analysis pkg v 1.0.0.11

analysis.py v 1.1.13.009
superscript.py v 0.0.5.002

.devcontainer/Dockerfile

@@ -0,0 +1,2 @@
+FROM python
+WORKDIR ~/

.devcontainer/devcontainer.json

@@ -0,0 +1,26 @@
+{
+    "name": "TRA Analysis Development Environment",
+    "build": {
+		"dockerfile": "Dockerfile",
+    },
+    "settings": { 
+		"terminal.integrated.shell.linux": "/bin/bash",
+		"python.pythonPath": "/usr/local/bin/python",
+		"python.linting.enabled": true,
+		"python.linting.pylintEnabled": true,
+		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
+		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
+		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
+		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
+		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
+		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
+		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
+		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
+		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint",
+		"python.testing.pytestPath": "/usr/local/py-utils/bin/pytest"
+	},
+	"extensions": [
+		"mhutchie.git-graph",
+	],
+	"postCreateCommand": "pip install -r analysis-master/analysis-amd64/requirements.txt"
+}

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.gitignore

@@ -1,2 +1,24 @@
-
 benchmark_data.csv
+data analysis/keys/keytemp.json
+data analysis/__pycache__/analysis.cpython-37.pyc
+apps/android/source/app/src/main/res/drawable-v24/uuh.png
+apps/android/source/app/src/main/java/com/example/titanscouting/tits.java
+
+data analysis/analysis.cp37-win_amd64.pyd
+data analysis/analysis/analysis.c
+data analysis/analysis/analysis.cp37-win_amd64.pyd
+data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.exp
+data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.lib
+data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.obj
+data analysis/test.ipynb
+data analysis/.ipynb_checkpoints/test-checkpoint.ipynb
+.vscode/settings.json
+.vscode
+data analysis/arthur_pull.ipynb
+data analysis/keys.txt
+data analysis/check_for_new_matches.ipynb
+data analysis/test.ipynb
+data analysis/visualize_pit.ipynb
+data analysis/config/keys.config
+analysis-master/analysis/__pycache__/
+data analysis/__pycache__/

CONTRIBUTING.md

@@ -0,0 +1 @@
+These sets of code is more unstable than an antimatter bear taunted with a barrel of fish. Add at your own risk.

LICENSE

@@ -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) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <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) <year>  <name of author>
+    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>.

README.md

@@ -0,0 +1,3 @@
+# tr2022-strategy
+Titan Robotics 2022 Strategy Team Repository
+Use at your own risk

analysis-master/analysis-amd64/analysis.egg-info/PKG-INFO

@@ -0,0 +1,14 @@
+Metadata-Version: 2.1
+Name: analysis
+Version: 1.0.0.11
+Summary: analysis package developed by Titan Scouting for The Red Alliance
+Home-page: https://github.com/titanscout2022/tr2022-strategy
+Author: The Titan Scouting Team
+Author-email: titanscout2022@gmail.com
+License: GNU General Public License v3.0
+Description: UNKNOWN
+Platform: UNKNOWN
+Classifier: Programming Language :: Python :: 3
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown

analysis-master/analysis-amd64/analysis.egg-info/SOURCES.txt

@@ -0,0 +1,13 @@
+setup.py
+analysis/__init__.py
+analysis/analysis.py
+analysis/glicko2.py
+analysis/regression.py
+analysis/titanlearn.py
+analysis/trueskill.py
+analysis/visualization.py
+analysis.egg-info/PKG-INFO
+analysis.egg-info/SOURCES.txt
+analysis.egg-info/dependency_links.txt
+analysis.egg-info/requires.txt
+analysis.egg-info/top_level.txt

analysis-master/analysis-amd64/analysis.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

analysis-master/analysis-amd64/analysis.egg-info/requires.txt

@@ -0,0 +1,6 @@
+numba
+numpy
+scipy
+scikit-learn
+six
+matplotlib

analysis-master/analysis-amd64/analysis.egg-info/top_level.txt

@@ -0,0 +1 @@
+analysis

analysis-master/analysis-amd64/analysis/analysis.py

@@ -0,0 +1,700 @@
+# Titan Robotics Team 2022: Data Analysis Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import analysis'
+#    this should be included in the local directory or environment variable
+#    this module has been optimized for multhreaded computing
+#    current benchmark of optimization: 1.33 times faster
+# setup:
+
+__version__ = "1.1.13.009"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    1.1.13.009:
+        - moved elo, glicko2, trueskill functions under class Metrics
+    1.1.13.008:
+        - moved Glicko2 to a seperate package
+    1.1.13.007:
+        - fixed bug with trueskill
+    1.1.13.006:
+        - cleaned up imports
+    1.1.13.005:
+        - cleaned up package
+    1.1.13.004:
+        - small fixes to regression to improve performance
+    1.1.13.003:
+        - filtered nans from regression
+    1.1.13.002:
+        - removed torch requirement, and moved Regression back to regression.py
+    1.1.13.001:
+        - bug fix with linear regression not returning a proper value
+        - cleaned up regression
+        - fixed bug with polynomial regressions
+    1.1.13.000:
+        - fixed all regressions to now properly work
+    1.1.12.006:
+        - fixed bg with a division by zero in histo_analysis
+    1.1.12.005:
+        - fixed numba issues by removing numba  from elo, glicko2 and trueskill
+    1.1.12.004:
+        - renamed gliko to glicko
+    1.1.12.003:
+        - removed depreciated code
+    1.1.12.002:
+        - removed team first time trueskill instantiation in favor of integration in superscript.py
+    1.1.12.001:
+        - improved readibility of regression outputs by stripping tensor data
+        - used map with lambda to acheive the improved readibility
+        - lost numba jit support with regression, and generated_jit hangs at execution
+        - TODO: reimplement correct numba integration in regression
+    1.1.12.000:
+        - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures
+    1.1.11.010:
+        - alphabeticaly ordered import lists
+    1.1.11.009:
+        - bug fixes
+    1.1.11.008:
+        - bug fixes
+    1.1.11.007:
+        - bug fixes
+    1.1.11.006:
+        - tested min and max
+        - bug fixes 
+    1.1.11.005:
+        - added min and max in basic_stats
+    1.1.11.004:
+        - bug fixes
+    1.1.11.003:
+        - bug fixes
+    1.1.11.002:
+        - consolidated metrics
+        - fixed __all__
+    1.1.11.001:
+        - added test/train split to RandomForestClassifier and RandomForestRegressor
+    1.1.11.000:
+        - added RandomForestClassifier and RandomForestRegressor
+        - note: untested
+    1.1.10.000:
+        - added numba.jit to remaining functions
+    1.1.9.002:
+        - kernelized PCA and KNN
+    1.1.9.001:
+        - fixed bugs with SVM and NaiveBayes
+    1.1.9.000:
+        - added SVM class, subclasses, and functions
+        - note: untested
+    1.1.8.000:
+        - added NaiveBayes classification engine
+        - note: untested
+    1.1.7.000:
+        - added knn()
+        - added confusion matrix to decisiontree()
+    1.1.6.002:
+        - changed layout of __changelog to be vscode friendly
+    1.1.6.001:
+        - added additional hyperparameters to decisiontree()
+    1.1.6.000:
+        - fixed __version__
+        - fixed __all__ order
+        - added decisiontree()
+    1.1.5.003:
+        - added pca
+    1.1.5.002:
+        - reduced import list
+        - added kmeans clustering engine
+    1.1.5.001:
+        - simplified regression by using .to(device)
+    1.1.5.000:
+        - added polynomial regression to regression(); untested
+    1.1.4.000:
+        - added trueskill()
+    1.1.3.002:
+        - renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2
+    1.1.3.001:
+        - changed glicko2() to return tuple instead of array
+    1.1.3.000:
+        - added glicko2_engine class and glicko()
+        - verified glicko2() accuracy
+    1.1.2.003:
+        - fixed elo()
+    1.1.2.002:
+        - added elo()
+        - elo() has bugs to be fixed
+    1.1.2.001:
+        - readded regrression import
+    1.1.2.000:
+        - integrated regression.py as regression class
+        - removed regression import
+        - fixed metadata for regression class
+        - fixed metadata for analysis class
+    1.1.1.001:
+        - regression_engine() bug fixes, now actaully regresses
+    1.1.1.000:
+        - added regression_engine()
+        - added all regressions except polynomial
+    1.1.0.007:
+        - updated _init_device()
+    1.1.0.006:
+        - removed useless try statements
+    1.1.0.005:
+        - removed impossible outcomes
+    1.1.0.004:
+        - added performance metrics (r^2, mse, rms)
+    1.1.0.003:
+        - resolved nopython mode for mean, median, stdev, variance
+    1.1.0.002:
+        - snapped (removed) majority of uneeded imports
+        - forced object mode (bad) on all jit
+        - TODO: stop numba complaining about not being able to compile in nopython mode
+    1.1.0.001:
+        - removed from sklearn import * to resolve uneeded wildcard imports
+    1.1.0.000:
+        - removed c_entities,nc_entities,obstacles,objectives from __all__
+        - applied numba.jit to all functions
+        - depreciated and removed stdev_z_split
+        - cleaned up histo_analysis to include numpy and numba.jit optimizations
+        - depreciated and removed all regression functions in favor of future pytorch optimizer
+        - depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
+        - optimized z_normalize using sklearn.preprocessing.normalize
+        - TODO: implement kernel/function based pytorch regression optimizer
+    1.0.9.000:
+        - refactored
+        - numpyed everything
+        - removed stats in favor of numpy functions
+    1.0.8.005:
+        - minor fixes
+    1.0.8.004:
+        - removed a few unused dependencies
+    1.0.8.003:
+        - added p_value function
+    1.0.8.002:
+        - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
+    1.0.8.001:
+        - refactors
+        - bugfixes
+    1.0.8.000:
+        - depreciated histo_analysis_old
+        - depreciated debug
+        - altered basic_analysis to take array data instead of filepath
+        - refactor
+        - optimization
+    1.0.7.002:
+        - bug fixes
+    1.0.7.001:
+        - bug fixes
+    1.0.7.000:
+        - added tanh_regression (logistical regression)
+        - bug fixes
+    1.0.6.005:
+        - added z_normalize function to normalize dataset
+        - bug fixes
+    1.0.6.004:
+        - bug fixes
+    1.0.6.003:
+        - bug fixes
+    1.0.6.002:
+        - bug fixes
+    1.0.6.001:
+        - corrected __all__ to contain all of the functions
+    1.0.6.000:
+        - added calc_overfit, which calculates two measures of overfit, error and performance
+        - added calculating overfit to optimize_regression
+    1.0.5.000:
+        - added optimize_regression function, which is a sample function to find the optimal regressions
+        - optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
+        - planned addition: overfit detection in the optimize_regression function
+    1.0.4.002:
+        - added __changelog__
+        - updated debug function with log and exponential regressions
+    1.0.4.001:
+        - added log regressions
+        - added exponential regressions
+        - added log_regression and exp_regression to __all__
+    1.0.3.008:
+        - added debug function to further consolidate functions
+    1.0.3.007:
+        - added builtin benchmark function
+        - added builtin random (linear) data generation function
+        - added device initialization (_init_device)
+    1.0.3.006:
+        - reorganized the imports list to be in alphabetical order
+        - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
+    1.0.3.005:
+        - major bug fixes
+        - updated historical analysis
+        - depreciated old historical analysis
+    1.0.3.004:
+        - added __version__, __author__, __all__
+        - added polynomial regression
+        - added root mean squared function
+        - added r squared function
+    1.0.3.003:
+        - bug fixes
+        - added c_entities
+    1.0.3.002:
+        - bug fixes
+        - added nc_entities, obstacles, objectives
+        - consolidated statistics.py to analysis.py
+    1.0.3.001:
+        - compiled 1d, column, and row basic stats into basic stats function
+    1.0.3.000:
+        - added historical analysis function
+    1.0.2.xxx:
+        - added z score test
+    1.0.1.xxx:
+        - major bug fixes
+    1.0.0.xxx:
+        - added loading csv
+        - added 1d, column, row basic stats
+"""
+
+__author__ = (
+    "Arthur Lu <learthurgo@gmail.com>",
+    "Jacob Levine <jlevine@imsa.edu>",
+)
+
+__all__ = [
+    'load_csv',
+    'basic_stats',
+    'z_score',
+    'z_normalize',
+    'histo_analysis',
+    'regression',
+    'elo',
+    'glicko2',
+    'trueskill',
+    'RegressionMetrics',
+    'ClassificationMetrics',
+    'kmeans',
+    'pca',
+    'decisiontree',
+    'knn_classifier',
+    'knn_regressor',
+    'NaiveBayes',
+    'SVM',
+    'random_forest_classifier',
+    'random_forest_regressor',
+    # all statistics functions left out due to integration in other functions
+]
+
+# now back to your regularly scheduled programming:
+
+# imports (now in alphabetical order! v 1.0.3.006):
+
+import csv
+from analysis import glicko2 as Glicko2
+import numba
+from numba import jit
+import numpy as np
+import scipy
+from scipy import *
+import sklearn
+from sklearn import *
+from analysis import trueskill as Trueskill
+
+class error(ValueError):
+    pass
+
+def load_csv(filepath):
+    with open(filepath, newline='') as csvfile:
+        file_array = np.array(list(csv.reader(csvfile)))
+        csvfile.close()
+    return file_array
+
+# expects 1d array
+@jit(forceobj=True)
+def basic_stats(data):
+
+    data_t = np.array(data).astype(float)
+
+    _mean = mean(data_t)
+    _median = median(data_t)
+    _stdev = stdev(data_t)
+    _variance = variance(data_t)
+    _min = npmin(data_t)
+    _max = npmax(data_t)
+
+    return _mean, _median, _stdev, _variance, _min, _max
+
+# returns z score with inputs of point, mean and standard deviation of spread
+@jit(forceobj=True)
+def z_score(point, mean, stdev):
+    score = (point - mean) / stdev
+    
+    return score
+
+# expects 2d array, normalizes across all axes
+@jit(forceobj=True)
+def z_normalize(array, *args):
+
+   array = np.array(array)
+   for arg in args:
+       array = sklearn.preprocessing.normalize(array, axis = arg)
+
+   return array
+
+@jit(forceobj=True)
+# expects 2d array of [x,y]
+def histo_analysis(hist_data):
+
+    if(len(hist_data[0]) > 2):
+
+        hist_data = np.array(hist_data)
+        derivative = np.array(len(hist_data) - 1, dtype = float)
+        t = np.diff(hist_data)
+        derivative = t[1] / t[0]
+        np.sort(derivative)
+
+        return basic_stats(derivative)[0], basic_stats(derivative)[3]
+
+    else:
+
+        return None
+
+def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
+
+    X = np.array(inputs)
+    y = np.array(outputs)
+
+    regressions = []
+
+    if 'lin' in args: # formula: ax + b
+
+        try:
+
+            def func(x, a, b):
+
+                return a * x + b
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
+
+    if 'log' in args: # formula: a log (b(x + c)) + d
+
+        try:
+
+            def func(x, a, b, c, d):
+
+                return a * np.log(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+            
+            pass
+
+    if 'exp' in args: # formula: a e ^ (b(x + c)) + d
+
+        try:        
+
+            def func(x, a, b, c, d):
+
+                return a * np.exp(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
+
+    if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
+        
+        inputs = np.array([inputs])
+        outputs = np.array([outputs])
+
+        plys = []
+        limit = len(outputs[0])
+
+        for i in range(2, limit):
+
+            model = sklearn.preprocessing.PolynomialFeatures(degree = i)
+            model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression())
+            model = model.fit(np.rot90(inputs), np.rot90(outputs))
+
+            params = model.steps[1][1].intercept_.tolist()
+            params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::])
+            params.flatten()
+            params = params.tolist()
+            
+            plys.append(params)
+
+        regressions.append(plys)
+
+    if 'sig' in args: # formula: a tanh (b(x + c)) + d
+
+        try:        
+
+            def func(x, a, b, c, d):
+
+                return a * np.tanh(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+           
+            pass
+
+    return regressions
+
+class Metrics:
+
+    def elo(starting_score, opposing_score, observed, N, K):
+
+        expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
+
+        return starting_score + K*(np.sum(observed) - np.sum(expected))
+
+    def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
+
+        player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
+
+        player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
+
+        return (player.rating, player.rd, player.vol)
+
+    def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
+
+        team_ratings = []
+
+        for team in teams_data:
+            team_temp = ()
+            for player in team:
+                player = Trueskill.Rating(player[0], player[1])
+                team_temp = team_temp + (player,)
+            team_ratings.append(team_temp)
+
+        return Trueskill.rate(team_ratings, ranks=observations)
+
+class RegressionMetrics():
+
+    def __new__(cls, predictions, targets):
+
+        return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
+
+    def r_squared(self, predictions, targets):  # assumes equal size inputs
+
+        return sklearn.metrics.r2_score(targets, predictions)
+
+    def mse(self, predictions, targets):
+
+        return sklearn.metrics.mean_squared_error(targets, predictions)
+
+    def rms(self, predictions, targets):
+
+        return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
+
+class ClassificationMetrics():
+
+    def __new__(cls, predictions, targets):
+
+        return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets)
+
+    def cm(self, predictions, targets):
+
+        return sklearn.metrics.confusion_matrix(targets, predictions)
+
+    def cr(self, predictions, targets):
+
+        return sklearn.metrics.classification_report(targets, predictions)
+
+@jit(nopython=True)
+def mean(data):
+
+    return np.mean(data)
+
+@jit(nopython=True)
+def median(data):
+
+    return np.median(data)
+
+@jit(nopython=True)
+def stdev(data):
+
+    return np.std(data)
+
+@jit(nopython=True)
+def variance(data):
+
+    return np.var(data)
+
+@jit(nopython=True)
+def npmin(data):
+
+    return np.amin(data)
+
+@jit(nopython=True)
+def npmax(data):
+
+    return np.amax(data)
+
+@jit(forceobj=True)
+def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
+
+    kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
+    kernel.fit(data)
+    predictions = kernel.predict(data)
+    centers = kernel.cluster_centers_
+
+    return centers, predictions
+
+@jit(forceobj=True)
+def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
+
+    kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)
+
+    return kernel.fit_transform(data)
+
+@jit(forceobj=True)
+def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
+
+    data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+    model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth)
+    model = model.fit(data_train,labels_train)
+    predictions = model.predict(data_test)
+    metrics = ClassificationMetrics(predictions, labels_test)
+
+    return model, metrics
+
+class KNN:
+
+    def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsClassifier()
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+
+        data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
+        model.fit(data_train, outputs_train)
+        predictions = model.predict(data_test)
+
+        return model, RegressionMetrics(predictions, outputs_test)
+
+class NaiveBayes:
+
+    def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+class SVM:
+
+    class CustomKernel:
+
+        def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
+
+            return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
+
+    class StandardKernel:
+
+        def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
+
+            return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
+
+    class PrebuiltKernel:
+
+        class Linear:
+
+            def __new__(cls):
+
+                return sklearn.svm.SVC(kernel = 'linear')
+
+        class Polynomial:
+
+            def __new__(cls, power, r_bias):
+
+                return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
+
+        class RBF:
+
+            def __new__(cls, gamma):
+
+                return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
+
+        class Sigmoid:
+
+            def __new__(cls, r_bias):
+
+                return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
+
+    def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
+
+        return kernel.fit(train_data, train_outputs)
+
+    def eval_classification(self, kernel, test_data, test_outputs):
+
+        predictions = kernel.predict(test_data)
+
+        return ClassificationMetrics(predictions, test_outputs)
+
+    def eval_regression(self, kernel, test_data, test_outputs):
+
+        predictions = kernel.predict(test_data)
+
+        return RegressionMetrics(predictions, test_outputs)
+
+def random_forest_classifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
+
+    data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+    kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
+    kernel.fit(data_train, labels_train)
+    predictions = kernel.predict(data_test)
+
+    return kernel, ClassificationMetrics(predictions, labels_test)
+
+def random_forest_regressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
+
+    data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+    kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
+    kernel.fit(data_train, outputs_train)
+    predictions = kernel.predict(data_test)
+
+    return kernel, RegressionMetrics(predictions, outputs_test)

