net_test_2.cpp

/**
 * Example of a neural network with reused edge weights
 * The system of equations associated with the net in this example is not regular
 * minimizes the function: ((2y+0.5)^2 + (2x+1)^2 + (2x + y + 0.25)^2 + (2x+1)^2 + 1 + (4.5x + 0.37)^2 ) /3
 * minimum [0.705493164] at (x, y) = (-1133/6290, -11193/62900) = (-0.180127186, -0.177949126)
 */

//
// Created by Michal on 7/17/18.
//

#include <vector>

#include "../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, 0};
    out = {0.5, 0};
    data_vec.emplace_back(std::make_pair(inp, out));

    inp = {1, 0.5, 0};
    out = {0.75, 0};
    data_vec.emplace_back(std::make_pair(inp, out));

    inp = {0, 0, 1.25};
    out = {0, 0.63};
    data_vec.emplace_back(std::make_pair(inp, out));
    DataSet ds(&data_vec);

    /* NETWORK DEFINITION */
    NeuralNetwork net;

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

    double b = 1;//bias
    NeuronLinear *i3 = new NeuronLinear( ); //f(x) = x + 1

    /* Output neurons */
    NeuronLinear *o1 = new NeuronLinear( );  //f(x) = x + 1
    NeuronLinear *o2 = new NeuronLinear( );  //f(x) = x + 1



    /* Adding neurons to the nets */
    size_t idx1 = net.add_neuron(i1, BIAS_TYPE::NO_BIAS);
    size_t idx2 = net.add_neuron(i2, BIAS_TYPE::NO_BIAS);
    size_t idx3 = net.add_neuron(o1, BIAS_TYPE::NEXT_BIAS);
    size_t idx4 = net.add_neuron(i3, BIAS_TYPE::NEXT_BIAS);
    size_t idx5 = net.add_neuron(o2, BIAS_TYPE::NEXT_BIAS);

    std::vector<double> *bv = net.get_parameter_ptr_biases();
    for(size_t i = 0; i < 3; ++i){
        bv->at(i) = 1.0;
    }

    /* 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, SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT); // weight index 0
    net.add_connection_simple(idx2, idx3, SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT); // weight index 1
    net.add_connection_simple(idx4, idx5, SIMPLE_CONNECTION_TYPE::EXISTING_WEIGHT, 0); // AGAIN weight index 0 - same weight!

    net.randomize_weights();

    /* specification of the input/output neurons */
    std::vector<size_t> net_input_neurons_indices(3);
    std::vector<size_t> net_output_neurons_indices(2);
    net_input_neurons_indices[0] = idx1;
    net_input_neurons_indices[1] = idx2;
    net_input_neurons_indices[2] = idx4;

    net_output_neurons_indices[0] = idx3;
    net_output_neurons_indices[1] = idx5;

    net.specify_input_neurons(net_input_neurons_indices);
    net.specify_output_neurons(net_output_neurons_indices);




    /* COMPLEX ERROR FUNCTION SPECIFICATION */
    MSE mse(&net, &ds);

//    double weights[2] = {-0.18012411, -0.17793740};
//    double weights[2] = {1, 1};

//    printf("evaluation of error at point (%f, %f) => %f\n", weights[0], weights[1], mse.eval(weights));

    /* TRAINING METHOD SETUP */
    unsigned int max_iters = 5000;


    //must encapsulate each of the partial error functions
    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 = 100;

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

    swarm_01.optimize(0.5, 0.02, 0.9);

    printf("evaluation of error: %f\n", mse.eval());

    return 0;
}