将随机模拟加入项目中，创建了高斯混合模型的工程
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x64/
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eigen-3.4.0/
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*.debug
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||||||
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||||||
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## Ignore Visual Studio temporary files, build results, and
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||||||
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## files generated by popular Visual Studio add-ons.
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||||||
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##
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||||||
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## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore
|
||||||
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|
||||||
|
# User-specific files
|
||||||
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*.rsuser
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||||||
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*.suo
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||||||
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*.user
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||||||
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*.userosscache
|
||||||
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*.sln.docstates
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||||||
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|
||||||
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# User-specific files (MonoDevelop/Xamarin Studio)
|
||||||
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*.userprefs
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||||||
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||||||
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# Mono auto generated files
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||||||
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mono_crash.*
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||||||
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||||||
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# Build results
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||||||
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[Dd]ebug/
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||||||
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[Dd]ebugPublic/
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||||||
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[Rr]elease/
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||||||
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[Rr]eleases/
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||||||
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x64/
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||||||
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x86/
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||||||
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[Ww][Ii][Nn]32/
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||||||
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[Aa][Rr][Mm]/
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||||||
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[Aa][Rr][Mm]64/
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||||||
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bld/
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||||||
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[Bb]in/
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||||||
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[Oo]bj/
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||||||
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[Ll]og/
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||||||
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[Ll]ogs/
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||||||
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||||||
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# Visual Studio 2015/2017 cache/options directory
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||||||
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.vs/
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||||||
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# Uncomment if you have tasks that create the project's static files in wwwroot
|
||||||
|
#wwwroot/
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||||||
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|
||||||
|
# Visual Studio 2017 auto generated files
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||||||
|
Generated\ Files/
|
||||||
|
|
||||||
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# MSTest test Results
|
||||||
|
[Tt]est[Rr]esult*/
|
||||||
|
[Bb]uild[Ll]og.*
|
||||||
|
|
||||||
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# NUnit
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||||||
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*.VisualState.xml
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||||||
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TestResult.xml
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||||||
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nunit-*.xml
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||||||
|
|
||||||
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# Build Results of an ATL Project
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||||||
|
[Dd]ebugPS/
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||||||
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[Rr]eleasePS/
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||||||
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dlldata.c
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||||||
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||||||
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# Benchmark Results
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||||||
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BenchmarkDotNet.Artifacts/
|
||||||
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|
||||||
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# .NET Core
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||||||
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project.lock.json
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||||||
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project.fragment.lock.json
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||||||
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artifacts/
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||||||
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|
||||||
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# ASP.NET Scaffolding
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||||||
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ScaffoldingReadMe.txt
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||||||
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|
||||||
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# StyleCop
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||||||
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StyleCopReport.xml
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||||||
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||||||
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# Files built by Visual Studio
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||||||
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*_i.c
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||||||
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*_p.c
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*_h.h
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||||||
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*.ilk
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||||||
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*.meta
|
||||||
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*.obj
|
||||||
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*.iobj
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||||||
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*.pch
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||||||
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*.pdb
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||||||
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*.ipdb
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||||||
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*.pgc
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||||||
|
*.pgd
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||||||
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*.rsp
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||||||
|
*.sbr
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||||||
|
*.tlb
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||||||
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*.tli
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||||||
|
*.tlh
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||||||
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*.tmp
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||||||
|
*.tmp_proj
|
||||||
|
*_wpftmp.csproj
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||||||
|
*.log
|
||||||
|
*.tlog
|
||||||
|
*.vspscc
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||||||
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*.vssscc
|
||||||
|
.builds
|
||||||
|
*.pidb
|
||||||
|
*.svclog
|
||||||
|
*.scc
|
||||||
|
|
||||||
|
# Chutzpah Test files
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||||||
|
_Chutzpah*
|
||||||
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|
||||||
|
# Visual C++ cache files
|
||||||
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ipch/
|
||||||
|
*.aps
|
||||||
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*.ncb
|
||||||
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*.opendb
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||||||
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*.opensdf
|
||||||
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*.sdf
|
||||||
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*.cachefile
|
||||||
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*.VC.db
|
||||||
|
*.VC.VC.opendb
|
||||||
|
|
||||||
|
# Visual Studio profiler
|
||||||
|
*.psess
|
||||||
|
*.vsp
|
||||||
|
*.vspx
|
||||||
|
*.sap
|
||||||
|
|
||||||
|
# Visual Studio Trace Files
|
||||||
|
*.e2e
|
||||||
|
|
||||||
|
# TFS 2012 Local Workspace
|
||||||
|
$tf/
|
||||||
|
|
||||||
|
# Guidance Automation Toolkit
|
||||||
|
*.gpState
|
||||||
|
|
||||||
|
# ReSharper is a .NET coding add-in
|
||||||
|
_ReSharper*/
|
||||||
|
*.[Rr]e[Ss]harper
|
||||||
|
*.DotSettings.user
|
||||||
|
|
||||||
|
# TeamCity is a build add-in
|
||||||
|
_TeamCity*
|
||||||
|
|
||||||
|
# DotCover is a Code Coverage Tool
|
||||||
|
*.dotCover
|
||||||
|
|
||||||
|
# AxoCover is a Code Coverage Tool
|
||||||
|
.axoCover/*
|
||||||
|
!.axoCover/settings.json
|
||||||
|
|
||||||
|
# Coverlet is a free, cross platform Code Coverage Tool
|
||||||
|
coverage*.json
|
||||||
|
coverage*.xml
|
||||||
|
coverage*.info
|
||||||
|
|
||||||
|
# Visual Studio code coverage results
|
||||||
|
*.coverage
|
||||||
|
*.coveragexml
|
||||||
|
|
||||||
|
# NCrunch
|
||||||
|
_NCrunch_*
|
||||||
|
.*crunch*.local.xml
|
||||||
|
nCrunchTemp_*
|
||||||
|
|
||||||
|
# MightyMoose
|
||||||
|
*.mm.*
|
||||||
|
AutoTest.Net/
|
||||||
|
|
||||||
|
# Web workbench (sass)
|
||||||
|
.sass-cache/
|
||||||
|
|
||||||
|
# Installshield output folder
|
||||||
|
[Ee]xpress/
|
||||||
|
|
||||||
|
# DocProject is a documentation generator add-in
|
||||||
|
DocProject/buildhelp/
|
||||||
|
DocProject/Help/*.HxT
|
||||||
|
DocProject/Help/*.HxC
|
||||||
|
DocProject/Help/*.hhc
|
||||||
|
DocProject/Help/*.hhk
|
||||||
|
DocProject/Help/*.hhp
|
||||||
|
DocProject/Help/Html2
|
||||||
|
DocProject/Help/html
|
||||||
|
|
||||||
|
# Click-Once directory
|
||||||
|
publish/
|
||||||
|
|
||||||
|
# Publish Web Output
|
||||||
|
*.[Pp]ublish.xml
|
||||||
|
*.azurePubxml
|
||||||
|
# Note: Comment the next line if you want to checkin your web deploy settings,
|
||||||
|
# but database connection strings (with potential passwords) will be unencrypted
|
||||||
|
*.pubxml
|
||||||
|
*.publishproj
|
||||||
|
|
||||||
|
# Microsoft Azure Web App publish settings. Comment the next line if you want to
|
||||||
|
# checkin your Azure Web App publish settings, but sensitive information contained
|
||||||
|
# in these scripts will be unencrypted
|
||||||
|
PublishScripts/
|
||||||
|
|
||||||
|
# NuGet Packages
|
||||||
|
*.nupkg
|
||||||
|
# NuGet Symbol Packages
|
||||||
|
*.snupkg
|
||||||
|
# The packages folder can be ignored because of Package Restore
|
||||||
|
**/[Pp]ackages/*
|
||||||
|
# except build/, which is used as an MSBuild target.
|
||||||
|
!**/[Pp]ackages/build/
|
||||||
|
# Uncomment if necessary however generally it will be regenerated when needed
|
||||||
|
#!**/[Pp]ackages/repositories.config
|
||||||
|
# NuGet v3's project.json files produces more ignorable files
|
||||||
|
*.nuget.props
|
||||||
|
*.nuget.targets
|
||||||
|
|
||||||
|
# Microsoft Azure Build Output
|
||||||
|
csx/
|
||||||
|
*.build.csdef
|
||||||
|
|
||||||
|
# Microsoft Azure Emulator
|
||||||
|
ecf/
|
||||||
|
rcf/
|
||||||
|
|
||||||
|
# Windows Store app package directories and files
|
||||||
|
AppPackages/
|
||||||
|
BundleArtifacts/
|
||||||
|
Package.StoreAssociation.xml
|
||||||
|
_pkginfo.txt
|
||||||
|
*.appx
|
||||||
|
*.appxbundle
|
||||||
|
*.appxupload
|
||||||
|
|
||||||
|
# Visual Studio cache files
|
||||||
|
# files ending in .cache can be ignored
|
||||||
|
*.[Cc]ache
|
||||||
|
# but keep track of directories ending in .cache
|
||||||
|
!?*.[Cc]ache/
|
||||||
|
|
||||||
|
# Others
|
||||||
|
ClientBin/
|
||||||
|
~$*
|
||||||
|
*~
|
||||||
|
*.dbmdl
|
||||||
|
*.dbproj.schemaview
|
||||||
|
*.jfm
|
||||||
|
*.pfx
|
||||||
|
*.publishsettings
|
||||||
|
orleans.codegen.cs
|
||||||
|
|
||||||
|
# Including strong name files can present a security risk
|
||||||
|
# (https://github.com/github/gitignore/pull/2483#issue-259490424)
|
||||||
|
#*.snk
|
||||||
|
|
||||||
|
# Since there are multiple workflows, uncomment next line to ignore bower_components
|
||||||
|
# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)
