diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100755
index 0000000000000000000000000000000000000000..810fce6e9bf2aa10265b85614db5ac65941ecf81
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,621 @@
+ 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
diff --git a/README.md b/README.md
deleted file mode 100644
index e9306769a7e24b1984ac54c38381390ebe0fd7d3..0000000000000000000000000000000000000000
--- a/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# sigma_faust
-
diff --git a/README.rst b/README.rst
new file mode 100644
index 0000000000000000000000000000000000000000..87d2ff9d78422f0f15dd9be89216bb88a26621b4
--- /dev/null
+++ b/README.rst
@@ -0,0 +1,53 @@
+sigma_faust
+===========
+
+Code to reproduce experiments in preprint *Learning a sum of sparse matrix
+products* by Moujahid Bou-Laouz, Valentin Emiya, Liva Ralaivola and Caroline
+Chaux (currently under review).
+
+Install
+-------
+
+Install requirements by creating an ``anaconda`` environment with Python 3.7
+or 3.9:
+
+ conda create -n sigma_faust python=3.7 pandas numpy matplotlib scipy scikit-learn sympy openpyxl
+
+or
+
+ conda create -n sigma_faust python=3.9 pandas numpy matplotlib scipy scikit-learn sympy openpyxl
+
+Then activate the environment and install ``pyfaust``:
+
+ conda activate sigma_faust
+ pip install pyfaust
+
+Download and extract 'sigma_faust' files, go to the source code root directory
+and run
+
+ pip install -e .
+
+
+Running the code
+----------------
+
+To reproduce the first experiment and the top two plots of Figure 1 (target
+matrix is composed of random entries), execute file `exp_icassp_random.py` to
+run the experiment and execute function `plot_random(N=256)` from
+`sigma_faust.plot_icassp_results` module to display the results.
+
+To reproduce the second experiment, the bottom plot of Figure 1 and Figure 2
+(target matrix is composed of data from MNIST),
+
+* download and locally extract the data from
+`https://archive.ics.uci .edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits`
+* run file `extract_matrices_from_datasets.py` after adapting path variable
+`root_dir` depending on the location of your data
+* run file `exp_icassp_data_matrices.py`.
+* execute function `plot_digits()` from
+`sigma_faust.plot_icassp_results` module to display the results.
+
+Bug report
+----------
+
+This code is in a preliminary version. Please report any bug to the authors.
\ No newline at end of file
diff --git a/VERSION b/VERSION
new file mode 100755
index 0000000000000000000000000000000000000000..33b12eac499c8c30bf2fe7a11d5d738d12cbf389
--- /dev/null
+++ b/VERSION
@@ -0,0 +1 @@
+sigma_faust:0.1
diff --git a/doc/copyright.rst b/doc/copyright.rst
new file mode 100755
index 0000000000000000000000000000000000000000..b8e4e7f46a27b5c4a8c52f1538801bbb74afdfef
--- /dev/null
+++ b/doc/copyright.rst
@@ -0,0 +1,47 @@
+Credits
+#######
+
+Copyright(c) 2019-2021
+----------------------
+
+* Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+* Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+* Université d'Aix-Marseille <http://www.univ-amu.fr/>
+* Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+* Université de Toulon <http://www.univ-tln.fr/>
+
+Contributors
+------------
+
+* Moujahid Bou-Laouz
+* Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+
+Description
+-----------
+
+`sigma_faust` is a Python implementation of algorithms and experiments proposed
+ in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+ Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+
+
+Version
+-------
+
+* sigma_faust version = 0.1
+
+Licence
+-------
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program. If not, see <http://www.gnu.org/licenses/>.
+
diff --git a/setup.cfg b/setup.cfg
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/setup.py b/setup.py
new file mode 100755
index 0000000000000000000000000000000000000000..c30ce3ed0b099a57d83003d72a56bfd45c3c9101
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+
+import os
+from setuptools import setup, find_packages
+import sys
+
+NAME = 'sigma_faust'
+DESCRIPTION = \
+ 'Learning a sum of sparse matrix products, Bou-Laouz et al., 2021.'
+LICENSE = 'GNU General Public License v3 (GPLv3)'
+URL = 'https://gitlab.lis-lab.fr/valentin.emiya/{}'.format(NAME)
+AUTHOR = 'Moujahid Bou-Laouz, Valentin Emiya'
+AUTHOR_EMAIL = ('valentin.emiya@lis-lab.fr')
+INSTALL_REQUIRES = ['numpy', 'scipy', 'matplotlib', 'pandas',
+ 'scikit-learn', 'sympy', 'openpyxl', 'pyfaust']
+CLASSIFIERS = [
+ 'Development Status :: 5 - Production/Stable',
+ 'Intended Audience :: Developers',
+ 'Intended Audience :: End Users/Desktop',
+ 'Intended Audience :: Science/Research',
+ 'Topic :: Scientific/Engineering :: Mathematics',
+ 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
+ 'Natural Language :: English',
+ 'Operating System :: MacOS :: MacOS X ',
+ 'Operating System :: POSIX :: Linux',
+ 'Programming Language :: Python :: 3.7']
+PYTHON_REQUIRES = '>=3.7'
+EXTRAS_REQUIRE = {
+ 'dev': ['coverage', 'pytest', 'pytest-cov', 'pytest-randomly'],
+ 'doc': ['nbsphinx', 'numpydoc', 'sphinx']}
+PROJECT_URLS = {'Bug Reports': URL + '/issues', 'Source': URL}
+KEYWORDS = 'matrix, decomposition, sparsity, algorithm'
+
+###############################################################################
+if sys.argv[-1] == 'setup.py':
+ print("To install, run 'python setup.py install'\n")
+
+if sys.version_info[:2] < (3, 7):
+ errmsg = '{} requires Python 3.7 or later ({[0]:d}.{[1]:d} detected).'
