function [V] = test_train_kernel() clear; more off; rand('state', 654321); n = 10; m = 5; classes = 4; cutoff = 5e-3; X = rand(n, m); Z = [ones(n, 1), X]; set_matrix(Z, "data->Z", "data->m+1"); y = [2 1 3 2 3 2 4 1 3 4]; set_matrix(y, "data->y", "1"); p = 1.2143; kappa = 0.90298; lambda = 0.00219038; epsilon = 1e-15; rho = ones(n, 1); K = zeros(n, n); # RBF kernel # exp(-gamma * norm(x1 - x2)^2) gamma = 0.348 for ii=1:n for jj=1:n K(ii, jj) = exp(-gamma * sum((X(ii, :) - X(jj, :)).^2)); end end [P, Sigma] = eig(K); eigenvalues = diag(Sigma); ratios = eigenvalues ./ eigenvalues(end, end); realP = fliplr(P(:, ratios > cutoff)); realSigma = sqrt(flipud(eigenvalues(ratios > cutoff))); assert_matrix(realSigma, "data->Sigma", "1"); r = sum(ratios > cutoff); fprintf("mu_assert(data->r == %i);\n", r); M = realP * diag(realSigma); size(M) assert_matrix(Z, "data->RAW", "data->m+1"); seedV = zeros(size(M, 2) + 1, classes - 1); [W, t] = msvmmaj(M, y, rho, p, kappa, lambda, epsilon, 'show', 0, seedV); V = [t'; W]; fprintf('\n'); assert_matrix_abs(V, "model->V", "model->K-1"); end function set_matrix(A, name, cols) for ii=1:size(A, 1) for jj=1:size(A, 2) fprintf("matrix_set(%s, %s, %i, %i, %.16f);\n", name, cols, ii-1, jj-1, A(ii, jj)); end end fprintf("\n"); end function assert_matrix(A, name, cols) for ii=1:size(A, 1) for jj=1:size(A, 2) fprintf(["mu_assert(fabs(matrix_get(%s, %s, %i, %i) -\n%.16f) <", ... " eps,\n\"Incorrect %s at %i, %i\");\n"], name, cols, ... ii-1, jj-1, A(ii, jj), name, ii-1, jj-1); end end fprintf("\n"); end function assert_matrix_abs(A, name, cols) for ii=1:size(A, 1) for jj=1:size(A, 2) fprintf(["mu_assert(fabs(fabs(matrix_get(%s, %s, %i, %i)) -\nfabs(%.16f)) <", ... " eps,\n\"Incorrect %s at %i, %i\");\n"], name, cols, ... ii-1, jj-1, A(ii, jj), name, ii-1, jj-1); end end fprintf("\n"); end