function D=GP_Algorithm(data,tau,max_m)% GP算法求關聯維和嵌入維%clc%---------------------------------------------------x=data;X = normalize_1(x);%---------------------------------------------------'disp('----- GP算法求關聯維和嵌入維 -----');% t = 1;m_vector = 1:max_m;r_vector = exp(-5:0.25:1);num_m = length(m_vector);num_r = length(r_vector);ln_Cr = zeros(num_m,num_r);%------------------------------------------------------% tictype_norm = 2;       % 使用范數類型 (缺省值為2)                    % type_norm = 0,1,2時，分別對應無窮范數、1范數和2范數block = 1;           % 分塊計計算關聯積分 - 分塊數 (缺省值為1)                    % t越大速度越快，但有誤差for i = 1:num_m    i    for j = 1:num_r        % 計算關聯積分S(m,N,r,t), 參見 <<混沌時間序列分析及應用>> P35 式(2.29)        m = m_vector(i);        r = r_vector(j);        %ln_Cr(i,j) = log(CorrelationIntegral(m,X,r,t)); % 缺省用法        ln_Cr(i,j) = log(CorrelationIntegral(m,X,r,tau,type_norm,block));     endend% t = tocsubplot(211)ln_r = log(r_vector);plot(ln_r,ln_Cr','+:');grid;xlabel('ln(r)'); ylabel('ln(C(r))');title(['norm = ',num2str(type_norm),', block = ',num2str(block),', t = ',num2str(tau)]);legend('m=2','m=3','m=4','m=5',4)subplot(212) %------------------------------------------------------    % 擬合線性區域for i=1:num_m   A=find(ln_Cr(i,:)~=-inf);   t=A(1);    LinearZone = [t:t+7];    F = polyfit(ln_r(LinearZone),ln_Cr(i,LinearZone),1);    D(i) = F(1);end plot(D,'+:'); grid;