I want to perform optimization on entropy in the program below

*Issue 1: I am having some difficulties optimizing the entropy in the program. I have applied the whole techniques in matlab library se(j)= -sim(j)*log10(sim(j))/log10(100); and sum= (-pc(j,k)*log(pc(j,k))/log10(100) )+ sum * Issue 2:When I implement to the program Fig 1 is not coming. I need an assistance on these issues

tp=zeros(10,10), fp=zeros(10,10); phh=zeros(10,10); pmm=zeros(10,10); pmh=zeros(10,10); phm=zeros(10,10); M=zeros(10,10); H=zeros(10,10); source=zeros(10,200,100); avgT=zeros(10,10); eval= zeros(1, 100); sim = zeros(10, 1); se=zeros(10,1); ce =zeros(10,1); pc= zeros(10,100); c=zeros(10,100); T=zeros(100,100,10);

actM=[1 1 1 1 1 0 0 0 0 0]; %cw2= [ 1 0 0 0 0 1 1 1 1 1] ; %tq=zeros(10,T); cw3=zeros(1,100); q=10; while q <50 d=1; T=zeros(10,100,100); while d <5 %%%%%%%%%%%%%%%%%%% iterating over percentage of malicious packets

phh=zeros(10,10); pmm=zeros(10,10); pmh=zeros(10,10); phm=zeros(10,10); M=zeros(10,10); H=zeros(10,10); iter=1; while iter < 10 %%%%%% look into the following code, it does not run for iterations %%%%%%%%%%%%%%%% generating multiple runs for the given percentage %%%%%%%%%%%%%%%% of malicious packets

           eval=zeros(1,100);
            rp=randperm(100);
            eval(rp(1:q)) = 1;
            cw1=bsc(eval,0.04);
            cw2=zeros(1,100);
            %%%%%%%%%%%formulating  malicious behavior
       %     for i = 1:100
             %if  i < q
             %    if eval(i)==1
             %    cw2(i)=0;

         %        else
          %   cw2(i)=1;

           %      end
           %  else 
         %        cw2(i)=eval(i);

          %     end
       %     end
       cw2=bsc(eval,(q/100));

     source=zeros(100,200,100);
sim = zeros(10, 1); se=zeros(10,1); ce =zeros(10,1);
pc= zeros(10,100);
c=zeros(10,100);
for i=1:100
    for j=1:10
         source(j,i,:) =eval;% cw1;
      if  rem(i,10)<(d )&& j<((10/2) +1)   &&i>1     %%%%%%%%%%%%%%%%%%%%malicious behavior %%%%%%%%%%%%%%
          source(j,i,:)=cw2;%bsc(eval,q/100);    
      end
    %  if rem(i,10)<d &&j>3&&j<6 %%%%%%%%%%%%%%to have different amounts
    %  of misbehavior
  %        source(j,i,:)=cw3;
   %   end
    for k=1:100
              if (i ==1)
              c(j,k)=1;
              end
              if  (i>1&& source(j,i,k) == source(j,i-1,k))
               c(j,k) =c(j,k)+1;
               end
     end

     sim(j)= similar(source(j,i,:),(eval));

    se(j)= -sim(j)*log10(sim(j))/log10(100);%%Issue 1
       if isnan(se(j))
          se(j)=1;
      end
        pc(j,:)=c(j,:)/i; pc(1,:)=c(1,:)/i;
      sum =0;
      for k=1:100

      sum= (-pc(j,k)*log(pc(j,k))/log10(100) )+ sum; %%%issue 2 % computing the entropy of each source for the whole code word

      end
      ce(j) =sum;
       if isnan(ce(j))
          ce(j)=1; 
       end
     if i <2

        T(d,i,j)=1-se(j); %%%%%%%%d is replaced by d
    else
     T(d,i,j)= (1-(se(j)+ce(j)))*0.5+ 0.5*T(d,i-1,j);
     T(d,i,j)= T(d,i,j)/max(T(d,:,j));

    end
    if (isnan(T(d,i,j)) )
         T(d,i,j)=0;
    end
    if (T(d,i,j)>1)
            T(d,i,j)=1;
    end
    if (T(d,i,j)<0)
            T(d,i,j)=0;
    end

    end
end

for i = 1: 10 % d*10 to 10 avgT(d,i)=mean(T(d,:,i)); %%%%%%%%%% replacing 'd 'by d if avgT(d,i)<0.75 M(d,i)=M(d,i)+1; if actM(i)==1 pmm (q/10,d)= pmm(q/10,d)+1; else pmh(q/10,d) =pmh(q/10,d)+1; end else H(d,i)=H(d,i)+1; if actM(i)==0 phh(q/10,d)=phh(q/10,d)+1; else phm(q/10,d) =phm(q/10,d)+1; end end end iter =iter+1; end % tp(q/10,d)=pmm(q/10,d)/(pmm(q/10,d)+phm(q/10,d)); tp(q/10,d)=pmm(q/10,d)/(pmm(q/10,d)+phm(q/10,d)); % fp(q/10,d)=pmh(q/10,d)/(phh(q/10,d)+pmh(q/10,d)); fp(q/10,d)=pmh(q/10,d)/(phh(q/10,d)+pmh(q/10,d)); d=d+1;

end

q =q+10; x=1:100; figure(q/10) plot(x,T(1,:,9),'-ob', x,T(1,:,2),'-dr', x, T(2,:,2),'-sg',x,T(3,:,2),'-m*',x,T(5,:,2),'-k^'); legend('Honest', 'Malicious, p =0.1, 10% recommendations are false', 'Malicious,p=0.2, 10% recommendations are false','Malicious,p=0.3, 10% recommendations are false','Malicious,p=0.5, 10% recommendations are false') xlabel('Iteration'); ylabel('RecommendationTrust'); title('Impact of size of recommendation vector');

end %% function file to the program function [ s ] = similar( X,y1 ) %UNTITLED Summary of this function goes here % Detailed explanation goes here com=0; [r1, sy]=size(y1); [r2 ,sx]=size(X); sy=sy-sum(y1(:)==999); sx=sx-sum(X(:)==999); for i=1:sx if (X(i)==y1(i) && X(i)<999) com=com+1; end end if(sx+sy-com>0) s=com/(sx+sy-com); else s=0; end end

function [sx] = Sim(X,y1,y2,y3 ) sx=(similar(X,y1)*similar(X,y2)*similar(X,y3))^(1/3);

end

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