Function test1

dlib/test/svr_linear_trainer.cpp:38–134

    template <typename scalar_type>
    void test1()
    {
        typedef matrix<scalar_type,0,1> sample_type;

        typedef radial_basis_kernel<sample_type> kernel_type;

        print_spinner();

        std::vector<sample_type> samples;
        std::vector<scalar_type> targets;

        // The first thing we do is pick a few training points from the sinc() function.
        sample_type m(1);
        for (scalar_type x = -10; x <= 4; x += 1)
        {
            m(0) = x;

            samples.push_back(m);
            targets.push_back(sinc(x)+1.1);
        }

        randomize_samples(samples, targets);

        empirical_kernel_map<kernel_type> ekm;
        ekm.load(kernel_type(0.1), samples);

        for (unsigned long i = 0; i < samples.size(); ++i)
            samples[i] = ekm.project(samples[i]);

        svr_linear_trainer<linear_kernel<sample_type> > linear_trainer;
        linear_trainer.set_epsilon(0.0001);
        linear_trainer.set_c(30);
        linear_trainer.set_epsilon_insensitivity(0.001);

        matrix<double> res = cross_validate_regression_trainer(linear_trainer, samples, targets, 5);
        dlog << LINFO << "MSE and R-Squared: "<< res;
        DLIB_TEST(res(0) < 1e-4);
        DLIB_TEST(res(1) > 0.99);

        dlib::rand rnd;

        samples.clear();
        targets.clear();
        std::vector<scalar_type> noisefree_targets;
        for (scalar_type x = 0; x <= 5; x += 0.1)
        {
            m(0) = x;
            samples.push_back(matrix_cast<scalar_type>(linpiece(m, linspace(0,5,20))));
            targets.push_back(x*x + rnd.get_random_gaussian());
            noisefree_targets.push_back(x*x);
        }
        linear_trainer.set_learns_nonnegative_weights(true);
        linear_trainer.set_epsilon_insensitivity(1.0);
        decision_function<linear_kernel<sample_type> > df2 = linear_trainer.train(samples, targets);

        print_spinner();
        res = test_regression_function(df2, samples, noisefree_targets);
        dlog << LINFO << "MSE and R-Squared: "<< res;