|
||||||
|
#bower_components/
|
||||||
|
|
||||||
|
# RIA/Silverlight projects
|
||||||
|
Generated_Code/
|
||||||
|
|
||||||
|
# Backup & report files from converting an old project file
|
||||||
|
# to a newer Visual Studio version. Backup files are not needed,
|
||||||
|
# because we have git ;-)
|
||||||
|
_UpgradeReport_Files/
|
||||||
|
Backup*/
|
||||||
|
UpgradeLog*.XML
|
||||||
|
UpgradeLog*.htm
|
||||||
|
ServiceFabricBackup/
|
||||||
|
*.rptproj.bak
|
||||||
|
|
||||||
|
# SQL Server files
|
||||||
|
*.mdf
|
||||||
|
*.ldf
|
||||||
|
*.ndf
|
||||||
|
|
||||||
|
# Business Intelligence projects
|
||||||
|
*.rdl.data
|
||||||
|
*.bim.layout
|
||||||
|
*.bim_*.settings
|
||||||
|
*.rptproj.rsuser
|
||||||
|
*- [Bb]ackup.rdl
|
||||||
|
*- [Bb]ackup ([0-9]).rdl
|
||||||
|
*- [Bb]ackup ([0-9][0-9]).rdl
|
||||||
|
|
||||||
|
# Microsoft Fakes
|
||||||
|
FakesAssemblies/
|
||||||
|
|
||||||
|
# GhostDoc plugin setting file
|
||||||
|
*.GhostDoc.xml
|
||||||
|
|
||||||
|
# Node.js Tools for Visual Studio
|
||||||
|
.ntvs_analysis.dat
|
||||||
|
node_modules/
|
||||||
|
|
||||||
|
# Visual Studio 6 build log
|
||||||
|
*.plg
|
||||||
|
|
||||||
|
# Visual Studio 6 workspace options file
|
||||||
|
*.opt
|
||||||
|
|
||||||
|
# Visual Studio 6 auto-generated workspace file (contains which files were open etc.)
|
||||||
|
*.vbw
|
||||||
|
|
||||||
|
# Visual Studio 6 auto-generated project file (contains which files were open etc.)
|
||||||
|
*.vbp
|
||||||
|
|
||||||
|
# Visual Studio 6 workspace and project file (working project files containing files to include in project)
|
||||||
|
*.dsw
|
||||||
|
*.dsp
|
||||||
|
|
||||||
|
# Visual Studio 6 technical files
|
||||||
|
*.ncb
|
||||||
|
*.aps
|
||||||
|
|
||||||
|
# Visual Studio LightSwitch build output
|
||||||
|
**/*.HTMLClient/GeneratedArtifacts
|
||||||
|
**/*.DesktopClient/GeneratedArtifacts
|
||||||
|
**/*.DesktopClient/ModelManifest.xml
|
||||||
|
**/*.Server/GeneratedArtifacts
|
||||||
|
**/*.Server/ModelManifest.xml
|
||||||
|
_Pvt_Extensions
|
||||||
|
|
||||||
|
# Paket dependency manager
|
||||||
|
.paket/paket.exe
|
||||||
|
paket-files/
|
||||||
|
|
||||||
|
# FAKE - F# Make
|
||||||
|
.fake/
|
||||||
|
|
||||||
|
# CodeRush personal settings
|
||||||
|
.cr/personal
|
||||||
|
|
||||||
|
# Python Tools for Visual Studio (PTVS)
|
||||||
|
__pycache__/
|
||||||
|
*.pyc
|
||||||
|
|
||||||
|
# Cake - Uncomment if you are using it
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||||||
|
# tools/**
|
||||||
|
# !tools/packages.config
|
||||||
|
|
||||||
|
# Tabs Studio
|
||||||
|
*.tss
|
||||||
|
|
||||||
|
# Telerik's JustMock configuration file
|
||||||
|
*.jmconfig
|
||||||
|
|
||||||
|
# BizTalk build output
|
||||||
|
*.btp.cs
|
||||||
|
*.btm.cs
|
||||||
|
*.odx.cs
|
||||||
|
*.xsd.cs
|
||||||
|
|
||||||
|
# OpenCover UI analysis results
|
||||||
|
OpenCover/
|
||||||
|
|
||||||
|
# Azure Stream Analytics local run output
|
||||||
|
ASALocalRun/
|
||||||
|
|
||||||
|
# MSBuild Binary and Structured Log
|
||||||
|
*.binlog
|
||||||
|
|
||||||
|
# NVidia Nsight GPU debugger configuration file
|
||||||
|
*.nvuser
|
||||||
|
|
||||||
|
# MFractors (Xamarin productivity tool) working folder
|
||||||
|
.mfractor/
|
||||||
|
|
||||||
|
# Local History for Visual Studio
|
||||||
|
.localhistory/
|
||||||
|
|
||||||
|
# Visual Studio History (VSHistory) files
|
||||||
|
.vshistory/
|
||||||
|
|
||||||
|
# BeatPulse healthcheck temp database
|
||||||
|
healthchecksdb
|
||||||
|
|
||||||
|
# Backup folder for Package Reference Convert tool in Visual Studio 2017
|
||||||
|
MigrationBackup/
|
||||||
|
|
||||||
|
# Ionide (cross platform F# VS Code tools) working folder
|
||||||
|
.ionide/
|
||||||
|
|
||||||
|
# Fody - auto-generated XML schema
|
||||||
|
FodyWeavers.xsd
|
||||||
|
|
||||||
|
# VS Code files for those working on multiple tools
|
||||||
|
.vscode/*
|
||||||
|
!.vscode/settings.json
|
||||||
|
!.vscode/tasks.json
|
||||||
|
!.vscode/launch.json
|
||||||
|
!.vscode/extensions.json
|
||||||
|
*.code-workspace
|
||||||
|
|
||||||
|
# Local History for Visual Studio Code
|
||||||
|
.history/
|
||||||
|
|
||||||
|
# Windows Installer files from build outputs
|
||||||
|
*.cab
|
||||||
|
*.msi
|
||||||
|
*.msix
|
||||||
|
*.msm
|
||||||
|
*.msp
|
||||||
|
|
||||||
|
# JetBrains Rider
|
||||||
|
*.sln.iml
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||||||
|
|
||||||
# ---> Python
|
# ---> Python
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||||||
# Byte-compiled / optimized / DLL files
|
# Byte-compiled / optimized / DLL files
|
||||||
__pycache__/
|
__pycache__/
|
||||||
|
|
|
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|
|
@ -0,0 +1,136 @@
|
||||||
|
<?xml version="1.0" encoding="utf-8"?>
|
||||||
|
<Project DefaultTargets="Build" ToolsVersion="15.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||||
|
<Import Project="..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.props" Condition="Exists('..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.props')" />
|
||||||
|
<PropertyGroup Label="Globals">
|
||||||
|
<CppWinRTOptimized>true</CppWinRTOptimized>
|
||||||
|
<CppWinRTRootNamespaceAutoMerge>true</CppWinRTRootNamespaceAutoMerge>
|
||||||
|
<CppWinRTGenerateWindowsMetadata>true</CppWinRTGenerateWindowsMetadata>
|
||||||
|
<MinimalCoreWin>true</MinimalCoreWin>
|
||||||
|
<VCProjectVersion>15.0</VCProjectVersion>
|
||||||
|
<ProjectGuid>{a2b67815-1235-4f7c-874d-4fccb3b0c738}</ProjectGuid>
|
||||||
|
<Keyword>Win32Proj</Keyword>
|
||||||
|
<RootNamespace>GMM</RootNamespace>
|
||||||
|
<WindowsTargetPlatformVersion Condition=" '$(WindowsTargetPlatformVersion)' == '' ">10.0</WindowsTargetPlatformVersion>
|
||||||
|
<WindowsTargetPlatformMinVersion>10.0.17134.0</WindowsTargetPlatformMinVersion>
|
||||||
|
</PropertyGroup>
|
||||||
|
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
|
||||||
|
<ItemGroup Label="ProjectConfigurations">
|
||||||
|
<ProjectConfiguration Include="Debug|Win32">
|
||||||
|
<Configuration>Debug</Configuration>
|
||||||
|
<Platform>Win32</Platform>
|
||||||
|
</ProjectConfiguration>
|
||||||
|
<ProjectConfiguration Include="Release|Win32">
|
||||||
|
<Configuration>Release</Configuration>
|
||||||
|
<Platform>Win32</Platform>
|
||||||
|
</ProjectConfiguration>
|
||||||
|
<ProjectConfiguration Include="Debug|x64">
|
||||||
|
<Configuration>Debug</Configuration>
|
||||||
|
<Platform>x64</Platform>
|
||||||
|
</ProjectConfiguration>
|
||||||
|
<ProjectConfiguration Include="Release|x64">
|
||||||
|
<Configuration>Release</Configuration>
|
||||||
|
<Platform>x64</Platform>
|
||||||
|
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||||||
|
/***************************************************************************
|
||||||
|
|
||||||
|
Module Name:
|
||||||
|
|
||||||
|
Gaussian Mixture Model with Diagonal Covariance Matrix
|
||||||
|
|
||||||
|
History:
|
||||||
|
|
||||||
|
2003/11/01 Fei Wang
|
||||||
|
2013 luxiaoxun
|
||||||
|
***************************************************************************/
|
||||||
|
#include <math.h>
|
||||||
|
#include <iostream>
|
||||||
|
#include <cassert>
|
||||||
|
#include "gmm.h"
|
||||||
|
#include "kmeans.h"
|
||||||
|
using namespace std;
|
||||||
|
|
||||||
|
GMM::GMM(int dimNum, int mixNum)
|
||||||
|
{
|
||||||
|
m_dimNum = dimNum;
|
||||||
|
m_mixNum = mixNum;
|
||||||
|
|
||||||
|
m_maxIterNum = 100;
|
||||||
|
m_endError = 0.001;
|
||||||
|
|
||||||
|
Allocate();
|
||||||
|
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
m_priors[i] = 1.0 / m_mixNum;
|
||||||
|
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
m_means[i][d] = 0;
|
||||||
|
m_vars[i][d] = 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
GMM::~GMM()
|
||||||
|
{
|
||||||
|
Dispose();
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::Allocate()
|
||||||
|
{
|
||||||
|
m_priors = new double[m_mixNum];
|
||||||
|
m_means = new double* [m_mixNum];
|
||||||
|
m_vars = new double* [m_mixNum];
|
||||||
|
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
m_means[i] = new double[m_dimNum];
|
||||||
|
m_vars[i] = new double[m_dimNum];
|
||||||
|
}
|
||||||
|
|
||||||
|
m_minVars = new double[m_dimNum];
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::Dispose()
|
||||||
|
{
|
||||||
|
delete[] m_priors;
|
||||||
|
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
delete[] m_means[i];
|
||||||
|
delete[] m_vars[i];
|
||||||
|
}
|
||||||
|
delete[] m_means;
|
||||||
|
delete[] m_vars;
|
||||||
|
|
||||||
|
delete[] m_minVars;
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::Copy(GMM* gmm)
|
||||||
|
{
|
||||||
|
assert(m_mixNum == gmm->m_mixNum && m_dimNum == gmm->m_dimNum);
|
||||||
|
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
m_priors[i] = gmm->Prior(i);
|
||||||
|
memcpy(m_means[i], gmm->Mean(i), sizeof(double) * m_dimNum);
|
||||||
|
memcpy(m_vars[i], gmm->Variance(i), sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
memcpy(m_minVars, gmm->m_minVars, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
double GMM::GetProbability(const double* sample)
|
||||||
|
{
|
||||||
|
double p = 0;
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
p += m_priors[i] * GetProbability(sample, i);
|
||||||
|
}
|
||||||
|
return p;
|
||||||
|
}
|
||||||
|
|
||||||
|
double GMM::GetProbability(const double* x, int j)
|
||||||
|
{
|
||||||
|
double p = 1;
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
p *= 1 / sqrt(2 * M_PI * m_vars[j][d]);
|
||||||
|
p *= exp(-0.5 * (x[d] - m_means[j][d]) * (x[d] - m_means[j][d]) / m_vars[j][d]);
|
||||||
|
}
|
||||||
|
return p;
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::Train(const char* sampleFileName)
|
||||||
|
{
|
||||||
|
//DumpSampleFile(sampleFileName);
|
||||||
|
Init(sampleFileName);
|
||||||
|
|
||||||
|
ifstream sampleFile(sampleFileName, ios_base::binary);
|
||||||
|
assert(sampleFile);
|
||||||
|
|
||||||
|
int size = 0;
|
||||||
|
sampleFile.seekg(0, ios_base::beg);
|
||||||
|
sampleFile.read((char*)&size, sizeof(int));
|
||||||
|
|
||||||
|
// Reestimation
|
||||||
|
bool loop = true;
|
||||||
|
double iterNum = 0;
|
||||||
|
double lastL = 0;
|
||||||
|
double currL = 0;
|
||||||
|
int unchanged = 0;
|
||||||
|
double* x = new double[m_dimNum]; // Sample data
|
||||||
|
double* next_priors = new double[m_mixNum];
|
||||||
|
double** next_vars = new double* [m_mixNum];
|
||||||
|
double** next_means = new double* [m_mixNum];
|
||||||
|
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
next_means[i] = new double[m_dimNum];
|
||||||
|
next_vars[i] = new double[m_dimNum];
|
||||||
|
}
|
||||||
|
|
||||||
|
while (loop)
|
||||||
|
{
|
||||||
|
// Clear buffer for reestimation
|
||||||
|
memset(next_priors, 0, sizeof(double) * m_mixNum);
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
memset(next_vars[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
memset(next_means[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
lastL = currL;
|
||||||
|
currL = 0;
|
||||||
|
|
||||||
|
// Predict
|
||||||
|
sampleFile.seekg(2 * sizeof(int), ios_base::beg);
|
||||||
|
for (int k = 0; k < size; k++)
|
||||||
|
{
|
||||||
|
sampleFile.read((char*)x, sizeof(double) * m_dimNum);
|
||||||
|
double p = GetProbability(x);
|
||||||
|
|
||||||
|
for (int j = 0; j < m_mixNum; j++)
|
||||||
|
{
|
||||||
|
double pj = GetProbability(x, j) * m_priors[j] / p;
|
||||||
|
next_priors[j] += pj;
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
next_means[j][d] += pj * x[d];
|
||||||
|
next_vars[j][d] += pj * x[d] * x[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
currL += (p > 1E-20) ? log10(p) : -20;
|
||||||
|
}
|
||||||
|
currL /= size;
|
||||||
|
|
||||||
|
// Reestimation: generate new priors, means and variances.
|
||||||
|
for (int j = 0; j < m_mixNum; j++)
|
||||||
|
{
|
||||||
|
m_priors[j] = next_priors[j] / size;
|
||||||
|
|
||||||
|
if (m_priors[j] > 0)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
m_means[j][d] = next_means[j][d] / next_priors[j];
|
||||||
|
m_vars[j][d] = next_vars[j][d] / next_priors[j] - m_means[j][d] * m_means[j][d];
|
||||||
|
if (m_vars[j][d] < m_minVars[d])
|
||||||
|
{
|
||||||
|
m_vars[j][d] = m_minVars[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Terminal conditions
|
||||||
|
iterNum++;
|
||||||
|
if (fabs(currL - lastL) < m_endError * fabs(lastL))
|
||||||
|
{
|
||||||
|
unchanged++;
|
||||||
|
}
|
||||||
|
if (iterNum >= m_maxIterNum || unchanged >= 3)
|
||||||
|
{
|
||||||
|
loop = false;
|
||||||
|
}
|
||||||
|
//--- Debug ---
|
||||||
|
//cout << "Iter: " << iterNum << ", Average Log-Probability: " << currL << endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
sampleFile.close();
|
||||||
|
delete[] next_priors;
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
delete[] next_means[i];
|
||||||
|
delete[] next_vars[i];
|
||||||
|
}
|
||||||
|
delete[] next_means;
|
||||||
|
delete[] next_vars;
|
||||||
|
delete[] x;
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::Train(double* data, int N)
|
||||||
|
{
|
||||||
|
Init(data, N);
|
||||||
|
|
||||||
|
int size = N;
|
||||||
|
|
||||||
|
// Reestimation
|
||||||
|
bool loop = true;
|
||||||
|
double iterNum = 0;
|
||||||
|
double lastL = 0;
|
||||||
|
double currL = 0;
|
||||||
|
int unchanged = 0;
|
||||||
|
double* x = new double[m_dimNum]; // Sample data
|
||||||
|
double* next_priors = new double[m_mixNum];
|
||||||
|
double** next_vars = new double* [m_mixNum];
|
||||||
|
double** next_means = new double* [m_mixNum];
|
||||||
|
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
next_means[i] = new double[m_dimNum];
|
||||||
|
next_vars[i] = new double[m_dimNum];
|
||||||
|
}
|
||||||
|
|
||||||
|
while (loop)
|
||||||
|
{
|
||||||
|
// Clear buffer for reestimation
|
||||||
|
memset(next_priors, 0, sizeof(double) * m_mixNum);
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
memset(next_vars[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
memset(next_means[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
lastL = currL;
|
||||||
|
currL = 0;
|
||||||
|
|
||||||
|
// Predict
|
||||||
|
for (int k = 0; k < size; k++)
|
||||||
|
{
|
||||||
|
for (int j = 0;j < m_dimNum;j++)
|
||||||
|
x[j] = data[k * m_dimNum + j];
|
||||||
|
double p = GetProbability(x);
|
||||||
|
|
||||||
|
for (int j = 0; j < m_mixNum; j++)
|
||||||
|
{
|
||||||
|
double pj = GetProbability(x, j) * m_priors[j] / p;
|
||||||
|
|
||||||
|
next_priors[j] += pj;
|
||||||
|
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
next_means[j][d] += pj * x[d];
|
||||||
|
next_vars[j][d] += pj * x[d] * x[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
currL += (p > 1E-20) ? log10(p) : -20;
|
||||||
|
}
|
||||||
|
currL /= size;
|
||||||
|
|
||||||
|
// Reestimation: generate new priors, means and variances.
