clc, clear, close all, warning off
%% Preprocessing - Just assign the data for Vertex
Sampling_iter=15;              
Total_Run=1;n=70;Sampling_Num=20;k=3;
Max_iter=1;
%zpi=0.9;
% global epsilon
Accuracy_Proposed=zeros(Max_iter,4);Index_Proposed=zeros(Max_iter,4);Accuracy_Original=zeros(Max_iter,4);Index_Original=zeros(Max_iter,4);
for iter=1:Max_iter
    [Data_Base_Model,Data_Bootstrap]= Dual_dataset(Total_Run,n,Sampling_Num,Sampling_iter,k,zpi);
    %% Parameters......................................................
    K=3;                              % Number of Classes
    epsilon=0.05;      % General Parameters
    C=1;beta=-1.5;mu=0.01; eta=0.1;       % there is no output for eta more than 2!
    degree=2;                         % Kernel degree
    Ic=550/2;                         % Stop Criterion
    for j=1:3
        % type='Linear';                % Kernel Types: Linear, polynomial, RBF, Tanh
        %% Model Selection
        % model=['Proposed';'Original'];                 % Models: Proposed, Original
        for i=1:1
            if j==1
                type='Linear';
            elseif j==2
                type='polynomial';
            elseif j==3
                type='RBF';
            elseif j==4
                type='Tanh';
            end
            switch i
                case 1                                  % Proposed-2
                    model='Proposed2';
                    data = Data_Bootstrap{1,1};
                    %Ap=Data_Base_Model{1,1};
                    [f1,f2,class,macc,index, Accuracy,f1max,f2max] = SVM(data,K,type,Ic,zpi,epsilon,C,beta,mu,eta,degree,model,j,iter,0); % Proceeding both
                    Accuracy_Proposed(iter,j)=macc; Index_Proposed(iter,j)=index;
                   % figure(j)
                   % plot(Accuracy,'r')
                  % plot(f1max,f2max,'ro')
                case 2                                  % Original
                    model='Original';
                    data = Data_Base_Model{1,1};
                     [f1,f2,class,macc,index, Accuracy,f1max,f2max] = SVM(data,K,type,Ic,epsilon,C,beta,mu,eta,degree,model,j,iter,zpi,~); % Proceeding both
                    
                     Accuracy_Original(iter,j)=macc; Index_Original(iter,j)=index;
                    %figure(j)
                   % hold on
                    %plot(Accuracy,'b')   %figure(3),legend('Proposed','RSSVM')
                   % plot(f1max,f2max,'bo')
            end
        end
    end
end
%% Main
Setting=[zpi, epsilon, C, beta, mu, eta, degree, 0];
Mixed_Proposed=[];
Mixed_Original=[];
for kernel_number=1:4
    Mixed_Proposed=[Mixed_Proposed, Accuracy_Proposed(:,kernel_number),Index_Proposed(:,kernel_number)];
    Mixed_Original=[Mixed_Original, Accuracy_Original(:,kernel_number),Index_Original(:,kernel_number)];
end
Mixed_Proposed=[Setting;Mixed_Proposed]
Mixed_Original=[Setting;Mixed_Original];
xlswrite('Mixed_Proposed', Mixed_Proposed);
xlswrite('Mixed_Original', Mixed_Original);
disp('finished')
%---------------------------------------------------------
function [Mixed_Proposed] = SimulatedData_ext(zpi,sig_U)
%% Preprocessing - Just assign the data for Vertex
global sig_U
Sampling_iter=100;              
Total_Run=1;n=300;Sampling_Num=7;k=3;
Max_iter=1;
Accuracy_Proposed=zeros(Max_iter,4);Index_Proposed=zeros(Max_iter,4);Accuracy_Original=zeros(Max_iter,4);Index_Original=zeros(Max_iter,4);
for iter=1:Max_iter
    [Data_Base_Model,Data_Bootstrap]= Dual_dataset(Total_Run,n,Sampling_Num,Sampling_iter,k,zpi);
    %% Parameters......................................................
    K=3;                              % Number of Classes
    epsilon=0.05;      % General Parameters
    C=1;beta=-1.5;mu=4; eta=0.1;       % there is no output for eta more than 2!
    degree=2;                         % Kernel degree
    Ic=300/2;                         % Stop Criterion
    for j=1:3
        % type='Linear';                % Kernel Types: Linear, polynomial, RBF, Tanh
        %% Model Selection
        % model=['Proposed';'Original'];                 % Models: Proposed, Original
        for i=1:1
            if j==1
                type='Linear';
            elseif j==2
                type='polynomial';
            elseif j==3
                type='RBF';
            elseif j==4
                type='Tanh';
            end
            switch i
                case 1                                  % Proposed-2
                    model='Proposed2';
                    data = Data_Bootstrap{1,1};
                    Ap=Data_Base_Model{1,1};
                    [f1,f2,class,macc,index, Accuracy,f1max,f2max] = SVM(data,K,type,Ic,zpi,epsilon,C,beta,mu,eta,degree,model,j,iter,Ap); % Proceeding both
                    Accuracy_Proposed(iter,j)=macc; Index_Proposed(iter,j)=index;
                case 2                                  % Original
                    model='Original';
                    data = Data_Base_Model{1,1};
                     [f1,f2,class,macc,index, Accuracy,f1max,f2max] = SVM(data,K,type,Ic,epsilon,C,beta,mu,eta,degree,model,j,iter,zpi,Ap); % Proceeding both
                    