+ print(errmsg.format(NAME, sys.version_info[:2]))
+ sys.exit(-1)
+
+
+def get_version():
+ v_text = open('VERSION').read().strip()
+ v_text_formted = '{"' + v_text.replace('\n', '","').replace(':', '":"')
+ v_text_formted += '"}'
+ v_dict = eval(v_text_formted)
+ return v_dict[NAME]
+
+
+def set_version(path, VERSION):
+ filename = os.path.join(path, '__init__.py')
+ buf = ""
+ for line in open(filename, "rb"):
+ if not line.decode("utf8").startswith("__version__ ="):
+ buf += line.decode("utf8")
+ f = open(filename, "wb")
+ f.write(buf.encode("utf8"))
+ f.write(('__version__ = "%s"\n' % VERSION).encode("utf8"))
+
+
+def setup_package():
+ """Setup function"""
+ # set version
+ VERSION = get_version()
+
+ here = os.path.abspath(os.path.dirname(__file__))
+ with open(os.path.join(here, 'README.rst'), encoding='utf-8') as f:
+ long_description = f.read()
+
+ mod_dir = NAME
+ set_version(mod_dir, get_version())
+ setup(name=NAME,
+ version=VERSION,
+ description=DESCRIPTION,
+ long_description=long_description,
+ url=URL,
+ author=AUTHOR,
+ author_email=AUTHOR_EMAIL,
+ license=LICENSE,
+ classifiers=CLASSIFIERS,
+ keywords=KEYWORDS,
+ packages=find_packages(exclude=['doc', 'dev']),
+ install_requires=INSTALL_REQUIRES,
+ python_requires=PYTHON_REQUIRES,
+ extras_require=EXTRAS_REQUIRE,
+ project_urls=PROJECT_URLS)
+
+
+if __name__ == "__main__":
+ setup_package()
diff --git a/sigma_faust/PALM.py b/sigma_faust/PALM.py
new file mode 100644
index 0000000000000000000000000000000000000000..56b1c51101cdb82112d4f6a1206ca7d4be4a6f91
--- /dev/null
+++ b/sigma_faust/PALM.py
@@ -0,0 +1,291 @@
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""
+Created on Tue May 16 13:23:40 2021
+
+ @author: Moujahid
+"""
+
+
+from pyfaust import Faust
+from pyfaust.fact import palm4msa
+from pyfaust.factparams \
+ import ParamsPalm4MSA, ConstraintList, StoppingCriterion
+import pyfaust
+import numpy as np
+from numpy import linalg as LA
+from scipy.sparse import random
+from numpy.linalg import multi_dot
+from scipy.stats import special_ortho_group
+from random import randint
+import math
+
+
+
+def asfortranarray(A):
+
+ return [np.asfortranarray(i) for i in A]
+
+def asfortranarray2(A):
+ return [asfortranarray(A[i]) for i in range(len(A))]
+
+
+def f(A):
+ #lors de la réinitilasition sur chaques itérations, des fois les facteurs sont denses/creuses
+ if type(A) is np.ndarray:
+ return A
+ else:
+ return A.toarray()
+
+
+def Faust_to_list(F):
+ return [np.asfortranarray(f(F.factors(i))) for i in range(F.numfactors())]
+
+def Erreur_r(A,B):
+ return LA.norm(A-B)/LA.norm(A)
+
+def multiplication(A,_lambda=1):
+ if len(A)==1:
+ return _lambda*A[0]
+ else:
+ return _lambda*multi_dot(A)
+
+
+def somme_multiplication(A,_lambda):
+ "A est une liste de liste"
+ "_lambda est une liste"
+ if len(A)==0:
+ return 0
+ if len(_lambda)==0:
+ _lambda=[1]*len(A)
+ S=[]
+ K=len(A)
+ for s in range(0,K):
+ S.append(multiplication(A[s],_lambda[s]))
+ S=sum(S)
+ return S
+
+
+
+#Palm pour le nouveau modèle
+#PALM2
+def Palm4smsa(A,K,J,cons,initfacts,initlambda,N,threshold=1e-3):
+ """
+ Parameters
+
+ ----------
+ A : Matrice a décomposer
+ K : somme
+ J : produit
+ cons : une liste dont chaque élément est un objet de la classe ConstraintList
+ initfacts : Initilisation de tout les KJ facteurs , une liste de K liste de J éléments
+ initlambda : Initialisation des scalaires lambda , une liste de K éléments
+ N : Nombre d'itérations
+
+ Returns
+ -------
+ F : facteurs
+ _lambda : scalaires
+ historique_lambda : valeurs des lambdas sur chaque itération
+ historique_facteurs : valeurs des facteurs sur chaque itération
+ erreur_relatif: erreur_relatif sur chaque itération
+ I : nombre d'itérations néccesaire pour atteindre le seuil
+
+
+ """
+
+
+ stop_crit=StoppingCriterion(num_its=1)
+
+ historique_lambda=[initlambda]
+ historique_facteurs=[initfacts]
+ S=somme_multiplication(initfacts,initlambda)
+ erreur_relatif=[LA.norm(A-S)/LA.norm(A)]
+ for i in range(N):
+ initlambda1=list(historique_lambda[len(historique_lambda)-1])