analysis-master/analysis-amd64/analysis/glicko2.py

@@ -0,0 +1,99 @@
+import math
+
+class Glicko2:
+    _tau = 0.5
+
+    def getRating(self):
+        return (self.__rating * 173.7178) + 1500 
+
+    def setRating(self, rating):
+        self.__rating = (rating - 1500) / 173.7178
+
+    rating = property(getRating, setRating)
+
+    def getRd(self):
+        return self.__rd * 173.7178
+
+    def setRd(self, rd):
+        self.__rd = rd / 173.7178
+
+    rd = property(getRd, setRd)
+        
+    def __init__(self, rating = 1500, rd = 350, vol = 0.06):
+
+        self.setRating(rating)
+        self.setRd(rd)
+        self.vol = vol
+            
+    def _preRatingRD(self):
+
+        self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
+        
+    def update_player(self, rating_list, RD_list, outcome_list):
+
+        rating_list = [(x - 1500) / 173.7178 for x in rating_list]
+        RD_list = [x / 173.7178 for x in RD_list]
+
+        v = self._v(rating_list, RD_list)
+        self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
+        self._preRatingRD()
+        
+        self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
+        
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * \
+                        (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        self.__rating += math.pow(self.__rd, 2) * tempSum
+        
+        
+    def _newVol(self, rating_list, RD_list, outcome_list, v):
+
+        i = 0
+        delta = self._delta(rating_list, RD_list, outcome_list, v)
+        a = math.log(math.pow(self.vol, 2))
+        tau = self._tau
+        x0 = a
+        x1 = 0
+        
+        while x0 != x1:
+            # New iteration, so x(i) becomes x(i-1)
+            x0 = x1
+            d = math.pow(self.__rating, 2) + v + math.exp(x0)
+            h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
+            / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
+            h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
+            (math.pow(self.__rating, 2) + v) \
+            / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
+            * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
+            x1 = x0 - (h1 / h2)
+
+        return math.exp(x1 / 2)
+        
+    def _delta(self, rating_list, RD_list, outcome_list, v):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        return v * tempSum
+        
+    def _v(self, rating_list, RD_list):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempE = self._E(rating_list[i], RD_list[i])
+            tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
+        return 1 / tempSum
+        
+    def _E(self, p2rating, p2RD):
+
+        return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
+                                    (self.__rating - p2rating)))
+        
+    def _g(self, RD):
+
+        return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
+        
+    def did_not_compete(self):
+
+        self._preRatingRD()

analysis-master/analysis-amd64/analysis/regression.py

@@ -0,0 +1,220 @@
+# Titan Robotics Team 2022: CUDA-based Regressions Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#   this module has been automatically inegrated into analysis.py, and should be callable as a class from the package
+#   this module is cuda-optimized and vectorized (except for one small part)
+# setup:
+
+__version__ = "1.0.0.004"
+
+# changelog should be viewed using print(analysis.regression.__changelog__)
+__changelog__ = """
+    1.0.0.004:
+        - bug fixes
+        - fixed changelog
+    1.0.0.003:
+        - bug fixes
+    1.0.0.002:
+        -Added more parameters to log, exponential, polynomial
+        -Added SigmoidalRegKernelArthur, because Arthur apparently needs
+        to train the scaling and shifting of sigmoids
+    1.0.0.001:
+        -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+        -already vectorized (except for polynomial generation) and CUDA-optimized
+"""
+
+__author__ = (
+    "Jacob Levine <jlevine@imsa.edu>",
+    "Arthur Lu <learthurgo@gmail.com>"
+)
+
+__all__ = [
+    'factorial',
+    'take_all_pwrs',
+    'num_poly_terms',
+    'set_device',
+    'LinearRegKernel',
+    'SigmoidalRegKernel',
+    'LogRegKernel',
+    'PolyRegKernel',
+    'ExpRegKernel',
+    'SigmoidalRegKernelArthur',
+    'SGDTrain',
+    'CustomTrain'
+]
+
+import torch
+
+global device
+
+device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
+
+#todo: document completely
+
+def set_device(self, new_device):
+    device=new_device
+
+class LinearRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def forward(self,mtx):
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return torch.matmul(self.weights,mtx)+long_bias
+
+class SigmoidalRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    sigmoid=torch.nn.Sigmoid()
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def forward(self,mtx):
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
+
+class SigmoidalRegKernelArthur():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    sigmoid=torch.nn.Sigmoid()
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class LogRegKernel():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class ExpRegKernel():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class PolyRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    power=None
+    def __init__(self, num_vars, power):
+        self.power=power
+        num_terms=self.num_poly_terms(num_vars, power)
+        self.weights=torch.rand(num_terms, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def num_poly_terms(self,num_vars, power):
+        if power == 0:
+            return 0
+        return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
+    def factorial(self,n):
+        if n==0:
+            return 1
+        else:
+            return n*self.factorial(n-1)
+    def take_all_pwrs(self, vec, pwr):
+        #todo: vectorize (kinda)
+        combins=torch.combinations(vec, r=pwr, with_replacement=True)
+        out=torch.ones(combins.size()[0]).to(device).to(torch.float)
+        for i in torch.t(combins).to(device).to(torch.float):
+            out *= i
+        if pwr == 1:
+            return out
+        else:
+            return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
+    def forward(self,mtx):
+        #TODO: Vectorize the last part
+        cols=[]
+        for i in torch.t(mtx):
+            cols.append(self.take_all_pwrs(i,self.power))
+        new_mtx=torch.t(torch.stack(cols))
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return torch.matmul(self.weights,new_mtx)+long_bias
+
+def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
+    optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
+    data_cuda=data.to(device)
+    ground_cuda=ground.to(device)
+    if (return_losses):
+        losses=[]
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                losses.append(ls.item())
+                ls.backward()
+                optim.step()
+        return [kernel,losses]
+    else:
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                ls.backward()
+                optim.step()
+        return kernel
+
+def CustomTrain(self, kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
+    data_cuda=data.to(device)
+    ground_cuda=ground.to(device)
+    if (return_losses):
+        losses=[]
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data)
+                ls=loss(pred,ground)
+                losses.append(ls.item())
+                ls.backward()
+                optim.step()
+        return [kernel,losses]
+    else:
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                ls.backward()
+                optim.step()
+        return kernel

analysis-master/analysis-amd64/analysis/titanlearn.py

@@ -0,0 +1,122 @@
+# Titan Robotics Team 2022: ML Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import titanlearn'
+#    this should be included in the local directory or environment variable
+#    this module is optimized for multhreaded computing
+#    this module learns from its mistakes far faster than 2022's captains
+# setup:
+
+__version__ = "2.0.1.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    2.0.1.001:
+        - removed matplotlib import
+        - removed graphloss()
+    2.0.1.000:
+        - added net, dataset, dataloader, and stdtrain template definitions
+        - added graphloss function
+    2.0.0.001:
+        - added clear functions
+    2.0.0.000:
+        - complete rewrite planned
+        - depreciated 1.0.0.xxx versions
+        - added simple training loop
+    1.0.0.xxx:
+        -added generation of ANNS, basic SGD training
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>,"
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+
+__all__ = [
+    'clear',
+    'net',
+    'dataset',
+    'dataloader',
+    'train',
+    'stdtrainer',
+    ]
+
+import torch
+from os import system, name
+import numpy as np
+
+def clear(): 
+    if name == 'nt': 
+        _ = system('cls') 
+    else: 
+        _ = system('clear') 
+
+class net(torch.nn.Module): #template for standard neural net
+    def __init__(self):
+        super(Net, self).__init__()
+        
+    def forward(self, input):
+        pass
+
+class dataset(torch.utils.data.Dataset): #template for standard dataset
+    
+    def __init__(self):
+        super(torch.utils.data.Dataset).__init__()
+        
+    def __getitem__(self, index):
+        pass
+    
+    def __len__(self):
+        pass
+
+def dataloader(dataset, batch_size, num_workers, shuffle = True):
+
+    return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
+
+def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
+
+    dataset_len = trainloader.dataset.__len__()
+    iter_count = 0
+    running_loss = 0
+    running_loss_list = []
+
+    for epoch in range(epochs):  # loop over the dataset multiple times
+
+        for i, data in enumerate(trainloader, 0):
+
+            inputs = data[0].to(device)
+            labels = data[1].to(device)
+
+            optimizer.zero_grad()
+
+            outputs = net(inputs)
+            loss = criterion(outputs, labels.to(torch.float))
+            
+            loss.backward()
+            optimizer.step()
+
+            # monitoring steps below
+
+            iter_count += 1
+            running_loss += loss.item()
+            running_loss_list.append(running_loss)
+            clear()
+
+            print("training on: " + device)
+            print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
+            print("current batch loss: " + str(loss.item))
+            print("running loss: " + str(running_loss / iter_count))
+        
+    return net, running_loss_list
+    print("finished training")
+
+def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
+
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    net = net.to(device)
+    criterion = criterion.to(device)
+    optimizer = optimizer.to(device)
+    trainloader = dataloader
+    
+    return train(device, net, epochs, trainloader, optimizer, criterion)

