main.cpp

/**
 * This file serves for testing of various examples, have fun!
 *
 * @author Michal Kravčenko
 * @date 14.6.18 -
 */

#include <iostream>
#include <cstdio>
#include <fstream>
#include <vector>
#include <utility>
#include <algorithm>

#include "4neuro.h"
#include "../CrossValidator/CrossValidator.h"

int main(int argc, char** argv){

    /* Read data from the file */
    l4n::CSVReader reader("/home/martin/Desktop/ANN_DATA_1_SET.txt", "\t", true);
    reader.read();

    /* Create data set for both the training and testing of the neural network */
    std::vector<unsigned int> inputs = {2,3,4,5,6,7,8,26,27,28};
    std::vector<unsigned int> outputs = {17,18,19,20,21,22,23,24,25};
    l4n::DataSet ds = reader.get_data_set(&inputs, &outputs);

    /* Normalize data in the set for easier training of the network */
    ds.normalize();

    /* Neural network construction */
    std::vector<unsigned int> neuron_numbers_in_layers = {10,10,10,9};
    l4n::FullyConnectedFFN nn(&neuron_numbers_in_layers, l4n::NEURON_TYPE::LOGISTIC);

    /* Error function */
    l4n::MSE mse(&nn, &ds);

    /* Domain */
    std::vector<double> domain_bounds(2 * (nn.get_n_weights() + nn.get_n_biases()));

    /* Training method */
//    for(size_t i = 0; i < domain_bounds.size() / 2; ++i){
//        domain_bounds[2 * i] = -10;
//        domain_bounds[2 * i + 1] = 10;
//    }
//    l4n::ParticleSwarm ps(&domain_bounds,
//                          1.711897,
//                          1.711897,
//                          0.711897,
//                          0.5,
//                          20,
//                          0.7,
//                          600,
//                          1000);
    l4n::GradientDescent gs(1e-3, 1);

    nn.randomize_weights();

    /* Cross - validation */
    l4n::CrossValidator cv(&gs, &mse);
    cv.run_k_fold_test(10, 3);

    /* Save network to the file */
    nn.save_text("test_net.4n");

    /* Check of the saved network */
    std::cout << "The original network info:" << std::endl;
    nn.print_stats();

    l4n::NeuralNetwork nn_loaded("test_net.4n");
    std::cout << "The loaded network info:" << std::endl;
    nn_loaded.print_stats();

    return 0;
}