|
||||||
|
for (int j = 0; j < m_mixNum; j++)
|
||||||
|
{
|
||||||
|
m_priors[j] = next_priors[j] / size;
|
||||||
|
|
||||||
|
if (m_priors[j] > 0)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
m_means[j][d] = next_means[j][d] / next_priors[j];
|
||||||
|
m_vars[j][d] = next_vars[j][d] / next_priors[j] - m_means[j][d] * m_means[j][d];
|
||||||
|
if (m_vars[j][d] < m_minVars[d])
|
||||||
|
{
|
||||||
|
m_vars[j][d] = m_minVars[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Terminal conditions
|
||||||
|
iterNum++;
|
||||||
|
if (fabs(currL - lastL) < m_endError * fabs(lastL))
|
||||||
|
{
|
||||||
|
unchanged++;
|
||||||
|
}
|
||||||
|
if (iterNum >= m_maxIterNum || unchanged >= 3)
|
||||||
|
{
|
||||||
|
loop = false;
|
||||||
|
}
|
||||||
|
|
||||||
|
//--- Debug ---
|
||||||
|
//cout << "Iter: " << iterNum << ", Average Log-Probability: " << currL << endl;
|
||||||
|
}
|
||||||
|
delete[] next_priors;
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
delete[] next_means[i];
|
||||||
|
delete[] next_vars[i];
|
||||||
|
}
|
||||||
|
delete[] next_means;
|
||||||
|
delete[] next_vars;
|
||||||
|
delete[] x;
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::Init(double* data, int N)
|
||||||
|
{
|
||||||
|
const double MIN_VAR = 1E-10;
|
||||||
|
|
||||||
|
KMeans* kmeans = new KMeans(m_dimNum, m_mixNum);
|
||||||
|
kmeans->SetInitMode(KMeans::InitUniform);
|
||||||
|
int* Label;
|
||||||
|
Label = new int[N];
|
||||||
|
kmeans->Cluster(data, N, Label);
|
||||||
|
|
||||||
|
int* counts = new int[m_mixNum];
|
||||||
|
double* overMeans = new double[m_dimNum]; // Overall mean of training data
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
counts[i] = 0;
|
||||||
|
m_priors[i] = 0;
|
||||||
|
memcpy(m_means[i], kmeans->GetMean(i), sizeof(double) * m_dimNum);
|
||||||
|
memset(m_vars[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
memset(overMeans, 0, sizeof(double) * m_dimNum);
|
||||||
|
memset(m_minVars, 0, sizeof(double) * m_dimNum);
|
||||||
|
|
||||||
|
int size = 0;
|
||||||
|
size = N;
|
||||||
|
|
||||||
|
double* x = new double[m_dimNum];
|
||||||
|
int label = -1;
|
||||||
|
|
||||||
|
for (int i = 0; i < size; i++)
|
||||||
|
{
|
||||||
|
for (int j = 0;j < m_dimNum;j++)
|
||||||
|
x[j] = data[i * m_dimNum + j];
|
||||||
|
label = Label[i];
|
||||||
|
|
||||||
|
// Count each Gaussian
|
||||||
|
counts[label]++;
|
||||||
|
double* m = kmeans->GetMean(label);
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
m_vars[label][d] += (x[d] - m[d]) * (x[d] - m[d]);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Count the overall mean and variance.
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
overMeans[d] += x[d];
|
||||||
|
m_minVars[d] += x[d] * x[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Compute the overall variance (* 0.01) as the minimum variance.
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
overMeans[d] /= size;
|
||||||
|
m_minVars[d] = max(MIN_VAR, 0.01 * (m_minVars[d] / size - overMeans[d] * overMeans[d]));
|
||||||
|
}
|
||||||
|
|
||||||
|
// Initialize each Gaussian.
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
m_priors[i] = 1.0 * counts[i] / size;
|
||||||
|
|
||||||
|
if (m_priors[i] > 0)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
m_vars[i][d] = m_vars[i][d] / counts[i];
|
||||||
|
|
||||||
|
// A minimum variance for each dimension is required.
|
||||||
|
if (m_vars[i][d] < m_minVars[d])
|
||||||
|
{
|
||||||
|
m_vars[i][d] = m_minVars[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
else
|
||||||
|
{
|
||||||
|
memcpy(m_vars[i], m_minVars, sizeof(double) * m_dimNum);
|
||||||
|
cout << "[WARNING] Gaussian " << i << " of GMM is not used!\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
delete kmeans;
|
||||||
|
delete[] x;
|
||||||
|
delete[] counts;
|
||||||
|
delete[] overMeans;
|
||||||
|
delete[] Label;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::Init(const char* sampleFileName)
|
||||||
|
{
|
||||||
|
const double MIN_VAR = 1E-10;
|
||||||
|
|
||||||
|
KMeans* kmeans = new KMeans(m_dimNum, m_mixNum);
|
||||||
|
kmeans->SetInitMode(KMeans::InitUniform);
|
||||||
|
kmeans->Cluster(sampleFileName, "gmm_init.tmp");
|
||||||
|
|
||||||
|
int* counts = new int[m_mixNum];
|
||||||
|
double* overMeans = new double[m_dimNum]; // Overall mean of training data
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
counts[i] = 0;
|
||||||
|
m_priors[i] = 0;
|
||||||
|
memcpy(m_means[i], kmeans->GetMean(i), sizeof(double) * m_dimNum);
|
||||||
|
memset(m_vars[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
memset(overMeans, 0, sizeof(double) * m_dimNum);
|
||||||
|
memset(m_minVars, 0, sizeof(double) * m_dimNum);
|
||||||
|
|
||||||
|
// Open the sample and label file to initialize the model
|
||||||
|
ifstream sampleFile(sampleFileName, ios_base::binary);
|
||||||
|
assert(sampleFile);
|
||||||
|
|
||||||
|
ifstream labelFile("gmm_init.tmp", ios_base::binary);
|
||||||
|
assert(labelFile);
|
||||||
|
|
||||||
|
int size = 0;
|
||||||
|
sampleFile.read((char*)&size, sizeof(int));
|
||||||
|
sampleFile.seekg(2 * sizeof(int), ios_base::beg);
|
||||||
|
labelFile.seekg(sizeof(int), ios_base::beg);
|
||||||
|
|
||||||
|
double* x = new double[m_dimNum];
|
||||||
|
int label = -1;
|
||||||
|
|
||||||
|
for (int i = 0; i < size; i++)
|
||||||
|
{
|
||||||
|
sampleFile.read((char*)x, sizeof(double) * m_dimNum);
|
||||||
|
labelFile.read((char*)&label, sizeof(int));
|
||||||
|
|
||||||
|
// Count each Gaussian
|
||||||
|
counts[label]++;
|
||||||
|
double* m = kmeans->GetMean(label);
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
m_vars[label][d] += (x[d] - m[d]) * (x[d] - m[d]);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Count the overall mean and variance.
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
overMeans[d] += x[d];
|
||||||
|
m_minVars[d] += x[d] * x[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Compute the overall variance (* 0.01) as the minimum variance.
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
overMeans[d] /= size;
|
||||||
|
m_minVars[d] = max(MIN_VAR, 0.01 * (m_minVars[d] / size - overMeans[d] * overMeans[d]));
|
||||||
|
}
|
||||||
|
|
||||||
|
// Initialize each Gaussian.
|
||||||
|
for (int i = 0; i < m_mixNum; i++)
|
||||||
|
{
|
||||||
|
m_priors[i] = 1.0 * counts[i] / size;
|
||||||
|
|
||||||
|
if (m_priors[i] > 0)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
m_vars[i][d] = m_vars[i][d] / counts[i];
|
||||||
|
|
||||||
|
// A minimum variance for each dimension is required.
|
||||||
|
if (m_vars[i][d] < m_minVars[d])
|
||||||
|
{
|
||||||
|
m_vars[i][d] = m_minVars[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
else
|
||||||
|
{
|
||||||
|
memcpy(m_vars[i], m_minVars, sizeof(double) * m_dimNum);
|
||||||
|
cout << "[WARNING] Gaussian " << i << " of GMM is not used!\n";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
delete kmeans;
|
||||||
|
delete[] x;
|
||||||
|
delete[] counts;
|
||||||
|
delete[] overMeans;
|
||||||
|
|
||||||
|
sampleFile.close();
|
||||||
|
labelFile.close();
|
||||||
|
}
|
||||||
|
|
||||||
|
void GMM::DumpSampleFile(const char* fileName)
|
||||||
|
{
|
||||||
|
ifstream sampleFile(fileName, ios_base::binary);
|
||||||
|
assert(sampleFile);
|
||||||
|
|
||||||
|
int size = 0;
|
||||||
|
sampleFile.read((char*)&size, sizeof(int));
|
||||||
|
cout << size << endl;
|
||||||
|
|
||||||
|
int dim = 0;
|
||||||
|
sampleFile.read((char*)&dim, sizeof(int));
|
||||||
|
cout << dim << endl;
|
||||||
|
|
||||||
|
double* f = new double[dim];
|
||||||
|
for (int i = 0; i < size; i++)
|
||||||
|
{
|
||||||
|
sampleFile.read((char*)f, sizeof(double) * dim);
|
||||||
|
|
||||||
|
cout << i << ":";
|
||||||
|
for (int j = 0; j < dim; j++)
|
||||||
|
{
|
||||||
|
cout << " " << f[j];
|
||||||
|
}
|
||||||
|
cout << endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
delete[] f;
|
||||||
|
sampleFile.close();
|
||||||
|
}
|
||||||
|
|
||||||
|
ostream& operator<<(ostream& out, GMM& gmm)
|
||||||
|
{
|
||||||
|
out << "<GMM>" << endl;
|
||||||
|
out << "<DimNum> " << gmm.m_dimNum << " </DimNum>" << endl;
|
||||||
|
out << "<MixNum> " << gmm.m_mixNum << " </MixNum>" << endl;
|
||||||
|
|
||||||
|
out << "<Prior> ";
|
||||||
|
for (int i = 0; i < gmm.m_mixNum; i++)
|
||||||
|
{
|
||||||
|
out << gmm.m_priors[i] << " ";
|
||||||
|
}
|
||||||
|
out << "</Prior>" << endl;
|
||||||
|
|
||||||
|
out << "<Mean>" << endl;
|
||||||
|
for (int i = 0; i < gmm.m_mixNum; i++)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < gmm.m_dimNum; d++)
|
||||||
|
{
|
||||||
|
out << gmm.m_means[i][d] << " ";
|
||||||
|
}
|
||||||
|
out << endl;
|
||||||
|
}
|
||||||
|
out << "</Mean>" << endl;
|
||||||
|
|
||||||
|
out << "<Variance>" << endl;
|
||||||
|
for (int i = 0; i < gmm.m_mixNum; i++)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < gmm.m_dimNum; d++)
|
||||||
|
{
|
||||||
|
out << gmm.m_vars[i][d] << " ";
|
||||||
|
}
|
||||||
|
out << endl;
|
||||||
|
}
|
||||||
|
out << "</Variance>" << endl;
|
||||||
|
|
||||||
|
out << "</GMM>" << endl;
|
||||||
|
|
||||||
|
return out;
|
||||||
|
}
|
||||||
|
|
||||||
|
istream& operator>>(istream& in, GMM& gmm)
|
||||||
|
{
|
||||||
|
char label[50];
|
||||||
|
in >> label; // "<GMM>"
|
||||||
|
assert(strcmp(label, "<GMM>") == 0);
|
||||||
|
|
||||||
|
gmm.Dispose();
|
||||||
|
|
||||||
|
in >> label >> gmm.m_dimNum >> label; // "<DimNum>"
|
||||||
|
in >> label >> gmm.m_mixNum >> label; // "<MixNum>"
|
||||||
|
|
||||||
|
gmm.Allocate();
|
||||||
|
|
||||||
|
in >> label; // "<Prior>"
|
||||||
|
for (int i = 0; i < gmm.m_mixNum; i++)
|
||||||
|
{
|
||||||
|
in >> gmm.m_priors[i];
|
||||||
|
}
|
||||||
|
in >> label;
|
||||||
|
|
||||||
|
in >> label; // "<Mean>"
|
||||||
|
for (int i = 0; i < gmm.m_mixNum; i++)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < gmm.m_dimNum; d++)
|
||||||
|
{
|
||||||
|
in >> gmm.m_means[i][d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
in >> label;
|
||||||
|
|
||||||
|
in >> label; // "<Variance>"
|
||||||
|
for (int i = 0; i < gmm.m_mixNum; i++)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < gmm.m_dimNum; d++)
|
||||||
|
{
|
||||||
|
in >> gmm.m_vars[i][d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
in >> label;
|
||||||
|
|
||||||
|
in >> label; // "</GMM>"
|
||||||
|
return in;
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,68 @@
|
||||||
|
/***************************************************************************
|
||||||
|
Module Name:
|
||||||
|
Gaussian Mixture Model with Diagonal Covariance Matrix
|
||||||
|
|
||||||
|
History:
|
||||||
|
2003/11/01 Fei Wang
|
||||||
|
2013 luxiaoxun
|
||||||
|
***************************************************************************/
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
#include <fstream>
|
||||||
|
#define M_PI 3.1415926535897932384626433832795
|
||||||
|
|
||||||
|
class GMM
|
||||||
|
{
|
||||||
|
public:
|
||||||
|
GMM(int dimNum = 1, int mixNum = 1);
|
||||||
|
~GMM();
|
||||||
|
|
||||||
|
void Copy(GMM* gmm);
|
||||||
|
|
||||||
|
void SetMaxIterNum(int i) { m_maxIterNum = i; }
|
||||||
|
void SetEndError(double f) { m_endError = f; }
|
||||||
|
|
||||||
|
int GetDimNum() { return m_dimNum; }
|
||||||
|
int GetMixNum() { return m_mixNum; }
|
||||||
|
int GetMaxIterNum() { return m_maxIterNum; }
|
||||||
|
double GetEndError() { return m_endError; }
|
||||||
|
|
||||||
|
double& Prior(int i) { return m_priors[i]; }
|
||||||
|
double* Mean(int i) { return m_means[i]; }
|
||||||
|
double* Variance(int i) { return m_vars[i]; }
|
||||||
|
|
||||||
|
void setPrior(int i, double val) { m_priors[i] = val; }
|
||||||
|
void setMean(int i, double* val) { for (int j = 0;j < m_dimNum;j++) m_means[i][j] = val[j]; }
|
||||||
|
void setVariance(int i, double* val) { for (int j = 0;j < m_dimNum;j++) m_vars[i][j] = val[j]; }
|
||||||
|
|
||||||
|
double GetProbability(const double* sample);
|
||||||
|
|
||||||
|
/* SampleFile: <size><dim><data>...*/
|
||||||
|
void Init(const char* sampleFileName);
|
||||||
|
void Train(const char* sampleFileName);
|
||||||
|
void Init(double* data, int N);
|
||||||
|
void Train(double* data, int N);
|
||||||
|
|
||||||
|
void DumpSampleFile(const char* fileName);
|
||||||
|
|
||||||
|
friend std::ostream& operator<<(std::ostream& out, GMM& gmm);
|
||||||
|
friend std::istream& operator>>(std::istream& in, GMM& gmm);
|
||||||
|
|
||||||
|
private:
|
||||||
|
int m_dimNum; // 样本维数
|
||||||
|
int m_mixNum; // Gaussian数目
|
||||||
|
double* m_priors; // Gaussian权重
|
||||||
|
double** m_means; // Gaussian均值
|
||||||
|
double** m_vars; // Gaussian方差
|
||||||
|
|
||||||
|
// A minimum variance is required. Now, it is the overall variance * 0.01.
