rightPanel.add(buttonPanel);
rightPanel.add(new JLabel("Global accuracy : " + acc*100 + "%"));
// Setup main layout
JPanel mainPanel = new JPanel(new BorderLayout(10, 10));
mainPanel.setBorder(BorderFactory.createEmptyBorder(10, 10, 10, 10));
mainPanel.add(panel, BorderLayout.WEST);
mainPanel.add(rightPanel, BorderLayout.CENTER);
frame.add(mainPanel);
frame.pack();
frame.setVisible(true);
predict(false);
//showPoint(currentPointIndex); // Show initial point
}
public double[] softmax(double outputs[]) {
double[] result = new double[outputs.length];
double sum = 0;
for (double v : outputs) {
sum += v;
}
for (int i=0; i<result.length; i++) {
result[i] = outputs[i]/sum;
}
return result;
}
// Update le tout pour le dessin custom
private void predictCustom() {
double[] inputs = panel.getData();
double[] outputs = softmax(nn.calculateOutputs(inputs));
String v = String.valueOf(getDataValue(outputs));
expectedResult.setText("");
predictedResult.setText("Predicted : " + v);
updateBars(outputs);
}
private void predict(boolean findError) {
DataPoint point = data.points[currentPointIndex];
panel.setData(Arrays.copyOf(point.inputs, point.inputs.length));
double[] inputs = panel.getData();
double[] outputs = softmax(nn.calculateOutputs(inputs));
double[] expectedOutputs = point.expectedOutputs;
expectedResult.setText("Expected : " + getDataValue(expectedOutputs));
predictedResult.setText("Predicted : " + getDataValue(outputs));
updateBars(outputs);
if (findError && (getDataValue(expectedOutputs) == getDataValue(outputs))) {
showRandomPoint(true);
}
}
private int getDataValue(double[] d) {
double max = 0;
int imax = 0;
for (int i=0; i<d.length; i++) {
double v = d[i];
if (v>max) {
max = v;
imax = i;
}
}
return imax;
}
private void showRandomPoint(boolean findError) {
currentPointIndex = random.nextInt(data.points.length);
//showPoint(currentPointIndex);
predict(findError);
}
private void updateBars(double[] values) {
for (int i = 0; i < 10; i++) {
bars[i].setValue(values[i]);
}
}
private BufferedImage createImageFromData(double[] data) {
BufferedImage image = new BufferedImage(28, 28, BufferedImage.TYPE_INT_RGB);
for (int y = 0; y < 28; y++) {
for (int x = 0; x < 28; x++) {
int value = (int) (data[y * 28 + x] * 255);
value = Math.max(0, Math.min(255, value));
int rgb = new Color(value, value, value).getRGB();
image.setRGB(x, y, rgb);
}
}
return image;
}
private class DrawingPanel extends JPanel {
private double[] data;
private BufferedImage currentImage;
private DrawingPanel(double[] data) {
this.data = data;
this.currentImage = createImageFromData(data);
setupMouseListeners();
setPreferredSize(new Dimension(28*PXSIZE, 28*PXSIZE));
}
public double[] getData() {
return data;
}
public void setData(double[] newData) {
this.data = Arrays.copyOf(newData, newData.length);
this.currentImage = createImageFromData(this.data);
repaint();
}
private void setupMouseListeners() {
addMouseListener(new MouseAdapter() {
@Override
public void mousePressed(MouseEvent e) {
updateCell(e.getX(), e.getY());
}
});
addMouseMotionListener(new MouseMotionAdapter() {
@Override
public void mouseDragged(MouseEvent e) {
updateCell(e.getX(), e.getY());
}
});
}
private void updateCell(int x, int y) {
int col = x / PXSIZE;
int row = y / PXSIZE;
int radius = 2;
double sigma = radius / 2.8;
if (col >= 0 && col < 28 && row >= 0 && row < 28) {
for (int dr = -radius; dr <= radius; dr++) {
for (int dc = -radius; dc <= radius; dc++) {
int c = col + dc;
int r = row + dr;
if (c >= 0 && c < 28 && r >= 0 && r < 28) {
double distance = Math.sqrt(dc * dc + dr * dr);
if (distance <= radius) {
// gauss
double exponent = -(distance * distance) / (2 * sigma * sigma);
double falloff = Math.exp(exponent);
int index = r * 28 + c;
data[index] = Math.min(1.0, Math.max(data[index]+falloff/2, falloff));
}
}
}
}
currentImage = createImageFromData(data);
repaint();
predictCustom();
}
}
public void reset() {
Arrays.fill(data, 0.0);
currentImage = createImageFromData(data);
repaint();
}
@Override
protected void paintComponent(Graphics g) {
super.paintComponent(g);
if (currentImage == null) return;
g.drawImage(currentImage, 0, 0, 28*PXSIZE, 28*PXSIZE, null);
}
}
private class BarComponent extends JComponent {
private double value = 0.0;
public void setValue(double value) {
this.value = Math.max(0.0, Math.min(1.0, value));
repaint();
}
@Override
protected void paintComponent(Graphics g) {
super.paintComponent(g);
int width = getWidth();
int height = getHeight();
// Draw background
g.setColor(Color.WHITE);
g.fillRect(0, 0, width, height);
// Draw filled bar
g.setColor(Color.BLACK);
int fillWidth = (int) (value * (width - 2));
g.fillRect(1, 1, fillWidth, height - 2);
// Draw border
g.setColor(Color.BLACK);
g.drawRect(0, 0, width - 1, height - 1);
}
@Override
public Dimension getPreferredSize() {
return new Dimension(200, 20);
}
}
public static void main(String[] args) {
if (!new File("data.dat").exists()) {
MNIST.loadTrain();
MNIST.loadEval();
MNIST.save();
}
else {
MNIST.load();
}
DataSet eval = new DataSet(1);
eval.loadFromArray(MNIST.getEvalImages(), MNIST.getEvalLabels());
String cachedNN = "nn_256_128.dat";
NeuralNetwork nn;
System.out.println("Try loading cached NN...");
try {
nn = NeuralNetwork.load(cachedNN);
System.out.println("Done.");
System.out.println(nn + "\n");
new MnistWindow(eval, nn);
} catch (ClassNotFoundException | IOException e) {
System.out.println(e);
System.out.println("Loading failed.");
}
}
}

