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"content": "*.json\n*.lnk\nsources/assets.zip\nsources/googleapi/GoogleClientIdentifiers.cs\ntest/*\ndatasource/*\nreleases/*\nml/*/data/*\nml/.vscode/*\n!datasource/!Start.bat\n!dalamud/*.json\n\n## Ignore Visual Studio temporary files, build results, and\n## files generated by popular Visual Studio add-ons.\n##\n## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore\n\n# User-specific files\n*.rsuser\n*.suo\n*.user\n*.userosscache\n*.sln.docstates\n\n# User-specific files (MonoDevelop/Xamarin Studio)\n*.userprefs\n\n# Mono auto generated files\nmono_crash.*\n\n# Build results\n[Dd]ebug/\n[Dd]ebugPublic/\n[Rr]elease/\n[Rr]eleases/\nx64/\nx86/\n[Aa][Rr][Mm]/\n[Aa][Rr][Mm]64/\nbld/\n[Bb]in/\n[Oo]bj/\n[Ll]og/\n\n# Visual Studio 2015/2017 cache/options directory\n.vs/\n# Uncomment if you have tasks that create the project's static files in wwwroot\n#wwwroot/\n\n# Visual Studio 2017 auto generated files\nGenerated\\ Files/\n\n# MSTest test Results\n[Tt]est[Rr]esult*/\n[Bb]uild[Ll]og.*\n\n# NUNIT\n*.VisualState.xml\nTestResult.xml\n\n# Build Results of an ATL Project\n[Dd]ebugPS/\n[Rr]eleasePS/\ndlldata.c\n\n# Benchmark Results\nBenchmarkDotNet.Artifacts/\n\n# .NET Core\nproject.lock.json\nproject.fragment.lock.json\nartifacts/\n\n# StyleCop\nStyleCopReport.xml\n\n# Files built by Visual Studio\n*_i.c\n*_p.c\n*_h.h\n*.ilk\n*.meta\n*.obj\n*.iobj\n*.pch\n*.pdb\n*.ipdb\n*.pgc\n*.pgd\n*.rsp\n*.sbr\n*.tlb\n*.tli\n*.tlh\n*.tmp\n*.tmp_proj\n*_wpftmp.csproj\n*.log\n*.vspscc\n*.vssscc\n.builds\n*.pidb\n*.svclog\n*.scc\n\n# Chutzpah Test files\n_Chutzpah*\n\n# Visual C++ cache files\nipch/\n*.aps\n*.ncb\n*.opendb\n*.opensdf\n*.sdf\n*.cachefile\n*.VC.db\n*.VC.VC.opendb\n\n# Visual Studio profiler\n*.psess\n*.vsp\n*.vspx\n*.sap\n\n# Visual Studio Trace Files\n*.e2e\n\n# TFS 2012 Local Workspace\n$tf/\n\n# Guidance Automation Toolkit\n*.gpState\n\n# ReSharper is a .NET coding add-in\n_ReSharper*/\n*.[Rr]e[Ss]harper\n*.DotSettings.user\n\n# JustCode is a .NET coding add-in\n.JustCode\n\n# TeamCity is a build add-in\n_TeamCity*\n\n# DotCover is a Code Coverage Tool\n*.dotCover\n\n# AxoCover is a Code Coverage Tool\n.axoCover/*\n!.axoCover/settings.json\n\n# Visual Studio code coverage results\n*.coverage\n*.coveragexml\n\n# NCrunch\n_NCrunch_*\n.*crunch*.local.xml\nnCrunchTemp_*\n\n# MightyMoose\n*.mm.*\nAutoTest.Net/\n\n# Web workbench (sass)\n.sass-cache/\n\n# Installshield output folder\n[Ee]xpress/\n\n# DocProject is a documentation generator add-in\nDocProject/buildhelp/\nDocProject/Help/*.HxT\nDocProject/Help/*.HxC\nDocProject/Help/*.hhc\nDocProject/Help/*.hhk\nDocProject/Help/*.hhp\nDocProject/Help/Html2\nDocProject/Help/html\n\n# Click-Once directory\npublish/\n\n# Publish Web Output\n*.[Pp]ublish.xml\n*.azurePubxml\n# Note: Comment the next line if you want to checkin your web deploy settings,\n# but database connection strings (with potential passwords) will be unencrypted\n*.pubxml\n*.publishproj\n\n# Microsoft Azure Web App publish settings. Comment the next line if you want to\n# checkin your Azure Web App publish settings, but sensitive information contained\n# in these scripts will be unencrypted\nPublishScripts/\n\n# NuGet Packages\n*.nupkg\n# The packages folder can be ignored because of Package Restore\n**/[Pp]ackages/*\n# except build/, which is used as an MSBuild target.\n!**/[Pp]ackages/build/\n# Uncomment if necessary however generally it will be regenerated when needed\n#!**/[Pp]ackages/repositories.config\n# NuGet v3's project.json files produces more ignorable files\n*.nuget.props\n*.nuget.targets\n\n# Microsoft Azure Build Output\ncsx/\n*.build.csdef\n\n# Microsoft Azure Emulator\necf/\nrcf/\n\n# Windows Store app package directories and files\nAppPackages/\nBundleArtifacts/\nPackage.StoreAssociation.xml\n_pkginfo.txt\n*.appx\n*.appxbundle\n*.appxupload\n\n# Visual Studio cache files\n# files ending in .cache can be ignored\n*.[Cc]ache\n# but keep track of directories ending in .cache\n!?*.[Cc]ache/\n\n# Others\nClientBin/\n~$*\n*~\n*.dbmdl\n*.dbproj.schemaview\n*.jfm\n*.pfx\n*.publishsettings\norleans.codegen.cs\n\n# Including strong name files can present a security risk\n# (https://github.com/github/gitignore/pull/2483#issue-259490424)\n#*.snk\n\n# Since there are multiple workflows, uncomment next line to ignore bower_components\n# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)\n#bower_components/\n\n# RIA/Silverlight projects\nGenerated_Code/\n\n# Backup & report files from converting an old project file\n# to a newer Visual Studio version. Backup files are not needed,\n# because we have git ;-)\n_UpgradeReport_Files/\nBackup*/\nUpgradeLog*.XML\nUpgradeLog*.htm\nServiceFabricBackup/\n*.rptproj.bak\n\n# SQL Server files\n*.mdf\n*.ldf\n*.ndf\n\n# Business Intelligence projects\n*.rdl.data\n*.bim.layout\n*.bim_*.settings\n*.rptproj.rsuser\n*- Backup*.rdl\n\n# Microsoft Fakes\nFakesAssemblies/\n\n# GhostDoc plugin setting file\n*.GhostDoc.xml\n\n# Node.js Tools for Visual Studio\n.ntvs_analysis.dat\nnode_modules/\n\n# Visual Studio 6 build log\n*.plg\n\n# Visual Studio 6 workspace options file\n*.opt\n\n# Visual Studio 6 auto-generated workspace file (contains which files were open etc.)\n*.vbw\n\n# Visual Studio LightSwitch build output\n**/*.HTMLClient/GeneratedArtifacts\n**/*.DesktopClient/GeneratedArtifacts\n**/*.DesktopClient/ModelManifest.xml\n**/*.Server/GeneratedArtifacts\n**/*.Server/ModelManifest.xml\n_Pvt_Extensions\n\n# Paket dependency manager\n.paket/paket.exe\npaket-files/\n\n# FAKE - F# Make\n.fake/\n\n# CodeRush personal settings\n.cr/personal\n\n# Python Tools for Visual Studio (PTVS)\n__pycache__/\n*.pyc\n\n# Cake - Uncomment if you are using it\n# tools/**\n# !tools/packages.config\n\n# Tabs Studio\n*.tss\n\n# Telerik's JustMock configuration file\n*.jmconfig\n\n# BizTalk build output\n*.btp.cs\n*.btm.cs\n*.odx.cs\n*.xsd.cs\n\n# OpenCover UI analysis results\nOpenCover/\n\n# Azure Stream Analytics local run output\nASALocalRun/\n\n# MSBuild Binary and Structured Log\n*.binlog\n\n# NVidia Nsight GPU debugger configuration file\n*.nvuser\n\n# MFractors (Xamarin productivity tool) working folder\n.mfractor/\n\n# Local History for Visual Studio\n.localhistory/\n\n# BeatPulse healthcheck temp database\nhealthchecksdb\n\n# Backup folder for Package Reference Convert tool in Visual Studio 2017\nMigrationBackup/"
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"path": "FFTriadBuddy.sln",
"content": "\nMicrosoft Visual Studio Solution File, Format Version 12.00\n# Visual Studio 15\nVisualStudioVersion = 15.0.27703.2026\nMinimumVisualStudioVersion = 10.0.40219.1\nProject(\"{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}\") = \"FFTriadBuddy\", \"sources\\FFTriadBuddy.csproj\", \"{4FE48C06-9E9C-46C9-A53E-CFE06EFDCBDC}\"\nEndProject\nGlobal\n\tGlobalSection(SolutionConfigurationPlatforms) = preSolution\n\t\tDebug|Any CPU = Debug|Any CPU\n\t\tRelease|Any CPU = Release|Any CPU\n\tEndGlobalSection\n\tGlobalSection(ProjectConfigurationPlatforms) = postSolution\n\t\t{4FE48C06-9E9C-46C9-A53E-CFE06EFDCBDC}.Debug|Any CPU.ActiveCfg = Debug|Any CPU\n\t\t{4FE48C06-9E9C-46C9-A53E-CFE06EFDCBDC}.Debug|Any CPU.Build.0 = Debug|Any CPU\n\t\t{4FE48C06-9E9C-46C9-A53E-CFE06EFDCBDC}.Release|Any CPU.ActiveCfg = Release|Any CPU\n\t\t{4FE48C06-9E9C-46C9-A53E-CFE06EFDCBDC}.Release|Any CPU.Build.0 = Release|Any CPU\n\tEndGlobalSection\n\tGlobalSection(SolutionProperties) = preSolution\n\t\tHideSolutionNode = FALSE\n\tEndGlobalSection\n\tGlobalSection(ExtensibilityGlobals) = postSolution\n\t\tSolutionGuid = {4BB10FD8-07CE-4000-A373-6D18CE17C244}\n\tEndGlobalSection\nEndGlobal\n"