|
||||||
|
double* m_minVars;
|
||||||
|
int m_maxIterNum; // The stopping criterion regarding the number of iterations
|
||||||
|
double m_endError; // The stopping criterion regarding the error
|
||||||
|
|
||||||
|
private:
|
||||||
|
// Return the "j"th pdf, p(x|j).
|
||||||
|
double GetProbability(const double* x, int j);
|
||||||
|
void Allocate();
|
||||||
|
void Dispose();
|
||||||
|
};
|
||||||
|
|
@ -0,0 +1,389 @@
|
||||||
|
/***************************************************************************
|
||||||
|
Module Name:
|
||||||
|
KMeans
|
||||||
|
|
||||||
|
History:
|
||||||
|
2003/10/16 Fei Wang
|
||||||
|
2013 luxiaoxun
|
||||||
|
***************************************************************************/
|
||||||
|
#include <stdlib.h>
|
||||||
|
#include <math.h>
|
||||||
|
#include <time.h>
|
||||||
|
#include <iostream>
|
||||||
|
#include <cassert>
|
||||||
|
#include "KMeans.h"
|
||||||
|
using namespace std;
|
||||||
|
|
||||||
|
|
||||||
|
KMeans::KMeans(int dimNum, int clusterNum)
|
||||||
|
{
|
||||||
|
m_dimNum = dimNum;
|
||||||
|
m_clusterNum = clusterNum;
|
||||||
|
|
||||||
|
m_means = new double* [m_clusterNum];
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
m_means[i] = new double[m_dimNum];
|
||||||
|
memset(m_means[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
m_initMode = InitRandom;
|
||||||
|
m_maxIterNum = 100;
|
||||||
|
m_endError = 0.001;
|
||||||
|
}
|
||||||
|
|
||||||
|
KMeans::~KMeans()
|
||||||
|
{
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
delete[] m_means[i];
|
||||||
|
}
|
||||||
|
delete[] m_means;
|
||||||
|
}
|
||||||
|
|
||||||
|
void KMeans::Cluster(const char* sampleFileName, const char* labelFileName)
|
||||||
|
{
|
||||||
|
// Check the sample file
|
||||||
|
ifstream sampleFile(sampleFileName, ios_base::binary);
|
||||||
|
assert(sampleFile);
|
||||||
|
|
||||||
|
int size = 0;
|
||||||
|
int dim = 0;
|
||||||
|
sampleFile.read((char*)&size, sizeof(int));
|
||||||
|
sampleFile.read((char*)&dim, sizeof(int));
|
||||||
|
assert(size >= m_clusterNum);
|
||||||
|
assert(dim == m_dimNum);
|
||||||
|
|
||||||
|
// Initialize model
|
||||||
|
Init(sampleFile);
|
||||||
|
|
||||||
|
// Recursion
|
||||||
|
double* x = new double[m_dimNum]; // Sample data
|
||||||
|
int label = -1; // Class index
|
||||||
|
double iterNum = 0;
|
||||||
|
double lastCost = 0;
|
||||||
|
double currCost = 0;
|
||||||
|
int unchanged = 0;
|
||||||
|
bool loop = true;
|
||||||
|
int* counts = new int[m_clusterNum];
|
||||||
|
double** next_means = new double* [m_clusterNum]; // New model for reestimation
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
next_means[i] = new double[m_dimNum];
|
||||||
|
}
|
||||||
|
|
||||||
|
while (loop)
|
||||||
|
{
|
||||||
|
memset(counts, 0, sizeof(int) * m_clusterNum);
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
memset(next_means[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
lastCost = currCost;
|
||||||
|
currCost = 0;
|
||||||
|
|
||||||
|
sampleFile.clear();
|
||||||
|
sampleFile.seekg(sizeof(int) * 2, ios_base::beg);
|
||||||
|
|
||||||
|
// Classification
|
||||||
|
for (int i = 0; i < size; i++)
|
||||||
|
{
|
||||||
|
sampleFile.read((char*)x, sizeof(double) * m_dimNum);
|
||||||
|
currCost += GetLabel(x, &label);
|
||||||
|
|
||||||
|
counts[label]++;
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
next_means[label][d] += x[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
currCost /= size;
|
||||||
|
|
||||||
|
// Reestimation
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
if (counts[i] > 0)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
next_means[i][d] /= counts[i];
|
||||||
|
}
|
||||||
|
memcpy(m_means[i], next_means[i], sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Terminal conditions
|
||||||
|
iterNum++;
|
||||||
|
if (fabs(lastCost - currCost) < m_endError * lastCost)
|
||||||
|
{
|
||||||
|
unchanged++;
|
||||||
|
}
|
||||||
|
if (iterNum >= m_maxIterNum || unchanged >= 3)
|
||||||
|
{
|
||||||
|
loop = false;
|
||||||
|
}
|
||||||
|
//DEBUG
|
||||||
|
//cout << "Iter: " << iterNum << ", Average Cost: " << currCost << endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Output the label file
|
||||||
|
ofstream labelFile(labelFileName, ios_base::binary);
|
||||||
|
assert(labelFile);
|
||||||
|
|
||||||
|
labelFile.write((char*)&size, sizeof(int));
|
||||||
|
sampleFile.clear();
|
||||||
|
sampleFile.seekg(sizeof(int) * 2, ios_base::beg);
|
||||||
|
|
||||||
|
for (int i = 0; i < size; i++)
|
||||||
|
{
|
||||||
|
sampleFile.read((char*)x, sizeof(double) * m_dimNum);
|
||||||
|
GetLabel(x, &label);
|
||||||
|
labelFile.write((char*)&label, sizeof(int));
|
||||||
|
}
|
||||||
|
|
||||||
|
sampleFile.close();
|
||||||
|
labelFile.close();
|
||||||
|
|
||||||
|
delete[] counts;
|
||||||
|
delete[] x;
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
delete[] next_means[i];
|
||||||
|
}
|
||||||
|
delete[] next_means;
|
||||||
|
}
|
||||||
|
|
||||||
|
//
|
||||||
|
void KMeans::Cluster(double* data, int N, int* Label)
|
||||||
|
{
|
||||||
|
int size = 0;
|
||||||
|
size = N;
|
||||||
|
|
||||||
|
assert(size >= m_clusterNum);
|
||||||
|
|
||||||
|
// Initialize model
|
||||||
|
Init(data, N);
|
||||||
|
|
||||||
|
// Recursion
|
||||||
|
double* x = new double[m_dimNum]; // Sample data
|
||||||
|
int label = -1; // Class index
|
||||||
|
double iterNum = 0;
|
||||||
|
double lastCost = 0;
|
||||||
|
double currCost = 0;
|
||||||
|
int unchanged = 0;
|
||||||
|
bool loop = true;
|
||||||
|
int* counts = new int[m_clusterNum];
|
||||||
|
double** next_means = new double* [m_clusterNum]; // New model for reestimation
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
next_means[i] = new double[m_dimNum];
|
||||||
|
}
|
||||||
|
|
||||||
|
while (loop)
|
||||||
|
{
|
||||||
|
memset(counts, 0, sizeof(int) * m_clusterNum);
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
memset(next_means[i], 0, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
lastCost = currCost;
|
||||||
|
currCost = 0;
|
||||||
|
|
||||||
|
// Classification
|
||||||
|
for (int i = 0; i < size; i++)
|
||||||
|
{
|
||||||
|
for (int j = 0; j < m_dimNum; j++)
|
||||||
|
x[j] = data[i * m_dimNum + j];
|
||||||
|
|
||||||
|
currCost += GetLabel(x, &label);
|
||||||
|
|
||||||
|
counts[label]++;
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
next_means[label][d] += x[d];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
currCost /= size;
|
||||||
|
|
||||||
|
// Reestimation
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
if (counts[i] > 0)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < m_dimNum; d++)
|
||||||
|
{
|
||||||
|
next_means[i][d] /= counts[i];
|
||||||
|
}
|
||||||
|
memcpy(m_means[i], next_means[i], sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Terminal conditions
|
||||||
|
iterNum++;
|
||||||
|
if (fabs(lastCost - currCost) < m_endError * lastCost)
|
||||||
|
{
|
||||||
|
unchanged++;
|
||||||
|
}
|
||||||
|
if (iterNum >= m_maxIterNum || unchanged >= 3)
|
||||||
|
{
|
||||||
|
loop = false;
|
||||||
|
}
|
||||||
|
|
||||||
|
//DEBUG
|
||||||
|
//cout << "Iter: " << iterNum << ", Average Cost: " << currCost << endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Output the label file
|
||||||
|
for (int i = 0; i < size; i++)
|
||||||
|
{
|
||||||
|
for (int j = 0; j < m_dimNum; j++)
|
||||||
|
x[j] = data[i * m_dimNum + j];
|
||||||
|
GetLabel(x, &label);
|
||||||
|
Label[i] = label;
|
||||||
|
}
|
||||||
|
delete[] counts;
|
||||||
|
delete[] x;
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
delete[] next_means[i];
|
||||||
|
}
|
||||||
|
delete[] next_means;
|
||||||
|
}
|
||||||
|
|
||||||
|
void KMeans::Init(double* data, int N)
|
||||||
|
{
|
||||||
|
int size = N;
|
||||||
|
|
||||||
|
if (m_initMode == InitRandom)
|
||||||
|
{
|
||||||
|
int inteval = size / m_clusterNum;
|
||||||
|
double* sample = new double[m_dimNum];
|
||||||
|
|
||||||
|
// Seed the random-number generator with current time
|
||||||
|
srand((unsigned)time(NULL));
|
||||||
|
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
int select = inteval * i + (inteval - 1) * rand() / RAND_MAX;
|
||||||
|
for (int j = 0; j < m_dimNum; j++)
|
||||||
|
sample[j] = data[select * m_dimNum + j];
|
||||||
|
memcpy(m_means[i], sample, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
delete[] sample;
|
||||||
|
}
|
||||||
|
else if (m_initMode == InitUniform)
|
||||||
|
{
|
||||||
|
double* sample = new double[m_dimNum];
|
||||||
|
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
int select = i * size / m_clusterNum;
|
||||||
|
for (int j = 0; j < m_dimNum; j++)
|
||||||
|
sample[j] = data[select * m_dimNum + j];
|
||||||
|
memcpy(m_means[i], sample, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
delete[] sample;
|
||||||
|
}
|
||||||
|
else if (m_initMode == InitManual)
|
||||||
|
{
|
||||||
|
// Do nothing
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void KMeans::Init(ifstream& sampleFile)
|
||||||
|
{
|
||||||
|
int size = 0;
|
||||||
|
sampleFile.seekg(0, ios_base::beg);
|
||||||
|
sampleFile.read((char*)&size, sizeof(int));
|
||||||
|
|
||||||
|
if (m_initMode == InitRandom)
|
||||||
|
{
|
||||||
|
int inteval = size / m_clusterNum;
|
||||||
|