+ initfacts1=list(historique_facteurs[len(historique_facteurs)-1])
+ for k in range(K):
+ R=somme_multiplication(initfacts1[:k],initlambda1[:k])+somme_multiplication(initfacts1[k+1:],initlambda1[k+1:])
+ param=ParamsPalm4MSA(cons[k],stop_crit,init_facts=initfacts1[k],\
+ init_lambda=initlambda1[k],is_update_way_R2L=True)
+ M=A-R
+ init_factsu,lambdau=palm4msa(M,param,ret_lambda=True)
+ X=Faust_to_list(init_factsu)
+ # On doit normaliser les facteurs de init_factsu, on divise le facteur qui a une norme différente de 1 par lambdau
+ X=[X[i] if np.isclose(LA.norm(X[i]),1,atol=1e-03) else X[i]/lambdau for i in range(len(X))]
+ initfacts1[k]=asfortranarray(X)
+ initlambda1[k]=lambdau
+
+
+ historique_lambda.append(initlambda1)
+ historique_facteurs.append(initfacts1)
+ S=somme_multiplication(initfacts1,initlambda1)
+ erreur_relatif.append(LA.norm(A-S)/LA.norm(M))
+ if abs(erreur_relatif[i]-erreur_relatif[i-1])<threshold:
+ break
+ print("l'algorithme a convergé pendant %d iteration" %(i+1))
+ I=i+1
+ F=historique_facteurs[i]
+ _lambda=historique_lambda[i]
+ return F,_lambda,erreur_relatif,historique_facteurs,historique_lambda,I
+
+
+
+# On doit normaliser le facteur de init_factsu, on divise le faust qui a norme différente de 1 par lambdau
+
+
+#Exemple : test : initlisation dans un minimum local
+if __name__ == "__main__":
+ import matplotlib.pyplot as plt
+ d=3
+ S_1=np.array([[1,2,0],[0,0,4],[0,0,0]],dtype=float)
+ S_2=np.array([[1,0,0],[0,2,0],[0,0,4]],dtype=float)
+ M1=np.dot(S_1,S_2)
+ S_3=np.array([[0,0,3],[1,1,2],[0,0,0]],dtype=float)
+ S_4=np.array([[0,1,1],[4,0,2],[4,0,0]],dtype=float)
+ M2=np.dot(S_3,S_4)
+ print(M1+M2)
+ M=M1+M2
+ s_1=np.count_nonzero(S_1)
+ s_2=np.count_nonzero(S_2)
+ s_3=np.count_nonzero(S_3)
+ s_4=np.count_nonzero(S_4)
+ #Normalisation :
+ B_1=S_1/LA.norm(S_1)
+ B_2=S_2/LA.norm(S_2)
+ B_3=S_3/LA.norm(S_3)
+ B_4=S_4/LA.norm(S_4)
+
+ #Initilalisation de lambda
+ N=100
+ K=1
+ J=2
+ C=[ConstraintList('sp',s_1,d,d,'sp',s_2,d,d),\
+ ConstraintList('sp',s_3,d,d,'sp',s_4,d,d)]
+
+ B=[[B_1,B_2],[B_3,B_4]]
+ L=[LA.norm(S_1)*LA.norm(S_2),LA.norm(S_3)*LA.norm(S_4)]
+ A=Palm4smsa(M,K=2,J=2,cons=C,initfacts=asfortranarray2(B),initlambda=L,N=N)
+
+
+
+
+
+ # 2eme exemple : test de convergence
+ d=3
+ D=np.diag((4.1,4,5))
+ S_1
+ M=D+S_1
+ s_1=np.count_nonzero(S_1)
+ d_1=np.count_nonzero(D)
+ M=S_1+D
+
+ B_1=S_1/LA.norm(S_1)
+ D_1=D/LA.norm(D)
+ N=100
+ K=2
+ C=[ConstraintList('sp',s_1,d,d,'sp',s_1,d,d),\
+ ConstraintList('sp',d_1,d,d)]
+
+ B=[[B_1,B_1],[D_1]]
+ L=[LA.norm(S_1),LA.norm(D)]
+ P1=Palm4smsa(M,K=2,J=1,cons=C,initfacts=asfortranarray2(B),initlambda=L,N=N)
+
+
+
+
+ n=range(P1[5]+1)
+
+ fig = plt.figure(2)
+ plt.plot(n,P1[2])
+ plt.title('test de convergence')
+
+
+ #Exemple aléatoire
+ d=100
+
+ S_1=random(d,d,density=0.1)
+ S_2=random(d,d,density=0.1)
+ Y=3*S_1.dot(S_2).toarray()
+ K=3
+ J=2
+ N=30
+ C=[ConstraintList('skperm',2,d,d,'skperm',2,d,d),\
+ ConstraintList('skperm',2,d,d,'skperm',2,d,d),\
+ ConstraintList('skperm',2,d,d,'skperm',2,d,d)]
+
+
+
+ B=[[1/2*np.identity(d),np.zeros((d,d))]]*3
+ A=[1.0,1.0,1.0]
+ P2=Palm4smsa(Y,K,J,cons=C,initfacts=asfortranarray2(B),initlambda=A,N=500,threshold=1e-4)
+
+ #Plot : itérations vs erreur
+ n=range(P2[5]+1)
+
+ fig = plt.figure(3)
+ plt.plot(n,P2[2])
+ plt.title("Fonction objectif")
+ plt.xlabel("Itération")
+ plt.ylabel("Erreur relative")
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/sigma_faust/__init__.py b/sigma_faust/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf8e54b05406908830d67a5c480541a7fd809a3f
--- /dev/null
+++ b/sigma_faust/__init__.py
@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+"""
+
+.. moduleauthor:: Valentin Emiya
+"""
+__version__ = "0.1"
diff --git a/sigma_faust/exp_icassp_data_matrices.py b/sigma_faust/exp_icassp_data_matrices.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0c816dc109fdfef625e41fdeaa8658e6f2fb5f4
--- /dev/null
+++ b/sigma_faust/exp_icassp_data_matrices.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""
+
+.. moduleauthor:: Valentin Emiya
+"""
+import numpy as np
+
