function varargout = demosvm(varargin)
% 非線性模式分析模型.
%
% 說明:
%  demosvm實現多類數據分類
%  用戶可以裝載數據，也可以手動創建
%
%  使用的訓練算法：
%  - OAA算法
%  - OAO 算法
%  - BSVM2算法
%  使用的核函數
%  -linear(線性核函數）
%  - rbf（徑向基核函數）
%  - poly（多項式核函數）
%  - sigmoid（S型核函數）
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @demosvm_OpeningFcn, ...
                   'gui_OutputFcn',  @demosvm_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% 系統初始化代碼。


% 系統可視化。
function demosvm_OpeningFcn(hObject, eventdata, handles, varargin)

%demosvm中使用以下結構體，來實現圖形窗口上的各個控件的數據傳送，進而實現各個控件的功能。
data=struct(...
     'pushbutton1', handles.pushbutton1,... %創建數據
     'pushbutton2', handles.pushbutton2,... %裝載數據
     'pushbutton3', handles.pushbutton3,... %重置數據
     'pushbutton4', handles.pushbutton4,... %訓練SVM
     'pushbutton5', handles.pushbutton5,... %關閉
     'pushbutton6', handles.pushbutton6,... %圖象導出
     'pushbutton7', handles.pushbutton7,... %計算SV
     'pushbutton8', handles.pushbutton8,... %決策邊界
     'popupmenu1', handles.popupmenu1,...%訓練算法
     'popupmenu2', handles.popupmenu2,...%核函數
     'popupmenu3', handles.popupmenu2,...%C參數
     'edit1', handles.edit1,...% 注釋窗口
     'axes1', handles.axes1);  %圖形顯示坐標軸

   set(gcf ,'UserData',data );% 將圖形窗口顯示在桌面上
  
   set(gcf,'Visible','on');
   drawnow;

handles.output = hObject;


guidata(hObject, handles);


function varargout = demosvm_OutputFcn(hObject, eventdata, handles) 

varargout{1} = handles.output;


% 創建數據回調函數
function pushbutton1_Callback(hObject, eventdata, handles)
n=5;
createdata('finite',n,'created',gcf);

% 裝載數據回調函數
function pushbutton2_Callback(hObject, eventdata, handles)

h = waitbar(0,'請等待...');%等待條行窗口
for i=1:100, 
waitbar(i/100)
end
close(h) 
data=get(gcf,'UserData');
[name,path]=uigetfile('*.mat','Open file');
 if name~=0,
      file.pathname=strcat(path,name); 
      file.path=path;
      file.name=name;
 
 if check2ddata( file.pathname )  
       set(data.pushbutton2,'UserData',file);
      cla;
       data=loadsets(gcf);  %   裝載結構型數據
       
      set(gcf,'UserData',data);
      set(handles.edit1,'String','數據已經裝載 !');

      else
         errordlg('This file does not contain required data.','Bad file','modal'); 
      end
   end


%重置數據回調函數
function pushbutton3_Callback(hObject, eventdata, handles)

cla;
set(handles.edit1,'String','數據已經清除，請重新裝載 !');
 
% 訓練SVM回調函數----軟件核心部分
function pushbutton4_Callback(hObject, eventdata, handles)

ker_inx = get( handles.popupmenu2, 'Value' );    %獲取核函數信息
   if ker_inx == 1;
      ker = 'linear';
      set( handles.edit3,'Enable','off');
   elseif ker_inx == 2;
      ker = 'rbf';
      set( handles.edit3,'Enable','on');
   elseif ker_inx == 3; 
      ker = 'poly';
      set( handles.edit3,'Enable','on');
   else
       ker = 'sigmoid';
      set( handles.edit3,'Enable','on');
   end
C_inx = get( handles.popupmenu3, 'Value' );    %獲取C參數信息
if C_inx == 1;
    C=10;
elseif C_inx == 2;
    C=50;
  else    C=100;
end
switch get( handles.popupmenu1, 'Value' )
    case 1
                 data=get(handles.axes1,'userdata');

                 options.solver = 'smo'; % 使用 SMO solver
                 options.ker = ker; % 使用核函數
                 options.arg = 1; % kernel argument
                 options.C = C; %調整參數
                 T1=cputime; 
                 model = oaasvm(data,options ); % 訓練
                 T=cputime-T1;%計算訓練所用時間
                 set(handles.pushbutton6,'userdata',T);%將時間變量傳遞出去
                 
                 %顯示圖形
                 axes(handles.axes1);cla;
                 ppatterns(data);
                 