215
src/main/java/fr/perceptron/NeuralNetwork.java Normal file
View File

@@ -0,0 +1,215 @@
package fr.perceptron;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serializable;
import java.util.Random;
// import java.util.concurrent.ExecutorService;
// import java.util.concurrent.Executors;
// import java.util.concurrent.Future;
public class NeuralNetwork implements Serializable {
public int[] sizes; // number of neurons for each layer of the neural network
public Layer[] layers;
private transient Random random;
private static final double initialLearningRate = 0.1d;
private static final double decayRate = 0.95d;
private static final double decayFactor = 10.0d;
public static final double COST_TOLERANCE = 10e-5;
public static final double COST_VARIATION_TOLERANCE = 10e-7;
public int epoch;
public double learningRate;
public NeuralNetwork(int[] layerSizes) {
this.sizes = layerSizes;
// the "input layer" doesn't truly exist so we don't add it
this.layers = new Layer[layerSizes.length - 1];
// init to a fixed seed to debug
this.random = new Random(0);
// set the epoch to 0 and the learning rate to the initial value
this.epoch = 0;
this.learningRate = initialLearningRate;
// create each layer with the number of input neurons corresponding
// to the number of output neurons of the previous index
for (int i = 0; i < layerSizes.length - 1; ++i) {
layers[i] = new Layer(layerSizes[i], layerSizes[i + 1], this.random);
}
}
// calculates the output of the Neural Network based on given input values
// the inputs are forwarded through each layer of the NN, using the forward()
// method of each layer.
public double[] calculateOutputs(double[] inputs) {
double[] layerOutput = inputs;
// forward the inputs through each layer
for (int i = 0; i < this.layers.length; ++i) {
layerOutput = this.layers[i].forward(layerOutput);
}
return layerOutput;
}
// returns an int that corresponds to the index of the highest activation
// value of an output. (i.e. the index of the max of all values of the array)
public int classify(double[] networkOutput) {
int maxActivationIndex = 0;
double maxActivation = Double.MIN_VALUE;
for (int i = 0; i < networkOutput.length; ++i) {
if (networkOutput[i] > maxActivation) {
maxActivation = networkOutput[i];
maxActivationIndex = i;
}
}
return maxActivationIndex;
}
public int classify(double input) {
// return 1 if input > 0 else 0
return (input > 0.5 ? 1 : 0);
}
// calculates the cost of an evaluation by comparing the values predicted
// with the expected output. The formula used for the cost is the
// mean square error, defined as :
// E(w) = 1/2 * sum( (expected - predicted) ^ 2)
public double calculateCost(double[] expected, double[] outputs) {
double cost = 0;
for (int i = 0; i < outputs.length; ++i) {
cost += Math.pow(expected[i] - outputs[i], 2);
}
return 0.5 * cost;
}
public void train(DataSet data) {
// Train the Neural Network using the given data set
// the data set is split into batches, and each batch is used to
// update the weights and biases of the Neural Network
// This implementation is sequential, but it could be parallelized (TODO)
DataSet currentSet = data.generateBatch();
while(currentSet != null) {
System.out.print(String.format("Batch %2d/%d\r", data.currentBatch, data.numBatches));
// for each point in the batch, calculate the outputs and update
// the gradients of the Neural Network
for (DataPoint currentPoint : currentSet.points) {