analysis-master/analysis-amd64/analysis/trueskill.py

@@ -0,0 +1,907 @@
+from __future__ import absolute_import
+
+from itertools import chain
+import math
+
+from six import iteritems
+from six.moves import map, range, zip
+from six import iterkeys
+
+import copy
+try:
+    from numbers import Number
+except ImportError:
+    Number = (int, long, float, complex)
+
+inf = float('inf')
+
+class Gaussian(object):
+    #: Precision, the inverse of the variance.
+    pi = 0
+    #: Precision adjusted mean, the precision multiplied by the mean.
+    tau = 0
+
+    def __init__(self, mu=None, sigma=None, pi=0, tau=0):
+        if mu is not None:
+            if sigma is None:
+                raise TypeError('sigma argument is needed')
+            elif sigma == 0:
+                raise ValueError('sigma**2 should be greater than 0')
+            pi = sigma ** -2
+            tau = pi * mu
+        self.pi = pi
+        self.tau = tau
+
+    @property
+    def mu(self):
+        return self.pi and self.tau / self.pi
+
+    @property
+    def sigma(self):
+        return math.sqrt(1 / self.pi) if self.pi else inf
+
+    def __mul__(self, other):
+        pi, tau = self.pi + other.pi, self.tau + other.tau
+        return Gaussian(pi=pi, tau=tau)
+
+    def __truediv__(self, other):
+        pi, tau = self.pi - other.pi, self.tau - other.tau
+        return Gaussian(pi=pi, tau=tau)
+
+    __div__ = __truediv__  # for Python 2
+
+    def __eq__(self, other):
+        return self.pi == other.pi and self.tau == other.tau
+
+    def __lt__(self, other):
+        return self.mu < other.mu
+
+    def __le__(self, other):
+        return self.mu <= other.mu
+
+    def __gt__(self, other):
+        return self.mu > other.mu
+
+    def __ge__(self, other):
+        return self.mu >= other.mu
+
+    def __repr__(self):
+        return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma)
+
+    def _repr_latex_(self):
+        latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma)
+        return '$%s$' % latex
+
+class Matrix(list):
+    def __init__(self, src, height=None, width=None):
+        if callable(src):
+            f, src = src, {}
+            size = [height, width]
+            if not height:
+                def set_height(height):
+                    size[0] = height
+                size[0] = set_height
+            if not width:
+                def set_width(width):
+                    size[1] = width
+                size[1] = set_width
+            try:
+                for (r, c), val in f(*size):
+                    src[r, c] = val
+            except TypeError:
+                raise TypeError('A callable src must return an interable '
+                                'which generates a tuple containing '
+                                'coordinate and value')
+            height, width = tuple(size)
+            if height is None or width is None:
+                raise TypeError('A callable src must call set_height and '
+                                'set_width if the size is non-deterministic')
+        if isinstance(src, list):
+            is_number = lambda x: isinstance(x, Number)
+            unique_col_sizes = set(map(len, src))
+            everything_are_number = filter(is_number, sum(src, []))
+            if len(unique_col_sizes) != 1 or not everything_are_number:
+                raise ValueError('src must be a rectangular array of numbers')
+            two_dimensional_array = src
+        elif isinstance(src, dict):
+            if not height or not width:
+                w = h = 0
+                for r, c in iterkeys(src):
+                    if not height:
+                        h = max(h, r + 1)
+                    if not width:
+                        w = max(w, c + 1)
+                if not height:
+                    height = h
+                if not width:
+                    width = w
+            two_dimensional_array = []
+            for r in range(height):
+                row = []
+                two_dimensional_array.append(row)
+                for c in range(width):
+                    row.append(src.get((r, c), 0))
+        else:
+            raise TypeError('src must be a list or dict or callable')
+        super(Matrix, self).__init__(two_dimensional_array)
+
+    @property
+    def height(self):
+        return len(self)
+
+    @property
+    def width(self):
+        return len(self[0])
+
+    def transpose(self):
+        height, width = self.height, self.width
+        src = {}
+        for c in range(width):
+            for r in range(height):
+                src[c, r] = self[r][c]
+        return type(self)(src, height=width, width=height)
+
+    def minor(self, row_n, col_n):
+        height, width = self.height, self.width
+        if not (0 <= row_n < height):
+            raise ValueError('row_n should be between 0 and %d' % height)
+        elif not (0 <= col_n < width):
+            raise ValueError('col_n should be between 0 and %d' % width)
+        two_dimensional_array = []
+        for r in range(height):
+            if r == row_n:
+                continue
+            row = []
+            two_dimensional_array.append(row)
+            for c in range(width):
+                if c == col_n:
+                    continue
+                row.append(self[r][c])
+        return type(self)(two_dimensional_array)
+
+    def determinant(self):
+        height, width = self.height, self.width
+        if height != width:
+            raise ValueError('Only square matrix can calculate a determinant')
+        tmp, rv = copy.deepcopy(self), 1.
+        for c in range(width - 1, 0, -1):
+            pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1))
+            pivot = tmp[r][c]
+            if not pivot:
+                return 0.
+            tmp[r], tmp[c] = tmp[c], tmp[r]
+            if r != c:
+                rv = -rv
+            rv *= pivot
+            fact = -1. / pivot
+            for r in range(c):
+                f = fact * tmp[r][c]
+                for x in range(c):
+                    tmp[r][x] += f * tmp[c][x]
+        return rv * tmp[0][0]
+
+    def adjugate(self):
+        height, width = self.height, self.width
+        if height != width:
+            raise ValueError('Only square matrix can be adjugated')
+        if height == 2:
+            a, b = self[0][0], self[0][1]
+            c, d = self[1][0], self[1][1]
+            return type(self)([[d, -b], [-c, a]])
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                sign = -1 if (r + c) % 2 else 1
+                src[r, c] = self.minor(r, c).determinant() * sign
+        return type(self)(src, height, width)
+
+    def inverse(self):
+        if self.height == self.width == 1:
+            return type(self)([[1. / self[0][0]]])
+        return (1. / self.determinant()) * self.adjugate()
+
+    def __add__(self, other):
+        height, width = self.height, self.width
+        if (height, width) != (other.height, other.width):
+            raise ValueError('Must be same size')
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = self[r][c] + other[r][c]
+        return type(self)(src, height, width)
+
+    def __mul__(self, other):
+        if self.width != other.height:
+            raise ValueError('Bad size')
+        height, width = self.height, other.width
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = sum(self[r][x] * other[x][c]
+                                for x in range(self.width))
+        return type(self)(src, height, width)
+
+    def __rmul__(self, other):
+        if not isinstance(other, Number):
+            raise TypeError('The operand should be a number')
+        height, width = self.height, self.width
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = other * self[r][c]
+        return type(self)(src, height, width)
+
+    def __repr__(self):
+        return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__())
+
+    def _repr_latex_(self):
+        rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self]
+        latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows)
+        return '$%s$' % latex
+
+def _gen_erfcinv(erfc, math=math):
+    def erfcinv(y):
+        """The inverse function of erfc."""
+        if y >= 2:
+            return -100.
+        elif y <= 0:
+            return 100.
+        zero_point = y < 1
+        if not zero_point:
+            y = 2 - y
+        t = math.sqrt(-2 * math.log(y / 2.))
+        x = -0.70711 * \
+            ((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t)
+        for i in range(2):
+            err = erfc(x) - y
+            x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err)
+        return x if zero_point else -x
+    return erfcinv
+
+def _gen_ppf(erfc, math=math):
+    erfcinv = _gen_erfcinv(erfc, math)
+    def ppf(x, mu=0, sigma=1):
+        return mu - sigma * math.sqrt(2) * erfcinv(2 * x)
+    return ppf
+
+def erfc(x):
+    z = abs(x)
+    t = 1. / (1. + z / 2.)
+    r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (
+        0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * (
+            0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * (
+                -0.82215223 + t * 0.17087277
+            )))
+        )))
+    )))
+    return 2. - r if x < 0 else r
+
+def cdf(x, mu=0, sigma=1):
+    return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2)))
+
+
+def pdf(x, mu=0, sigma=1):
+    return (1 / math.sqrt(2 * math.pi) * abs(sigma) *
+            math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2)))
+
+ppf = _gen_ppf(erfc)
+
+def choose_backend(backend):
+    if backend is None:  # fallback
+        return cdf, pdf, ppf
+    elif backend == 'mpmath':
+        try:
+            import mpmath
+        except ImportError:
+            raise ImportError('Install "mpmath" to use this backend')
+        return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath)
+    elif backend == 'scipy':
+        try:
+            from scipy.stats import norm
+        except ImportError:
+            raise ImportError('Install "scipy" to use this backend')
+        return norm.cdf, norm.pdf, norm.ppf
+    raise ValueError('%r backend is not defined' % backend)
+
+def available_backends():
+    backends = [None]
+    for backend in ['mpmath', 'scipy']:
+        try:
+            __import__(backend)
+        except ImportError:
+            continue
+        backends.append(backend)
+    return backends
+
+class Node(object):
+
+    pass
+
+class Variable(Node, Gaussian):
+
+    def __init__(self):
+        self.messages = {}
+        super(Variable, self).__init__()
+
+    def set(self, val):
+        delta = self.delta(val)
+        self.pi, self.tau = val.pi, val.tau
+        return delta
+
+    def delta(self, other):
+        pi_delta = abs(self.pi - other.pi)
+        if pi_delta == inf:
+            return 0.
+        return max(abs(self.tau - other.tau), math.sqrt(pi_delta))
+
+    def update_message(self, factor, pi=0, tau=0, message=None):
+        message = message or Gaussian(pi=pi, tau=tau)
+        old_message, self[factor] = self[factor], message
+        return self.set(self / old_message * message)
+
+    def update_value(self, factor, pi=0, tau=0, value=None):
+        value = value or Gaussian(pi=pi, tau=tau)
+        old_message = self[factor]
+        self[factor] = value * old_message / self
+        return self.set(value)
+
+    def __getitem__(self, factor):
+        return self.messages[factor]
+
+    def __setitem__(self, factor, message):
+        self.messages[factor] = message
+
+    def __repr__(self):
+        args = (type(self).__name__, super(Variable, self).__repr__(),
+                len(self.messages), '' if len(self.messages) == 1 else 's')
+        return '<%s %s with %d connection%s>' % args
+
+
+class Factor(Node):
+
+    def __init__(self, variables):
+        self.vars = variables
+        for var in variables:
+            var[self] = Gaussian()
+
+    def down(self):
+        return 0
+
+    def up(self):
+        return 0
+
+    @property
+    def var(self):
+        assert len(self.vars) == 1
+        return self.vars[0]
+
+    def __repr__(self):
+        args = (type(self).__name__, len(self.vars),
+                '' if len(self.vars) == 1 else 's')
+        return '<%s with %d connection%s>' % args
+
+
+class PriorFactor(Factor):
+
+    def __init__(self, var, val, dynamic=0):
+        super(PriorFactor, self).__init__([var])
+        self.val = val
+        self.dynamic = dynamic
+
+    def down(self):
+        sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2)
+        value = Gaussian(self.val.mu, sigma)
+        return self.var.update_value(self, value=value)
+
+
+class LikelihoodFactor(Factor):
+
+    def __init__(self, mean_var, value_var, variance):
+        super(LikelihoodFactor, self).__init__([mean_var, value_var])
+        self.mean = mean_var
+        self.value = value_var
+        self.variance = variance
+
+    def calc_a(self, var):
+        return 1. / (1. + self.variance * var.pi)
+
+    def down(self):
+        # update value.
+        msg = self.mean / self.mean[self]
+        a = self.calc_a(msg)
+        return self.value.update_message(self, a * msg.pi, a * msg.tau)
+
+    def up(self):
+        # update mean.
+        msg = self.value / self.value[self]
+        a = self.calc_a(msg)
+        return self.mean.update_message(self, a * msg.pi, a * msg.tau)
+
+
+class SumFactor(Factor):
+
+    def __init__(self, sum_var, term_vars, coeffs):
+        super(SumFactor, self).__init__([sum_var] + term_vars)
+        self.sum = sum_var
+        self.terms = term_vars
+        self.coeffs = coeffs
+
+    def down(self):
+        vals = self.terms
+        msgs = [var[self] for var in vals]
+        return self.update(self.sum, vals, msgs, self.coeffs)
+
+    def up(self, index=0):
+        coeff = self.coeffs[index]
+        coeffs = []
+        for x, c in enumerate(self.coeffs):
+            try:
+                if x == index:
+                    coeffs.append(1. / coeff)
+                else:
+                    coeffs.append(-c / coeff)
+            except ZeroDivisionError:
+                coeffs.append(0.)
+        vals = self.terms[:]
+        vals[index] = self.sum
+        msgs = [var[self] for var in vals]
+        return self.update(self.terms[index], vals, msgs, coeffs)
+
+    def update(self, var, vals, msgs, coeffs):
+        pi_inv = 0
+        mu = 0
+        for val, msg, coeff in zip(vals, msgs, coeffs):
+            div = val / msg
+            mu += coeff * div.mu
+            if pi_inv == inf:
+                continue
+            try:
+                # numpy.float64 handles floating-point error by different way.
+                # For example, it can just warn RuntimeWarning on n/0 problem
+                # instead of throwing ZeroDivisionError.  So div.pi, the
+                # denominator has to be a built-in float.
+                pi_inv += coeff ** 2 / float(div.pi)
+            except ZeroDivisionError:
+                pi_inv = inf
+        pi = 1. / pi_inv
+        tau = pi * mu
+        return var.update_message(self, pi, tau)
+
+
+class TruncateFactor(Factor):
+
+    def __init__(self, var, v_func, w_func, draw_margin):
+        super(TruncateFactor, self).__init__([var])
+        self.v_func = v_func
+        self.w_func = w_func
+        self.draw_margin = draw_margin
+
+    def up(self):
+        val = self.var
+        msg = self.var[self]
+        div = val / msg
+        sqrt_pi = math.sqrt(div.pi)
+        args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi)
+        v = self.v_func(*args)
+        w = self.w_func(*args)
+        denom = (1. - w)
+        pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom
+        return val.update_value(self, pi, tau)
+
+#: Default initial mean of ratings.
+MU = 25.
+#: Default initial standard deviation of ratings.
+SIGMA = MU / 3
+#: Default distance that guarantees about 76% chance of winning.
+BETA = SIGMA / 2
+#: Default dynamic factor.
+TAU = SIGMA / 100
+#: Default draw probability of the game.
+DRAW_PROBABILITY = .10
+#: A basis to check reliability of the result.
+DELTA = 0.0001
+
+
+def calc_draw_probability(draw_margin, size, env=None):
+    if env is None:
+        env = global_env()
+    return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1
+
+
+def calc_draw_margin(draw_probability, size, env=None):
+    if env is None:
+        env = global_env()
+    return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta
+
+
+def _team_sizes(rating_groups):
+    team_sizes = [0]
+    for group in rating_groups:
+        team_sizes.append(len(group) + team_sizes[-1])
+    del team_sizes[0]
+    return team_sizes
+
+
+def _floating_point_error(env):
+    if env.backend == 'mpmath':
+        msg = 'Set "mpmath.mp.dps" to higher'
+    else:
+        msg = 'Cannot calculate correctly, set backend to "mpmath"'
+    return FloatingPointError(msg)
+
+
+class Rating(Gaussian):
+    def __init__(self, mu=None, sigma=None):
+        if isinstance(mu, tuple):
+            mu, sigma = mu
+        elif isinstance(mu, Gaussian):
+            mu, sigma = mu.mu, mu.sigma
+        if mu is None:
+            mu = global_env().mu
+        if sigma is None:
+            sigma = global_env().sigma
+        super(Rating, self).__init__(mu, sigma)
+
+    def __int__(self):
+        return int(self.mu)
+
+    def __long__(self):
+        return long(self.mu)
+
+    def __float__(self):
+        return float(self.mu)
+
+    def __iter__(self):
+        return iter((self.mu, self.sigma))
+
+    def __repr__(self):
+        c = type(self)
+        args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma)
+        return '%s(mu=%.3f, sigma=%.3f)' % args
+
+
+class TrueSkill(object):
+    def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+                 draw_probability=DRAW_PROBABILITY, backend=None):
+        self.mu = mu
+        self.sigma = sigma
+        self.beta = beta
+        self.tau = tau
+        self.draw_probability = draw_probability
+        self.backend = backend
+        if isinstance(backend, tuple):
+            self.cdf, self.pdf, self.ppf = backend
+        else:
+            self.cdf, self.pdf, self.ppf = choose_backend(backend)
+
+    def create_rating(self, mu=None, sigma=None):
+        if mu is None:
+            mu = self.mu
+        if sigma is None:
+            sigma = self.sigma
+        return Rating(mu, sigma)
+
+    def v_win(self, diff, draw_margin):
+        x = diff - draw_margin
+        denom = self.cdf(x)
+        return (self.pdf(x) / denom) if denom else -x
+
+    def v_draw(self, diff, draw_margin):
+        abs_diff = abs(diff)
+        a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+        denom = self.cdf(a) - self.cdf(b)
+        numer = self.pdf(b) - self.pdf(a)
+        return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1)
+
+    def w_win(self, diff, draw_margin):
+        x = diff - draw_margin
+        v = self.v_win(diff, draw_margin)
+        w = v * (v + x)
+        if 0 < w < 1:
+            return w
+        raise _floating_point_error(self)
+
+    def w_draw(self, diff, draw_margin):
+        abs_diff = abs(diff)
+        a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+        denom = self.cdf(a) - self.cdf(b)
+        if not denom:
+            raise _floating_point_error(self)
+        v = self.v_draw(abs_diff, draw_margin)
+        return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom
+
+    def validate_rating_groups(self, rating_groups):
+        # check group sizes
+        if len(rating_groups) < 2:
+            raise ValueError('Need multiple rating groups')
+        elif not all(rating_groups):
+            raise ValueError('Each group must contain multiple ratings')
+        # check group types
+        group_types = set(map(type, rating_groups))
+        if len(group_types) != 1:
+            raise TypeError('All groups should be same type')
+        elif group_types.pop() is Rating:
+            raise TypeError('Rating cannot be a rating group')
+        # normalize rating_groups
+        if isinstance(rating_groups[0], dict):
+            dict_rating_groups = rating_groups
+            rating_groups = []
+            keys = []
+            for dict_rating_group in dict_rating_groups:
+                rating_group, key_group = [], []
+                for key, rating in iteritems(dict_rating_group):
+                    rating_group.append(rating)
+                    key_group.append(key)
+                rating_groups.append(tuple(rating_group))
+                keys.append(tuple(key_group))
+        else:
+            rating_groups = list(rating_groups)
+            keys = None
+        return rating_groups, keys
+
+    def validate_weights(self, weights, rating_groups, keys=None):
+        if weights is None:
+            weights = [(1,) * len(g) for g in rating_groups]
+        elif isinstance(weights, dict):
+            weights_dict, weights = weights, []
+            for x, group in enumerate(rating_groups):
+                w = []
+                weights.append(w)
+                for y, rating in enumerate(group):
+                    if keys is not None:
+                        y = keys[x][y]
+                    w.append(weights_dict.get((x, y), 1))
+        return weights
+
+    def factor_graph_builders(self, rating_groups, ranks, weights):
+        flatten_ratings = sum(map(tuple, rating_groups), ())
+        flatten_weights = sum(map(tuple, weights), ())
+        size = len(flatten_ratings)
+        group_size = len(rating_groups)
+        # create variables
+        rating_vars = [Variable() for x in range(size)]
+        perf_vars = [Variable() for x in range(size)]
+        team_perf_vars = [Variable() for x in range(group_size)]
+        team_diff_vars = [Variable() for x in range(group_size - 1)]
+        team_sizes = _team_sizes(rating_groups)
+        # layer builders
+        def build_rating_layer():
+            for rating_var, rating in zip(rating_vars, flatten_ratings):
+                yield PriorFactor(rating_var, rating, self.tau)
+        def build_perf_layer():
+            for rating_var, perf_var in zip(rating_vars, perf_vars):
+                yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2)
+        def build_team_perf_layer():
+            for team, team_perf_var in enumerate(team_perf_vars):
+                if team > 0:
+                    start = team_sizes[team - 1]
+                else:
+                    start = 0
+                end = team_sizes[team]
+                child_perf_vars = perf_vars[start:end]
+                coeffs = flatten_weights[start:end]
+                yield SumFactor(team_perf_var, child_perf_vars, coeffs)
+        def build_team_diff_layer():
+            for team, team_diff_var in enumerate(team_diff_vars):
+                yield SumFactor(team_diff_var,
+                                team_perf_vars[team:team + 2], [+1, -1])
+        def build_trunc_layer():
+            for x, team_diff_var in enumerate(team_diff_vars):
+                if callable(self.draw_probability):
+                    # dynamic draw probability
+                    team_perf1, team_perf2 = team_perf_vars[x:x + 2]
+                    args = (Rating(team_perf1), Rating(team_perf2), self)
+                    draw_probability = self.draw_probability(*args)
+                else:
+                    # static draw probability
+                    draw_probability = self.draw_probability
+                size = sum(map(len, rating_groups[x:x + 2]))
+                draw_margin = calc_draw_margin(draw_probability, size, self)
+                if ranks[x] == ranks[x + 1]:  # is a tie?
+                    v_func, w_func = self.v_draw, self.w_draw
+                else:
+                    v_func, w_func = self.v_win, self.w_win
+                yield TruncateFactor(team_diff_var,
+                                     v_func, w_func, draw_margin)
+        # build layers
+        return (build_rating_layer, build_perf_layer, build_team_perf_layer,
+                build_team_diff_layer, build_trunc_layer)
+
+    def run_schedule(self, build_rating_layer, build_perf_layer,
+                     build_team_perf_layer, build_team_diff_layer,
+                     build_trunc_layer, min_delta=DELTA):
+        if min_delta <= 0:
+            raise ValueError('min_delta must be greater than 0')
+        layers = []
+        def build(builders):
+            layers_built = [list(build()) for build in builders]
+            layers.extend(layers_built)
+            return layers_built
+        # gray arrows
+        layers_built = build([build_rating_layer,
+                              build_perf_layer,
+                              build_team_perf_layer])
+        rating_layer, perf_layer, team_perf_layer = layers_built
+        for f in chain(*layers_built):
+            f.down()
+        # arrow #1, #2, #3
+        team_diff_layer, trunc_layer = build([build_team_diff_layer,
+                                              build_trunc_layer])
+        team_diff_len = len(team_diff_layer)
+        for x in range(10):
+            if team_diff_len == 1:
+                # only two teams
+                team_diff_layer[0].down()
+                delta = trunc_layer[0].up()
+            else:
+                # multiple teams
+                delta = 0
+                for x in range(team_diff_len - 1):
+                    team_diff_layer[x].down()
+                    delta = max(delta, trunc_layer[x].up())
+                    team_diff_layer[x].up(1)  # up to right variable
+                for x in range(team_diff_len - 1, 0, -1):
+                    team_diff_layer[x].down()
+                    delta = max(delta, trunc_layer[x].up())
+                    team_diff_layer[x].up(0)  # up to left variable
+            # repeat until to small update
+            if delta <= min_delta:
+                break
+        # up both ends
+        team_diff_layer[0].up(0)
+        team_diff_layer[team_diff_len - 1].up(1)
+        # up the remainder of the black arrows
+        for f in team_perf_layer:
+            for x in range(len(f.vars) - 1):
+                f.up(x)
+        for f in perf_layer:
+            f.up()
+        return layers
+
+    def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA):
+        rating_groups, keys = self.validate_rating_groups(rating_groups)
+        weights = self.validate_weights(weights, rating_groups, keys)
+        group_size = len(rating_groups)
+        if ranks is None:
+            ranks = range(group_size)
+        elif len(ranks) != group_size:
+            raise ValueError('Wrong ranks')
+        # sort rating groups by rank
+        by_rank = lambda x: x[1][1]
+        sorting = sorted(enumerate(zip(rating_groups, ranks, weights)),
+                         key=by_rank)
+        sorted_rating_groups, sorted_ranks, sorted_weights = [], [], []
+        for x, (g, r, w) in sorting:
+            sorted_rating_groups.append(g)
+            sorted_ranks.append(r)
+            # make weights to be greater than 0
+            sorted_weights.append(max(min_delta, w_) for w_ in w)
+        # build factor graph
+        args = (sorted_rating_groups, sorted_ranks, sorted_weights)
+        builders = self.factor_graph_builders(*args)
+        args = builders + (min_delta,)
+        layers = self.run_schedule(*args)
+        # make result
+        rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups)
+        transformed_groups = []
+        for start, end in zip([0] + team_sizes[:-1], team_sizes):
+            group = []
+            for f in rating_layer[start:end]:
+                group.append(Rating(float(f.var.mu), float(f.var.sigma)))
+            transformed_groups.append(tuple(group))
+        by_hint = lambda x: x[0]
+        unsorting = sorted(zip((x for x, __ in sorting), transformed_groups),
+                           key=by_hint)
+        if keys is None:
+            return [g for x, g in unsorting]
+        # restore the structure with input dictionary keys
+        return [dict(zip(keys[x], g)) for x, g in unsorting]
+
+    def quality(self, rating_groups, weights=None):
+        rating_groups, keys = self.validate_rating_groups(rating_groups)
+        weights = self.validate_weights(weights, rating_groups, keys)
+        flatten_ratings = sum(map(tuple, rating_groups), ())
+        flatten_weights = sum(map(tuple, weights), ())
+        length = len(flatten_ratings)
+        # a vector of all of the skill means
+        mean_matrix = Matrix([[r.mu] for r in flatten_ratings])
+        # a matrix whose diagonal values are the variances (sigma ** 2) of each
+        # of the players.
+        def variance_matrix(height, width):
+            variances = (r.sigma ** 2 for r in flatten_ratings)
+            for x, variance in enumerate(variances):
+                yield (x, x), variance
+        variance_matrix = Matrix(variance_matrix, length, length)
+        # the player-team assignment and comparison matrix
+        def rotated_a_matrix(set_height, set_width):
+            t = 0
+            for r, (cur, _next) in enumerate(zip(rating_groups[:-1],
+                                                rating_groups[1:])):
+                for x in range(t, t + len(cur)):
+                    yield (r, x), flatten_weights[x]
+                    t += 1
+                x += 1
+                for x in range(x, x + len(_next)):
+                    yield (r, x), -flatten_weights[x]
+            set_height(r + 1)
+            set_width(x + 1)
+        rotated_a_matrix = Matrix(rotated_a_matrix)
+        a_matrix = rotated_a_matrix.transpose()
+        # match quality further derivation
+        _ata = (self.beta ** 2) * rotated_a_matrix * a_matrix
+        _atsa = rotated_a_matrix * variance_matrix * a_matrix
+        start = mean_matrix.transpose() * a_matrix
+        middle = _ata + _atsa
+        end = rotated_a_matrix * mean_matrix
+        # make result
+        e_arg = (-0.5 * start * middle.inverse() * end).determinant()
+        s_arg = _ata.determinant() / middle.determinant()
+        return math.exp(e_arg) * math.sqrt(s_arg)
+
+    def expose(self, rating):
+        k = self.mu / self.sigma
+        return rating.mu - k * rating.sigma
+
+    def make_as_global(self):
+        return setup(env=self)
+
+    def __repr__(self):
+        c = type(self)
+        if callable(self.draw_probability):
+            f = self.draw_probability
+            draw_probability = '.'.join([f.__module__, f.__name__])
+        else:
+            draw_probability = '%.1f%%' % (self.draw_probability * 100)
+        if self.backend is None:
+            backend = ''
+        elif isinstance(self.backend, tuple):
+            backend = ', backend=...'
+        else:
+            backend = ', backend=%r' % self.backend
+        args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma,
+                self.beta, self.tau, draw_probability, backend)
+        return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, '
+                'draw_probability=%s%s)' % args)
+
+
+def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None):
+    if env is None:
+        env = global_env()
+    ranks = [0, 0 if drawn else 1]
+    teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta)
+    return teams[0][0], teams[1][0]
+
+
+def quality_1vs1(rating1, rating2, env=None):
+    if env is None:
+        env = global_env()
+    return env.quality([(rating1,), (rating2,)])
+
+
+def global_env():
+    try:
+        global_env.__trueskill__
+    except AttributeError:
+        # setup the default environment
+        setup()
+    return global_env.__trueskill__
+
+
+def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+          draw_probability=DRAW_PROBABILITY, backend=None, env=None):
+    if env is None:
+        env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend)
+    global_env.__trueskill__ = env
+    return env
+
+
+def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA):
+    return global_env().rate(rating_groups, ranks, weights, min_delta)
+
+
+def quality(rating_groups, weights=None):
+    return global_env().quality(rating_groups, weights)
+
+
+def expose(rating):
+    return global_env().expose(rating)

analysis-master/analysis-amd64/analysis/visualization.py

@@ -0,0 +1,34 @@
+# Titan Robotics Team 2022: Visualization Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import visualization'
+#    this should be included in the local directory or environment variable
+#    fancy
+# setup:
+
+__version__ = "1.0.0.000"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    1.0.0.000:
+        - created visualization.py
+        - added graphloss()
+        - added imports
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>,"
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+
+__all__ = [
+    'graphloss',
+    ]
+
+import matplotlib.pyplot as plt
+
+def graphloss(losses):
+
+    x = range(0, len(losses))
+    plt.plot(x, losses)
+    plt.show()

analysis-master/analysis-amd64/build.sh

@@ -0,0 +1 @@
+python setup.py sdist bdist_wheel || python3 setup.py sdist bdist_wheel

