www.gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\stprtool\svm\svmdemo.m
function result = svmdemo(action,hfigure,varargin)
% SVMDEMO demo on the Support Vector Machines.
%
% SVMDEMO demonstrates use of the algorithms which
% solves the SVM problem with the both linear (L1)
% and quadratic (L2) penalization of classification
% violations. The input data must be 2-dimensional
% and can be interactively created by the user.
%
% Following algorithms can be tested:
%
% - Sequential Minimal Optimizer (SMO) solving SVM-L1.
% - SVM-L1 solved by the Matlab Optimization toolbox.
% - Kernel Schlesinger-Kozinec's algortihm solving SVM-L2.
% - SVM-L2 solved by the Matlab Optimization toolbox.
%
% Control:
% Algorithm - algorithm for testing.
% Kernel - non-linear kernel.
% Kernel argument - argument of the non-linear kernel.
% C-constant - trade-off (regularization) constant.
% parameters - parameters of the selected algorithm.
% background - if selected then the background color
% denotes the sign and the intenzity denotes the value
% of the found decision function.
%
% FIG2EPS - exports screen to the PostScript file.
% Load data - loads input training sets from file.
% Create data - calls program for creating point sets.
% Reset - clears the screen.
% Train SVM - trains and displays the SVM classifer.
% Info - calls the info box.
% Close - close the program.
%
% See also SMO, SVMMOT, KERNELSK, SVM2MOT, KPERCEPTR, SVM.
%
% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
%
% Modifications
% 6-march-2002, V.Franc
% 23-oct-2001, V.Franc
BORDER=0.2; % minimal space between axis and points
DATA_IDENT='Finite sets, Enumeration'; % file identifier
ALGOS=['SMO (L1) ';...
'Optim toolbox (L1) ';...
'Kernel-SK (L2) ';...
'Optim toolbox (L2) ';...
'Kernel-Perceptron '];
KERNELS=['Linear ';...
'Polynomial';...
'RBF '];
SMO_PARAM = 'epsilon,tolerance';
DEF_SMO_PARAM = '1e-3,1e-3';
KERNELSK_PARAM = 'epsilon,iter_limit';
DEF_KERNELSK_PARAM = '1e-3,inf';
KPERCEPTR_PARAM = 'tmax';
DEF_KPERCEPTR_PARAM = 'inf';
% if number of arguments is less then 1, that means first call of this
% function. Every other calls set up at least argument action
if nargin < 1,
action = 'initialize';
end
% what action is required ?
switch lower(action)
case 'initialize'
% == Initialize user interface control and figure =======
% == Figure =============================================
left=0.1;
width=0.8;
bottom=0.1;
height=0.8;
hfigure=figure('Name','Support Vector Machines', ...
'Visible','off',...
'NumberTitle','off', ...
'Units','normalized', ...
'Position',[left bottom width height],...
'Units','normalized', ...
'tag','svmdemo');
% == Axes =========================================
left=0.1;
width=0.65;
bottom=0.35;
height=0.60;
haxes=axes(...
'Units','normalized', ...
'UserData',[],...
'Position',[left bottom width height]);
xlabel('feature x');
ylabel('feature y');
% == Comment window =================================
% Comment Window frame
bottom=0.05;
height=0.2;
uicontrol( ...
'Style','frame', ...
'Units','normalized', ...
'Position',[left bottom width height], ...
'BackgroundColor',[0.5 0.5 0.5]);
% Text label
uicontrol( ...
'Style','text', ...
'Units','normalized', ...
'Position',[left height-0.01 width 0.05], ...
'BackgroundColor',[0.5 0.5 0.5], ...
'ForegroundColor',[1 1 1], ...
'String','Comment Window');
% Edit window
border=0.01;
hconsole=uicontrol( ...
'Style','edit', ...
'HorizontalAlignment','left', ...
'Units','normalized', ...
'Max',10, ...
'BackgroundColor',[1 1 1], ...
'Position',[left+border bottom width-2*border height-0.05], ...