double[] values = this.calculateOutputs(currentPoint.inputs);
this.updateGradients(currentPoint, values);
}
currentSet = data.generateBatch();
}
System.out.println();
// update the epoch and learning rate
this.epoch++;
this.learningRate = initialLearningRate
* Math.pow(decayRate, this.epoch / decayFactor);
}
public void updateGradients(DataPoint point, double[] values) {
// update the gradients of the Neural Network
int finalLayer = this.layers.length - 1;
double[][] layerErrors = new double[finalLayer + 1][];
layerErrors[finalLayer] = new double[this.layers[finalLayer].numNeurons];
// calculate the delta for the output layer
for (int i = 0; i < layerErrors[finalLayer].length; i++) {
layerErrors[finalLayer][i] = -(point.expectedOutputs[i] - values[i])
* Layer.activationDerivativeFunction(this.layers[finalLayer].rawLayerData[i]);
}
// calculate the delta for the hidden layers
for (int i = finalLayer - 1; i >= 0; i--) {
int layerSize = this.layers[i].numNeurons;
layerErrors[i] = new double[layerSize];
for (int j = 0; j < layerSize; j++) {
layerErrors[i][j] = 0;
for (int k = 0; k < this.layers[i+1].numNeurons; k++) {
layerErrors[i][j] += this.layers[i + 1].getWeight(j, k) * layerErrors[i + 1][k];
}
layerErrors[i][j] *= Layer.activationDerivativeFunction(layers[i].rawLayerData[j]);
}
}
// update the weights and biases of each layer
for (int l = 0; l < this.layers.length; l++) { // for each layer
for (int j = 0; j < this.layers[l].numNeurons; j++) { // for each neuron
for (int i = 0; i < this.layers[l].numInputNeurons; i++) { // for each input neuron
double w = this.layers[l].getWeight(i, j);
// update the weight using the delta of the neuron
// find the raw data of the neuron
// if l == 0, the raw data is the input data
double rawData = (l == 0) ? point.inputs[i] : layers[l - 1].rawLayerData[i];
// update the weight
// w = w - learningRate * delta * f(rawData)
w = w - this.learningRate * layerErrors[l][j]
* Layer.activationFunction(rawData);
// set the new weight
this.layers[l].setWeight(i, j, w);
}
// update the biases
this.layers[l].biases[j] -= this.learningRate * layerErrors[l][j];
}
}
}
public double evaluateTotalCost(DataSet data) {
double res = 0.0;
for (int i = 0; i < data.numPoints; ++i) {
DataPoint point = data.points[i];
double[] outputs = calculateOutputs(point.inputs);
res += calculateCost(point.expectedOutputs, outputs);
}
return res / data.numPoints;
}
public double evaluateAccuracy(DataSet data) {
int correct = 0;
for (int i = 0; i < data.numPoints; ++i) {
DataPoint point = data.points[i];
double[] outputs = calculateOutputs(point.inputs);
int predicted = classify(outputs);
int expected = classify(point.expectedOutputs);
if (predicted == expected) {
correct++;
}
}
return (double) correct / data.numPoints;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("NeuralNetwork{\n");
for (int i = 0; i < this.layers.length; ++i) {
sb.append(" " + this.layers[i].toString() + "\n");
}
sb.append("}");
return sb.toString();
}
public void save(String filename) throws IOException {
try (ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(filename))) {
oos.writeObject(this);
}
}
public static NeuralNetwork load(String filename) throws IOException, ClassNotFoundException {
try (ObjectInputStream ois = new ObjectInputStream(new FileInputStream(filename))) {
NeuralNetwork nn = (NeuralNetwork) ois.readObject();
nn.random = new Random(0);
return nn;
}
}
}