                 D=struct('solver',options.solver,'ker',options.ker,'arg',options.arg,'C',options.C,'model',model);
                 set(handles.pushbutton2,'userdata',D);
                
                 text=sprintf(...
                                ['訓練算法：OAA算法\n',...
                                 '核函數: %s \n',...
                                 '訓練所費時間:  %.6f\n'],...
                                 ker,T);
                
    case 2
                 data=get(handles.axes1,'userdata');
                 options.solver = 'smo'; 
                 options.ker = ker; 
                 options.arg = 1; 
                 options.C = C; 
                 T1=cputime;
                 model = oaosvm(data,options); 
                 T=cputime-T1;
                 set(handles.pushbutton6,'userdata',T);
                
                 axes(handles.axes1);cla;
                 ppatterns(data);

                 D=struct('solver',options.solver,'ker',options.ker,'arg',options.arg,'C',options.C,'model',model);
                 set(handles.pushbutton2,'userdata',D); 
                
                 
                 text=sprintf(...
                                ['訓練算法：OAO算法\n',...
                                 '核函數: %s \n',...
                                 '訓練所費時間:  %.6f\n'],...
                                 ker,T);
    case 3
                 data=get(handles.axes1,'userdata');
                 
                 options.solver = 'smo'; % 使用 smo solver
                 options.ker = ker; 
                 options.arg = 1; 
                 options.C = C; 
                 T1=cputime;
                 model = bsvm2(data,options); % training
                 T=cputime-T1;
                 set(handles.pushbutton6,'userdata',T);
                
                 % 顯示圖形
                 axes(handles.axes1);cla;
                 ppatterns(data);
                 
                 D=struct('solver',options.solver,'ker',options.ker,'arg',options.arg,'C', options.C,'model',model);
                 set(handles.pushbutton2,'userdata',D); 
                 
                 text=sprintf(...
                                ['訓練算法：BSVM2算法\n',...
                                 '核函數: %s \n',...
                                '訓練所費時間:  %.6f\n'],...
                                 ker,T);
end

set( handles.edit1,'String', text );%將結果顯示到注釋窗口

%關閉窗口回調函數
function pushbutton5_Callback(hObject, eventdata, handles)

close(gcf);

%圖象導出回調函數
function pushbutton6_Callback(hObject, eventdata, handles)
h=get(gcf,'CurrentAxes');
fig2jpg(h);


%計算SV回調函數
function pushbutton7_Callback(hObject, eventdata, handles)

        data=get(handles.axes1,'userdata');
        D=get(handles.pushbutton2,'userdata');
        model=D.model;
        axes(handles.axes1);cla;
        
        nsv = model.nsv;%將訓練算法中支持向量個數的變量nsv傳遞出來
        time=get(handles.pushbutton6,'userdata');%將計算CPU時間參數T傳遞出來
        ppatterns(data);
        ppatterns(model.sv.X,'ko',12);
        set(handles.pushbutton2,'userdata',D);
       
   ker_inx = get( handles.popupmenu2, 'Value' );  %獲取核函數信息
   if ker_inx == 1;
      ker = 'linear';
   elseif ker_inx == 2;
      ker = 'rbf';
   elseif ker_inx == 3;
      ker = 'poly';
   else
      ker = 'sigmoid';
   end


   %要輸出的內容控制
   if get( handles.popupmenu1, 'Value' )==1
           text1=sprintf(...
                        ['訓練算法：OAA算法\n',...
                         '核函數: %s \n',...
                         '訓練所費時間:  %.6f\n',...
                         '支持向量的個數: %d\n'],...
                                 ker,time,nsv );
       elseif get( handles.popupmenu1, 'Value' )==2
           text1=sprintf(...
                        ['訓練算法：OAO算法\n',...
                         '核函數: %s \n',...
                         '訓練所費時間:  %.6f\n',...
                         '支持向量的個數: %d\n'],...
                                 ker,time,nsv );
       else
           text1=sprintf(...
                        ['訓練算法：BSVM2算法\n',...
                         '核函數: %s \n',...
                         '訓練所費時間:  %.6f\n',...
                         '支持向量的個數: %d\n'],...
                                 ker,time,nsv );
        end

        set( handles.edit1,'String', text1 );
      

