SlideShare a Scribd company logo
1 of 80
SHOGUN

 2011 4   23       9      CV       PRML
               @yasutomo57jp (   @inco_san   )
SHOGUN
   1                       SHOGUN

 2011 4   23       9      CV       PRML
               @yasutomo57jp (   @inco_san   )
SHOGUN
*
OpenCV


 * http://d.hatena.ne.jp/takmin/20110306/1299423617
• SHOGUN
• SHOGUN
• 1        SHOGUN
• SHOGUN
• 1                     SHOGUN
 •   Static Interface
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
    •   libshogun
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
    •   libshogun
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
    •   libshogun
SHOGUN
SHOGUN
•
SHOGUN
•
    •   SVM   !
SHOGUN
•
    •           SVM                    !

        • SVM         OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT
SHOGUN
•
    •           SVM                       !

        • SVM          OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT



        •             Linear, Polynomial, Gaussian and Sigmoid Kernel
SHOGUN
•
    •           SVM                       !

        • SVM          OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT



        •             Linear, Polynomial, Gaussian and Sigmoid Kernel



    •
SHOGUN
• SVM             !!

 • LDA : Linear Discriminant Analysis
 • LPM : Linear Programming Machine
 • (Kernel) Perceptron
 • HMM
SHOGUN


•
•
SHOGUN
Q. Matlab
Q. Matlab
Octave
Python
Python
Q. C++
         …
…
…
SHOGUN
SHOGUN
• Static Interface
    •
    •
•    Modular Interface
    • Python Octave
    •
•    libshogun
    •   C++
    •
• Static Interface
    •
    •
•    Modular Interface
    • Python Octave
    •
•    libshogun
    •   C++
    •
Windows
Cygwin

http://www.shogun-toolbox.org/#releases
Windows
                                           Linux (Ubuntu)
Cygwin
                                          sudo apt-get install shogun
http://www.shogun-toolbox.org/#releases
Windows
                                           Linux (Ubuntu)
Cygwin
                                          sudo apt-get install shogun
http://www.shogun-toolbox.org/#releases



          Mac
sudo port install shogun
Windows
                                           Linux (Ubuntu)
Cygwin
                                          sudo apt-get install shogun
http://www.shogun-toolbox.org/#releases



          Mac
sudo port install shogun                                       OK
SVM
        ••      libsvm
                                                    (Cmdline   )


set_kernel GAUSSIAN REAL 10 1.2
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                libsvm
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                libsvm
c1                                                    C      1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline             )


set_kernel GAUSSIAN REAL 10 1.2                                                  (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify                                                       out.txt
••      libsvm
                                                                   (Cmdline             )


set_kernel SIGMOID REAL 50 3 0                                                   (cache, gamma, coeff)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify                                                       out.txt
SVM

        ••      svmlight
                                                    (Cmdline   )


set_kernel GAUSSIAN REAL 10 1.2
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                 libsvm
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                 libsvm
c1                                                    C      1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline             )


set_kernel GAUSSIAN REAL 10 1.2                                                  (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify                                                       out.txt
Python

• sg            ( from sg import sg                )

 • sg                                         OK

   • Cmdline       set_feature TEST data.dat

   • Python        sg(‘set_feature’, ‘TEST’, ‘data.dat’)


     http://www.shogun-toolbox.org/doc/static_tutorial.html
• SHOGUN
• SHOGUN
•3
• SHOGUN
•3
 • Static Interface,Modular Interface, libshogun
• SHOGUN
•3
 • Static Interface,Modular Interface, libshogun
 •          Static Interface
• SHOGUN
•3
  • Static Interface,Modular Interface, libshogun
  •          Static Interface

•
• SHOGUN
•3
  • Static Interface,Modular Interface, libshogun
  •          Static Interface

•
• SHOGUN
•3
  • Static Interface,Modular Interface, libshogun
  •          Static Interface

•
           Modular Interface

More Related Content

What's hot

Programming using Open Mp
Programming using Open MpProgramming using Open Mp
Programming using Open Mp
Anshul Sharma
 
OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2
Amar Kapadia
 
DataMapper on Infinispan
DataMapper on InfinispanDataMapper on Infinispan
DataMapper on Infinispan
Lance Ball
 
ZK_Arch_notes_20081121
ZK_Arch_notes_20081121ZK_Arch_notes_20081121
ZK_Arch_notes_20081121
WANGCHOU LU
 
