x2_fitting.cpp

#include <iostream>

#include <4neuro_public.h>

void optimize_via_particle_swarm(l4n::NeuralNetwork& net,
                                 l4n::ErrorFunction& ef) {

    /* TRAINING METHOD SETUP */
    std::vector<double> domain_bounds(2 * (net.get_n_weights() + net.get_n_biases()));

    for (size_t i = 0; i < domain_bounds.size() / 2; ++i) {
        domain_bounds[2 * i]     = -10;
        domain_bounds[2 * i + 1] = 10;
    }

    double c1          = 1.7;
    double c2          = 1.7;
    double w           = 0.7;
    size_t n_particles = 100;
    size_t iter_max    = 150;

    /* if the maximal velocity from the previous step is less than 'gamma' times the current maximal velocity, then one
     * terminating criterion is met */
    double gamma = 0.5;

    /* if 'delta' times 'n' particles are in the centroid neighborhood given by the radius 'epsilon', then the second
     * terminating criterion is met ('n' is the total number of particles) */
    double epsilon = 0.02;
    double delta   = 0.7;

    l4n::ParticleSwarm swarm_01(
        &domain_bounds,
        c1,
        c2,
        w,
        gamma,
        epsilon,
        delta,
        n_particles,
        iter_max
    );
    swarm_01.optimize(ef);

    net.copy_parameter_space(swarm_01.get_parameters());

    /* ERROR CALCULATION */
    std::cout << "Run finished! Error of the network[Particle swarm]: " << ef.eval(nullptr) << std::endl;
    std::cout
        << "***********************************************************************************************************************"
        << std::endl;
}

void optimize_via_gradient_descent(l4n::NeuralNetwork& net,
                                   l4n::ErrorFunction& ef) {

    std::cout
        << "***********************************************************************************************************************"
        << std::endl;
    l4n::GradientDescentBB gd(1e-4,
                              100,
                              15000);

    gd.optimize(ef);

    net.copy_parameter_space(gd.get_parameters());

    /* ERROR CALCULATION */
    double err = ef.eval(nullptr);
    std::cout << "Run finished! Error of the network[Gradient descent]: " << err << std::endl;

    /* Just for validation test purposes - NOT necessary for the example to work! */
    if(err > 0.002) {
        throw std::runtime_error("Training was incorrect!");
    }
}

int main() {

    l4n::CSVReader reader("../../data/x2_data.txt",
                          " ",
                          true);
    reader.read();

    std::vector<unsigned int>     input_ind  = {0};
    std::vector<unsigned int>     output_ind = {1};
    std::shared_ptr<l4n::DataSet> ds         = reader.get_data_set(&input_ind,
                                                                   &output_ind);

    std::vector<unsigned int>     neuron_numbers_in_layers = {1, 15, 1};
    std::vector<l4n::NEURON_TYPE> hidden_type_v            = {l4n::NEURON_TYPE::LOGISTIC};
    l4n::FullyConnectedFFN        net(&neuron_numbers_in_layers,
                                      &hidden_type_v);

    l4n::MSE mse(&net,
                 ds.get());

    net.randomize_parameters();

    optimize_via_particle_swarm(net, mse);

    optimize_via_gradient_descent(net, mse);

    /* Print fit comparison with real data */
    std::vector<double> output;
    output.resize(1);

    for(auto e : *ds->get_data()) {
        for(auto inp_e : e.first) {
            std::cout << inp_e << " ";
        }
        std::cout << e.second.at(0) << " ";
        net.eval_single(e.first, output);
        std::cout << output.at(0) << std::endl;
    }

    return 0;
}