                     Accuracy_Original(iter,j)=macc; Index_Original(iter,j)=index;
            end
        end
    end
end
%% Main
Setting=[zpi, epsilon, C, beta, mu, eta, degree, 0];
Mixed_Proposed=[];
Mixed_Original=[];
for kernel_number=1:4
    Mixed_Proposed=[Mixed_Proposed, Accuracy_Proposed(:,kernel_number),Index_Proposed(:,kernel_number)];
    Mixed_Original=[Mixed_Original, Accuracy_Original(:,kernel_number),Index_Original(:,kernel_number)];
end
Mixed_Proposed=[Setting;Mixed_Proposed];
Mixed_Original=[Setting;Mixed_Original];

end

%-------------------------------------
function [f1,f2,class,macc,index, Accuracy,f1max,f2max] = SVM(data,K,type,Ic,zpi,epsilon,C,beta,mu,eta,degree,model,j,iter,Ap)
%UNTITLED2 Summary of this function goes here
%   Detailed explanation goes here
%Normalising data......................................
data(:,1:end-1)=data(:,1:end-1)/(max(max(data(:,1:end-1))));
realclass=data(:,size(data,2));
dim=size(data,1);
V=Find_V(K);             % V matrix
A=data; A(:,end)=[];                           % Removing Label Column
e1=ones(size(A,1),1);e2=ones(2*size(A,1),1);   % Vector e

% Kernel = My_Kernel(A,dim,degree,mu,beta,type,Ap);
Kernel = My_Kernel(A,dim,degree,mu,beta,type);
[E_hat,F_hat]=Find_E_F(K,V,dim,data,zpi);               % producing E and F matrices for E_hat and F_hat.
[H,B]=Find_H_B(E_hat,F_hat,Kernel,e1,e2,epsilon,zpi,model);  % 
% Algorithm 2 .................................
[f1,f2,class,Accuracy,f1max,f2max] = Algorithm2(K,dim,Ic,eta,H,B,C,Kernel,E_hat,realclass,zpi);
% Output
[macc,index]=max(Accuracy);
SIMplot(f1max,f2max,realclass,model,dim,j,iter);
end