double* sample = new double[m_dimNum];
|
||||||
|
|
||||||
|
// Seed the random-number generator with current time
|
||||||
|
srand((unsigned)time(NULL));
|
||||||
|
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
int select = inteval * i + (inteval - 1) * rand() / RAND_MAX;
|
||||||
|
int offset = sizeof(int) * 2 + select * sizeof(double) * m_dimNum;
|
||||||
|
|
||||||
|
sampleFile.seekg(offset, ios_base::beg);
|
||||||
|
sampleFile.read((char*)sample, sizeof(double) * m_dimNum);
|
||||||
|
memcpy(m_means[i], sample, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
delete[] sample;
|
||||||
|
}
|
||||||
|
else if (m_initMode == InitUniform)
|
||||||
|
{
|
||||||
|
double* sample = new double[m_dimNum];
|
||||||
|
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
int select = i * size / m_clusterNum;
|
||||||
|
int offset = sizeof(int) * 2 + select * sizeof(double) * m_dimNum;
|
||||||
|
|
||||||
|
sampleFile.seekg(offset, ios_base::beg);
|
||||||
|
sampleFile.read((char*)sample, sizeof(double) * m_dimNum);
|
||||||
|
memcpy(m_means[i], sample, sizeof(double) * m_dimNum);
|
||||||
|
}
|
||||||
|
|
||||||
|
delete[] sample;
|
||||||
|
}
|
||||||
|
else if (m_initMode == InitManual)
|
||||||
|
{
|
||||||
|
// Do nothing
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
double KMeans::GetLabel(const double* sample, int* label)
|
||||||
|
{
|
||||||
|
double dist = -1;
|
||||||
|
for (int i = 0; i < m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
double temp = CalcDistance(sample, m_means[i], m_dimNum);
|
||||||
|
if (temp < dist || dist == -1)
|
||||||
|
{
|
||||||
|
dist = temp;
|
||||||
|
*label = i;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return dist;
|
||||||
|
}
|
||||||
|
|
||||||
|
double KMeans::CalcDistance(const double* x, const double* u, int dimNum)
|
||||||
|
{
|
||||||
|
double temp = 0;
|
||||||
|
for (int d = 0; d < dimNum; d++)
|
||||||
|
{
|
||||||
|
temp += (x[d] - u[d]) * (x[d] - u[d]);
|
||||||
|
}
|
||||||
|
return sqrt(temp);
|
||||||
|
}
|
||||||
|
|
||||||
|
ostream& operator<<(ostream& out, KMeans& kmeans)
|
||||||
|
{
|
||||||
|
out << "<KMeans>" << endl;
|
||||||
|
out << "<DimNum> " << kmeans.m_dimNum << " </DimNum>" << endl;
|
||||||
|
out << "<ClusterNum> " << kmeans.m_clusterNum << " </CluterNum>" << endl;
|
||||||
|
|
||||||
|
out << "<Mean>" << endl;
|
||||||
|
for (int i = 0; i < kmeans.m_clusterNum; i++)
|
||||||
|
{
|
||||||
|
for (int d = 0; d < kmeans.m_dimNum; d++)
|
||||||
|
{
|
||||||
|
out << kmeans.m_means[i][d] << " ";
|
||||||
|
}
|
||||||
|
out << endl;
|
||||||
|
}
|
||||||
|
out << "</Mean>" << endl;
|
||||||
|
|
||||||
|
out << "</KMeans>" << endl;
|
||||||
|
return out;
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,57 @@
|
||||||
|
/***************************************************************************
|
||||||
|
Module Name:
|
||||||
|
KMeans
|
||||||
|
|
||||||
|
History:
|
||||||
|
2003/10/16 Fei Wang
|
||||||
|
2013 luxiaoxun
|
||||||
|
***************************************************************************/
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
#include <fstream>
|
||||||
|
|
||||||
|
class KMeans
|
||||||
|
{
|
||||||
|
public:
|
||||||
|
enum InitMode
|
||||||
|
{
|
||||||
|
InitRandom,
|
||||||
|
InitManual,
|
||||||
|
InitUniform,
|
||||||
|
};
|
||||||
|
|
||||||
|
KMeans(int dimNum = 1, int clusterNum = 1);
|
||||||
|
~KMeans();
|
||||||
|
|
||||||
|
void SetMean(int i, const double* u) { memcpy(m_means[i], u, sizeof(double) * m_dimNum); }
|
||||||
|
void SetInitMode(int i) { m_initMode = i; }
|
||||||
|
void SetMaxIterNum(int i) { m_maxIterNum = i; }
|
||||||
|
void SetEndError(double f) { m_endError = f; }
|
||||||
|
|
||||||
|
double* GetMean(int i) { return m_means[i]; }
|
||||||
|
int GetInitMode() { return m_initMode; }
|
||||||
|
int GetMaxIterNum() { return m_maxIterNum; }
|
||||||
|
double GetEndError() { return m_endError; }
|
||||||
|
|
||||||
|
|
||||||
|
/* SampleFile: <size><dim><data>...
|
||||||
|
LabelFile: <size><label>...
|
||||||
|
*/
|
||||||
|
void Cluster(const char* sampleFileName, const char* labelFileName);
|
||||||
|
void Init(std::ifstream& sampleFile);
|
||||||
|
void Init(double* data, int N);
|
||||||
|
void Cluster(double* data, int N, int* Label);
|
||||||
|
friend std::ostream& operator<<(std::ostream& out, KMeans& kmeans);
|
||||||
|
|
||||||
|
private:
|
||||||
|
int m_dimNum;
|
||||||
|
int m_clusterNum;
|
||||||
|
double** m_means;
|
||||||
|
|
||||||
|
int m_initMode;
|
||||||
|
int m_maxIterNum; // The stopping criterion regarding the number of iterations
|
||||||
|
double m_endError; // The stopping criterion regarding the error
|
||||||
|
|
||||||
|
double GetLabel(const double* x, int* label);
|
||||||
|
double CalcDistance(const double* x, const double* u, int dimNum);
|
||||||
|
};
|
||||||
|
|
@ -0,0 +1,325 @@
|
||||||
|
#include <iostream>
|
||||||
|
#include <iomanip>
|
||||||
|
#include <fstream>
|
||||||
|
#include <sstream>
|
||||||
|
#include <algorithm>
|
||||||
|
#include <random>
|
||||||
|
#include <unordered_map>
|
||||||
|
#include <omp.h>
|
||||||
|
#include <time.h>
|
||||||
|
#include <string>
|
||||||
|
#include <vector>
|
||||||
|
#include <queue>
|
||||||
|
#ifdef _WIN32
|
||||||
|
#include <io.h>
|
||||||
|
#include <process.h>
|
||||||
|
#define F_OK 0
|
||||||
|
#else
|
||||||
|
#include <unistd.h>
|
||||||
|
#endif
|
||||||
|
#include <mat.h>
|
||||||
|
#include "gmm.h"
|
||||||
|
using namespace std;
|
||||||
|
using std::cout;
|
||||||
|
using std::vector;
|
||||||
|
|
||||||
|
/* 从mat文件中读取给定名称的矩阵数据，并获取矩阵的行列数值 */
|
||||||
|
template<typename T>
|
||||||
|
T* ReadMatlabMat(const string &filePath, const string &mtxName, int *pRowNum, int *pColNum) {
|
||||||
|
T* dst = nullptr;
|
||||||
|
MATFile* pMatFile = nullptr;
|
||||||
|
mxArray* pMxArray = nullptr;
|
||||||
|
int rowNum, colNum;
|
||||||
|
double* matData;
|
||||||
|
pMatFile = matOpen(filePath.c_str(), "r"); //打开.mat文件
|
||||||
|
if (pMatFile == nullptr) {
|
||||||
|
cerr << "filePath is error!" << endl;
|
||||||
|
return nullptr;
|
||||||
|
}
|
||||||
|
pMxArray = matGetVariable(pMatFile, mtxName.c_str()); //获取.mat文件里面名为matrixName的矩阵
|
||||||
|
rowNum = mxGetM(pMxArray);
|
||||||
|
colNum = mxGetN(pMxArray);
|
||||||
|
cout << rowNum << " " << colNum << endl;
|
||||||
|
matData = (double*)mxGetData(pMxArray); //获取指针
|
||||||
|
|
||||||
|
dst = new T[rowNum * colNum];
|
||||||
|
for (int i = 0; i < rowNum; ++i) {
|
||||||
|
for (int j = 0; j < colNum; ++j) {
|
||||||
|
dst[i * colNum + j] = T(matData[j * rowNum + i]);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
mxDestroyArray(pMxArray); //释放内存
|
||||||
|
matClose(pMatFile); // 关闭文件
|
||||||
|
|
||||||
|
*pRowNum = rowNum;
|
||||||
|
*pColNum = colNum;
|
||||||
|
return dst;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 将数据写入mat文件中，用给定的名称命名 */
|
||||||
|
template<typename T>
|
||||||
|
bool SaveMatrix(T* src, MATFile* pMatFile, string matrixName, int rowNum, int colNum)
|
||||||
|
{
|
||||||
|
//转置存储
|
||||||
|
int datasize = colNum * rowNum;
|
||||||
|
double* mtxData = new double[datasize];//待存储数据转为double格式
|
||||||
|
// memset(mtxData, 0, datasize * sizeof(double));
|
||||||
|
for (int i = 0; i < rowNum; i++)
|
||||||
|
{
|
||||||
|
for (int j = 0; j < colNum; j++)
|
||||||
|
{
|
||||||
|
mtxData[j * rowNum + i] = double(src[i * colNum + j]);
|
||||||
|
// *(mtxData + j * rowNum + i) = (double)src[i * colNum + j]; 可消除警告
|
||||||
|
}
|
||||||
|
}
|
||||||
|
mxArray* pWriteArray = NULL;//matlab格式矩阵
|
||||||
|
if (pMatFile == nullptr)
|
||||||
|
{
|
||||||
|
cerr << "mat file pointer is error!" << endl;
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
//创建一个rowNum*colNum的矩阵
|
||||||
|
pWriteArray = mxCreateDoubleMatrix(rowNum, colNum, mxREAL);
|
||||||
|
//把data的值赋给pWriteArray指针
|
||||||
|
memcpy((void*)(mxGetPr(pWriteArray)), (void*)mtxData, sizeof(double) * datasize);
|
||||||
|
//给矩阵命名为matrixName
|
||||||
|
matPutVariable(pMatFile, matrixName.c_str(), pWriteArray);
|
||||||
|
|
||||||
|
mxDestroyArray(pWriteArray);//release resource
|
||||||
|
delete[]mtxData;//release resource
|
||||||
|
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 将x向量放到宽度为binWidth大小的桶中，功能类似matlab的hist*/
|
||||||
|
void PutXtoBin(double* x, int xSize, double binWidth, vector<double>& vXBin, vector<double>& vYBin) {
|
||||||
|
double maxX = 0.0;
|
||||||
|
for (int i = 0; i < xSize; ++i) {
|
||||||
|
if (maxX < x[i]) maxX = x[i];
|
||||||
|
}
|
||||||
|
int binSize = (int)((maxX + binWidth / 2) / binWidth + 1);
|
||||||
|
double binMaxVal = (binSize - 1) * binWidth;
|
||||||
|
if (binMaxVal > maxX) { // 确保最后一个bin不大于maxX，而且不小于maxX-binWidth
|
||||||
|
binSize -= 1;
|
||||||
|
}
|
||||||
|
vXBin.resize(xSize);
|
||||||
|
vYBin.resize(binSize);
|
||||||
|
for (int i = 0; i < binSize; ++i) vYBin[i] = 0;
|
||||||
|
for (int i = 0; i < xSize; ++i) {
|
||||||
|
int binIdx = (int)((x[i] + binWidth / 2) / binWidth);
|
||||||
|
if (binIdx >= binSize) binIdx = binSize - 1;
|
||||||
|
vYBin[binIdx] += 1;
|
||||||
|
// vXBin[i] = binIdx * binWidth;
|
||||||
|
}
|
||||||
|
// 按大小顺序将修改后的x数值存储在vXBin中，点的顺序不同，训练出的高斯混合模型参数会有一些不同。
|
||||||
|
int xIdx = 0;
|
||||||
|
for (int i = 0; i < binSize; ++i) {
|
||||||
|
for (int j = 0; j < vYBin[i]; ++j) {
|
