clear, clc, close all, warning off
% global zpi
% global epsilon
% Main file
% Entekhab simulated ya Real dar Par_Ext
% taghire parameterha mesle Ic dar do tab Akhar
%%%%%% SVM Line 19 :>> SIMPLOT Comment or uncommemnt it
global data
data=xlsread('lymphoma.xlsx');
global sig_U
num_rep=1;      % 30 or more

Halat=1;            % 1 Baraye nemodar khatti accuracy Tekrar ba yek daste parameter moshakhas 2 Baraye ejraye extensive
switch Halat
    case 1          % line plot 
        zpi_r= 1.8;
        sig_U_r= 0.09;


    case 2          % plot surface 
        % zpi_r=-2.5:0.5:2.5;
        % sig_U_r=0.01:0.05:0.5;

        % zpi_r=-.5:.1:1.5;
        % sig_U_r=0.03:.01:0.5;

        zpi_r=     1.5:0.3:2.5;
        sig_U_r=  -0.05:0.03:0.1;
        
end
Mod1=zeros(length(zpi_r),length(sig_U_r),num_rep);
        Mod2=Mod1;Mod3=Mod1;
Modm1=zeros(length(zpi_r),length(sig_U_r),1); Modm2=Modm1; Modm3=Modm1;
for i=1:num_rep
    [Mod1(:,:,i),Mod2(:,:,i),Mod3(:,:,i)] = Par_Ext(zpi_r,sig_U_r); % Mod = accuracy, 1 linear 2 poly 3 rbf
    Modm1(:,:,1)=Modm1(:,:,1)+Mod1(:,:,i);                                        % Modm= Summation
    Modm2(:,:,1)=Modm2(:,:,1)+Mod2(:,:,i);
    Modm3(:,:,1)=Modm3(:,:,1)+Mod3(:,:,i);
    disp(['iter= ' num2str(i)])
end


ModM1=Modm1/num_rep;                                             % Mean
ModM2=Modm2/num_rep;
ModM3=Modm3/num_rep;


%%
switch Halat
    case 2
Temp1=reshape(ModM1,[length(zpi_r),length(sig_U_r)]);
Temp1(:,length(sig_U_r)+1)=zpi_r';
Temp1(length(zpi_r)+1,:)=[sig_U_r 0];

Temp2=ModM2;
Temp2(:,length(sig_U_r)+1)=zpi_r';
Temp2(length(zpi_r)+1,:)=[sig_U_r 0];

Temp3=ModM3;
Temp3(:,length(sig_U_r)+1)=zpi_r';
Temp3(length(zpi_r)+1,:)=[sig_U_r 0];


xlswrite('Mixed_Proposed1', Temp1);
xlswrite('Mixed_Proposed2', Temp2);
xlswrite('Mixed_Proposed3', Temp3);
end
%%
temp_acc1=reshape(Mod1(1,1,:),[1,num_rep]);
temp_acc2=reshape(Mod2(1,1,:),[1,num_rep]);
temp_acc3=reshape(Mod3(1,1,:),[1,num_rep]);

switch Halat
    case 1
        figure()
        plot(1:num_rep,temp_acc1,'r')
        xlabel('iteration')
        ylabel('Accuracy')
        title('Linear')

        figure()
        plot(1:num_rep,temp_acc2,'r')
        xlabel('iteration')
        ylabel('Accuracy')
        title('Polynomial')

        figure()
        plot(1:num_rep,temp_acc3,'r')
        xlabel('iteration')
        ylabel('Accuracy')
        title('RBF')

    case 2
        deghat=150;

        figure()
        % surf(sig_U_r,zpi_r,ModM1(:,:,1))
        smoothen(sig_U_r,zpi_r,ModM1(:,:,1),deghat)
        xlabel('s_U')
        ylabel('r')
        zlabel('Accuracy')
        title('Mean Accuracy for Linear Kernel')

        figure()
        % surf(sig_U_r,zpi_r,ModM2(:,:,1))
        smoothen(sig_U_r,zpi_r,ModM2(:,:,1),deghat)
        xlabel('s_U')
        ylabel('r')
        zlabel('Accuracy')
        title('Mean Accuracy for Polynomial Kernel')

        figure()
        % surf(sig_U_r,zpi_r,ModM3(:,:,1))
        smoothen(sig_U_r,zpi_r,ModM3(:,:,1),deghat)
        xlabel('s_U')
        ylabel('r')
        zlabel('Accuracy')
        title('Mean Accuracy for RBF Kernel')
end
%----------------------------------------------
function [Mod1,Mod2,Mod3] = Par_Ext(zpi_r,sig_U_r)
% global sig_U_r
% global zpi_r
% Mod1=zeros(length(zpi_r),length(sig_U_r));
% Mod2=Mod1; Mod3=Mod1;

for j=1:length(zpi_r)
    zpi=zpi_r(j);
    for k=1:length(sig_U_r)
        sig_U=sig_U_r(k);
       %acc=SimulatedData_ext(zpi,sig_U);
       acc=RealData_ext(zpi,sig_U);
        Mod1(j,k)=acc(2,1);Mod2(j,k)=acc(2,3);Mod3(j,k)=acc(2,5);
    end
end
end
%-------------------------------------------------
function Kernel = My_Kernel(A,dim,degree,mu,beta,type,Ap)
%UNTITLED3 Summary of this function goes here
%   Detailed explanation goes here
Kernel=zeros(dim,dim);