20140419 oedo rubykaigi04
20140419 oedo rubykaigi0420140419 oedo rubykaigi04
20140419 oedo rubykaigi04
Hiroshi SHIBATA
 
20140425 ruby conftaiwan2014
20140425 ruby conftaiwan201420140425 ruby conftaiwan2014
20140425 ruby conftaiwan2014
Hiroshi SHIBATA
 

What's hot (20)

Programming using Open Mp
Programming using Open MpProgramming using Open Mp
Programming using Open Mp
 
TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011
 
Ffmpeg
FfmpegFfmpeg
Ffmpeg
 
OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2
 
Find bottleneck and tuning in Java Application
Find bottleneck and tuning in Java ApplicationFind bottleneck and tuning in Java Application
Find bottleneck and tuning in Java Application
 
DataMapper on Infinispan
DataMapper on InfinispanDataMapper on Infinispan
DataMapper on Infinispan
 
RubyGems 3 & 4
RubyGems 3 & 4RubyGems 3 & 4
RubyGems 3 & 4
 
Complex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBoxComplex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBox
 
Tips of Malloc & Free
Tips of Malloc & FreeTips of Malloc & Free
Tips of Malloc & Free
 
TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011
 
ZK_Arch_notes_20081121
ZK_Arch_notes_20081121ZK_Arch_notes_20081121
ZK_Arch_notes_20081121
 
Ruby 2.4 Internals
Ruby 2.4 InternalsRuby 2.4 Internals
Ruby 2.4 Internals
 
20140419 oedo rubykaigi04
20140419 oedo rubykaigi0420140419 oedo rubykaigi04
20140419 oedo rubykaigi04
 
Lec7
Lec7Lec7
Lec7
 
Configuration management II - Terraform
Configuration management II - TerraformConfiguration management II - Terraform
Configuration management II - Terraform
 
How to distribute Ruby to the world
How to distribute Ruby to the worldHow to distribute Ruby to the world
How to distribute Ruby to the world
 
20140925 rails pacific
20140925 rails pacific20140925 rails pacific
20140925 rails pacific
 
RubyGems 3 & 4
RubyGems 3 & 4RubyGems 3 & 4
RubyGems 3 & 4
 
20140425 ruby conftaiwan2014
20140425 ruby conftaiwan201420140425 ruby conftaiwan2014
20140425 ruby conftaiwan2014
 
JVM Internals (2015)
JVM Internals (2015)JVM Internals (2015)
JVM Internals (2015)
 

Similar to SHOGUN使ってみました

Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machines
butest
 
Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machines
butest
 
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X SandboxMeder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
DefconRussia
 

Similar to SHOGUN使ってみました (20)

Jvm internals
Jvm internalsJvm internals
Jvm internals
 
Jvm Performance Tunning
Jvm Performance TunningJvm Performance Tunning
Jvm Performance Tunning
 
Jvm Performance Tunning
Jvm Performance TunningJvm Performance Tunning
Jvm Performance Tunning
 
Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machines
 
Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machines
 
Beirut Java User Group JVM presentation
Beirut Java User Group JVM presentationBeirut Java User Group JVM presentation
Beirut Java User Group JVM presentation
 
Новый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоныНовый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоны
 
Software Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in JavaSoftware Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in Java
 
Introduction to LAVA Workload Scheduler
Introduction to LAVA Workload SchedulerIntroduction to LAVA Workload Scheduler
Introduction to LAVA Workload Scheduler
 
New features in Ruby 2.5
New features in Ruby 2.5New features in Ruby 2.5
New features in Ruby 2.5
 
Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108
 
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
 
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X SandboxMeder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
 
Grow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM StackGrow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM Stack
 
Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018
 
Python + GDB = Javaデバッガ
Python + GDB = JavaデバッガPython + GDB = Javaデバッガ
Python + GDB = Javaデバッガ
 
DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)
 
[BGOUG] Java GC - Friend or Foe
[BGOUG] Java GC - Friend or Foe[BGOUG] Java GC - Friend or Foe
[BGOUG] Java GC - Friend or Foe
 
Adding a BOLT pass
Adding a BOLT passAdding a BOLT pass
Adding a BOLT pass
 
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container DayQuantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
 

More from Yasutomo Kawanishi

画像処理でのPythonの利用
画像処理でのPythonの利用画像処理でのPythonの利用
画像処理でのPythonの利用
Yasutomo Kawanishi
 