209
src/main/java/fr/perceptron/Window.java Normal file
View File

@@ -0,0 +1,209 @@
package fr.perceptron;
import javax.swing.JFrame;
import javax.swing.JPanel;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.Graphics;
import java.awt.event.KeyEvent;
import java.awt.event.KeyListener;
import java.awt.image.BufferedImage;
public class Window {
public Canvas canvas;
public Window() {}
public void init(NeuralNetwork nn, DataSet data) {
// Set the canvas to be the main frame's content pane
this.canvas = new Canvas(800, 800);
this.canvas.setBackground(Color.white);
this.canvas.setPreferredSize(new Dimension(800, 800));
this.canvas.setVisible(true);
JFrame mainFrame = new JFrame("Perceptron");
mainFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
mainFrame.setSize(800, 800);
mainFrame.add(this.canvas);
mainFrame.addKeyListener(canvas);
mainFrame.setResizable(false);
mainFrame.setLocationRelativeTo(null); // Center the window
mainFrame.pack();
mainFrame.setVisible(true);
mainFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
this.canvas.data = data;
this.canvas.nn = nn;
this.canvas.updatePixels();
}
public void update() {
// Update the canvas with the neural network's output
canvas.updatePixels();
}
}
class Canvas extends JPanel implements KeyListener {
private byte[][] pixels;
private int width;
private int height;
public DataSet data;
private float zoomLevel;
public NeuralNetwork nn;
private static final Color[] COLORS = {
new Color(0xc06699ff, true), // light blue
new Color(0xc0ff6666, true), // light red
};
public Canvas(int width, int height) {
setBackground(Color.white);
this.pixels = new byte[height][width];
this.width = width;
this.height = height;
// this.zoomLevel = 2.0f; // linear training set
// this.zoomLevel = 0.75f; // moon training set
this.zoomLevel = 0.125f; // two circles training set
// Enable double buffering
setDoubleBuffered(true);
}
public Dimension getPreferredSize() {
return new Dimension(800, 800);
}
public void paintComponent(Graphics g) {
super.paintComponent(g);
// Clear the canvas
g.setColor(Color.WHITE);
g.fillRect(0, 0, getWidth(), getHeight());
// Draw the axes
this.drawAxis(g);
// Draw the points
this.drawDataPoints(g);
// draw the pixels from the pixels array to the canvas
// for (int y = 0; y < height; y++) {
// for (int x = 0; x < width; x++) {
// // Set the color based on the pixel value
// g.setColor(COLORS[pixels[y][x]]);
// // Draw one pixel
// g.fillRect(x, y, 1, 1);
// }
// }
// Draw the pixels from the pixels array to the canvas
// use a BufferedImage for better performance
BufferedImage img = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
// Set the color based on the pixel value
img.setRGB(x, y, COLORS[pixels[y][x]].getRGB());
}
}
g.drawImage(img, 0, 0, null);
}
public void updatePixels() {
System.out.println("Updating pixels...");
int midX = width / 2;
int midY = height / 2;
// Initialize a new pixels array
byte[][] newPixels = new byte[height][width];
// Use parallel processing to compute pixel values
java.util.stream.IntStream.range(0, height).parallel().forEach(y -> {
for (int x = 0; x < width; x++) {
// Normalize the pixel coordinates to the range [-2, 2]
double[] inputs = {
// Normalize the pixel coordinates to the range [-2, 2]
(double) (x - midX) / (width / 4 * this.zoomLevel),
(double) -(y - midY) / (height / 4 * this.zoomLevel),
};
// Calculate the output of the neural network
double[] output = nn.calculateOutputs(inputs);
int classification = nn.classify(output[0]);
// Store the computed value in the temporary array
newPixels[y][x] = (byte) classification;
}
});
// Replace the old pixels array with the new one
this.pixels = newPixels;
System.out.println("Done updating pixels.");
// Repaint the canvas
repaint();
}
public void drawAxis(Graphics g) {
// Draw the axes
g.setColor(Color.BLACK);
g.drawLine(0, height / 2, width, height / 2); // X-axis
g.drawLine(width / 2, 0, width / 2, height); // Y-axis
// Add labels to the axes
g.drawString("X", width - 20, height / 2 + 15);
g.drawString("Y", width / 2 - 15, 15);
// g.drawString("0", width / 2 + 5, height / 2 + 15);
// g.drawString("1", width - 20, height / 2 - 5);
// g.drawString("-1", width / 2 + 5, height - 5);
// g.drawString("-1", 5, height / 2 + 15);
// g.drawString("1", width / 2 + 5, 15);
}
public void drawDataPoints(Graphics g) {
// Draw the data points
g.setColor(Color.BLACK);
for (int i = 0; i < this.data.points.length; i++) {
int x = (int) (this.zoomLevel * this.data.points[i].inputs[0] * (width / 4) + (width / 2));
int y = (int) (this.zoomLevel * -this.data.points[i].inputs[1] * (height / 4) + (height / 2));
if (this.data.points[i].expectedOutputs[0] == 1.0) {
// Draw a circle
g.drawOval(x, y, 8, 8);
} else {
// Draw a cross
g.drawLine(x - 4, y, x + 4, y);
g.drawLine(x, y - 4, x, y + 4);
}
}
}
@Override
public void keyPressed(KeyEvent e) {
// Check if the key pressed is UP or DOWN
if (e.getKeyCode() == KeyEvent.VK_UP) {
this.zoomLevel += 0.125f;
System.out.println("Zoom level: " + this.zoomLevel);
} else if (e.getKeyCode() == KeyEvent.VK_DOWN) {
this.zoomLevel -= 0.125f;
System.out.println("Zoom level: " + this.zoomLevel);
}
if (this.zoomLevel < 0.125f) {
this.zoomLevel = 0.125f;
}
}
@Override
public void keyReleased(KeyEvent e) {}
@Override
public void keyTyped(KeyEvent e) {
if (e.getKeyChar() == 'u') {
// Update the pixels
updatePixels();
repaint();
}
}
}