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"path": "LICENSE",
"content": "MIT License\n\nCopyright (c) 2019 MgAl2O4\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "README.md",
"content": "# FFTriadBuddy\r\nHelper program for Triple Triad minigame in [Final Fantasy XIV](https://www.finalfantasyxiv.com/), NPC matches only. \r\n(All used icons are property of SQUARE-ENIX Ltd. All rights reserved)\r\n\r\nFeatures:\r\n\r\n* suggest next move in standalone simulation\r\n* suggest next move with in-game overlay (screenshot based)\r\n* organize cards and npc fights\r\n* prepare deck for given npc\r\n* all game rules are supported\r\n* **super secret** Mini Cactpot mode for in-game overlay\r\n\r\nDalamud plugin can be found here: https://github.com/MgAl2O4/FFTriadBuddyDalamud\r\n\r\n\r\n## Instructions\r\n1. Download [latest release](https://github.com/MgAl2O4/FFTriadBuddy/releases/latest)\r\n2. Unpack and run. \r\n\r\nProgram will attempt to auto update on startup from this repository.\r\n\r\n## Known issues\r\n\r\nUI buttons glitching out after mouse over: \r\nPlease refer to [this bug report](https://github.com/MgAl2O4/FFTriadBuddy/issues/53#issuecomment-879286853) for solution, it's a known incompatibility between UI framework and Nahimic software.\r\n\r\n\r\n## Bug reports\r\n\r\nBug happens. Whenever it's related to Play: Screenshot mode not recognizing images/cards correctly, please include a screenshot in bug report. Ideally, the one saved by tool in secret-debug-mode, which is exact input image for processing.\r\n* make sure that game show broken state and board is not obscured\r\n* switch to Play: Simulate tab, press [F12] and hit \"Apply rule\" button\r\n* look for screenshot-source-xx.jpg file, saved next to tool's executable\r\n\r\n## Translation\r\n\r\nLocalization of game data (cards, npcs, rules, tournaments, etc) is created from client datamining and is more or less automatic. Tool's UI is now separate from it, but relies on people to contribute translations.\r\n\r\nYou can help with translation here: https://crowdin.com/project/fftriadbuddy\r\n\r\n\r\nContact: MgAl2O4@protonmail.com\r\n"
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"path": "assets/data/cards.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
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"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
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"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
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{
"path": "assets/data/npcs.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "assets/data/tournaments.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "datasource/!Start.bat",
"content": "@echo off\nsetlocal\n\nif not exist SaintCoinach goto MISSING_SAINT\nif not exist SetUserVars.bat goto MISSING_PATH\ncall SetUserVars.bat\nif %GamePath%==\"\" goto MISSING_PATH \n\necho Game path: %GamePath%\necho.\necho Export data using commands:\necho ui 87000 88999\necho allexd\necho exit\necho.\n\npushd SaintCoinach\nif exist SaintCoinach.History.zip ( del SaintCoinach.History.zip > nul )\nSaintCoinach.Cmd.exe %GamePath%\npopd\n\nFOR /F \"tokens=* USEBACKQ\" %%F IN (`dir SaintCoinach\\2* /A:D /B`) DO (\nSET DataPath=%%F\n)\n\necho.\nrmdir export /s /q > nul\necho Copying exported data from: %DataPath%\n\nxcopy SaintCoinach\\%DataPath%\\*.* export\\ /e > nul\n\ndel ..\\assets\\icons\\*.png\nxcopy export\\ui\\icon\\087000\\*.png ..\\assets\\icons\\ /s > nul\nxcopy export\\ui\\icon\\088000\\*.png ..\\assets\\icons\\ /s > nul\n\nrmdir SaintCoinach\\%DataPath% /s /q > nul\n\n:GIT_MIRRORS\nSET /P CANDOWNLOAD=Do you want to download from github mirrors (curl required) (Y/[N])? \nIF /I \"%CANDOWNLOAD%\" NEQ \"Y\" GOTO EXPORTED\n\nrem raw blobs with URL like:\nrem https://raw.githubusercontent.com/thewakingsands/ffxiv-datamining-cn/master/ENpcResident.csv\ncall :CURL_WORKER cn thewakingsands/ffxiv-datamining-cn master\ncall :CURL_WORKER ko Ra-Workspace/ffxiv-datamining-ko master/csv\ngoto EXPORTED\n\n:CURL_WORKER\necho Downloading from: %2...\nfor %%F in (ENpcResident Item PlaceName TripleTriadCard TripleTriadCardType TripleTriadRule TripleTriadCompetition TripleTriadCardResident TripleTriadResident) do ( \n\tcurl https://raw.githubusercontent.com/%2/%3/%%F.csv --output export\\exd-all\\%%F.%1.csv --silent\n)\nexit /b\n\n:EXPORTED\necho Done! \necho.\necho Run DEBUG build with -dataConvert cmdline to process data tables.\necho Output logs will show all needed information\ngoto FINISHED\n\n:MISSING_PATH\necho Game path not set, edit SetUserVars.bat and update GamePath variable\necho.\necho Example: \"C:\\Games\\SquareEnix\\FINAL FANTASY XIV - A Realm Reborn\"\necho set GamePath=\"C:\\Games\\SquareEnix\\FINAL FANTASY XIV - A Realm Reborn\" > SetUserVars.bat\ngoto FINISHED\n\n:MISSING_SAINT\necho Can't find SaintCoinach binaries!\necho.\necho Grab latest release of SaintCoinach.Cmd from github:\necho https://github.com/xivapi/SaintCoinach/releases\necho.\necho Expected path: datasource/SaintCoinach/SaintCoinach.Cmd.exe\ngoto FINISHED\n\n:FINISHED\necho.\npause"
},
{
"path": "ml/patternMatch/nn.py",
"content": "import tensorflow as tf\nimport numpy as np\nimport json\nimport textwrap\n\nfrom tensorflow import keras\nfrom tqdm.keras import TqdmCallback\n\nclass NNTraining():\n def __init__(self, inputFile, outputFile, labelKey=\"output\"):\n path = 'data/'\n self.inputFile = path + inputFile\n self.outputFile = path + outputFile\n self.labelKey = labelKey\n pass\n \n def printToLines(self, prefix, values, suffix):\n longstr = prefix + ', '.join(('%ff' % v) for v in values) + suffix\n return textwrap.wrap(longstr, 250)\n\n def loadData(self):\n with open(self.inputFile) as file:\n training_sets = json.load(file)\n\n inputs = []\n outputs = []\n \n for elem in training_sets[\"dataset\"]:\n inputs.append(elem[\"input\"])\n outputs.append(elem[self.labelKey])\n\n return inputs, outputs\n \n\n def writeCodeFile(self, model, codeSuffix):\n lines = []\n\n for i in range(len(model.layers)):\n layer = model.layers[i]\n print('Layer[%i]:' % i)\n print(' input_shape:', layer.input_shape)\n print(' output_shape:', layer.output_shape)\n weights = layer.get_weights()\n for w in weights:\n print(' w.shape:', w.shape)\n print(' use_bias:', layer.use_bias)\n print(' activation:', layer.activation)\n\n if (len(weights) == 2 and layer.use_bias):\n listWeights = np.reshape(weights[0], -1)\n listBias = np.reshape(weights[1], -1)\n lines += self.printToLines('Layer%s%iW = new float[]{' % (codeSuffix, i), listWeights, '};')\n lines += self.printToLines('Layer%s%iB = new float[]{' % (codeSuffix, i), listBias, '};')\n\n with open(self.outputFile, \"w\") as file:\n for line in lines:\n file.write(line)\n file.write(\"\\n\")\n\n\n def run(self, numHidden1, numHidden2=0, numEpochs=20, batchSize=512, codeSuffix=''):\n x_train, y_train = self.loadData()\n x_train = np.array(x_train, np.float32)\n numClasses = max(y_train) + 1\n\n train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n train_data = train_data.repeat().shuffle(5000).batch(batchSize).prefetch(1)\n\n layers = []\n if (numHidden1 > 0):\n layers += [ tf.keras.layers.Dense(numHidden1, activation='relu') ]\n if (numHidden2 > 0):\n layers += [ tf.keras.layers.Dense(numHidden2, activation='relu') ]\n \n layers += [ tf.keras.layers.Dense(numClasses) ];\n model = tf.keras.Sequential(layers)\n model.compile(optimizer='adam',\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n metrics=['accuracy'])\n\n model.fit(train_data,\n epochs=numEpochs,\n steps_per_epoch=100,\n verbose=0, callbacks=[TqdmCallback(verbose=2)])\n\n self.writeCodeFile(model, codeSuffix)\n"
