gp23.m

混沌理论中的可预测分析中的GP算法程序

 function [ln_r,ln_C]=GP23(data,N,tau,min_m,max_m,ss)
 load v20.txt
 data=v20;
 N=length(data);
 tau=2;ss=2;
 min_m=2;max_m=15;
 for m=min_m:max_m
     Y=reconstitution(data,N,m,tau);
     M=N-(m-1)*tau;
     for i=1:M-1
         for j=i+1:M
             d(i,j)=max(abs(Y(:,i)-Y(:,j)));
         end
      end
  max_d=max(max(d));
  d(1,1)=max_d;
  min_d=min(min(d));
  delt=(max_d-min_d)/ss;
   for k=1:10
      r=min_d+k*delt;
      C(k)=correlation_integral(Y,M,r);
      ln_C(m,k)=log(C(k));
      ln_r(m,k)=log(r);
      z= ln_C(m,k)/ ln_r(m,k);
    end
  plot(ln_r(m,:),ln_C(m,:));
  xlabel('lnr');
 ylabel('lnC(r)');
  hold on;
 end
function X=reconstitution(data,N,m,tau)
M=N-(m-1)*tau;
for j=1:M
    for i=1:m
        X(i,j)=data((i-1)*tau+j);
    end
end
function sita=heaviside(r,d)
if(r-d)<0
    sita=0;
else sita=1;
end
function C_I=correlation_integral(X,M,r)
sum_H=0;
for i=1:M
   for j=i+1:M
       d=norm((X(:,i)-X(:,j)),inf);
       sita=heaviside(r,d);
       sum_H=sum_H+sita;
    end
 end
 C_I=2*sum_H/(M*(M-1));