analysis-master/analysis-amd64/build/lib/analysis/analysis.py

@@ -0,0 +1,700 @@
+# Titan Robotics Team 2022: Data Analysis Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import analysis'
+#    this should be included in the local directory or environment variable
+#    this module has been optimized for multhreaded computing
+#    current benchmark of optimization: 1.33 times faster
+# setup:
+
+__version__ = "1.1.13.009"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    1.1.13.009:
+        - moved elo, glicko2, trueskill functions under class Metrics
+    1.1.13.008:
+        - moved Glicko2 to a seperate package
+    1.1.13.007:
+        - fixed bug with trueskill
+    1.1.13.006:
+        - cleaned up imports
+    1.1.13.005:
+        - cleaned up package
+    1.1.13.004:
+        - small fixes to regression to improve performance
+    1.1.13.003:
+        - filtered nans from regression
+    1.1.13.002:
+        - removed torch requirement, and moved Regression back to regression.py
+    1.1.13.001:
+        - bug fix with linear regression not returning a proper value
+        - cleaned up regression
+        - fixed bug with polynomial regressions
+    1.1.13.000:
+        - fixed all regressions to now properly work
+    1.1.12.006:
+        - fixed bg with a division by zero in histo_analysis
+    1.1.12.005:
+        - fixed numba issues by removing numba  from elo, glicko2 and trueskill
+    1.1.12.004:
+        - renamed gliko to glicko
+    1.1.12.003:
+        - removed depreciated code
+    1.1.12.002:
+        - removed team first time trueskill instantiation in favor of integration in superscript.py
+    1.1.12.001:
+        - improved readibility of regression outputs by stripping tensor data
+        - used map with lambda to acheive the improved readibility
+        - lost numba jit support with regression, and generated_jit hangs at execution
+        - TODO: reimplement correct numba integration in regression
+    1.1.12.000:
+        - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures
+    1.1.11.010:
+        - alphabeticaly ordered import lists
+    1.1.11.009:
+        - bug fixes
+    1.1.11.008:
+        - bug fixes
+    1.1.11.007:
+        - bug fixes
+    1.1.11.006:
+        - tested min and max
+        - bug fixes 
+    1.1.11.005:
+        - added min and max in basic_stats
+    1.1.11.004:
+        - bug fixes
+    1.1.11.003:
+        - bug fixes
+    1.1.11.002:
+        - consolidated metrics
+        - fixed __all__
+    1.1.11.001:
+        - added test/train split to RandomForestClassifier and RandomForestRegressor
+    1.1.11.000:
+        - added RandomForestClassifier and RandomForestRegressor
+        - note: untested
+    1.1.10.000:
+        - added numba.jit to remaining functions
+    1.1.9.002:
+        - kernelized PCA and KNN
+    1.1.9.001:
+        - fixed bugs with SVM and NaiveBayes
+    1.1.9.000:
+        - added SVM class, subclasses, and functions
+        - note: untested
+    1.1.8.000:
+        - added NaiveBayes classification engine
+        - note: untested
+    1.1.7.000:
+        - added knn()
+        - added confusion matrix to decisiontree()
+    1.1.6.002:
+        - changed layout of __changelog to be vscode friendly
+    1.1.6.001:
+        - added additional hyperparameters to decisiontree()
+    1.1.6.000:
+        - fixed __version__
+        - fixed __all__ order
+        - added decisiontree()
+    1.1.5.003:
+        - added pca
+    1.1.5.002:
+        - reduced import list
+        - added kmeans clustering engine
+    1.1.5.001:
+        - simplified regression by using .to(device)
+    1.1.5.000:
+        - added polynomial regression to regression(); untested
+    1.1.4.000:
+        - added trueskill()
+    1.1.3.002:
+        - renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2
+    1.1.3.001:
+        - changed glicko2() to return tuple instead of array
+    1.1.3.000:
+        - added glicko2_engine class and glicko()
+        - verified glicko2() accuracy
+    1.1.2.003:
+        - fixed elo()
+    1.1.2.002:
+        - added elo()
+        - elo() has bugs to be fixed
+    1.1.2.001:
+        - readded regrression import
+    1.1.2.000:
+        - integrated regression.py as regression class
+        - removed regression import
+        - fixed metadata for regression class
+        - fixed metadata for analysis class
+    1.1.1.001:
+        - regression_engine() bug fixes, now actaully regresses
+    1.1.1.000:
+        - added regression_engine()
+        - added all regressions except polynomial
+    1.1.0.007:
+        - updated _init_device()
+    1.1.0.006:
+        - removed useless try statements
+    1.1.0.005:
+        - removed impossible outcomes
+    1.1.0.004:
+        - added performance metrics (r^2, mse, rms)
+    1.1.0.003:
+        - resolved nopython mode for mean, median, stdev, variance
+    1.1.0.002:
+        - snapped (removed) majority of uneeded imports
+        - forced object mode (bad) on all jit
+        - TODO: stop numba complaining about not being able to compile in nopython mode
+    1.1.0.001:
+        - removed from sklearn import * to resolve uneeded wildcard imports
+    1.1.0.000:
+        - removed c_entities,nc_entities,obstacles,objectives from __all__
+        - applied numba.jit to all functions
+        - depreciated and removed stdev_z_split
+        - cleaned up histo_analysis to include numpy and numba.jit optimizations
+        - depreciated and removed all regression functions in favor of future pytorch optimizer
+        - depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
+        - optimized z_normalize using sklearn.preprocessing.normalize
+        - TODO: implement kernel/function based pytorch regression optimizer
+    1.0.9.000:
+        - refactored
+        - numpyed everything
+        - removed stats in favor of numpy functions
+    1.0.8.005:
+        - minor fixes
+    1.0.8.004:
+        - removed a few unused dependencies
+    1.0.8.003:
+        - added p_value function
+    1.0.8.002:
+        - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
+    1.0.8.001:
+        - refactors
+        - bugfixes
+    1.0.8.000:
+        - depreciated histo_analysis_old
+        - depreciated debug
+        - altered basic_analysis to take array data instead of filepath
+        - refactor
+        - optimization
+    1.0.7.002:
+        - bug fixes
+    1.0.7.001:
+        - bug fixes
+    1.0.7.000:
+        - added tanh_regression (logistical regression)
+        - bug fixes
+    1.0.6.005:
+        - added z_normalize function to normalize dataset
+        - bug fixes
+    1.0.6.004:
+        - bug fixes
+    1.0.6.003:
+        - bug fixes
+    1.0.6.002:
+        - bug fixes
+    1.0.6.001:
+        - corrected __all__ to contain all of the functions
+    1.0.6.000:
+        - added calc_overfit, which calculates two measures of overfit, error and performance
+        - added calculating overfit to optimize_regression
+    1.0.5.000:
+        - added optimize_regression function, which is a sample function to find the optimal regressions
+        - optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
+        - planned addition: overfit detection in the optimize_regression function
+    1.0.4.002:
+        - added __changelog__
+        - updated debug function with log and exponential regressions
+    1.0.4.001:
+        - added log regressions
+        - added exponential regressions
+        - added log_regression and exp_regression to __all__
+    1.0.3.008:
+        - added debug function to further consolidate functions
+    1.0.3.007:
+        - added builtin benchmark function
+        - added builtin random (linear) data generation function
+        - added device initialization (_init_device)
+    1.0.3.006:
+        - reorganized the imports list to be in alphabetical order
+        - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
+    1.0.3.005:
+        - major bug fixes
+        - updated historical analysis
+        - depreciated old historical analysis
+    1.0.3.004:
+        - added __version__, __author__, __all__
+        - added polynomial regression
+        - added root mean squared function
+        - added r squared function
+    1.0.3.003:
+        - bug fixes
+        - added c_entities
+    1.0.3.002:
+        - bug fixes
+        - added nc_entities, obstacles, objectives
+        - consolidated statistics.py to analysis.py
+    1.0.3.001:
+        - compiled 1d, column, and row basic stats into basic stats function
+    1.0.3.000:
+        - added historical analysis function
+    1.0.2.xxx:
+        - added z score test
+    1.0.1.xxx:
+        - major bug fixes
+    1.0.0.xxx:
+        - added loading csv
+        - added 1d, column, row basic stats
+"""
+
+__author__ = (
+    "Arthur Lu <learthurgo@gmail.com>",
+    "Jacob Levine <jlevine@imsa.edu>",
+)
+
+__all__ = [
+    'load_csv',
+    'basic_stats',
+    'z_score',
+    'z_normalize',
+    'histo_analysis',
+    'regression',
+    'elo',
+    'glicko2',
+    'trueskill',
+    'RegressionMetrics',
+    'ClassificationMetrics',
+    'kmeans',
+    'pca',
+    'decisiontree',
+    'knn_classifier',
+    'knn_regressor',
+    'NaiveBayes',
+    'SVM',
+    'random_forest_classifier',
+    'random_forest_regressor',
+    # all statistics functions left out due to integration in other functions
+]
+
+# now back to your regularly scheduled programming:
+
+# imports (now in alphabetical order! v 1.0.3.006):
+
+import csv
+from analysis import glicko2 as Glicko2
+import numba
+from numba import jit
+import numpy as np
+import scipy
+from scipy import *
+import sklearn
+from sklearn import *
+from analysis import trueskill as Trueskill
+
+class error(ValueError):
+    pass
+
+def load_csv(filepath):
+    with open(filepath, newline='') as csvfile:
+        file_array = np.array(list(csv.reader(csvfile)))
+        csvfile.close()
+    return file_array
+
+# expects 1d array
+@jit(forceobj=True)
+def basic_stats(data):
+
+    data_t = np.array(data).astype(float)
+
+    _mean = mean(data_t)
+    _median = median(data_t)
+    _stdev = stdev(data_t)
+    _variance = variance(data_t)
+    _min = npmin(data_t)
+    _max = npmax(data_t)
+
+    return _mean, _median, _stdev, _variance, _min, _max
+
+# returns z score with inputs of point, mean and standard deviation of spread
+@jit(forceobj=True)
+def z_score(point, mean, stdev):
+    score = (point - mean) / stdev
+    
+    return score
+
+# expects 2d array, normalizes across all axes
+@jit(forceobj=True)
+def z_normalize(array, *args):
+
+   array = np.array(array)
+   for arg in args:
+       array = sklearn.preprocessing.normalize(array, axis = arg)
+
+   return array
+
+@jit(forceobj=True)
+# expects 2d array of [x,y]
+def histo_analysis(hist_data):
+
+    if(len(hist_data[0]) > 2):
+
+        hist_data = np.array(hist_data)
+        derivative = np.array(len(hist_data) - 1, dtype = float)
+        t = np.diff(hist_data)
+        derivative = t[1] / t[0]
+        np.sort(derivative)
+
+        return basic_stats(derivative)[0], basic_stats(derivative)[3]
+
+    else:
+
+        return None
+
+def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
+
+    X = np.array(inputs)
+    y = np.array(outputs)
+
+    regressions = []
+
+    if 'lin' in args: # formula: ax + b
+
+        try:
+
+            def func(x, a, b):
+
+                return a * x + b
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
+
+    if 'log' in args: # formula: a log (b(x + c)) + d
+
+        try:
+
+            def func(x, a, b, c, d):
+
+                return a * np.log(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+            
+            pass
+
+    if 'exp' in args: # formula: a e ^ (b(x + c)) + d
+
+        try:        
+
+            def func(x, a, b, c, d):
+
+                return a * np.exp(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
+
+    if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
+        
+        inputs = np.array([inputs])
+        outputs = np.array([outputs])
+
+        plys = []
+        limit = len(outputs[0])
+
+        for i in range(2, limit):
+
+            model = sklearn.preprocessing.PolynomialFeatures(degree = i)
+            model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression())
+            model = model.fit(np.rot90(inputs), np.rot90(outputs))
+
+            params = model.steps[1][1].intercept_.tolist()
+            params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::])
+            params.flatten()
+            params = params.tolist()
+            
+            plys.append(params)
+
+        regressions.append(plys)
+
+    if 'sig' in args: # formula: a tanh (b(x + c)) + d
+
+        try:        
+
+            def func(x, a, b, c, d):
+
+                return a * np.tanh(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+           
+            pass
+
+    return regressions
+
+class Metrics:
+
+    def elo(starting_score, opposing_score, observed, N, K):
+
+        expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
+
+        return starting_score + K*(np.sum(observed) - np.sum(expected))
+
+    def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
+
+        player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
+
+        player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
+
+        return (player.rating, player.rd, player.vol)
+
+    def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
+
+        team_ratings = []
+
+        for team in teams_data:
+            team_temp = ()
+            for player in team:
+                player = Trueskill.Rating(player[0], player[1])
+                team_temp = team_temp + (player,)
+            team_ratings.append(team_temp)
+
+        return Trueskill.rate(team_ratings, ranks=observations)
+
+class RegressionMetrics():
+
+    def __new__(cls, predictions, targets):
+
+        return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
+
+    def r_squared(self, predictions, targets):  # assumes equal size inputs
+
+        return sklearn.metrics.r2_score(targets, predictions)
+
+    def mse(self, predictions, targets):
+
+        return sklearn.metrics.mean_squared_error(targets, predictions)
+
+    def rms(self, predictions, targets):
+
+        return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
+
+class ClassificationMetrics():
+
+    def __new__(cls, predictions, targets):
+
+        return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets)
+
+    def cm(self, predictions, targets):
+
+        return sklearn.metrics.confusion_matrix(targets, predictions)
+
+    def cr(self, predictions, targets):
+
+        return sklearn.metrics.classification_report(targets, predictions)
+
+@jit(nopython=True)
+def mean(data):
+
+    return np.mean(data)
+
+@jit(nopython=True)
+def median(data):
+
+    return np.median(data)
+
+@jit(nopython=True)
+def stdev(data):
+
+    return np.std(data)
+
+@jit(nopython=True)
+def variance(data):
+
+    return np.var(data)
+
+@jit(nopython=True)
+def npmin(data):
+
+    return np.amin(data)
+
+@jit(nopython=True)
+def npmax(data):
+
+    return np.amax(data)
+
+@jit(forceobj=True)
+def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
+
+    kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
+    kernel.fit(data)
+    predictions = kernel.predict(data)
+    centers = kernel.cluster_centers_
+
+    return centers, predictions
+
+@jit(forceobj=True)
+def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
+
+    kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)
+
+    return kernel.fit_transform(data)
+
+@jit(forceobj=True)
+def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
+
+    data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+    model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth)
+    model = model.fit(data_train,labels_train)
+    predictions = model.predict(data_test)
+    metrics = ClassificationMetrics(predictions, labels_test)
+
+    return model, metrics
+
+class KNN:
+
+    def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsClassifier()
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+
+        data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
+        model.fit(data_train, outputs_train)
+        predictions = model.predict(data_test)
+
+        return model, RegressionMetrics(predictions, outputs_test)
+
+class NaiveBayes:
+
+    def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+    def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
+
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
+
+        return model, ClassificationMetrics(predictions, labels_test)
+
+class SVM:
+
+    class CustomKernel:
+
+        def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
+
+            return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
+
+    class StandardKernel:
+
+        def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
+
+            return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
+
+    class PrebuiltKernel:
+
+        class Linear:
+
+            def __new__(cls):
+
+                return sklearn.svm.SVC(kernel = 'linear')
+
+        class Polynomial:
+
+            def __new__(cls, power, r_bias):
+
+                return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
+
+        class RBF:
+
+            def __new__(cls, gamma):
+
+                return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
+
+        class Sigmoid:
+
+            def __new__(cls, r_bias):
+
+                return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
+
+    def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
+
+        return kernel.fit(train_data, train_outputs)
+
+    def eval_classification(self, kernel, test_data, test_outputs):
+
+        predictions = kernel.predict(test_data)
+
+        return ClassificationMetrics(predictions, test_outputs)
+
+    def eval_regression(self, kernel, test_data, test_outputs):
+
+        predictions = kernel.predict(test_data)
+
+        return RegressionMetrics(predictions, test_outputs)
+
+def random_forest_classifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
+
+    data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+    kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
+    kernel.fit(data_train, labels_train)
+    predictions = kernel.predict(data_test)
+
+    return kernel, ClassificationMetrics(predictions, labels_test)
+
+def random_forest_regressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
+
+    data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+    kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
+    kernel.fit(data_train, outputs_train)
+    predictions = kernel.predict(data_test)
+
+    return kernel, RegressionMetrics(predictions, outputs_test)

analysis-master/analysis-amd64/build/lib/analysis/glicko2.py

@@ -0,0 +1,99 @@
+import math
+
+class Glicko2:
+    _tau = 0.5
+
+    def getRating(self):
+        return (self.__rating * 173.7178) + 1500 
+
+    def setRating(self, rating):
+        self.__rating = (rating - 1500) / 173.7178
+
+    rating = property(getRating, setRating)
+
+    def getRd(self):
+        return self.__rd * 173.7178
+
+    def setRd(self, rd):
+        self.__rd = rd / 173.7178
+
+    rd = property(getRd, setRd)
+        
+    def __init__(self, rating = 1500, rd = 350, vol = 0.06):
+
+        self.setRating(rating)
+        self.setRd(rd)
+        self.vol = vol
+            
+    def _preRatingRD(self):
+
+        self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
+        
+    def update_player(self, rating_list, RD_list, outcome_list):
+
+        rating_list = [(x - 1500) / 173.7178 for x in rating_list]
+        RD_list = [x / 173.7178 for x in RD_list]
+
+        v = self._v(rating_list, RD_list)
+        self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
+        self._preRatingRD()
+        
+        self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
+        
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * \
+                        (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        self.__rating += math.pow(self.__rd, 2) * tempSum
+        
+        
+    def _newVol(self, rating_list, RD_list, outcome_list, v):
+
+        i = 0
+        delta = self._delta(rating_list, RD_list, outcome_list, v)
+        a = math.log(math.pow(self.vol, 2))
+        tau = self._tau
+        x0 = a
+        x1 = 0
+        
+        while x0 != x1:
+            # New iteration, so x(i) becomes x(i-1)
+            x0 = x1
+            d = math.pow(self.__rating, 2) + v + math.exp(x0)
+            h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
+            / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
+            h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
+            (math.pow(self.__rating, 2) + v) \
+            / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
+            * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
+            x1 = x0 - (h1 / h2)
+
+        return math.exp(x1 / 2)
+        
+    def _delta(self, rating_list, RD_list, outcome_list, v):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        return v * tempSum
+        
+    def _v(self, rating_list, RD_list):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempE = self._E(rating_list[i], RD_list[i])
+            tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
+        return 1 / tempSum
+        
+    def _E(self, p2rating, p2RD):
+
+        return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
+                                    (self.__rating - p2rating)))
+        
+    def _g(self, RD):
+
+        return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
+        
+    def did_not_compete(self):
+
+        self._preRatingRD()

analysis-master/analysis-amd64/build/lib/analysis/regression.py

@@ -0,0 +1,220 @@
+# Titan Robotics Team 2022: CUDA-based Regressions Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#   this module has been automatically inegrated into analysis.py, and should be callable as a class from the package
+#   this module is cuda-optimized and vectorized (except for one small part)
+# setup:
+
+__version__ = "1.0.0.004"
+
+# changelog should be viewed using print(analysis.regression.__changelog__)
+__changelog__ = """
+    1.0.0.004:
+        - bug fixes
+        - fixed changelog
+    1.0.0.003:
+        - bug fixes
+    1.0.0.002:
+        -Added more parameters to log, exponential, polynomial
+        -Added SigmoidalRegKernelArthur, because Arthur apparently needs
+        to train the scaling and shifting of sigmoids
+    1.0.0.001:
+        -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+        -already vectorized (except for polynomial generation) and CUDA-optimized
+"""
+
+__author__ = (
+    "Jacob Levine <jlevine@imsa.edu>",
+    "Arthur Lu <learthurgo@gmail.com>"
+)
+
+__all__ = [
+    'factorial',
+    'take_all_pwrs',
+    'num_poly_terms',
+    'set_device',
+    'LinearRegKernel',
+    'SigmoidalRegKernel',
+    'LogRegKernel',
+    'PolyRegKernel',
+    'ExpRegKernel',
+    'SigmoidalRegKernelArthur',
+    'SGDTrain',
+    'CustomTrain'
+]
+
+import torch
+
+global device
+
+device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
+
+#todo: document completely
+
+def set_device(self, new_device):
+    device=new_device
+
+class LinearRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def forward(self,mtx):
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return torch.matmul(self.weights,mtx)+long_bias
+
+class SigmoidalRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    sigmoid=torch.nn.Sigmoid()
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def forward(self,mtx):
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
+
+class SigmoidalRegKernelArthur():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    sigmoid=torch.nn.Sigmoid()
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class LogRegKernel():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class ExpRegKernel():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class PolyRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    power=None
+    def __init__(self, num_vars, power):
+        self.power=power
+        num_terms=self.num_poly_terms(num_vars, power)
+        self.weights=torch.rand(num_terms, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def num_poly_terms(self,num_vars, power):
+        if power == 0:
+            return 0
+        return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
+    def factorial(self,n):
+        if n==0:
+            return 1
+        else:
+            return n*self.factorial(n-1)
+    def take_all_pwrs(self, vec, pwr):
+        #todo: vectorize (kinda)
+        combins=torch.combinations(vec, r=pwr, with_replacement=True)
+        out=torch.ones(combins.size()[0]).to(device).to(torch.float)
+        for i in torch.t(combins).to(device).to(torch.float):
+            out *= i
+        if pwr == 1:
+            return out
+        else:
+            return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
+    def forward(self,mtx):
+        #TODO: Vectorize the last part
+        cols=[]
+        for i in torch.t(mtx):
+            cols.append(self.take_all_pwrs(i,self.power))
+        new_mtx=torch.t(torch.stack(cols))
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return torch.matmul(self.weights,new_mtx)+long_bias
+
+def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
+    optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
+    data_cuda=data.to(device)
+    ground_cuda=ground.to(device)
+    if (return_losses):
+        losses=[]
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                losses.append(ls.item())
+                ls.backward()
+                optim.step()
+        return [kernel,losses]
+    else:
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                ls.backward()
+                optim.step()
+        return kernel
+
+def CustomTrain(self, kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
+    data_cuda=data.to(device)
+    ground_cuda=ground.to(device)
+    if (return_losses):
+        losses=[]
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data)
+                ls=loss(pred,ground)
+                losses.append(ls.item())
+                ls.backward()
+                optim.step()
+        return [kernel,losses]
+    else:
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                ls.backward()
+                optim.step()
+        return kernel