'Enable','inactive',...
'String','');
% == Buttons ===========================================
% Export to EPS
width=0.1;
left=0.75-width;
bottom=0.95;
height=0.04;
hbt_close = uicontrol(...
'Units','Normalized', ...
'Callback','fig2eps(gcf)',...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','FIG2EPS');
% Close button
left=0.8;
bottom=0.05;
height=0.045;
width=0.15;
hbt_close = uicontrol(...
'Units','Normalized', ...
'Callback','close(gcf)',...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','Close');
% Info button: call stanard info box
bottom=bottom+1.5*height;
hbt_info = uicontrol(...
'Units','Normalized', ...
'Callback','svmdemo(''info'',gcf)',...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','Info');
% Train SVM button
bottom=bottom+1.5*height;
hbt_train = uicontrol(...
'Units','Normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','Train SVM', ...
'Callback','svmdemo(''train'',gcf)');
% Reset button
bottom=bottom+height;
hbt_reset = uicontrol(...
'Units','Normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','Reset', ...
'Callback','svmdemo(''reset'',gcf)');
% Creat data
bottom=bottom+1.5*height;
hbt_creat = uicontrol(...
'Units','Normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','Create data', ...
'Callback','svmdemo(''creatdata'',gcf)');
% Load data
bottom=bottom+1*height;
hbt_load = uicontrol(...
'Units','Normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','Load data', ...
'Callback','svmdemo(''getfile'',gcf)');
% == Popup menus ======================================
bottom=0.95-height;
htx_algo=uicontrol( ...
'Style','text', ...
'Units','normalized', ...
'Position',[left bottom width height], ...
'String','Algorithm');
% popup menu
bottom=bottom-height*0.8;
hpu_algo=uicontrol( ...
'Style','popup', ...
'Units','normalized', ...
'CallBack','svmdemo(''algo_handler'',gcf)',...
'Position',[left bottom width height], ...
'String',ALGOS);
% pop menu - kernel
bottom=bottom-height*1.2;
htx_kernel=uicontrol( ...
'Style','text', ...
'Units','normalized', ...
'Position',[left bottom width height], ...
'String','Kernel');
% popup menu
bottom=bottom-height*.8;
hpu_kernel=uicontrol( ...
'Style','popup', ...
'Units','normalized', ...
'CallBack','svmdemo(''kernel_handler'',gcf)',...
'Position',[left bottom width height], ...
'String',KERNELS);
% == Edit line ========================================
% kernel argument
bottom=bottom-1.2*height;
htx_arg=uicontrol( ...
'Style','text', ...
'Units','normalized', ...
'Position',[left bottom width 0.9*height], ...
'Enable','off',...
'String','Kernel argument');
bottom=bottom-height*.8;
hed_arg = uicontrol(...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'Style','edit',...
'Enable','off',...
'CallBack','svmdemo(''arg_handler'',gcf)',...
'String','1');
% C const
bottom=bottom-1.2*height;
htx_cconst=uicontrol( ...
'Style','text', ...
'Units','normalized', ...
'Position',[left bottom width 0.9*height], ...
'Enable','on',...
'String','C-constant');
bottom=bottom-height*.8;
hed_cconst = uicontrol(...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'Style','edit',...
'Enable','on',...
'CallBack','svmdemo(''cconst_handler'',gcf)',...
'String','100');
% parameters of the algortihm
bottom=bottom-1.2*height;
htx_param=uicontrol( ...
'Style','text', ...
'Units','normalized', ...
'Position',[left bottom width 0.9*height], ...
'Enable','on',...
'String',SMO_PARAM);
bottom=bottom-height*.8;
hed_param = uicontrol(...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'Style','edit',...
'Enable','on',...
'CallBack','svmdemo(''param_handler'',gcf)',...
'String',DEF_SMO_PARAM);
% == Check boxes ==============================================
bottom=bottom-height*1.2;
hxb_background = uicontrol(...
'Style','checkbox', ...
'Units','normalized', ...
