spxy.zip_AOG_spxy _spxy样本挑选_zip

function [m,dminmax] = spxy(X,Y,N) % Algorithm for Sample set Partitioning based on joint X-Y distances % [m,dminmax] = spxy(X,Y,N); % % X --> Matrix of instrumental responses % Y --> Matrix of parameters % N --> Number of samples to be selected (minimum of 2) % % m --> Indexes of the selected samples % % dminmax(1) = 0; % dminmax(2) = Joint XY distance between the two first samples selected by the algorithm % dminmax(i) = Smallest joint XY distance between the i-th selected sample and the previously selected ones (i > 2) % % Reference: % R. K. H. Galvao, M. C. U. Araujo, G. E. Jose, M. J. C. Pontes, E. C. Silva, T. C. B. Saldanha % A method for calibration and validation subset partitioning % Talanta, vol. 67, pp. 736-740, 2005. % % Web site: www.ele.ita.br/~kawakami/spa/ dminmax = zeros(1,N); % Initializes the vector of minimum distances M = size(X,1); % Number of rows in X (samples) samples = 1:M; % Auto-scales the Y matrix for i=1:size(Y,2) % For each parameter in Y yi = Y(:,i); Y(:,i) = (yi - mean(yi))/std(yi); end D = zeros(M,M); % Initializes the matrix of X distances Dy = zeros(M,M); % Initializes the matrix of Y distances for i=1:M-1 xa = X(i,:); ya = Y(i,:); for j = i+1:M xb = X(j,:); yb = Y(j,:); D(i,j) = norm(xa - xb); Dy(i,j) = norm(ya - yb); end end Dmax = max(max(D)); Dymax = max(max(Dy)); D = D/Dmax + Dy/Dymax; % Combines the distances in X and Y % D: Upper Triangular Matrix % D(i,j) = Euclidean distance between objects i and j (j > i) [maxD,index_row] = max(D); % maxD = Row vector containing the largest element of each column in D % index_row(n) = Index of the row with the largest element in the n-th column [dummy,index_column] = max(maxD); % index_column = column corresponding to the largest element in matrix D m(1) = index_row(index_column); m(2) = index_column; dminmax(2) = D(m(1),m(2)); for i = 3:N % This routine determines the distances between each sample still available for selection and each of the samples already selected pool = setdiff(samples,m); % pool = Samples still available for selection dmin = zeros(1,M-i+1); % Initializes the vector of minimum distances between each sample in pool and the samples already selected for j = 1:(M-i+1) % For each sample xa still available for selection indexa = pool(j); % indexa = index of the j-th sample in pool (still available for selection) d = zeros(1,i-1); % Initializes the vector of distances between the j-th sample in pool and the samples already selected for k = 1:(i-1) % The distance with respect to each sample already selected is analyzed indexb = m(k); % indexb = index of the k-th sample already selected if indexa < indexb d(k) = D(indexa,indexb); else d(k) = D(indexb,indexa); end end dmin(j) = min(d); end % The selected sample corresponds to the largest dmin [dminmax(i),index] = max(dmin); m(i) = pool(index); end