%------------------------------------
function [Data_Base_Model,Data_Bootstrap] = Dual_dataset(Total_Run,n,Sampling_Num,Sampling_iter,k,zpi)
%UNTITLED2 Summary of this function goes here
%   Detailed explanation goes here
global data
% Total_Run=1;         % Total iteration(r)
% n=150;               % Dimension of each creation
Data_Base_Model{Total_Run}={};
Data_Bootstrap{Total_Run}={};
for ij=1:Total_Run
    %% Ba Jaygozari- ya 11, 12 % darand ya 13 , 14
  %  data1 = DatasetCreatorIris(n);         % for creating data artificial (active line 11 & 12)
   % data = sortrows(data1, size(data1, 2)); 
    %D=load('IrisTrain.mat');                  %real  Mahalle dataset
    %data=D.data_6July;                        % Real data
    SamplingData=data(:,1:end-1);
    Data_Base_Model{ij}=data(:,1:end);
    size_end=size(data,2);
    %% R1,R2,R3
    r1=0;r2=0;r3=0;
    for i=1:size(data,1)
        if data(i,end)==1
            r1=r1+1;
        elseif data(i,end)==2
            r2=r2+1;
        elseif data(i,end)==3
            r3=r3+1;
        end
    end
    %%
    % k=3;                                                    % Number of classes
    % Sampling_iter=25;                                       % Number of Samples
    % Sampling_Num=7;                                         % Volume of Samples
    dimension=Total_Run*Sampling_iter*Sampling_Num;         % Dimension of random numbers
    R1=randi(r1,1,dimension);
    R2=randi(r2,1,dimension); R2=R2+r1;
    R3=randi(r3,1,dimension); R3=R3+r1+r2;
    R=[R1,R2,R3];                                           % Creating all random numbers
    Sample_Matrix=zeros(1,size(SamplingData,2));            % Accelerating
    Sample_Mean=zeros(1,size(SamplingData,2));              % Accelerating
    m=0;
    Store_sample=[];
    for i=1:Sampling_iter*k
        m=(i-1)*Sampling_Num;
        for j=1:Sampling_Num
            m=m+1;
            Sample_Matrix(1,:)=Sample_Matrix+SamplingData(R(m),:);
            Store_sample(j,:)= SamplingData(R(m),:);   % x_i
        end
        Sample_Mean(i,:)=Sample_Matrix/Sampling_Num; % x^bar
        dev=0;
        for ii=1:Sampling_Num
            dev=dev+(Store_sample(ii,:)-Sample_Mean(i,:)).^2;
        end

        Dev(i,:)=dev/Sampling_Num;
        Dev(i,:)=sqrt(Dev(i,:));
        Sample_Matrix=zeros(1,size(SamplingData,2));
    end


    Sample_Mean(1:Sampling_iter,size_end)=1;
    Sample_Mean(Sampling_iter+1:2*Sampling_iter,size_end)=2;
    Sample_Mean(2*Sampling_iter+1:3*Sampling_iter,size_end)=3;
    Dev=Dev*(zpi-1);
    Dev(1:Sampling_iter,size_end)=1;
    Dev(Sampling_iter+1:2*Sampling_iter,size_end)=2;
    Dev(2*Sampling_iter+1:3*Sampling_iter,size_end)=3;


    Data_Bootstrap{ij}=Sample_Mean+Dev;
    Data_Bootstrap{ij}(:,size_end)=Data_Bootstrap{1,1}(:,size_end)/2;

end
end<pre>An Error occurred while handling another error:
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Stack trace:
#0 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php(339): yii\web\Response-&gt;sendHeaders()
#1 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/ErrorHandler.php(136): yii\web\Response-&gt;send()
#2 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/ErrorHandler.php(135): yii\web\ErrorHandler-&gt;renderException()
#3 [internal function]: yii\base\ErrorHandler-&gt;handleException()
#4 {main}
Previous exception:
yii\web\HeadersAlreadySentException: Headers already sent in  on line 0. in /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php:366
Stack trace:
#0 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php(339): yii\web\Response-&gt;sendHeaders()
#1 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Application.php(656): yii\web\Response-&gt;send()
#2 /var/www/html/prof-homepages/vendor/faravaghi/yii2-filemanager/models/Files.php(696): yii\base\Application-&gt;end()
#3 /var/www/html/prof-homepages/vendor/faravaghi/yii2-filemanager/controllers/FilesController.php(484): faravaghi\filemanager\models\Files-&gt;getFile()
#4 [internal function]: faravaghi\filemanager\controllers\FilesController-&gt;actionGetFile()
#5 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/InlineAction.php(57): call_user_func_array()
#6 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Controller.php(180): yii\base\InlineAction-&gt;runWithParams()
#7 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Module.php(528): yii\base\Controller-&gt;runAction()
#8 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Application.php(103): yii\base\Module-&gt;runAction()
#9 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Application.php(386): yii\web\Application-&gt;handleRequest()
#10 /var/www/html/prof-homepages/backend/web/index.php(16): yii\base\Application-&gt;run()
#11 {main}</pre>