 
%決策邊界回調函數
function pushbutton8_Callback(hObject, eventdata, handles)


data=get(handles.axes1,'userdata');
D=get(handles.pushbutton2,'userdata');
model=D.model;

axes(handles.axes1);cla;
ppatterns(data);
ppatterns(model.sv.X,'ko',12);
pboundary(model);

      nsv = model.nsv;
      time=get(handles.pushbutton6,'userdata');
      trnerr = model.trnerr;%將訓練算法中計算分類錯誤的參數傳出
       
 ker_inx = get( handles.popupmenu2, 'Value' );    %獲取核函數信息
   if ker_inx == 1;
      ker = 'linear';
   elseif ker_inx == 2;
      ker = 'rbf';
   elseif ker_inx == 3;
      ker = 'poly';
   else
      ker = 'sigmoid';
   end

       
        if get( handles.popupmenu1, 'Value' )==1
           text1=sprintf(...
                        ['訓練算法：OAA算法\n',...
                         '核函數: %s \n',...
                         '訓練所費時間:  %.6f\n',...
                         '支持向量的個數: %d\n',...
                         '分類錯誤率:  %.2f%%'],...
                                 ker,time,nsv ,100*trnerr);
       elseif get( handles.popupmenu1, 'Value' )==2
           text1=sprintf(...
                        ['訓練算法：OAO算法\n',...
                         '核函數: %s \n',...
                         '訓練所費時間:  %.6f\n',...
                         '支持向量的個數: %d\n',...
                          '分類錯誤率:  %.2f%%'],...
                                 ker,time,nsv ,100*trnerr );
       else
           text1=sprintf(...
                        ['訓練算法：BSVM2算法\n',...
                         '核函數: %s \n',...
                         '訓練所費時間:  %.6f\n',...
                         '支持向量的個數: %d\n',...
                          '分類錯誤率:  %.2f%%'],...
                                 ker,time,nsv,100*trnerr );
        end

        set( handles.edit1,'String', text1 );
      

%訓練算法下拉菜單
function popupmenu1_Callback(hObject, eventdata, handles)

function popupmenu1_CreateFcn(hObject, eventdata, handles)


handles.popupmenu1=get(hObject,'Value');%將菜單選項值傳出

if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end


% 核函數下拉菜單
function popupmenu2_Callback(hObject, eventdata, handles)

function popupmenu2_CreateFcn(hObject, eventdata, handles)


handles.popupmenu2=get(hObject,'Value');%將菜單選項值傳出


if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end


% C懲罰系數下拉菜單
function popupmenu3_Callback(hObject, eventdata, handles)

function popupmenu3_CreateFcn(hObject, eventdata, handles)


handles.popupmenu3=get(hObject,'Value');%將菜單選項值傳出


if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end

%注釋窗口
function edit1_Callback(hObject, eventdata, handles)


function edit1_CreateFcn(hObject, eventdata, handles)

if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end




% MENU設計及回調函數 


%創建數據
function Untitled_2_Callback(hObject, eventdata, handles)
n=5;
createdata('finite',n,'created',gcf);

%裝載數據
function Untitled_3_Callback(hObject, eventdata, handles)


h = waitbar(0,'Please wait...');
for i=1:100, 
waitbar(i/100)
end
close(h) 
data=get( gcf,'UserData');
[name,path]=uigetfile('*.mat','Open file');
 if name~=0,
      file.pathname=strcat(path,name); 
      file.path=path;
      file.name=name;
 
 if check2ddata( file.pathname )  
       set(data.pushbutton2,'UserData',file);
      cla;
       data=loadsets(gcf);         
  
      set(gcf,'UserData',data);
      set(data.edit1,'String','數據已經裝載，請選擇下一步 !');
  
      else
         errordlg('This file does not contain required data.','Bad file','modal'); 
      end
 end


% --------------------------------------------------------------------
function Untitled_1_Callback(hObject, eventdata, handles)



% 關閉
function Untitled_4_Callback(hObject, eventdata, handles)

close(gcf);

% --------------------------------------------------------------------
function Untitled_5_Callback(hObject, eventdata, handles)



% 調用OAA算法演示模型
function Untitled_6_Callback(hObject, eventdata, handles)
OAA_Multi_C_SVM;

% 調用OAO算法演示模型
function Untitled_7_Callback(hObject, eventdata, handles)

OAO_Multi_SVM;

% 調用BSVM2算法演示模型
function Untitled_8_Callback(hObject, eventdata, handles)

Multi_BSVM__L2_soft_margi

% --------------------------------------------------------------------
function Untitled_9_Callback(hObject, eventdata, handles)


% --------------------------------------------------------------------
function Untitled_10_Callback(hObject, eventdata, handles)
helpwin(mfilename);