+from sigma_faust.exp_icassp_nb_factors import experience, get_list_of_n_factors
+from sigma_faust.PALM import somme_multiplication
+
+
+if __name__ == '__main__':
+ print(get_list_of_n_factors(32))
+ print(get_list_of_n_factors(64))
+ print(get_list_of_n_factors(128))
+ print(get_list_of_n_factors(100))
+
+ import pickle
+
+ res = {}
+ for dataset in ['digits', 'LSVT_voice_rehabilitation', 'breast_cancer']:
+ M = np.load(f'data/{dataset}.npy')
+ assert M.shape[0] == M.shape[1]
+ data_size = M.shape[0]
+ print('*' * 80)
+ print(f'Dataset {dataset} - Data size {data_size}')
+ print('*' * 80)
+ for n_factors in get_list_of_n_factors(data_size)[:2]:
+ print('=' * 60)
+ print(f'{n_factors} factors')
+ print('=' * 60)
+ if n_factors * data_size * 2 > data_size ** 2:
+ break
+ relative_error, n_terms_list, res_factors = \
+ experience(data_dim=data_size, n_factors=n_factors,
+ n_runs_init=0, data_type=dataset)
+ M_est = [somme_multiplication(*f) for f in res_factors]
+ res[dataset, data_size, n_factors] = \
+ relative_error, n_terms_list, M_est
+ # filename = f'icassp_{dataset}_{data_size}_{n_factors}'
+ # title = f'Data size : {data_size} - Budget : {n_factors}'
+ # for additional_curves in (False, True):
+ # plot_results(relative_error, n_terms_list,
+ # filename=filename, title=title,
+ # additional_curves=additional_curves)
+ with open('res_icassp_nb_factors_datasets.pickle', 'wb') as file:
+ pickle.dump(res, file)
diff --git a/sigma_faust/exp_icassp_nb_factors.py b/sigma_faust/exp_icassp_nb_factors.py
new file mode 100644
index 0000000000000000000000000000000000000000..78d9dd71428b305f88737877ed192beb961051b4
--- /dev/null
+++ b/sigma_faust/exp_icassp_nb_factors.py
@@ -0,0 +1,266 @@
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""
+
+.. moduleauthor:: Valentin Emiya
+"""
+import numpy as np
+import numpy.linalg as LA
+from sympy import divisors
+
+from pyfaust.proj import splincol
+
+from sigma_faust.hierarchique \
+ import StoppingCriterion, ParamsPalm4MSA, ParamsHierarchical, \
+ asfortranarray, asfortranarray2, \
+ palm4msa, hierarchical, \
+ Palm4smsa, Palm4smsa_hierarchique, Palm4smsa_hierarchique2, \
+ somme_multiplication
+
+
+def compute_relative_error(est_matrix, ref_matrix):
+ return LA.norm(ref_matrix - est_matrix) / LA.norm(ref_matrix)
+
+
+def experience(data_dim=32, n_factors=5, n_runs_data=1, n_runs_init=10,
+ data_type='randn'):
+ """
+
+ Parameters
+ ----------
+ data_dim : taille de la matrice aléatoire
+ n_factors : Nombre de facteurs
+
+ Returns
+ -------
+ la fonction génére un graphe de comparaison des algos
+
+ """
+ if n_runs_data > 1:
+ raise NotImplementedError()
+
+ lincol_sparsity = 2 # number of non-zero per row and col
+ n_iter_max = 30
+ n_iter_max = 100
+ n_terms_list = divisors(n_factors)
+ # n_terms_list.remove(n_factors)
+ # n_terms_list = [n_factors,]
+ relative_error = {}
+
+ # Data generation
+ if data_type == 'randn':
+ M = np.random.randn(data_dim, data_dim)
+ else:
+ M = np.load(f'data/{data_type}.npy')
+ assert M.shape[0] == M.shape[1]
+ data_dim = M.shape[0]
+
+ # run PALM4MSA with Id/0 initialisation
+ cons = [splincol((data_dim, data_dim), lincol_sparsity)
+ for _ in range(n_factors)]
+ stop_crit = StoppingCriterion(num_its=n_iter_max)
+ param = ParamsPalm4MSA(cons, stop_crit, constant_step_size=False)
+ # param = ParamsPalm4MSA(cons, stop_crit, constant_step_size=True)
+ # Factorisation avec palm4msa avec l'inisialisation naturelle
+ M_est = palm4msa(M, param).toarray()
+ relative_error['PALM4MSA Id'] = compute_relative_error(est_matrix=M_est,
+ ref_matrix=M)
+
+ # run PALM4MSA with random initialisation
+ relative_error['PALM4MSA Rd'] = []
+ # Random comme intiliastion
+ for i in range(n_runs_init):
+ I_Factors = [np.random.randn(data_dim, data_dim)] * n_factors
+ param = ParamsPalm4MSA(cons, stop_crit,
+ init_facts=asfortranarray(I_Factors),
+ is_update_way_R2L=True,
+ constant_step_size=True)
+ param = ParamsPalm4MSA(cons, stop_crit,
+ init_facts=asfortranarray(I_Factors),
+ is_update_way_R2L=True,
+ constant_step_size=False)
+ M_est = palm4msa(M, param).toarray()