'ListboxTop',0, ...
'Position',[left bottom width height], ...
'String','Background');
% ============================================================
data=struct(...
'bt_close',hbt_close,...
'bt_train',hbt_train,...
'bt_reset',hbt_reset,...
'bt_info',hbt_info,...
'bt_load',hbt_load,...
'bt_creat',hbt_creat,...
'pu_algo',hpu_algo,...
'pu_kernel', hpu_kernel,...
'ed_arg', hed_arg,...
'tx_arg', htx_arg,...
'tx_cconst', htx_cconst,...
'ed_cconst', hed_cconst,...
'tx_param', htx_param,...
'ed_param', hed_param,...
'console',hconsole,...
'axes',haxes,...
'xb_background',hxb_background);
set(hfigure,'UserData',data );
% Reset
svmdemo('reset',hfigure);
% Put figure on desktop
set(hfigure,'Visible','on');
drawnow;
%== Trains SVM and displays result ================================
case 'train'
data = get( hfigure, 'UserData');
trn = get( data.axes, 'UserData' );
if isempty( trn ),
return;
end
C = str2num(get( data.ed_cconst, 'String' ));
ker_inx = get( data.pu_kernel, 'Value' );
if ker_inx == 1,
ker = 'linear';
elseif ker_inx == 2;
ker = 'poly';
else
ker = 'rbf';
end
arg = str2num(get( data.ed_arg, 'String' ));
[Alpha,bias] = svm_train( data, trn, ker, arg, C );
% focus on the axes
axes( data.axes );
% Clear axes
clrchild( data.axes);
% get options
options.background = get( data.xb_background, 'Value');
% plot decision function
psvm( trn.X,trn.I,Alpha,bias,ker,arg, options );
%== Handler for Algorithm popup menu ==========================
case 'algo_handler'
data=get(hfigure,'UserData');
% which algorithm ?
switch get(data.pu_algo, 'Value' )
case 1 % SMO
set(data.tx_cconst,'Enable','on');
set(data.ed_cconst,'Enable','on');
set(data.tx_param,'Enable','on');
set(data.tx_param,'String',SMO_PARAM);
set(data.ed_param,'Enable','on');
set(data.ed_param,'String',DEF_SMO_PARAM);
case 2 % Matlab toolbox
set(data.tx_cconst,'Enable','on');
set(data.ed_cconst,'Enable','on');
set(data.tx_param,'Enable','off');
set(data.ed_param,'Enable','off');
case 3 % Kernel-SK
set(data.tx_cconst,'Enable','on');
set(data.ed_cconst,'Enable','on');
set(data.tx_param,'Enable','on');
set(data.ed_param,'Enable','on');
set(data.tx_param,'String',KERNELSK_PARAM);
set(data.ed_param,'String',DEF_KERNELSK_PARAM);
case 4 % Matlab toolbox
set(data.tx_cconst,'Enable','on');
set(data.ed_cconst,'Enable','on');
set(data.tx_param,'Enable','off');
set(data.ed_param,'Enable','off');
case 5 % Kernel Perceptron
set(data.tx_cconst,'Enable','off');
set(data.ed_cconst,'Enable','off');
set(data.tx_param,'Enable','on');
set(data.ed_param,'Enable','on');
set(data.tx_param,'String',KPERCEPTR_PARAM);
set(data.ed_param,'String',DEF_KPERCEPTR_PARAM);
end
%== Handler for C-const edit line ===========================
case 'cconst_handler'
data=get(hfigure,'UserData');
C = str2num(get(data.ed_cconst,'String'));
if C <= 0,
C = 100;
end
set( data.ed_cconst,'String',num2str(C));
%== Handle for arg edit line ===========================
case 'arg_handler'
data=get(hfigure,'UserData');
arg = str2num(get(data.ed_arg,'String'));
if arg < 0,
arg = 1;
end
set( data.ed_arg, 'String',num2str( arg));
%== Handle for kernel pop up menu ===========================
case 'kernel_handler'
data=get(hfigure,'UserData');
ker_inx = get( data.pu_kernel,'Value');
if ker_inx >= 2,
set( data.ed_arg,'Enable','on');
set( data.tx_arg,'Enable','on');
else
set( data.ed_arg,'Enable','off');
set( data.tx_arg,'Enable','off');
end