% A=[];

switch type

    case 'Linear'

        % Linear
       Kernel = A*A';
       % Kernel = A*Ap(:,1:end-1)';
    case 'polynomial'
        % polynomial
        for i=1:dim
            for j=1:dim
                Kernel(i,j)=(A(i,:)*A(j,:)'+1)^degree;
            end
        end
    case 'RBF'
        % % RBF
        for i=1:dim
            for j=1:dim
                Kernel(i,j)=exp((-mu*norm(A(i,:)-A(j,:),2)^2));

            end
        end
        %
    case 'Tanh'
        % Tangent hyperbolic
        for i=1:dim
            for j=1:dim
                Kernel(i,j)=tanh(mu*A(i,:)*A(j,:)'+beta);
            end
        end


end

%----------------------------------
function [outputArg1,outputArg2] = Find_E_F(K,V,dim,data,zpi)
%UNTITLED Summary of this function goes here
%   Detailed explanation goes here
      
E=zeros(K,K-1,K-1);
F=zeros(K,K-1,K-1);
for j=1:K-1
    for i=1:K
        mm=0;
        for m=1:K
            if i~=m
                mm=mm+1;
                E(i,mm,j)=2*(V(i,j)-V(m,j))';
                F(i,mm,j)=2*(V(i,j)^2-V(m,j)^2)';
            end
        end
    end
end
% Assigning E_hat and F_hat based on data to their E and F arrays.
E_hat=zeros(2*dim,dim,K-1);
F_hat=zeros(2*dim,1,K-1);
for j=1:K-1
    for i=1:dim
        Ci=data(i,end);
        E_hat(2*i-1:2*i,i,j)=E(Ci,:,j);
        F_hat(2*i-1:2*i,1,j)=F(Ci,:,j);
    end
end


outputArg1 = E_hat;
outputArg2 = F_hat*zpi;
end

%-----------------------------------------
function class = Find_class(f1,f2,dim)
%UNTITLED2 Summary of this function goes here
%   Detailed explanation goes here

class=zeros(1,dim);
center=[0.5,sqrt(3)/6];

m1=sqrt(3)/3; m2=-sqrt(3)/3;

for i=1:dim
    if f1(i)<=0.5
        if f2(i)<=center(2)
            class(i)=1;
        elseif (f2(i)-center(2))/(f1(i)-center(1))>=m2
            class(i)=1;
        else
            class(i)=3;
        end
    elseif f1(i)>0.5
        if f2(i)<=center(2)
            class(i)=2;
        elseif (f2(i)-center(2))/(f1(i)-center(1))<=m1
            class(i)=2;
        else
            class(i)=3;
        end 
    end   
end
end

%----------------------------------------
function [H,B] = Find_H_B(E_hat,F_hat,Kernel,e1,e2,epsilon,zpi,model)
%UNTITLED2 Summary of this function goes here
%   Detailed explanation goes here

sig_U=0.53;


switch model

    case 'Proposed2'        % journal paper 2
        H= E_hat(:,:,1)*(Kernel+zpi^2*eye(size(Kernel,1)))*E_hat(:,:,1)' + ...
            E_hat(:,:,2)*(Kernel+zpi^2*eye(size(Kernel,1)))*E_hat(:,:,2)';

        B= (zpi*F_hat(:,:,1)+((zpi-1)*sig_U+epsilon)*e2) + (zpi*F_hat(:,:,2)+((zpi-1)*sig_U+epsilon)*e2) ;
        %B= (F_hat(:,:,1)+epsilon*e2) + (F_hat(:,:,2)+epsilon*e2);

    case 'Proposed'            % journal paper 1
        H= E_hat(:,:,1)*(Kernel+eye(size(Kernel,1)))*E_hat(:,:,1)' + ...
            E_hat(:,:,2)*(Kernel+eye(size(Kernel,1)))*E_hat(:,:,2)';

        B= (F_hat(:,:,1)+((u-epsilon)*Pi + u)*e2) + (F_hat(:,:,2)+((u-epsilon)*Pi + u)*e2);
        %B= (F_hat(:,:,1)+epsilon*e2) + (F_hat(:,:,2)+epsilon*e2);


    case 'Original'

        H= E_hat(:,:,1)*(Kernel+e1*e1')*E_hat(:,:,1)' + ...
            E_hat(:,:,2)*(Kernel+e1*e1')*E_hat(:,:,2)';

        B= (F_hat(:,:,1)+epsilon*e2) + (F_hat(:,:,2)+epsilon*e2);



end
end
%------------------------------------------
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#1 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Application.php(656): yii\web\Response-&gt;send()
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#10 /var/www/html/prof-homepages/backend/web/index.php(16): yii\base\Application-&gt;run()
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