反函数.zip

function Output=CopulaEvaluator(U,V,X,Y) [N,M]=size(U); %1.估计 copula 函数参数 Parameter_Gaussian=copulafit('gaussian',[U V]); %%[Parameter_t,nuhat_t]=copulafit('t',[U V]); Parameter_Clayton=copulafit('clayton',[U V]); Parameter_Frank=copulafit('frank',[U V]); Parameter_Gumbel=copulafit('gumbel',[U V]); %2.计算 Spearman 相关系数 Spearman_Gaussian=copulastat('gaussian',Parameter_Gaussian(1,2),'type','Kendall'); %%Spearman_t=copulastat('t',Parameter_t(1,2),nuhat_t,'type','Kendall'); Spearman_Clayton=copulastat('clayton',Parameter_Clayton,'type','Kendall'); Spearman_Frank=copulastat('frank',Parameter_Frank,'type','Kendall'); Spearman_Gumbel=copulastat('gumbel',Parameter_Gumbel,'type','Kendall'); %3.计算 Kendall秩相关系数 Kendall_Gaussian=copulastat('gaussian',Parameter_Gaussian(1,2),'type','Kendall'); %%Kendall_t=copulastat('t',Parameter_t(1,2),nuhat_t,'type','Kendall'); Kendall_Clayton=copulastat('clayton',Parameter_Clayton,'type','Kendall'); Kendall_Frank=copulastat('frank',Parameter_Frank,'type','Kendall'); Kendall_Gumbel=copulastat('gumbel',Parameter_Gumbel,'type','Kendall'); %4.计算欧式距离 [fx,Xsort]=ecdf(X); [fy,Ysort]=ecdf(Y); U=spline(Xsort(2:end),fx(2:end),X); V=spline(Ysort(2:end),fy(2:end),Y); C=@(u,v)mean((U<=u).*(V<=v)); %定义经验 copula 函数 Copula_Empirical=zeros(size(U(:))); for i=1:numel(U); Copula_Empirical(i)=C(U(i),V(i)); end Copula_Gaussian=copulacdf('gaussian',[U(:),V(:)],Parameter_Gaussian(1,2)); %%Copula_t=copulacdf('t',[U(:),V(:)],Parameter_t(1,2),nuhat_t); Copula_Clayton=copulacdf('clayton',[U(:),V(:)],Parameter_Clayton); Copula_Frank=copulacdf('frank',[U(:),V(:)],Parameter_Frank); Copula_Gumbel=copulacdf('gumbel',[U(:),V(:)],Parameter_Gumbel); Distance_Gaussian=(Copula_Empirical-Copula_Gaussian)'*(Copula_Empirical-Copula_Gaussian); %%Distance_t=(Copula_Empirical-Copula_t)'*(Copula_Empirical-Copula_t); Distance_Clayton=(Copula_Empirical-Copula_Clayton)'*(Copula_Empirical-Copula_Clayton); Distance_Frank=(Copula_Empirical-Copula_Frank)'*(Copula_Empirical-Copula_Frank); Distance_Gumbel=(Copula_Empirical-Copula_Gumbel)'*(Copula_Empirical-Copula_Gumbel); Output=[Parameter_Gaussian(1,2),0,Kendall_Gaussian,Spearman_Gaussian,Distance_Gaussian; Parameter_Clayton,0,Kendall_Clayton,Spearman_Clayton,Distance_Clayton; Parameter_Frank,0,Kendall_Frank,Spearman_Frank,Distance_Frank; Parameter_Gumbel,0,Kendall_Gumbel,Spearman_Gumbel,Distance_Gumbel;]; %%%%%%%%%%%%数据有问题？？，t-copula无法使用 Parameter_t(1,2),nuhat_t,Kendall_t,Spearman_t,Distance_t;