analysis-master/analysis-amd64/build/lib/analysis/titanlearn.py

@@ -0,0 +1,122 @@
+# Titan Robotics Team 2022: ML Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import titanlearn'
+#    this should be included in the local directory or environment variable
+#    this module is optimized for multhreaded computing
+#    this module learns from its mistakes far faster than 2022's captains
+# setup:
+
+__version__ = "2.0.1.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    2.0.1.001:
+        - removed matplotlib import
+        - removed graphloss()
+    2.0.1.000:
+        - added net, dataset, dataloader, and stdtrain template definitions
+        - added graphloss function
+    2.0.0.001:
+        - added clear functions
+    2.0.0.000:
+        - complete rewrite planned
+        - depreciated 1.0.0.xxx versions
+        - added simple training loop
+    1.0.0.xxx:
+        -added generation of ANNS, basic SGD training
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>,"
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+
+__all__ = [
+    'clear',
+    'net',
+    'dataset',
+    'dataloader',
+    'train',
+    'stdtrainer',
+    ]
+
+import torch
+from os import system, name
+import numpy as np
+
+def clear(): 
+    if name == 'nt': 
+        _ = system('cls') 
+    else: 
+        _ = system('clear') 
+
+class net(torch.nn.Module): #template for standard neural net
+    def __init__(self):
+        super(Net, self).__init__()
+        
+    def forward(self, input):
+        pass
+
+class dataset(torch.utils.data.Dataset): #template for standard dataset
+    
+    def __init__(self):
+        super(torch.utils.data.Dataset).__init__()
+        
+    def __getitem__(self, index):
+        pass
+    
+    def __len__(self):
+        pass
+
+def dataloader(dataset, batch_size, num_workers, shuffle = True):
+
+    return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
+
+def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
+
+    dataset_len = trainloader.dataset.__len__()
+    iter_count = 0
+    running_loss = 0
+    running_loss_list = []
+
+    for epoch in range(epochs):  # loop over the dataset multiple times
+
+        for i, data in enumerate(trainloader, 0):
+
+            inputs = data[0].to(device)
+            labels = data[1].to(device)
+
+            optimizer.zero_grad()
+
+            outputs = net(inputs)
+            loss = criterion(outputs, labels.to(torch.float))
+            
+            loss.backward()
+            optimizer.step()
+
+            # monitoring steps below
+
+            iter_count += 1
+            running_loss += loss.item()
+            running_loss_list.append(running_loss)
+            clear()
+
+            print("training on: " + device)
+            print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
+            print("current batch loss: " + str(loss.item))
+            print("running loss: " + str(running_loss / iter_count))
+        
+    return net, running_loss_list
+    print("finished training")
+
+def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
+
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    net = net.to(device)
+    criterion = criterion.to(device)
+    optimizer = optimizer.to(device)
+    trainloader = dataloader
+    
+    return train(device, net, epochs, trainloader, optimizer, criterion)

analysis-master/analysis-amd64/build/lib/analysis/trueskill.py

@@ -0,0 +1,907 @@
+from __future__ import absolute_import
+
+from itertools import chain
+import math
+
+from six import iteritems
+from six.moves import map, range, zip
+from six import iterkeys
+
+import copy
+try:
+    from numbers import Number
+except ImportError:
+    Number = (int, long, float, complex)
+
+inf = float('inf')
+
+class Gaussian(object):
+    #: Precision, the inverse of the variance.
+    pi = 0
+    #: Precision adjusted mean, the precision multiplied by the mean.
+    tau = 0
+
+    def __init__(self, mu=None, sigma=None, pi=0, tau=0):
+        if mu is not None:
+            if sigma is None:
+                raise TypeError('sigma argument is needed')
+            elif sigma == 0:
+                raise ValueError('sigma**2 should be greater than 0')
+            pi = sigma ** -2
+            tau = pi * mu
+        self.pi = pi
+        self.tau = tau
+
+    @property
+    def mu(self):
+        return self.pi and self.tau / self.pi
+
+    @property
+    def sigma(self):
+        return math.sqrt(1 / self.pi) if self.pi else inf
+
+    def __mul__(self, other):
+        pi, tau = self.pi + other.pi, self.tau + other.tau
+        return Gaussian(pi=pi, tau=tau)
+
+    def __truediv__(self, other):
+        pi, tau = self.pi - other.pi, self.tau - other.tau
+        return Gaussian(pi=pi, tau=tau)
+
+    __div__ = __truediv__  # for Python 2
+
+    def __eq__(self, other):
+        return self.pi == other.pi and self.tau == other.tau
+
+    def __lt__(self, other):
+        return self.mu < other.mu
+
+    def __le__(self, other):
+        return self.mu <= other.mu
+
+    def __gt__(self, other):
+        return self.mu > other.mu
+
+    def __ge__(self, other):
+        return self.mu >= other.mu
+
+    def __repr__(self):
+        return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma)
+
+    def _repr_latex_(self):
+        latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma)
+        return '$%s$' % latex
+
+class Matrix(list):
+    def __init__(self, src, height=None, width=None):
+        if callable(src):
+            f, src = src, {}
+            size = [height, width]
+            if not height:
+                def set_height(height):
+                    size[0] = height
+                size[0] = set_height
+            if not width:
+                def set_width(width):
+                    size[1] = width
+                size[1] = set_width
+            try:
+                for (r, c), val in f(*size):
+                    src[r, c] = val
+            except TypeError:
+                raise TypeError('A callable src must return an interable '
+                                'which generates a tuple containing '
+                                'coordinate and value')
+            height, width = tuple(size)
+            if height is None or width is None:
+                raise TypeError('A callable src must call set_height and '
+                                'set_width if the size is non-deterministic')
+        if isinstance(src, list):
+            is_number = lambda x: isinstance(x, Number)
+            unique_col_sizes = set(map(len, src))
+            everything_are_number = filter(is_number, sum(src, []))
+            if len(unique_col_sizes) != 1 or not everything_are_number:
+                raise ValueError('src must be a rectangular array of numbers')
+            two_dimensional_array = src
+        elif isinstance(src, dict):
+            if not height or not width:
+                w = h = 0
+                for r, c in iterkeys(src):
+                    if not height:
+                        h = max(h, r + 1)
+                    if not width:
+                        w = max(w, c + 1)
+                if not height:
+                    height = h
+                if not width:
+                    width = w
+            two_dimensional_array = []
+            for r in range(height):
+                row = []
+                two_dimensional_array.append(row)
+                for c in range(width):
+                    row.append(src.get((r, c), 0))
+        else:
+            raise TypeError('src must be a list or dict or callable')
+        super(Matrix, self).__init__(two_dimensional_array)
+
+    @property
+    def height(self):
+        return len(self)
+
+    @property
+    def width(self):
+        return len(self[0])
+
+    def transpose(self):
+        height, width = self.height, self.width
+        src = {}
+        for c in range(width):
+            for r in range(height):
+                src[c, r] = self[r][c]
+        return type(self)(src, height=width, width=height)
+
+    def minor(self, row_n, col_n):
+        height, width = self.height, self.width
+        if not (0 <= row_n < height):
+            raise ValueError('row_n should be between 0 and %d' % height)
+        elif not (0 <= col_n < width):
+            raise ValueError('col_n should be between 0 and %d' % width)
+        two_dimensional_array = []
+        for r in range(height):
+            if r == row_n:
+                continue
+            row = []
+            two_dimensional_array.append(row)
+            for c in range(width):
+                if c == col_n:
+                    continue
+                row.append(self[r][c])
+        return type(self)(two_dimensional_array)
+
+    def determinant(self):
+        height, width = self.height, self.width
+        if height != width:
+            raise ValueError('Only square matrix can calculate a determinant')
+        tmp, rv = copy.deepcopy(self), 1.
+        for c in range(width - 1, 0, -1):
+            pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1))
+            pivot = tmp[r][c]
+            if not pivot:
+                return 0.
+            tmp[r], tmp[c] = tmp[c], tmp[r]
+            if r != c:
+                rv = -rv
+            rv *= pivot
+            fact = -1. / pivot
+            for r in range(c):
+                f = fact * tmp[r][c]
+                for x in range(c):
+                    tmp[r][x] += f * tmp[c][x]
+        return rv * tmp[0][0]
+
+    def adjugate(self):
+        height, width = self.height, self.width
+        if height != width:
+            raise ValueError('Only square matrix can be adjugated')
+        if height == 2:
+            a, b = self[0][0], self[0][1]
+            c, d = self[1][0], self[1][1]
+            return type(self)([[d, -b], [-c, a]])
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                sign = -1 if (r + c) % 2 else 1
+                src[r, c] = self.minor(r, c).determinant() * sign
+        return type(self)(src, height, width)
+
+    def inverse(self):
+        if self.height == self.width == 1:
+            return type(self)([[1. / self[0][0]]])
+        return (1. / self.determinant()) * self.adjugate()
+
+    def __add__(self, other):
+        height, width = self.height, self.width
+        if (height, width) != (other.height, other.width):
+            raise ValueError('Must be same size')
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = self[r][c] + other[r][c]
+        return type(self)(src, height, width)
+
+    def __mul__(self, other):
+        if self.width != other.height:
+            raise ValueError('Bad size')
+        height, width = self.height, other.width
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = sum(self[r][x] * other[x][c]
+                                for x in range(self.width))
+        return type(self)(src, height, width)
+
+    def __rmul__(self, other):
+        if not isinstance(other, Number):
+            raise TypeError('The operand should be a number')
+        height, width = self.height, self.width
+        src = {}
+        for r in range(height):
+            for c in range(width):
+                src[r, c] = other * self[r][c]
+        return type(self)(src, height, width)
+
+    def __repr__(self):
+        return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__())
+
+    def _repr_latex_(self):
+        rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self]
+        latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows)
+        return '$%s$' % latex
+
+def _gen_erfcinv(erfc, math=math):
+    def erfcinv(y):
+        """The inverse function of erfc."""
+        if y >= 2:
+            return -100.
+        elif y <= 0:
+            return 100.
+        zero_point = y < 1
+        if not zero_point:
+            y = 2 - y
+        t = math.sqrt(-2 * math.log(y / 2.))
+        x = -0.70711 * \
+            ((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t)
+        for i in range(2):
+            err = erfc(x) - y
+            x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err)
+        return x if zero_point else -x
+    return erfcinv
+
+def _gen_ppf(erfc, math=math):
+    erfcinv = _gen_erfcinv(erfc, math)
+    def ppf(x, mu=0, sigma=1):
+        return mu - sigma * math.sqrt(2) * erfcinv(2 * x)
+    return ppf
+
+def erfc(x):
+    z = abs(x)
+    t = 1. / (1. + z / 2.)
+    r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (
+        0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * (
+            0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * (
+                -0.82215223 + t * 0.17087277
+            )))
+        )))
+    )))
+    return 2. - r if x < 0 else r
+
+def cdf(x, mu=0, sigma=1):
+    return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2)))
+
+
+def pdf(x, mu=0, sigma=1):
+    return (1 / math.sqrt(2 * math.pi) * abs(sigma) *
+            math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2)))
+
+ppf = _gen_ppf(erfc)
+
+def choose_backend(backend):
+    if backend is None:  # fallback
+        return cdf, pdf, ppf
+    elif backend == 'mpmath':
+        try:
+            import mpmath
+        except ImportError:
+            raise ImportError('Install "mpmath" to use this backend')
+        return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath)
+    elif backend == 'scipy':
+        try:
+            from scipy.stats import norm
+        except ImportError:
+            raise ImportError('Install "scipy" to use this backend')
+        return norm.cdf, norm.pdf, norm.ppf
+    raise ValueError('%r backend is not defined' % backend)
+
+def available_backends():
+    backends = [None]
+    for backend in ['mpmath', 'scipy']:
+        try:
+            __import__(backend)
+        except ImportError:
+            continue
+        backends.append(backend)
+    return backends
+
+class Node(object):
+
+    pass
+
+class Variable(Node, Gaussian):
+
+    def __init__(self):
+        self.messages = {}
+        super(Variable, self).__init__()
+
+    def set(self, val):
+        delta = self.delta(val)
+        self.pi, self.tau = val.pi, val.tau
+        return delta
+
+    def delta(self, other):
+        pi_delta = abs(self.pi - other.pi)
+        if pi_delta == inf:
+            return 0.
+        return max(abs(self.tau - other.tau), math.sqrt(pi_delta))
+
+    def update_message(self, factor, pi=0, tau=0, message=None):
+        message = message or Gaussian(pi=pi, tau=tau)
+        old_message, self[factor] = self[factor], message
+        return self.set(self / old_message * message)
+
+    def update_value(self, factor, pi=0, tau=0, value=None):
+        value = value or Gaussian(pi=pi, tau=tau)
+        old_message = self[factor]
+        self[factor] = value * old_message / self
+        return self.set(value)
+
+    def __getitem__(self, factor):
+        return self.messages[factor]
+
+    def __setitem__(self, factor, message):
+        self.messages[factor] = message
+
+    def __repr__(self):
+        args = (type(self).__name__, super(Variable, self).__repr__(),
+                len(self.messages), '' if len(self.messages) == 1 else 's')
+        return '<%s %s with %d connection%s>' % args
+
+
+class Factor(Node):
+
+    def __init__(self, variables):
+        self.vars = variables
+        for var in variables:
+            var[self] = Gaussian()
+
+    def down(self):
+        return 0
+
+    def up(self):
+        return 0
+
+    @property
+    def var(self):
+        assert len(self.vars) == 1
+        return self.vars[0]
+
+    def __repr__(self):
+        args = (type(self).__name__, len(self.vars),
+                '' if len(self.vars) == 1 else 's')
+        return '<%s with %d connection%s>' % args
+
+
+class PriorFactor(Factor):
+
+    def __init__(self, var, val, dynamic=0):
+        super(PriorFactor, self).__init__([var])
+        self.val = val
+        self.dynamic = dynamic
+
+    def down(self):
+        sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2)
+        value = Gaussian(self.val.mu, sigma)
+        return self.var.update_value(self, value=value)
+
+
+class LikelihoodFactor(Factor):
+
+    def __init__(self, mean_var, value_var, variance):
+        super(LikelihoodFactor, self).__init__([mean_var, value_var])
+        self.mean = mean_var
+        self.value = value_var
+        self.variance = variance
+
+    def calc_a(self, var):
+        return 1. / (1. + self.variance * var.pi)
+
+    def down(self):
+        # update value.
+        msg = self.mean / self.mean[self]
+        a = self.calc_a(msg)
+        return self.value.update_message(self, a * msg.pi, a * msg.tau)
+
+    def up(self):
+        # update mean.
+        msg = self.value / self.value[self]
+        a = self.calc_a(msg)
+        return self.mean.update_message(self, a * msg.pi, a * msg.tau)
+
+
+class SumFactor(Factor):
+
+    def __init__(self, sum_var, term_vars, coeffs):
+        super(SumFactor, self).__init__([sum_var] + term_vars)
+        self.sum = sum_var
+        self.terms = term_vars
+        self.coeffs = coeffs
+
+    def down(self):
+        vals = self.terms
+        msgs = [var[self] for var in vals]
+        return self.update(self.sum, vals, msgs, self.coeffs)
+
+    def up(self, index=0):
+        coeff = self.coeffs[index]
+        coeffs = []
+        for x, c in enumerate(self.coeffs):
+            try:
+                if x == index:
+                    coeffs.append(1. / coeff)
+                else:
+                    coeffs.append(-c / coeff)
+            except ZeroDivisionError:
+                coeffs.append(0.)
+        vals = self.terms[:]
+        vals[index] = self.sum
+        msgs = [var[self] for var in vals]
+        return self.update(self.terms[index], vals, msgs, coeffs)
+
+    def update(self, var, vals, msgs, coeffs):
+        pi_inv = 0
+        mu = 0
+        for val, msg, coeff in zip(vals, msgs, coeffs):
+            div = val / msg
+            mu += coeff * div.mu
+            if pi_inv == inf:
+                continue
+            try:
+                # numpy.float64 handles floating-point error by different way.
+                # For example, it can just warn RuntimeWarning on n/0 problem
+                # instead of throwing ZeroDivisionError.  So div.pi, the
+                # denominator has to be a built-in float.
+                pi_inv += coeff ** 2 / float(div.pi)
+            except ZeroDivisionError:
+                pi_inv = inf
+        pi = 1. / pi_inv
+        tau = pi * mu
+        return var.update_message(self, pi, tau)
+
+
+class TruncateFactor(Factor):
+
+    def __init__(self, var, v_func, w_func, draw_margin):
+        super(TruncateFactor, self).__init__([var])
+        self.v_func = v_func
+        self.w_func = w_func
+        self.draw_margin = draw_margin
+
+    def up(self):
+        val = self.var
+        msg = self.var[self]
+        div = val / msg
+        sqrt_pi = math.sqrt(div.pi)
+        args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi)
+        v = self.v_func(*args)
+        w = self.w_func(*args)
+        denom = (1. - w)
+        pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom
+        return val.update_value(self, pi, tau)
+
+#: Default initial mean of ratings.
+MU = 25.
+#: Default initial standard deviation of ratings.
+SIGMA = MU / 3
+#: Default distance that guarantees about 76% chance of winning.
+BETA = SIGMA / 2
+#: Default dynamic factor.
+TAU = SIGMA / 100
+#: Default draw probability of the game.
+DRAW_PROBABILITY = .10
+#: A basis to check reliability of the result.
+DELTA = 0.0001
+
+
+def calc_draw_probability(draw_margin, size, env=None):
+    if env is None:
+        env = global_env()
+    return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1
+
+
+def calc_draw_margin(draw_probability, size, env=None):
+    if env is None:
+        env = global_env()
+    return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta
+
+
+def _team_sizes(rating_groups):
+    team_sizes = [0]
+    for group in rating_groups:
+        team_sizes.append(len(group) + team_sizes[-1])
+    del team_sizes[0]
+    return team_sizes
+
+
+def _floating_point_error(env):
+    if env.backend == 'mpmath':
+        msg = 'Set "mpmath.mp.dps" to higher'
+    else:
+        msg = 'Cannot calculate correctly, set backend to "mpmath"'
+    return FloatingPointError(msg)
+
+
+class Rating(Gaussian):
+    def __init__(self, mu=None, sigma=None):
+        if isinstance(mu, tuple):
+            mu, sigma = mu
+        elif isinstance(mu, Gaussian):
+            mu, sigma = mu.mu, mu.sigma
+        if mu is None:
+            mu = global_env().mu
+        if sigma is None:
+            sigma = global_env().sigma
+        super(Rating, self).__init__(mu, sigma)
+
+    def __int__(self):
+        return int(self.mu)
+
+    def __long__(self):
+        return long(self.mu)
+
+    def __float__(self):
+        return float(self.mu)
+
+    def __iter__(self):
+        return iter((self.mu, self.sigma))
+
+    def __repr__(self):
+        c = type(self)
+        args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma)
+        return '%s(mu=%.3f, sigma=%.3f)' % args
+
+
+class TrueSkill(object):
+    def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+                 draw_probability=DRAW_PROBABILITY, backend=None):
+        self.mu = mu
+        self.sigma = sigma
+        self.beta = beta
+        self.tau = tau
+        self.draw_probability = draw_probability
+        self.backend = backend
+        if isinstance(backend, tuple):
+            self.cdf, self.pdf, self.ppf = backend
+        else:
+            self.cdf, self.pdf, self.ppf = choose_backend(backend)
+
+    def create_rating(self, mu=None, sigma=None):
+        if mu is None:
+            mu = self.mu
+        if sigma is None:
+            sigma = self.sigma
+        return Rating(mu, sigma)
+
+    def v_win(self, diff, draw_margin):
+        x = diff - draw_margin
+        denom = self.cdf(x)
+        return (self.pdf(x) / denom) if denom else -x
+
+    def v_draw(self, diff, draw_margin):
+        abs_diff = abs(diff)
+        a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+        denom = self.cdf(a) - self.cdf(b)
+        numer = self.pdf(b) - self.pdf(a)
+        return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1)
+
+    def w_win(self, diff, draw_margin):
+        x = diff - draw_margin
+        v = self.v_win(diff, draw_margin)
+        w = v * (v + x)
+        if 0 < w < 1:
+            return w
+        raise _floating_point_error(self)
+
+    def w_draw(self, diff, draw_margin):
+        abs_diff = abs(diff)
+        a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+        denom = self.cdf(a) - self.cdf(b)
+        if not denom:
+            raise _floating_point_error(self)
+        v = self.v_draw(abs_diff, draw_margin)
+        return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom
+
+    def validate_rating_groups(self, rating_groups):
+        # check group sizes
+        if len(rating_groups) < 2:
+            raise ValueError('Need multiple rating groups')
+        elif not all(rating_groups):
+            raise ValueError('Each group must contain multiple ratings')
+        # check group types
+        group_types = set(map(type, rating_groups))
+        if len(group_types) != 1:
+            raise TypeError('All groups should be same type')
+        elif group_types.pop() is Rating:
+            raise TypeError('Rating cannot be a rating group')
+        # normalize rating_groups
+        if isinstance(rating_groups[0], dict):
+            dict_rating_groups = rating_groups
+            rating_groups = []
+            keys = []
+            for dict_rating_group in dict_rating_groups:
+                rating_group, key_group = [], []
+                for key, rating in iteritems(dict_rating_group):
+                    rating_group.append(rating)
+                    key_group.append(key)
+                rating_groups.append(tuple(rating_group))
+                keys.append(tuple(key_group))
+        else:
+            rating_groups = list(rating_groups)
+            keys = None
+        return rating_groups, keys
+
+    def validate_weights(self, weights, rating_groups, keys=None):
+        if weights is None:
+            weights = [(1,) * len(g) for g in rating_groups]
+        elif isinstance(weights, dict):
+            weights_dict, weights = weights, []
+            for x, group in enumerate(rating_groups):
+                w = []
+                weights.append(w)
+                for y, rating in enumerate(group):
+                    if keys is not None:
+                        y = keys[x][y]
+                    w.append(weights_dict.get((x, y), 1))
+        return weights
+
+    def factor_graph_builders(self, rating_groups, ranks, weights):
+        flatten_ratings = sum(map(tuple, rating_groups), ())
+        flatten_weights = sum(map(tuple, weights), ())
+        size = len(flatten_ratings)
+        group_size = len(rating_groups)
+        # create variables
+        rating_vars = [Variable() for x in range(size)]
+        perf_vars = [Variable() for x in range(size)]
+        team_perf_vars = [Variable() for x in range(group_size)]
+        team_diff_vars = [Variable() for x in range(group_size - 1)]
+        team_sizes = _team_sizes(rating_groups)
+        # layer builders
+        def build_rating_layer():
+            for rating_var, rating in zip(rating_vars, flatten_ratings):
+                yield PriorFactor(rating_var, rating, self.tau)
+        def build_perf_layer():
+            for rating_var, perf_var in zip(rating_vars, perf_vars):
+                yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2)
+        def build_team_perf_layer():
+            for team, team_perf_var in enumerate(team_perf_vars):
+                if team > 0:
+                    start = team_sizes[team - 1]
+                else:
+                    start = 0
+                end = team_sizes[team]
+                child_perf_vars = perf_vars[start:end]
+                coeffs = flatten_weights[start:end]
+                yield SumFactor(team_perf_var, child_perf_vars, coeffs)
+        def build_team_diff_layer():
+            for team, team_diff_var in enumerate(team_diff_vars):
+                yield SumFactor(team_diff_var,
+                                team_perf_vars[team:team + 2], [+1, -1])
+        def build_trunc_layer():
+            for x, team_diff_var in enumerate(team_diff_vars):
+                if callable(self.draw_probability):
+                    # dynamic draw probability
+                    team_perf1, team_perf2 = team_perf_vars[x:x + 2]
+                    args = (Rating(team_perf1), Rating(team_perf2), self)
+                    draw_probability = self.draw_probability(*args)
+                else:
+                    # static draw probability
+                    draw_probability = self.draw_probability
+                size = sum(map(len, rating_groups[x:x + 2]))
+                draw_margin = calc_draw_margin(draw_probability, size, self)
+                if ranks[x] == ranks[x + 1]:  # is a tie?
+                    v_func, w_func = self.v_draw, self.w_draw
+                else:
+                    v_func, w_func = self.v_win, self.w_win
+                yield TruncateFactor(team_diff_var,
+                                     v_func, w_func, draw_margin)
+        # build layers
+        return (build_rating_layer, build_perf_layer, build_team_perf_layer,
+                build_team_diff_layer, build_trunc_layer)
+
+    def run_schedule(self, build_rating_layer, build_perf_layer,
+                     build_team_perf_layer, build_team_diff_layer,
+                     build_trunc_layer, min_delta=DELTA):
+        if min_delta <= 0:
+            raise ValueError('min_delta must be greater than 0')
+        layers = []
+        def build(builders):
+            layers_built = [list(build()) for build in builders]
+            layers.extend(layers_built)
+            return layers_built
+        # gray arrows
+        layers_built = build([build_rating_layer,
+                              build_perf_layer,
+                              build_team_perf_layer])
+        rating_layer, perf_layer, team_perf_layer = layers_built
+        for f in chain(*layers_built):
+            f.down()
+        # arrow #1, #2, #3
+        team_diff_layer, trunc_layer = build([build_team_diff_layer,
+                                              build_trunc_layer])
+        team_diff_len = len(team_diff_layer)
+        for x in range(10):
+            if team_diff_len == 1:
+                # only two teams
+                team_diff_layer[0].down()
+                delta = trunc_layer[0].up()
+            else:
+                # multiple teams
+                delta = 0
+                for x in range(team_diff_len - 1):
+                    team_diff_layer[x].down()
+                    delta = max(delta, trunc_layer[x].up())
+                    team_diff_layer[x].up(1)  # up to right variable
+                for x in range(team_diff_len - 1, 0, -1):
+                    team_diff_layer[x].down()
+                    delta = max(delta, trunc_layer[x].up())
+                    team_diff_layer[x].up(0)  # up to left variable
+            # repeat until to small update
+            if delta <= min_delta:
+                break
+        # up both ends
+        team_diff_layer[0].up(0)
+        team_diff_layer[team_diff_len - 1].up(1)
+        # up the remainder of the black arrows
+        for f in team_perf_layer:
+            for x in range(len(f.vars) - 1):
+                f.up(x)
+        for f in perf_layer:
+            f.up()
+        return layers
+
+    def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA):
+        rating_groups, keys = self.validate_rating_groups(rating_groups)
+        weights = self.validate_weights(weights, rating_groups, keys)
+        group_size = len(rating_groups)
+        if ranks is None:
+            ranks = range(group_size)
+        elif len(ranks) != group_size:
+            raise ValueError('Wrong ranks')
+        # sort rating groups by rank
+        by_rank = lambda x: x[1][1]
+        sorting = sorted(enumerate(zip(rating_groups, ranks, weights)),
+                         key=by_rank)
+        sorted_rating_groups, sorted_ranks, sorted_weights = [], [], []
+        for x, (g, r, w) in sorting:
+            sorted_rating_groups.append(g)
+            sorted_ranks.append(r)
+            # make weights to be greater than 0
+            sorted_weights.append(max(min_delta, w_) for w_ in w)
+        # build factor graph
+        args = (sorted_rating_groups, sorted_ranks, sorted_weights)
+        builders = self.factor_graph_builders(*args)
+        args = builders + (min_delta,)
+        layers = self.run_schedule(*args)
+        # make result
+        rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups)
+        transformed_groups = []
+        for start, end in zip([0] + team_sizes[:-1], team_sizes):
+            group = []
+            for f in rating_layer[start:end]:
+                group.append(Rating(float(f.var.mu), float(f.var.sigma)))
+            transformed_groups.append(tuple(group))
+        by_hint = lambda x: x[0]
+        unsorting = sorted(zip((x for x, __ in sorting), transformed_groups),
+                           key=by_hint)
+        if keys is None:
+            return [g for x, g in unsorting]
+        # restore the structure with input dictionary keys
+        return [dict(zip(keys[x], g)) for x, g in unsorting]
+
+    def quality(self, rating_groups, weights=None):
+        rating_groups, keys = self.validate_rating_groups(rating_groups)
+        weights = self.validate_weights(weights, rating_groups, keys)
+        flatten_ratings = sum(map(tuple, rating_groups), ())
+        flatten_weights = sum(map(tuple, weights), ())
+        length = len(flatten_ratings)
+        # a vector of all of the skill means
+        mean_matrix = Matrix([[r.mu] for r in flatten_ratings])
+        # a matrix whose diagonal values are the variances (sigma ** 2) of each
+        # of the players.
+        def variance_matrix(height, width):
+            variances = (r.sigma ** 2 for r in flatten_ratings)
+            for x, variance in enumerate(variances):
+                yield (x, x), variance
+        variance_matrix = Matrix(variance_matrix, length, length)
+        # the player-team assignment and comparison matrix
+        def rotated_a_matrix(set_height, set_width):
+            t = 0
+            for r, (cur, _next) in enumerate(zip(rating_groups[:-1],
+                                                rating_groups[1:])):
+                for x in range(t, t + len(cur)):
+                    yield (r, x), flatten_weights[x]
+                    t += 1
+                x += 1
+                for x in range(x, x + len(_next)):
+                    yield (r, x), -flatten_weights[x]
+            set_height(r + 1)
+            set_width(x + 1)
+        rotated_a_matrix = Matrix(rotated_a_matrix)
+        a_matrix = rotated_a_matrix.transpose()
+        # match quality further derivation
+        _ata = (self.beta ** 2) * rotated_a_matrix * a_matrix
+        _atsa = rotated_a_matrix * variance_matrix * a_matrix
+        start = mean_matrix.transpose() * a_matrix
+        middle = _ata + _atsa
+        end = rotated_a_matrix * mean_matrix
+        # make result
+        e_arg = (-0.5 * start * middle.inverse() * end).determinant()
+        s_arg = _ata.determinant() / middle.determinant()
+        return math.exp(e_arg) * math.sqrt(s_arg)
+
+    def expose(self, rating):
+        k = self.mu / self.sigma
+        return rating.mu - k * rating.sigma
+
+    def make_as_global(self):
+        return setup(env=self)
+
+    def __repr__(self):
+        c = type(self)
+        if callable(self.draw_probability):
+            f = self.draw_probability
+            draw_probability = '.'.join([f.__module__, f.__name__])
+        else:
+            draw_probability = '%.1f%%' % (self.draw_probability * 100)
+        if self.backend is None:
+            backend = ''
+        elif isinstance(self.backend, tuple):
+            backend = ', backend=...'
+        else:
+            backend = ', backend=%r' % self.backend
+        args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma,
+                self.beta, self.tau, draw_probability, backend)
+        return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, '
+                'draw_probability=%s%s)' % args)
+
+
+def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None):
+    if env is None:
+        env = global_env()
+    ranks = [0, 0 if drawn else 1]
+    teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta)
+    return teams[0][0], teams[1][0]
+
+
+def quality_1vs1(rating1, rating2, env=None):
+    if env is None:
+        env = global_env()
+    return env.quality([(rating1,), (rating2,)])
+
+
+def global_env():
+    try:
+        global_env.__trueskill__
+    except AttributeError:
+        # setup the default environment
+        setup()
+    return global_env.__trueskill__
+
+
+def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+          draw_probability=DRAW_PROBABILITY, backend=None, env=None):
+    if env is None:
+        env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend)
+    global_env.__trueskill__ = env
+    return env
+
+
+def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA):
+    return global_env().rate(rating_groups, ranks, weights, min_delta)
+
+
+def quality(rating_groups, weights=None):
+    return global_env().quality(rating_groups, weights)
+
+
+def expose(rating):
+    return global_env().expose(rating)