},
{
"path": "ml/patternMatch/run-everything.bat",
"content": "@echo off\n\nset ToolPath=%localappdata%/Programs/Python/Python38\n\nfor %%i in (train-*.py) do %ToolPath%/python %%i\n\npause"
},
{
"path": "ml/patternMatch/train-cactpot.py",
"content": "from nn import NNTraining\n\ntraining = NNTraining(inputFile='ml-cactpot.json', outputFile='ml-cactpot.txt')\ntraining.run(numHidden1=80, numEpochs=500)\n"
},
{
"path": "ml/patternMatch/train-triad.py",
"content": "from nn import NNTraining\n\ntraining = NNTraining(inputFile='ml-triad.json', outputFile='ml-triad.txt')\ntraining.run(numHidden1=64, numEpochs=200)\n"
},
{
"path": "ml/solver/agents/agent.py",
"content": "class Agent:\n def __init__(self, game):\n pass\n\n def findAction(self, game, state):\n raise RuntimeError()\n\n def findTrainingAction(self, game, state):\n raise RuntimeError()\n\n def onTrainingGameStart(self, game, playerId):\n pass\n\n def onTrainingGameEnd(self, game, playerId):\n pass\n\n def onTrainingStep(self, game, playerId, state, action, nextState, reward):\n pass\n\n def train(self):\n pass\n\n def save(self, name):\n pass\n\n def load(self, name):\n pass\n\n def getTrainingDetails(self):\n return {}\n\n def generateModelCode(self, name):\n pass\n"
},
{
"path": "ml/solver/agents/agentDQN.py",
"content": "import os\nimport math\nimport numpy as np\nfrom .agent import Agent\nfrom utils.trainingMemory import TrainingMemoryCircular\nfrom utils.estimatorTorch import EstimatorModel\n\n# Deep Q Network\n#\nclass AgentDQN(Agent):\n def __init__(self, game):\n self.memorySize = 1 * 1000 * 1000\n self.batchSize = 256\n self.numLayersHidden = [500, 500]\n self.learningRate = 0.0001\n self.discountFactor = 0.99\n self.epsilonStart = 1\n self.epsilonEnd = 0.1\n self.epsilonDecay = 2000\n\n self.numActions = game.getMaxActions()\n numInputs = len(game.getState(0))\n\n self.estimatorQ = EstimatorModel(numInputs, self.numLayersHidden, self.numActions, self.learningRate)\n self.estimatorTarget = EstimatorModel(numInputs, self.numLayersHidden, self.numActions, self.learningRate)\n self.replayMemory = TrainingMemoryCircular(self.memorySize)\n self.epsilon = self.epsilonStart\n self.numSteps = 0\n self.historyLoss = []\n self.historyReward = []\n self.trainedOnce = False\n\n\n def findAction(self, game, state):\n probs = self.estimatorQ.predict(np.expand_dims(state, 0))[0]\n probs = self.sanitizeActions(game, state, probs)\n return np.argmax(probs)\n\n def findTrainingAction(self, game, state):\n useRandom = np.random.random() < self.epsilon\n if useRandom or not self.trainedOnce:\n allowedActions = game.getAllowedActions(state)\n return np.random.choice(allowedActions)\n\n return self.findAction(game, state)\n\n def onTrainingStep(self, game, playerId, state, action, nextState, reward):\n self.numSteps += 1\n self.epsilon = self.epsilonEnd + ((self.epsilonStart - self.epsilonEnd) * math.exp(-1.0 * self.numSteps / self.epsilonDecay))\n\n memorySample = (state, action, nextState, reward, game.isFinished())\n self.historyReward.append(reward)\n self.replayMemory.add(memorySample)\n if len(self.replayMemory) >= self.batchSize:\n self.trainOnBatch(game)\n\n def train(self):\n updateTargetCheckpoint = 'updateTarget.tmp'\n self.estimatorQ.save(updateTargetCheckpoint)\n self.estimatorTarget.load(updateTargetCheckpoint)\n os.remove(updateTargetCheckpoint)\n self.trainAvgLoss = np.average(self.historyLoss)\n self.trainAvgReward = np.average(self.historyReward)\n self.historyLoss = []\n self.historyReward = []\n\n def save(self, name):\n self.estimatorQ.save(name)\n\n def load(self, name):\n self.estimatorQ.load(name)\n self.estimatorTarget.load(name)\n\n def getTrainingDetails(self):\n return {\n 'epsilon': self.epsilon,\n 'memory': len(self.replayMemory) / self.replayMemory.capacity,\n 'loss': self.trainAvgLoss,\n 'reward': self.trainAvgReward,\n }\n\n def generateModelCode(self, name):\n self.estimatorQ.generateModelCode(name)\n\n def sanitizeActions(self, game, state, actionValues, badValue = -np.inf):\n maskedValues = badValue * np.ones(self.numActions, dtype=float)\n allowedActions = game.getAllowedActions(state)\n maskedValues[allowedActions] = actionValues[allowedActions]\n return maskedValues\n\n def trainOnBatch(self, game):\n states, actions, nextStates, rewards, dones = self.replayMemory.sample(self.batchSize)\n\n states = np.array(states)\n targets = self.estimatorTarget.predict(states)\n for i in range(self.batchSize):\n targets[i] = self.sanitizeActions(game, states[i], targets[i], badValue=-1)\n\n bestActions = np.argmax(targets, axis=1)\n nextTargets = self.estimatorTarget.predict(nextStates)\n predictedRewards = rewards + (np.invert(dones).astype(np.float) * self.discountFactor * nextTargets[np.arange(self.batchSize), bestActions])\n\n loss = self.estimatorQ.fit(states, actions, predictedRewards)\n self.historyLoss.append(loss)\n\n self.trainedOnce = True\n"
},
{
"path": "ml/solver/agents/agentRandom.py",
"content": "import random\nfrom .agent import Agent\n\nclass AgentRandom(Agent):\n def __init__(self, game):\n self.randGen = random.Random()\n\n def setSeed(self, seedValue):\n self.randGen = random.Random(seedValue)\n\n def findAction(self, game, state):\n actions = game.getAllowedActions(state)\n return self.randGen.choice(actions)\n\n def findTrainingAction(self, game, state):\n return self.findAction(game, state)\n"
},
{
"path": "ml/solver/environment.py",
"content": "class Environment(object):\n def __init__(self, game, playerAgents):\n self.game = game\n self.players = playerAgents\n\n def runTrainingGame(self):\n self.game.init()\n\n for idx in range(len(self.players)):\n self.players[idx].onTrainingGameStart(self.game, idx)\n\n while not self.game.isFinished():\n playerId = self.game.getCurrentPlayer()\n state = self.game.getState(playerId)\n\n action = self.players[playerId].findTrainingAction(self.game, state)\n self.game.step(action)\n\n # TODO: reuse nextState in next step, it contains relative data relative to player though (board owner, card visibility)\n nextState = self.game.getState(playerId)\n reward = self.game.getReward(playerId)\n self.players[playerId].onTrainingStep(self.game, playerId, state, action, nextState, reward)\n\n for idx in range(len(self.players)):\n self.players[idx].onTrainingGameEnd(self.game, idx)\n\n def runEvalGame(self):\n self.game.init()\n\n while not self.game.isFinished():\n playerId = self.game.getCurrentPlayer()\n state = self.game.getState(playerId)\n\n action = self.players[playerId].findAction(self.game, state)\n self.game.step(action)\n\n playerRewards = []\n for idx in range(len(self.players)):\n playerRewards.append(self.game.getReward(idx))\n\n return playerRewards\n"
},
{
"path": "ml/solver/gameSession.py",