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
Yasutomo Kawanishi
 

More from Yasutomo Kawanishi (12)

TransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by TransformerTransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by Transformer
 
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
 
Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~
 
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
 
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
 
サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識
 
Pythonによる画像処理について
Pythonによる画像処理についてPythonによる画像処理について
Pythonによる画像処理について
 
ACCV2014参加報告
ACCV2014参加報告ACCV2014参加報告
ACCV2014参加報告
 
背景モデリングに関する研究など
背景モデリングに関する研究など背景モデリングに関する研究など
背景モデリングに関する研究など
 
画像処理でのPythonの利用
画像処理でのPythonの利用画像処理でのPythonの利用
画像処理でのPythonの利用
 
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
 
SNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシングSNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシング
 

Recently uploaded

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Recently uploaded (20)

Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 

SHOGUN使ってみました

  • 1. SHOGUN 2011 4 23 9 CV PRML @yasutomo57jp ( @inco_san )
  • 2. SHOGUN 1 SHOGUN 2011 4 23 9 CV PRML @yasutomo57jp ( @inco_san )
  • 5.
  • 6.
  • 9. • SHOGUN • 1 SHOGUN • Static Interface
  • 10. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN
  • 11. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface
  • 12. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( )
  • 13. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
  • 14. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
  • 15. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
  • 18. SHOGUN • • SVM !
  • 19. SHOGUN • • SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT
  • 20. SHOGUN • • SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT • Linear, Polynomial, Gaussian and Sigmoid Kernel
  • 21. SHOGUN • • SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT • Linear, Polynomial, Gaussian and Sigmoid Kernel •
  • 22. SHOGUN • SVM !! • LDA : Linear Discriminant Analysis • LPM : Linear Programming Machine • (Kernel) Perceptron • HMM
  • 26.
  • 31. Q. C++
  • 32.
  • 33.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40. • Static Interface • • • Modular Interface • Python Octave • • libshogun • C++ •
  • 41. • Static Interface • • • Modular Interface • Python Octave • • libshogun • C++ •
  • 42.
  • 43.
  • 45. Windows Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases
  • 46. Windows Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases Mac sudo port install shogun
  • 47. Windows Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases Mac sudo port install shogun OK
  • 48.
  • 49. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 50. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 51. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 52. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 53. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 54. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 55. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 56. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 57. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 58. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 59. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
  • 60. •• libsvm (Cmdline ) set_kernel SIGMOID REAL 50 3 0 (cache, gamma, coeff) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
  • 61. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 62. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 63. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 64. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 65. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 66. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 67. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 68. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 69. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 70. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 71. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
  • 72. Python • sg ( from sg import sg ) • sg OK • Cmdline set_feature TEST data.dat • Python sg(‘set_feature’, ‘TEST’, ‘data.dat’) http://www.shogun-toolbox.org/doc/static_tutorial.html
  • 73.
  • 76. • SHOGUN •3 • Static Interface,Modular Interface, libshogun
  • 77. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface
  • 78. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface •
  • 79. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface •
  • 80. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface • Modular Interface

Editor's Notes

  1. \n
  2. \n
  3. \n
  4. \n
  5. \n
  6. \n
  7. \n
  8. \n
  9. \n
  10. \n
  11. \n
  12. \n
  13. \n
  14. \n
  15. \n
  16. \n
  17. \n
  18. \n
  19. \n
  20. \n
  21. \n
  22. \n
  23. \n
  24. \n
  25. \n
  26. \n
  27. \n
  28. \n
  29. \n
  30. \n
  31. \n
  32. \n
  33. \n
  34. \n
  35. \n
  36. \n
  37. \n
  38. \n
  39. \n
  40. \n
  41. \n
  42. \n
  43. \n
  44. \n
  45. \n
  46. \n
  47. \n
  48. \n
  49. \n
  50. \n
  51. \n
  52. \n
  53. \n
  54. \n
  55. \n
  56. \n
  57. \n
  58. \n
  59. \n
  60. \n
  61. \n
  62. \n
  63. \n
  64. \n
  65. \n
  66. \n
  67. \n
  68. \n
  69. \n
  70. \n
  71. \n
  72. \n
  73. \n
  74. \n
  75. \n
  76. \n
  77. \n
  78. \n