92
stats/stats.py Normal file
View File

@@ -0,0 +1,92 @@
#/usr/bin/env python3
# -*- coding: utf-8 -*-
# Simple script to show statistics a Neural Network training session.
# - Imports
from matplotlib import pyplot as plt
# Read the data from the file
def read_data(file_path: str) -> list:
with open(file_path, 'r') as file:
lines = file.readlines()
return [line.strip() for line in lines if line.strip()]
# Parse the data from the file
# This function takes the data read from the file and parses it.
# Each line contains multiple values separated by commas.
# The function extracts the values and returns them as a list of lists.
def parse_data(data: list, numEntries: int) -> list[list]:
parsed_data = [[] for _ in range(numEntries)]
for line in data:
values = line.split(',')
for i in range(numEntries):
try:
parsed_data[i].append(float(values[i]))
except (ValueError, IndexError):
print(f"Error parsing line: {line}")
continue
return parsed_data
# Plot the data using matplotlib
def plot_data(
data: list,
title: str,
xlabel: str,
ylabel: str,
log_scale: bool = False
) -> None:
# Plot the data
plt.figure(figsize=(10, 5))
plt.plot(data, label=title)
# Add labels and title
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.legend()
plt.grid()
# Show the plot
if log_scale:
plt.yscale('log')
plt.show()
def plot_accuracies(
learn_rates: list,
train: list,
eval: list
) -> None:
# Create 2 subplots, one for the learning rates in relation to the epochs
# and one for the training and evaluation accuracies
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10))
# Plot the learning rates
ax1.plot(learn_rates, label='Learning Rate')
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Learning Rate')
ax1.set_title('Learning Rate vs Epochs')
ax1.legend()
ax1.grid()
# Plot the accuracies
ax2.plot(train, label='Training Accuracy')
ax2.plot(eval, label='Evaluation Accuracy')
ax2.set_xlabel('Epochs')
ax2.set_ylabel('Accuracy')
ax2.set_title('Training and Evaluation Accuracy vs Epochs')
ax2.legend()
ax2.grid()
# Show the plots
plt.tight_layout()
plt.show()
# Main function to execute the script
def main():
# Read the data from the file
data = read_data('stats/stats_256_128.txt')
# Parse the data
learn_rates, data_accuracy, eval_accuracy = parse_data(data, 3)
# Plot the accuracies
plot_accuracies(learn_rates, data_accuracy, eval_accuracy)
if __name__ == "__main__":
main()
# - End of script

BIN
stats/stats.txt LFS Normal file
View File

Binary file not shown.

BIN
stats/stats_256_128.txt LFS Normal file
View File

Binary file not shown.