+ relative_error['PALM4MSA Rd'].append(
+ compute_relative_error(est_matrix=M_est, ref_matrix=M))
+
+ # run Hierarchical PALM4MSA
+ fact_cons = [splincol(M.shape, lincol_sparsity)
+ for _ in range(n_factors - 1)]
+ res_cons = [splincol(M.shape, int(data_dim / (2 + i)))
+ for i in range(n_factors - 2)] \
+ + [splincol(M.shape, lincol_sparsity)]
+ stop_crit1 = StoppingCriterion(num_its=n_iter_max)
+ stop_crit2 = StoppingCriterion(num_its=n_iter_max)
+ param = ParamsHierarchical(fact_cons, res_cons,
+ stop_crit1, stop_crit2,
+ is_update_way_R2L=True)
+ M_est = hierarchical(M, param).toarray()
+ relative_error['H-PALM4MSA'] = compute_relative_error(est_matrix=M_est,
+ ref_matrix=M)
+
+ relative_error['PALM4SMSA Id'] = []
+ relative_error['G-PALM4SMSA Id'] = []
+ relative_error['PALM4SMSA Rd'] = []
+ relative_error['G-PALM4SMSA Rd'] = []
+ relative_error['G-PALM4SMSA H'] = []
+ res_factors = []
+ for n_terms in n_terms_list:
+ n_factors_per_term = n_factors // n_terms
+ C = [[splincol(M.shape, lincol_sparsity)
+ for _ in range(n_factors_per_term)]] * n_terms
+
+ # run PALM4SMSA and G-PALM4SMSA using PALM4MSA with Id/0 initialisation
+ I_Factors = [[np.identity(data_dim)] * (n_factors_per_term - 1)
+ + [np.zeros((data_dim, data_dim))]] * n_terms
+ I_lambda = [1.0] * n_terms
+ F2 = Palm4smsa(M, n_terms, n_factors_per_term, cons=C,
+ initfacts=asfortranarray2(I_Factors),
+ initlambda=I_lambda, N=n_iter_max)[0:2]
+ relative_error['PALM4SMSA Id'].append(compute_relative_error(
+ est_matrix=somme_multiplication(F2[0], F2[1]), ref_matrix=M
+ ))
+ F3 = Palm4smsa_hierarchique(M, n_terms, n_factors_per_term, cons=C,
+ initfacts=asfortranarray2(I_Factors),
+ initlambda=I_lambda,
+ N=n_iter_max, N_1=n_iter_max)
+ relative_error['G-PALM4SMSA Id'].append(compute_relative_error(
+ est_matrix=somme_multiplication(F3[0], F3[1]), ref_matrix=M
+ ))
+
+ # run PALM4SMSA and G-PALM4SMSA using PALM4MSA with random initialisation
+ relative_error['PALM4SMSA Rd'].append([])
+ relative_error['G-PALM4SMSA Rd'].append([])
+ for i in range(n_runs_init):
+ I_Factors = [[np.random.randn(data_dim, data_dim)]
+ * n_factors_per_term] * n_terms
+ F2_random = Palm4smsa(M, n_terms, n_factors_per_term,
+ cons=C, initfacts=asfortranarray2(I_Factors),
+ initlambda=I_lambda, N=n_iter_max)[0:2]
+ relative_error['PALM4SMSA Rd'][-1].append(compute_relative_error(
+ est_matrix=somme_multiplication(F2_random[0], F2_random[1]),
+ ref_matrix=M
+ ))
+ F3_random = Palm4smsa_hierarchique(
+ M, n_terms, n_factors_per_term, cons=C,
+ initfacts=asfortranarray2(I_Factors), initlambda=I_lambda,
+ N=n_iter_max, N_1=n_iter_max)
+ relative_error['G-PALM4SMSA Rd'][-1].append(compute_relative_error(
+ est_matrix=somme_multiplication(F3_random[0], F3_random[1]),
+ ref_matrix=M
+ ))
+
+ # run G-PALM4SMSA using H-PALM4MSA (with Id initialisation)
+ if n_factors_per_term == 1:
+ F = [[splincol(M.shape, lincol_sparsity)]] * n_terms
+ R = [[splincol(M.shape, 1)]] * n_terms
+ else:
+ F = [[splincol(M.shape, lincol_sparsity)
+ for _ in range(n_factors_per_term - 1)]] * n_terms
+ R = [[splincol(M.shape, int(data_dim / (2 * i)))
+ for i in range(1, n_factors_per_term)]] * n_terms
+ F3_h = Palm4smsa_hierarchique2(M, n_terms, n_factors_per_term,
+ fact_cons=F, res_cons=R,
+ N_1=n_iter_max, N_2=n_iter_max,
+ N_3=n_iter_max)
+ res_factors.append(F3_h)
+ relative_error['G-PALM4SMSA H'].append(compute_relative_error(
+ est_matrix=somme_multiplication(F3_h[0], F3_h[1]),
+ ref_matrix=M
+ ))
+
+ return relative_error, n_terms_list, res_factors
+ ########
+
+
+def get_list_of_n_factors(data_dim, extended_list=False):
+ n_min = int(np.ceil(np.log2(data_dim)))
+ n_range = list(range(n_min, int(data_dim / 2)))
+ n_divisors = [len(divisors(n)) for n in n_range]
+
+ n_div_max = -1
+ n_range_max = []
+ n_divisors_max = []
+ for i in range(len(n_range)):
+ if n_divisors[i] >= n_div_max:
+ if n_divisors[i] > n_div_max or extended_list:
+ n_div_max = n_divisors[i]
+ n_range_max.append(n_range[i])
+ n_divisors_max.append(n_divisors[i])
+ print(f'{n_range[i]} has {n_divisors[i]} divisors.')