%== Calls data creator ==========================================
case 'creatdata'
creatset('finite',2,'svmdemo','created',hfigure);
% == Loads recently created data ================================
case 'created'
% get handler and make this figure active
figure(hfigure);
data = get(hfigure,'UserData');
% get file name
path=varargin{1};
name=varargin{2};
pathname=strcat(path,name);
if checkdat(pathname,DATA_IDENT,2,[0 0])==1,
file.pathname=pathname;
file.path=path;
file.name=name;
set( data.bt_load,'UserData',file);
svmdemo('loadsets',hfigure);
svmdemo('reset',hfigure);
else
errordlg('This file does not contain required data.','Bad file','modal');
end
% == Calls standard open file dialog ==========================
case 'getfile'
data=get(hfigure,'UserData');
[name,path]=uigetfile('*.mat','Open file');
if name~=0,
file.pathname=strcat(path,name);
file.path=path;
file.name=name;
if checkdat(file.pathname,DATA_IDENT,2,[0 0])==1,
set(data.bt_load,'UserData',file);
svmdemo('loadsets',hfigure);
svmdemo('reset',hfigure);
else
errordlg('This file does not contain required data.','Bad file','modal');
end
end
case 'loadsets'
% == Load data from file ==========================================
data = get( hfigure,'UserData' );
% Clear axes
clrchild( data.axes);
% set x and y axes labels
xlabel('feature x');
ylabel('feature y');
% Get file name with sets
file=get( data.bt_load,'UserData');
% Load sets
trn = load(file.pathname );
% store loaded sets
set( data.axes,'UserData', trn);
% focus on axes
axes( data.axes );
% plots points
ppatterns( trn.X, trn.I, 5);
drawnow
% == Reset ==========================================================
case 'reset'
data = get(hfigure,'UserData');
% Clear axes
clrchild( data.axes);
% get data set
trn = get( data.axes, 'UserData');
% get file
file = get( data.bt_load,'UserData');
% create comment
if isempty( trn ) == 0,
consoletext=sprintf('Data loaded.\nSelect algorithm and press Train SVM button.\n');
titletext=sprintf('File: %s, # of points K = %d', file.name , size(trn.X,2));
set( data.axes, 'XLimMode','auto', 'YLimMode','auto');
ppatterns( trn.X, trn.I, 5 );
else
consoletext=sprintf('No data loaded.\nPress Load data button.\n');
titletext='';
pos=get( data.axes,'Position');
fsize=min(pos(3),pos(4))/10;
setaxis( data.axes,[-1 1 -1 1]);
% axis([-1 1 -1 1]);
builtin('text',0,0,'Press ''Load data'' button.',...
'HorizontalAlignment','center',...
'FontUnits','normalized',...
'Clipping','on',...
'FontSize',fsize);
end
% show comment
set( data.console,'String',consoletext );
% print title
pos=get( data.axes,'Position');
fsize=(1-pos(2)-pos(4))*1;
title(titletext,...
'Parent', data.axes,...
'VerticalAlignment','bottom',...
'HorizontalAlignment','left',...
'FontUnits','normalized',...
'Units','normalized',...
'Position',[0 1 0],...
'FontSize',fsize);
% == Calls Matlab`s info box ==================================
case 'info'
helpwin(mfilename);
end
return;
%===============================================================
function [Alpha,bias] = svm_train( data, trn, ker, arg, C )
if strcmpi( ker, 'linear'),
strarg = '-';
else
strarg = num2str( arg);
end
param = str2num( get( data.ed_param,'String'));
switch get( data.pu_algo, 'Value' ),
case 1 % Sequential Minimal Optimizer
% if length(param) ~= length( str2num(DEF_SMO_PARAM)),
% param = str2num(DEF_SMO_PARAM);
% end
[Alpha,bias,nsv,kercnt,trn_err,margin]=...
smo(trn.X,trn.I,ker,arg,C, param(1),param(2) );
text = sprintf(...
