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function [V] = test_optimize()
clear;
more off;
rand('state', 902183);
n = 8;
m = 3;
K = 4;
X = rand(n, m);
set_matrix(X);
y = [2 1 3 2 3 2 4 1];
set_vector(y);
p = 1.2143;
kappa = 0.90298;
lambda = 0.00219038;
epsilon = 1e-15;
rho = rand(n, 1);
set_vector(rho);
[W, t] = msvmmaj(X, y, rho, p, kappa, lambda, epsilon);
V = [t'; W];
assert_matrix(V, "model->V", "model->K-1");
end
function set_matrix(A)
for i=1:size(A, 1)
for j=1:size(A, 2)
fprintf('matrix_set(A, %i, %i, %i, %.16f);\n', size(A, 2), i-1, j-1, A(i, j));
end
end
end
function set_vector(a)
for i=1:numel(a)
fprintf('A[%i] = %.16f;\n', i-1, a(i));
end
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
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