+ return n_range_max
+
+
+if __name__ == '__main__':
+ import pickle
+ res = {}
+ for data_size in (32, 64, 128):
+ print('*' * 80)
+ print(f'Data size {data_size}')
+ print('*' * 80)
+ for n_factors in get_list_of_n_factors(data_dim=data_size):
+ print('=' * 60)
+ print(f'{n_factors} factors')
+ print('=' * 60)
+ if n_factors * data_size * 2 > data_size ** 2:
+ break
+ relative_error, n_terms_list, res_factors = \
+ experience(data_dim=data_size, n_factors=n_factors,
+ n_runs_init=0)
+ res[data_size, n_factors] = relative_error, n_terms_list
+ with open('res_icassp_nb_factors.pickle', 'wb') as file:
+ pickle.dump(res, file)
+
+ # filename = f'icassp_{data_size}_{n_factors}'
+ # title = f'Data size : {data_size} - Budget : {n_factors}'
+ # for additional_curves in (False, True):
+ # plot_results(relative_error, n_terms_list,
+ # filename=filename, title=title,
+ # additional_curves=additional_curves)
diff --git a/sigma_faust/exp_icassp_random.py b/sigma_faust/exp_icassp_random.py
new file mode 100644
index 0000000000000000000000000000000000000000..01b133d5cf4eb47c6da8d13a7f0495f6564fde70
--- /dev/null
+++ b/sigma_faust/exp_icassp_random.py
@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""
+
+.. moduleauthor:: Valentin Emiya
+"""
+import pickle
+
+from sigma_faust.exp_icassp_nb_factors import experience
+
+res = {}
+data_size = 256
+print('*' * 80)
+print(f'Data size {data_size}')
+print('*' * 80)
+for n_factors in [8, 24]:
+ print('=' * 60)
+ print(f'{n_factors} factors')
+ print('=' * 60)
+ if n_factors * data_size * 2 > data_size ** 2:
+ break
+ relative_error, n_terms_list, res_factors = \
+ experience(data_dim=data_size, n_factors=n_factors,
+ n_runs_init=0)
+ res[data_size, n_factors] = relative_error, n_terms_list
+ with open('res_icassp_nb_factors_256.pickle', 'wb') as file:
+ pickle.dump(res, file)
diff --git a/sigma_faust/extract_matrices_from_datasets.py b/sigma_faust/extract_matrices_from_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..17298b25a0e8fd7f7af35683c239a20d72f5a714
--- /dev/null
+++ b/sigma_faust/extract_matrices_from_datasets.py
@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""
+
+.. moduleauthor:: Valentin Emiya
+"""
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import sklearn.datasets as skldata
+
+
+def extract_matrix(dataset, root_dir, out_dir=Path('./data')):
+ Path(out_dir).mkdir(exist_ok=True)
+ if dataset == 'LSVT_voice_rehabilitation':
+ root_dir = root_dir / 'LSVT_voice_rehabilitation'
+ df = pd.read_excel(root_dir / 'LSVT_voice_rehabilitation.xlsx')
+ M = df.to_numpy()[:126, :126]
+ elif dataset == 'digits':
+ data = skldata.load_digits()
+ M = data['data']
+ else:
+ raise ValueError(f'Unknown dataset {dataset}')
+
+ data_dim = np.min(M.shape)
+ M = M[:data_dim, :data_dim]
+ np.save(out_dir / f'{dataset}.npy', M)
+
+
+if __name__ == '__main__':
+ # Adapt this path depending on the location of your data
+ root_dir = Path('/Users/valentin/data/UCI/icassp21')
+ for dataset in ('LSVT_voice_rehabilitation', 'digits'):
+ try:
+ extract_matrix(dataset=dataset, root_dir=root_dir)
+ except ValueError as e:
+ print(e)
+ except FileNotFoundError as e:
+ print(f'Dataset {dataset} not available: {e}')
diff --git a/sigma_faust/hierarchique.py b/sigma_faust/hierarchique.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ab5a1e563523f4dfb350b5d1249cf43b159bc7c
--- /dev/null
+++ b/sigma_faust/hierarchique.py
@@ -0,0 +1,176 @@
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""
+Created on Wed May 26 12:13:20 2021
+
+@author: Admin
+"""
+import copy
+
+from pyfaust.fact import hierarchical
+from pyfaust.proj import *
+
+from sigma_faust.PALM import * # fichier
+
+
+def Palm4smsa_hierarchique(A,K,J,cons,initfacts,initlambda,N,N_1=None):
+ '''
+ Décomposition using palm4msa
+ Parameters
+ ----------
+ A : array a décomposer
+ K : nombre de terme
+ J : nombre de facteur
+ cons : contrainte, une liste de liste objet : pyfaust.proj
+ initfacts : liste de liste, initialisation facteur
+ initlambda : liste initisialisation scalaire
+ N : Nombre d'itération palm4msa
