27796720simple-SVM.rar_SVM 分类器_svm matlab_svm 分类 matlab_训练SVM分类

function [c, u, yup, nope] = svm_test(data, pesos, vect, b, escala) % [c u] = svm_test(data, pesos, vect, b, escala) % % data - data to be tested % pesos - weights % vect - support vectors % b - bias % escala - width of centers (8, 16, 4) % % c - % correct classifications % u - % uncorrect classifications % yup - indices of correctly classified patterns % nope - indices of incorrectly classified patterns % % Hugh Pasika 1997 [r, c]=size(data); z=zeros(size(data(:,1))); for i=1:length(pesos) z=z+pesos(i)*exp(-((data(:,1)-vect(i,1)).^2+(data(:,2)-vect(i,2)).^2)/escala); end z = z+b; z = sign(z); V=[data z]; % classification accuracy c1 = find(V(:,5) == V(:,6)); c2 = find(V(:,5) == 0 & V(:,6) == -1); yup = [c1' c2']'; c=length(yup); u1 = find(V(:,5) == 1 & V(:,6) == -1); u2 = find(V(:,5) == 0 & V(:,6) == 1); nope = [u1' u2']'; u=length(nope); nope = [u1 zeros(size(u1)); u2 ones(size(u2))]; fprintf(1,'Percent correct: %5.2f\n', 100*c/r) fprintf(1,'Percent incorrect: %5.2f\n', 100*u/r)