"content": "from gamelogic.triadCard import TriadCardDB\nfrom gamelogic.triadDeck import TriadDeck\nfrom gamelogic.triadGame import TriadGame, TriadGameState\nfrom gamelogic.triadMods import TriadMod\nimport random\n\nclass GameSession():\n def __init__(self, useModifiers = False):\n self.randomSeed = 0\n self.useModifiers = useModifiers\n self.init()\n\n def init(self):\n self.game = TriadGame()\n if self.useModifiers:\n self.game.mods = TriadMod.generateMods()\n\n self.game.redDeck = TriadDeck.generateDeckPlayer()\n self.game.blueDeck = TriadDeck.generateDeckPlayer()\n self.game.blueDeck.makeAllVisible()\n\n self.game.state = TriadGameState.BlueTurn if (random.random() < 0.5) else TriadGameState.RedTurn\n self.game.initModifiers()\n self.game.onTurnStart()\n\n\n def step(self, action):\n cardIdx = int(action / 9)\n boardIdx = action % 9\n\n ownerId = TriadGame.OwnerIdBlue if (self.game.state == TriadGameState.BlueTurn) else TriadGame.OwnerIdRed\n result = self.game.placeCardFromDeck(boardIdx, cardIdx, ownerId)\n if not result:\n raise RuntimeError('Step failed, random seed:', self.randomSeed)\n\n def isFinished(self):\n return (self.game.state != TriadGameState.BlueTurn) and (self.game.state != TriadGameState.RedTurn)\n\n def getCurrentPlayer(self):\n return 0 if self.game.state == TriadGameState.BlueTurn else 1\n\n @staticmethod\n def getMaxActions():\n return 9 * 5\n\n def getAllowedActions(self, state):\n actions = []\n for idxB in range(9):\n # is board pos available? (avail flag = 1)\n if state[idxB] == 1:\n for idxC in range(5):\n # is card available? (avail flag = 1)\n if state[idxC + 9] == 1:\n actions.append((idxC * 9) + idxB)\n return actions\n\n def getReward(self, playerId):\n if self.game.state == TriadGameState.EndBlueWin:\n return 1 if playerId == 0 else -1\n if self.game.state == TriadGameState.EndRedWin:\n return -1 if playerId == 0 else 1\n if self.game.state == TriadGameState.EndDraw:\n return 0.1\n return 0\n\n def getState(self, playerId):\n state = []\n\n # precalc values\n ownerPlayer = TriadGame.OwnerIdBlue if playerId == 0 else TriadGame.OwnerIdRed\n deckPlayer = self.game.blueDeck if playerId == 0 else self.game.redDeck\n deckOpp = self.game.redDeck if playerId == 1 else self.game.blueDeck\n cardInfoPlayer = deckPlayer.getCardsInfo(True)\n cardInfoOpp = deckOpp.getCardsInfo()\n\n # one-hot: valid board placement\n if self.game.forcedBoardIdx >= 0:\n state += [1 if (pos == self.game.forcedBoardIdx) else 0 for pos in range(len(self.game.owner))]\n else:\n state += [1 if (ownerId == 0) else 0 for ownerId in self.game.owner]\n\n # one-hot: valid cards for move's owner\n if self.game.forcedCardIdx >= 0:\n state += [1 if (cardIdx == self.game.forcedCardIdx) else 0 for cardIdx in range(5)]\n else:\n state += [1 if (cardState != TriadDeck.cardNone) else 0 for cardState in deckPlayer.state]\n\n # one-hot: active modifiers\n allGameMods = [mod.name for mod in TriadMod.modDB]\n activeMods = [mod.name for mod in self.game.mods]\n state += [1 if (s in activeMods) else 0 for s in allGameMods]\n # value of type modes\n state += self.game.typeMod\n\n # board cells: [ relative ownerId, type, sides 0..3]\n for pos in range(len(self.game.owner)):\n cellInfo = [0, 0, 0, 0, 0, 0]\n if self.game.owner[pos] != 0:\n cardOb = self.game.board[pos]\n cellInfo = [1 if self.game.owner[pos] == ownerPlayer else -1, cardOb.cardType, cardOb.sides[0], cardOb.sides[1], cardOb.sides[2], cardOb.sides[3]]\n\n state += cellInfo\n\n # card data for move owner & opponent\n for i in range(5):\n if cardInfoPlayer[i][0] != 0:\n sides = cardInfoPlayer[i][2].sides\n state += [ cardInfoPlayer[i][0], sides[0], sides[1], sides[2], sides[3], cardInfoPlayer[i][3] ]\n else:\n state += [ 0, 0, 0, 0, 0, 0 ]\n\n if cardInfoOpp[i][0] != 0:\n sides = cardInfoOpp[i][2].sides if (cardInfoOpp[i][2] != None) else TriadCardDB.mapAvgSides[cardInfoOpp[i][3]]\n state += [ cardInfoOpp[i][0], cardInfoOpp[i][1], sides[0], sides[1], sides[2], sides[3], cardInfoOpp[i][3] ]\n else:\n state += [ 0, 0, 0, 0, 0, 0, 0 ]\n\n return state\n"
},
{
"path": "ml/solver/gamelogic/triadCard.py",
"content": "from xml.dom import minidom\n\nclass TriadCard:\n def __init__(self, name, rarity, cardType, sideUp, sideLeft, sideDown, sideRight):\n self.name = name\n self.rarity = rarity\n self.cardType = cardType\n self.sides = [ sideUp, sideLeft, sideDown, sideRight ]\n self.valid = True\n self.id = 0\n\n def __str__(self):\n if self.valid == False:\n return \"invalid\"\n return 'type:%s, sides:[T:%i, L:%i, D:%i, R:%i], name:%s %s' % (\n self.cardType,\n self.sides[0], self.sides[1], self.sides[2], self.sides[3],\n self.name, '*' * (self.rarity + 1))\n\nclass TriadCardDB:\n typeList = ['None', 'Primal', 'Scion', 'Beastman', 'Garlean']\n rarityList = ['Common', 'Uncommon', 'Rare', 'Epic', 'Legendary']\n cards = []\n # [rarity] = list of cards\n mapRarity = []\n mapAvgSides = []\n\n @staticmethod\n def load():\n assetFolder = '../../assets/data/'\n if (len(TriadCardDB.cards) > 0):\n return\n\n unknownCard = TriadCard('', 0, 0, 0, 0, 0, 0)\n unknownCard.valid = False\n TriadCardDB.cards = [ unknownCard ]\n\n TriadCardDB.mapRarity = []\n TriadCardDB.mapAvgSides = []\n for i in range(len(TriadCardDB.rarityList)):\n TriadCardDB.mapRarity.append([])\n TriadCardDB.mapAvgSides.append([1,1,1,1])\n\n cardNames = {}\n locXml = minidom.parse(assetFolder + 'loc.xml')\n locElems = locXml.getElementsByTagName('loc')\n for elem in locElems:\n locType = int(elem.attributes['type'].value)\n if locType == 3:\n cardNames[elem.attributes['id'].value] = elem.attributes['en'].value\n\n cardXml = minidom.parse(assetFolder + 'cards.xml')\n cardElems = cardXml.getElementsByTagName('card')\n for elem in cardElems:\n cardOb = TriadCard(\n cardNames[elem.attributes['id'].value],\n int(elem.attributes['rarity'].value),\n int(elem.attributes['type'].value),\n int(elem.attributes['up'].value),\n int(elem.attributes['lt'].value),\n int(elem.attributes['dn'].value),\n int(elem.attributes['rt'].value))\n\n cardOb.id = len(TriadCardDB.cards)\n TriadCardDB.cards.append(cardOb)\n TriadCardDB.mapRarity[cardOb.rarity].append(cardOb)\n\n for i in range(len(TriadCardDB.rarityList)):\n avgSidesAcc = [0, 0, 0, 0]\n for card in TriadCardDB.mapRarity[i]:\n avgSidesAcc[0] += card.sides[0]\n avgSidesAcc[1] += card.sides[1]\n avgSidesAcc[2] += card.sides[2]\n avgSidesAcc[3] += card.sides[3]\n\n for side in range(4):\n TriadCardDB.mapAvgSides[i][side] = avgSidesAcc[side] / len(TriadCardDB.mapRarity[i])\n\n print('Loaded cards:',len(TriadCardDB.cards))\n\n @staticmethod\n def find(name):\n for card in TriadCardDB.cards:\n if (card.name == name):\n return card\n return TriadCardDB.cards[0]\n\n\n# load cards on startup\nTriadCardDB.load()\n"
},
{
"path": "ml/solver/gamelogic/triadDeck.py",
