net_test_1.cpp

/**
 * Basic example using particle swarm method to train the network
 * (result 0, -1/4)
 */

//
// Created by martin on 7/16/18.
//

#include <vector>

#include "4neuro.h"

int main() {

    /* TRAIN DATA DEFINITION */
    std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
    std::vector<double> inp, out;

    inp = {0, 1};
    out = {0.5};
    data_vec.emplace_back(std::make_pair(inp, out));

    inp = {1, 0.5};
    out = {0.75};
    data_vec.emplace_back(std::make_pair(inp, out));

    DataSet ds(&data_vec);

    /* NETWORK DEFINITION */
    NeuralNetwork net;

    /* Input neurons */
    NeuronLinear *i1 = new NeuronLinear(0.0, 1.0);  //f(x) = x
    NeuronLinear *i2 = new NeuronLinear(0.0, 1.0);  //f(x) = x

    /* Output neuron */
    NeuronLinear *o1 = new NeuronLinear(1.0, 2.0);  //f(x) = 2x + 1



    /* Adding neurons to the net */
    int idx1 = net.add_neuron(i1);
    int idx2 = net.add_neuron(i2);
    int idx3 = net.add_neuron(o1);

    /* Adding connections */
    //net.add_connection_simple(idx1, idx3, -1, 1.0);
    //net.add_connection_simple(idx2, idx3, -1, 1.0);
    net.add_connection_simple(idx1, idx3);
    net.add_connection_simple(idx2, idx3);

    //net.randomize_weights();

    /* specification of the input/output neurons */
    std::vector<size_t> net_input_neurons_indices(2);
    std::vector<size_t> net_output_neurons_indices(1);
    net_input_neurons_indices[0] = idx1;
    net_input_neurons_indices[1] = idx2;

    net_output_neurons_indices[0] = idx3;

    net.specify_input_neurons(net_input_neurons_indices);
    net.specify_output_neurons(net_output_neurons_indices);
    /* ERROR FUNCTION SPECIFICATION */
    MSE mse(&net, &ds);

    /* TRAINING METHOD SETUP */
    unsigned int n_edges = 2;
    unsigned int dim = n_edges, max_iters = 2000;


    double domain_bounds[4] = {-800.0, 800.0, -800.0, 800.0};

    double c1 = 0.5, c2 = 1.5, w = 0.8;

    unsigned int n_particles = 10;

    ParticleSwarm swarm_01(&mse, domain_bounds, c1, c2, w, n_particles, max_iters);

    swarm_01.optimize(0.5, 0.02);

    return 0;
}