analysis-master/analysis-amd64/build/lib/analysis/visualization.py

@@ -0,0 +1,34 @@
+# Titan Robotics Team 2022: Visualization Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import visualization'
+#    this should be included in the local directory or environment variable
+#    fancy
+# setup:
+
+__version__ = "1.0.0.000"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    1.0.0.000:
+        - created visualization.py
+        - added graphloss()
+        - added imports
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>,"
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+
+__all__ = [
+    'graphloss',
+    ]
+
+import matplotlib.pyplot as plt
+
+def graphloss(losses):
+
+    x = range(0, len(losses))
+    plt.plot(x, losses)
+    plt.show()

analysis-master/analysis-amd64/docker/Dockerfile

@@ -0,0 +1,5 @@
+FROM python
+WORKDIR ~/
+COPY ./ ./
+RUN pip install -r requirements.txt
+CMD ["bash"]

analysis-master/analysis-amd64/docker/start-docker.sh

@@ -0,0 +1,3 @@
+cd ..
+docker build -t tra-analysis-amd64-dev -f docker/Dockerfile .
+docker run -it tra-analysis-amd64-dev

analysis-master/analysis-amd64/requirements.txt

@@ -0,0 +1,6 @@
+numba
+numpy
+scipy
+scikit-learn
+six
+matplotlib

analysis-master/analysis-amd64/setup.py

@@ -0,0 +1,26 @@
+import setuptools
+
+requirements = []
+
+with open("requirements.txt", 'r') as file:
+    for line in file:
+        requirements.append(line)
+
+setuptools.setup(
+    name="analysis",
+    version="1.0.0.011",
+    author="The Titan Scouting Team",
+    author_email="titanscout2022@gmail.com",
+    description="analysis package developed by Titan Scouting for The Red Alliance",
+    long_description="",
+    long_description_content_type="text/markdown",
+    url="https://github.com/titanscout2022/tr2022-strategy",
+    packages=setuptools.find_packages(),
+    install_requires=requirements,
+    license = "GNU General Public License v3.0",
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "Operating System :: OS Independent",
+    ],
+    python_requires='>=3.6',
+)

analysis-master/analysis-arm64/docker/start-docker.sh

@@ -0,0 +1,3 @@
+cd ..
+docker build -t tra-analysis-amd64-dev -f docker/Dockerfile .
+docker run -it tra-analysis-amd64-dev

data analysis/config/competition.config

@@ -0,0 +1 @@
+2020ilch

data analysis/config/stats.config

@@ -0,0 +1,14 @@
+balls-blocked,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-collected,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-lower-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-lower-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-started,basic_stats,historical_analyss,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-upper-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-upper-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+wheel-mechanism
+low-balls
+high-balls
+wheel-success
+strategic-focus
+climb-mechanism
+attitude

data analysis/data.py

@@ -0,0 +1,102 @@
+import requests
+import pymongo
+import pandas as pd
+import time
+
+def pull_new_tba_matches(apikey, competition, cutoff):
+    api_key= apikey 
+    x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
+    out = []
+    for i in x.json():
+        if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
+            out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
+    return out
+
+def get_team_match_data(apikey, competition, team_num):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_scouting
+    mdata = db.matchdata
+    out = {}
+    for i in mdata.find({"competition" : competition, "team_scouted": team_num}):
+        out[i['match']] = i['data']
+    return pd.DataFrame(out)
+
+def get_team_pit_data(apikey, competition, team_num):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_scouting
+    mdata = db.pitdata
+    out = {}
+    return mdata.find_one({"competition" : competition, "team_scouted": team_num})["data"]
+
+def get_team_metrics_data(apikey, competition, team_num):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_processing
+    mdata = db.team_metrics
+    return mdata.find_one({"competition" : competition, "team": team_num})
+
+def unkeyify_2l(layered_dict):
+    out = {}
+    for i in layered_dict.keys():
+        add = []
+        sortkey = []
+        for j in layered_dict[i].keys():
+            add.append([j,layered_dict[i][j]])
+        add.sort(key = lambda x: x[0])
+        out[i] = list(map(lambda x: x[1], add))
+    return out
+
+def get_match_data_formatted(apikey, competition):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_scouting
+    mdata = db.teamlist
+    x=mdata.find_one({"competition":competition})
+    out = {}
+    for i in x:
+        try:
+            out[int(i)] = unkeyify_2l(get_team_match_data(apikey, competition, int(i)).transpose().to_dict())
+        except:
+            pass
+    return out
+
+def get_pit_data_formatted(apikey, competition):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_scouting
+    mdata = db.teamlist
+    x=mdata.find_one({"competition":competition})
+    out = {}
+    for i in x:
+        try:
+            out[int(i)] = get_team_pit_data(apikey, competition, int(i))
+        except:
+            pass
+    return out
+
+def push_team_tests_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
+    client = pymongo.MongoClient(apikey)
+    db = client[dbname]
+    mdata = db[colname]
+    mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "data" : data}, True)
+
+def push_team_metrics_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_metrics"):
+    client = pymongo.MongoClient(apikey)
+    db = client[dbname]
+    mdata = db[colname]
+    mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "metrics" : data}, True)
+
+def push_team_pit_data(apikey, competition, variable, data, dbname = "data_processing", colname = "team_pit"):
+    client = pymongo.MongoClient(apikey)
+    db = client[dbname]
+    mdata = db[colname]
+    mdata.replace_one({"competition" : competition, "variable": variable}, {"competition" : competition, "variable" : variable, "data" : data}, True)
+
+def get_analysis_flags(apikey, flag):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_processing
+    mdata = db.flags
+    return mdata.find_one({flag:{"$exists":True}})
+
+def set_analysis_flags(apikey, flag, data):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_processing
+    mdata = db.flags
+    return mdata.replace_one({flag:{"$exists":True}}, data, True)

data analysis/generate_data.py

@@ -1,16 +0,0 @@
-import random
-
-def generate(filename, x, y, low, high):
-
-    file = open(filename, "w")
-
-    for i in range (0, y, 1):
-
-        temp = ""
-        
-        for j in range (0, x - 1, 1):
-
-            temp = str(random.uniform(low, high)) +  ","  + temp
-
-        temp = temp + str(random.uniform(low, high))
-        file.write(temp + "\n")