"content": "from .triadCard import *\nimport random\n\nclass TriadDeck():\n cardNone = 0\n cardHidden = 1\n cardVisible = 2\n\n def __init__(self):\n self.cards = [ None, None, None, None, None ]\n self.state = [ TriadDeck.cardNone, TriadDeck.cardNone, TriadDeck.cardNone, TriadDeck.cardNone, TriadDeck.cardNone ]\n self.usedRarityCount = [ 0 ] * len(TriadCardDB.rarityList)\n self.expectedRarityCount = [0, 0, 3, 1, 1]\n self.numAvail = 0\n\n def initialize(self, cards, state):\n self.cards = cards\n self.numAvail = len(cards)\n for i in range(len(self.state)):\n self.state[i] = state\n\n def hasCard(self, idx):\n return self.state[idx] != TriadDeck.cardNone\n\n def useCard(self, idx):\n card = self.cards[idx]\n self.cards[idx] = None\n self.state[idx] = TriadDeck.cardNone\n self.numAvail -= 1\n self.usedRarityCount[card.rarity] += 1\n return card\n\n def makeAllVisible(self):\n for i in range(len(self.state)):\n self.state[i] = TriadDeck.cardVisible\n\n def onRestart(self):\n self.numAvail = len(self.cards)\n self.usedRarityCount = [ 0 ] * len(TriadCardDB.rarityList)\n\n def getCardsInfo(self, forceVisible = False):\n result = []\n remainingRarity = self.expectedRarityCount.copy()\n hiddenList = []\n for i in range(len(self.cards)):\n if (self.state[i] == TriadDeck.cardNone):\n result.append([0, 0, None, 0])\n elif (self.state[i] == TriadDeck.cardVisible) or forceVisible:\n cardOb = self.cards[i]\n remainingRarity[cardOb.rarity] -= 1\n result.append([1, 1, cardOb, cardOb.rarity])\n else:\n result.append([])\n hiddenList.append(i)\n\n if len(hiddenList) > 0:\n for i in range(len(remainingRarity)):\n remainingRarity[i] -= self.usedRarityCount[i]\n\n if remainingRarity[i] < 0:\n if i < (len(remainingRarity) - 1):\n remainingRarity[i + 1] += remainingRarity[i]\n remainingRarity[i] = 0\n else:\n remainingRarity[i] = min(remainingRarity[i], len(hiddenList))\n if remainingRarity[i] > 0:\n for iR in range(remainingRarity[i]):\n cardIdx = hiddenList.pop()\n result[cardIdx] = [1, 0, None, i]\n\n return result\n\n def __str__(self):\n stateDesc = ['none','hidden','visible']\n desc = []\n for i in range(len(self.state)):\n desc += [ '[%i]:%s (%i*%s)' % (\n i,\n self.cards[i].name if isinstance(self.cards[i], TriadCard) else \"--\",\n (self.cards[i].rarity + 1) if isinstance(self.cards[i], TriadCard) else 0,\n \"\" if self.state[i] == TriadDeck.cardVisible else \", \" + stateDesc[self.state[i]]\n )]\n\n return ', '.join(desc)\n\n @staticmethod\n def generateDeckForRarityRange(state, minRarity, maxRarity):\n cards = []\n while len(cards) < 5:\n rarity = random.randrange(minRarity, maxRarity + 1)\n cardIdx = random.randrange(len(TriadCardDB.mapRarity[rarity]))\n testCard = TriadCardDB.mapRarity[rarity][cardIdx]\n if (testCard in cards) == False:\n cards.append(testCard)\n\n deck = TriadDeck()\n deck.initialize(cards, state)\n return deck\n\n @staticmethod\n def generateDeckRandom(state):\n return TriadDeck.generateDeckForRarityRange(state, 0, 4)\n\n @staticmethod\n def generateDeckNpc(minRarity, maxRarity):\n return TriadDeck.generateDeckForRarityRange(TriadDeck.cardHidden, minRarity, maxRarity)\n\n @staticmethod\n def generateDeckPlayer():\n randR4 = random.randrange(len(TriadCardDB.mapRarity[4]))\n randR3 = random.randrange(len(TriadCardDB.mapRarity[3]))\n cards = [ TriadCardDB.mapRarity[4][randR4], TriadCardDB.mapRarity[3][randR3] ]\n\n while len(cards) < 5:\n rarity = random.randrange(3)\n cardIdx = random.randrange(len(TriadCardDB.mapRarity[rarity]))\n testCard = TriadCardDB.mapRarity[rarity][cardIdx]\n if (testCard in cards) == False:\n cards.append(testCard)\n\n deck = TriadDeck()\n deck.initialize(cards, TriadDeck.cardHidden)\n return deck\n"
},
{
"path": "ml/solver/gamelogic/triadGame.py",
"content": "from .triadCard import TriadCardDB\nfrom enum import Enum\n\nclass TriadGameState(Enum):\n Unknown = 0\n BlueTurn = 1\n RedTurn = 2\n EndBlueWin = 3\n EndDraw = 4\n EndRedWin = 5\n\nclass TriadGame:\n OwnerIdBlue = 1\n OwnerIdRed = 2\n\n ModTurnStart = 0\n ModCardPlaced = 1\n ModAllPlaced = 2\n ModCaptureNei = 3\n ModCaptureWeight = 4\n ModCaptureCondition = 5\n ModOverrides = 6\n\n cachedNeis = []\n\n def __init__(self):\n self.board = [ None ] * 9\n self.owner = [ 0 ] * 9\n self.typeMod = [ 0 ] * len(TriadCardDB.typeList)\n self.blueDeck = None\n self.redDeck = None\n self.mods = []\n self.modOverrides = [ False ] * TriadGame.ModOverrides\n self.numRestarts = 0\n self.mapPlaced = [ 0, 0, 0 ]\n self.forcedCardIdx = -1\n self.forcedBoardIdx = -1\n self.state = TriadGameState.Unknown\n self.cachedSideValues = [[ 0, 0, 0, 0]] * 9\n self.debug = False\n\n def getNumCardsPlaced(self):\n return sum(self.mapPlaced)\n\n def getNumCardsByOwner(self, ownerId):\n return sum([1 if (testId == ownerId) else 0 for testId in self.owner])\n\n def getCardSideValue(self, card, side):\n return max(1, min(10, card.sides[side] + self.typeMod[card.cardType]))\n\n def getCardOppositeSideValue(self, card, side):\n return self.getCardSideValue(card, (side + 2) % 4)\n\n def initModifiers(self):\n self.modOverrides = [ False ] * TriadGame.ModOverrides\n for mod in self.mods:\n mod.onMatchStart(self)\n for idx in mod.overrides:\n self.modOverrides[idx] = True\n\n def restartGame(self):\n self.board = [ None ] * 9\n self.owner = [ 0 ] * 9\n self.typeMod = [ 0 ] * len(TriadCardDB.typeList)\n self.numRestarts += 1\n self.mapPlaced = [ 0, 0, 0 ]\n self.cachedSideValues = [[ 0, 0, 0, 0]] * 9\n if self.debug:\n print('restartGame',self.numRestarts)\n\n def placeCardFromDeck(self, pos, idx, ownerId):\n deck = self.blueDeck if ownerId == TriadGame.OwnerIdBlue else self.redDeck\n isValid = self.isMoveValid(deck, pos, idx)\n if isValid:\n card = deck.useCard(idx)\n self.mapPlaced[ownerId] += 1\n self.placeCard(pos, card, ownerId)\n return True\n\n return False\n\n def isMoveValid(self, deck, pos, idx):\n if (self.forcedBoardIdx >= 0 and self.forcedBoardIdx != pos):\n return False\n if (self.forcedCardIdx >= 0 and self.forcedCardIdx != idx):\n return False\n if (not deck.hasCard(idx) or self.owner[pos] != 0):\n return False\n return True\n\n def placeCard(self, pos, card, ownerId):\n if self.debug:\n print('Place card:%s, pos:%i, ownerId:%i' % (card, pos, ownerId))\n\n if ((self.owner[pos] == 0) and card != None and card.valid):\n self.setBoardRaw(pos, card, ownerId)\n\n allowCombo = False\n if self.modOverrides[TriadGame.ModCardPlaced]:\n for mod in self.mods:\n mod.onCardPlaced(self, pos)\n allowCombo = allowCombo or mod.allowCombo\n\n comboCounter = 0\n comboList = self.getCaptures(pos, comboCounter)\n while (allowCombo and len(comboList) > 0):\n comboCounter += 1\n nextCombo = []\n for pos in comboList:\n nextComboPart = self.getCaptures(pos, comboCounter)\n nextCombo = nextCombo + nextComboPart\n comboList = nextCombo\n\n totalPlaced = sum(self.mapPlaced)\n if (totalPlaced == len(self.board)):\n self.onAllCardsPlaced()\n\n # count again, all placed may trigger sudden death rule\n totalPlaced = sum(self.mapPlaced)\n if (totalPlaced < len(self.board)):\n self.onTurnStart()\n else:\n print('ERROR: failed to place card:%s, pos:%i, ownerId:%i' % (card, pos, ownerId))\n self.showState('DEBUG')\n\n def setBoardRaw(self, pos, card, ownerId):\n self.board[pos] = card\n self.owner[pos] = ownerId\n self.state = TriadGameState.BlueTurn if (ownerId == TriadGame.OwnerIdRed) else TriadGameState.RedTurn\n self.updateCachedValuesForCard(pos)\n\n def updateCachedValuesForCard(self, pos):\n card = self.board[pos]\n if card != None:\n self.cachedSideValues[pos] = [ self.getCardSideValue(card, side) for side in range(4) ]\n else:\n self.cachedSideValues[pos] = [0, 0, 0, 0]\n\n def onTurnStart(self):\n self.forcedBoardIdx = -1\n self.forcedCardIdx = -1\n if (self.state == TriadGameState.BlueTurn or self.state == TriadGameState.RedTurn) and self.modOverrides[TriadGame.ModTurnStart]:\n for mod in self.mods:\n mod.onTurnStart(self)\n\n def onAllCardsPlaced(self):\n numPlayerCards = self.blueDeck.numAvail + self.getNumCardsByOwner(TriadGame.OwnerIdBlue)\n numPlayerOwnedToWin = 5\n if self.debug:\n print('onAllCardsPlaced, player:%i+%i vs %i' % (self.blueDeck.numAvail, self.getNumCardsByOwner(TriadGame.OwnerIdBlue), numPlayerOwnedToWin))\n\n if (numPlayerCards > numPlayerOwnedToWin):\n self.state = TriadGameState.EndBlueWin\n elif (numPlayerCards == numPlayerOwnedToWin):\n self.state = TriadGameState.EndDraw\n else:\n self.state = TriadGameState.EndRedWin\n\n if self.modOverrides[TriadGame.ModAllPlaced]:\n for mod in self.mods:\n mod.onAllCardsPlaced(self)\n\n\n @staticmethod\n