+ N_1 : Nombre d'itération palm4smsa
+
+ Returns
+ -------
+ Factors
+ scalars
+
+ '''
+ stop_crit=StoppingCriterion(num_its=N)
+ Factors=[]
+ scalars=[]
+ for k in range(K):
+ if np.allclose(A-somme_multiplication(Factors,scalars),np.zeros(A.shape))==True:
+ break
+ else:
+ param=ParamsPalm4MSA(cons[k],stop_crit,init_facts=initfacts[k],\
+ init_lambda=initlambda[k],is_update_way_R2L=True)
+ F,_lambda=palm4msa(A-somme_multiplication(Factors,scalars),param,ret_lambda=True)
+ F1=Faust_to_list(F)
+ F1=[F1[i] if np.isclose(LA.norm(F1[i]),1,atol=1e-03) else F1[i]/_lambda for i in range(len(F1))]
+ Factors.append(F1)
+ scalars.append(_lambda)
+ if N_1!=None:
+ if k==0:
+ continue
+ else:
+ F2,_lambda2,U=Palm4smsa(A,k+1,J,cons[:k+1],Factors,scalars,N_1)[:3]
+ Factors=F2
+ scalars=_lambda2
+
+ return Factors,scalars
+
+
+def Palm4smsa_hierarchique2(A,K,J,fact_cons,res_cons,N_1,N_2,N_3=None):
+ '''
+ Décomposition using hierarchical palm4msa : initilisation not possible : pas très grave puisque le palm4msa n'est pas sensible a l'iniisailisation
+ Parameters
+ ----------
+ A : array a décomposer
+ K : nombre de terme
+ J : nombre de facteur
+ fact_cons : contrainte des facteurs, une liste de liste objet : pyfaust.proj
+ res_cons : contrainte des résidus, une liste de liste objet : pyfaust.proj
+ initlambda : liste initisialisation scalaire
+ N_1 : Nombre d'itération palm4msa
+ N_2 : Nombre d'itération palm4msa global
+ N_3 : Nombre d'itération palm4smsa
+
+ Returns
+ -------
+ Factors
+ scalars
+
+ '''
+ stop_crit1=StoppingCriterion(num_its=N_1)
+ stop_crit2=StoppingCriterion(num_its=N_2)
+ cons=copy.deepcopy(fact_cons)
+ # si on utilisie *k on a :
+ cons[0].append(res_cons[0][-1])
+ Factors=[]
+ scalars=[]
+ for k in range(K):
+ if np.allclose(A-somme_multiplication(Factors,scalars),np.zeros(A.shape))==True:
+ break
+ else:
+ param=ParamsHierarchical(fact_cons[k], res_cons[k], stop_crit1, stop_crit2,is_update_way_R2L=True)
+ F,_lambda=hierarchical(A-somme_multiplication(Factors,scalars),param,ret_lambda=True)
+ F1=Faust_to_list(F)
+ F1=[F1[i] if np.isclose(LA.norm(F1[i]),1,atol=1e-03) else F1[i]/_lambda for i in range(len(F1))]
+ Factors.append(F1)
+ scalars.append(_lambda)
+ if N_3!=None:
+ if k==0:
+ continue
+ else:
+ F2,_lambda2=Palm4smsa(A,k+1,J,cons[:k+1],Factors,scalars,N_3)[:2]
+ Factors=F2
+ scalars=_lambda2
+
+ return Factors,scalars
+
+if __name__ == "__main__":
+ #test
+ d=10
+
+ M=10*random(d,d,density=1).toarray()
+ K=2
+ J=6
+ D=[[splincol(M.shape,2) for i in range(6)],[splincol(M.shape,2) for i in range(6)]]
+
+
+ I=[[np.identity(d)]*6,[M]*6]
+ A=[1.0,1.0]
+ H1,H2=Palm4smsa_hierarchique(M,K,J,cons=D,initfacts=asfortranarray2(I),initlambda=A,N=500,N_1=5000)
+ print(LA.norm(M-somme_multiplication(H1,H2))/LA.norm(M))
+ K=2
+ J=3
+ F=[[splincol(M.shape, 2) for i in range(2)]]*2
+ R=[[splincol(M.shape,10),splincol(M.shape,2)]]*2
+ H3,H4=Palm4smsa_hierarchique2(M,K,J,fact_cons=F,res_cons=R,N_1=500,N_2=500,N_3=500)
+ print(LA.norm(M-somme_multiplication(H3,H4))/LA.norm(M))
diff --git a/sigma_faust/plot_icassp_results.py b/sigma_faust/plot_icassp_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c0d86f90e5b6072f113b2ddf5a8caa9a8f1340a
--- /dev/null
+++ b/sigma_faust/plot_icassp_results.py
@@ -0,0 +1,214 @@
+# -*- coding: utf-8 -*-
+# ######### COPYRIGHT #########
+# Credits
+# #######
+#
+# Copyright(c) 2019-2021
+# ----------------------
+#
+# * Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>
+# * Institut de Mathématiques de Marseille <https://www.i2m.univ-amu.fr/>
+# * Université d'Aix-Marseille <http://www.univ-amu.fr/>
+# * Centre National de la Recherche Scientifique <http://www.cnrs.fr/>
+# * Université de Toulon <http://www.univ-tln.fr/>
+#
+# Contributors
+# ------------
+#
+# * Moujahid Bou-Laouz
+# * Valentin Emiya <firstname.lastname_AT_lis-lab.fr>
+#
+# Description
+# -----------
+#
+# `sigma_faust` is a Python implementation of algorithms and experiments proposed
+# in paper *Learning a sum of sparse matrix products* by Moujahid Bou-Laouz,
+# Valentin Emiya, Liva Ralaivola and Caroline Chaux in 2021.