data analysis/get_team_rankings.py

@@ -0,0 +1,59 @@
+import data as d
+from analysis import analysis as an
+import pymongo
+import operator
+
+def load_config(file):
+    config_vector = {}
+    file = an.load_csv(file)
+    for line in file[1:]:
+        config_vector[line[0]] = line[1:]
+
+    return (file[0][0], config_vector)
+
+def get_metrics_processed_formatted(apikey, competition):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_scouting
+    mdata = db.teamlist
+    x=mdata.find_one({"competition":competition})
+    out = {}
+    for i in x:
+        try:
+            out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
+        except:
+            pass
+    return out
+
+def main():
+
+    apikey = an.load_csv("keys.txt")[0][0]
+    tbakey = an.load_csv("keys.txt")[1][0]
+
+    competition, config = load_config("config.csv")
+
+    metrics = get_metrics_processed_formatted(apikey, competition)
+
+    elo = {}
+    gl2 = {}
+
+    for team in metrics:
+
+        elo[team] = metrics[team]["metrics"]["elo"]["score"]
+        gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
+
+    elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
+    gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
+
+    for team in elo:
+
+        print("teams sorted by elo:")
+        print("" + str(team) + " | " + str(elo[team]))
+
+    print("*"*25)
+
+    for team in gl2:
+
+        print("teams sorted by glicko2:")
+        print("" + str(team) + " | " + str(gl2[team]))
+
+main()

data analysis/requirements.txt

@@ -0,0 +1,4 @@
+requests
+pymongo
+pandas
+dnspython

data analysis/superscript.py

@@ -0,0 +1,378 @@
+# Titan Robotics Team 2022: Superscript Script
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+# setup:
+
+__version__ = "0.0.5.002"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    0.0.5.002:
+        - made changes due to refactoring of analysis
+    0.0.5.001:
+        - text fixes
+        - removed matplotlib requirement
+    0.0.5.000:
+        - improved user interface
+    0.0.4.002:
+        - removed unessasary code
+    0.0.4.001:
+        - fixed bug where X range for regression was determined before sanitization
+        - better sanitized data
+    0.0.4.000:
+        - fixed spelling issue in __changelog__
+        - addressed nan bug in regression
+        - fixed errors on line 335 with metrics calling incorrect key "glicko2"
+        - fixed errors in metrics computing 
+    0.0.3.000:
+        - added analysis to pit data
+    0.0.2.001:
+        - minor stability patches
+        - implemented db syncing for timestamps
+        - fixed bugs
+    0.0.2.000:
+        - finalized testing and small fixes
+    0.0.1.004:
+        - finished metrics implement, trueskill is bugged
+    0.0.1.003:
+        - working
+    0.0.1.002:
+        - started implement of metrics
+    0.0.1.001:
+        - cleaned up imports
+    0.0.1.000:
+        - tested working, can push to database
+    0.0.0.009:
+        - tested working
+        - prints out stats for the time being, will push to database later
+    0.0.0.008:
+        - added data import
+        - removed tba import
+        - finished main method
+    0.0.0.007:
+        - added load_config
+        - optimized simpleloop for readibility
+        - added __all__ entries
+        - added simplestats engine
+        - pending testing
+    0.0.0.006:
+        - fixes
+    0.0.0.005:
+        - imported pickle
+        - created custom database object
+    0.0.0.004:
+        - fixed simpleloop to actually return a vector
+    0.0.0.003:
+        - added metricsloop which is unfinished
+    0.0.0.002:
+        - added simpleloop which is untested until data is provided
+    0.0.0.001:
+        - created script
+        - added analysis, numba, numpy imports
+"""
+
+__author__ = (
+    "Arthur Lu <learthurgo@gmail.com>",
+    "Jacob Levine <jlevine@imsa.edu>",
+)
+
+__all__ = [
+    "main", 
+    "load_config",
+    "simpleloop",
+    "simplestats",
+    "metricsloop"
+]
+
+# imports:
+
+from analysis import analysis as an
+import data as d
+import numpy as np
+from os import system, name
+from pathlib import Path
+import time
+import warnings
+
+def main():
+    warnings.filterwarnings("ignore")
+    while(True):
+
+        current_time = time.time()
+        print("[OK] time: " + str(current_time))
+
+        start = time.time()
+        config = load_config(Path("config/stats.config"))
+        competition = an.load_csv(Path("config/competition.config"))[0][0]
+        print("[OK] configs loaded")
+
+        apikey = an.load_csv(Path("config/keys.config"))[0][0]
+        tbakey = an.load_csv(Path("config/keys.config"))[1][0]
+        print("[OK] loaded keys")
+
+        previous_time = d.get_analysis_flags(apikey, "latest_update")
+
+        if(previous_time == None):
+
+            d.set_analysis_flags(apikey, "latest_update", 0)
+            previous_time = 0
+
+        else:
+
+            previous_time = previous_time["latest_update"]
+
+        print("[OK] analysis backtimed to: " + str(previous_time))
+
+        print("[OK] loading data")
+        start = time.time()
+        data = d.get_match_data_formatted(apikey, competition)
+        pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
+        print("[OK] loaded data in " + str(time.time() - start) + " seconds")
+
+        print("[OK] running tests")
+        start = time.time()
+        results = simpleloop(data, config)
+        print("[OK] finished tests in " + str(time.time() - start) + " seconds")
+
+        print("[OK] running metrics")
+        start = time.time()
+        metricsloop(tbakey, apikey, competition, previous_time)
+        print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
+
+        print("[OK] running pit analysis")
+        start = time.time()
+        pit = pitloop(pit_data, config)
+        print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
+
+        d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
+        
+        print("[OK] pushing to database")
+        start = time.time()
+        push_to_database(apikey, competition, results, pit)
+        print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
+
+        clear()
+
+def clear(): 
+    
+    # for windows 
+    if name == 'nt': 
+        _ = system('cls') 
+  
+    # for mac and linux(here, os.name is 'posix') 
+    else: 
+        _ = system('clear')
+
+def load_config(file):
+    config_vector = {}
+    file = an.load_csv(file)
+    for line in file:
+        config_vector[line[0]] = line[1:]
+
+    return config_vector
+
+def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
+
+    return_vector = {}
+    for team in data:
+        variable_vector = {}
+        for variable in data[team]:
+            test_vector = {}
+            variable_data = data[team][variable]
+            if(variable in tests):
+                for test in tests[variable]:
+                    test_vector[test] = simplestats(variable_data, test)
+            else:
+                pass      
+            variable_vector[variable] = test_vector
+        return_vector[team] = variable_vector
+
+    return return_vector
+
+def simplestats(data, test):
+
+    data = np.array(data)
+    data = data[np.isfinite(data)]
+    ranges = list(range(len(data)))
+
+    if(test == "basic_stats"):
+        return an.basic_stats(data)
+
+    if(test == "historical_analysis"):
+        return an.histo_analysis([ranges, data])
+
+    if(test == "regression_linear"):
+        return an.regression(ranges, data, ['lin'])
+
+    if(test == "regression_logarithmic"):
+        return an.regression(ranges, data, ['log'])
+
+    if(test == "regression_exponential"):
+        return an.regression(ranges, data, ['exp'])
+
+    if(test == "regression_polynomial"):
+        return an.regression(ranges, data, ['ply'])
+
+    if(test == "regression_sigmoidal"):
+        return an.regression(ranges, data, ['sig'])
+
+def push_to_database(apikey, competition, results, pit):
+
+    for team in results:
+
+        d.push_team_tests_data(apikey, competition, team, results[team])
+
+    for variable in pit:
+
+        d.push_team_pit_data(apikey, competition, variable, pit[variable])
+
+def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
+
+    elo_N = 400
+    elo_K = 24
+
+    matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
+
+    red = {}
+    blu = {}
+
+    for match in matches:
+
+        red = load_metrics(apikey, competition, match, "red")
+        blu = load_metrics(apikey, competition, match, "blue")
+ 
+        elo_red_total = 0
+        elo_blu_total = 0
+
+        gl2_red_score_total = 0
+        gl2_blu_score_total = 0
+
+        gl2_red_rd_total = 0
+        gl2_blu_rd_total = 0
+
+        gl2_red_vol_total = 0
+        gl2_blu_vol_total = 0
+
+        for team in red:
+
+            elo_red_total += red[team]["elo"]["score"]
+
+            gl2_red_score_total += red[team]["gl2"]["score"]
+            gl2_red_rd_total += red[team]["gl2"]["rd"]
+            gl2_red_vol_total += red[team]["gl2"]["vol"]
+
+        for team in blu:
+
+            elo_blu_total += blu[team]["elo"]["score"]
+
+            gl2_blu_score_total += blu[team]["gl2"]["score"]
+            gl2_blu_rd_total += blu[team]["gl2"]["rd"]
+            gl2_blu_vol_total += blu[team]["gl2"]["vol"]
+
+        red_elo = {"score": elo_red_total / len(red)}
+        blu_elo = {"score": elo_blu_total / len(blu)}
+
+        red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
+        blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
+
+
+        if(match["winner"] == "red"):
+
+            observations = {"red": 1, "blu": 0}
+
+        elif(match["winner"] == "blue"):
+
+            observations = {"red": 0, "blu": 1}
+
+        else:
+
+            observations = {"red": 0.5, "blu": 0.5}
+
+        red_elo_delta = an.Metrics.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
+        blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
+
+        new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metrics.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
+        new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metrics.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
+
+        red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
+        blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
+
+        for team in red:
+
+            red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
+
+            red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
+            red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
+            red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
+
+        for team in blu:
+
+            blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
+
+            blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
+            blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
+            blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
+
+        temp_vector = {}
+        temp_vector.update(red)
+        temp_vector.update(blu)
+
+        for team in temp_vector:
+
+            d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
+
+def load_metrics(apikey, competition, match, group_name):
+
+    group = {}
+
+    for team in match[group_name]:
+
+        db_data = d.get_team_metrics_data(apikey, competition, team)
+
+        if d.get_team_metrics_data(apikey, competition, team) == None:
+
+            elo = {"score": 1500}
+            gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
+            ts = {"mu": 25, "sigma": 25/3}
+
+            #d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
+
+            group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
+
+        else:
+
+            metrics = db_data["metrics"]
+
+            elo = metrics["elo"]
+            gl2 = metrics["gl2"]
+            ts = metrics["ts"]
+
+            group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
+
+    return group
+
+def pitloop(pit, tests):
+
+    return_vector = {}
+    for team in pit:
+        for variable in pit[team]:
+            if(variable in tests):
+                if(not variable in return_vector):
+                    return_vector[variable] = []
+                return_vector[variable].append(pit[team][variable])
+
+    return return_vector
+
+main()
+
+"""
+Metrics Defaults:
+
+elo starting score = 1500
+elo N = 400
+elo K = 24
+
+gl2 starting score = 1500
+gl2 starting rd = 350
+gl2 starting vol = 0.06
+"""

data analysis/visualize_pit.py

@@ -0,0 +1,59 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
+import matplotlib.pyplot as plt
+import data as d
+import pymongo
+
+
+# %%
+def get_pit_variable_data(apikey, competition):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_processing
+    mdata = db.team_pit
+    out = {}
+    return mdata.find()
+
+
+# %%
+def get_pit_variable_formatted(apikey, competition):
+    temp = get_pit_variable_data(apikey, competition)
+    out = {}
+    for i in temp:
+        out[i["variable"]] = i["data"]
+    return out
+
+
+# %%
+pit = get_pit_variable_formatted("mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true", "2020ilch")
+
+
+# %%
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# %%
+fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=(80,15))
+
+i = 0
+
+for variable in pit:
+
+    ax[i].hist(pit[variable])
+    ax[i].invert_xaxis()
+
+    ax[i].set_xlabel('')
+    ax[i].set_ylabel('Frequency')
+    ax[i].set_title(variable)
+
+    plt.yticks(np.arange(len(pit[variable])))
+
+    i+=1
+
+plt.show()
+
+
+# %%
+
+