def getBoardPos(x, y):\n return x + (y * 3)\n\n def getCaptures(self, pos, comboCounter):\n neiInfo = TriadGame.cachedNeis[pos]\n if self.debug:\n print('getCaptures(',pos,', combo:',comboCounter,'), neis:',neiInfo)\n\n comboList = []\n allowMods = comboCounter == 0\n\n if allowMods and self.modOverrides[TriadGame.ModCaptureNei]:\n for mod in self.mods:\n comboListPart = mod.getCaptureNeis(self, pos, neiInfo)\n if self.debug:\n print('>> capture(',mod.name,') = ',comboListPart)\n\n if (len(comboListPart) > 0):\n comboList = comboList + comboListPart\n\n for side, neiPos in neiInfo:\n if self.debug:\n print('>> side:%i, neiPos:%i' % (side, neiPos))\n\n if ((self.owner[neiPos] != 0) and (self.owner[neiPos] != self.owner[pos])):\n sideV = self.cachedSideValues[pos][side]\n neiV = self.cachedSideValues[neiPos][(side + 2) % 4] # opposite side\n\n if allowMods and self.modOverrides[TriadGame.ModCaptureWeight]:\n for mod in self.mods:\n sideV, neiV = mod.getCaptureWeights(self, sideV, neiV)\n\n if self.debug:\n print(' sideV:%i neiV:%i' % (sideV, neiV))\n\n canCaptureNei = sideV > neiV\n\n if allowMods and self.modOverrides[TriadGame.ModCaptureCondition]:\n for mod in self.mods:\n overrideCapture, newcanCaptureNei = mod.getCaptureCondition(self, neiV, sideV)\n if overrideCapture:\n canCaptureNei = newcanCaptureNei\n\n if canCaptureNei:\n if self.debug:\n print(' captured!')\n\n self.owner[neiPos] = self.owner[pos]\n if comboCounter > 0:\n comboList.append(neiPos)\n\n return comboList\n\n\n def showState(self, debugName):\n print('[%s] ==> State: %s' % (debugName, self.state.name))\n for i in range(len(self.owner)):\n if (self.owner[i] == 0):\n print(' [%i] empty' % i)\n else:\n print(' [%i] %s owner:%i' % (i, str(self.board[i]), self.owner[i]))\n\n print('Type mods:', '(empty)' if (sum(self.typeMod) == 0) else '')\n for i in range(len(self.typeMod)):\n if (self.typeMod[i] != 0):\n print(' [%i] %i' % (i,self.typeMod[i]))\n\n print('Player hand:', '(empty)' if (self.blueDeck == None or self.blueDeck.numAvail == 0) else '')\n if self.blueDeck != None:\n for i in range(5):\n print(' [%i]: %s' % (i, str(self.blueDeck.cards[i])))\n\n print('Opponent hand:', '(empty)' if (self.redDeck == None or self.redDeck.numAvail == 0) else '')\n if self.redDeck != None:\n for i in range(5):\n print(' [%i]: %s, state:%s' % (i, str(self.redDeck.cards[i]), self.redDeck.state[i]))\n\n print('Modifiers:', '(empty)' if (len(self.mods) == 0) else '')\n for i in range(len(self.mods)):\n print(' [%i]: %s' % (i, self.mods[i].name))\n\n if (self.forcedCardIdx >= 0 or self.forcedBoardIdx >= 0):\n print('Forced card:%i, placement:%i' % (self.forcedCardIdx, self.forcedBoardIdx))\n\n @staticmethod\n def staticInit():\n for i in range(9):\n boardX = i % 3\n boardY = int(i / 3)\n info = []\n # [up, left, down, right]\n if boardY > 0:\n info.append([0, TriadGame.getBoardPos(boardX, boardY - 1)])\n if boardX < 2:\n info.append([1, TriadGame.getBoardPos(boardX + 1, boardY)])\n if boardY < 2:\n info.append([2, TriadGame.getBoardPos(boardX, boardY + 1)])\n if boardX > 0:\n info.append([3, TriadGame.getBoardPos(boardX - 1, boardY)])\n\n TriadGame.cachedNeis.append(info)\n\nTriadGame.staticInit()\n"
},
{
"path": "ml/solver/gamelogic/triadMods.py",
"content": "import random\nfrom .triadDeck import TriadDeck\nfrom .triadGame import TriadGame, TriadGameState\nimport numpy as np\n\nclass TriadMod:\n allowCombo = False\n name = '??'\n blockedMods = []\n modDB = []\n\n def onMatchStart(self, game):\n pass\n def onTurnStart(self, game):\n pass\n def onCardPlaced(self, game, pos):\n pass\n def onAllCardsPlaced(self, game):\n pass\n def getCaptureNeis(self, game, pos, neis):\n return []\n def getCaptureWeights(self, game, cardNum, neiNum):\n return cardNum, neiNum\n def getCaptureCondition(self, game, cardNum, neiNum):\n return False, False\n\n @staticmethod\n def generateMods(maxMods = 4):\n numMods = random.randrange(0, maxMods + 1)\n mods = []\n blocked = []\n\n while len(mods) < numMods:\n idx = random.randrange(0, len(TriadMod.modDB))\n testMod = TriadMod.modDB[idx]\n if any(testMod.name in s for s in blocked):\n continue\n\n mods.append(testMod)\n blocked += testMod.blockedMods\n blocked.append(testMod.name)\n\n return mods\n\n\n# reverse: capture when number of lower\nclass TriadModReverse(TriadMod):\n def __init__(self):\n self.name = 'Reverse'\n self.overrides = [TriadGame.ModCaptureCondition]\n\n def getCaptureCondition(self, game, cardNum, neiNum):\n return True, cardNum < neiNum\n\nTriadMod.modDB.append(TriadModReverse())\n\n# fallen ace: 1 captures 10/A\nclass TriadModFallenAce(TriadMod):\n def __init__(self):\n self.name = 'FallenAce'\n self.overrides = [TriadGame.ModCaptureWeight]\n\n def getCaptureWeights(self, game, cardNum, neiNum):\n if (cardNum == 10) and (neiNum == 1):\n return 0, 1\n elif (cardNum == 1) and (neiNum == 10):\n return 1, 0\n else:\n return cardNum, neiNum\n\nTriadMod.modDB.append(TriadModFallenAce())\n\n# same: capture when 2+ sides have the same values\nclass TriadModSame(TriadMod):\n def __init__(self):\n self.name = 'Same'\n self.allowCombo = True\n self.overrides = [TriadGame.ModCaptureNei]\n\n def getCaptureNeis(self, game, pos, neis):\n captureNeis = []\n sameNeis = []\n for side,neiPos in neis:\n if (game.owner[neiPos] != 0):\n sideV = game.cachedSideValues[pos][side]\n neiV = game.cachedSideValues[neiPos][(side + 2) % 4] # opp side\n if (sideV == neiV):\n sameNeis.append(neiPos)\n if (game.owner[neiPos] != game.owner[pos]):\n captureNeis.append(neiPos)\n\n captureList = []\n if (len(sameNeis) >= 2) and (len(captureNeis) > 0):\n captureList = captureNeis\n\n return captureList\n\nTriadMod.modDB.append(TriadModSame())\n\n# plus: capture when 2+ sides have the same sum of values\nclass TriadModPlus(TriadMod):\n def __init__(self):\n self.name = 'Plus'\n self.allowCombo = True\n self.overrides = [TriadGame.ModCaptureNei]\n\n def getCaptureNeis(self, game, pos, neis):\n cardOwner = game.owner[pos]\n captureList = []\n for side,neiPos in neis:\n if game.owner[neiPos] != 0:\n if (game.owner[neiPos] != cardOwner):\n sideV = game.cachedSideValues[pos][side]\n neiV = game.cachedSideValues[neiPos][(side + 2) % 4] # opp side\n totalV = sideV + neiV\n captured = False\n\n for vsSide,vsNeiPos in neis:\n if ((game.owner[vsNeiPos] != 0) and (vsSide != side)):\n vsSideV = game.cachedSideValues[pos][vsSide]\n vsSideNeiV = game.cachedSideValues[vsNeiPos][(vsSide + 2) % 4] # opp side\n vsSideTotalV = vsSideV + vsSideNeiV\n\n if (vsSideTotalV == totalV):\n captured = True\n if (game.owner[vsNeiPos] != cardOwner):\n game.owner[vsNeiPos] = cardOwner\n captureList.append(vsNeiPos)\n\n if captured:\n game.owner[neiPos] = cardOwner\n captureList.append(neiPos)\n\n return captureList\n\nTriadMod.modDB.append(TriadModPlus())\n\n# ascention: type mod goes up after each placement\nclass TriadModAscention(TriadMod):\n def __init__(self):\n self.name = 'Ascention'\n self.blockedMods = ['Descention']\n self.overrides = [TriadGame.ModCardPlaced]\n\n def onCardPlaced(self, game, pos):\n cardType = game.board[pos].cardType\n if (cardType> 0):\n game.typeMod[cardType] += 1\n\nTriadMod.modDB.append(TriadModAscention())\n\n# descention: type mod goes down after each placement\nclass TriadModDescention(TriadMod):\n def __init__(self):\n self.name = 'Descention'\n self.blockedMods = ['Ascention']\n self.overrides = [TriadGame.ModCardPlaced]\n\n def onCardPlaced(self, game, pos):\n cardType = game.board[pos].cardType\n