||||||
|
vXBin[xIdx++] = i * binWidth;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 将标准高斯模型训练出的参数转换成自定义的系数, 并返回拟合后的Y值向量 */
|
||||||
|
struct cmpFunc {
|
||||||
|
bool operator()(const pair<double, double>& a, const pair<double, double>& b) { return a.first < b.first; }
|
||||||
|
};
|
||||||
|
void GMMToFactorEY(GMM& gmm, double binWidth, vector<double> &vYBin, vector<double>& vFactor, vector<double>& vEY) {
|
||||||
|
/* 需要调整曲线的权重，来拟合高斯曲线，而不是用概率密度 */
|
||||||
|
double zoomFactorSum = 0.0;
|
||||||
|
int valNum = 0;
|
||||||
|
vEY.resize(vYBin.size());
|
||||||
|
int topM = vYBin.size() / 4;
|
||||||
|
if (topM < 1) topM = 1;
|
||||||
|
/* 用堆排序的方式取前topM个最大值, 用来计算缩放参数*/
|
||||||
|
priority_queue<pair<double, double>, vector<pair<double, double> >, cmpFunc> pqTopM;
|
||||||
|
|
||||||
|
for (int i = 0; i < vYBin.size(); ++i) {
|
||||||
|
double xVal = i * binWidth;
|
||||||
|
double probVal = gmm.GetProbability(&xVal);
|
||||||
|
vEY[i] = probVal;
|
||||||
|
pqTopM.push(make_pair(vYBin[i], probVal));
|
||||||
|
}
|
||||||
|
|
||||||
|
for (int i = 0; i < topM; ++i) {
|
||||||
|
pair<double, double> topEle = pqTopM.top();
|
||||||
|
pqTopM.pop();
|
||||||
|
cout << topEle.first << '\t' << topEle.second << endl;
|
||||||
|
zoomFactorSum += topEle.first / topEle.second;
|
||||||
|
}
|
||||||
|
cout << endl;
|
||||||
|
|
||||||
|
double zoomFactor = zoomFactorSum / topM;
|
||||||
|
|
||||||
|
for (int i = 0; i < vEY.size(); ++i) {
|
||||||
|
vEY[i] *= zoomFactor;
|
||||||
|
}
|
||||||
|
vFactor.clear();
|
||||||
|
vFactor.push_back(zoomFactor * gmm.Prior(0) / sqrt(2 * M_PI * *gmm.Variance(0)));
|
||||||
|
vFactor.push_back(*gmm.Mean(0));
|
||||||
|
vFactor.push_back(sqrt(2 * *gmm.Variance(0)));
|
||||||
|
vFactor.push_back(zoomFactor * gmm.Prior(1) / sqrt(2 * M_PI * *gmm.Variance(1)));
|
||||||
|
vFactor.push_back(*gmm.Mean(1));
|
||||||
|
vFactor.push_back(sqrt(2 * *gmm.Variance(1)));
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 计算平均数 */
|
||||||
|
template <typename T>
|
||||||
|
T Average(vector<T>& vVal) {
|
||||||
|
T sumVal = T(0);
|
||||||
|
for (int i = 0; i < vVal.size(); ++i) {
|
||||||
|
sumVal += vVal[i];
|
||||||
|
}
|
||||||
|
return sumVal / vVal.size();
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 计算平方的均值 */
|
||||||
|
template <typename T>
|
||||||
|
T SquareAverage(vector<T>& vVal) {
|
||||||
|
vector<T> vSquare(vVal.size());
|
||||||
|
for (int i = 0; i < vVal.size(); ++i) {
|
||||||
|
vSquare[i] = vVal[i] * vVal[i];
|
||||||
|
}
|
||||||
|
return Average(vSquare);
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 计算向量x和y的相关距离, 向量维度必须相等*/
|
||||||
|
double CorrelationDistance(vector<double>& vX, vector<double>& vY) {
|
||||||
|
vector<double> vXY(vX.size());
|
||||||
|
for (int i = 0; i < vXY.size(); ++i) {
|
||||||
|
vXY[i] = vX[i] * vY[i];
|
||||||
|
}
|
||||||
|
double uv = Average(vXY);
|
||||||
|
double uu = SquareAverage(vX);
|
||||||
|
double vv = SquareAverage(vY);
|
||||||
|
|
||||||
|
double dist = 1.0 - uv / sqrt(uu * vv);
|
||||||
|
|
||||||
|
return abs(dist);
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 处理matlab的mat文件中包含的待拟合的数据 */
|
||||||
|
void processMatData(const string& filePath) {
|
||||||
|
|
||||||
|
double* hs = nullptr;
|
||||||
|
int rowNum = 0;
|
||||||
|
int colNum = 0;
|
||||||
|
|
||||||
|
clock_t begin, finish;
|
||||||
|
double total_cov = 0;
|
||||||
|
double total_cov2 = 0;
|
||||||
|
|
||||||
|
begin = clock();
|
||||||
|
hs = ReadMatlabMat<double>(filePath, "hs", &rowNum, &colNum);
|
||||||
|
ofstream gmmOfs("mat_gmm.debug");
|
||||||
|
ofstream gmmOfs2("mat_gmm2.debug");
|
||||||
|
ofstream xyOfs("xy_cpp.debug");
|
||||||
|
ofstream brOfs("br.debug");
|
||||||
|
vector<double>vXBin;
|
||||||
|
vector<double>vYBin;
|
||||||
|
vector<double>vEY;
|
||||||
|
vector<double>vFactor;
|
||||||
|
/* 用来保存数据，存入mat文件 */
|
||||||
|
vector<double>vDist(rowNum);
|
||||||
|
vector<double>vFactorAll;
|
||||||
|
for (int i = 0; i < rowNum; ++i) {
|
||||||
|
PutXtoBin(hs + i * colNum, colNum, 0.2, vXBin, vYBin);
|
||||||
|
// for (int m = 0; m < vYBin.size(); ++m) xyOfs<< fixed << setprecision(1) << 0.2 * m << ' ';
|
||||||
|
// xyOfs << endl;
|
||||||
|
// for (int m = 0; m < vYBin.size(); ++m) xyOfs << (int)vYBin[m] << ' ';
|
||||||
|
// xyOfs << endl;
|
||||||
|
|
||||||
|
GMM gmm(1, 2); // 1维， 2个高斯模型
|
||||||
|
gmm.Train(vXBin.data(), vXBin.size());
|
||||||
|
total_cov += *gmm.Variance(0);
|
||||||
|
gmmOfs << gmm << endl;
|
||||||
|
|
||||||
|
GMMToFactorEY(gmm, 0.2, vYBin, vFactor, vEY);
|
||||||
|
vDist[i] = CorrelationDistance(vYBin, vEY);
|
||||||
|
vFactorAll.insert(vFactorAll.end(), vFactor.begin(), vFactor.end());
|
||||||
|
|
||||||
|
brOfs << CorrelationDistance(vYBin, vEY) << endl;
|
||||||
|
for (int j = 0; j < vFactor.size(); ++j) brOfs << vFactor[j] << ", ";
|
||||||
|
|
||||||
|
GMM gmm2(1, 2);
|
||||||
|
gmm2.Train(hs + i * colNum, colNum);
|
||||||
|
total_cov2 += *gmm2.Variance(0);
|
||||||
|
gmmOfs2 << gmm2 << endl;
|
||||||
|
}
|
||||||
|
/* 写入matlab文件 */
|
||||||
|
MATFile* pMatFile = matOpen("D:\\save_br.mat", "w");
|
||||||
|
SaveMatrix<double>(vFactorAll.data(), pMatFile, "factor", rowNum, 6);
|
||||||
|
SaveMatrix<double>(vDist.data(), pMatFile, "correlation", rowNum, 1);
|
||||||
|
matClose(pMatFile);
|
||||||
|
|
||||||
|
gmmOfs.close();
|
||||||
|
gmmOfs2.close();
|
||||||
|
xyOfs.close();
|
||||||
|
brOfs.close();
|
||||||
|
finish = clock();
|
||||||
|
cout << "Total cov: " << total_cov << endl;
|
||||||
|
cout << "Total cov2: " << total_cov2 << endl;
|
||||||
|
cout << "Total time:" << (double)(finish - begin) / CLOCKS_PER_SEC << endl;
|
||||||
|
|
||||||
|
//MATFile* pMatFile = matOpen("D:\\save_hs.mat", "w");
|
||||||
|
//SaveMatrix<double>(hs, pMatFile, "hs_saved", rowNum, colNum);
|
||||||
|
//matClose(pMatFile);
|
||||||
|
|
||||||
|
delete[] hs;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 处理已经转换成txt的文本数据 */
|
||||||
|
void processTxtData(const string& filePath) {
|
||||||
|
clock_t begin, finish;
|
||||||
|
double total_cov = 0;
|
||||||
|
ifstream ifs(filePath, ios::in);
|
||||||
|
|
||||||
|
begin = clock();
|
||||||
|
ofstream gmmOfs("txt_gmm.debug");
|
||||||
|
while (!ifs.eof()) {
|
||||||
|
vector<double> vec_point;
|
||||||
|
string x_str, y_str;
|
||||||
|
if (!getline(ifs, x_str)) break;
|
||||||
|
if (!getline(ifs, y_str)) break;
|
||||||
|
// cout << x_str << endl << y_str << endl;
|
||||||
|
|
||||||
|
stringstream ss_x(x_str);
|
||||||
|
stringstream ss_y(y_str);
|
||||||
|
|
||||||
|
float x, y;
|
||||||
|
|
||||||
|
while (ss_x >> x && ss_y >> y) {
|
||||||
|
vec_point.resize(vec_point.size() + y);
|
||||||
|
for (int i = vec_point.size() - y; i < vec_point.size(); ++i)
|
||||||
|
vec_point[i] = x;
|
||||||
|
}
|
||||||
|
if (vec_point.size() == 0) continue;
|
||||||
|
|
||||||
|
GMM gmm(1, 2); // 1维， 2个高斯模型
|
||||||
|
gmm.Train(vec_point.data(), vec_point.size());
|
||||||
|
// cout << *gmm.Mean(0) << endl;
|
||||||
|
total_cov += *gmm.Variance(0);
|
||||||
|
gmmOfs << gmm << endl;
|
||||||
|
}
|
||||||
|
gmmOfs.close();
|
||||||
|
finish = clock();
|
||||||
|
cout << "Total cov: " << total_cov << endl;
|
||||||
|
cout << "Total time:" << (double)(finish - begin) / CLOCKS_PER_SEC << endl;
|
||||||
|
|
||||||
|
if (ifs.is_open())
|
||||||
|
ifs.close();
|
||||||
|
}
|
||||||
|
|
||||||
|
int main(int argc, char** argv) {
|
||||||
|
|
||||||
|
//if (argc != 2)
|
||||||
|
// cerr << "This program should take one argument(input data file)!" << endl;
|
||||||
|
|
||||||
|
|
||||||
|
// processMatData(argv[1]);
|
||||||
|
processMatData("D:\\Twirls\\runtime\\ALS_test\\1775\\twirls_id_abs2class_hs.mat");
|
||||||
|
processTxtData("D:\\Twirls\\backup\\xy.txt");
|
||||||
|
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
<?xml version="1.0" encoding="utf-8"?>
|
||||||
|
<packages>
|
||||||
|
<package id="Microsoft.Windows.CppWinRT" version="2.0.210806.1" targetFramework="native" />
|
||||||
|
</packages>
|
||||||
|
|
@ -0,0 +1,30 @@
|
||||||
|
========================================================================
|
||||||
|
C++/WinRT GMM Project Overview
|
||||||
|
========================================================================
|
||||||
|
|
||||||
|
This project demonstrates how to get started consuming Windows Runtime
|
||||||
|
classes directly from standard C++, using platform projection headers
|
||||||
|
generated from Windows SDK metadata files.
|
||||||
|
|
||||||
|
Steps to generate and consume SDK platform projection:
|
||||||
|
1. Build project initially to generate platform projection headers into
|
||||||
|
your Generated Files folder.
|
||||||
|
2. Include a projection namespace header in your pch.h, such as
|
||||||
|
<winrt/Windows.Foundation.h>.
|
||||||
|
3. Consume winrt namespace and any Windows Runtime namespaces, such as
|
||||||
|
winrt::Windows::Foundation, from source code.
|
||||||
|
4. Initialize apartment via init_apartment() and consume winrt classes.
|
||||||
|
|
||||||
|
Steps to generate and consume a projection from third party metadata:
|
||||||
|
1. Add a WinMD reference by right-clicking the References project node
|
||||||
|
and selecting "Add Reference...". In the Add References dialog,
|
||||||
|
browse to the component WinMD you want to consume and add it.