dep/2019/analysis/analysis-low.py

@@ -0,0 +1,944 @@
+# Titan Robotics Team 2022: Data Analysis Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#   this should be imported as a python module using 'import analysis'
+#   this should be included in the local directory or environment variable
+#   this module has not been optimized for multhreaded computing
+# number of easter eggs: 2
+# setup:
+
+__version__ = "1.0.9.000"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+1.0.9.000:
+    - refactored
+    - numpyed everything
+    - removed stats in favor of numpy functions
+1.0.8.005:
+    - minor fixes
+1.0.8.004:
+    - removed a few unused dependencies
+1.0.8.003:
+    - added p_value function
+1.0.8.002:
+    - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
+1.0.8.001:
+    - refactors
+    - bugfixes
+1.0.8.000:
+    - depreciated histo_analysis_old
+    - depreciated debug
+    - altered basic_analysis to take array data instead of filepath
+    - refactor
+    - optimization
+1.0.7.002:
+    - bug fixes
+1.0.7.001:
+    - bug fixes
+1.0.7.000:
+    - added tanh_regression (logistical regression)
+    - bug fixes
+1.0.6.005:
+    - added z_normalize function to normalize dataset
+    - bug fixes
+1.0.6.004:
+    - bug fixes
+1.0.6.003:
+    - bug fixes
+1.0.6.002:
+    - bug fixes
+1.0.6.001:
+    - corrected __all__ to contain all of the functions
+1.0.6.000:
+    - added calc_overfit, which calculates two measures of overfit, error and performance
+    - added calculating overfit to optimize_regression
+1.0.5.000:
+    - added optimize_regression function, which is a sample function to find the optimal regressions
+    - optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
+    - planned addition: overfit detection in the optimize_regression function
+1.0.4.002:
+    - added __changelog__
+    - updated debug function with log and exponential regressions
+1.0.4.001:
+    - added log regressions
+    - added exponential regressions
+    - added log_regression and exp_regression to __all__
+1.0.3.008:
+    - added debug function to further consolidate functions
+1.0.3.007:
+    - added builtin benchmark function
+    - added builtin random (linear) data generation function
+    - added device initialization (_init_device)
+1.0.3.006:
+    - reorganized the imports list to be in alphabetical order
+    - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
+1.0.3.005:
+    - major bug fixes
+    - updated historical analysis
+    - depreciated old historical analysis
+1.0.3.004:
+    - added __version__, __author__, __all__
+    - added polynomial regression
+    - added root mean squared function
+    - added r squared function
+1.0.3.003:
+    - bug fixes
+    - added c_entities
+1.0.3.002:
+    - bug fixes
+    - added nc_entities, obstacles, objectives
+    - consolidated statistics.py to analysis.py
+1.0.3.001:
+    - compiled 1d, column, and row basic stats into basic stats function
+1.0.3.000:
+    - added historical analysis function
+1.0.2.xxx:
+    - added z score test
+1.0.1.xxx:
+    - major bug fixes
+1.0.0.xxx:
+    - added loading csv
+    - added 1d, column, row basic stats
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+)
+
+__all__ = [
+    '_init_device',
+    'c_entities',
+    'nc_entities',
+    'obstacles',
+    'objectives',
+    'load_csv',
+    'basic_stats',
+    'z_score',
+    'z_normalize',
+    'stdev_z_split',
+    'histo_analysis',
+    'poly_regression',
+    'log_regression',
+    'exp_regression',
+    'r_squared',
+    'rms',
+    'calc_overfit',
+    'strip_data',
+    'optimize_regression',
+    'select_best_regression',
+    'basic_analysis',
+    # all statistics functions left out due to integration in other functions
+]
+
+# now back to your regularly scheduled programming:
+
+# imports (now in alphabetical order! v 1.0.3.006):
+
+from bisect import bisect_left, bisect_right
+import collections
+import csv
+from decimal import Decimal
+import functools
+from fractions import Fraction
+from itertools import groupby
+import math
+import matplotlib
+import numbers
+import numpy as np
+import pandas
+import random
+import scipy
+from scipy.optimize import curve_fit
+from scipy import stats
+from sklearn import *
+# import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
+import time
+import torch
+
+class error(ValueError):
+    pass
+
+def _init_device(setting, arg):  # initiates computation device for ANNs
+    if setting == "cuda":
+        try:
+            return torch.device(setting + ":" + str(arg) if torch.cuda.is_available() else "cpu")
+        except:
+            raise error("could not assign cuda or cpu")
+    elif setting == "cpu":
+        try:
+            return torch.device("cpu")
+        except:
+            raise error("could not assign cpu")
+    else:
+        raise error("specified device does not exist")
+
+def load_csv(filepath):
+    with open(filepath, newline='') as csvfile:
+        file_array = np.array(list(csv.reader(csvfile)))
+        csvfile.close()
+    return file_array
+
+# data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
+def basic_stats(data, method, arg):
+
+    if method == 'debug':
+        return "basic_stats requires 3 args: data, mode, arg; where data is data to be analyzed, mode is an int from 0 - 2 depending on type of analysis (by column or by row) and is only applicable to 2d arrays (for 1d arrays use mode 1), and arg is row/column number for mode 1 or mode 2; function returns: [mean, median, mode, stdev, variance]"
+
+    if method == "1d" or method == 0:
+
+        data_t = np.array(data).astype(float)
+
+        _mean = mean(data_t)
+        _median = median(data_t)
+        try:
+            _mode = mode(data_t)
+        except:
+            _mode = None
+        try:
+            _stdev = stdev(data_t)
+        except:
+            _stdev = None
+        try:
+            _variance = variance(data_t)
+        except:
+            _variance = None
+
+        return _mean, _median, _mode, _stdev, _variance
+    """
+    elif method == "column" or method == 1:
+
+        c_data = []
+        c_data_sorted = []
+
+        for i in data:
+            try:
+                c_data.append(float(i[arg]))
+            except:
+                pass
+
+        _mean = mean(c_data)
+        _median = median(c_data)
+        try:
+            _mode = mode(c_data)
+        except:
+            _mode = None
+        try:
+            _stdev = stdev(c_data)
+        except:
+            _stdev = None
+        try:
+            _variance = variance(c_data)
+        except:
+            _variance = None
+
+        return _mean, _median, _mode, _stdev, _variance
+    
+    elif method == "row" or method == 2:
+
+        r_data = []
+
+        for i in range(len(data[arg])):
+            r_data.append(float(data[arg][i]))
+
+        _mean = mean(r_data)
+        _median = median(r_data)
+        try:
+            _mode = mode(r_data)
+        except:
+            _mode = None
+        try:
+            _stdev = stdev(r_data)
+        except:
+            _stdev = None
+        try:
+            _variance = variance(r_data)
+        except:
+            _variance = None
+
+        return _mean, _median, _mode, _stdev, _variance
+    
+    else:
+        raise error("method error")
+    """
+
+
+# returns z score with inputs of point, mean and standard deviation of spread
+def z_score(point, mean, stdev):
+    score = (point - mean) / stdev
+    return score
+
+# mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized
+def z_normalize(x, y, mode):
+
+    x_norm = np.array().astype(float)
+    y_norm = np.array().astype(float)
+
+    mean = 0
+    stdev = 0
+
+    if mode == 'x':
+        _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
+
+        for i in range(0, len(x), 1):
+            x_norm.append(z_score(x[i], _mean, _stdev))
+
+        return x_norm, y
+
+    if mode == 'y':
+        _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
+
+        for i in range(0, len(y), 1):
+            y_norm.append(z_score(y[i], _mean, _stdev))
+
+        return x, y_norm
+
+    if mode == 'both':
+        _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
+
+        for i in range(0, len(x), 1):
+            x_norm.append(z_score(x[i], _mean, _stdev))
+
+        _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
+
+        for i in range(0, len(y), 1):
+            y_norm.append(z_score(y[i], _mean, _stdev))
+
+        return x_norm, y_norm
+
+    else:
+
+        return error('method error')
+
+
+# returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score
+def stdev_z_split(mean, stdev, delta, low_bound, high_bound):
+
+    z_split = np.array().astype(float)
+    i = low_bound
+
+    while True:
+        z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) *
+                             math.e ** (-0.5 * (((i - mean) / stdev) ** 2))))
+        i = i + delta
+        if i > high_bound:
+            break
+
+    return z_split
+
+
+def histo_analysis(hist_data, delta, low_bound, high_bound):
+
+    if hist_data == 'debug':
+        return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
+
+    derivative = []
+
+    for i in range(0, len(hist_data), 1):
+        try:
+            derivative.append(float(hist_data[i - 1]) - float(hist_data[i]))
+        except:
+            pass
+
+    derivative_sorted = sorted(derivative, key=int)
+    mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0]
+    stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3]
+
+    predictions = []
+    pred_change = 0
+
+    i = low_bound
+
+    while True:
+        if i > high_bound:
+            break
+
+        try:
+            pred_change = mean_derivative + i * stdev_derivative
+        except:
+            pred_change = mean_derivative
+
+        predictions.append(float(hist_data[-1:][0]) + pred_change)
+
+        i = i + delta
+
+    return predictions
+
+
+def poly_regression(x, y, power):
+
+    if x == "null":  # if x is 'null', then x will be filled with integer points between 1 and the size of y
+        x = []
+
+        for i in range(len(y)):
+            print(i)
+            x.append(i + 1)
+
+    reg_eq = scipy.polyfit(x, y, deg=power)
+    eq_str = ""
+
+    for i in range(0, len(reg_eq), 1):
+        if i < len(reg_eq) - 1:
+            eq_str = eq_str + str(reg_eq[i]) + \
+                "*(z**" + str(len(reg_eq) - i - 1) + ")+"
+        else:
+            eq_str = eq_str + str(reg_eq[i]) + \
+                "*(z**" + str(len(reg_eq) - i - 1) + ")"
+
+    vals = []
+
+    for i in range(0, len(x), 1):
+        z = x[i]
+
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return [eq_str, _rms, r2_d2]
+
+
+def log_regression(x, y, base):
+
+    x_fit = []
+
+    for i in range(len(x)):
+        try:
+            # change of base for logs
+            x_fit.append(np.log(x[i]) / np.log(base))
+        except:
+            pass
+
+    # y = reg_eq[0] * log(x, base) + reg_eq[1]
+    reg_eq = np.polyfit(x_fit, y, 1)
+    q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + \
+        str(base) + "))+" + str(reg_eq[1])
+    vals = []
+
+    for i in range(len(x)):
+        z = x[i]
+
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return eq_str, _rms, r2_d2
+
+
+def exp_regression(x, y, base):
+
+    y_fit = []
+
+    for i in range(len(y)):
+        try:
+            # change of base for logs
+            y_fit.append(np.log(y[i]) / np.log(base))
+        except:
+            pass
+
+    # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
+    reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit))
+    eq_str = "(" + str(base) + "**(" + \
+        str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
+    vals = []
+
+    for i in range(len(x)):
+        z = x[i]
+
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return eq_str, _rms, r2_d2
+
+
+def tanh_regression(x, y):
+
+    def tanh(x, a, b, c, d):
+
+        return a * np.tanh(b * (x - c)) + d
+
+    reg_eq = np.float64(curve_fit(tanh, np.array(x), np.array(y))[0]).tolist()
+    eq_str = str(reg_eq[0]) + " * np.tanh(" + str(reg_eq[1]) + \
+        "*(z - " + str(reg_eq[2]) + ")) + " + str(reg_eq[3])
+    vals = []
+
+    for i in range(len(x)):
+        z = x[i]
+        try:
+            exec("vals.append(" + eq_str + ")")
+        except:
+            pass
+
+    _rms = rms(vals, y)
+    r2_d2 = r_squared(vals, y)
+
+    return eq_str, _rms, r2_d2
+
+
+def r_squared(predictions, targets):  # assumes equal size inputs
+
+    return metrics.r2_score(np.array(targets), np.array(predictions))
+
+
+def rms(predictions, targets):  # assumes equal size inputs
+
+    _sum = 0
+
+    for i in range(0, len(targets), 1):
+        _sum = (targets[i] - predictions[i]) ** 2
+
+    return float(math.sqrt(_sum / len(targets)))
+
+
+def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
+
+    # performance overfit = performance(train) - performance(test) where performance is r^2
+    # error overfit = error(train) - error(test) where error is rms; biased towards smaller values
+
+    vals = []
+
+    for i in range(0, len(x_test), 1):
+
+        z = x_test[i]
+
+        exec("vals.append(" + equation + ")")
+
+    r2_test = r_squared(vals, y_test)
+    rms_test = rms(vals, y_test)
+
+    return r2_train - r2_test
+
+
+def strip_data(data, mode):
+
+    if mode == "adam":  # x is the row number, y are the data
+        pass
+
+    if mode == "eve":  # x are the data, y is the column number
+        pass
+
+    else:
+        raise error("mode error")
+
+
+# _range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000
+def optimize_regression(x, y, _range, resolution):
+    # usage not: for demonstration purpose only, performance is shit
+    if type(resolution) != int:
+        raise error("resolution must be int")
+
+    x_train = x
+    y_train = []
+
+    for i in range(len(y)):
+        y_train.append(float(y[i]))
+
+    x_test = []
+    y_test = []
+
+    for i in range(0, math.floor(len(x) * 0.5), 1):
+        index = random.randint(0, len(x) - 1)
+
+        x_test.append(x[index])
+        y_test.append(float(y[index]))
+
+        x_train.pop(index)
+        y_train.pop(index)
+
+    #print(x_train, x_test)
+    #print(y_train, y_test)
+
+    eqs = []
+    rmss = []
+    r2s = []
+
+    for i in range(0, _range + 1, 1):
+        try:
+            x, y, z = poly_regression(x_train, y_train, i)
+            eqs.append(x)
+            rmss.append(y)
+            r2s.append(z)
+        except:
+            pass
+
+    for i in range(1, 100 * resolution + 1):
+        try:
+            x, y, z = exp_regression(x_train, y_train, float(i / resolution))
+            eqs.append(x)
+            rmss.append(y)
+            r2s.append(z)
+        except:
+            pass
+
+    for i in range(1, 100 * resolution + 1):
+        try:
+            x, y, z = log_regression(x_train, y_train, float(i / resolution))
+            eqs.append(x)
+            rmss.append(y)
+            r2s.append(z)
+        except:
+            pass
+
+    try:
+        x, y, z = tanh_regression(x_train, y_train)
+
+        eqs.append(x)
+        rmss.append(y)
+        r2s.append(z)
+    except:
+        pass
+
+    # marks all equations where r2 = 1 as they 95% of the time overfit the data
+    for i in range(0, len(eqs), 1):
+        if r2s[i] == 1:
+            eqs[i] = ""
+            rmss[i] = ""
+            r2s[i] = ""
+
+    while True:  # removes all equations marked for removal
+        try:
+            eqs.remove('')
+            rmss.remove('')
+            r2s.remove('')
+        except:
+            break
+
+    overfit = []
+
+    for i in range(0, len(eqs), 1):
+
+        overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
+
+    return eqs, rmss, r2s, overfit
+
+
+def select_best_regression(eqs, rmss, r2s, overfit, selector):
+
+    b_eq = ""
+    b_rms = 0
+    b_r2 = 0
+    b_overfit = 0
+
+    ind = 0
+
+    if selector == "min_overfit":
+
+        ind = np.argmin(overfit)
+
+        b_eq = eqs[ind]
+        b_rms = rmss[ind]
+        b_r2 = r2s[ind]
+        b_overfit = overfit[ind]
+
+    if selector == "max_r2s":
+
+        ind = np.argmax(r2s)
+        b_eq = eqs[ind]
+        b_rms = rmss[ind]
+        b_r2 = r2s[ind]
+        b_overfit = overfit[ind]
+
+    return b_eq, b_rms, b_r2, b_overfit
+
+
+def p_value(x, y):  # takes 2 1d arrays
+
+    return stats.ttest_ind(x, y)[1]
+
+
+# assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
+def basic_analysis(data):
+
+    row = len(data)
+    column = []
+
+    for i in range(0, row, 1):
+        column.append(len(data[i]))
+
+    column_max = max(column)
+    row_b_stats = []
+    row_histo = []
+
+    for i in range(0, row, 1):
+        row_b_stats.append(basic_stats(data, "row", i))
+        row_histo.append(histo_analysis(data[i], 0.67449, -0.67449, 0.67449))
+
+    column_b_stats = []
+
+    for i in range(0, column_max, 1):
+        column_b_stats.append(basic_stats(data, "column", i))
+
+    return[row_b_stats, column_b_stats, row_histo]
+
+
+def benchmark(x, y):
+
+    start_g = time.time()
+    generate_data("data/data.csv", x, y, -10, 10)
+    end_g = time.time()
+
+    start_a = time.time()
+    basic_analysis("data/data.csv")
+    end_a = time.time()
+
+    return [(end_g - start_g), (end_a - start_a)]
+
+
+def generate_data(filename, x, y, low, high):
+
+    file = open(filename, "w")
+
+    for i in range(0, y, 1):
+        temp = ""
+
+        for j in range(0, x - 1, 1):
+            temp = str(random.uniform(low, high)) + "," + temp
+
+        temp = temp + str(random.uniform(low, high))
+        file.write(temp + "\n")
+
+def mean(data):
+
+    return np.mean(data)
+
+def median(data):
+
+    return np.median(data)
+
+def mode(data):
+
+    return np.argmax(np.bincount(data))
+
+def stdev(data):
+
+    return np.std(data)
+
+def variance(data):
+
+    return np.var(data)
+
+"""
+
+class StatisticsError(ValueError):
+    pass
+
+
+def _sum(data, start=0):
+    count = 0
+    n, d = _exact_ratio(start)
+    partials = {d: n}
+    partials_get = partials.get
+    T = _coerce(int, type(start))
+    for typ, values in groupby(data, type):
+        T = _coerce(T, typ)  # or raise TypeError
+        for n, d in map(_exact_ratio, values):
+            count += 1
+            partials[d] = partials_get(d, 0) + n
+    if None in partials:
+
+        total = partials[None]
+        assert not _isfinite(total)
+    else:
+
+        total = sum(Fraction(n, d) for d, n in sorted(partials.items()))
+    return (T, total, count)
+
+
+def _isfinite(x):
+    try:
+        return x.is_finite()  # Likely a Decimal.
+    except AttributeError:
+        return math.isfinite(x)  # Coerces to float first.
+
+
+def _coerce(T, S):
+
+    assert T is not bool, "initial type T is bool"
+
+    if T is S:
+        return T
+
+    if S is int or S is bool:
+        return T
+    if T is int:
+        return S
+
+    if issubclass(S, T):
+        return S
+    if issubclass(T, S):
+        return T
+
+    if issubclass(T, int):
+        return S
+    if issubclass(S, int):
+        return T
+
+    if issubclass(T, Fraction) and issubclass(S, float):
+        return S
+    if issubclass(T, float) and issubclass(S, Fraction):
+        return T
+
+    msg = "don't know how to coerce %s and %s"
+    raise TypeError(msg % (T.__name__, S.__name__))
+
+
+def _exact_ratio(x):
+
+    try:
+
+        if type(x) is float or type(x) is Decimal:
+            return x.as_integer_ratio()
+        try:
+
+            return (x.numerator, x.denominator)
+        except AttributeError:
+            try:
+
+                return x.as_integer_ratio()
+            except AttributeError:
+
+                pass
+    except (OverflowError, ValueError):
+
+        assert not _isfinite(x)
+        return (x, None)
+    msg = "can't convert type '{}' to numerator/denominator"
+    raise TypeError(msg.format(type(x).__name__))
+
+
+def _convert(value, T):
+
+    if type(value) is T:
+
+        return value
+    if issubclass(T, int) and value.denominator != 1:
+        T = float
+    try:
+
+        return T(value)
+    except TypeError:
+        if issubclass(T, Decimal):
+            return T(value.numerator) / T(value.denominator)
+        else:
+            raise
+
+
+def _counts(data):
+
+    table = collections.Counter(iter(data)).most_common()
+    if not table:
+        return table
+
+    maxfreq = table[0][1]
+    for i in range(1, len(table)):
+        if table[i][1] != maxfreq:
+            table = table[:i]
+            break
+    return table
+
+
+def _find_lteq(a, x):
+
+    i = bisect_left(a, x)
+    if i != len(a) and a[i] == x:
+        return i
+    raise ValueError
+
+
+def _find_rteq(a, l, x):
+
+    i = bisect_right(a, x, lo=l)
+    if i != (len(a) + 1) and a[i - 1] == x:
+        return i - 1
+    raise ValueError
+
+
+def _fail_neg(values, errmsg='negative value'):
+
+    for x in values:
+        if x < 0:
+            raise StatisticsError(errmsg)
+        yield x
+def mean(data):
+
+    if iter(data) is data:
+        data = list(data)
+    n = len(data)
+    if n < 1:
+        raise StatisticsError('mean requires at least one data point')
+    T, total, count = _sum(data)
+    assert count == n
+    return _convert(total / n, T)
+
+
+def median(data):
+
+    data = sorted(data)
+    n = len(data)
+    if n == 0:
+        raise StatisticsError("no median for empty data")
+    if n % 2 == 1:
+        return data[n // 2]
+    else:
+        i = n // 2
+        return (data[i - 1] + data[i]) / 2
+
+
+def mode(data):
+
+    table = _counts(data)
+    if len(table) == 1:
+        return table[0][0]
+    elif table:
+        raise StatisticsError(
+            'no unique mode; found %d equally common values' % len(table)
+        )
+    else:
+        raise StatisticsError('no mode for empty data')
+
+
+def _ss(data, c=None):
+
+    if c is None:
+        c = mean(data)
+    T, total, count = _sum((x - c)**2 for x in data)
+
+    U, total2, count2 = _sum((x - c) for x in data)
+    assert T == U and count == count2
+    total -= total2**2 / len(data)
+    assert not total < 0, 'negative sum of square deviations: %f' % total
+    return (T, total)
+
+
+def variance(data, xbar=None):
+
+    if iter(data) is data:
+        data = list(data)
+    n = len(data)
+    if n < 2:
+        raise StatisticsError('variance requires at least two data points')
+    T, ss = _ss(data, xbar)
+    return _convert(ss / (n - 1), T)
+
+
+def stdev(data, xbar=None):
+
+    var = variance(data, xbar)
+    try:
+        return var.sqrt()
+    except AttributeError:
+        return math.sqrt(var)
+"""

dep/2019/analysis/compile.bat

@@ -0,0 +1,2 @@
+python setup.py build_ext --inplace
+pause

dep/2019/analysis/compile.sh

@@ -0,0 +1 @@
+python setup.py build_ext --inplace

dep/2019/analysis/setup.py

@@ -0,0 +1,5 @@
+from distutils.core import setup
+from Cython.Build import cythonize
+
+setup(name='analysis',
+      ext_modules=cythonize("analysis.py"))

dep/2019/apps/android/apk/debug/output.json

@@ -0,0 +1 @@
+[{"outputType":{"type":"APK"},"apkInfo":{"type":"MAIN","splits":[],"versionCode":1,"versionName":"1.0","enabled":true,"outputFile":"app-debug.apk","fullName":"debug","baseName":"debug"},"path":"app-debug.apk","properties":{}}]

dep/2019/apps/android/source/.gitignore

@@ -0,0 +1,13 @@
+*.iml
+.gradle
+/local.properties
+/.idea/caches
+/.idea/libraries
+/.idea/modules.xml
+/.idea/workspace.xml
+/.idea/navEditor.xml
+/.idea/assetWizardSettings.xml
+.DS_Store
+/build
+/captures
+.externalNativeBuild

dep/2019/apps/android/source/.idea/codeStyles/Project.xml

@@ -0,0 +1,29 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <Objective-C-extensions>
+      <file>
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Import" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Macro" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Typedef" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Enum" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Constant" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Global" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Struct" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="FunctionPredecl" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Function" />
+      </file>
+      <class>
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Property" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Synthesize" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InitMethod" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="StaticMethod" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InstanceMethod" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="DeallocMethod" />
+      </class>
+      <extensions>
+        <pair source="cpp" header="h" fileNamingConvention="NONE" />
+        <pair source="c" header="h" fileNamingConvention="NONE" />
+      </extensions>
+    </Objective-C-extensions>
+  </code_scheme>
+</component>

dep/2019/apps/android/source/.idea/gradle.xml

@@ -0,0 +1,18 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="GradleSettings">
+    <option name="linkedExternalProjectsSettings">
+      <GradleProjectSettings>
+        <option name="distributionType" value="DEFAULT_WRAPPED" />
+        <option name="externalProjectPath" value="$PROJECT_DIR$" />
+        <option name="modules">
+          <set>
+            <option value="$PROJECT_DIR$" />
+            <option value="$PROJECT_DIR$/app" />
+          </set>
+        </option>
+        <option name="resolveModulePerSourceSet" value="false" />
+      </GradleProjectSettings>
+    </option>
+  </component>
+</project>

dep/2019/apps/android/source/.idea/misc.xml

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" project-jdk-name="11" project-jdk-type="JavaSDK">
+    <output url="file://$PROJECT_DIR$/build/classes" />
+  </component>
+  <component name="ProjectType">
+    <option name="id" value="Android" />
+  </component>
+</project>

dep/2019/apps/android/source/.idea/runConfigurations.xml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="RunConfigurationProducerService">
+    <option name="ignoredProducers">
+      <set>
+        <option value="org.jetbrains.plugins.gradle.execution.test.runner.AllInPackageGradleConfigurationProducer" />
+        <option value="org.jetbrains.plugins.gradle.execution.test.runner.TestClassGradleConfigurationProducer" />
+        <option value="org.jetbrains.plugins.gradle.execution.test.runner.TestMethodGradleConfigurationProducer" />
+      </set>
+    </option>
+  </component>
+</project>

dep/2019/apps/android/source/.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/../../.." vcs="Git" />
+  </component>
+</project>

dep/2019/apps/android/source/app/.gitignore

@@ -0,0 +1 @@
+/build

dep/2019/apps/android/source/app/build.gradle

@@ -0,0 +1,28 @@
+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 28
+    defaultConfig {
+        applicationId "com.example.titanscouting"
+        minSdkVersion 16
+        targetSdkVersion 28
+        versionCode 1
+        versionName "1.0"
+        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
+    }
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+}
+
+dependencies {
+    implementation fileTree(dir: 'libs', include: ['*.jar'])
+    implementation 'com.android.support:appcompat-v7:28.0.0'
+    implementation 'com.android.support.constraint:constraint-layout:1.1.3'
+    testImplementation 'junit:junit:4.12'
+    androidTestImplementation 'com.android.support.test:runner:1.0.2'
+    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
+}

dep/2019/apps/android/source/app/proguard-rules.pro

@@ -0,0 +1,21 @@
+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

dep/2019/apps/android/source/app/release/output.json

@@ -0,0 +1 @@
+[{"outputType":{"type":"APK"},"apkInfo":{"type":"MAIN","splits":[],"versionCode":1,"versionName":"1.0","enabled":true,"outputFile":"app-release.apk","fullName":"release","baseName":"release"},"path":"app-release.apk","properties":{}}]

dep/2019/apps/android/source/app/src/androidTest/java/com/example/titanscouting/ExampleInstrumentedTest.java

@@ -0,0 +1,26 @@
+package com.example.titanscouting;
+
+import android.content.Context;
+import android.support.test.InstrumentationRegistry;
+import android.support.test.runner.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getTargetContext();
+
+        assertEquals("com.example.titanscouting", appContext.getPackageName());
+    }
+}

dep/2019/apps/android/source/app/src/main/AndroidManifest.xml

@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.example.titanscouting">
+
+    <uses-permission android:name="android.permission.INTERNET" />
+
+    <application
+        android:allowBackup="true"
+        android:icon="@mipmap/ic_launcher"
+        android:label="@string/app_name"
+        android:roundIcon="@drawable/binoculars_big"
+        android:supportsRtl="true"
+        android:theme="@style/AppTheme"
+        android:usesCleartextTraffic="true">
+        <activity android:name=".tits"></activity>
+        <activity android:name=".launcher">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN" />
+
+                <category android:name="android.intent.category.LAUNCHER" />
+            </intent-filter>
+        </activity>
+        <activity android:name=".MainActivity">
+
+        </activity>
+    </application>
+
+</manifest>