static void test_inc_x(size_t i, const double *dx, const size_t *jdx, size_t ndx)
{
size_t j, k;
if (jdx) {
for (k = 0; k < ndx; k++) {
X[i * P + jdx[k]] += dx[k];
}
} else if (ndx) {
assert(ndx == P);
for (j = 0; j < P; j++) {
X[i * P + j] += dx[j];
}
}
recompute();
mlogit_set_x(&MLOGIT, i, 0, P, X + i * P);
//print_error("\tx...");
test_x();
//print_error("ok\n\tmean...");
test_mean();
//print_error("ok\n\tcov...");
test_cov();
//print_error("ok\n");
}
int main (void)
{
test_c ('?');
test_d_i (0xdeadbeef, 0xdeadbeefL);
test_x ('?', 0xdead, 0xdeadbeef);
test_a_double (0.0);
test_e_double (0.0);
test_f_double (0.0);
test_g_double (0.0);
test_a_long_double ();
test_e_long_double (0.0);
test_f_long_double ();
test_g_long_double ();
test_s (0);
test_n ();
test_percent ();
if (nfails)
{
__builtin_printf ("%u out of %u tests failed\n", nfails, ntests);
__builtin_abort ();
}
return 0;
}
int main(){
std::vector<std::vector<double> >test_x(3);
test_x[0].push_back(3);test_x[0].push_back(3);
test_x[1].push_back(4);test_x[1].push_back(3);
test_x[2].push_back(1);test_x[2].push_back(1);
std::vector<int> test_y(3);
test_y[0] = 1;
test_y[1] = 1;
test_y[2] = -1;
DualPerception *model = new DualPerception(1, 1.0, 100);
model->train(test_x,test_y);
model->printPerceptronModel();
}