if (cardType> 0):\n game.typeMod[cardType] -= 1\n\nTriadMod.modDB.append(TriadModDescention())\n\n# sudden death: restart game on draw, keeping card ownership, max 3 times\nclass TriadModSuddenDeath(TriadMod):\n def __init__(self):\n self.name = 'SuddenDeath'\n self.overrides = [TriadGame.ModAllPlaced]\n\n def onAllCardsPlaced(self, game):\n if ((game.state == TriadGameState.EndDraw) and (game.numRestarts < 3)):\n for i in range(len(game.owner)):\n if (game.owner[i] == 0):\n continue\n deck = game.blueDeck if (game.owner[i] == TriadGame.OwnerIdBlue) else game.redDeck\n for freeIdx in range(5):\n if not deck.hasCard(freeIdx):\n deck.cards[freeIdx] = game.board[i]\n deck.state[freeIdx] = TriadDeck.cardVisible\n break\n\n nextTurn = TriadGameState.BlueTurn if (game.mapPlaced[TriadGame.OwnerIdBlue] < 5) else TriadGameState.RedTurn\n game.restartGame()\n game.state = nextTurn\n game.blueDeck.onRestart()\n game.redDeck.onRestart()\n\nTriadMod.modDB.append(TriadModSuddenDeath())\n\n# all open: makes all opponent cards on hand visible\nclass TriadModAllOpen(TriadMod):\n def __init__(self):\n self.name = 'AllOpen'\n self.blockedMods = ['ThreeOpen']\n self.overrides = []\n\n def onMatchStart(self, game):\n game.redDeck.makeAllVisible()\n\nTriadMod.modDB.append(TriadModAllOpen())\n\n# three open: makes random 3 opponent cards on hand visible\nclass TriadModThreeOpen(TriadMod):\n def __init__(self):\n self.name = 'ThreeOpen'\n self.blockedMods = ['AllOpen']\n self.overrides = []\n\n def onMatchStart(self, game):\n random3 = np.random.choice(range(5), 3, False)\n for idx in random3:\n game.redDeck.state[idx] = TriadDeck.cardVisible\n pass\n\nTriadMod.modDB.append(TriadModThreeOpen())\n\n# order: forces card selection order (sequential from hand)\nclass TriadModOrder(TriadMod):\n def __init__(self):\n self.name = 'Order'\n self.blockedMods = ['Chaos']\n self.overrides = [TriadGame.ModTurnStart]\n\n def onTurnStart(self, game):\n deck = game.blueDeck if (game.state == TriadGameState.BlueTurn) else game.blueDeck\n for i in range(len(deck.cards)):\n if deck.hasCard(i):\n game.forcedCardIdx = i\n break\n\nTriadMod.modDB.append(TriadModOrder())\n\n# chaos: forces board placement order (random)\nclass TriadModChaos(TriadMod):\n def __init__(self):\n self.name = 'Chaos'\n self.blockedMods = ['Order']\n self.overrides = [TriadGame.ModTurnStart]\n\n def onTurnStart(self, game):\n availPos = []\n for i in range(len(game.owner)):\n if (game.owner[i] == 0):\n availPos.append(i)\n\n if (len(availPos) > 0):\n game.forcedBoardIdx = np.random.choice(availPos)\n\n\nTriadMod.modDB.append(TriadModChaos())\n\n# swap: swaps a card between player and opponent (1 each) at match start\nclass TriadModSwap(TriadMod):\n def __init__(self):\n self.name = 'Swap'\n self.overrides = []\n\n def onMatchStart(self, game):\n playerIdx = np.random.randint(0, 5)\n opponentIdx = np.random.randint(0, 5)\n\n swapCard = game.redDeck.cards[opponentIdx]\n game.redDeck.cards[opponentIdx] = game.blueDeck.cards[playerIdx]\n game.redDeck.state[opponentIdx] = TriadDeck.cardVisible\n game.blueDeck.cards[playerIdx] = swapCard\n if game.debug:\n print('SWAP player[%i] <> opp[%i]' % (playerIdx, opponentIdx))\n\nTriadMod.modDB.append(TriadModSwap())\nprint('Loaded game modifiers:',len(TriadMod.modDB))\n"
},
{
"path": "ml/solver/main.py",
"content": "from gameSession import GameSession\nfrom environment import Environment\nfrom agents.agentRandom import AgentRandom\nfrom agents.agentDQN import AgentDQN\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport time\n\ndef trainModel():\n numEpochs = 20\n numGamesToTrain = 200\n numGamesToEval = 1000\n\n game = GameSession(useModifiers=True)\n trainingAgent = AgentDQN(game)\n randomAgent = AgentRandom(game)\n\n learningEnv = Environment(game, [trainingAgent, trainingAgent])\n evalEnv = Environment(game, [trainingAgent, randomAgent])\n\n # load checkpoint data\n #trainingAgent.load('data/model.tmp')\n\n print('Start training, epochs:',numEpochs)\n timeStart = time.time()\n history = []\n for epochIdx in range(numEpochs):\n # play a lot and learn and stuff\n for _ in tqdm(range(numGamesToTrain), leave=False, desc='Training'.ljust(15)):\n learningEnv.runTrainingGame()\n\n # training update after exploration step\n trainingAgent.train()\n\n # eval with same seed for every iteration\n randomAgent.setSeed(0)\n score = 0\n for _ in tqdm(range(numGamesToEval), leave=False, desc='Evaluating'.ljust(15)):\n rewards = evalEnv.runEvalGame()\n score += rewards[0]\n\n # collect info about training to see if it's actually working (lol, who am i kidding, it just fails constantly T.T)\n stepDetails = trainingAgent.getTrainingDetails()\n stepDetails['score'] = score\n\n history.append(stepDetails)\n print('[%d] %s' % (epochIdx, stepDetails))\n\n timeElapsed = time.time() - timeStart\n print('Done. Total time:%s, epoch avg:%.0fs' % (time.strftime(\"%H:%M:%Ss\", time.gmtime(timeElapsed)), timeElapsed / numEpochs))\n\n trainingAgent.save('data/model.tmp')\n trainingAgent.generateModelCode('data/model.cs')\n return history\n\ndef showTrainingData(history):\n epochs = range(len(history))\n fig, axs = plt.subplots(2, sharex=True)\n axs[0].title.set_text('Reward')\n axs[0].plot(epochs, [stepDetails['reward'] for stepDetails in history])\n axs[1].title.set_text('Loss')\n axs[1].plot(epochs, [stepDetails['loss'] for stepDetails in history])\n plt.xlabel('Epochs')\n plt.show()\n\nif __name__ == \"__main__\":\n history = trainModel()\n showTrainingData(history)\n"
},
{
"path": "ml/solver/perfTests.py",
"content": "import numpy as np\nimport random\nfrom tqdm import tqdm\n\nfrom gameSession import GameSession\nfrom environment import Environment\nfrom agents.agentRandom import AgentRandom\nfrom agents.agentDQN import AgentDQN\n\ndef runTestPlayRandomGames(numGames, reproSeed):\n if reproSeed < 0:\n print('Running',numGames,'random test games...')\n\n for _ in tqdm(range(numGames)):\n seed = random.randrange(999999)\n random.seed(seed)\n np.random.seed(seed)\n\n session = GameSession()\n session.randomSeed = seed\n while not session.isFinished():\n playerId = session.getCurrentPlayer()\n state = session.getState(playerId)\n actions = session.getAllowedActions(state)\n action = random.choice(actions)\n session.step(action)\n else:\n random.seed(reproSeed)\n np.random.seed(reproSeed)\n\n session = GameSession()\n session.game.debug = True\n session.randomSeed = reproSeed\n\n print('DEBUG game')\n print(' mods:', [mod.name for mod in session.game.mods])\n print(' blue:', session.game.blueDeck)\n print(' red:', session.game.redDeck)\n\n while not session.isFinished():\n playerId = session.getCurrentPlayer()\n state = session.getState(playerId)\n actions = session.getAllowedActions(state)\n action = random.choice(actions)\n\n print('Move, player:%d numActions:%d (board:%d + card:%d) => action:%d (board:%d + card:%d)' %\n (playerId, len(actions),\n sum([ state[i] for i in range(9) ]),\n sum([ state[9 + i] for i in range(5) ]),\n action, action % 9, int(action / 9)))\n\n session.step(action)\n\n\ndef runTestAgentEval(numGames, agentName):\n print('Running',numGames,'eval games for agent',agentName)\n\n game = GameSession()\n evalAgent = AgentRandom(game)\n if agentName == 'dqn':\n evalAgent = AgentDQN(game)\n\n env = Environment(game, [evalAgent, AgentRandom(game)])\n\n score = 0\n for _ in tqdm(range(numGames)):\n rewards = env.runEvalGame()\n score += rewards[0]\n\n\nif __name__ == \"__main__\":\n runTestPlayRandomGames(10000, reproSeed=-1)\n runTestAgentEval(10000, 'random')\n runTestAgentEval(10000, 'dqn')\n"
