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Challenge in running some plots

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%I want to plot the True Positive Rate of different percentages of "q" bits %modified ( ranges of 10,20,30,40,and 50) with the constant value of p =0.1; as a single graph. The one already done is with q =10% with different variations of p.  


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%The main file below 



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);
       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



%%%%%%%%%%%%%%%%%%%%%%%%The function path below 

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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Challenge in running some plots elvindavin@gmail.com - 2017-09-25 09:41 -0700

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