+#
+#
+# Version
+# -------
+#
+# * sigma_faust version = 0.1
+#
+# Licence
+# -------
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# ######### COPYRIGHT #########
+"""
+
+.. moduleauthor:: Valentin Emiya
+"""
+import pickle
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def plot_results(relative_error, n_terms_list, filename, title,
+ additional_curves=False, curve_style='o:'):
+ plt.figure()
+ k = 'PALM4SMSA Id'
+ plt.plot(n_terms_list, relative_error[k], curve_style,
+ label='PALM4$\Sigma$MSA')
+ if additional_curves:
+ k = 'PALM4MSA Id'
+ plt.plot(1, relative_error[k], '+', label=k)
+
+ if additional_curves:
+ k = 'PALM4SMSA Rd'
+ # plt.errorbar(n_terms_list,
+ # [np.mean(x) for x in relative_error[k]],
+ # [np.std(x) for x in relative_error[k]],
+ # label=k)
+ plt.plot(n_terms_list, [np.mean(x) for x in relative_error[k]], label=k)
+ k = 'PALM4MSA Rd'
+ # plt.errorbar(1, np.mean(relative_error[k]), np.std(relative_error[k]),
+ # marker='x', label=k)
+ plt.plot(1, np.mean(relative_error[k]), marker='x', label=k)
+
+ k = 'G-PALM4SMSA Id'
+ plt.plot(n_terms_list, relative_error[k], curve_style,
+ label='Greedy PALM4$\Sigma$MSA')
+
+ if additional_curves:
+ k = 'G-PALM4SMSA Rd'
+ # plt.errorbar(n_terms_list,
+ # [np.mean(x) for x in relative_error[k]],
+ # [np.std(x) for x in relative_error[k]],
+ # label=k)
+ plt.plot(n_terms_list, [np.mean(x) for x in relative_error[k]], label=k)
+ k = 'G-PALM4SMSA H'
+ print(title, np.min(relative_error[k]))
+ plt.plot(n_terms_list, relative_error[k], curve_style,
+ label='Greedy PALM4$\Sigma$MSA H')
+ if additional_curves:
+ k = 'H-PALM4MSA'
+ plt.plot(1, relative_error[k], '.', label=k)
+ ry = 0.05
+ plt.legend()
+ plt.grid()
+ plt.xlabel('Number of terms ($K$)')
+ plt.ylabel('Relative MSE')
+ yl = plt.ylim()
+ plt.ylim(0-ry, min(1+ry, yl[1]))
+ plt.title(title)
+ # plt.yscale('log')
+ if additional_curves:
+ plt.savefig(f'{filename}_extended.pdf')
+ else:
+ plt.savefig(f'{filename}.pdf')
+ plt.close()
+
+
+def plot_random(N=None):
+ if N == 256:
+ with open('2021-10-05_res_icassp_nb_factors.pickle', 'rb') as file:
+ res = pickle.load(file)
+ else:
+ with open('res_icassp_nb_factors.pickle', 'rb') as file:
+ res = pickle.load(file)
+
+ for data_size, n_factors in res:
+ if N is not None and N != data_size:
+ continue
+ relative_error, n_terms_list = res[data_size, n_factors]
+ filename = f'icassp_{data_size}_{n_factors}'
+ title = f'Gaussian matrix $N={data_size}$ $B={n_factors}$'
+ for additional_curves in (False, True):
+ plot_results(relative_error, n_terms_list,
+ filename=filename, title=title, curve_style='o:',
+ additional_curves=additional_curves)
+ plot_results(relative_error, n_terms_list,
+ filename=filename + '_o', title=title, curve_style='o',
+ additional_curves=additional_curves)
+
+
+def plot_random256():
+ with open('res_icassp_nb_factors_datasets_copie.pickle', 'rb') as file:
+ res = pickle.load(file)
+
+ for data_size, n_factors in res:
+ relative_error, n_terms_list = res[data_size, n_factors]
+ filename = f'icassp_{data_size}_{n_factors}'
+ title = f'Gaussian matrix $N={data_size}$ $B={n_factors}$'
+ for additional_curves in (False, True):
+ plot_results(relative_error, n_terms_list,
+ filename=filename, title=title, curve_style='o:',
+ additional_curves=additional_curves)
+ plot_results(relative_error, n_terms_list,
+ filename=filename + '_o', title=title, curve_style='o',
+ additional_curves=additional_curves)
+
+
+def plot_digits():
+ with open('res_icassp_nb_factors_datasets.pickle', 'rb') as file:
+ res = pickle.load(file)
+
+ for k in res:
+ dataset, data_size, n_factors = k
+ if dataset != 'digits' or n_factors != 6:
+ continue
+ relative_error, n_terms_list, M_est = res[k]
+ filename = f'icassp_{dataset}_{data_size}_{n_factors}'
+ title = f'Dataset MNIST $N={data_size}$ $B={n_factors}$'
+ additional_curves = False
+ plot_results(relative_error, n_terms_list,
+ filename=filename, title=title,
+ additional_curves=additional_curves,
+ curve_style='o:')
+ plot_results(relative_error, n_terms_list,
+ filename=filename+'_o', title=title,
+ additional_curves=additional_curves,
+ curve_style='o')
+
+ n_examples = 10
+ for i, M in enumerate(M_est):
+ for n in range(n_examples):
+ plt.figure()
+ plt.imshow(-np.reshape(M[n, :], (8, 8)), cmap='gray')
+ frame = plt.gca()
+ frame.axes.get_xaxis().set_visible(False)
+ frame.axes.get_yaxis().set_visible(False)
+ plt.savefig(f'digits{n}_K{n_terms_list[i]}.pdf',
+ bbox_inches='tight')
+ plt.close()
+ M = np.load(f'data/digits.npy')
+ for n in range(n_examples):
+ plt.figure()
+ plt.imshow(-np.reshape(M[n, :], (8, 8)), cmap='gray')
+ frame = plt.gca()
+ frame.axes.get_xaxis().set_visible(False)
+ frame.axes.get_yaxis().set_visible(False)
+ plt.savefig(f'digits{n}_ref.pdf', bbox_inches='tight')
+ plt.close()
+ w = '.1\\textwidth'
+ for n in range(n_examples):
+ print(f'\includegraphics[width={w}]'
+ + '{' + f'figures/digits{n}_ref.pdf' + '}')
+ print(f'\includegraphics[width={w}]'
+ + '{' + f'figures/digits{n}_K1.pdf' + '}')
+ print(f'\includegraphics[width={w}]'
+ + '{' + f'figures/digits{n}_K3.pdf' + '}')
+ print(f'\includegraphics[width={w}]'
+ + '{' + f'figures/digits{n}_K6.pdf' + '}')
+ print('\\\\')
+
+
+if __name__ == '__main__':
+ plot_digits()
+ plot_random()
+ plot_random(128)
+ plot_random(256)
\ No newline at end of file