},
{
"path": "ml/solver/utils/codeGenerator.py",
"content": "import os\nimport textwrap\n\nclass CodeGenerator():\n def __init__(self):\n self.layers = []\n\n def addLayer(self, weights, biases, activation):\n self.layers.append((weights, biases, activation))\n\n def save(self, name):\n lines = []\n lines.append('// Auto generated model data')\n lines.append('private void InitModel()')\n lines.append('{')\n\n numInputs = self.layers[0][0].shape[1]\n lines.append('\\tNumInputs = %d;' % numInputs)\n\n numOutputs = [str(layer[0].shape[0]) for layer in self.layers]\n lines.append('\\tNumOutputs = {%s};' % ', '.join(numOutputs))\n\n for i in range(len(self.layers)):\n lines.append('\\tLayer%d_W = %s;' % (i, self.convertToCSArray(self.layers[i][0])))\n lines.append('\\tLayer%d_B = %s;' % (i, self.convertToCSArray(self.layers[i][1])))\n\n lines.append('}')\n\n file = open(name, 'w')\n for line in lines:\n wrapLines = textwrap.wrap(line, width=120, tabsize=4, subsequent_indent='\\t\\t')\n for fileLine in wrapLines:\n file.write(fileLine + '\\n')\n\n file.close()\n print('Saved generated code:',name)\n\n def convertToCSArray(self, arr):\n values = []\n if len(arr.shape) == 1:\n values = [str(x) for x in arr]\n else:\n for i in range(arr.shape[0]):\n values.append(self.convertToCSArray(arr[i]))\n\n return '{' + ', '.join(values) + '}'\n"
},
{
"path": "ml/solver/utils/estimatorTorch.py",
"content": "import numpy as np\nimport torch\nimport torch.nn as nn\nfrom .codeGenerator import CodeGenerator\n\ntorchDevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nclass EstimatorNN(nn.Module):\n def __init__(self, numInputs, numHiddenArr, numOutputs):\n super(EstimatorNN, self).__init__()\n\n layerSizes = [ numInputs ] + numHiddenArr\n modelArgs = [nn.Flatten()]\n for i in range(len(layerSizes) - 1):\n modelArgs.append(nn.Linear(layerSizes[i], layerSizes[i + 1]))\n modelArgs.append(nn.ReLU())\n modelArgs.append(nn.Linear(layerSizes[-1], numOutputs))\n\n self.model = nn.Sequential(*modelArgs)\n\n def forward(self, s):\n return self.model(s)\n\n def generateModelCode(self, name):\n sd = self.state_dict()\n weightSuffix = \".weight\"\n biasSuffix = \".bias\"\n layers = []\n\n # layers will be in the same order as model\n for entryName in sd.keys():\n if entryName.endswith(weightSuffix):\n biasName = entryName.replace(weightSuffix, biasSuffix)\n if biasName in sd.keys():\n layers.append({\n 'w': sd[entryName].cpu().numpy(),\n 'b':sd[biasName].cpu().numpy(),\n 'act':'relu',\n 'name':entryName.replace(weightSuffix, '')\n })\n\n layers[-1]['act'] = ''\n codeGen = CodeGenerator()\n for layer in layers:\n codeGen.addLayer(layer['w'], layer['b'], layer['act'])\n codeGen.save(name)\n\n\nclass EstimatorModel:\n def __init__(self, numInputs, numHiddenArr, numOutput, learningRate):\n self.nn = EstimatorNN(numInputs, numHiddenArr, numOutput).to(torchDevice)\n self.nn.eval()\n self.lossFunc = nn.MSELoss(reduction='mean')\n self.optimizer = torch.optim.Adam(self.nn.parameters(), lr=learningRate)\n\n def predict(self, input):\n with torch.no_grad():\n input = torch.tensor(input).float().to(torchDevice)\n return self.nn(input).cpu().numpy()\n\n def fit(self, inputS, inputA, targetAR):\n self.nn.train()\n\n inputS = torch.tensor(inputS).float().to(torchDevice)\n inputA = torch.tensor(inputA).long().to(torchDevice)\n targetAR = torch.tensor(targetAR).float().to(torchDevice)\n\n outputAProbs = self.nn(inputS)\n outputAR = torch.gather(outputAProbs, dim=-1, index=inputA.unsqueeze(-1)).squeeze(-1)\n loss = self.lossFunc(outputAR, targetAR)\n\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n self.nn.eval()\n return loss.item()\n\n def save(self, name):\n torch.save(self.nn.state_dict(), name)\n\n def load(self, name):\n self.nn.load_state_dict(torch.load(name))\n self.nn.eval()\n\n def generateModelCode(self, name):\n self.nn.generateModelCode(name)\n"
},
{
"path": "ml/solver/utils/trainingMemory.py",
"content": "from collections import deque\nimport random\n\nclass TrainingMemory:\n def __init__(self, capacity):\n self.capacity = capacity\n self.clear()\n\n def add(self, sampleTuple):\n self.numWrites += 1\n if len(self) < self.capacity:\n self.buffer.append(sampleTuple)\n return True\n return False\n\n def clear(self):\n self.numWrites = 0\n self.buffer = deque([], maxlen=self.capacity)\n\n def __len__(self):\n return len(self.buffer)\n\n def sample(self, batchSize):\n samples = random.sample(self.buffer, batchSize)\n return zip(*samples)\n\n\nclass TrainingMemoryCircular(TrainingMemory):\n def add(self, sampleTuple):\n isAdded = super().add(sampleTuple)\n if not isAdded:\n writeIdx = self.numWrites % self.capacity\n self.buffer[writeIdx] = sampleTuple\n\n\nclass TrainingMemoryReservoir(TrainingMemory):\n def add(self, sampleTuple):\n isAdded = super().add(sampleTuple)\n if not isAdded:\n # Algorithm R\n writeIdx = random.randint(0, self.numWrites)\n if writeIdx < self.capacity:\n self.buffer[writeIdx] = sampleTuple\n"
},
{
"path": "sources/App.config",
"content": "\n\n \n \n \n\n"
},
{
"path": "sources/FFTriadBuddy.csproj",
"content": "\n\n \n \n Debug\n AnyCPU\n {4FE48C06-9E9C-46C9-A53E-CFE06EFDCBDC}\n WinExe\n FFTriadBuddy\n FFTriadBuddy\n v4.8\n 512\n true\n false\n \n publish\\\n true\n Disk\n false\n Foreground\n 7\n Days\n false\n false\n true\n 0\n 1.0.0.%2a\n false\n true\n \n \n AnyCPU\n true\n full\n false\n bin\\Debug\\\n DEBUG;TRACE\n prompt\n 4\n true\n false\n \n \n AnyCPU\n pdbonly\n true\n bin\\Release\\\n TRACE\n prompt\n 4\n true\n false\n \n \n card.ico\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 4.0\n \n \n \n \n \n \n \n MSBuild:Compile\n Designer\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n AdjustCardDialog.xaml\n \n \n AdjustHashDialog.xaml\n \n \n DialogWindow.xaml\n \n \n FavDeckEditDialog.xaml\n \n \n FavDeckPreview.xaml\n \n \n OverlayWindowTransparent.xaml\n \n \n OverlayWindowInteractive.xaml\n \n \n \n \n \n NumTextBox.xaml\n \n \n \n PageCards.xaml\n \n \n PageInfo.xaml\n \n \n PageNpcs.xaml\n \n \n PageScreenshot.xaml\n \n \n PlayerDeckPreview.xaml\n \n \n PageSimulate.xaml\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n CardGridView.xaml\n \n \n CardView.xaml\n \n \n DeckView.xaml\n \n \n \n PageSetup.xaml\n \n \n SearchableComboBox.xaml\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n MSBuild:Compile\n Designer\n \n \n App.xaml\n Code\n \n \n MainWindow.xaml\n Code\n \n \n Designer\n MSBuild:Compile\n \n \n MSBuild:Compile\n Designer\n \n \n MSBuild:Compile\n Designer\n \n \n MSBuild:Compile\n Designer\n \n \n MSBuild:Compile\n Designer\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n MSBuild:Compile\n Designer\n \n \n Designer\n MSBuild:Compile\n \n \n \n \n \n \n \n \n \n ResXFileCodeGenerator\n Designer\n \n \n SettingsSingleFileGenerator\n Settings.Designer.cs\n \n \n True\n Settings.settings\n True\n \n \n Designer\n MSBuild:Compile\n \n \n Designer\n MSBuild:Compile\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n False\n Microsoft .NET Framework 4.6.1 %28x86 and x64%29\n true\n \n \n False\n .NET Framework 3.5 SP1\n false\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n (\\d+)\n %(myAssemblyInfo.Version)\n $([System.Text.RegularExpressions.Regex]::Match($(In), $(Pattern)))\n $(SolutionDir)\\releases\\release-v$(MajorVersion).zip\n $(SolutionDir)\\releases\\temp\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n