|
||||||
|
2. Build the project once to generate projection headers for the
|
||||||
|
referenced WinMD file under the "Generated Files" subfolder.
|
||||||
|
3. As above, include projection headers in pch or source code
|
||||||
|
to consume projected Windows Runtime classes.
|
||||||
|
|
||||||
|
========================================================================
|
||||||
|
Learn more about C++/WinRT here:
|
||||||
|
http://aka.ms/cppwinrt/
|
||||||
|
========================================================================
|
||||||
|
|
@ -1,3 +1,4 @@
|
||||||
# twirls
|
# twirls
|
||||||
|
|
||||||
重构matlab代码，改成python，加速文献感知
|
重构matlab代码，改成python，加速文献感知
|
||||||
|
先将matlab耗时的部分改成c++，通过文件的方式进行交互
|
||||||
|
|
@ -0,0 +1,16 @@
|
||||||
|
<?xml version="1.0" encoding="utf-8"?>
|
||||||
|
<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||||
|
<ImportGroup Label="PropertySheets" />
|
||||||
|
<PropertyGroup Label="UserMacros" />
|
||||||
|
<!--
|
||||||
|
To customize common C++/WinRT project properties:
|
||||||
|
* right-click the project node
|
||||||
|
* expand the Common Properties item
|
||||||
|
* select the C++/WinRT property page
|
||||||
|
|
||||||
|
For more advanced scenarios, and complete documentation, please see:
|
||||||
|
https://github.com/Microsoft/cppwinrt/tree/master/nuget
|
||||||
|
-->
|
||||||
|
<PropertyGroup />
|
||||||
|
<ItemDefinitionGroup />
|
||||||
|
</Project>
|
||||||
|
|
@ -0,0 +1,127 @@
|
||||||
|
<?xml version="1.0" encoding="utf-8"?>
|
||||||
|
<Project DefaultTargets="Build" ToolsVersion="15.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||||
|
<Import Project="..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.props" Condition="Exists('..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.props')" />
|
||||||
|
<PropertyGroup Label="Globals">
|
||||||
|
<CppWinRTOptimized>true</CppWinRTOptimized>
|
||||||
|
<CppWinRTRootNamespaceAutoMerge>true</CppWinRTRootNamespaceAutoMerge>
|
||||||
|
<CppWinRTGenerateWindowsMetadata>true</CppWinRTGenerateWindowsMetadata>
|
||||||
|
<MinimalCoreWin>true</MinimalCoreWin>
|
||||||
|
<VCProjectVersion>15.0</VCProjectVersion>
|
||||||
|
<ProjectGuid>{d3a55022-91f5-4db2-85a6-a25ae12cc840}</ProjectGuid>
|
||||||
|
<Keyword>Win32Proj</Keyword>
|
||||||
|
<RootNamespace>RandSim</RootNamespace>
|
||||||
|
<WindowsTargetPlatformVersion Condition=" '$(WindowsTargetPlatformVersion)' == '' ">10.0</WindowsTargetPlatformVersion>
|
||||||
|
<WindowsTargetPlatformMinVersion>10.0.17134.0</WindowsTargetPlatformMinVersion>
|
||||||
|
</PropertyGroup>
|
||||||
|
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
|
||||||
|
<ItemGroup Label="ProjectConfigurations">
|
||||||
|
<ProjectConfiguration Include="Debug|Win32">
|
||||||
|
<Configuration>Debug</Configuration>
|
||||||
|
<Platform>Win32</Platform>
|
||||||
|
</ProjectConfiguration>
|
||||||
|
<ProjectConfiguration Include="Release|Win32">
|
||||||
|
<Configuration>Release</Configuration>
|
||||||
|
<Platform>Win32</Platform>
|
||||||
|
</ProjectConfiguration>
|
||||||
|
<ProjectConfiguration Include="Debug|x64">
|
||||||
|
<Configuration>Debug</Configuration>
|
||||||
|
<Platform>x64</Platform>
|
||||||
|
</ProjectConfiguration>
|
||||||
|
<ProjectConfiguration Include="Release|x64">
|
||||||
|
<Configuration>Release</Configuration>
|
||||||
|
<Platform>x64</Platform>
|
||||||
|
</ProjectConfiguration>
|
||||||
|
</ItemGroup>
|
||||||
|
<PropertyGroup Label="Configuration">
|
||||||
|
<ConfigurationType>Application</ConfigurationType>
|
||||||
|
<PlatformToolset>v143</PlatformToolset>
|
||||||
|
<PlatformToolset Condition="'$(VisualStudioVersion)' == '16.0'">v142</PlatformToolset>
|
||||||
|
<PlatformToolset Condition="'$(VisualStudioVersion)' == '15.0'">v141</PlatformToolset>
|
||||||
|
<PlatformToolset Condition="'$(VisualStudioVersion)' == '14.0'">v140</PlatformToolset>
|
||||||
|
<CharacterSet>Unicode</CharacterSet>
|
||||||
|
</PropertyGroup>
|
||||||
|
<PropertyGroup Condition="'$(Configuration)'=='Debug'" Label="Configuration">
|
||||||
|
<UseDebugLibraries>true</UseDebugLibraries>
|
||||||
|
<LinkIncremental>true</LinkIncremental>
|
||||||
|
</PropertyGroup>
|
||||||
|
<PropertyGroup Condition="'$(Configuration)'=='Release'" Label="Configuration">
|
||||||
|
<UseDebugLibraries>false</UseDebugLibraries>
|
||||||
|
<WholeProgramOptimization>true</WholeProgramOptimization>
|
||||||
|
<LinkIncremental>false</LinkIncremental>
|
||||||
|
</PropertyGroup>
|
||||||
|
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||||
|
<ImportGroup Label="ExtensionSettings">
|
||||||
|
</ImportGroup>
|
||||||
|
<ImportGroup Label="Shared">
|
||||||
|
</ImportGroup>
|
||||||
|
<ImportGroup Label="PropertySheets">
|
||||||
|
<Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
|
||||||
|
</ImportGroup>
|
||||||
|
<ImportGroup Label="PropertySheets">
|
||||||
|
<Import Project="PropertySheet.props" />
|
||||||
|
</ImportGroup>
|
||||||
|
<PropertyGroup Label="UserMacros" />
|
||||||
|
<ItemDefinitionGroup>
|
||||||
|
<ClCompile>
|
||||||
|
<PrecompiledHeader>NotUsing</PrecompiledHeader>
|
||||||
|
<PrecompiledHeaderFile>pch.h</PrecompiledHeaderFile>
|
||||||
|
<PrecompiledHeaderOutputFile>$(IntDir)pch.pch</PrecompiledHeaderOutputFile>
|
||||||
|
<PreprocessorDefinitions>_CONSOLE;WIN32_LEAN_AND_MEAN;WINRT_LEAN_AND_MEAN;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||||
|
<WarningLevel>Level4</WarningLevel>
|
||||||
|
<AdditionalOptions>%(AdditionalOptions) /permissive- /bigobj</AdditionalOptions>
|
||||||
|
</ClCompile>
|
||||||
|
</ItemDefinitionGroup>
|
||||||
|
<ItemDefinitionGroup Condition="'$(Configuration)'=='Debug'">
|
||||||
|
<ClCompile>
|
||||||
|
<Optimization>Disabled</Optimization>
|
||||||
|
<PreprocessorDefinitions>_DEBUG;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||||
|
</ClCompile>
|
||||||
|
<Link>
|
||||||
|
<SubSystem>Console</SubSystem>
|
||||||
|
<GenerateWindowsMetadata>false</GenerateWindowsMetadata>
|
||||||
|
</Link>
|
||||||
|
</ItemDefinitionGroup>
|
||||||
|
<ItemDefinitionGroup Condition="'$(Platform)'=='Win32'">
|
||||||
|
<ClCompile>
|
||||||
|
<PreprocessorDefinitions>WIN32;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||||
|
</ClCompile>
|
||||||
|
</ItemDefinitionGroup>
|
||||||
|
<ItemDefinitionGroup Condition="'$(Configuration)'=='Release'">
|
||||||
|
<ClCompile>
|
||||||
|
<Optimization>MaxSpeed</Optimization>
|
||||||
|
<FunctionLevelLinking>true</FunctionLevelLinking>
|
||||||
|
<IntrinsicFunctions>true</IntrinsicFunctions>
|
||||||
|
<PreprocessorDefinitions>NDEBUG;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||||
|
</ClCompile>
|
||||||
|
<Link>
|
||||||
|
<SubSystem>Console</SubSystem>
|
||||||
|
<EnableCOMDATFolding>true</EnableCOMDATFolding>
|
||||||
|
<OptimizeReferences>true</OptimizeReferences>
|
||||||
|
<GenerateWindowsMetadata>false</GenerateWindowsMetadata>
|
||||||
|
</Link>
|
||||||
|
</ItemDefinitionGroup>
|
||||||
|
<ItemGroup>
|
||||||
|
<ClInclude Include="rand_sim.h" />
|
||||||
|
</ItemGroup>
|
||||||
|
<ItemGroup>
|
||||||
|
<ClCompile Include="rand_sim.cpp" />
|
||||||
|
</ItemGroup>
|
||||||
|
<ItemGroup>
|
||||||
|
<None Include="packages.config" />
|
||||||
|
<None Include="PropertySheet.props" />
|
||||||
|
<Text Include="readme.txt">
|
||||||
|
<DeploymentContent>false</DeploymentContent>
|
||||||
|
</Text>
|
||||||
|
</ItemGroup>
|
||||||
|
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||||
|
<ImportGroup Label="ExtensionTargets">
|
||||||
|
<Import Project="..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.targets" Condition="Exists('..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.targets')" />
|
||||||
|
</ImportGroup>
|
||||||
|
<Target Name="EnsureNuGetPackageBuildImports" BeforeTargets="PrepareForBuild">
|
||||||
|
<PropertyGroup>
|
||||||
|
<ErrorText>这台计算机上缺少此项目引用的 NuGet 程序包。使用“NuGet 程序包还原”可下载这些程序包。有关更多信息，请参见 http://go.microsoft.com/fwlink/?LinkID=322105。缺少的文件是 {0}。</ErrorText>
|
||||||
|
</PropertyGroup>
|
||||||
|
<Error Condition="!Exists('..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.props')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.props'))" />
|
||||||
|
<Error Condition="!Exists('..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.Windows.CppWinRT.2.0.210806.1\build\native\Microsoft.Windows.CppWinRT.targets'))" />
|
||||||
|
</Target>
|
||||||
|
</Project>
|
||||||
|
|
@ -0,0 +1,34 @@
|
||||||
|
<?xml version="1.0" encoding="utf-8"?>
|
||||||
|
<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||||
|
<ItemGroup>
|
||||||
|
<Filter Include="Source Files">
|
||||||
|
<UniqueIdentifier>{4FC737F1-C7A5-4376-A066-2A32D752A2FF}</UniqueIdentifier>
|
||||||
|
<Extensions>cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx</Extensions>
|
||||||
|
</Filter>
|
||||||
|
<Filter Include="Header Files">
|
||||||
|
<UniqueIdentifier>{93995380-89BD-4b04-88EB-625FBE52EBFB}</UniqueIdentifier>
|
||||||
|
<Extensions>h;hh;hpp;hxx;hm;inl;inc;xsd</Extensions>
|
||||||
|
</Filter>
|
||||||
|
<Filter Include="Resource Files">
|
||||||
|
<UniqueIdentifier>{67DA6AB6-F800-4c08-8B7A-83BB121AAD01}</UniqueIdentifier>
|
||||||
|
<Extensions>rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms</Extensions>
|
||||||
|
</Filter>
|
||||||
|
</ItemGroup>
|
||||||
|
<ItemGroup>
|
||||||
|
<ClInclude Include="rand_sim.h">
|
||||||
|
<Filter>Header Files</Filter>
|
||||||
|
</ClInclude>
|
||||||
|
</ItemGroup>
|
||||||
|
<ItemGroup>
|
||||||
|
<ClCompile Include="rand_sim.cpp">
|
||||||
|
<Filter>Source Files</Filter>
|
||||||
|
</ClCompile>
|
||||||
|
</ItemGroup>
|
||||||
|
<ItemGroup>
|
||||||
|
<None Include="PropertySheet.props" />
|
||||||
|
<None Include="packages.config" />
|
||||||
|
</ItemGroup>
|
||||||
|
<ItemGroup>
|
||||||
|
<Text Include="readme.txt" />
|
||||||
|
</ItemGroup>
|
||||||
|
</Project>
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
<?xml version="1.0" encoding="utf-8"?>
|
||||||
|
<packages>
|
||||||
|
<package id="Microsoft.Windows.CppWinRT" version="2.0.210806.1" targetFramework="native" />
|
||||||
|
</packages>
|
||||||
|
|
@ -0,0 +1,184 @@
|
||||||
|
#include <iostream>
|
||||||
|
#include <fstream>
|
||||||
|
#include <algorithm>
|
||||||
|
#include <random>
|
||||||
|
#include <unordered_map>
|
||||||
|
#include <omp.h>
|
||||||
|
#include <time.h>
|
||||||
|
#include <string>
|
||||||
|
#ifdef _WIN32
|
||||||
|
#include <io.h>
|
||||||
|
#include <process.h>
|
||||||
|
#define F_OK 0
|
||||||
|
#else
|
||||||
|
#include <unistd.h>
|
||||||
|
#endif
|
||||||
|
#include "rand_sim.h"
|
||||||
|
using namespace std;
|
||||||
|
|
||||||
|
void txtReaderVvs(vector<vector<string>>& vecVecStr, string cf) {
