I need assistance of the intitials represent in the algorithm i was provided

I want to use this Entropy based recommendation algorithm in fishing out some malicious nodes in internet of thing security analysis but I am having a little challenges about what the initials I highlighted represent. For instance tp represent "true positive", just like fp represent "false positive" Please I need help

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 < 40 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 < 20 %%%%%% 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);
       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)*log10(pc(j,k))/log10(100) )+ sum;  % 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

Answer 1

[Steven Lord]

I recommend you ask the person from whom you obtained that code for assistance in understanding what the code is doing. One first step in that process, in my opinion, should be to use descriptive variable names and add comments explaining the purposes behind the non-obvious lines of code.

For instance, rather than 'tp' or 'fp' I would use 'truePositive' or 'falsePositive' as variable names. The shorter names will be faster to type (though tab completion will mitigate that somewhat) but the longer names will be faster to understand.

[Image Analyst]

I whole-heartedly agree. Right now that is an impenetrable alphabet soup mess of a program that no one can follow. Tell the author to get professional and put in comments and use descriptive variable names. As is it, that code is not maintainable or even understandable.