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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
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