|
||||||
|
filebuf fb;
|
||||||
|
string fileName = cf;
|
||||||
|
if (fb.open(fileName.c_str(), ios::in) == NULL) {
|
||||||
|
cout << "error" << endl;
|
||||||
|
}
|
||||||
|
istream is(&fb);
|
||||||
|
string lineInfo;
|
||||||
|
while (getline(is, lineInfo)) {
|
||||||
|
int i = 0;
|
||||||
|
vector<string> vecStr;
|
||||||
|
string tmp;
|
||||||
|
while (i < lineInfo.length()) {
|
||||||
|
while (i < lineInfo.length() && lineInfo[i] != ' ') {
|
||||||
|
tmp += lineInfo[i++];
|
||||||
|
}
|
||||||
|
if (!tmp.empty()) {
|
||||||
|
transform(tmp.begin(), tmp.end(), tmp.begin(), ::tolower);
|
||||||
|
vecStr.push_back(tmp);
|
||||||
|
}
|
||||||
|
tmp.clear();
|
||||||
|
++i;
|
||||||
|
}
|
||||||
|
vecVecStr.push_back(vecStr);
|
||||||
|
}
|
||||||
|
fb.close();
|
||||||
|
}
|
||||||
|
template <class AUTO>
|
||||||
|
void txtWriterVvs(vector<vector<AUTO>>& vecVecStr, string cf) {
|
||||||
|
ofstream os;
|
||||||
|
string fileName = cf;
|
||||||
|
os.open(fileName, ios::out);
|
||||||
|
|
||||||
|
for (auto vecStr : vecVecStr) {
|
||||||
|
for (auto s : vecStr)
|
||||||
|
os << s << " ";
|
||||||
|
os << "" << endl;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void txtReaderVs(vector<string>& vecStr, string cf) {
|
||||||
|
filebuf fb;
|
||||||
|
string fileName = cf;
|
||||||
|
if (fb.open(fileName.c_str(), ios::in) == NULL) {
|
||||||
|
cout << "error" << endl;
|
||||||
|
}
|
||||||
|
istream is(&fb);
|
||||||
|
string lineInfo;
|
||||||
|
while (getline(is, lineInfo)) {
|
||||||
|
transform(lineInfo.begin(), lineInfo.end(), lineInfo.begin(), ::tolower);
|
||||||
|
vecStr.push_back(lineInfo);
|
||||||
|
}
|
||||||
|
fb.close();
|
||||||
|
}
|
||||||
|
void txtWriterVs(vector<string>& vecStr, string cf) {
|
||||||
|
ofstream os;
|
||||||
|
string fileName = cf;
|
||||||
|
os.open(fileName, ios::out);
|
||||||
|
|
||||||
|
for (auto s : vecStr) {
|
||||||
|
os << s << endl;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
/* run this program using the console pauser or add your own getch, system("pause") or input loop */
|
||||||
|
int main(int argc, char** argv) {
|
||||||
|
//input : dicr, wd2, wd1s;
|
||||||
|
vector<string> dicr;
|
||||||
|
vector<vector<string>> wd2;
|
||||||
|
vector<vector<string>> wd1s;
|
||||||
|
|
||||||
|
string dicrName = "F:\\myWork\\20191129\\dicr.txt";
|
||||||
|
string wd2Name = "F:\\myWork\\20191129\\wd2.txt";
|
||||||
|
string wd1sName = "F:\\myWork\\20191129\\wd1s.txt";
|
||||||
|
string ZrName = "F:\\myWork\\20191129\\Zr.txt";
|
||||||
|
int loopNum;
|
||||||
|
|
||||||
|
if (argc != 6) {
|
||||||
|
cout << "Unexpected count of input arguments! Please input paths of dicr, wd2, wd1s, Zr and loop num! eg: F:\\myWork\\dicr.txt or 1000" << endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
dicrName = argv[1];
|
||||||
|
wd2Name = argv[2];
|
||||||
|
wd1sName = argv[3];
|
||||||
|
ZrName = argv[4];
|
||||||
|
loopNum = atoi(argv[5]);
|
||||||
|
|
||||||
|
if (access(dicrName.c_str(), F_OK) == -1) {
|
||||||
|
cout << "Invalid dicr file path! Please input a right path!" << endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (access(wd2Name.c_str(), F_OK) == -1) {
|
||||||
|
cout << "Invalid wd2 file path! Please input a right path!" << endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (access(wd1sName.c_str(), F_OK) == -1) {
|
||||||
|
cout << "Invalid wd1s file path! Please input a right path!" << endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
txtReaderVs(dicr, dicrName);
|
||||||
|
txtReaderVvs(wd2, wd2Name);
|
||||||
|
txtReaderVvs(wd1s, wd1sName);
|
||||||
|
// cout<<"/************* Input file read finished! ***************/"<<endl;
|
||||||
|
//cout<<dicr.size()<<" "<<wd2.size()<<" "<<wd1s.size()<<endl;
|
||||||
|
//for(int i=0;i<50;++i)
|
||||||
|
// cout<<wd1s[i].size()<<endl;
|
||||||
|
unordered_map<string, int> dicrHashMap;
|
||||||
|
for (int i = 0;i < dicr.size();++i)
|
||||||
|
dicrHashMap[dicr[i]] = i;
|
||||||
|
//output : Zr;
|
||||||
|
vector<vector<int>> Zr(loopNum, vector<int>(dicr.size(), 0));
|
||||||
|
vector<int> randNums(wd2.size(), 0);
|
||||||
|
for (int i = 0; i < wd2.size(); ++i) {
|
||||||
|
randNums[i] = i;
|
||||||
|
}
|
||||||
|
//cout<<"12345"<<endl;
|
||||||
|
clock_t start, end;
|
||||||
|
start = clock();
|
||||||
|
//cout<<dicr.size()<<endl;
|
||||||
|
//cout<<wd2.size()<<" "<<wd2[0].size()<<endl;
|
||||||
|
//cout<<wd1s.size()<<" "<<wd1s[0].size()<<endl;
|
||||||
|
//#pragma omp parallel for
|
||||||
|
for (int i = 0; i < loopNum; ++i) {
|
||||||
|
clock_t begin = clock();
|
||||||
|
//#pragma omp atomic
|
||||||
|
/*vector<int> randNums(wd2.size(),0);
|
||||||
|
for(int i = 0; i < wd2.size(); ++i){
|
||||||
|
randNums[i] = i;
|
||||||
|
}*/
|
||||||
|
std::random_device rd;
|
||||||
|
std::shuffle(randNums.begin(), randNums.end(), std::default_random_engine(rd()));
|
||||||
|
//cout<<i<<endl;
|
||||||
|
for (int j = 0; j < wd1s.size(); ++j) {
|
||||||
|
vector<string> wd2rCell = wd2[randNums[j]];
|
||||||
|
//cout<<"step 1"<<endl;
|
||||||
|
vector<int> f(dicr.size(), 0);
|
||||||
|
//cout<<"step 2"<<endl;
|
||||||
|
for (int k = 0;k < wd2rCell.size();++k) {
|
||||||
|
string tmp = wd2rCell[k];
|
||||||
|
//cout<<tmp<<" "<<k<<endl;
|
||||||
|
if (dicrHashMap.find(tmp) != dicrHashMap.end()) {
|
||||||
|
//cout<<dicrHashMap[tmp]<<endl;
|
||||||
|
f[dicrHashMap[tmp]] = 1;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
//cout<<"step 3"<<endl;
|
||||||
|
for (int k = 0;k < dicr.size();++k) {
|
||||||
|
if (f[k] == 1)
|
||||||
|
Zr[i][k]++;
|
||||||
|
}
|
||||||
|
//cout<<"step 4"<<endl;
|
||||||
|
}
|
||||||
|
clock_t finish = clock();
|
||||||
|
double loopTime = (double)(finish - begin) / CLOCKS_PER_SEC;
|
||||||
|
//cout<<"Loop : "<<i<<" Time consuming :"<<loopTime<<"s"<<endl;
|
||||||
|
}
|
||||||
|
end = clock();
|
||||||
|
double endtime = (double)(end - start) / CLOCKS_PER_SEC;
|
||||||
|
//cout<<"Total time:"<<endtime<<endl;
|
||||||
|
txtWriterVvs(Zr, ZrName);
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,3 @@
|
||||||
|
#pragma once
|
||||||
|
#include <winrt/Windows.Foundation.h>
|
||||||
|
#include <winrt/Windows.Foundation.Collections.h>
|
||||||
|
|
@ -0,0 +1,30 @@
|
||||||
|
========================================================================
|
||||||
|
C++/WinRT RandSim Project Overview
|
||||||
|
========================================================================
|
||||||
|
|
||||||
|
This project demonstrates how to get started consuming Windows Runtime
|
||||||
|
classes directly from standard C++, using platform projection headers
|
||||||
|
generated from Windows SDK metadata files.
|
||||||
|
|
||||||
|
Steps to generate and consume SDK platform projection:
|
||||||
|
1. Build project initially to generate platform projection headers into
|
||||||
|
your Generated Files folder.
|
||||||
|
2. Include a projection namespace header in your pch.h, such as
|
||||||
|
<winrt/Windows.Foundation.h>.
|
||||||
|
3. Consume winrt namespace and any Windows Runtime namespaces, such as
|
||||||
|
winrt::Windows::Foundation, from source code.
|
||||||
|
4. Initialize apartment via init_apartment() and consume winrt classes.
|
||||||
|
|
||||||
|
Steps to generate and consume a projection from third party metadata:
|
||||||
|
1. Add a WinMD reference by right-clicking the References project node
|
||||||
|
and selecting "Add Reference...". In the Add References dialog,
|
||||||
|
browse to the component WinMD you want to consume and add it.
|
||||||
|
2. Build the project once to generate projection headers for the
|
||||||
|
referenced WinMD file under the "Generated Files" subfolder.
|
||||||
|
3. As above, include projection headers in pch or source code
|
||||||
|
to consume projected Windows Runtime classes.
|
||||||
|
|
||||||
|
========================================================================
|
||||||
|
Learn more about C++/WinRT here:
|
||||||
|
http://aka.ms/cppwinrt/
|
||||||
|
========================================================================
|
||||||
|
|
@ -0,0 +1,41 @@
|
||||||
|
|
||||||
|
Microsoft Visual Studio Solution File, Format Version 12.00
|
||||||
|
# Visual Studio Version 17
|
||||||
|
VisualStudioVersion = 17.0.32014.148
|
||||||
|
MinimumVisualStudioVersion = 10.0.40219.1
|
||||||
|
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "GMM", "GMM\GMM.vcxproj", "{A2B67815-1235-4F7C-874D-4FCCB3B0C738}"
|
||||||
|
EndProject
|
||||||
|
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "RandSim", "RandSim\RandSim.vcxproj", "{D3A55022-91F5-4DB2-85A6-A25AE12CC840}"
|
||||||
|
EndProject
|
||||||
|
Global
|
||||||
|
GlobalSection(SolutionConfigurationPlatforms) = preSolution
|
||||||
|
Debug|x64 = Debug|x64
|
||||||
|
Debug|x86 = Debug|x86
|
||||||
|
Release|x64 = Release|x64
|
||||||
|
Release|x86 = Release|x86
|
||||||
|
EndGlobalSection
|
||||||
|
GlobalSection(ProjectConfigurationPlatforms) = postSolution
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Debug|x64.ActiveCfg = Debug|x64
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Debug|x64.Build.0 = Debug|x64
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Debug|x86.ActiveCfg = Debug|Win32
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Debug|x86.Build.0 = Debug|Win32
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Release|x64.ActiveCfg = Release|x64
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Release|x64.Build.0 = Release|x64
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Release|x86.ActiveCfg = Release|Win32
|
||||||
|
{A2B67815-1235-4F7C-874D-4FCCB3B0C738}.Release|x86.Build.0 = Release|Win32
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Debug|x64.ActiveCfg = Debug|x64
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Debug|x64.Build.0 = Debug|x64
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Debug|x86.ActiveCfg = Debug|Win32
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Debug|x86.Build.0 = Debug|Win32
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Release|x64.ActiveCfg = Release|x64
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Release|x64.Build.0 = Release|x64
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Release|x86.ActiveCfg = Release|Win32
|
||||||
|
{D3A55022-91F5-4DB2-85A6-A25AE12CC840}.Release|x86.Build.0 = Release|Win32
|
||||||
|
EndGlobalSection
|
||||||
|
GlobalSection(SolutionProperties) = preSolution
|
||||||
|
HideSolutionNode = FALSE
|
||||||
|
EndGlobalSection
|
||||||
|
GlobalSection(ExtensibilityGlobals) = postSolution
|
||||||
|
SolutionGuid = {250954D6-DDDE-4F05-AE0F-FF61CD93D1E1}
|
||||||
|
EndGlobalSection
|
||||||
|
EndGlobal
|
||||||
Loading…
Reference in New Issue