['SVM (L1) by Sequential Minimal Optimizer\n',...
'Kernel: %s (%s), C: %.4f\n',...
'Kernel evaluations: %d\n',...
'Number of Support Vectors: %d\n',...
'Margin: %.4f\n',...
'Training error: %.2f%%'],...
ker, strarg, C, kercnt, nsv, margin, 100*trn_err );
case 2 % Matlab Optimization toolbox (L1)
[Alpha,bias,nsv,exitflag,flps,margin,trn_err]=...
svmmot(trn.X,trn.I,ker,arg,C);
text = sprintf(...
['SVM (L1) by QP from the Matlab Optimization Toolbox\n',...
'Kernel: %s (%s), C: %.4f\n',...
'Optimization: %s\n',...
'Number of Support Vectors: %d\n',...
'Floating point operations: %d\n',...
'Margin: %.4f\n',...
'Training error: %.2f%%'],...
ker, strarg, C, exitflag, nsv, flps, margin,trn_err);
case 3 % Kernel Schlesinger-Kozinec's algorithm (L2)
% if length(param) ~= length( str2num(DEF_KERNELSK_PARAM)),
% param = str2num(DEF_KERNELSK_PARAM);
% end
% [Alpha,bias,sol,t,flps,margin,up,lo,trn_err]=...
% kernelsk(trn.X,trn.I,param(1),ker,arg,param(2),C);
% [Alpha,bias,sol,t,flps,margin,up,lo]=...
% kerskfmat(trn.X,trn.I,param(1),ker,arg,param(2),C);
% inx1=find(trn.I==1);
% inx2=find(trn.I==2);
[Alpha,bias,sol,t,kercnt,margin,trn_err]= ...
kernelskf(trn.X, trn.I,[1 param(1)],ker,arg,param(2),C);
% Alpha = zeros(1,length(Alpha1)+length(Alpha2));
% Alpha(inx1) = Alpha1;
% Alpha(inx2) = Alpha2;
nsv = length( find( Alpha > 1e-9 ));
if( sol == 1), sol = 'found'; else sol = 'not found'; end
text = sprintf(...
['SVM (L2) by Kernel Schlesinger-Kozinec`s algorithm\n',...
'Kernel: %s (%s), C: %.4f\n',...
'Solution: %s\n', ...
'Number of Support Vectors: %d\n',...
'Kernel evaluations: %d\n',...
'Margin: %.4f\n',...
'Training error: %.2f%%'],...
ker, strarg, C, sol, nsv, kercnt, margin, trn_err*100 );
case 4 % Matlab Optimization toolbox (L2)
[Alpha,bias,nsv,exitflag,flps,margin,trn_err]=...
svm2mot(trn.X,trn.I,ker,arg,C);
text = sprintf(...
['SVM (L2) by QP from the Matlab Optimization Toolbox\n',...
'Kernel: %s (%s), C: %.4f\n',...
'Optimization: %s\n',...
'Number of Support Vectors: %d\n',...
'Floating point operations: %d\n',...
'Margin: %.4f\n',...
'Training error: %.2f%%'], ...
ker, strarg, C, exitflag, nsv, flps, margin, trn_err*100);
case 5 % Kernel Perceptron
[Alpha,bias,sol,t,nsv]=kperceptr(trn.X,trn.I,ker,arg,param(1));
if( sol == 1), sol = 'found'; else sol = 'not found'; end
text = sprintf(...
['Kernel Perceptron\n',...
'Kernel: %s (%s)\n',...
'Solution: %s\n',...
'Number of iterations: %d\n',...
'Number of Support Vectors: %d'], ...
ker, strarg, sol, t, nsv);
end
% show comment
set( data.console,'String', text );
return;